From 94504e71f4941e3c3b2d33da269f286a2d1386ed Mon Sep 17 00:00:00 2001 From: Manu Ponnapati Date: Thu, 24 Oct 2024 14:36:18 -0700 Subject: [PATCH 1/9] add docs --- docs/Makefile | 20 ++++++++ docs/conf.py | 32 ++++++++++++ docs/index.rst | 33 ++++++++++++ docs/make.bat | 35 +++++++++++++ docs/source/ldp.agent.rst | 69 +++++++++++++++++++++++++ docs/source/ldp.alg.optimizer.rst | 45 ++++++++++++++++ docs/source/ldp.alg.rst | 77 ++++++++++++++++++++++++++++ docs/source/ldp.graph.modules.rst | 45 ++++++++++++++++ docs/source/ldp.graph.rst | 85 +++++++++++++++++++++++++++++++ docs/source/ldp.llms.rst | 37 ++++++++++++++ docs/source/ldp.rst | 56 ++++++++++++++++++++ docs/source/modules.rst | 7 +++ 12 files changed, 541 insertions(+) create mode 100644 docs/Makefile create mode 100644 docs/conf.py create mode 100644 docs/index.rst create mode 100644 docs/make.bat create mode 100644 docs/source/ldp.agent.rst create mode 100644 docs/source/ldp.alg.optimizer.rst create mode 100644 docs/source/ldp.alg.rst create mode 100644 docs/source/ldp.graph.modules.rst create mode 100644 docs/source/ldp.graph.rst create mode 100644 docs/source/ldp.llms.rst create mode 100644 docs/source/ldp.rst create mode 100644 docs/source/modules.rst diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..d4bb2cbb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 00000000..8d620650 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,32 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information +import sphinx_nameko_theme +import sys,os + +project = 'ldp' +copyright = '2024, James Braza, Siddharth Narayanan' +author = 'James Braza, Siddharth Narayanan' +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.viewcode' +] +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [] + +templates_path = ['_templates'] +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] +sys.path.insert(0, os.path.abspath('../../')) + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'nameko' +html_theme_path = [sphinx_nameko_theme.get_html_theme_path()] \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 00000000..5b03942e --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,33 @@ +.. ldp documentation master file, created by + sphinx-quickstart on Thu Oct 24 14:27:08 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to ldp's documentation! +=============================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + ldp.agent + ldp.alg + ldp.graph + ldp + modules + ldp.llms + ldp.graph.modules + ldp.alg.optimizer diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 00000000..32bb2452 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/source/ldp.agent.rst b/docs/source/ldp.agent.rst new file mode 100644 index 00000000..146fe429 --- /dev/null +++ b/docs/source/ldp.agent.rst @@ -0,0 +1,69 @@ +ldp.agent package +================= + +Submodules +---------- + +ldp.agent.agent module +---------------------- + +.. automodule:: ldp.agent.agent + :members: + :undoc-members: + :show-inheritance: + +ldp.agent.agent\_client module +------------------------------ + +.. automodule:: ldp.agent.agent_client + :members: + :undoc-members: + :show-inheritance: + +ldp.agent.interactive\_agent module +----------------------------------- + +.. automodule:: ldp.agent.interactive_agent + :members: + :undoc-members: + :show-inheritance: + +ldp.agent.memory\_agent module +------------------------------ + +.. automodule:: ldp.agent.memory_agent + :members: + :undoc-members: + :show-inheritance: + +ldp.agent.react\_agent module +----------------------------- + +.. automodule:: ldp.agent.react_agent + :members: + :undoc-members: + :show-inheritance: + +ldp.agent.simple\_agent module +------------------------------ + +.. automodule:: ldp.agent.simple_agent + :members: + :undoc-members: + :show-inheritance: + +ldp.agent.tree\_of\_thoughts\_agent module +------------------------------------------ + +.. automodule:: ldp.agent.tree_of_thoughts_agent + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp.agent + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/ldp.alg.optimizer.rst b/docs/source/ldp.alg.optimizer.rst new file mode 100644 index 00000000..f05f2c58 --- /dev/null +++ b/docs/source/ldp.alg.optimizer.rst @@ -0,0 +1,45 @@ +ldp.alg.optimizer package +========================= + +Submodules +---------- + +ldp.alg.optimizer.ape module +---------------------------- + +.. automodule:: ldp.alg.optimizer.ape + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.optimizer.memory module +------------------------------- + +.. automodule:: ldp.alg.optimizer.memory + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.optimizer.opt module +---------------------------- + +.. automodule:: ldp.alg.optimizer.opt + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.optimizer.replay\_buffers module +---------------------------------------- + +.. automodule:: ldp.alg.optimizer.replay_buffers + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp.alg.optimizer + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/ldp.alg.rst b/docs/source/ldp.alg.rst new file mode 100644 index 00000000..621e6817 --- /dev/null +++ b/docs/source/ldp.alg.rst @@ -0,0 +1,77 @@ +ldp.alg package +=============== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + ldp.alg.optimizer + +Submodules +---------- + +ldp.alg.algorithms module +------------------------- + +.. automodule:: ldp.alg.algorithms + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.beam\_search module +--------------------------- + +.. automodule:: ldp.alg.beam_search + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.callbacks module +------------------------ + +.. automodule:: ldp.alg.callbacks + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.datasets module +----------------------- + +.. automodule:: ldp.alg.datasets + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.rollout module +---------------------- + +.. automodule:: ldp.alg.rollout + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.runners module +---------------------- + +.. automodule:: ldp.alg.runners + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.tree\_search module +--------------------------- + +.. automodule:: ldp.alg.tree_search + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp.alg + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/ldp.graph.modules.rst b/docs/source/ldp.graph.modules.rst new file mode 100644 index 00000000..28b36540 --- /dev/null +++ b/docs/source/ldp.graph.modules.rst @@ -0,0 +1,45 @@ +ldp.graph.modules package +========================= + +Submodules +---------- + +ldp.graph.modules.llm\_call module +---------------------------------- + +.. automodule:: ldp.graph.modules.llm_call + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.modules.react module +------------------------------ + +.. automodule:: ldp.graph.modules.react + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.modules.reflect module +-------------------------------- + +.. automodule:: ldp.graph.modules.reflect + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.modules.thought module +-------------------------------- + +.. automodule:: ldp.graph.modules.thought + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp.graph.modules + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/ldp.graph.rst b/docs/source/ldp.graph.rst new file mode 100644 index 00000000..9fc73995 --- /dev/null +++ b/docs/source/ldp.graph.rst @@ -0,0 +1,85 @@ +ldp.graph package +================= + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + ldp.graph.modules + +Submodules +---------- + +ldp.graph.async\_torch module +----------------------------- + +.. automodule:: ldp.graph.async_torch + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.common\_ops module +---------------------------- + +.. automodule:: ldp.graph.common_ops + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.gradient\_estimators module +------------------------------------- + +.. automodule:: ldp.graph.gradient_estimators + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.loss\_ops module +-------------------------- + +.. automodule:: ldp.graph.loss_ops + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.memory module +----------------------- + +.. automodule:: ldp.graph.memory + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.op\_utils module +-------------------------- + +.. automodule:: ldp.graph.op_utils + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.ops module +-------------------- + +.. automodule:: ldp.graph.ops + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.torch\_ops module +--------------------------- + +.. automodule:: ldp.graph.torch_ops + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp.graph + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/ldp.llms.rst b/docs/source/ldp.llms.rst new file mode 100644 index 00000000..31d1b865 --- /dev/null +++ b/docs/source/ldp.llms.rst @@ -0,0 +1,37 @@ +ldp.llms package +================ + +Submodules +---------- + +ldp.llms.chat module +-------------------- + +.. automodule:: ldp.llms.chat + :members: + :undoc-members: + :show-inheritance: + +ldp.llms.embeddings module +-------------------------- + +.. automodule:: ldp.llms.embeddings + :members: + :undoc-members: + :show-inheritance: + +ldp.llms.prompts module +----------------------- + +.. automodule:: ldp.llms.prompts + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp.llms + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/ldp.rst b/docs/source/ldp.rst new file mode 100644 index 00000000..df50fc31 --- /dev/null +++ b/docs/source/ldp.rst @@ -0,0 +1,56 @@ +ldp package +=========== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + ldp.agent + ldp.alg + ldp.graph + ldp.llms + +Submodules +---------- + +ldp.data\_structures module +--------------------------- + +.. automodule:: ldp.data_structures + :members: + :undoc-members: + :show-inheritance: + +ldp.main module +--------------- + +.. automodule:: ldp.main + :members: + :undoc-members: + :show-inheritance: + +ldp.shims module +---------------- + +.. automodule:: ldp.shims + :members: + :undoc-members: + :show-inheritance: + +ldp.utils module +---------------- + +.. automodule:: ldp.utils + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/source/modules.rst b/docs/source/modules.rst new file mode 100644 index 00000000..f473f842 --- /dev/null +++ b/docs/source/modules.rst @@ -0,0 +1,7 @@ +ldp +=== + +.. toctree:: + :maxdepth: 4 + + ldp From 3d9010572f2140411f71a36faeb5fda58b618f54 Mon Sep 17 00:00:00 2001 From: Manu Ponnapati Date: Thu, 24 Oct 2024 14:53:31 -0700 Subject: [PATCH 2/9] autodoc --- docs/conf.py | 19 +++++++------------ docs/index.rst | 20 +++++--------------- docs/{source => }/ldp.agent.rst | 0 docs/{source => }/ldp.alg.optimizer.rst | 0 docs/{source => }/ldp.alg.rst | 0 docs/{source => }/ldp.graph.modules.rst | 0 docs/{source => }/ldp.graph.rst | 0 docs/{source => }/ldp.llms.rst | 0 docs/{source => }/ldp.rst | 0 docs/{source => }/modules.rst | 0 10 files changed, 12 insertions(+), 27 deletions(-) rename docs/{source => }/ldp.agent.rst (100%) rename docs/{source => }/ldp.alg.optimizer.rst (100%) rename docs/{source => }/ldp.alg.rst (100%) rename docs/{source => }/ldp.graph.modules.rst (100%) rename docs/{source => }/ldp.graph.rst (100%) rename docs/{source => }/ldp.llms.rst (100%) rename docs/{source => }/ldp.rst (100%) rename docs/{source => }/modules.rst (100%) diff --git a/docs/conf.py b/docs/conf.py index 8d620650..3cc86ebb 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -5,28 +5,23 @@ # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information -import sphinx_nameko_theme -import sys,os project = 'ldp' -copyright = '2024, James Braza, Siddharth Narayanan' -author = 'James Braza, Siddharth Narayanan' -extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.viewcode' -] +copyright = '2024, FutureHouse' +author = 'FutureHouse' + # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration -extensions = [] +extensions = ['sphinx.ext.todo', 'sphinx.ext.viewcode', 'sphinx.ext.autodoc'] templates_path = ['_templates'] exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] -sys.path.insert(0, os.path.abspath('../../')) + # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output -html_theme = 'nameko' -html_theme_path = [sphinx_nameko_theme.get_html_theme_path()] \ No newline at end of file +html_theme = 'alabaster' +html_static_path = ['_static'] diff --git a/docs/index.rst b/docs/index.rst index 5b03942e..75b5c770 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,5 +1,5 @@ .. ldp documentation master file, created by - sphinx-quickstart on Thu Oct 24 14:27:08 2024. + sphinx-quickstart on Thu Oct 24 14:50:30 2024. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. @@ -7,9 +7,12 @@ Welcome to ldp's documentation! =============================== .. toctree:: - :maxdepth: 2 + :maxdepth: 4 :caption: Contents: + modules + + Indices and tables @@ -18,16 +21,3 @@ Indices and tables * :ref:`genindex` * :ref:`modindex` * :ref:`search` - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - ldp.agent - ldp.alg - ldp.graph - ldp - modules - ldp.llms - ldp.graph.modules - ldp.alg.optimizer diff --git a/docs/source/ldp.agent.rst b/docs/ldp.agent.rst similarity index 100% rename from docs/source/ldp.agent.rst rename to docs/ldp.agent.rst diff --git a/docs/source/ldp.alg.optimizer.rst b/docs/ldp.alg.optimizer.rst similarity index 100% rename from docs/source/ldp.alg.optimizer.rst rename to docs/ldp.alg.optimizer.rst diff --git a/docs/source/ldp.alg.rst b/docs/ldp.alg.rst similarity index 100% rename from docs/source/ldp.alg.rst rename to docs/ldp.alg.rst diff --git a/docs/source/ldp.graph.modules.rst b/docs/ldp.graph.modules.rst similarity index 100% rename from docs/source/ldp.graph.modules.rst rename to docs/ldp.graph.modules.rst diff --git a/docs/source/ldp.graph.rst b/docs/ldp.graph.rst similarity index 100% rename from docs/source/ldp.graph.rst rename to docs/ldp.graph.rst diff --git a/docs/source/ldp.llms.rst b/docs/ldp.llms.rst similarity index 100% rename from docs/source/ldp.llms.rst rename to docs/ldp.llms.rst diff --git a/docs/source/ldp.rst b/docs/ldp.rst similarity index 100% rename from docs/source/ldp.rst rename to docs/ldp.rst diff --git a/docs/source/modules.rst b/docs/modules.rst similarity index 100% rename from docs/source/modules.rst rename to docs/modules.rst From d9cc26d95c80c6a368623680144514930ebe9c5b Mon Sep 17 00:00:00 2001 From: Manu Ponnapati Date: Thu, 24 Oct 2024 15:00:57 -0700 Subject: [PATCH 3/9] sphix --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index a0d851d2..b152d6a4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -95,6 +95,7 @@ enable_error_code = [ "unimported-reveal", "unreachable", "unused-ignore", + "sphinx" ] # Shows a short summary line after error messages. error_summary = false From 53a49f63a0f36ce440aa13c36e312e3ecc5ef6fd Mon Sep 17 00:00:00 2001 From: Manu Ponnapati Date: Thu, 24 Oct 2024 15:05:16 -0700 Subject: [PATCH 4/9] pyproject --- docs/conf.py | 17 ++++++++--------- pyproject.toml | 2 +- 2 files changed, 9 insertions(+), 10 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 3cc86ebb..5fb7a54a 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -6,22 +6,21 @@ # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information -project = 'ldp' -copyright = '2024, FutureHouse' -author = 'FutureHouse' +project = "ldp" +copyright = "2024, FutureHouse" +author = "FutureHouse" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration -extensions = ['sphinx.ext.todo', 'sphinx.ext.viewcode', 'sphinx.ext.autodoc'] - -templates_path = ['_templates'] -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] +extensions = ["sphinx.ext.todo", "sphinx.ext.viewcode", "sphinx.ext.autodoc"] +templates_path = ["_templates"] +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output -html_theme = 'alabaster' -html_static_path = ['_static'] +html_theme = "alabaster" +html_static_path = ["_static"] diff --git a/pyproject.toml b/pyproject.toml index b152d6a4..44ba1a26 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -90,12 +90,12 @@ enable_error_code = [ "redundant-cast", "redundant-expr", "redundant-self", + "sphinx", "truthy-bool", "truthy-iterable", "unimported-reveal", "unreachable", "unused-ignore", - "sphinx" ] # Shows a short summary line after error messages. error_summary = false From 87fd92931b7687bec7c291746139e6ef1e71639c Mon Sep 17 00:00:00 2001 From: Manu Ponnapati Date: Thu, 24 Oct 2024 15:11:41 -0700 Subject: [PATCH 5/9] conf --- docs/conf.py | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 5fb7a54a..c19011ce 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -7,7 +7,6 @@ # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information project = "ldp" -copyright = "2024, FutureHouse" author = "FutureHouse" # -- General configuration --------------------------------------------------- From 16e7d45fba43034df5f311ab2fa1c6f89d3dc71d Mon Sep 17 00:00:00 2001 From: Manu Ponnapati Date: Thu, 24 Oct 2024 15:18:04 -0700 Subject: [PATCH 6/9] napoleon --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index c19011ce..c831fc97 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -12,7 +12,7 @@ # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration -extensions = ["sphinx.ext.todo", "sphinx.ext.viewcode", "sphinx.ext.autodoc"] +extensions = ["sphinx.ext.todo", "sphinx.ext.viewcode", "sphinx.ext.autodoc", "sphinx.ext.napoleon"] templates_path = ["_templates"] exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] From 2791c22cf219f35e406e46a1f5a84658a0aa9206 Mon Sep 17 00:00:00 2001 From: Manu Ponnapati Date: Thu, 24 Oct 2024 15:18:46 -0700 Subject: [PATCH 7/9] remove from mypy --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 44ba1a26..a0d851d2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -90,7 +90,6 @@ enable_error_code = [ "redundant-cast", "redundant-expr", "redundant-self", - "sphinx", "truthy-bool", "truthy-iterable", "unimported-reveal", From e432c262c5be42c44f072c7b24aa642ff4950b2e Mon Sep 17 00:00:00 2001 From: Manu Ponnapati Date: Thu, 24 Oct 2024 15:22:34 -0700 Subject: [PATCH 8/9] pyprojec --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index a0d851d2..5aa7ba73 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -431,5 +431,6 @@ dev-dependencies = [ "pytest-timer[colorama]", "pytest-xdist", "pytest>=8", # Pin to keep recent - "refurb>=2", # Pin to keep recent + "refurb>=2", # Pin to keep recent, + "sphinx" ] From 076188503ee402a229d1bb21dbc8ffc290c02e38 Mon Sep 17 00:00:00 2001 From: Manu Ponnapati Date: Fri, 25 Oct 2024 11:39:26 -0700 Subject: [PATCH 9/9] html --- docs/_build/doctrees/environment.pickle | Bin 0 -> 6300260 bytes docs/_build/doctrees/index.doctree | Bin 0 -> 4973 bytes docs/_build/doctrees/ldp.agent.doctree | Bin 0 -> 573482 bytes docs/_build/doctrees/ldp.alg.doctree | Bin 0 -> 821383 bytes .../_build/doctrees/ldp.alg.optimizer.doctree | Bin 0 -> 424436 bytes docs/_build/doctrees/ldp.doctree | Bin 0 -> 385217 bytes docs/_build/doctrees/ldp.graph.doctree | Bin 0 -> 1085546 bytes .../_build/doctrees/ldp.graph.modules.doctree | Bin 0 -> 141756 bytes docs/_build/doctrees/ldp.llms.doctree | Bin 0 -> 565658 bytes docs/_build/doctrees/modules.doctree | Bin 0 -> 2710 bytes docs/_build/html/.buildinfo | 4 + docs/_build/html/_modules/index.html | 137 + docs/_build/html/_modules/ldp/agent.html | 133 + .../_build/html/_modules/ldp/agent/agent.html | 295 ++ .../html/_modules/ldp/agent/agent_client.html | 260 ++ .../_modules/ldp/agent/interactive_agent.html | 225 ++ .../html/_modules/ldp/agent/memory_agent.html | 235 ++ .../html/_modules/ldp/agent/react_agent.html | 255 ++ .../html/_modules/ldp/agent/simple_agent.html | 217 ++ .../ldp/agent/tree_of_thoughts_agent.html | 251 ++ .../html/_modules/ldp/alg/algorithms.html | 226 ++ .../html/_modules/ldp/alg/beam_search.html 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    +
    +
    + + +
    + +

    All modules for which code is available

    +     + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/agent.html b/docs/_build/html/_modules/ldp/agent.html new file mode 100644 index 00000000..e5806a0e --- /dev/null +++ b/docs/_build/html/_modules/ldp/agent.html @@ -0,0 +1,133 @@ + + + + + + + ldp.agent — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.agent

    +from enum import StrEnum
+
+
+
    
+[docs]
+class DefaultLLMModelNames(StrEnum):
+    """Defaults for LLM models, pin exact versions for performance stability."""
+
+    OPENAI = "gpt-4o-2024-08-06"  # Cheap, fast, and decent
    
+
+
+
+# ruff: noqa: E402  # Avoid circular imports
+
+from .agent import Agent, AgentConfig
+from .agent_client import HTTPAgentClient, make_simple_agent_server
+from .memory_agent import MemoryAgent
+from .react_agent import ReActAgent
+from .simple_agent import SimpleAgent, SimpleAgentState
+from .tree_of_thoughts_agent import TreeofThoughtsAgent
+
+__all__ = [
+    "Agent",
+    "AgentConfig",
+    "DefaultLLMModelNames",
+    "HTTPAgentClient",
+    "MemoryAgent",
+    "ReActAgent",
+    "SimpleAgent",
+    "SimpleAgentState",
+    "TreeofThoughtsAgent",
+    "make_simple_agent_server",
+]
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/agent/agent.html b/docs/_build/html/_modules/ldp/agent/agent.html new file mode 100644 index 00000000..fe722289 --- /dev/null +++ b/docs/_build/html/_modules/ldp/agent/agent.html @@ -0,0 +1,295 @@ + + + + + + + ldp.agent.agent — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.agent.agent

    +from __future__ import annotations
+
+import json
+from abc import ABC, abstractmethod
+from collections.abc import Collection, Iterable, Mapping, Sequence
+from typing import Any, Generic, TypeVar
+
+import numpy as np
+from aviary.message import Message
+from aviary.tools import Tool, ToolRequestMessage
+from pydantic import BaseModel, ConfigDict, Field, JsonValue
+
+from ldp.graph import Op, OpResult
+
+try:
+    # So we can skip torch objects when looking for Ops
+    import torch
+except ImportError:
+    # If torch is not available, then it won't be used in an Agent anyway
+    torch = None  # type: ignore[assignment]
+
+TAgentState = TypeVar("TAgentState")
+
+
+# A global registry of all Agent subclasses, so we can look them up by name
+_AGENT_REGISTRY: dict[str, type[Agent]] = {}
+
+
+
    
+[docs]
+class Agent(ABC, Generic[TAgentState]):
+    def __init_subclass__(cls, **kwargs):
+        """Ensure Ops have unique names and subclasses are in _AGENT_REGISTRY."""
+        super().__init_subclass__(**kwargs)
+
+        original_init = cls.__init__
+
+        def init_with_op_naming(self, *args, **kwargs):
+            original_init(self, *args, **kwargs)
+
+            # loop through Ops and give them proper names
+            for name, op in _find_ops(self):
+                op.set_name(name)
+
+        cls.__init__ = init_with_op_naming  # type: ignore[method-assign]
+
+        # Register the Agent subclass.
+        _AGENT_REGISTRY[cls.__name__] = cls
+
+
    
+[docs]
+    @abstractmethod
+    async def get_asv(
+        self, agent_state: TAgentState, obs: list[Message]
+    ) -> tuple[OpResult[ToolRequestMessage], TAgentState, float]:
+        """
+        Get new action, state, and value given state and observation messages.
+
+        NOTE: the method's name has action listed before state to help you realize it's
+        a new state.
+
+        Args:
+            agent_state: Optional current agent state, pass None if irrelevant.
+                This can be something like agent memory.
+            obs: Most recent list of observation messages from the environment's steps.
+                If more observations than the most recent list are necessary, track them
+                in the agent state.
+
+        Returns:
+            Three-tuple of new action, new agent state, and estimated value. The
+                agent_state is returned as a copy so that you can safely mutate it
+                without affecting the original. The estimated value is the agent's
+                estimate of the future rewards given the input state and observations,
+                and is used for RL training.  If estimated value doesn't matter, just
+                return 0. The value could also come from a Q-value evaluated at the
+                action chosen by the agent.
+        """
    
+
+
+
    
+[docs]
+    @abstractmethod
+    async def init_state(self, tools: list[Tool]) -> TAgentState:
+        """Initializes the first agent state with the provided tools."""
    
+
+
+
    
+[docs]
+    def named_ops(self) -> Iterable[tuple[str, Op]]:
+        """Analogous to torch.nn.Module.named_parameters()."""
+        return _find_ops(self)
    
+
+
+
    
+[docs]
+    @classmethod
+    def from_name(cls, name: str, **kwargs) -> Agent:
+        return _AGENT_REGISTRY[name](**kwargs)
    
+
    
+
+
+
+
    
+[docs]
+class AgentConfig(BaseModel):
+    """Configuration for specifying the type of agent i.e. the subclass of Agent above."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    agent_type: str = Field(
+        description="The type of agent to be used. "
+        "This should be a subclass of Agent above.",
+    )
+    agent_kwargs: dict[str, JsonValue] = Field(
+        default_factory=dict,
+        description="Keyword arguments to pass to the agent's constructor.",
+    )
+
+
    
+[docs]
+    def construct_agent(self) -> Agent:
+        return Agent.from_name(self.agent_type, **self.agent_kwargs)
    
+
+
+    def __hash__(self) -> int:
+        return hash(self.agent_type + json.dumps(self.agent_kwargs, sort_keys=True))
    
+
+
+
+def _find_ops(  # noqa: C901
+    root: object, root_name: str = "", visited: set[int] | None = None
+) -> Iterable[tuple[str, Op]]:
+    """Recursive function to find children that are Ops and the attr chain to reach them.
+
+    E.g. if root.module.op is an Op, then we will yield ("module.op", root.module.op).
+    These are not fully qualified names, but more like "locally qualified names". In the above
+    example, "root." + "module.op" is the fully qualified name.
+    This is an internal function - Agent.named_ops() should usually suffice.
+
+    Args:
+        root: Any object that might hold Ops.
+        root_name: The name of the root object. Defaults to empty string and is passed as an arg to
+            make this method recursive.
+        visited: a set of visited node IDs to avoid loops. Defaults to None.
+
+    Yields:
+        Two-tuple of (locally qualified name, Op) pairs
+    """
+    # Recursive function to find children that are Ops and the
+    # attribute chain to reach them.
+    if visited is None:
+        visited = set()
+
+    if isinstance(root, Op):
+        yield root_name, root
+        # Assume an Op may not have sub-Ops. I think this is sound, since
+        # we wouldn't be tracking the compute graph properly if it did.
+        return
+
+    if "__pydantic_parent_namespace__" in root_name:
+        # Skip Pydantic internals
+        return
+
+    # Don't recurse into PyTorch objects because they won't contain Ops
+    if torch is not None and (  # type: ignore[redundant-expr]
+        isinstance(root, torch.Tensor | torch.nn.Module)
+    ):
+        return
+
+    # Similarly for numpy
+    if isinstance(root, np.ndarray):
+        return
+
+    # loop through 3 types of containers: dicts, collections, and objects
+    if isinstance(root, Mapping):
+        named_attrs: Any = root.items()
+    elif isinstance(root, Sequence | Collection) and not isinstance(root, str | bytes):
+        named_attrs = enumerate(root)
+    elif hasattr(root, "__dict__"):
+        # object?
+        named_attrs = root.__dict__.items()
+    else:
+        # couldn't descend
+        return
+
+    for k, v in named_attrs:
+        id_v = id(v)
+        if id_v not in visited:
+            # only visit each object once - avoid loops, etc.
+            visited.add(id_v)
+            if root_name:
+                k = f"{root_name}.{k}"
+            yield from _find_ops(v, root_name=k, visited=visited)
+
    + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/agent/agent_client.html b/docs/_build/html/_modules/ldp/agent/agent_client.html new file mode 100644 index 00000000..d520c0cb --- /dev/null +++ b/docs/_build/html/_modules/ldp/agent/agent_client.html @@ -0,0 +1,260 @@ + + + + + + + ldp.agent.agent_client — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.agent.agent_client

    +import os
+import secrets
+from typing import TYPE_CHECKING, Annotated, TypeVar
+
+import httpx
+from aviary.message import Message
+from aviary.tools import Messages, Tool, ToolRequestMessage, ToolsAdapter
+from pydantic import BaseModel
+
+from ldp.graph import OpResult, get_training_mode
+
+from .agent import Agent
+from .simple_agent import SimpleAgentState
+
+if TYPE_CHECKING:
+    from fastapi import FastAPI
+
+TSerializableAgentState = TypeVar("TSerializableAgentState", bound=BaseModel)
+
+
+
    
+[docs]
+class HTTPAgentClient(Agent[TSerializableAgentState]):
+    """Interact with an Agent running in a server via POST requests."""
+
+    def __init__(
+        self,
+        agent_state_type: type[TSerializableAgentState],
+        server_url: str,
+        request_headers: httpx._types.HeaderTypes | None = None,
+        request_timeout: float | None = None,
+    ):
+        super().__init__()
+        self._agent_state_type = agent_state_type
+        self._request_url = server_url
+        self._request_headers = request_headers
+        self._request_timeout = request_timeout
+
+
    
+[docs]
+    async def get_asv(
+        self,
+        agent_state: TSerializableAgentState,
+        obs: list[Message],
+    ) -> tuple[OpResult[ToolRequestMessage], TSerializableAgentState, float]:
+        async with httpx.AsyncClient() as client:
+            response = await client.post(
+                f"{self._request_url}/get_asv",
+                json={
+                    "agent_state": agent_state.model_dump(),
+                    "obs": [m.model_dump() for m in obs],
+                    "training": get_training_mode(),
+                },
+                headers=self._request_headers,
+                timeout=self._request_timeout,
+            )
+            response.raise_for_status()
+            response_data = response.json()
+            return (
+                OpResult.from_dict(ToolRequestMessage, response_data[0]),
+                self._agent_state_type(**response_data[1]),
+                response_data[2],
+            )
    
+
+
+
    
+[docs]
+    async def init_state(self, tools: list[Tool]) -> TSerializableAgentState:
+        async with httpx.AsyncClient() as client:
+            response = await client.post(
+                f"{self._request_url}/init_state",
+                json=ToolsAdapter.dump_python(tools),
+                headers=self._request_headers,
+                timeout=self._request_timeout,
+            )
+            response.raise_for_status()
+            return self._agent_state_type(**response.json())
    
+
    
+
+
+
+
    
+[docs]
+def make_simple_agent_server(
+    agent: Agent[SimpleAgentState], render_docs: bool = False
+) -> "FastAPI":
+    """
+    Make a FastAPI app designed to work with the above HTTPAgentClient.
+
+    Here's how this works:
+    1. There is an entity orchestrating an Agent's interactions with an Environment.
+       A simple example of this is an integration test that sequentially calls
+       Agent.get_asv and Environment.step.
+    2. That entity is given the above HTTPAgentClient. Any Agent.init_state or
+       Agent.get_asv calls the orchestration entity makes are actually
+       POST requests under the hood. The agent's "brains" aren't local.
+    3. Remotely, this server code is running, and is where the actual Agent logic lives.
+       An example of this is a remote server containing GPU(s).
+    """
+    try:
+        from fastapi import Body, Depends, FastAPI, HTTPException, status
+        from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
+    except ModuleNotFoundError as exc:
+        raise ImportError(
+            "Please install aviary with the 'server' extra like so:"
+            " `pip install aviary[server]`."
+        ) from exc
+
+    asgi_app = FastAPI(
+        title=f"aviary.Agent {type(agent).__name__}",
+        description="Serve inference endpoints for an aviary.Agent with a SimpleAgentState",
+        # Only render Swagger docs if local since we don't have a login here
+        docs_url="/docs" if render_docs else None,
+        redoc_url="/redoc" if render_docs else None,
+    )
+    auth_scheme = HTTPBearer()
+
+    async def validate_token(
+        token: Annotated[HTTPAuthorizationCredentials, Depends(auth_scheme)],
+    ) -> HTTPAuthorizationCredentials:
+        # NOTE: don't use os.environ.get() to avoid possible empty string matches, and
+        # to have clearer server failures if the AUTH_TOKEN env var isn't present
+        if not secrets.compare_digest(token.credentials, os.environ["AUTH_TOKEN"]):
+            raise HTTPException(
+                status_code=status.HTTP_401_UNAUTHORIZED,
+                detail="Incorrect bearer token",
+                headers={"WWW-Authenticate": "Bearer"},
+            )
+        return token
+
+    @asgi_app.get("/info")
+    def info(
+        _: Annotated[HTTPAuthorizationCredentials, Depends(validate_token)],
+    ) -> dict[str, str]:
+        """Get agent metadata, useful for debugging."""
+        return {"agent_type": type(agent).__name__}
+
+    @asgi_app.post("/get_asv")
+    async def get_asv(
+        agent_state: SimpleAgentState,
+        obs: Messages,
+        _: Annotated[HTTPAuthorizationCredentials, Depends(validate_token)],
+        training: Annotated[bool, Body()] = True,
+    ) -> tuple[dict, SimpleAgentState, float]:
+        if training:
+            raise NotImplementedError("Training is not yet supported.")
+        action, agent_state, vhat = await agent.get_asv(agent_state, obs)
+        return action.to_dict(), agent_state, vhat
+
+    @asgi_app.post("/init_state")
+    async def init_state(
+        tools: list[Tool],
+        _: Annotated[HTTPAuthorizationCredentials, Depends(validate_token)],
+    ) -> SimpleAgentState:
+        return await agent.init_state(tools)
+
+    return asgi_app
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/agent/interactive_agent.html b/docs/_build/html/_modules/ldp/agent/interactive_agent.html new file mode 100644 index 00000000..89da7c21 --- /dev/null +++ b/docs/_build/html/_modules/ldp/agent/interactive_agent.html @@ -0,0 +1,225 @@ + + + + + + + ldp.agent.interactive_agent — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.agent.interactive_agent

    +import contextlib
+import json
+from typing import Any
+
+from aviary.message import Message
+from aviary.tools import Tool, ToolRequestMessage
+from aviary.tools.base import ToolCall
+
+from ldp.graph import IdentityOp, OpResult, compute_graph
+
+from .agent import Agent
+from .simple_agent import SimpleAgentState
+
+MISSING_DEFAULT = object()
+CLEAR = "CLEAR"  # Clears the current input and allows the user to start over, e.g. if they made a mistake
+EXIT = "EXIT"  # Exits the agent by raising a RuntimeError. Makes it possible to interrupt a rollout
+
+
+
    
+[docs]
+class InteractiveAgent(Agent[SimpleAgentState]):
+    """An "agent" that provides an interface for human users to interact with environments."""
+
+    def __init__(self):
+        self.action_op = IdentityOp[ToolRequestMessage]()
+
+
    
+[docs]
+    async def init_state(self, tools: list[Tool]) -> SimpleAgentState:
+        print()  # add a newline to flush any progress bars, etc
+        print("AVAILABLE TOOLS:\n" + ("-" * 80))
+        for tool in tools:
+            info = tool.info
+            docstring = f"{info.name}("
+            arg_docs = []
+            for pname, pprops in info.parameters.properties.items():
+                docstring += self._get_param_string(pname, pprops) + ", "
+                arg_doc = "   " + pname
+                if "description" in pprops:
+                    arg_doc += ": " + pprops.get("description", "")
+                arg_docs.append(arg_doc)
+
+            docstring = docstring.rstrip(", ") + "):\n"
+            docstring += "   " + info.description + "\n\n"
+            docstring += "\n".join(arg_docs)
+            docstring += "\n"
+            print(docstring)
+        print("-" * 80)
+        return SimpleAgentState(tools=tools)
    
+
+
+
    
+[docs]
+    @compute_graph()
+    async def get_asv(  # noqa: C901
+        self, agent_state: SimpleAgentState, obs: list[Message]
+    ) -> tuple[OpResult[ToolRequestMessage], SimpleAgentState, float]:
+        print()  # add a newline to flush any progress bars, etc
+        print("OBSERVATIONS:\n" + ("-" * 80))
+        for msg in obs:
+            print((msg.content or "") + "\n")
+        print("-" * 80)
+
+        next_agent_state = agent_state.get_next_state(obs)
+
+        tool: Tool | None = None
+        while not tool:
+            tool_choice = input(">>> Select tool by name: ")
+            if tool_choice == CLEAR:
+                continue
+            if tool_choice == EXIT:
+                raise RuntimeError("User requested to kill the agent.")
+
+            tool = next(
+                (t for t in agent_state.tools if t.info.name == tool_choice), None
+            )
+            if not tool:
+                print(
+                    f"Tool '{tool_choice}' not found. Please select from the above tools."
+                )
+
+        params: dict[str, Any] = {}
+        for pname, pprops in tool.info.parameters.properties.items():
+            pdefault = pprops.get("default", MISSING_DEFAULT)
+            prompt = f">>> Enter parameter ({self._get_param_string(pname, pprops)}): "
+            while True:
+                value = input(prompt)
+                if value == CLEAR:
+                    return await self.get_asv(agent_state, obs)  # just start over
+                if value == EXIT:
+                    raise RuntimeError("User requested to kill the agent.")
+
+                with contextlib.suppress(json.JSONDecodeError):
+                    # lets us load ints, etc. Otherwise, assume it's a string
+                    value = json.loads(value)
+                if not value:
+                    if pdefault is MISSING_DEFAULT:
+                        print("Parameter is required.")
+                        continue
+
+                    value = pdefault
+
+                params[pname] = value
+                break
+
+        tool_call = ToolCall.from_tool(tool, **params)
+        action = await self.action_op(ToolRequestMessage(tool_calls=[tool_call]))
+
+        next_agent_state.messages = [*next_agent_state.messages, action.value]
+
+        return action, next_agent_state, 0.0
    
+
+
+    @staticmethod
+    def _get_param_string(pname: str, pprops: dict[str, Any]) -> str:
+        pstring = pname
+        if ptype := (pprops.get("type") or "Any"):
+            pstring += f": {ptype}"
+
+        if (pdefault := pprops.get("default", MISSING_DEFAULT)) is not MISSING_DEFAULT:
+            pstring += f" = {pdefault}"
+        return pstring
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/agent/memory_agent.html b/docs/_build/html/_modules/ldp/agent/memory_agent.html new file mode 100644 index 00000000..588d3cd1 --- /dev/null +++ b/docs/_build/html/_modules/ldp/agent/memory_agent.html @@ -0,0 +1,235 @@ + + + + + + + ldp.agent.memory_agent — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.agent.memory_agent

    +"""This module defines the MemoryAgent class, which extends a base agent model with memory.
+
+capabilities. The MemoryAgent can pick and invoke tools based on the stored and retrieved
+memories, formatted using specified prompts. A memory is typically a set of previous trajectories
+"""
+
+from collections.abc import Awaitable, Callable, Iterable
+from typing import ClassVar, cast
+
+from aviary.message import Message
+from aviary.tools import ToolRequestMessage
+from pydantic import ConfigDict, Field
+
+from ldp.graph import (
+    FxnOp,
+    Memory,
+    MemoryModel,
+    MemoryOp,
+    OpResult,
+    PromptOp,
+    compute_graph,
+)
+from ldp.llms.prompts import indent_xml
+
+from .simple_agent import SimpleAgent, SimpleAgentState
+
+
+async def _default_query_factory(messages: Iterable[Message]) -> str:
+    return "\n\n".join([str(m) for m in messages if m.role != "system"])
+
+
+
    
+[docs]
+class MemoryAgent(SimpleAgent):
+    """
+    Simple agent that can pick and invoke tools with memory.
+
+    NOTE: the MemoryAgent does not maintain an explicit value estimate,
+    it simply supplies previous trajectories via the prompt.
+    As such, the value estimate vhat will always be zero.
+    """
+
+    # Working around https://github.com/pydantic/pydantic/issues/10551
+    default_query_factory: ClassVar[Callable[[Iterable[Message]], Awaitable[str]]] = (
+        _default_query_factory
+    )
+
+    prompt: str = Field(
+        default=(
+            "<episode-memories>\n<description>\n"
+            "These are relevant memories from previous attempts at similar tasks, "
+            "along with the action taken and the discounted cumulative reward from that action. "
+            "A negative reward is failure, a positive reward is success.\n"
+            "</description>{memories}</episode-memories>\n\n"
+            "Considering the memories, choose the next action."
+        ),
+        description="Prompt that includes the memories.",
+    )
+    query_factory: Callable[[Iterable[Message]], Awaitable[str]] = Field(
+        default=default_query_factory,
+        description=(
+            "Async function to generate a Memory query string from messages. It's async"
+            " so this can involve an LLM completion if desired."
+        ),
+        exclude=True,
+    )
+    memory_prompt: str = Field(
+        default="<memory><obs>{input}</obs><action>{output}</action><reward>{value}</reward></memory>",
+        description="Prompt for formatting an individual memory. "
+        "Use XML instead of JSON to avoid potential escaping issues.",
+    )
+    num_memories: int = Field(
+        default=MemoryModel.DEFAULT_MEMORY_MATCHES,
+        description="Number of memories to retrieve from MemoryOp",
+    )
+    # Freeze to ensure the only mutation happens in either the agent state (which is
+    # passed around) or in the internal Ops
+    model_config = ConfigDict(frozen=True)
+
+    @staticmethod
+    def _format_memories(prompt: str, memories: Iterable[Memory]) -> str:
+        return indent_xml(
+            "\n".join([
+                prompt.format(**m.model_dump(exclude={"run_id", "template"}))
+                for m in memories
+            ])
+        )
+
+    @staticmethod
+    def _package_messages(
+        msgs: list[Message], memory_prompt: str, use_memories: bool
+    ) -> list[Message]:
+        if use_memories:
+            return [*msgs, Message(content=memory_prompt)]
+        return msgs
+
+    def __init__(self, memory_model: MemoryModel | None = None, **kwargs):
+        super().__init__(**kwargs)
+        self._query_factory_op = FxnOp[str](self.query_factory)
+        self._memory_op = MemoryOp(memory_model)
+        self._format_memory_op = FxnOp(self._format_memories)
+        self._prompt_op = PromptOp(self.prompt)
+        self._package_op = FxnOp(self._package_messages)
+
+
    
+[docs]
+    @compute_graph()
+    async def get_asv(
+        self, agent_state: SimpleAgentState, obs: list[Message]
+    ) -> tuple[OpResult[ToolRequestMessage], SimpleAgentState, float]:
+        next_state = agent_state.get_next_state(obs)
+
+        memories = await self._memory_op(
+            query=await self._query_factory_op(next_state.messages),
+            matches=self.num_memories,
+        )
+        packaged_messages = await self._package_op(
+            next_state.messages,
+            memory_prompt=await self._prompt_op(
+                memories=await self._format_memory_op(self.memory_prompt, memories)
+            ),
+            use_memories=bool(memories.value),
+        )
+        result = cast(
+            OpResult[ToolRequestMessage],
+            await self._llm_call_op(
+                await self._config_op(), msgs=packaged_messages, tools=next_state.tools
+            ),
+        )
+        next_state.messages = [*next_state.messages, result.value]
+        return result, next_state, 0.0
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/agent/react_agent.html b/docs/_build/html/_modules/ldp/agent/react_agent.html new file mode 100644 index 00000000..1dba1962 --- /dev/null +++ b/docs/_build/html/_modules/ldp/agent/react_agent.html @@ -0,0 +1,255 @@ + + + + + + + ldp.agent.react_agent — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.agent.react_agent

    +import logging
+from typing import Any, Self, cast
+
+from aviary.message import MalformedMessageError, Message
+from aviary.tools import Tool, ToolRequestMessage, ToolResponseMessage
+from pydantic import BaseModel, ConfigDict, Field
+from tenacity import (
+    Future,
+    RetryCallState,
+    before_sleep_log,
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+)
+
+from ldp.graph import OpResult, compute_graph
+from ldp.graph.modules.react import (
+    ACT_DEFAULT_PROMPT_TEMPLATE,
+    REACT_DEFAULT_PROMPT_TEMPLATE,
+    ReActModule,
+    ToolDescriptionMethods,
+)
+
+from . import DefaultLLMModelNames
+from .agent import Agent
+from .simple_agent import SimpleAgentState
+
+logger = logging.getLogger(__name__)
+
+
+
    
+[docs]
+class ReActAgent(BaseModel, Agent[SimpleAgentState]):
+    """An Act or ReAct Agent built to work with chat models.
+
+    Paper: https://arxiv.org/abs/2210.03629
+
+    The ReAct style is like so, and note Act style has no 'Thought: ' entries:
+    System:
+        Answer the following questions as best you can. You have access to the following tools:
+
+            {tools}
+
+            Use the following format:
+
+            Thought: you should always think about what to do
+            Action: the action to take, should be one of [{tool_names}]
+            Action Input: the input to the action
+            Observation: the result of the action
+            ... (this Thought/Action/Action Input/Observation can repeat N times)
+    User:
+        {questions}
+    Assistant:
+        Thought:
+        Action:
+        Action Input:
+    User:
+        Observation:
+    Assistant:
+        Thought:
+        Action:
+        Action Input:
+    ...
+
+    One notable design decision is that ReAct's state does not necessarily
+    track ToolRequestMessage. Recall that aviary is in a partially observable
+    domain, meaning we don't need to have perfect symmetry with Environments.
+    Instead, ReActAgent's state stores a ReAct-style message history, where the
+    messages are plain Message (and not a ToolRequestMessage).
+    """
+
+    # Freeze to ensure the only mutation happens in either the agent state (which is
+    # passed around) or in the internal Ops
+    model_config = ConfigDict(frozen=True)
+
+    llm_model: dict[str, Any] = Field(
+        default={
+            "model": DefaultLLMModelNames.OPENAI.value,
+            "temperature": 0.1,
+            "logprobs": True,
+            "top_logprobs": 1,
+        },
+        description="Starting configuration for the LLM model.",
+    )
+    sys_prompt: str = Field(
+        default=REACT_DEFAULT_PROMPT_TEMPLATE,
+        description="Learnable system prompt template, defaults to ReAct.",
+    )
+    tool_description_method: ToolDescriptionMethods = Field(
+        default=ToolDescriptionMethods.STR,
+        description="Method used to describe the tools, defaults to 'str' description.",
+    )
+
+
    
+[docs]
+    @classmethod
+    def make_act_agent(cls, **kwargs) -> Self:
+        return cls(sys_prompt=ACT_DEFAULT_PROMPT_TEMPLATE, **kwargs)
    
+
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._react_module = ReActModule(
+            self.llm_model, self.sys_prompt, self.tool_description_method
+        )
+
+
    
+[docs]
+    async def init_state(self, tools: list[Tool]) -> SimpleAgentState:
+        return SimpleAgentState(tools=tools)
    
+
+
+
    
+[docs]
+    @staticmethod
+    def after_retry_failure_log(retry_state: RetryCallState):
+        logger.error(
+            f"Failed across {retry_state.attempt_number} attempts to run get_asv given"
+            f" arguments {retry_state.args} and kwargs {retry_state.kwargs}."
+        )
+        # NOTE: this blows up with the underlying exception... it isn't wrapped in a
+        # RetryError like normal tenacity
+        return cast(Future, retry_state.outcome).result()
    
+
+
+
    
+[docs]
+    @retry(
+        retry=retry_if_exception_type(MalformedMessageError),
+        before_sleep=before_sleep_log(logger, logging.WARNING),
+        stop=stop_after_attempt(5),
+        retry_error_callback=after_retry_failure_log,
+    )
+    @compute_graph()
+    async def get_asv(
+        self, agent_state: SimpleAgentState, obs: list[Message]
+    ) -> tuple[OpResult[ToolRequestMessage], SimpleAgentState, float]:
+        next_state = agent_state.get_next_state(
+            obs=[
+                Message(content=f"Observation: {m.content}")
+                if isinstance(m, ToolResponseMessage)
+                else m
+                for m in obs
+            ]
+        )
+
+        final_result, react_message = await self._react_module(
+            messages=next_state.messages, tools=next_state.tools
+        )
+        next_state.messages = [*next_state.messages, react_message]
+        return final_result, next_state, 0.0
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/agent/simple_agent.html b/docs/_build/html/_modules/ldp/agent/simple_agent.html new file mode 100644 index 00000000..012a7c1e --- /dev/null +++ b/docs/_build/html/_modules/ldp/agent/simple_agent.html @@ -0,0 +1,217 @@ + + + + + + + ldp.agent.simple_agent — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.agent.simple_agent

    +from __future__ import annotations
+
+from typing import Any, Self, cast
+
+from aviary.message import Message
+from aviary.tools import Tool, ToolRequestMessage, ToolResponseMessage
+from pydantic import BaseModel, ConfigDict, Field
+
+from ldp.graph import ConfigOp, LLMCallOp, OpResult, compute_graph
+from ldp.llms import prepend_sys
+
+from . import DefaultLLMModelNames
+from .agent import Agent
+
+
+
    
+[docs]
+class SimpleAgentState(BaseModel):
+    """Simple bucket for an Agent to access tools and store messages."""
+
+    tools: list[Tool] = Field(default_factory=list)
+    messages: list[ToolRequestMessage | ToolResponseMessage | Message] = Field(
+        default_factory=list
+    )
+
+
    
+[docs]
+    def get_next_state(
+        self,
+        obs: list[Message] | None = None,
+        tools: list[Tool] | None = None,
+        **kwargs,
+    ) -> Self:
+        """
+        Get the next agent state without mutating the optional prior state.
+
+        Do not mutate self here, just read from it.
+
+        Args:
+            obs: Optional observation messages to use in creating the next state.
+            tools: Optional list of tools available to the agent. If unspecified, these
+                should be pulled from the prior_state.
+            kwargs: Additional keyword arguments to pass to this class's constructor.
+
+        Returns:
+            The next agent state (which is not an in-place change to self).
+        """
+        return type(self)(
+            tools=tools if tools is not None else self.tools,
+            messages=self.messages + (obs or []),
+            **kwargs,
+        )
    
+
    
+
+
+
+
    
+[docs]
+class SimpleAgent(BaseModel, Agent[SimpleAgentState]):
+    """Simple agent that can pick and invoke tools with a language model.
+
+    It does not have a system prompt because it's meant to be lightweight.
+    """
+
+    # Freeze to ensure the only mutation happens in either the agent state (which is
+    # passed around) or in the internal Ops
+    model_config = ConfigDict(frozen=True)
+
+    llm_model: dict[str, Any] = Field(
+        default={"model": DefaultLLMModelNames.OPENAI.value, "temperature": 0.1},
+        description="Starting configuration for the LLM model. Trainable.",
+    )
+    sys_prompt: str | None = Field(
+        default=None,
+        description=(
+            "Opt-in system prompt. If one is passed, the system prompt is not set up to"
+            " be trainable, because this class is meant to be quite simple as far as"
+            " possible hyperparameters."
+        ),
+    )
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._config_op = ConfigOp[dict](config=self.llm_model)
+        self._llm_call_op = LLMCallOp()
+
+
    
+[docs]
+    async def init_state(self, tools: list[Tool]) -> SimpleAgentState:
+        return SimpleAgentState(tools=tools)
    
+
+
+
    
+[docs]
+    @compute_graph()
+    async def get_asv(
+        self, agent_state: SimpleAgentState, obs: list[Message]
+    ) -> tuple[OpResult[ToolRequestMessage], SimpleAgentState, float]:
+        next_state = agent_state.get_next_state(obs)
+
+        messages = (
+            prepend_sys(next_state.messages, sys_content=self.sys_prompt)
+            if self.sys_prompt is not None
+            else next_state.messages
+        )
+        result = cast(
+            OpResult[ToolRequestMessage],
+            await self._llm_call_op(
+                await self._config_op(), msgs=messages, tools=next_state.tools
+            ),
+        )
+        next_state.messages = [*next_state.messages, result.value]
+        return result, next_state, 0.0
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/agent/tree_of_thoughts_agent.html b/docs/_build/html/_modules/ldp/agent/tree_of_thoughts_agent.html new file mode 100644 index 00000000..f8bca7ff --- /dev/null +++ b/docs/_build/html/_modules/ldp/agent/tree_of_thoughts_agent.html @@ -0,0 +1,251 @@ + + + + + + + ldp.agent.tree_of_thoughts_agent — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.agent.tree_of_thoughts_agent

    +"""Module for the Tree of Thoughts agent.
+
+This module defines the Tree of Thoughts agent which uses a language model to generate and evaluate possible
+steps in a puzzle or problem-solving environment. The agent employs a tree search mechanism to explore different
+solutions and selects the most promising ones based on evaluations.
+
+This module is based on the following paper: https://openreview.net/forum?id=5Xc1ecxO1h
+
+Note: TreeofThoughtsAgent is currently tested as a baseline agent for Game of 24. It does not yet support tool calls
+that operate on the intermediate reasoning steps. This would probably entail a redefinition of the POMDP to
+undertake intermediate reasoning steps as environment steps.
+"""
+
+import logging
+from collections.abc import Callable
+from typing import Any
+
+from aviary.message import Message
+from aviary.tools import Tool, ToolCall, ToolRequestMessage
+from pydantic import BaseModel, ConfigDict, Field
+
+from ldp.graph import FxnOp, LLMCallOp, OpResult, compute_graph, get_call_id, op_call
+from ldp.llms import prepend_sys
+
+from . import DefaultLLMModelNames
+from .agent import Agent
+from .simple_agent import SimpleAgentState
+
+logger = logging.getLogger(__name__)
+
+
+
    
+[docs]
+class TreeofThoughtsAgent(BaseModel, Agent[SimpleAgentState]):
+    """Tree of Thoughts Agent.
+
+    This agent uses a tree search mechanism combined with an LLM to generate and evaluate
+    possible steps in a problem-solving environment. It is designed to explore different solutions
+    and select the most promising ones based on a heuristic evaluation function.
+    """
+
+    # Freeze to ensure the only mutation happens in either the agent state (which is
+    # passed around) or in the internal Ops
+    model_config = ConfigDict(frozen=True)
+
+    llm_model: dict[str, Any] = Field(
+        default={"model": DefaultLLMModelNames.OPENAI.value, "temperature": 0.1},
+        description="Starting configuration for the LLM model.",
+    )
+    value_prompt_func: Callable[[str, str], str] = Field(
+        default=lambda x, y: f"Value prompt for input: {x}, current path: {y}",
+        description="Function to format value prompt template.",
+    )
+    proposal_prompt_func: Callable[[str, str], str] = Field(
+        default=lambda x, y: f"Proposal prompt for input: {x}, current path: {y}",
+        description="Function to format proposal prompt template.",
+    )
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._prepend_op = FxnOp(prepend_sys)
+        self._llm_call_op = LLMCallOp()
+
+
    
+[docs]
+    async def init_state(self, tools: list[Tool]) -> SimpleAgentState:
+        return SimpleAgentState(tools=tools)
    
+
+
+
    
+[docs]
+    @compute_graph()
+    async def get_asv(  # type: ignore[override]
+        self,
+        agent_state: SimpleAgentState,
+        obs: list[Message],
+        eval_function: Callable[[str, list[str]], float],
+        n_steps: int = 0,
+        n_select_samples: int = 0,
+    ) -> tuple[OpResult[ToolRequestMessage], SimpleAgentState, float]:
+        """Generate and evaluate possible steps in the problem-solving process.
+
+        Args:
+            agent_state: The current state of the agent.
+            obs: The observations provided to the agent.
+            eval_function: Function to evaluate the generated paths in the tree.
+            n_steps: Number of steps to generate. Defaults to 0. Dictated by the environment.
+            n_select_samples: Number of tree nodes to select to explore in each step. Defaults to 0.
+
+        Returns:
+            The result of the operation, the new state of the agent, and the number representing the value (0).
+        """
+        new_state = agent_state.get_next_state()
+
+        x = str(obs[0].content)  # Current problem input
+        current_paths = [""]  # current candidate paths through the tree
+
+        for step in range(n_steps):
+            logger.info(f"Step {step}")
+
+            # propose candidate paths
+            candidate_paths = []
+            for path in current_paths:
+                proposal_prompt_init = self.proposal_prompt_func(x, path)
+                proposal_msgs = await self._prepend_op(
+                    new_state.messages, sys_content=proposal_prompt_init
+                )
+                proposal = await self._llm_call_op(self.llm_model, msgs=proposal_msgs)
+                # Append candidate paths to the current paths
+                candidate_paths += [
+                    path + _ + "\n"
+                    for _ in (proposal.value.content or "").split("\n")
+                    if _
+                ]
+
+            # score candidate paths
+            values = []
+            for path in candidate_paths:
+                value_prompt_init = self.value_prompt_func(x, path)
+                value_msgs = await self._prepend_op(
+                    new_state.messages, sys_content=value_prompt_init
+                )
+                value_outputs = await self._llm_call_op(self.llm_model, msgs=value_msgs)
+                values.append(eval_function(path, [value_outputs.value.content or ""]))
+
+            # greedy selection
+            values_with_index = [(v, i) for i, v in enumerate(values)]
+            sorted_values = sorted(values_with_index, key=lambda x: x[0], reverse=True)
+            select_ids = [i for _, i in sorted_values[:n_select_samples]]
+            select_new_paths = [candidate_paths[select_id] for select_id in select_ids]
+            current_paths = select_new_paths
+
+        # Generate tool calls for the selected answer
+        tool_calls = [
+            ToolCall.from_tool(tool, *[current_paths[0]]) for tool in new_state.tools
+        ]
+        result = ToolRequestMessage(content=current_paths[0], tool_calls=tool_calls)
+
+        new_state.messages = [*new_state.messages, result]
+        async with op_call():
+            op_result: OpResult[ToolRequestMessage] = OpResult(
+                call_id=get_call_id(),
+                op_name="TreeofThoughtsAgentOp",
+                op_class_name=type(self).__name__,
+                value=result,
+            )
+        return op_result, new_state, 0.0
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/alg/algorithms.html b/docs/_build/html/_modules/ldp/alg/algorithms.html new file mode 100644 index 00000000..c02eae30 --- /dev/null +++ b/docs/_build/html/_modules/ldp/alg/algorithms.html @@ -0,0 +1,226 @@ + + + + + + + ldp.alg.algorithms — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.alg.algorithms

    +import itertools
+from collections.abc import Sequence
+from typing import Any
+
+import networkx as nx
+from aviary.message import Message, join
+from aviary.tools import Tool, ToolRequestMessage
+
+from ldp.graph import OpResult
+from ldp.graph.ops import GradOutType
+
+
+
    
+[docs]
+def to_network(  # noqa: C901
+    op_result: OpResult,
+    max_label_height: int | None = None,
+    max_label_width: int | None = None,
+    G: "nx.MultiDiGraph | None" = None,
+) -> "nx.MultiDiGraph":
+    """
+    Populate a NetworkX graph from the input op result's computation graph.
+
+    How to export Graphviz .dot file: nx.drawing.nx_pydot.write_dot(G, "file.dot")
+    How to render with Graphviz: nx.drawing.nx_pydot.to_pydot(G).write_png("file.png")
+    Online Graphviz renderer: https://dreampuf.github.io/GraphvizOnline/
+
+    Args:
+        op_result: Starting op result to recurse parent op calls and results.
+        max_label_height: Optional max label height (lines).
+        max_label_width: Optional max label width (chars).
+        G: Optional graph to add nodes/edges to. Allows this to be a recursive function.
+
+    Returns:
+        Populated a NetworkX multi-edge directed graph.
+    """
+
+    def gvizify(x: Any) -> str:
+        """Stringify and then escape colons for Graphviz labels."""
+        if isinstance(x, OpResult):
+            x = x.value
+        if isinstance(x, Sequence):
+            if isinstance(x[0], Message):
+                x = join(x)
+            elif isinstance(x[0], Tool):
+                x = "\n".join(f"Tool {t.info.name}" for t in x)
+        elif isinstance(x, ToolRequestMessage):
+            # reformatting tool calls to make them easier to read
+            x = str(x).split(" for tool calls: ")
+            x = "\n".join(x).replace("; ", "\n")
+        result = (
+            "\n".join(
+                # Replace double quotes since they can interfere with colon escapes
+                # Strip here to avoid trailing spaces in the labels
+                x_line[:max_label_width].replace('"', "'").strip()
+                for i, x_line in enumerate(str(x).split("\n"))
+                if not max_label_height or i < max_label_height
+            )
+        ).strip()  # Remove trailing newlines
+        return result if ":" not in result else f'"{result}"'  # Escape colons
+
+    call_id = op_result.call_id
+    assert (
+        call_id is not None
+    ), "to_network currently assumes a compute graph is available"
+    ctx = op_result.ctx
+
+    op_result_str = gvizify(op_result)
+    op_result_node = gvizify(f"{op_result_str}\n{call_id.fwd_id}")
+    if G is None:
+        # TODO: figure out a way to use OpResult.get_compute_graph(), which builds
+        # a nx.DiGraph.
+        G = nx.MultiDiGraph()
+
+    op_call_str = gvizify(f"{ctx.op_name}:{call_id.fwd_id}")
+    if op_call_str in G:
+        # We have already visited this node - can skip.
+        return G
+
+    G.add_node(op_result_node, style="dotted", label=op_result_str)
+    G.add_edge(op_call_str, op_result_node)
+
+    if (
+        result_grad := ctx.get(key="grad_output", call_id=call_id, default=None)
+    ) is not None:
+        G.add_edge(
+            op_result_node,
+            op_call_str,
+            label=gvizify(result_grad),
+            style="dotted",
+        )
+
+    input_args, input_kwargs = op_result.inputs
+    grads = ctx.get(key="grad_input", call_id=call_id, default=None)
+    if grads is None:
+        arg_grads: list[GradOutType | None] = [None] * len(input_args)
+        kwarg_grads: dict[str, GradOutType | None] = dict.fromkeys(input_kwargs)
+    else:
+        arg_grads, kwarg_grads = grads
+
+    args_and_grads = itertools.chain(
+        zip(input_args, arg_grads, strict=True),
+        ((arg, kwarg_grads[key]) for key, arg in input_kwargs.items()),
+    )
+
+    for arg, grad in args_and_grads:
+        arg_str = gvizify(arg)
+
+        if not isinstance(arg, OpResult):
+            G.add_node(arg_str, style="dotted")
+
+        else:
+            arg_str = gvizify(f"{arg_str}\n{arg.call_id.fwd_id}")
+            G = to_network(
+                arg,
+                max_label_height=max_label_height,
+                max_label_width=max_label_width,
+                G=G,
+            )
+
+        G.add_edge(arg_str, op_call_str)
+        if grad is not None:
+            G.add_edge(op_call_str, arg_str, label=gvizify(grad), style="dotted")
+
+    return G
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/alg/beam_search.html b/docs/_build/html/_modules/ldp/alg/beam_search.html new file mode 100644 index 00000000..07142dfe --- /dev/null +++ b/docs/_build/html/_modules/ldp/alg/beam_search.html @@ -0,0 +1,329 @@ + + + + + + + ldp.alg.beam_search — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.alg.beam_search

    +import asyncio
+import uuid
+from collections.abc import Awaitable, Callable, Sequence
+from contextlib import suppress
+from typing import NamedTuple
+
+from aviary.env import Environment
+
+from ldp.agent.agent import Agent, TAgentState
+from ldp.data_structures import Trajectory, Transition
+
+from .callbacks import Callback
+from .rollout import AgentError, EnvError, TEnv, reraise_exc_as
+
+
+
    
+[docs]
+class Beam(NamedTuple):
+    # An ongoing beam contains two things: the trajectory up to now
+    # and the environment that the last action was sampled from. We
+    # need both to continue sampling the next step.
+    traj: Trajectory
+    env: Environment
    
+
+
+
+
    
+[docs]
+class BeamSearchRollout:
+    def __init__(
+        self,
+        agent: Agent,
+        beam_width: int,
+        samples_per_beam: int,
+        env_clone_fn: Callable[[TEnv], Awaitable[TEnv]],
+        agent_clone_fn: Callable[[TAgentState], TAgentState],
+        scoring_fn: Callable[[Trajectory], Awaitable[float]],
+        replay_actions_on_clone: bool = False,
+        callbacks: Sequence[Callback] | None = None,
+        catch_agent_failures: bool = True,
+        catch_env_failures: bool = True,
+        verbose: bool = False,
+    ):
+        self.agent = agent
+
+        self.catch_agent_failures = catch_agent_failures
+        self.catch_env_failures = catch_env_failures
+
+        self.verbose = verbose
+
+        self.traj_buffer: dict[str, Trajectory] = {}
+        self.search_buffer: dict[str, list[Trajectory]] = {}
+
+        self.beam_width = beam_width
+        self.samples_per_beam = samples_per_beam
+
+        self.env_clone_fn = env_clone_fn
+        self.agent_clone_fn = agent_clone_fn
+        self.scoring_fn = scoring_fn
+        self.replay_actions_on_clone = replay_actions_on_clone
+
+        self.callbacks = callbacks or []
+
+
    
+[docs]
+    async def sample_trajectories(
+        self,
+        environments: Sequence[Environment],
+        max_steps: int | None = None,
+    ) -> list[Trajectory]:
+        self.traj_buffer.clear()
+        traj_ids = [uuid.uuid4().hex for _ in environments]
+
+        tasks = [
+            self._rollout(traj_id, env, max_steps)
+            for traj_id, env in zip(traj_ids, environments, strict=True)
+        ]
+        await asyncio.gather(*tasks)
+
+        return [self.traj_buffer[traj_id] for traj_id in traj_ids]
    
+
+
+    async def _rollout(
+        self, traj_id: str, env: Environment, max_steps: int | None
+    ) -> None:
+        with suppress(AgentError, EnvError):
+            # for samples_per_beam==1. we want to ensemble and pick the highest-scoring one
+            n_seeds = 1 if self.samples_per_beam > 1 else self.beam_width
+
+            done_beams: list[Beam] = []
+            beams = [
+                Beam(traj=Trajectory(traj_id=f"{traj_id}:{i}"), env=env)
+                for i in range(n_seeds)
+            ]
+            # will be replaced if rollout is successful
+            self.traj_buffer[traj_id] = beams[0].traj
+            self.search_buffer[traj_id] = []
+
+            with reraise_exc_as(EnvError, self.catch_env_failures):
+                init_obs, tools = await env.reset()
+            await asyncio.gather(*[
+                c.after_env_reset(traj_id, init_obs, tools) for c in self.callbacks
+            ])
+
+            with reraise_exc_as(AgentError, self.catch_agent_failures):
+                seed_agent_states = await asyncio.gather(
+                    *(self.agent.init_state(tools) for _ in range(n_seeds))
+                )
+            # TODO: implement after_agent_init_state callback
+
+            while len(done_beams) < self.beam_width and beams:
+                new_beams = []
+                for beam, seed_agent_state in zip(
+                    beams, seed_agent_states, strict=True
+                ):
+                    for i_sample in range(self.samples_per_beam):
+                        new_env = await self._clone_env(beam)
+                        if new_env is None:
+                            continue
+
+                        agent_state = self.agent_clone_fn(seed_agent_state)
+                        obs = (
+                            beam.traj.steps[-1].next_observation
+                            if beam.traj.steps
+                            else init_obs.copy()
+                        )
+
+                        await asyncio.gather(*[
+                            callback.before_transition(
+                                traj_id, self.agent, env, agent_state, obs
+                            )
+                            for callback in self.callbacks
+                        ])
+
+                        with reraise_exc_as(AgentError, self.catch_agent_failures):
+                            (
+                                action,
+                                next_agent_state,
+                                vhat,
+                            ) = await self.agent.get_asv(agent_state, obs)
+                        await asyncio.gather(*[
+                            callback.after_agent_get_asv(
+                                traj_id, action, next_agent_state, vhat
+                            )
+                            for callback in self.callbacks
+                        ])
+
+                        with reraise_exc_as(EnvError, self.catch_env_failures):
+                            next_obs, reward, done, trunc = await new_env.step(
+                                action.value
+                            )
+                        await asyncio.gather(*[
+                            callback.after_env_step(
+                                traj_id, next_obs, reward, done, trunc
+                            )
+                            for callback in self.callbacks
+                        ])
+
+                        step = Transition(
+                            timestep=len(beam.traj.steps),
+                            agent_state=agent_state,
+                            next_agent_state=next_agent_state,
+                            observation=obs,
+                            next_observation=next_obs,
+                            action=action,
+                            reward=reward,
+                            done=done,
+                            truncated=trunc,
+                            value=0.0,  # will be filled in
+                        )
+                        await asyncio.gather(*[
+                            callback.after_transition(traj_id, self.agent, env, step)
+                            for callback in self.callbacks
+                        ])
+
+                        new_beam = Beam(
+                            traj=Trajectory(
+                                traj_id=beam.traj.traj_id + f":{i_sample}",  # type: ignore[operator]
+                                steps=[*beam.traj.steps, step],
+                            ),
+                            env=new_env,
+                        )
+                        step.value = await self.scoring_fn(new_beam.traj)
+                        self.search_buffer[traj_id].append(new_beam.traj)
+
+                        if (
+                            not new_beam.traj.done
+                            and max_steps is not None
+                            and len(new_beam.traj.steps) >= max_steps
+                        ):
+                            last_step = new_beam.traj.steps[-1]
+                            last_step.done = last_step.truncated = True
+
+                        if new_beam.traj.done:
+                            done_beams.append(new_beam)
+                        else:
+                            new_beams.append(new_beam)
+
+                new_beams.sort(key=lambda b: b.traj.steps[-1].value, reverse=True)
+                beams, discarded = (
+                    new_beams[: self.beam_width],
+                    new_beams[self.beam_width :],
+                )
+                seed_agent_states = [b.traj.steps[-1].next_agent_state for b in beams]
+                await asyncio.gather(*[d.env.close() for d in discarded])
+
+            await asyncio.gather(*[b.env.close() for b in beams])
+
+            self.traj_buffer[traj_id] = max(
+                done_beams,
+                key=lambda b: (b.traj.steps[-1].truncated, b.traj.steps[-1].value),
+            ).traj
+
+    async def _clone_env(self, beam: Beam) -> Environment | None:
+        try:
+            with reraise_exc_as(EnvError, self.catch_env_failures):
+                # I'm not sure how to type hint this
+                env = await self.env_clone_fn(beam.env)  # type: ignore[arg-type]
+                if self.replay_actions_on_clone:
+                    # Some envs can't be cloned, so instead replay.
+                    # We rely on env_clone_fn to properly reset the env if needed.
+                    # We assume a deterministic env, so the return values are discarded.
+                    for step in beam.traj.steps:
+                        if step.action is not None:
+                            _ = await env.step(step.action.value)
+                return env
+        except EnvError:
+            return None
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/alg/callbacks.html b/docs/_build/html/_modules/ldp/alg/callbacks.html new file mode 100644 index 00000000..9fadfc52 --- /dev/null +++ b/docs/_build/html/_modules/ldp/alg/callbacks.html @@ -0,0 +1,758 @@ + + + + + + + ldp.alg.callbacks — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.alg.callbacks

    +import json
+import logging
+import os
+import time
+from collections import defaultdict
+from collections.abc import Collection, Iterable, Sequence
+from pathlib import Path
+from typing import Any, cast
+
+import aiofiles
+from aviary.env import Environment, TaskDataset
+from aviary.message import Message
+from aviary.tools import MessagesAdapter, Tool, ToolRequestMessage
+
+from ldp.agent import Agent
+from ldp.data_structures import Trajectory, Transition
+from ldp.graph import OpCtx, OpResult
+
+try:
+    import wandb
+except ImportError:
+    wandb = None  # type: ignore[assignment]
+
+logger = logging.getLogger(__name__)
+
+
+
    
+[docs]
+class Callback:
+    """Base class for callbacks used by RolloutManager/Evaluator/OnlineTrainer.
+
+    Pseudocode to demonstrate how callback methods are invoked (marked as *):
+
+    RolloutManager.sample_trajectories():
+        env.reset()
+        callback.after_env_reset() *
+        agent.init_state()
+        callback.after_agent_init_state() *
+        while not done:
+            callback.before_transition() *
+            agent.get_asv()
+            callback.after_agent_get_asv() *
+            env.step()
+            callback.after_env_step() *
+            callback.after_transition() *
+
+    Evaluator.evaluate / OnlineTrainer._eval_loop():
+        callback.before_eval_loop() *
+        for batch in eval_dataset:
+            rollout_manager.sample_trajectories()
+            callback.after_eval_step() *
+        callback.after_eval_loop() *
+
+    OfflineTrainer / OnlineTrainer.train():
+        for batch in train_dataset:
+            rollout_manager.sample_trajectories() # if online
+            optimizer.aggregate()
+            if updating_optimizer:
+                optimizer.update()
+                callback.after_update() *
+            callback.after_train_step() *
+    """
+
+
    
+[docs]
+    async def before_transition(
+        self,
+        traj_id: str,
+        agent: Agent,
+        env: Environment,
+        agent_state: Any,
+        obs: list[Message],
+    ) -> None:
+        """Invoked by runners before each transition and after agent and env reset."""
    
+
+
+
    
+[docs]
+    async def after_agent_init_state(self, traj_id: str, init_state: Any) -> None:
+        """Invoked by runners after agent.init_state()."""
    
+
+
+
    
+[docs]
+    async def after_agent_get_asv(
+        self,
+        traj_id: str,
+        action: OpResult[ToolRequestMessage],
+        next_agent_state: Any,
+        value: float,
+    ) -> None:
+        """Invoked by runners after agent.get_asv()."""
    
+
+
+
    
+[docs]
+    async def after_env_reset(
+        self, traj_id: str, obs: list[Message], tools: list[Tool]
+    ) -> None:
+        """Invoked by runners after env.reset()."""
    
+
+
+
    
+[docs]
+    async def after_env_step(
+        self, traj_id: str, obs: list[Message], reward: float, done: bool, trunc: bool
+    ) -> None:
+        """Invoked by runners after env.step()."""
    
+
+
+
    
+[docs]
+    async def after_transition(
+        self, traj_id: str, agent: Agent, env: Environment, transition: Transition
+    ) -> None:
+        """Invoked by runners after each transition."""
    
+
+
+
    
+[docs]
+    async def after_train_step(self, trajectories: Sequence[Trajectory]) -> None:
+        """Invoked by OnlineTrainer after each training step."""
    
+
+
+
    
+[docs]
+    async def before_eval_loop(self) -> None:
+        """Invoked by Evaluator and OnlineTrainer before the evaluation loop."""
    
+
+
+
    
+[docs]
+    async def after_eval_step(self, trajectories: Sequence[Trajectory]) -> None:
+        """Invoked by Evaluator and OnlineTrainer after each evaluation step."""
    
+
+
+
    
+[docs]
+    async def after_eval_loop(self) -> None:
+        """Invoked by Evaluator and OnlineTrainer after the evaluation loop."""
    
+
+
+
    
+[docs]
+    async def after_update(self) -> None:
+        """Invoked by OnlineTrainer after each optimizer.update() call."""
    
+
    
+
+
+
+
    
+[docs]
+class TrajectoryFileCallback(Callback):
+    """Callback that writes trajectories to a file."""
+
+    def __init__(self, output_dir: os.PathLike | str):
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+
+        self.out_files: dict[str, Path] = {}
+        self.trajs: dict[str, Trajectory] = defaultdict(Trajectory)
+
+    def _make_filename(self, traj_id: str, env: Environment) -> str:
+        """Create the filename for the output file."""
+        return f"{traj_id}.jsonl"
+
+
    
+[docs]
+    async def before_transition(
+        self,
+        traj_id: str,
+        agent: Agent,
+        env: Environment,
+        agent_state: Any,
+        obs: list[Message],
+    ) -> None:
+        if traj_id not in self.out_files:
+            self.out_files[traj_id] = self.output_dir / self._make_filename(
+                traj_id, env
+            )
    
+
+
+
    
+[docs]
+    async def after_transition(
+        self, traj_id: str, agent: Agent, env: Environment, transition: Transition
+    ) -> None:
+        assert traj_id in self.out_files
+        traj = self.trajs[traj_id]
+        traj.steps.append(transition)
+        # TODO: make this async?
+        traj.to_jsonl(self.out_files[traj_id])
    
+
+
+
    
+[docs]
+    def cleanup(self) -> None:
+        for out_file in self.out_files.values():
+            if out_file.exists():
+                out_file.unlink()
    
+
    
+
+
+
+
    
+[docs]
+class RolloutDebugDumpCallback(Callback):
+    """Dump JSONL files for each agent and environment step to a directory."""
+
+    def __init__(self, output_dir: os.PathLike | str):
+        """Initialize.
+
+        Args:
+            output_dir: Directory to place JSONL files.
+        """
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+
+        self.out_files: dict[str, Path] = {}
+
+    def _get_out_file(self, traj_id: str) -> Path:
+        if traj_id not in self.out_files:
+            self.out_files[traj_id] = self.output_dir / f"{traj_id}.jsonl"
+        return self.out_files[traj_id]
+
+
    
+[docs]
+    async def before_transition(
+        self,
+        traj_id: str,
+        agent: Agent,
+        env: Environment,
+        agent_state,
+        obs: list[Message],
+    ) -> None:
+        self.start = time.time()
    
+
+
+    def _get_elapsed_time(self, reset: bool = True) -> float:
+        elapsed = time.time() - self.start
+        if reset:
+            self.start = time.time()
+        return elapsed
+
+
    
+[docs]
+    async def after_agent_get_asv(
+        self,
+        traj_id: str,
+        action: OpResult[ToolRequestMessage],
+        next_agent_state: Any,
+        value: float,
+    ) -> None:
+        log_jsonl = json.dumps({
+            "event": "AGENT_GET_ASV",
+            "elapsed": self._get_elapsed_time(),
+            "action": action.value.model_dump(),
+            "value": value,
+        })
+        async with aiofiles.open(self._get_out_file(traj_id), "a") as f:
+            await f.write(log_jsonl + "\n")
    
+
+
+
    
+[docs]
+    async def after_env_step(
+        self, traj_id: str, obs: list[Message], reward: float, done: bool, trunc: bool
+    ) -> None:
+        log_jsonl = json.dumps({
+            "event": "ENV_STEP",
+            "elapsed": self._get_elapsed_time(),
+            "obs": MessagesAdapter.dump_python(obs),
+            "reward": reward,
+            "done": done,
+            "truncated": trunc,
+        })
+        async with aiofiles.open(self._get_out_file(traj_id), "a") as f:
+            await f.write(log_jsonl + "\n")
    
+
    
+
+
+
+
    
+[docs]
+class ComputeTrajectoryMetricsMixin:
+    """Mixin for TaskDataset classes to enable them to compute metrics."""
+
+    # Tools or tool names to include in trajectory metrics
+    tools_to_track: Collection[str | Tool] = set()
+
+
    
+[docs]
+    def compute_trajectory_metrics(
+        self,
+        trajectories: Sequence[Trajectory],
+    ) -> dict[str, list[float]]:
+        metrics: dict[str, list[float]] = {
+            "reward": [
+                sum(step.reward for step in traj.steps) for traj in trajectories
+            ],
+            "truncation_rate": [
+                sum(step.truncated for step in traj.steps) for traj in trajectories
+            ],
+            "avg_value": [
+                sum(step.value for step in traj.steps) / len(traj.steps)
+                for traj in trajectories
+            ],
+            "num_steps": [len(traj.steps) for traj in trajectories],
+            "failures": [traj.failed for traj in trajectories],
+        }
+        for tool in self.tools_to_track:  # Default of empty set means this is not run
+            if isinstance(tool, Tool):
+                tool = tool.info.name
+            metrics[f"tool_{tool}"] = [
+                sum(
+                    sum(tc.function.name == tool for tc in s.action.value.tool_calls)
+                    for s in traj.steps
+                    if isinstance(s.action, OpResult)
+                )
+                for traj in trajectories
+            ]
+        return metrics
    
+
    
+
+
+
+
    
+[docs]
+class TrajectoryMetricsCallback(Callback):
+    """
+    Compute metrics that are defined by task datasets.
+
+    NOTE: evaluation portion's after_eval_step/loop() is not concurrency safe because
+    trajectories should be stored in the order of after_eval_step() calls.
+    """
+
+    def __init__(
+        self,
+        train_dataset: TaskDataset | None = None,
+        eval_dataset: TaskDataset | None = None,
+        track_tool_usage: bool = False,
+    ):
+        self._datasets = train_dataset, eval_dataset
+        self._track_tool_usage = track_tool_usage
+        for ds in self._datasets:
+            if ds and not isinstance(ds, ComputeTrajectoryMetricsMixin):
+                raise ValueError(
+                    f"Dataset {ds} didn't implement"
+                    f" {ComputeTrajectoryMetricsMixin.__name__}, which is required for"
+                    " this callback."
+                )
+        self._train_metrics_fn = (
+            train_dataset.compute_trajectory_metrics if train_dataset else None  # type: ignore[attr-defined]
+        )
+        self._eval_metrics_fn = (
+            eval_dataset.compute_trajectory_metrics if eval_dataset else None  # type: ignore[attr-defined]
+        )
+
+        self._train_metrics: dict[str, list[float]] | None = None
+        self._eval_metrics: dict[str, list[float]] = {}
+
+
    
+[docs]
+    async def after_env_reset(
+        self, traj_id: str, obs: list[Message], tools: list[Tool]
+    ) -> None:
+        for ds in (ds for ds in self._datasets if ds):
+            if self._track_tool_usage:
+                cast(ComputeTrajectoryMetricsMixin, ds).tools_to_track = {
+                    t.info.name for t in tools
+                }
    
+
+
+
    
+[docs]
+    async def after_train_step(self, trajectories: Sequence[Trajectory]) -> None:
+        if self._train_metrics_fn is not None:
+            self._train_metrics = self._train_metrics_fn(trajectories)
    
+
+
+
    
+[docs]
+    async def after_eval_step(self, trajectories: Sequence[Trajectory]) -> None:
+        if self._eval_metrics_fn is not None:
+            for k, v in self._eval_metrics_fn(trajectories).items():
+                if k not in self._eval_metrics:
+                    # Don't use defaultdict - error prone in user code
+                    self._eval_metrics[k] = []
+                self._eval_metrics[k].extend(v)
    
+
+
+
    
+[docs]
+    async def after_eval_loop(self) -> None:
+        self._eval_metrics.clear()
    
+
    
+
+
+
+
    
+[docs]
+class MeanMetricsCallback(TrajectoryMetricsCallback):
+    """Take a mean of all metrics."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._train_means: dict[str, float] | None = None
+        self._eval_means: dict[str, float] | None = None
+
+
    
+[docs]
+    async def after_train_step(self, trajectories: Sequence[Trajectory]) -> None:
+        await super().after_train_step(trajectories)
+        if self._train_metrics is not None:
+            # may be None if train_dataset was not provided
+            self._train_means = self._compute_means(self._train_metrics)
    
+
+
+
    
+[docs]
+    async def after_eval_loop(self) -> None:
+        if self._eval_metrics:
+            # may be empty if eval_dataset was not provided
+            self._eval_means = self._compute_means(self._eval_metrics)
+        await super().after_eval_loop()
    
+
+
+    @staticmethod
+    def _compute_means(metrics: dict[str, list[float]]) -> dict[str, float]:
+        return {k: sum(v) / len(v) for k, v in metrics.items()}
+
+    @property
+    def train_means(self) -> dict[str, float]:
+        if self._train_means is None:
+            raise RuntimeError(
+                "Training means are only available after this callback is invoked."
+            )
+        return self._train_means
+
+    @property
+    def eval_means(self) -> dict[str, float]:
+        if self._eval_means is None:
+            raise RuntimeError(
+                "Evaluation means are only available after this callback is invoked."
+            )
+        return self._eval_means
    
+
+
+
+
    
+[docs]
+class WandBLoggingCallback(TrajectoryMetricsCallback):
+    def __init__(self, *args, **kwargs):
+        if wandb is None:
+            raise ImportError(
+                f"{type(self).__name__} processing requires the 'monitor' extra for"
+                " 'wandb'. Please: `pip install aviary-internal[monitor]`."
+            )
+        super().__init__(*args, **kwargs)
+
+        self._num_train_step = 0
+
+
    
+[docs]
+    async def after_train_step(self, trajectories: Sequence[Trajectory]) -> None:
+        await super().after_train_step(trajectories)
+        self._num_train_step += 1
+
+        if self._train_metrics is None:
+            return
+
+        # Each wandb.log() increments the wandb step by 1. Log the training step here
+        # so we can use it as an x-axis for training metrics that are logged by different
+        # wandb.log() calls.
+        wandb.log(
+            {
+                f"train/{key}_mean": sum(vals) / len(vals)
+                for key, vals in self._train_metrics.items()
+            }
+            | {"train/step": self._num_train_step}
+        )
    
+
+
+
    
+[docs]
+    async def after_eval_loop(self) -> None:
+        if not self._eval_metrics:
+            return
+
+        wandb.log({
+            f"eval/{key}_mean": (sum(vals) / len(vals) if vals else None)
+            for key, vals in self._eval_metrics.items()
+        })
+
+        await super().after_eval_loop()
    
+
    
+
+
+
+
    
+[docs]
+class ClearContextCallback(Callback):
+    def __init__(self, op_names: Iterable[str] | None = None):
+        self._op_names = op_names
+
+
    
+[docs]
+    async def after_eval_step(self, trajectories: Sequence[Trajectory]) -> None:
+        OpCtx.clear_contexts(self._op_names)
    
+
+
+
    
+[docs]
+    async def after_update(self) -> None:
+        OpCtx.clear_contexts(self._op_names)
    
+
    
+
+
+
+
    
+[docs]
+class LoggingCallback(MeanMetricsCallback):
+    """Custom callback for logging filtered metrics (e.g., pass rates) to the console.
+
+    This callback extends the `MeanMetricsCallback` and allows logging of user-specified metrics
+    after each training step and after the evaluation loop. It calculates the specified metrics
+    (e.g., pass rates) from the trajectories and logs the results.
+    """
+
+    def __init__(
+        self,
+        train_dataset: TaskDataset | None = None,
+        eval_dataset: TaskDataset | None = None,
+        metrics_to_log: Collection[str] | None = None,
+    ):
+        """Initialize the callback with a list of metric keys to log.
+
+        Args:
+            train_dataset: The training dataset for computing metrics.
+            eval_dataset: The evaluation dataset for computing metrics.
+            metrics_to_log: Optional metric names (e.g., ["pass"]) to log.
+                            If left as default of None, all metrics will be logged.
+        """
+        super().__init__(train_dataset, eval_dataset)
+        self.metrics_to_log = (
+            metrics_to_log or set()
+        )  # If no metrics provided, log all by default
+
+    def _log_filtered_metrics(self, metrics: dict[str, float], step_type: str) -> None:
+        """Helper function to log only the specified metrics.
+
+        Args:
+            metrics: Dictionary of calculated means for the current step (e.g., train or eval).
+            step_type: The type of step (e.g., "Train" or "Eval") for logging purposes.
+        """
+        if self.metrics_to_log:
+            for metric in self.metrics_to_log:
+                if metric in metrics:
+                    logger.info(
+                        f"{metric.upper()} RATE ({step_type}): {metrics[metric]:.5f}"
+                    )
+        else:
+            # Log all metrics if no specific ones are provided
+            logger.info(f"{step_type} Metrics: {metrics}")
+
+
    
+[docs]
+    async def after_train_step(self, trajectories: Sequence[Trajectory]) -> None:
+        """Log metrics and pass rate after each training step.
+
+        This method is called after every training step, calculating and logging
+        the training metrics and pass rate.
+
+        Args:
+            trajectories: A sequence of trajectories from the training step.
+        """
+        await super().after_train_step(trajectories)  # Call the parent to compute means
+        if self.train_means:
+            self._log_filtered_metrics(self.train_means, step_type="Train")
    
+
+
+
    
+[docs]
+    async def after_eval_loop(self) -> None:
+        """Log metrics and pass rate after the evaluation loop.
+
+        This method is called after the evaluation loop finishes, calculating and logging
+        the evaluation metrics and pass rate.
+        """
+        await super().after_eval_loop()  # Call the parent to compute means
+        if self.eval_means:
+            self._log_filtered_metrics(self.eval_means, step_type="Eval")
    
+
    
+
+
+
+
    
+[docs]
+class TerminalPrintingCallback(Callback):
+    """Callback that prints action, observation, and timing information to the terminal."""
+
+    def __init__(self):
+        self.start_time = None
+        # try now, rather than start running and die
+        try:
+            from rich.pretty import pprint  # noqa: F401
+        except ImportError as e:
+            raise ImportError(
+                f"rich is required for {type(self).__name__}. Please install it with `pip install rich`."
+            ) from e
+
+
    
+[docs]
+    async def before_transition(
+        self,
+        traj_id: str,
+        agent: Agent,
+        env: Environment,
+        agent_state: Any,
+        obs: list[Message],
+    ) -> None:
+        """Start the timer before each transition."""
+        self.start_time = time.time()
    
+
+
+
    
+[docs]
+    async def after_agent_get_asv(
+        self,
+        traj_id: str,
+        action: OpResult[ToolRequestMessage],
+        next_agent_state: Any,
+        value: float,
+    ) -> None:
+        from rich.pretty import pprint
+
+        print("\nAction:")
+        pprint(action.value, expand_all=True)
    
+
+
+
    
+[docs]
+    async def after_env_step(
+        self, traj_id: str, obs: list[Message], reward: float, done: bool, trunc: bool
+    ) -> None:
+        from rich.pretty import pprint
+
+        # Compute elapsed time
+        if self.start_time is not None:
+            elapsed_time = time.time() - self.start_time
+            self.start_time = None  # Reset timer
+        else:
+            elapsed_time = 0.0
+        print("\nObservation:")
+        pprint(obs, expand_all=True)
+        print(f"Elapsed time: {elapsed_time:.2f} seconds")
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/alg/datasets.html b/docs/_build/html/_modules/ldp/alg/datasets.html new file mode 100644 index 00000000..cb2f59a5 --- /dev/null +++ b/docs/_build/html/_modules/ldp/alg/datasets.html @@ -0,0 +1,114 @@ + + + + + + + ldp.alg.datasets — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.alg.datasets

    +from aviary.env import TASK_DATASET_REGISTRY
+from aviary.env import DummyTaskDataset as _DummyTaskDataset
+
+from .callbacks import ComputeTrajectoryMetricsMixin
+
+
+
    
+[docs]
+class DummyTaskDataset(_DummyTaskDataset, ComputeTrajectoryMetricsMixin):
+    pass
    
+
+
+
+TASK_DATASET_REGISTRY["dummy"] = "ldp.alg.datasets", "DummyTaskDataset"
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/alg/optimizer.html b/docs/_build/html/_modules/ldp/alg/optimizer.html new file mode 100644 index 00000000..30aa6a46 --- /dev/null +++ b/docs/_build/html/_modules/ldp/alg/optimizer.html @@ -0,0 +1,194 @@ + + + + + + + ldp.alg.optimizer — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.alg.optimizer

    +from typing import Any, cast
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from ldp.agent import Agent, MemoryAgent, ReActAgent
+from ldp.alg.optimizer.ape import APEOpt, APEScoreFn
+from ldp.alg.optimizer.memory import MemoryFactory, MemoryOpt, PositiveMemoryOpt
+from ldp.alg.optimizer.opt import _OPTIMIZER_REGISTRY, ChainedOptimizer, Optimizer
+
+_DEFAULT_OPTIMIZER_ERROR_MSG = (
+    "Didn't yet implement an optimizer of type {opt_type} for {agent_type}."
+)
+AUTOSELECT_BEST_OPTIMIZER = None
+
+
+
    
+[docs]
+class OptimizerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    optimizer_type: str | None = AUTOSELECT_BEST_OPTIMIZER
+    optimizer_kwargs: dict[str, Any] = Field(default_factory=dict)
    
+
+
+
+_DEFAULT_OPTIMIZER_MAP: dict[type[Agent], type[Optimizer]] = {
+    MemoryAgent: MemoryOpt,
+    ReActAgent: APEOpt,
+}
+
+
+
    
+[docs]
+def default_optimizer_factory(
+    agent: Agent,
+    optimizer_cls: str | type[Optimizer] | None = AUTOSELECT_BEST_OPTIMIZER,
+    **optimizer_kwargs,
+) -> Optimizer:
+    """A method that constructs a default optimizer for commonly-used agents.
+
+    Args:
+        agent: Agent to construct the optimizer for.
+        optimizer_cls: The optimizer class to use. If not specified, we will try a
+            default based on the provided agent.
+        optimizer_kwargs: Arguments forwarded to optimizer_cls.
+
+    Returns:
+        Instantiated optimizer.
+    """
+    if isinstance(optimizer_cls, str):
+        try:
+            optimizer_cls = _OPTIMIZER_REGISTRY[optimizer_cls]
+        except KeyError:
+            raise TypeError(
+                f"Optimizer class not supported by default_optimizer_factory: {optimizer_cls}"
+            ) from None
+
+    if optimizer_cls is None:
+        optimizer_cls = _DEFAULT_OPTIMIZER_MAP.get(agent.__class__)
+
+    # convince mypy that optimizer_cls is a type from here on
+    optimizer_cls = cast(type, optimizer_cls)
+
+    if isinstance(agent, MemoryAgent):
+        if optimizer_cls != MemoryOpt:
+            raise NotImplementedError(
+                _DEFAULT_OPTIMIZER_ERROR_MSG.format(
+                    opt_type=optimizer_cls.__name__, agent_type=MemoryAgent.__name__
+                )
+            )
+        return MemoryOpt.from_agent(agent, **optimizer_kwargs)
+    if isinstance(agent, ReActAgent):
+        if optimizer_cls != APEOpt:
+            raise NotImplementedError(
+                _DEFAULT_OPTIMIZER_ERROR_MSG.format(
+                    opt_type=optimizer_cls.__name__, agent_type=ReActAgent.__name__
+                )
+            )
+        return APEOpt.from_agent(agent, **optimizer_kwargs)
+    raise TypeError(f"Unsupported agent type: {agent.__class__.__name__}")
    
+
+
+
+__all__ = [
+    "APEOpt",
+    "APEScoreFn",
+    "ChainedOptimizer",
+    "MemoryFactory",
+    "MemoryOpt",
+    "Optimizer",
+    "OptimizerConfig",
+    "PositiveMemoryOpt",
+    "default_optimizer_factory",
+]
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/alg/optimizer/ape.html b/docs/_build/html/_modules/ldp/alg/optimizer/ape.html new file mode 100644 index 00000000..a8c456d9 --- /dev/null +++ b/docs/_build/html/_modules/ldp/alg/optimizer/ape.html @@ -0,0 +1,428 @@ + + + + + + + ldp.alg.optimizer.ape — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.alg.optimizer.ape

    +from __future__ import annotations
+
+import asyncio
+import logging
+from collections import UserDict
+from enum import StrEnum, auto
+from typing import Any, Self, cast
+
+from aviary.message import Message
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    JsonValue,
+    RootModel,
+    ValidationError,
+    model_validator,
+)
+
+from ldp.agent import ReActAgent
+from ldp.alg.optimizer.opt import Optimizer
+from ldp.data_structures import Trajectory
+from ldp.graph import LLMCallOp, OpResult, PromptOp
+from ldp.llms import LLMModel, LLMResult
+
+logger = logging.getLogger(__name__)
+
+
+
    
+[docs]
+class APEScoreFn(StrEnum):
+    # Use the reward as the APE score (as proposed in the paper).
+    # Goal is to maximize this score.
+    REWARD = auto()
+    # Use the gradient of the output of the PromptOp as the APE score.
+    # Goal is to push this to zero.
+    GRADIENT = auto()
    
+
+
+
+class _FormatDict(UserDict):
+    """Custom dictionary that stores missing items."""
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.key_set: set[str] = set()
+
+    def __missing__(self, key: str) -> str:
+        self.key_set.add(key)
+        return key
+
+
+
    
+[docs]
+def get_formatted_variables(s: str) -> set[str]:
+    """Returns the set of variables implied by the format string."""
+    format_dict = _FormatDict()
+    s.format_map(format_dict)
+    return format_dict.key_set
    
+
+
+
+
    
+[docs]
+class OutputPrompt(BaseModel):
+    prompt: str = Field(description="Prompt for language model")
    
+
+
+
+
    
+[docs]
+class Example(BaseModel):
+    input: JsonValue
+    output: JsonValue
+    score: float
    
+
+
+
+ExampleList = RootModel[list[Example]]
+
+
+
    
+[docs]
+class APEOpt(BaseModel, Optimizer):
+    """
+    Basic optimizer that acts as an Automatic Prompt Engineer (APE).
+
+    Paper: https://openreview.net/pdf?id=92gvk82DE-
+
+    Details:
+    - This implements the "forward mode generation" strategy.
+    - The score function used is the gradient (float) at the output of the
+      PromptOp being optimized. A zero gradient means the prompt was "good",
+      and a non-zero gradient means we can learn from the prompt.
+    - Possible improvements include:
+        - Extending the score function to the LLM result's logprobs
+        - Iterating with Monte Carlo Search
+        - Use of memory for further example augmentation
+    """
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    ### Configuration
+    max_examples: int | None = Field(
+        default=50,  # Comes from APE paper section 5.2
+        description=(
+            "Max number of examples to include in the below query_prompt, or None for"
+            " no limit. The paper mentions that more examples produce better prompt"
+            " proposals."
+        ),
+    )
+    system_prompt: str = (
+        "We are optimizing prompts for a language model."
+        " The model sees a prompt, an input, and then generates an output."
+    )
+    query_prompt: str = (
+        "Here are correct example outputs that the language model and prompt should produce:"
+        "\n{good_examples}"
+        '\n\nThe current prompt is: "{prompt}"'
+        "\n\nWhich resulted in the following incorrect input, output, and {score}:"
+        "\n{examples}"
+        "\n\nRevise the current prompt to improve the outputs."
+        " Your proposed prompt should be concise, correct, and specify the desired output format."
+    )
+    llm: LLMModel = Field(
+        default_factory=LLMModel,
+        description=(
+            "LLM used to update the prompt inside the PromptOp. The paper mentions that"
+            " larger models produce better prompt proposals."
+        ),
+    )
+
+    prompt_op: PromptOp = Field(description="PromptOp to be optimized.")
+    llm_call_op: LLMCallOp = Field(description="LLMCallOp to be optimized.")
+
+    score_fn: APEScoreFn = APEScoreFn.REWARD
+    good_reward_threshold: float | None = Field(
+        default=None,
+        description="If using reward as the score_fn, then a good example is defined by "
+        "reward>=good_reward_threshold.",
+    )
+    reward_discount: float = 1.0
+
+    ### State
+    examples: list[Example] = Field(default_factory=list)
+    good_examples: list[Example] = Field(default_factory=list)
+    steps: int = 0
+    trace: list[str] = Field(
+        default_factory=list, description="History of prompts used."
+    )
+
+
    
+[docs]
+    @model_validator(mode="after")
+    def validate_score_fn(self):
+        if self.score_fn == APEScoreFn.REWARD:
+            if self.good_reward_threshold is None:
+                raise ValueError(
+                    "good_reward_threshold must be set if using reward as the score function"
+                )
+            self._score_str = "rewards"
+        elif self.score_fn == APEScoreFn.GRADIENT:
+            # The gradient into the prompt op is the (signed) backpropagated error, and "gradient" would
+            # be confusing to the model in the prompt.
+            self._score_str = "errors"
+        else:
+            raise ValueError(f"Invalid score function {self.score_fn}")
+
+        return self
    
+
+
+
    
+[docs]
+    def model_post_init(self, __context: Any) -> None:
+        if self.prompt_op.prompt not in self.trace:
+            self.trace.append(self.prompt_op.prompt)
+
+        # Make sure updates are not run concurrently
+        self._update_lock = asyncio.Lock()
    
+
+
+
    
+[docs]
+    @classmethod
+    def from_agent(cls, agent: ReActAgent, **kwargs) -> Self:
+        return cls(
+            llm_call_op=agent._react_module.tool_select_module.llm_call_op,
+            prompt_op=agent._react_module.prompt_op,
+            **kwargs,
+        )
    
+
+
+
    
+[docs]
+    def aggregate_trajectory(self, trajectory: Trajectory) -> None:
+        if trajectory.failed:
+            return
+
+        if self.score_fn == APEScoreFn.REWARD:
+            d_returns = trajectory.compute_discounted_returns(self.reward_discount)
+
+        for i_step, step in enumerate(trajectory.steps):
+            action_call_id = cast(OpResult, step.action).call_id
+            llm_call_ids = self.llm_call_op.get_call_ids({action_call_id.run_id})
+
+            if self.score_fn == APEScoreFn.GRADIENT:
+                prompt_call_id, *extra_prompt_call_ids = self.prompt_op.get_call_ids({
+                    action_call_id.run_id
+                })
+                # TODO: loosen this restriction once grad acc/topological traversal are done
+                assert (
+                    not extra_prompt_call_ids
+                ), "APE only supports one prompt call per run"
+
+            for llm_call_id in llm_call_ids:
+                result = cast(
+                    LLMResult | None,
+                    self.llm_call_op.ctx.get(llm_call_id, "result"),
+                )
+                if result is None or not result.messages or not result.prompt:
+                    continue
+                # (x: first prompt's user message's content, y: AI response's content)
+                x = next(
+                    cast(str, m.content) for m in result.prompt if m.role == "user"
+                )
+                y = cast(str, result.messages[0].content)
+
+                if self.score_fn == APEScoreFn.GRADIENT:
+                    score = self.prompt_op.ctx.get(
+                        prompt_call_id, "grad_output", default=None
+                    )
+                    if score is None:
+                        # backprop did not reach this op call - move on
+                        continue
+                    is_good = score == 0
+                else:
+                    score = d_returns[i_step]  # pylint: disable=possibly-used-before-assignment
+                    is_good = score >= cast(float, self.good_reward_threshold)
+
+                example = Example(input=x, output=y, score=score)
+                (self.good_examples if is_good else self.examples).append(example)
    
+
+
+
    
+[docs]
+    async def update(self) -> None:
+        async with self._update_lock:
+            if not self.examples:
+                raise ValueError("No examples to update the prompt with.")
+
+            new_p = await self._get_updated_prompt(
+                self.examples, self.good_examples, self.prompt_op.prompt
+            )
+            # Check any template vars remain, and if some were added or
+            # lost, discard this new prompt
+            if new_p != self.prompt_op.prompt and get_formatted_variables(
+                new_p
+            ) != get_formatted_variables(self.prompt_op.prompt):
+                logger.warning(
+                    "Update broke prompt templating."
+                    f"\n\nNew prompt:\n{new_p}"
+                    f"\n\nPrior prompt:\n{self.prompt_op.prompt}"
+                )
+            else:
+                if new_p == self.prompt_op.prompt:
+                    logger.warning("Update did not change the prompt.")
+                self.examples.clear()
+                self.prompt_op.prompt = new_p
+                self.trace.append(new_p)
+            self.steps += 1
    
+
+
+    def _prepare_examples(self, examples: list[Example]) -> str:
+        if not examples:
+            return ""
+        if self.max_examples and len(examples) > self.max_examples:
+            if self.score_fn == APEScoreFn.GRADIENT:
+                # Return examples closest to decision boundary,
+                # aka ones with the lowest L1-normalized error
+                # NOTE: this pairs with our setting of Example.score = PromptOp's output
+                # gradient inside the update method, so examples with error values closer to
+                # 0 are defined to be higher quality
+                examples = sorted(examples, key=lambda e: abs(e.score))[
+                    : self.max_examples
+                ]
+            else:
+                # In reward mode, we want to show the examples with the highest reward, per the paper
+                # TODO: consider whether uniform sampling is better
+                examples = sorted(examples, key=lambda e: -e.score)[: self.max_examples]
+        return ExampleList.model_validate(examples).model_dump_json()
+
+    async def _get_updated_prompt(
+        self, examples: list[Example], good_examples: list[Example], prompt: str
+    ) -> str:
+        messages = [
+            Message(
+                role="system",
+                content=self.system_prompt,
+            ),
+            Message(
+                role="user",
+                content=self.query_prompt.format(
+                    examples=self._prepare_examples(examples),
+                    good_examples=self._prepare_examples(good_examples),
+                    prompt=prompt,
+                    score=self._score_str,
+                ),
+            ),
+            Message(
+                role="assistant",
+                content=OutputPrompt(prompt=prompt).model_dump_json(indent=2),
+            ),
+            Message(
+                content=(
+                    "You responded without changing the prompt. Don't forget to revise"
+                    " the prompt."
+                )
+            ),
+        ]
+        result = await self.llm.call(messages, output_type=OutputPrompt)
+        message_content = cast(str, cast(list[Message], result.messages)[-1].content)
+        try:
+            return OutputPrompt.model_validate_json(message_content).prompt
+        except ValidationError:
+            return prompt
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/alg/optimizer/memory.html b/docs/_build/html/_modules/ldp/alg/optimizer/memory.html new file mode 100644 index 00000000..d4f155fb --- /dev/null +++ b/docs/_build/html/_modules/ldp/alg/optimizer/memory.html @@ -0,0 +1,285 @@ + + + + + + + ldp.alg.optimizer.memory — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.alg.optimizer.memory

    +from __future__ import annotations
+
+import logging
+from collections.abc import Iterable
+from itertools import product
+from typing import ClassVar, Protocol, Self, cast, runtime_checkable
+
+from pydantic import BaseModel, ConfigDict, Field, JsonValue
+
+from ldp.agent import MemoryAgent
+from ldp.alg.optimizer.opt import Optimizer
+from ldp.data_structures import Trajectory
+from ldp.graph import CallID, Memory, MemoryOp, Op, OpResult
+from ldp.graph.ops import TOutput
+
+logger = logging.getLogger(__name__)
+
+
+
    
+[docs]
+@runtime_checkable
+class MemoryFactory(Protocol):
+    async def __call__(
+        self,
+        memory_op: MemoryOp,
+        output_op: Op[TOutput],
+        memory_template: str,
+        example_buffer: Iterable[tuple[CallID, CallID, float, JsonValue]],
+    ) -> list[Memory]:
+        """
+        Create new memories from the example buffer.
+
+        Args:
+            memory_op: MemoryOp whose context contains the MemoryModel's query and input.
+            output_op: Op whose context contains an output that can be correlated with
+                how good the outcome was.
+            memory_template: Template used for the Memory's string representation.
+            example_buffer: Buffer of 4-tuples containing the memory_op's call ID, the
+                output_op's call ID, the current discounted return, and arbitrary JSON
+                metadata (which can be used to add task-specific metadata the memory).
+
+        Returns:
+            New Memories created.
+        """
    
+
+
+
+async def _default_memory_factory(
+    memory_op: MemoryOp,
+    output_op: Op[TOutput],
+    memory_template: str,
+    example_buffer: Iterable[tuple[CallID, CallID, float, JsonValue]],
+) -> list[Memory]:
+    return [
+        Memory.from_ops(
+            memory_op,
+            mem_call_id,
+            output_op,
+            output_call_id,
+            d_return,
+            template=memory_template,
+            metadata=metadata,
+        )
+        for mem_call_id, output_call_id, d_return, metadata in example_buffer
+    ]
+
+
+
    
+[docs]
+class MemoryOpt(BaseModel, Optimizer):
+    """Trainer for memory agents. By default it is a minimizer.
+
+    This optimizer simply adds memories to the MemoryOp using a memory factory.
+    """
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    # Working around https://github.com/pydantic/pydantic/issues/10551
+    default_memory_factory: ClassVar[MemoryFactory] = _default_memory_factory
+
+    ### Configuration
+    memory_op: MemoryOp
+    output_op: Op
+    reward_discount: float = 1.0
+    memory_factory: MemoryFactory = Field(
+        default=default_memory_factory,
+        description=(
+            "Async function to make Memories from an example buffer. It's async so this"
+            " can involve an LLM completion if desired."
+        ),
+        exclude=True,
+    )
+    memory_template: str = Field(
+        default="Input: {input}\nOutput: {output}\nReward: {value}",
+        description="Template for a Memory's string representation.",
+    )
+
+    ### State
+    steps: int = 0
+    example_buffer: list[tuple[CallID, CallID, float, JsonValue]] = Field(
+        default_factory=list
+    )
+
+
    
+[docs]
+    @classmethod
+    def from_agent(cls, agent: MemoryAgent, **kwargs) -> Self:
+        return cls(memory_op=agent._memory_op, output_op=agent._llm_call_op, **kwargs)
    
+
+
+    def _memory_filter(
+        self, call_id: CallID, memory_op: MemoryOp, d_return: float
+    ) -> bool:
+        # only keep memories that backprop reached, i.e. those that were used in
+        # achieving the reward
+        return memory_op.ctx.get(call_id, "grad_output", default=None) is not None
+
+
    
+[docs]
+    def aggregate_trajectory(self, trajectory: Trajectory) -> None:
+        # NOTE: this is a little dangerous. This optimizer currently
+        # does not check which memory op calls are upstream of output op calls,
+        # besides making sure they belong to the same run.
+        # This is not a problem if we have no branching in the compute graph
+        # between the memory op and the *final* output op.
+        # TODO: fix the above using OpResult.traverse() to find the upstream calls
+
+        if trajectory.failed:
+            return
+
+        d_returns = trajectory.compute_discounted_returns(self.reward_discount)
+
+        for step, d_return in zip(trajectory.steps, d_returns, strict=True):
+            output_run_id = cast(OpResult, step.action).call_id.run_id
+            mem_call_ids = {
+                m
+                for m in self.memory_op.get_call_ids({output_run_id})
+                if self._memory_filter(m, self.memory_op, d_return)
+            }
+            output_call_ids = self.output_op.get_call_ids({output_run_id})
+            if len(mem_call_ids) > 1 and len(output_call_ids) > 1:
+                raise ValueError(
+                    "Multiple memory or output calls in a single run - this violates"
+                    " our 1-1 correspondence assumption."
+                )
+
+            self.example_buffer.extend(
+                (
+                    *x,
+                    d_return,
+                    {
+                        "timestep": step.timestep,
+                        "done": step.done,
+                        "truncated": step.truncated,
+                    },
+                )
+                for x in product(mem_call_ids, output_call_ids)
+            )
    
+
+
+
    
+[docs]
+    async def update(self) -> None:
+        """Create new memories from the example buffer and add them to MemoryOp."""
+        for memory in await self.memory_factory(  # pylint: disable=too-many-function-args
+            self.memory_op, self.output_op, self.memory_template, self.example_buffer
+        ):
+            await self.memory_op.memory_model.add_memory(memory)
+        self.steps += 1
+        self.example_buffer.clear()
    
+
    
+
+
+
+
    
+[docs]
+class PositiveMemoryOpt(MemoryOpt):
+    def _memory_filter(
+        self, call_id: CallID, memory_op: MemoryOp, d_return: float
+    ) -> bool:
+        # only keep positive memories
+        return d_return > 0 and super()._memory_filter(call_id, memory_op, d_return)
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/alg/optimizer/opt.html b/docs/_build/html/_modules/ldp/alg/optimizer/opt.html new file mode 100644 index 00000000..8b01f57b --- /dev/null +++ b/docs/_build/html/_modules/ldp/alg/optimizer/opt.html @@ -0,0 +1,185 @@ + + + + + + + ldp.alg.optimizer.opt — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.alg.optimizer.opt

    +from __future__ import annotations
+
+import logging
+from abc import ABC, abstractmethod
+from collections.abc import Iterable
+
+from ldp.data_structures import Trajectory
+from ldp.shims import tqdm
+
+logger = logging.getLogger(__name__)
+
+
+# Registry for all optimizers
+_OPTIMIZER_REGISTRY: dict[str, type[Optimizer]] = {}
+
+
+
    
+[docs]
+class Optimizer(ABC):
+    """Base class for all optimizers."""
+
+    def __init_subclass__(cls) -> None:
+        # Register each optimizer subclass
+        _OPTIMIZER_REGISTRY[cls.__name__] = cls
+        return super().__init_subclass__()
+
+
    
+[docs]
+    def aggregate(
+        self, trajectories: Iterable[Trajectory], show_pbar: bool = False
+    ) -> None:
+        """Aggregate trajectories to construct training samples."""
+        trajectories_with_pbar = tqdm(
+            trajectories,
+            desc="Aggregating trajectories",
+            ncols=0,
+            mininterval=1,
+            disable=not show_pbar,
+        )
+        for trajectory in trajectories_with_pbar:
+            self.aggregate_trajectory(trajectory)
    
+
+
+
    
+[docs]
+    @abstractmethod
+    def aggregate_trajectory(self, trajectory: Trajectory) -> None:
+        """Aggregate transitions from a single trajectory to construct training samples."""
    
+
+
+
    
+[docs]
+    @abstractmethod
+    async def update(self) -> None:
+        """Update the model based on the aggregated samples."""
    
+
    
+
+
+
+
    
+[docs]
+class ChainedOptimizer(Optimizer):
+    """An optimizer that runs a sequence of sub-optimizers in the order they are provided."""
+
+    def __init__(self, *optimizers: Optimizer):
+        self.optimizers = optimizers
+
+
    
+[docs]
+    def aggregate(
+        self, trajectories: Iterable[Trajectory], show_pbar: bool = False
+    ) -> None:
+        for optimizer in self.optimizers:
+            optimizer.aggregate(trajectories, show_pbar=show_pbar)
    
+
+
+
    
+[docs]
+    async def update(self) -> None:
+        for optimizer in self.optimizers:
+            await optimizer.update()
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/alg/optimizer/replay_buffers.html b/docs/_build/html/_modules/ldp/alg/optimizer/replay_buffers.html new file mode 100644 index 00000000..904db5f8 --- /dev/null +++ b/docs/_build/html/_modules/ldp/alg/optimizer/replay_buffers.html @@ -0,0 +1,145 @@ + + + + + + + ldp.alg.optimizer.replay_buffers — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.alg.optimizer.replay_buffers

    +import random
+from collections import UserList
+
+
+
    
+[docs]
+class CircularReplayBuffer(UserList[dict]):
+
    
+[docs]
+    def resize(self, size: int):
+        if len(self) > size:
+            self.data = self.data[-size:]
    
+
+
+
    
+[docs]
+    def batched_iter(
+        self, batch_size: int, shuffle: bool = True, infinite: bool = False
+    ):
+        while True:
+            indices = list(range(len(self)))
+            if shuffle:
+                random.shuffle(indices)
+
+            for i in range(0, len(self), batch_size):
+                keys = self.data[0].keys()
+
+                batch: dict[str, list] = {k: [] for k in keys}
+                for j in indices[i : i + batch_size]:
+                    if self.data[j].keys() != keys:
+                        raise RuntimeError(
+                            "Found buffer element with inconsistent keys"
+                        )
+
+                    for k in keys:
+                        batch[k].append(self.data[j][k])
+
+                yield batch
+
+            if not infinite:
+                break
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/alg/rollout.html b/docs/_build/html/_modules/ldp/alg/rollout.html new file mode 100644 index 00000000..56a34b41 --- /dev/null +++ b/docs/_build/html/_modules/ldp/alg/rollout.html @@ -0,0 +1,435 @@ + + + + + + + ldp.alg.rollout — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.alg.rollout

    +import asyncio
+import itertools
+import logging
+import uuid
+from collections.abc import Callable, Iterator, Sequence
+from contextlib import contextmanager, nullcontext
+from typing import Any, TypeVar, overload
+
+from aviary.env import Environment
+from aviary.message import Message
+
+from ldp.agent import Agent
+from ldp.data_structures import Trajectory, Transition
+
+from .callbacks import Callback
+
+logger = logging.getLogger(__name__)
+
+
+TEnv = TypeVar("TEnv", bound=Environment)
+
+
+
    
+[docs]
+class CaughtError(Exception):
+    """Base class for reraised exceptions when catching is enabled."""
+
+    def __init__(self, original_exc: Exception):
+        self.original_exc = original_exc
+
+    exc_type = "undefined"
    
+
+
+
+
    
+[docs]
+class AgentError(CaughtError):
+    exc_type = "agent"
    
+
+
+
+
    
+[docs]
+class EnvError(CaughtError):
+    exc_type = "env"
    
+
+
+
+
    
+[docs]
+@contextmanager
+def reraise_exc_as(reraise: type[CaughtError], enabled: bool) -> Iterator[None]:
+    try:
+        yield
+    except Exception as e:
+        if enabled:
+            logging.exception(f"Caught {reraise.exc_type} exception.")
+            raise reraise(e) from e
+        raise
    
+
+
+
+
    
+[docs]
+class RolloutManager:
+    def __init__(
+        self,
+        agent: Agent,
+        catch_agent_failures: bool = True,
+        catch_env_failures: bool = True,
+        callbacks: Sequence[Callback] | None = None,
+        concurrency_limit: int | None = None,
+    ):
+        self.agent = agent
+
+        self.catch_agent_failures = catch_agent_failures
+        self.catch_env_failures = catch_env_failures
+
+        self.concurrency_limiter = (
+            asyncio.Semaphore(concurrency_limit) if concurrency_limit else nullcontext()
+        )
+
+        self.traj_buffer: dict[str, Trajectory] = {}
+        self.callbacks = callbacks or []
+
+    @overload
+    async def sample_trajectories(  # noqa: D418
+        self,
+        environment_factory: Callable[[], TEnv],
+        batch_size: int = 1,
+        max_steps: int | None = None,
+    ) -> list[tuple[Trajectory, TEnv]]:
+        """Run rollouts in parallel, using a factory to construct environments.
+
+        We will construct `batch_size` environments and run rollouts on each of them.
+        If `max_steps` is set, rollouts will be truncated at this value. If a rollout
+        has fewer than `max_steps`, then a new environment will be constructed and another
+        rollout will be started until `max_steps` is reached.
+
+        Args:
+            environment_factory: A no-argument callable that returns
+                an environment instance
+            batch_size (int, optional): Defaults to 1.
+            max_steps (int | None, optional): Max steps per rollout. Defaults to None (see above).
+
+        Returns:
+            list[tuple[Trajectory, Environment]]: A list of (trajectory, environment) tuples: one per rollout.
+        """
+
+    @overload
+    async def sample_trajectories(  # noqa: D418
+        self,
+        environments: Sequence[Environment],
+        max_steps: int | None = None,
+    ) -> list[Trajectory]:
+        """Run rollouts in parallel on a list of provided environments.
+
+        Args:
+            environments: A list of environments to run rollouts on.
+            max_steps: Max steps per rollout. Defaults to None, in which case the rollouts are run
+                until environment returns done.
+        """
+
+
    
+[docs]
+    async def sample_trajectories(self, **kwargs):
+        if "environment_factory" in kwargs:
+            assert (
+                "environments" not in kwargs
+            ), "Cannot use environment_factory with environments"
+
+            return await self._sample_trajectories_from_env_factory(
+                kwargs["environment_factory"],
+                kwargs.get("batch_size", 1),
+                kwargs.get("max_steps"),
+            )
+
+        if "environments" in kwargs:
+            assert (
+                "environment_factory" not in kwargs
+            ), "Cannot use environments with environment_factory"
+            return await self._sample_trajectories_from_envs(
+                kwargs["environments"], kwargs.get("max_steps")
+            )
+
+        raise TypeError(
+            "sample_trajectories() missing required "
+            "arguments 'environment_factory' or 'environments'"
+        )
    
+
+
+    async def _sample_trajectories_from_env_factory(
+        self,
+        environment_factory: Callable[[], Environment],
+        batch_size: int = 1,
+        max_steps: int | None = None,
+    ) -> list[tuple[Trajectory, Environment]]:
+        self.traj_buffer.clear()
+
+        async def rollout_with_args(idx: int, **rollout_kwargs):
+            return idx, await self._rollout(**rollout_kwargs), rollout_kwargs
+
+        accumulated_steps = [0] * batch_size
+        # submit initial batch of tasks
+        tasks = [
+            asyncio.create_task(
+                rollout_with_args(
+                    idx,
+                    traj_id=uuid.uuid4().hex,
+                    env=environment_factory(),
+                    max_steps=max_steps,
+                )
+            )
+            for idx in range(batch_size)
+        ]
+
+        results = []
+        while tasks:
+            done, pending = await asyncio.wait(
+                tasks, return_when=asyncio.FIRST_COMPLETED
+            )
+            new_tasks = []
+            for task in done:
+                idx, traj, kwargs = await task
+                results.append((traj, kwargs["env"]))
+                accumulated_steps[idx] += len(traj.steps)
+                if (
+                    max_steps is not None
+                    and (remaining_steps := max_steps - accumulated_steps[idx]) > 0
+                ):
+                    # submit another task if we haven't reached max_steps
+                    new_task = asyncio.create_task(
+                        rollout_with_args(
+                            idx,
+                            traj_id=uuid.uuid4().hex,
+                            env=environment_factory(),
+                            max_steps=remaining_steps,
+                        )
+                    )
+                    new_tasks.append(new_task)
+
+            tasks = list(pending) + new_tasks
+
+        return results
+
+    async def _sample_trajectories_from_envs(
+        self,
+        environments: Sequence[Environment],
+        max_steps: int | None = None,
+    ) -> list[Trajectory]:
+        self.traj_buffer.clear()
+
+        traj_ids = [uuid.uuid4().hex for _ in range(len(environments))]
+        await asyncio.gather(
+            *(
+                self._rollout(*args, max_steps=max_steps)
+                for args in zip(traj_ids, environments, strict=True)
+            )
+        )
+        return [self.traj_buffer[traj_id] for traj_id in traj_ids]
+
+    async def _rollout(
+        self,
+        traj_id: str,
+        env: Environment,
+        max_steps: int | None,
+    ) -> Trajectory:
+        trajectory = Trajectory(traj_id=traj_id)
+
+        async def store_step(step: Transition):
+            await asyncio.gather(*[
+                callback.after_transition(traj_id, self.agent, env, step)
+                for callback in self.callbacks
+            ])
+            trajectory.steps.append(step)
+
+        # Set default values to store in the buffer in case reset/init_state fail
+        obs: list[Message] = []
+        agent_state: Any = None
+
+        try:
+            with reraise_exc_as(EnvError, enabled=self.catch_env_failures):
+                obs, tools = await env.reset()
+            await asyncio.gather(*[
+                c.after_env_reset(traj_id, obs, tools) for c in self.callbacks
+            ])
+
+            with reraise_exc_as(AgentError, enabled=self.catch_agent_failures):
+                agent_state = await self.agent.init_state(tools)
+            await asyncio.gather(*[
+                c.after_agent_init_state(traj_id, agent_state) for c in self.callbacks
+            ])
+
+            for timestep in itertools.count():
+                step = await self._take_step(timestep, traj_id, env, agent_state, obs)
+
+                if timestep + 1 == max_steps and not step.done:
+                    # Mark as truncated if we hit max_steps and the state is not terminal.
+                    # Do it before store_step(), so that callbacks can access this info
+                    step.truncated = True
+
+                # We assume the below won't throw a CaughtError
+                await store_step(step)
+
+                # set things up for the next iteration
+                agent_state = step.next_agent_state
+                obs = step.next_observation
+
+                if step.done or step.truncated:
+                    break
+
+        except CaughtError as e:
+            # NOTE: This trajectory should not be used for regular training.
+            # We save the last transition here for debugging, etc.
+            await store_step(
+                Transition(
+                    timestep=len(trajectory.steps),
+                    agent_state=agent_state,
+                    next_agent_state=None,
+                    observation=obs,
+                    next_observation=[],
+                    action=None,
+                    done=True,
+                    metadata={"exception": repr(e.original_exc)},
+                )
+            )
+
+        self.traj_buffer[traj_id] = trajectory
+        return trajectory
+
+    async def _take_step(
+        self,
+        timestep: int,
+        traj_id: str,
+        env: Environment,
+        agent_state: Any,
+        obs: list[Message],
+    ) -> Transition:
+        async with self.concurrency_limiter:
+            await asyncio.gather(*[
+                callback.before_transition(traj_id, self.agent, env, agent_state, obs)
+                for callback in self.callbacks
+            ])
+
+            with reraise_exc_as(AgentError, enabled=self.catch_agent_failures):
+                (
+                    action,
+                    next_agent_state,
+                    value,
+                ) = await self.agent.get_asv(agent_state, obs)
+            await asyncio.gather(*[
+                callback.after_agent_get_asv(traj_id, action, next_agent_state, value)
+                for callback in self.callbacks
+            ])
+
+            with reraise_exc_as(EnvError, enabled=self.catch_env_failures):
+                next_obs, reward, done, trunc = await env.step(action.value)
+            await asyncio.gather(*[
+                callback.after_env_step(traj_id, next_obs, reward, done, trunc)
+                for callback in self.callbacks
+            ])
+
+            return Transition(
+                timestep=timestep,
+                agent_state=agent_state,
+                next_agent_state=next_agent_state,
+                action=action,
+                reward=reward,
+                value=value,
+                observation=obs,
+                next_observation=next_obs,
+                done=done,
+                truncated=trunc,
+            )
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/alg/runners.html b/docs/_build/html/_modules/ldp/alg/runners.html new file mode 100644 index 00000000..181c2d0b --- /dev/null +++ b/docs/_build/html/_modules/ldp/alg/runners.html @@ -0,0 +1,482 @@ + + + + + + + ldp.alg.runners — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.alg.runners

    +from __future__ import annotations
+
+import asyncio
+import math
+import random
+from collections.abc import Sequence
+from typing import cast
+
+from aviary.env import Environment, TaskDataset
+from pydantic import BaseModel, ConfigDict, Field
+
+from ldp.agent import Agent
+from ldp.alg.optimizer import Optimizer
+from ldp.data_structures import Trajectory
+from ldp.graph import OpResult, eval_mode, train_mode
+from ldp.shims import tqdm, trange
+
+from .callbacks import Callback, ClearContextCallback
+from .rollout import RolloutManager
+
+
+async def _run_eval_loop(
+    dataset: TaskDataset,
+    rollout_manager: RolloutManager,
+    batch_size: int,
+    num_iterations: int | None,
+    max_rollout_steps: int | None,
+    callbacks: Sequence[Callback],
+    shuffle: bool = False,
+) -> None:
+    await asyncio.gather(*[callback.before_eval_loop() for callback in callbacks])
+
+    if num_iterations is None:
+        try:
+            num_iterations = math.ceil(len(dataset) / batch_size)
+        except TypeError:
+            raise ValueError(
+                "If num_iterations is not provided, the "
+                "dataset must be finite and implement __len__."
+            ) from None
+    if not num_iterations:
+        return
+
+    with tqdm(
+        desc="Evaluation Iterations", ncols=0, total=num_iterations, leave=False
+    ) as pbar:
+        # We use pbar.n as a counter for the number of training steps
+        while pbar.n < num_iterations:
+            for batch in dataset.iter_batches(batch_size, shuffle=shuffle):
+                trajectories = await rollout_manager.sample_trajectories(
+                    environments=batch, max_steps=max_rollout_steps
+                )
+
+                # Close the environment after we have sampled from it,
+                # in case it needs to tear down resources.
+                await asyncio.gather(*(env.close() for env in batch))
+
+                await asyncio.gather(*[
+                    callback.after_eval_step(trajectories) for callback in callbacks
+                ])
+                pbar.update()
+
+                if pbar.n == num_iterations:
+                    break
+
+    await asyncio.gather(*[callback.after_eval_loop() for callback in callbacks])
+
+
+
    
+[docs]
+class EvaluatorConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    batch_size: int = 1
+    num_eval_iterations: int | None = Field(
+        None,
+        description="Number of eval iterations. "
+        "If not provided, will exhaust the dataset. "
+        "If 0, will not run the eval loop. ",
+    )
+    max_rollout_steps: int | None = None
+    catch_agent_failures: bool = True
+    catch_env_failures: bool = True
+    clear_ctx_at_each_iter: bool = False
+
+
    
+[docs]
+    def make_rollout_manager(
+        self, agent: Agent, callbacks: Sequence[Callback]
+    ) -> RolloutManager:
+        return RolloutManager(
+            agent=agent,
+            callbacks=callbacks,
+            catch_agent_failures=self.catch_agent_failures,
+            catch_env_failures=self.catch_env_failures,
+        )
    
+
    
+
+
+
+
    
+[docs]
+class Evaluator:
+    def __init__(
+        self,
+        config: EvaluatorConfig,
+        agent: Agent,
+        dataset: TaskDataset,
+        callbacks: Sequence[Callback] | None = None,
+    ):
+        self.config = config
+        self.agent = agent
+        self.dataset = dataset
+        self.callbacks = callbacks or []
+        if self.config.clear_ctx_at_each_iter:
+            clear_cb = ClearContextCallback()
+            self.callbacks = [*self.callbacks, clear_cb] if callbacks else [clear_cb]
+        self.rollout_manager = self.config.make_rollout_manager(agent, self.callbacks)
+
+    @eval_mode()
+    async def evaluate(self, **kwargs) -> None:
+        """Run the agent over the provided dataset in eval mode."""
+        return await self.run(**kwargs)
+
+
    
+[docs]
+    async def run(self, **kwargs) -> None:
+        """Run the agent over the provided dataset.
+
+        This method does not set training mode, so it can be used to collect
+        trajectories for offline training.
+        """
+        await _run_eval_loop(
+            dataset=self.dataset,
+            rollout_manager=self.rollout_manager,
+            batch_size=self.config.batch_size,
+            num_iterations=self.config.num_eval_iterations,
+            max_rollout_steps=self.config.max_rollout_steps,
+            callbacks=self.callbacks,
+            **kwargs,
+        )
    
+
    
+
+
+
+
    
+[docs]
+class OnlineTrainerConfig(EvaluatorConfig):
+    batch_size: int
+    num_train_iterations: int = Field(
+        ge=0,
+        description=(
+            "Number of iterations (at one batch per iteration) to process during"
+            " training, and setting to 0 skips training."
+        ),
+    )
+    num_rollouts_per_env: int = Field(
+        default=1,
+        description=(
+            "Number of rollouts to execute for each environment per training iteration."
+        ),
+    )
+    update_every: int = Field(
+        default=1,
+        description="Number of training iterations between optimizer update calls.",
+        ge=1,
+    )
+    eval_every: int | None = Field(
+        None,
+        description=(
+            "If set, will repeatedly evaluate on the validation set after this many"
+            " iterations. If unset (default), no evaluation is performed."
+        ),
+    )
+    eval_before: bool = Field(
+        default=True,
+        description=(
+            "If True (default), evaluate on the validation set before kicking off"
+            " training."
+        ),
+    )
+    clear_ctx_at_each_iter: bool = False
    
+
+
+
+
    
+[docs]
+class OnlineTrainer:
+    def __init__(
+        self,
+        config: OnlineTrainerConfig,
+        agent: Agent,
+        optimizer: Optimizer,
+        train_dataset: TaskDataset,
+        eval_dataset: TaskDataset | None = None,
+        callbacks: Sequence[Callback] | None = None,
+    ):
+        if config.eval_every is not None and eval_dataset is None:
+            raise ValueError("Must specify eval_dataset if eval_every is set")
+
+        self.config = config
+        self.agent = agent
+        self.train_dataset = train_dataset
+        self.eval_dataset = eval_dataset
+        self.optimizer = optimizer
+        self.callbacks = callbacks or []
+        if self.config.clear_ctx_at_each_iter:
+            clear_cb = ClearContextCallback()
+            self.callbacks = [*self.callbacks, clear_cb] if callbacks else [clear_cb]
+        self.rollout_manager = self.config.make_rollout_manager(
+            agent=agent, callbacks=self.callbacks
+        )
+
+
    
+[docs]
+    async def train(self) -> None:
+        if self.config.eval_before:
+            await self.evaluate()
+
+        with tqdm(
+            desc="Training Iterations", ncols=0, total=self.config.num_train_iterations
+        ) as pbar:
+            # We use pbar.n as a counter for the number of training steps
+            while pbar.n < self.config.num_train_iterations:
+                for batch in self.train_dataset.iter_batches(
+                    self.config.batch_size, shuffle=True
+                ):
+                    await self._training_step(pbar.n, batch)
+                    pbar.update()  # Increment pbar.n by 1
+
+                    if (
+                        self.config.eval_every is not None
+                        and pbar.n % self.config.eval_every == 0
+                    ):
+                        await self.evaluate()
+
+                    if pbar.n == self.config.num_train_iterations:
+                        break  # Will also break out of the outer while loop
+
+        await self.evaluate()
    
+
+
+    @eval_mode()
+    async def evaluate(self, **kwargs) -> None:
+        await _run_eval_loop(
+            dataset=cast(TaskDataset, self.eval_dataset),
+            rollout_manager=self.rollout_manager,
+            batch_size=self.config.batch_size,
+            num_iterations=self.config.num_eval_iterations,
+            max_rollout_steps=self.config.max_rollout_steps,
+            callbacks=self.callbacks,
+            **kwargs,
+        )
+
+    @train_mode()
+    async def _training_step(self, i_iter: int, envs: Sequence[Environment]) -> None:
+        training_batch: list[Trajectory] = []
+
+        for _ in range(self.config.num_rollouts_per_env):
+            trajectories = await self.rollout_manager.sample_trajectories(
+                environments=envs, max_steps=self.config.max_rollout_steps
+            )
+
+            # Close the environments after we have sampled from them, in case they need to tear down resources.
+            await asyncio.gather(*[env.close() for env in envs])
+
+            training_batch.extend(traj for traj in trajectories if not traj.failed)
+
+        await self._optimizer_step(i_iter, training_batch)
+
+        await asyncio.gather(*[
+            callback.after_train_step(trajectories) for callback in self.callbacks
+        ])
+
+    async def _optimizer_step(
+        self, i_iter: int, training_batch: Sequence[Trajectory]
+    ) -> None:
+        for traj in training_batch:
+            for step in traj.steps:
+                # TODO: make this async
+                # step.action is not None because we checked traj.failed above
+                cast(OpResult, step.action).compute_grads()
+
+        self.optimizer.aggregate(training_batch)
+
+        if (i_iter + 1) % self.config.update_every == 0:
+            await self.optimizer.update()
+
+            await asyncio.gather(*[
+                callback.after_update() for callback in self.callbacks
+            ])
    
+
+
+
+
    
+[docs]
+class OfflineTrainerConfig(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    batch_size: int
+    update_every: int = Field(
+        default=1,
+        description="Number of training iterations to run before updating the model.",
+        ge=1,
+    )
+    clear_ctx_at_each_iter: bool = False
    
+
+    # TODO: add some concept of eval loops
+
+
+
    
+[docs]
+class OfflineTrainer:
+    def __init__(
+        self,
+        config: OfflineTrainerConfig,
+        agent: Agent,
+        optimizer: Optimizer,
+        train_trajectories: list[Trajectory],
+        callbacks: Sequence[Callback] | None = None,
+    ):
+        self.config = config
+        self.agent = agent
+        self.optimizer = optimizer
+        # copy so we can shuffle
+        self.train_trajectories = train_trajectories.copy()
+        self.callbacks = callbacks or []
+        if self.config.clear_ctx_at_each_iter:
+            clear_cb = ClearContextCallback()
+            self.callbacks = [*self.callbacks, clear_cb] if callbacks else [clear_cb]
+
+
    
+[docs]
+    async def train(self) -> None:
+        random.shuffle(self.train_trajectories)
+
+        full_batch = len(self.train_trajectories) <= self.config.batch_size
+
+        if full_batch:
+            # Separating out the full batch case lets the user run a single update()
+            # step even if train_trajectories is empty. This can be useful if the
+            # optimizer is pre-populated with offline training data, for example.
+            batch = self.train_trajectories
+
+            self.optimizer.aggregate(batch, show_pbar=True)
+            await self.optimizer.update()
+
+            await asyncio.gather(*[
+                callback.after_update() for callback in self.callbacks
+            ])
+
+            await asyncio.gather(*[
+                callback.after_train_step(batch) for callback in self.callbacks
+            ])
+
+        else:
+            for training_step, i_batch_start in enumerate(
+                trange(
+                    0,
+                    len(self.train_trajectories),
+                    self.config.batch_size,
+                    desc="Training Iterations",
+                    ncols=0,
+                )
+            ):
+                batch = self.train_trajectories[
+                    i_batch_start : i_batch_start + self.config.batch_size
+                ]
+
+                self.optimizer.aggregate(batch)
+
+                if (training_step + 1) % self.config.update_every == 0:
+                    await self.optimizer.update()
+                    await asyncio.gather(*[
+                        callback.after_update() for callback in self.callbacks
+                    ])
+
+                await asyncio.gather(*[
+                    callback.after_train_step(batch) for callback in self.callbacks
+                ])
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/alg/tree_search.html b/docs/_build/html/_modules/ldp/alg/tree_search.html new file mode 100644 index 00000000..d5b83a0f --- /dev/null +++ b/docs/_build/html/_modules/ldp/alg/tree_search.html @@ -0,0 +1,276 @@ + + + + + + + ldp.alg.tree_search — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.alg.tree_search

    +import asyncio
+import logging
+import uuid
+from collections.abc import Awaitable, Callable, Sequence
+from typing import Any
+
+from aviary.message import Message
+from aviary.utils import is_coroutine_callable
+
+from ldp.agent import Agent
+from ldp.data_structures import TransitionTree
+
+from .callbacks import Callback
+from .rollout import (
+    AgentError,
+    CaughtError,
+    EnvError,
+    RolloutManager,
+    TEnv,
+    reraise_exc_as,
+)
+
+logger = logging.getLogger(__name__)
+
+
+
    
+[docs]
+class TreeSearchRollout(RolloutManager):
+    def __init__(
+        self,
+        agent: Agent,
+        branching_factor: int,
+        env_clone_fn: Callable[[TEnv], Awaitable[TEnv]] | Callable[[TEnv], TEnv],
+        catch_agent_failures: bool = True,
+        catch_env_failures: bool = True,
+        callbacks: Sequence[Callback] | None = None,
+        concurrency_limit: int | None = None,
+        target_reward: float | None = None,
+    ):
+        super().__init__(
+            agent,
+            catch_agent_failures=catch_agent_failures,
+            catch_env_failures=catch_env_failures,
+            callbacks=callbacks,
+            concurrency_limit=concurrency_limit,
+        )
+
+        self.branching_factor = branching_factor
+        self.target_reward = (
+            target_reward if target_reward is not None else float("inf")
+        )
+        self.target_reward_hit: set[str] = set()
+
+        self.env_clone_fn = env_clone_fn
+
+
    
+[docs]
+    async def sample_trees(
+        self,
+        environments: Sequence[TEnv],
+        max_depth: int | None = None,
+    ) -> list[TransitionTree]:
+        return await asyncio.gather(*[
+            self.sample_tree(env, max_depth) for env in environments
+        ])
    
+
+
+
    
+[docs]
+    async def sample_tree(self, env: TEnv, max_depth: int | None) -> TransitionTree:
+        max_depth_f = max_depth if max_depth is not None else float("inf")
+        tree = TransitionTree(root_id=str(uuid.uuid4()))
+
+        try:
+            with reraise_exc_as(EnvError, enabled=self.catch_env_failures):
+                obs, tools = await env.reset()
+            await asyncio.gather(*[
+                c.after_env_reset(tree.root_id, obs, tools) for c in self.callbacks
+            ])
+
+            with reraise_exc_as(AgentError, enabled=self.catch_agent_failures):
+                agent_state = await self.agent.init_state(tools)
+            await asyncio.gather(*[
+                c.after_agent_init_state(tree.root_id, agent_state)
+                for c in self.callbacks
+            ])
+        except CaughtError:
+            return tree
+
+        await self._descend(
+            tree=tree,
+            prev_step_id=tree.root_id,
+            env=env,
+            agent_state=agent_state,
+            obs=obs,
+            prev_timestep=-1,
+            prev_cumulative_reward=0.0,
+            max_depth=max_depth_f,
+        )
+
+        return tree
    
+
+
+    async def _descend(
+        self,
+        tree: TransitionTree,
+        prev_step_id: str,
+        env: TEnv,
+        agent_state: Any,
+        obs: list[Message],
+        prev_timestep: int,
+        prev_cumulative_reward: float,
+        max_depth: float,
+    ) -> None:
+        # Descend one level in the tree, by adding branching_factor children to the branch
+        # Then, recurse on each child
+
+        if tree.root_id in self.target_reward_hit:
+            # If at least one branch hit the target reward, stop descending
+            return
+
+        timestep = prev_timestep + 1
+
+        async def inner_descend(idx: int) -> None:
+            if is_coroutine_callable(self.env_clone_fn):
+                cloned_env = await self.env_clone_fn(env)  # type: ignore[arg-type, misc]
+            else:
+                cloned_env = self.env_clone_fn(env)  # type: ignore[arg-type]
+
+            # Descend one step
+            step_id = f"{prev_step_id}:{idx}"
+            try:
+                step = await self._take_step(
+                    timestep, step_id, cloned_env, agent_state, obs
+                )
+            except CaughtError:
+                # If we failed, do not extend the branch - just give up on this path
+                return
+
+            if timestep + 1 == max_depth and not step.done:
+                # Mark as truncated if we hit max_steps and the state is not terminal.
+                step.truncated = True
+
+            await asyncio.gather(*[
+                callback.after_transition(step_id, self.agent, cloned_env, step)
+                for callback in self.callbacks
+            ])
+
+            tree.add_transition(step_id, step)
+
+            cumulative_reward = prev_cumulative_reward + step.reward
+            if cumulative_reward >= self.target_reward:
+                # signal other descents to stop too
+                self.target_reward_hit.add(tree.root_id)
+                return
+
+            if step.done or step.truncated:
+                return
+
+            # Recurse
+            await self._descend(
+                tree=tree,
+                prev_step_id=step_id,
+                env=cloned_env,
+                agent_state=step.next_agent_state,
+                obs=step.next_observation,
+                prev_timestep=timestep,
+                prev_cumulative_reward=cumulative_reward,
+                max_depth=max_depth,
+            )
+
+        # Add branching_factor children
+        await asyncio.gather(*[
+            inner_descend(idx) for idx in range(self.branching_factor)
+        ])
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/data_structures.html b/docs/_build/html/_modules/ldp/data_structures.html new file mode 100644 index 00000000..630e8c3b --- /dev/null +++ b/docs/_build/html/_modules/ldp/data_structures.html @@ -0,0 +1,500 @@ + + + + + + + ldp.data_structures — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.data_structures

    +from __future__ import annotations
+
+import json
+import logging
+import os
+from collections.abc import Callable, Hashable
+from typing import Any, ClassVar, Self, cast
+from uuid import UUID
+
+import networkx as nx
+from aviary.message import Message, join
+from aviary.tools import ToolRequestMessage, ToolResponseMessage
+from pydantic import BaseModel, ConfigDict, Field, JsonValue, field_validator
+
+from ldp.graph import OpResult
+from ldp.utils import discounted_returns
+
+logger = logging.getLogger(__name__)
+
+
+
    
+[docs]
+class Transition(BaseModel):
+    model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid")
+
+    # Sentinel value for missing observation, as opposed to empty observation
+    # Only used for tests; a user should never use this.
+    NO_OBSERVATION: ClassVar[list[Message]] = []
+
+    timestep: int = Field(description="Zero-indexed MDP timestep t.")
+
+    agent_state: Any = Field(
+        description=(
+            "Agent.get_asv's input. This is `s_t` in RL terms. Note that `s_0` comes"
+            " from `Agent.init_state()`"
+        )
+    )
+    next_agent_state: Any = Field(
+        description="Agent.get_asv's output. This is s_t+1 in RL terms."
+    )
+
+    observation: list[ToolResponseMessage | Message] = Field(
+        description="Agent.get_asv's input. This is o_t in RL terms."
+    )
+    next_observation: list[ToolResponseMessage | Message] = Field(
+        description="Environment.step output. This is o_t+1 in RL terms."
+    )
+
+    action: OpResult[ToolRequestMessage] | None = Field(
+        default=None, description="Agent.get_asv output. This is a_t in RL terms."
+    )
+
+    reward: float = Field(
+        default=0.0, description="Environment.step output. This is r_t in RL terms."
+    )
+
+    truncated: bool = Field(
+        default=False, description="timestep t's Environment.step output."
+    )
+    done: bool = Field(
+        default=False, description="timestep t's Environment.step output."
+    )
+    value: float = Field(
+        default=0.0,
+        description=(
+            "Value estimate output from timestep t's Agent.get_asv. This is v(s_t)"
+            " [state value function] or q(s_t, a_t) [state-action value]."
+        ),
+    )
+    # JsonValue so we can serialize
+    metadata: dict[str, JsonValue] = Field(default_factory=dict)
+
+
    
+[docs]
+    @field_validator("action", mode="before")
+    @classmethod
+    def construct_action(
+        cls, action: OpResult[ToolRequestMessage] | dict | None
+    ) -> OpResult[ToolRequestMessage] | None:
+        if isinstance(action, dict):
+            return OpResult.from_dict(ToolRequestMessage, action)
+        return action
    
+
+
+    @property
+    def failed(self) -> bool:
+        """Get if an exception was encountered during rollout, for convenience.
+
+        If True, this transition should not be trained on.
+        Failed transitions are for debugging purposes.
+        """
+        return bool(self.metadata.get("exception"))
+
+
    
+[docs]
+    def model_dump_json(self, *, indent: int | None = None, **kwargs) -> str:
+        # The kwargs for model_dump are the same as super().model_dump_json,
+        # with the exception of indent.
+        dump = self.model_dump(**kwargs)
+        if self.action is not None:
+            dump["action"] = self.action.to_dict()
+        return json.dumps(dump, indent=indent)
    
+
    
+
+
+
+
    
+[docs]
+class Trajectory(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    traj_id: str | None = None
+    steps: list[Transition] = Field(default_factory=list)
+
+    @property
+    def failed(self) -> bool:
+        return any(step.failed for step in self.steps)
+
+    @property
+    def done(self) -> bool:
+        if not self.steps:
+            return False
+        return self.steps[-1].done
+
+
    
+[docs]
+    def to_jsonl(self, filename: str | os.PathLike) -> None:
+        with open(filename, "w") as f:
+            f.write(json.dumps(self.traj_id) + "\n")
+            f.writelines(s.model_dump_json() + "\n" for s in self.steps)
    
+
+
+
    
+[docs]
+    @classmethod
+    def from_jsonl(cls, filename: str | os.PathLike) -> Self:
+        with open(filename) as f:
+            reader = iter(f)
+            traj = cls(traj_id=json.loads(next(reader)))
+            for json_line in reader:
+                traj.steps.append(Transition(**json.loads(json_line)))
+        return traj
    
+
+
+
    
+[docs]
+    def compute_discounted_returns(self, discount: float = 1.0) -> list[float]:
+        """Compute the discounted returns for each step in the trajectory."""
+        return discounted_returns(
+            rewards=[step.reward for step in self.steps],
+            terminated=[step.truncated for step in self.steps],
+            discount=discount,
+        )
    
+
    
+
+
+
+
    
+[docs]
+class TransitionTree:
+    def __init__(self, root_id: str | UUID):
+        """A tree of transitions.
+
+        If A->B is an edge in this tree, then A and B are consecutive
+        transitions in an LDP. Any path from the root node to a terminal
+        node constitutes a complete LDP.
+
+        A node may be assigned an arbitrary weight, which will be treated
+        as a relative probability of sampling that node. For example, if
+        A(weight=1) and B(w=2) are both children of the same node,
+        then we treat B as twice as likely as A.
+
+        Args:
+            root_id: A unique identifier for the root node of the tree.
+                All IDs of transitions added to this tree must begin with
+                the same root_id.
+        """
+        self.root_id = str(root_id)
+
+        self.tree = nx.DiGraph()  # the actual tree
+        self.rev_tree = nx.DiGraph()  # the same as self.tree, but with reversed edges
+
+        self._add_node(self.root_id, transition=None, weight=1.0)
+
+    def _add_node(
+        self, step_id: str, transition: Transition | None, weight: float
+    ) -> None:
+        self.tree.add_node(step_id, transition=transition, weight=weight)
+        self.rev_tree.add_node(step_id)
+
+    def _add_edge(self, parent_step_id: str, child_step_id: str) -> None:
+        self.tree.add_edge(parent_step_id, child_step_id)
+        self.rev_tree.add_edge(child_step_id, parent_step_id)
+
+
    
+[docs]
+    def get_transition(self, step_id: str) -> Transition:
+        if step_id == self.root_id:
+            raise ValueError("Root node has no transition.")
+
+        return cast(Transition, self.tree.nodes[step_id]["transition"])
    
+
+
+
    
+[docs]
+    def get_weight(self, step_id: str) -> float:
+        return cast(float, self.tree.nodes[step_id]["weight"])
    
+
+
+
    
+[docs]
+    def add_transition(
+        self, step_id: str, step: Transition, weight: float = 1.0
+    ) -> None:
+        """Add a transition to the tree.
+
+        Args:
+            step_id: A unique identifier for the root node of the tree.
+                The expected form of the step ID is "{parent step ID}:{step index}".
+            step: The transition to add.
+            weight: Weight of the transition. Defaults to 1.0.
+        """
+        root_id, *step_ids = step_id.split(":")
+        assert (
+            root_id == self.root_id
+        ), f"Step ID {step_id} does not start with root ID {self.root_id}"
+        assert step_ids, "Step ID cannot be the same as the root ID."
+        # TODO: maybe this should be warning?
+        assert (
+            step_id not in self.tree
+        ), f"Step ID {step_id} already exists in the tree."
+
+        self._add_node(step_id, transition=step, weight=weight)
+
+        parent_id = ":".join([root_id, *step_ids[:-1]])
+        if parent_id in self.tree:
+            self._add_edge(parent_id, step_id)
    
+
+
+
    
+[docs]
+    def get_trajectories(self) -> list[Trajectory]:
+        """Return a list of trajectories.
+
+        Since each path from the root node to a terminal node defines
+        a unique trajectory, N(terminal node) trajectories will be returned.
+        The trajectory ID will be set to the ID of the terminal step.
+
+        Note that we include failed and truncated trajectories; it is up to the
+        caller to decide what to do them.
+
+        Returns:
+            All trajectories in this tree.
+        """
+        trajs = []
+        step: Transition | None
+
+        for step_id, step in self.tree.nodes(data="transition"):
+            if not step:
+                # root node
+                continue
+
+            is_terminal = (
+                # check terminal conditions in increasing order of expense
+                step.done
+                or step.truncated
+                or step.failed
+                or self.tree.out_degree(step_id) == 0
+            )
+
+            if not is_terminal:
+                continue
+
+            # set the ID to the terminal node, which uniquely identifies the path
+            traj = Trajectory(traj_id=step_id)
+            # Build the trajectory up from a terminal node
+            current_step: Transition | None = step
+            current_step_id = step_id
+
+            # Walk backwards towards the root (current_step=None)
+            while current_step:
+                traj.steps.append(current_step)
+
+                parent_step_id, *extra = list(self.rev_tree.successors(current_step_id))
+                assert not extra, f"Expected a single parent, but got {len(extra) + 1}"
+
+                current_step_id = parent_step_id
+                current_step = self.tree.nodes[parent_step_id]["transition"]
+
+            # would've added things in reverse order, so fix that here
+            traj.steps.sort(key=lambda x: x.timestep)
+            trajs.append(traj)
+
+        return trajs
    
+
+
+
    
+[docs]
+    def assign_mc_value_estimates(self, discount_factor: float = 1.0) -> None:
+        """Assign Monte Carlo state-action value estimates to each transition (in-place).
+
+        Args:
+            discount_factor: The discount factor to use when computing cumulative
+                future rewards.
+        """
+        for step_id in nx.topological_sort(self.rev_tree):
+            step: Transition | None = self.tree.nodes[step_id]["transition"]
+            if step is None:
+                continue
+
+            if children := list(self.tree.successors(step_id)):
+                # V_{t+1}(s') = sum_{a'} p(a'|s') * Q_{t+1}(s', a')
+                # Here we assume p(a'|s') is uniform over the sampled actions..
+                # TODO: don't make that assumption where a logprob is available
+                weights = [self.get_weight(child_id) for child_id in children]
+                steps = [self.get_transition(child_id) for child_id in children]
+                v_tp1 = sum(
+                    w * step.value for w, step in zip(weights, steps, strict=True)
+                ) / sum(weights)
+            else:
+                v_tp1 = 0.0
+
+            # Q_t(s_t, a_t) = r_{t+1} + gamma * V_{t+1}(s_{t+1})
+            # (we are assuming the environment is deterministic)
+            step.value = step.reward + discount_factor * v_tp1
    
+
+
+
    
+[docs]
+    def merge_identical_nodes(
+        self,
+        agent_state_hash_fn: Callable[[Any], Hashable],
+        observation_hash_fn: Callable[
+            [list[ToolResponseMessage | Message]], Hashable
+        ] = join,
+        next_observation_hash_fn: Callable[
+            [list[ToolResponseMessage | Message]], Hashable
+        ] = join,
+    ) -> TransitionTree:
+        """Merge nodes with identical (state, observation, action)s. Returns a new tree.
+
+        Args:
+            agent_state_hash_fn: A function that returns a hashable representation
+                of the agent state of a transition.
+            observation_hash_fn: A function that returns a hashable representation
+                of the observation messages of a transition.
+            next_observation_hash_fn: A function that returns a hashable representation
+                of the next observation messages of a transition.
+        """
+        new_tree = TransitionTree(self.root_id)
+
+        # step hash -> step ID
+        seen_hash_to_step_id: dict[int, str] = {}
+        # old step ID -> new step ID
+        node_remap: dict[str, str] = {self.root_id: self.root_id}
+
+        for step_id in nx.topological_sort(self.tree):
+            step: Transition | None = self.tree.nodes[step_id]["transition"]
+            if step is None:
+                continue
+
+            state_hash = agent_state_hash_fn(step.agent_state)
+
+            if step.action is not None:
+                tool_request_msg = step.action.value
+                # NOTE: we are ignoring tool call ID in the comparison of tool requests.
+                # Thus, the tool call ID is excluded from the hash, so we can't just
+                # simply call str(tool_request_msg)
+                action_str = (tool_request_msg.content or "") + " ".join(
+                    str(tc) for tc in tool_request_msg.tool_calls
+                )
+            else:
+                action_str = ""
+
+            step_hash = hash((
+                state_hash,
+                action_str,
+                # (s, a, o): works for deterministic envs
+                observation_hash_fn(step.observation),
+                # (s, a, o, o'): works for both deterministic and stochastic envs
+                next_observation_hash_fn(step.next_observation),
+            ))
+            step_weight = self.get_weight(step_id)
+
+            if step_hash in seen_hash_to_step_id:  # Seen: merge
+                merged_step_id = node_remap[step_id] = seen_hash_to_step_id[step_hash]
+                # Not sure if this is the fastest way to do this
+                new_tree.tree.nodes[merged_step_id]["weight"] += step_weight
+            else:  # Unseen: don't merge
+                node_remap[step_id] = seen_hash_to_step_id[step_hash] = step_id
+                parent_id = node_remap[":".join(step_id.split(":")[:-1])]
+
+                # manually add transitions, since the step_id substring relationship
+                # will be broken
+                new_tree._add_node(step_id, transition=step, weight=step_weight)
+                new_tree._add_edge(parent_id, step_id)
+
+        return new_tree
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/graph/async_torch.html b/docs/_build/html/_modules/ldp/graph/async_torch.html new file mode 100644 index 00000000..5c3a09aa --- /dev/null +++ b/docs/_build/html/_modules/ldp/graph/async_torch.html @@ -0,0 +1,315 @@ + + + + + + + ldp.graph.async_torch — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.graph.async_torch

    +__all__ = ["AsyncTorchModule", "async_protect_torch_call"]
+
+import asyncio
+import operator
+import time
+from abc import ABC, abstractmethod
+from collections.abc import Callable
+from contextlib import nullcontext
+from typing import Any
+from uuid import UUID, uuid4
+
+try:
+    import torch
+    from torch import nn
+    from torch.utils.data import default_collate
+except ImportError:
+    raise ImportError(
+        "ldp.graph.async_torch requires PyTorch as a dependency. "
+        "Please run `pip install ldp[nn]`."
+    ) from None
+
+_TORCH_LOCK = asyncio.Lock()
+
+# Supported devices here: https://pytorch.org/docs/stable/amp.html#torch.autocast
+_AUTOCAST_DEVICES = {"cpu", "cuda", "hpu", "xpu"}
+
+
+def _get_autocast_context(dtype: torch.dtype | None, device_type: str):
+    return (
+        nullcontext()
+        if dtype is None or device_type not in _AUTOCAST_DEVICES
+        else torch.autocast(dtype=dtype, device_type=device_type)
+    )
+
+
+def _get_grad_context(no_grad: bool):
+    return torch.no_grad() if no_grad else nullcontext()
+
+
+
    
+[docs]
+def async_protect_torch_call(
+    module: nn.Module,
+    module_call_fn: Callable = lambda m, *args, **kwargs: m(*args, **kwargs),
+    no_grad: bool = False,
+    autocast_dtype: torch.dtype | None = None,
+    autocast_device_type=None,
+) -> Callable:
+    async def wrapped_call(*args, **kwargs):
+        async with _TORCH_LOCK:
+            with (
+                _get_grad_context(no_grad),
+                _get_autocast_context(autocast_dtype, autocast_device_type),
+            ):
+                return module_call_fn(module, *args, **kwargs)
+
+    return wrapped_call
    
+
+
+
+# TODO: make max_wait_interval adaptive. We can use a heuristic like
+# half the average time for a single call. If it's not provided, enable
+# adaptive mode.
+
+
+class AsyncBufferedWorker(ABC):
+    """Abstract class for a worker that buffers inputs and processes them in batches."""
+
+    def __init__(
+        self,
+        batch_size: int,
+        max_wait_interval: float,
+        collate_fn: Callable = lambda x: x,
+        decollate_fn: Callable = lambda x: x,
+    ):
+        """Initialize.
+
+        Args:
+            batch_size: The target batch size to use when calling the module. As soon as
+                batch_size calls are made, a forward pass is executed.
+            max_wait_interval: The maximum time (sec) to wait for a batch to fill up
+                before executing the calls we have buffered.
+            collate_fn: A function to pre-process a list of inputs into a batch. Defaults to a
+                no-op.
+            decollate_fn: Kind of like the opposite of collate_fn. This function should take
+                 the batched output and return an ordered list of outputs. Defaults to no-op.
+        """
+        self.batch_size = batch_size
+        self.timeout = max_wait_interval
+        self.collate_fn = collate_fn
+        self.decollate_fn = decollate_fn
+
+        self._work_buffer: list[tuple[float, UUID, dict[str, Any]]] = []
+        self._result_buffer: dict[UUID, Any] = {}
+        self._lock = asyncio.Lock()
+
+    async def __call__(self, **kwargs):
+        request_id = uuid4()
+        request_ts = time.time()
+
+        async with self._lock:
+            # Make sure only one coroutine is using the work buffer at a time
+            self._work_buffer.append((request_ts, request_id, kwargs))
+
+        while True:
+            async with self._lock:
+                # Only one coroutine allowed in here when:
+                # - modifying the result buffer
+                # - modifying the work buffer
+
+                if request_id in self._result_buffer:
+                    # Our request was fulfilled by this or another coroutine!
+                    return self._result_buffer.pop(request_id)
+
+                # Try to run a batch.
+                await self._maybe_process_batch()
+
+            # Sleep, to let another coroutine take over if it needs to
+            await asyncio.sleep(0.0)
+
+    async def _maybe_process_batch(self):
+        """If the buffer is >= batch size or we have been waiting long enough, process the old batch.
+
+        If neither condition is met, do nothing.
+        """
+        now = time.time()
+
+        # sort by oldest requests first
+        self._work_buffer.sort(key=operator.itemgetter(0))
+
+        if (
+            len(self._work_buffer) >= self.batch_size
+            or now - self._work_buffer[0][0] > self.timeout
+        ):
+            # if we're over batch size or have at least one input waiting for
+            # more than timeout, pull out a batch to run
+            batch = self._work_buffer[: self.batch_size]
+            self._work_buffer = self._work_buffer[self.batch_size :]
+
+            # Construct the batch tensors
+            sample_kwargs = [x[2] for x in batch]
+            batch_kwargs = self.collate_fn(sample_kwargs)
+
+            batched_results = await self._batched_call(batch_kwargs)
+            request_ids = [x[1] for x in batch]
+            results = self.decollate_fn(batched_results)
+            self._result_buffer.update(zip(request_ids, results, strict=True))
+
+    @abstractmethod
+    async def _batched_call(self, batch_kwargs: dict[str, Any]):
+        """Logic to call the worker on a batch of inputs."""
+
+
+
    
+[docs]
+class AsyncTorchModule(AsyncBufferedWorker):
+    def __init__(
+        self,
+        module: nn.Module,
+        batch_size: int,
+        max_wait_interval: float,
+        collate_fn: Callable = default_collate,
+        decollate_fn: Callable = list,
+        module_call_fn: Callable = lambda m, *args, **kwargs: m(*args, **kwargs),
+    ):
+        """A wrapper around a torch.nn.Module that allows for async calls.
+
+        Usage:
+        ```python
+        my_model = nn.Linear(2, 2)
+        async_model = AsyncTorchModule(my_model, batch_size=4, max_wait_interval=0.01)
+
+        result = await asyncio.gather(*[
+            async_model(input=torch.rand(2)) for _ in range(10)
+        ])
+        ```
+        In the above example, note that we are making 10 calls with a batch size of 4.
+        The first two groups of 4 will be batched and executed as they arrive. The last 2
+        will wait for max_wait_interval and then execute.
+
+        NOTE: This module is not thread-safe and currently always operates in no_grad() mode.
+        It may be possible to relax the latter constraint.
+
+        Args:
+            module: The PyTorch module to wrap.
+            batch_size: See parent class.
+            max_wait_interval: See parent class.
+            collate_fn: A PyTorch collate function to use when batching inputs. Defaults to
+                the PyTorch default_collate.
+            decollate_fn: Kind of like the opposite of collate_fn. This function should take
+                 the batched output and return an ordered list of outputs. Defaults to list.
+            module_call_fn: Function that allows for customizing the call to the module.
+        """
+        super().__init__(
+            batch_size=batch_size,
+            max_wait_interval=max_wait_interval,
+            collate_fn=collate_fn,
+            decollate_fn=decollate_fn,
+        )
+        self.module = module
+        self.module_call_fn = module_call_fn
+
+    def _get_dtype_and_device(self) -> tuple[torch.dtype, torch.device]:
+        param = next(self.module.parameters())
+        return param.dtype, param.device
+
+    async def _batched_call(self, batch_kwargs: dict[str, Any]):
+        # Call the module and store results
+        # To be safe, set _TORCH_LOCK to prevent other
+        # coroutines from messing with torch state while running.
+        async with _TORCH_LOCK:
+            dtype, device = self._get_dtype_and_device()
+            with torch.no_grad(), _get_autocast_context(dtype, device.type):
+                return self.module_call_fn(self.module, **batch_kwargs)
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/graph/common_ops.html b/docs/_build/html/_modules/ldp/graph/common_ops.html new file mode 100644 index 00000000..38ae331c --- /dev/null +++ b/docs/_build/html/_modules/ldp/graph/common_ops.html @@ -0,0 +1,596 @@ + + + + + + + ldp.graph.common_ops — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.graph.common_ops

    +"""This module contains commonly-used Op implementations."""
+
+from __future__ import annotations
+
+import asyncio
+import functools
+import inspect
+import logging
+from collections.abc import Awaitable, Callable
+from functools import lru_cache
+from typing import Generic, TypeVar, cast, overload
+
+import numpy as np
+import tree
+from aviary.message import Message
+from aviary.tools import Tool, ToolRequestMessage
+from aviary.utils import is_coroutine_callable
+from pydantic import BaseModel
+
+from ldp.llms import (
+    EmbeddingModel,
+    HybridEmbeddingModel,
+    LiteEmbeddingModel,
+    LLMModel,
+    LLMResult,
+    SparseEmbeddingModel,
+)
+
+from .gradient_estimators import assign_constant_grads
+from .memory import Memory, MemoryModel, UIndexMemoryModel
+from .op_utils import CallID, get_call_id, get_training_mode
+from .ops import GradInType, Op, OpCtx, ResultOrValue, TOutput
+
+logger = logging.getLogger(__name__)
+
+
+
    
+[docs]
+def logsumexp(a: np.ndarray | list[float]) -> float:
+    a_max = np.max(a)
+    return a_max + np.log(np.sum(np.exp(a - a_max)))
    
+
+
+
+
    
+[docs]
+class IdentityOp(Op[TOutput]):
+    """
+    An operation that simply returns the input value.
+
+    NOTE: this op is equivalent to FxnOp(lambda x: x).
+    """
+
+
    
+[docs]
+    async def forward(self, value: TOutput) -> TOutput:
+        # We assume value already has the correct run_id from its producer
+        return value
    
+
+
+
    
+[docs]
+    @classmethod
+    def backward(
+        cls,
+        ctx: OpCtx,
+        input_args: list[ResultOrValue],
+        input_kwargs: dict[str, ResultOrValue],
+        grad_output: tree.Structure,
+        call_id: CallID,
+    ) -> GradInType:
+        return [], {"value": grad_output}
    
+
    
+
+
+
+
    
+[docs]
+class StopGradOp(IdentityOp[TOutput]):
+    """Pass through Op that terminates gradients in the backward pass."""
+
+
    
+[docs]
+    @classmethod
+    def backward(
+        cls,
+        ctx: OpCtx,
+        input_args: list[ResultOrValue],
+        input_kwargs: dict[str, ResultOrValue],
+        grad_output: tree.Structure,
+        call_id: CallID,
+    ) -> GradInType:
+        return assign_constant_grads(input_args, input_kwargs, None)
    
+
    
+
+
+
+TConfig = TypeVar("TConfig", bound=BaseModel | dict)
+
+
+
    
+[docs]
+class ConfigOp(Op[TConfig], Generic[TConfig]):
+    """An operation that contains a configuration object."""
+
+    def __init__(self, config: TConfig):
+        self.config = config
+
+
    
+[docs]
+    async def forward(self) -> TConfig:
+        return self.config
    
+
+
+
    
+[docs]
+    @classmethod
+    def backward(
+        cls,
+        ctx: OpCtx,
+        input_args: list[ResultOrValue],
+        input_kwargs: dict[str, ResultOrValue],
+        grad_output: tree.Structure,
+        call_id: CallID,
+    ) -> GradInType:
+        # Check that the grad_output structure is consistent with our config
+        tree.assert_same_structure(
+            grad_output, ctx.get(call_id, "output").value, check_types=False
+        )
+
+        # Terminate here - we're a leaf since a ConfigOp takes no inputs
+        return [], {}
    
+
    
+
+
+
+TResult = TypeVar("TResult")
+
+
+
    
+[docs]
+class Cacheable(Generic[TResult]):
+    def __init__(self, co: Awaitable[TResult]) -> None:
+        self.co = co
+        self.done = False
+        self.result: TResult | None = None
+        self.lock = asyncio.Lock()
+
+
    
+[docs]
+    async def get_result(self) -> TResult | None:
+        async with self.lock:
+            if not self.done:
+                self.result = await self.co
+                self.done = True
+            return self.result
    
+
+
+    def __await__(self):
+        return self.get_result().__await__()
    
+
+
+
+
    
+[docs]
+def async_cache(func):
+    @functools.lru_cache(maxsize=1024)
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        return Cacheable(co=func(*args, **kwargs))
+
+    return wrapper
    
+
+
+
+
    
+[docs]
+class FxnOp(Op[TOutput]):
+    """
+    Wrap a function for a straight through gradient approximation for all args/kwargs.
+
+    Basically, consider the fxn as a transform upon the inputs during the forward pass,
+    and propagating the same gradient for all inputs during the backward pass.
+    """
+
+    def __init__(
+        self,
+        fxn: Callable[..., TOutput] | Callable[..., Awaitable[TOutput]],
+        cache: bool = False,
+        fxn_name: str | None = None,  # useful for lambdas
+    ):
+        if cache:
+            self.fxn = (
+                async_cache(fxn) if is_coroutine_callable(fxn) else lru_cache()(fxn)
+            )
+        else:
+            self.fxn = fxn
+
+        try:
+            self.fxn_name = fxn_name or fxn.__name__
+        except AttributeError:  # unittest.mock.Mock or lambda
+            self.fxn_name = str(fxn)
+
+        # override forward args with the signature of the function
+        fwd_sig = inspect.signature(self.fxn)
+        self._fwd_args = list(fwd_sig.parameters.values())
+
+    def __repr__(self) -> str:
+        return f"{type(self).__name__} {self.fxn_name} ({id(self)})"
+
+
    
+[docs]
+    async def forward(self, *args, **kwargs) -> TOutput:
+        if is_coroutine_callable(self.fxn):
+            return await self.fxn(*args, **kwargs)
+        return self.fxn(*args, **kwargs)
    
+
+
+
    
+[docs]
+    @classmethod
+    def backward(
+        cls,
+        ctx: OpCtx,
+        input_args: list[ResultOrValue],
+        input_kwargs: dict[str, ResultOrValue],
+        grad_output: tree.Structure,
+        call_id: CallID,
+    ) -> GradInType:
+        return assign_constant_grads(input_args, input_kwargs, 0.0)
    
+
    
+
+
+
+
    
+[docs]
+class PromptOp(FxnOp[str]):
+    """An operation that formats kwargs into a prompt string."""
+
+    async def _fxn(
+        self, prompt_kwargs: dict[str, str] | None = None, **kwargs: str
+    ) -> str:
+        return self.prompt.format(**{**(prompt_kwargs or {}), **kwargs})
+
+    def __init__(self, prompt: str):
+        self.prompt = prompt
+        super().__init__(fxn=self._fxn, cache=False)
+
+    def __repr__(self) -> str:
+        # we want to use Op.__repr__, not FxnOp.__repr__
+        return super(FxnOp, self).__repr__()
    
+
+
+
+
    
+[docs]
+class LLMCallOp(Op[Message]):
+    """An operation for LLM calls interaction."""
+
+    def __init__(self, num_samples_logprob_estimate: int = 0) -> None:
+        super().__init__()
+        self.num_samples_partition_estimate = num_samples_logprob_estimate
+
+    @overload
+    async def forward(
+        self,
+        config: dict,
+        msgs: list[Message],
+        tools: list[Tool] = ...,
+        tool_choice: Tool | str | None = LLMModel.TOOL_CHOICE_REQUIRED,
+    ) -> ToolRequestMessage: ...
+
+    @overload
+    async def forward(
+        self,
+        config: dict,
+        msgs: list[Message],
+        tools: None = None,
+        tool_choice: str | None = LLMModel.TOOL_CHOICE_REQUIRED,
+    ) -> Message: ...
+
+
    
+[docs]
+    async def forward(
+        self,
+        config: dict,
+        msgs: list[Message],
+        tools: list[Tool] | None = None,
+        tool_choice: Tool | str | None = LLMModel.TOOL_CHOICE_REQUIRED,
+    ) -> Message:
+        model = LLMModel(config=config)
+
+        result = await model.call(messages=msgs, tools=tools, tool_choice=tool_choice)
+        if result.messages is None:
+            raise ValueError("No messages returned")
+
+        # if not set, assume temp = 1. TODO: when would it not be set?
+        temperature: float = (result.config or {}).get("temperature", 1.0)
+
+        # Compute a Monte Carlo estimate of the logprob of this sequence at the given temperature.
+        logprob = await self.compute_logprob(
+            raw_log_p=result.logprob,
+            temperature=temperature,
+            model=model,
+            messages=msgs,
+            tools=tools,
+            tool_choice=tool_choice,
+        )
+
+        call_id = get_call_id()
+        self.ctx.update(call_id, "result", result)
+        # This is the logprob of this sequence according to the raw model, without
+        # any temperature/top-p distribution shaping.
+        self.ctx.update(call_id, "raw_logprob", result.logprob)
+
+        self.ctx.update(call_id, "temperature", temperature)
+        self.ctx.update(call_id, "logprob", logprob)
+
+        return result.messages[0]
    
+
+
+
    
+[docs]
+    async def compute_logprob(
+        self,
+        raw_log_p: float | None,
+        temperature: float,
+        model: LLMModel,
+        **model_kwargs,
+    ) -> float | None:
+        """This method computes a Monte Carlo estimate of logprob for a given temperature.
+
+        It takes as input the logprob at T=1. The derivation is in Section 5.1 of the Aviary notes.
+        """
+        if temperature == 1:
+            return raw_log_p
+
+        if temperature == 0:
+            return 1.0
+
+        if raw_log_p is None or self.num_samples_partition_estimate == 0:
+            return None
+
+        # TODO: Try using n completions from a single API call. Need to modify LLMModel.call to do this, since
+        # it currently only checks completion.choices[0]. Would reduce cost for long prompts.
+        # TODO: think about whether sampling params besides temperature need to be accounted for, like top_p
+        results = await asyncio.gather(*[
+            model.call(temperature=1, **model_kwargs)
+            for _ in range(self.num_samples_partition_estimate)
+        ])
+        temp_factor = 1.0 / temperature - 1.0
+
+        # Partition function estimate:
+        # Z_T = E_P[ e^(lnP/T - lnP) ]
+        log_Z_T = logsumexp([
+            temp_factor * cast(float, result.logprob) for result in results
+        ]) - np.log(self.num_samples_partition_estimate)
+
+        return (raw_log_p / temperature) - log_Z_T
    
+
+
+
    
+[docs]
+    @classmethod
+    def backward(
+        cls,
+        ctx: OpCtx,
+        input_args: list[ResultOrValue],
+        input_kwargs: dict[str, ResultOrValue],
+        grad_output: tree.Structure,
+        call_id: CallID,
+    ) -> GradInType:
+        # By default, we want to descend into config, but not msgs/tools/tool_choice
+        # Essentially: we can think of each config field as an independent parameter,
+        # but not necessarily each message or tool.
+
+        # tree.map_structure allows us to assign a gradient of 0 to all fields of config
+        grad_config = tree.map_structure(lambda _: 0.0, input_kwargs["config"])
+        grad_kwargs = {"config": grad_config}
+        for arg in ("msgs", "tools", "tool_choice"):
+            if arg in input_kwargs:
+                grad_kwargs[arg] = 0.0
+
+        return [], grad_kwargs
    
+
+
+
    
+[docs]
+    def get_examples(self) -> list[tuple[LLMResult, float]]:
+        examples = [
+            (
+                self.ctx.get(c, "result", None),
+                # get 'model' kwarg from grad_input
+                # use default of None if not found
+                self.ctx.get(c, "grad_input", default=([], {}))[1].get("model", None),
+            )
+            for c in self.get_call_ids()
+        ]
+        # filter out the None values
+        return [(e, w) for e, w in examples if e is not None]
    
+
    
+
+
+
+
    
+[docs]
+class MemoryOp(Op[list[Memory]]):
+    """An operation for managing memory retrieval and storage."""
+
+    def __init__(self, memory_model: MemoryModel | None = None):
+        super().__init__()
+        self.memory_model = memory_model or UIndexMemoryModel(
+            embedding_model=EmbeddingModel.from_name("sparse")
+        )
+
+
    
+[docs]
+    async def forward(
+        self,
+        query: str,
+        input: str | None = None,  # noqa: A002
+        matches: int = 3,
+    ) -> list[Memory]:
+        """Retrieve relevant memories based on a query."""
+        if get_training_mode():
+            call_id = get_call_id()
+            self.ctx.update(call_id, "query", query)
+            self.ctx.update(call_id, "memory_input", input)
+        return await self.memory_model.get_memory(query, matches)
    
+
+
+
    
+[docs]
+    @classmethod
+    def backward(
+        cls,
+        ctx: OpCtx,
+        input_args: list[ResultOrValue],
+        input_kwargs: dict[str, ResultOrValue],
+        grad_output: tree.Structure,
+        call_id: CallID,
+    ) -> GradInType:
+        """Backward pass for memory retrieval - goes back to item."""
+        return assign_constant_grads(input_args, input_kwargs, 0.0)
    
+
    
+
+
+
+
    
+[docs]
+class EmbeddingOp(Op):
+    """A general operation for embedding text using LiteLLM."""
+
+    def __init__(
+        self,
+        *,
+        dense_embedding: str = "text-embedding-3-small",
+        dense_embedding_dim: int = 512,
+        sparse_embedding_dim: int = 0,
+        **embedding_model_kwargs,
+    ):
+        if "timeout" not in embedding_model_kwargs:
+            embedding_model_kwargs.setdefault("timeout", 60)
+        emb_models: list[EmbeddingModel] = []
+        if dense_embedding_dim > 0:
+            emb_models.append(
+                LiteEmbeddingModel(
+                    name=dense_embedding,
+                    dimensions=dense_embedding_dim,
+                    embed_kwargs=embedding_model_kwargs,
+                )
+            )
+        if sparse_embedding_dim > 0:
+            emb_models.append(SparseEmbeddingModel(dimensions=sparse_embedding_dim))
+        self.embedding = HybridEmbeddingModel(models=emb_models)
+
+
    
+[docs]
+    async def forward(self, string_input: str) -> np.ndarray:
+        return await self.embedding.embed_text(string_input)
    
+
+
+
    
+[docs]
+    @classmethod
+    def backward(
+        cls,
+        ctx: OpCtx,
+        input_args,
+        input_kwargs,
+        grad_output: tree.Structure,
+        call_id: CallID,
+    ) -> GradInType:
+        return [], {"string_input": None}
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/graph/gradient_estimators.html b/docs/_build/html/_modules/ldp/graph/gradient_estimators.html new file mode 100644 index 00000000..f6e91c27 --- /dev/null +++ b/docs/_build/html/_modules/ldp/graph/gradient_estimators.html @@ -0,0 +1,363 @@ + + + + + + + ldp.graph.gradient_estimators — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.graph.gradient_estimators

    +"""This module defines various gradient estimators that can be patched in during backward passes."""
+
+from __future__ import annotations
+
+import logging
+from functools import partial
+from typing import Any
+
+import tree
+
+from .op_utils import CallID
+from .ops import GradInType, OpCtx, OpResult, ResultOrValue
+
+try:
+    import torch
+
+    from .torch_ops import TorchOp
+
+except ImportError:
+    torch = None  # type: ignore[assignment]
+
+logger = logging.getLogger(__name__)
+
+
+
    
+[docs]
+def assign_constant_grads(
+    input_args: list[ResultOrValue],
+    input_kwargs: dict[str, ResultOrValue],
+    value: Any,
+    descend: bool = True,
+):
+    if not descend:
+        return [value] * len(input_args), dict.fromkeys(input_kwargs, value)
+
+    # descend into nested objects
+    arg_grads = [
+        tree.map_structure(lambda _: value, OpResult.unwrap_value(arg))
+        for arg in input_args
+    ]
+    kwarg_grads = {
+        k: tree.map_structure(lambda _: value, OpResult.unwrap_value(v))
+        for k, v in input_kwargs.items()
+    }
+    return arg_grads, kwarg_grads
    
+
+
+
+
    
+[docs]
+def straight_through_estimator(
+    ctx: OpCtx,  # noqa: ARG001
+    input_args: list[ResultOrValue],
+    input_kwargs: dict[str, ResultOrValue],
+    grad_output: tree.Structure,
+    call_id: CallID,  # noqa: ARG001
+    descend: bool = True,
+) -> GradInType:
+    return assign_constant_grads(input_args, input_kwargs, grad_output, descend=descend)
    
+
+
+
+
    
+[docs]
+def stop_grad(
+    ctx: OpCtx,  # noqa: ARG001
+    input_args: list[ResultOrValue],
+    input_kwargs: dict[str, ResultOrValue],
+    grad_output: tree.Structure,  # noqa: ARG001
+    call_id: CallID,  # noqa: ARG001
+) -> GradInType:
+    # don't descend - want gradients to stop at the OpResult level
+    return assign_constant_grads(input_args, input_kwargs, None, descend=False)
    
+
+
+
+
    
+[docs]
+def zero_estimator(
+    ctx: OpCtx,  # noqa: ARG001
+    input_args: list[ResultOrValue],
+    input_kwargs: dict[str, ResultOrValue],
+    grad_output: tree.Structure,  # noqa: ARG001
+    call_id: CallID,  # noqa: ARG001
+) -> GradInType:
+    """Sets the gradient of all inputs to zero.
+
+    Note that this is not the same as truncating the compute graph (stop_grad),
+    since upstream nodes can still optimize their logprobs. The zero estimator
+    the unbiased choice if we have no information about the gradient.
+    """
+    return assign_constant_grads(input_args, input_kwargs, 0.0)
    
+
+
+
+
    
+[docs]
+def llm_straight_through_estimator(
+    ctx: OpCtx,  # noqa: ARG001
+    input_args: list[ResultOrValue],
+    input_kwargs: dict[str, ResultOrValue],
+    grad_output: tree.Structure,
+    call_id: CallID,  # noqa: ARG001
+) -> GradInType:
+    """Straight-through for an LLM: descend into the config, but not msgs/tools/tool_calls.
+
+    See LLMCallOp.backward() for more details on this choice.
+    Don't bother checking that input_args/input_kwargs have the right structure,
+    since compute_grads() will raise if not.
+    """
+    config_grad = tree.map_structure(
+        lambda _: grad_output, OpResult.unwrap_value(input_kwargs["config"])
+    )
+    grad_args = [grad_output] * len(input_args)
+    grad_kwargs = {"config": config_grad}
+    if "msgs" in input_kwargs:
+        grad_kwargs["msgs"] = grad_output
+    if "tools" in input_kwargs:
+        grad_kwargs["tools"] = grad_output
+
+    return grad_args, grad_kwargs
    
+
+
+
+
    
+[docs]
+def assign_default_grads(
+    input_grads: GradInType,
+    input_args: list[ResultOrValue],
+    input_kwargs: dict[str, ResultOrValue],
+    default_grad_val: float = 0.0,
+) -> GradInType:
+    """Sets a default value of default_grad_val for every element in input_grads.
+
+    Example:
+    - input_kwargs = {"a": {"b": 1, "c": 2}},
+    - input_grad_kwargs = {"a": {"b": 0.1}}
+    Output: input_grads[1] = {"a": {"b": 0.1, "c": default_grad_val}}
+
+    Returns:
+        GradInType: A tuple containing the updated input_grad_args and
+            input_grad_kwargs with default values assigned where necessary.
+    """
+
+    def get_nested_value(data: tree.Structure, path: list) -> Any:
+        """Traverse given path over data and return the value at the end of the path."""
+        try:
+            current_value = data
+            for key in path:
+                current_value = current_value[key]
+        except (KeyError, IndexError):
+            return None  # If path not found, return None (than default_grad_val will be assigned)
+        else:
+            return current_value
+
+    def assign_default_gradients(
+        input_grads: tree.Structure, path: list, _value: Any
+    ) -> Any:
+        """Assign default_grad_val where grads are missing."""
+        return get_nested_value(input_grads, path) or default_grad_val
+
+    input_args_kwargs = (input_args, input_kwargs)
+    input_grads = tree.map_structure_with_path(
+        partial(assign_default_gradients, input_grads),
+        input_args_kwargs,
+    )
+
+    tree.assert_same_structure(input_grads, input_args_kwargs)
+    return input_grads
    
+
+
+
+
    
+[docs]
+class TorchParamBackwardEstimator:
+    """
+    Gradient estimator for `TorchOp` internal parameters.
+
+    This estimator computes gradients with respect to the internal parameters of a
+    `torch.nn.Module` by calling the `backward` method of the estimator instead of the default
+    `backward` method of `TorchOp`. Computed gradients are stored in the context of the operation
+    under the key `"grad_params"`.
+
+    Examples:
+        >>> torch_module = torch.nn.Sequential(
+        ...     torch.nn.Linear(4, 4),
+        ...     torch.nn.Linear(4, 1),
+        ... )
+        >>> torch_op = TorchOp(torch_module)
+        >>> estimator = TorchParamBackwardEstimator(torch_module)
+        >>> result = await torch_op(torch.randn(4, requires_grad=True))
+        >>> result.compute_grads(backward_fns={"TorchOp": estimator.backward})
+
+    Note:
+        This estimator is only compatible with `TorchOp` operations.
+    """
+
+    def __init__(self, module: torch.nn.Module):
+        if torch is None:
+            raise RuntimeError(
+                f"PyTorch library not found. Unable to use {type(self).__name__} class. "
+                "To install PyTorch dependencies, please run `pip install ldp[nn]`."
+            )
+        self.params = dict(module.named_parameters())
+
+
    
+[docs]
+    def backward(
+        self,
+        ctx: OpCtx,
+        input_args: list[ResultOrValue],
+        input_kwargs: dict[str, ResultOrValue],
+        grad_output: tree.Structure,
+        call_id: CallID,
+    ) -> GradInType:
+        tensor_args, tensor_kwargs = ctx.get(call_id, TorchOp.CTX_TENSOR_INPUT_KEY)
+        n_pos_args = len(tensor_args)
+        n_pos_kwargs = len(tensor_kwargs)
+        output = ctx.get(call_id, "output").value
+
+        if not isinstance(grad_output, torch.Tensor):
+            grad_output = torch.tensor(
+                grad_output,
+                dtype=output.dtype,
+            )
+        grad_output = grad_output.to(output.device)
+
+        while grad_output.ndim < output.ndim:
+            # Assume we can broadcast, so expand dims
+            # e.g. if output.shape = (2, 1, 1) and grad_output is a scalar
+            # then we want to expand to (1, 1, 1) and then broadcast
+            grad_output = grad_output.unsqueeze(-1)
+
+        if output.shape != grad_output.shape:
+            raise RuntimeError(
+                f"Output shape {output.shape} does not match grad_output shape {grad_output.shape}"
+            )
+
+        gradients = torch.autograd.grad(
+            output,
+            [*tensor_args, *tensor_kwargs.values(), *self.params.values()],
+            grad_outputs=grad_output,
+        )
+
+        grad_args = [grad.detach().cpu().float() for grad in gradients[:n_pos_args]]
+        grad_kwargs = {
+            k: grad.detach().cpu().float()
+            for k, grad in zip(
+                tensor_kwargs.keys(), gradients[n_pos_args:n_pos_kwargs], strict=True
+            )
+        }
+        grad_params = {
+            name: grad.detach().cpu().float()
+            for name, grad in zip(
+                self.params.keys(), gradients[n_pos_kwargs:], strict=True
+            )
+        }
+
+        ctx.update(call_id=call_id, key="grad_params", value=grad_params)
+
+        return grad_args, grad_kwargs
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/graph/loss_ops.html b/docs/_build/html/_modules/ldp/graph/loss_ops.html new file mode 100644 index 00000000..df94fb87 --- /dev/null +++ b/docs/_build/html/_modules/ldp/graph/loss_ops.html @@ -0,0 +1,144 @@ + + + + + + + ldp.graph.loss_ops — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.graph.loss_ops

    +"""This module contains loss Op implementations."""
+
+from typing import TYPE_CHECKING
+
+import tree
+
+from .op_utils import CallID
+from .ops import GradInType, Op, OpCtx
+
+if TYPE_CHECKING:
+    import numpy.typing as npt
+    import torch
+
+
+
    
+[docs]
+class MSELossOp(Op):
+
    
+[docs]
+    async def forward(
+        self,
+        prediction: "npt.NDArray | torch.Tensor",
+        target: "npt.NDArray | torch.Tensor",
+    ) -> "float | torch.Tensor":
+        return ((prediction - target) ** 2).mean()
    
+
+
+
    
+[docs]
+    @classmethod
+    def backward(
+        cls,
+        ctx: OpCtx,
+        input_args,
+        input_kwargs,
+        grad_output: tree.Structure,
+        call_id: CallID,
+    ) -> GradInType:
+        prediction = input_kwargs["prediction"]
+        target = input_kwargs["target"]
+        grad = 2 * (prediction - target)
+        return [], {"prediction": grad, "target": None}
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/graph/memory.html b/docs/_build/html/_modules/ldp/graph/memory.html new file mode 100644 index 00000000..30c3f12b --- /dev/null +++ b/docs/_build/html/_modules/ldp/graph/memory.html @@ -0,0 +1,315 @@ + + + + + + + ldp.graph.memory — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.graph.memory

    +import asyncio
+from abc import ABC, abstractmethod
+from collections.abc import AsyncIterator
+from contextlib import asynccontextmanager
+from typing import TYPE_CHECKING, Any, ClassVar, Generic, Self, TypeVar, cast
+from uuid import UUID
+
+import numpy as np
+import numpy.typing as npt
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    JsonValue,
+    PrivateAttr,
+    field_validator,
+    model_validator,
+)
+from usearch.index import Index
+
+from ldp.llms import EmbeddingModel
+
+if TYPE_CHECKING:
+    from .common_ops import MemoryOp
+    from .op_utils import CallID
+    from .ops import Op, OpResult, TOutput
+
+
+
    
+[docs]
+class Memory(BaseModel):
+    """A single memory about an input, output, and value tuple.
+
+    A memory is a record of an input, output, and resulting value. Typically used
+    for prompting a language model. Or, it could be about a whole forward pass where
+    the input is the observation and the output is the action taken.
+    The query is optionally different and used for
+    retrieving the memory. For example, it could be much larger because it won't
+    be formatted in the resulting prompt.
+    """
+
+    query: str = Field(
+        description="String to be embedded into a retrieval key for a memory index."
+    )
+    input: str | None = Field(
+        default=None,
+        description=(
+            "Some input (e.g. prompt to LLM, observation). If None (default), the input"
+            " is set to match the query."
+        ),
+    )
+    output: str = Field(description="Some output (e.g. tool selection).")
+    value: float | str = Field(
+        description="Measure of the output's quality (e.g. loss)."
+    )
+    metadata: JsonValue = Field(
+        default_factory=dict,
+        description=(
+            "Optional JSON metadata to store with the memory. An example is storing"
+            " information an optimizer can use at training time."
+        ),
+    )
+    run_id: UUID | None = Field(
+        default=None,
+        description=(
+            "Associate run_id for debugging purposes to trace "
+            "which forward pass generated the memory."
+        ),
+    )
+    template: str = "Input: {input}\nOutput: {output}\nValue: {value}"
+
+
    
+[docs]
+    @model_validator(mode="before")
+    @classmethod
+    def ensure_query(cls, data: Any) -> Any:
+        """Copy input to match the query if input is None."""
+        if isinstance(data, dict) and data.get("input") is None:
+            data["input"] = data["query"]
+        return data
    
+
+
+    def __str__(self) -> str:
+        return self.template.format(**self.model_dump())
+
+
    
+[docs]
+    @classmethod
+    def from_ops(
+        cls,
+        mem_op: "MemoryOp",
+        mem_call_id: "CallID",
+        output_op: "Op[TOutput]",
+        output_call_id: "CallID",
+        value: float,
+        **kwargs,
+    ) -> Self:
+        """Create from a MemoryOp, output Op, and their call IDs."""
+        query: str = mem_op.ctx.get(mem_call_id, "query")
+        memory_input: str | None = mem_op.ctx.get(mem_call_id, "memory_input")
+        output_result: OpResult[TOutput] = output_op.ctx.get(output_call_id, "output")
+        return cls(
+            query=query,
+            input=memory_input if memory_input is not None else query,
+            output=str(output_result.value),
+            value=value,
+            run_id=output_call_id.run_id,
+            **kwargs,
+        )
    
+
    
+
+
+
+TIndex = TypeVar("TIndex")
+
+
+
    
+[docs]
+class MemoryModel(BaseModel, Generic[TIndex], ABC):
+    """A collection of memories with retrieval."""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    embedding_model: EmbeddingModel = Field(
+        default_factory=lambda: EmbeddingModel.from_name(
+            "hybrid-text-embedding-3-small"
+        )
+    )
+    memories: dict[int, Memory] = Field(default_factory=dict)
+    _index: TIndex
+    _index_lock: asyncio.Lock = PrivateAttr(default_factory=asyncio.Lock)
+
+
    
+[docs]
+    @field_validator("memories")
+    @classmethod
+    def enforce_empty(cls, v: dict) -> dict:
+        if v:
+            raise ValueError("Memories must be empty at construction time.")
+        return v
    
+
+
+
    
+[docs]
+    async def add_memory(self, memory: Memory) -> None:
+        key = await self._add_to_index(
+            embedding=await self.embedding_model.embed_text(memory.query)
+        )
+        self.memories[key] = memory
    
+
+
+    DEFAULT_MEMORY_MATCHES: ClassVar[int] = 3
+
+
    
+[docs]
+    async def get_memory(
+        self, query: str, matches: int = DEFAULT_MEMORY_MATCHES
+    ) -> list[Memory]:
+        return await self._search_index(
+            embedding=await self.embedding_model.embed_text(query), matches=matches
+        )
    
+
+
+    def __len__(self) -> int:
+        return len(self.memories)
+
+
    
+[docs]
+    @asynccontextmanager
+    async def safe_access_index(self) -> AsyncIterator[TIndex]:
+        """Get the internal Index under the protection of an internal Lock."""
+        # pylint bug, SEE: https://github.com/pylint-dev/pylint/issues/9813
+        async with self._index_lock:  # pylint: disable=not-async-context-manager
+            yield self._index
    
+
+
+    @abstractmethod
+    async def _add_to_index(self, embedding: np.ndarray) -> int:
+        """Add an embedding to the internal Index and return its key."""
+
+    @abstractmethod
+    async def _search_index(
+        self, embedding: np.ndarray, matches: int = DEFAULT_MEMORY_MATCHES
+    ) -> list[Memory]:
+        """Search the internal Index, returning a 'matches' amount of Memories."""
    
+
+
+
+
    
+[docs]
+class UIndexMemoryModel(MemoryModel[Index]):
+    """Memory model using a U-Search index."""
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        if not self.embedding_model.dimensions:
+            raise TypeError("Specify dimensions to the embedding model.")
+        self._index = Index(ndim=self.embedding_model.dimensions)
+
+    async def _add_to_index(self, embedding: np.ndarray) -> int:
+        async with self.safe_access_index() as index:
+            added_value = cast(
+                npt.NDArray[np.int_], index.add(len(self.memories), embedding)
+            )
+            return added_value.item()
+
+    async def _search_index(
+        self, embedding: np.ndarray, matches: int = MemoryModel.DEFAULT_MEMORY_MATCHES
+    ) -> list[Memory]:
+        async with self.safe_access_index() as index:
+            search_matches = index.search(embedding, matches)
+        # mypy doesn't respect "old style" __getitem__/__len__ as iterable,
+        # so we have this ignore. SEE: https://github.com/python/mypy/issues/9737
+        return [self.memories[m.key] for m in search_matches]  # type: ignore[union-attr]
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/graph/modules/llm_call.html b/docs/_build/html/_modules/ldp/graph/modules/llm_call.html new file mode 100644 index 00000000..a8f91bf3 --- /dev/null +++ b/docs/_build/html/_modules/ldp/graph/modules/llm_call.html @@ -0,0 +1,131 @@ + + + + + + + ldp.graph.modules.llm_call — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.graph.modules.llm_call

    +from collections.abc import Callable, Iterable
+from typing import Any, Generic, TypeVar
+
+from aviary.message import Message
+
+from ldp.graph import ConfigOp, FxnOp, LLMCallOp, OpResult, compute_graph
+
+TParsedMessage = TypeVar("TParsedMessage", bound=Message)
+
+
+
    
+[docs]
+class ParsedLLMCallModule(Generic[TParsedMessage]):
+    """Module for a processing-based tool selection, with a learnable configuration."""
+
+    def __init__(
+        self, llm_model: dict[str, Any], parser: Callable[..., TParsedMessage]
+    ):
+        self.config_op = ConfigOp[dict](config=llm_model)
+        self.llm_call_op = LLMCallOp()
+        self.parse_msg_op = FxnOp(parser)
+
+    @compute_graph()
+    async def __call__(
+        self, messages: Iterable[Message], *parse_args, **parse_kwargs
+    ) -> tuple[OpResult[TParsedMessage], Message]:
+        raw_result = await self.llm_call_op(await self.config_op(), msgs=messages)
+        return await self.parse_msg_op(
+            raw_result, *parse_args, **parse_kwargs
+        ), raw_result.value
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/graph/modules/react.html b/docs/_build/html/_modules/ldp/graph/modules/react.html new file mode 100644 index 00000000..99c9bf69 --- /dev/null +++ b/docs/_build/html/_modules/ldp/graph/modules/react.html @@ -0,0 +1,366 @@ + + + + + + + ldp.graph.modules.react — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.graph.modules.react

    +import ast
+import re
+import textwrap
+from collections.abc import Iterable
+from enum import StrEnum
+from typing import Any
+
+from aviary.message import EMPTY_CONTENT_BASE_MSG, MalformedMessageError, Message
+from aviary.tools import Tool, ToolCall, ToolRequestMessage
+
+from ldp.graph import FxnOp, OpResult, PromptOp, compute_graph
+from ldp.llms import prepend_sys
+
+from .llm_call import ParsedLLMCallModule
+
+_DEFAULT_PROMPT_TEMPLATE = textwrap.dedent(
+    """    Answer the following questions as best you can. You have access to the following tools:
+
+    {{tools}}
+
+    Use the following format:
+
+    {fields}
+    ... (this {fields_description} can repeat N times)
+
+    Example:
+
+    {example}"""
+)
+REACT_DEFAULT_PROMPT_TEMPLATE = _DEFAULT_PROMPT_TEMPLATE.format(
+    fields=(
+        "Thought: you should always think about what to do"
+        "\nAction: the action to take, should be one of [{tool_names}]"
+        "\nAction Input: comma separated list of inputs to action as python tuple"
+        "\nObservation: the result of the action"
+    ),
+    fields_description="Thought/Action/Action Input/Observation",
+    example=(
+        "Thought: I need to use the get_weather tool"
+        "\nAction: get_weather"
+        '\nAction Input: "New York", 7'
+        "\nObservation: The 7 day forecast for New York is [...]"
+    ),
+)
+ACT_DEFAULT_PROMPT_TEMPLATE = _DEFAULT_PROMPT_TEMPLATE.format(
+    fields=(
+        "Action: the action to take, should be one of [{tool_names}]"
+        "\nAction Input: comma separated list of inputs to action as python tuple"
+        "\nObservation: the result of the action"
+    ),
+    fields_description="Action/Action Input/Observation",
+    example=(
+        "Action: get_weather"
+        '\nAction Input: "New York", 7'
+        "\nObservation: The 7 day forecast for New York is [...]"
+    ),
+)
+
+
+
    
+[docs]
+def parse_message(m: Message, tools: list[Tool]) -> ToolRequestMessage:  # noqa: C901
+    """
+    Parse an Act or ReAct Message into a ToolRequestMessage.
+
+    Args:
+        m: Input raw message.
+        tools: Tools used to confirm a valid tool selection
+
+    Returns:
+        Parsed ToolRequestMessage.
+    """
+    if not m.content:
+        raise MalformedMessageError(
+            f"{EMPTY_CONTENT_BASE_MSG} of type {type(m).__name__}."
+        )
+
+    message_content = m.content
+    # strip (and overwrite) up to end of action input
+    loc = message_content.find("Action Input:")
+    if loc != -1:
+        loc = message_content.find("\n", loc)
+        message_content = message_content[: loc if loc > 0 else None]
+    # we need to override the message too - don't want the model to hallucinate
+    m.content = message_content
+
+    action_args: tuple[Any, ...] = ()
+    # https://regex101.com/r/qmqZ7Z/1
+    action_input = re.search(r"Input:[ \t]*([ \S]*)", m.content)
+    # only parse if it takes arguments
+    if action_input and action_input.group(1).strip():
+        input_str = action_input.group(1).strip()
+        # if it has commas and no quotes, it's almost certainly a tuple without
+        # parentheses, so we add them
+        if "," in input_str and not (
+            input_str.startswith("(") and input_str.endswith(")")
+        ):
+            input_str = f"({input_str})"
+        try:
+            if input_str.startswith("(") and input_str.endswith(")"):
+                # Handle tuples and quoted strings inside
+                if '"' not in input_str and "'" not in input_str:
+                    # Add quotes around each element within parentheses if they are not already quoted
+                    # and if they are not purely numbers. There may exist a killer regex for this
+                    # but I am a simple man
+
+                    # just catches things like "1.1".isnumeric() == False
+                    # so we can't just use isnumeric
+                    def is_number(s: str) -> bool:
+                        try:
+                            float(s)
+                        except ValueError:
+                            return False
+                        return True
+
+                    input_str = ", ".join(
+                        f'"{e.strip()}"' if not is_number(e) else str(e)
+                        for e in input_str.strip("()").split(",")
+                        if e.strip()
+                    )
+                    input_str = f"({input_str})"
+                eval_result = ast.literal_eval(input_str)
+                action_args = (
+                    (eval_result,)
+                    if not isinstance(eval_result, tuple)
+                    else eval_result
+                )
+            else:
+                # Convert to int or float if possible
+                try:
+                    action_args = (ast.literal_eval(input_str),)
+                except (ValueError, SyntaxError):
+                    action_args = (input_str,)
+        except Exception as exc:
+            raise MalformedMessageError(
+                f"Action Input {input_str} could not be parsed."
+            ) from exc
+
+        if len(action_args) == 1 and isinstance(action_args[0], tuple):
+            action_args = action_args[0]
+
+    action = re.search(r"Action:[ \t]*(\S*)", m.content)
+    if not action:
+        raise MalformedMessageError("Action not emitted.")
+    tool_name = action.group(1).strip()
+    # have to match up name to tool to line up args in order
+    try:
+        tool = next(t for t in tools if t.info.name == tool_name)
+    except StopIteration as exc:
+        raise MalformedMessageError(f"Tool {tool_name} not found in tools.") from exc
+    if len(action_args) < len(tool.info.parameters.required):
+        raise MalformedMessageError(
+            f"Action Input {action_args!r} shorter than {tool.info.name!r} tool's"
+            " parameters."
+        )
+
+    # Anecdotally we've observed thought also often captures the action
+    # NOTE: for Act agents there is no Thought, so the regex will return None
+    thought = re.search(r"Thought:[ \t]*(.*)", m.content)
+    return ToolRequestMessage(
+        content=thought.group(1) if thought else None,
+        tool_calls=[ToolCall.from_tool(tool, *action_args)],
+    )
    
+
+
+
+
    
+[docs]
+class ToolDescriptionMethods(StrEnum):
+    """Possible methods of describing the tools."""
+
+    STR = "describe_str"
+    XML = "describe_xml"
+    JSON = "describe_json"
+
+
    
+[docs]
+    def get_prompt_prefix(self) -> str:
+        """Get the prefix to put in front of the prompt."""
+        if self == self.STR:
+            return ""
+        if self == self.JSON:
+            return "Tools are specified with a JSON schema."
+        return "Tools are specified with an XML schema."
    
+
    
+
+
+
+
    
+[docs]
+class ReActModule:
+    """An Act or ReAct module built to work with chat models.
+
+    Paper: https://arxiv.org/abs/2210.03629
+
+    The ReAct style is like so, and note Act style has no 'Thought: ' entries:
+    System:
+        Answer the following questions as best you can. You have access to the following tools:
+
+            {tools}
+
+            Use the following format:
+
+            Thought: you should always think about what to do
+            Action: the action to take, should be one of [{tool_names}]
+            Action Input: the input to the action
+            Observation: the result of the action
+            ... (this Thought/Action/Action Input/Observation can repeat N times)
+    User:
+        {questions}
+    Assistant:
+        Thought:
+        Action:
+        Action Input:
+    User:
+        Observation:
+    Assistant:
+        Thought:
+        Action:
+        Action Input:
+    ...
+    """
+
+
    
+[docs]
+    @staticmethod
+    def parse_message(m: Message, tools: list[Tool]) -> ToolRequestMessage:
+        return parse_message(m, tools)
    
+
+
+    async def _create_system_prompt(self, tools: list[Tool]) -> OpResult[str]:
+        tool_info = "\n".join([
+            getattr(t.info, self._tool_description_method)() for t in tools
+        ])
+        if prefix := self._tool_description_method.get_prompt_prefix():
+            tool_info = f"{prefix}\n{tool_info}"
+        tool_names = ", ".join([t.info.name for t in tools])
+        return await self.prompt_op(
+            schema_type=self._tool_description_method.value,
+            tools=tool_info.strip(),
+            tool_names=tool_names,
+        )
+
+    def __init__(
+        self,
+        llm_model: dict[str, Any],
+        sys_prompt: str = REACT_DEFAULT_PROMPT_TEMPLATE,
+        tool_description_method: ToolDescriptionMethods = ToolDescriptionMethods.STR,
+    ):
+        self.prompt_op = PromptOp(sys_prompt)
+        self._tool_description_method = tool_description_method
+        llm_model["stop"] = ["Observation:"]
+        self.package_msg_op = FxnOp(prepend_sys)
+        self.tool_select_module = ParsedLLMCallModule[ToolRequestMessage](
+            llm_model=llm_model, parser=self.parse_message
+        )
+
+    @compute_graph()
+    async def __call__(
+        self, messages: Iterable[Message], tools: list[Tool]
+    ) -> tuple[OpResult[ToolRequestMessage], Message]:
+        packaged_msgs = await self.package_msg_op(
+            messages, sys_content=await self._create_system_prompt(tools)
+        )
+        return await self.tool_select_module(packaged_msgs, tools=tools)  # type: ignore[arg-type]
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/graph/modules/reflect.html b/docs/_build/html/_modules/ldp/graph/modules/reflect.html new file mode 100644 index 00000000..72b6af7f --- /dev/null +++ b/docs/_build/html/_modules/ldp/graph/modules/reflect.html @@ -0,0 +1,164 @@ + + + + + + + ldp.graph.modules.reflect — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.graph.modules.reflect

    +from typing import Any
+
+from aviary.message import Message
+from pydantic import BaseModel, Field
+
+from ldp.graph import ConfigOp, FxnOp, LLMCallOp, PromptOp, compute_graph
+from ldp.graph.ops import ResultOrValue
+from ldp.llms import append_to_sys
+from ldp.llms.prompts import indent_xml
+
+
+
    
+[docs]
+class ReflectModuleConfig(BaseModel):
+    """Configuration for the ReflectModuleConfig."""
+
+    llm_model: dict[str, Any] = Field(
+        default={"model": "gpt-3.5-turbo"},
+        description="Starting configuration for the LLM model.",
+    )
    
+
+
+
+
    
+[docs]
+class ReflectModule:
+    """A module that simply gives an LLM to reflect on an input."""
+
+    def __init__(self, start_config: ReflectModuleConfig):
+        self.llm_call_op = LLMCallOp()
+        self.prompt_op = PromptOp(
+            "Consider a proposed response based on context. Reflect on the response"
+            " within <thought> tags then conclude with a possibly revised response"
+            " within <final-response> tags."
+        )
+        self.config_op = ConfigOp[ReflectModuleConfig](config=start_config)
+        self.llm_config_op = FxnOp[dict](lambda c: c.llm_model)
+        self.package_fxn = FxnOp(append_to_sys)
+
+        def extract_msg(msg: Message, backup_response: str) -> str:
+            msg_str = msg.content
+            if msg_str and "<final-response>" in msg_str:
+                return msg_str.split("<final-response>")[1].split("</final-response>")[
+                    0
+                ]
+            if msg_str and "<response>" in msg_str:
+                return msg_str.split("<response>")[1].split("</response>")[0]
+            return backup_response
+
+        self.extract_msg = FxnOp(extract_msg)
+
+    @compute_graph()
+    async def __call__(
+        self, context: ResultOrValue[str], response: ResultOrValue[str]
+    ) -> ResultOrValue[str]:
+        llm_config = await self.llm_config_op(await self.config_op())
+        sys_str = await self.prompt_op()
+        user_str = indent_xml(
+            f"<context>{context}</context><response>{response}</response>"
+        )
+        msg = await self.package_fxn(user_str, sys_str)
+        llm_result = await self.llm_call_op(llm_config, msg)
+        return await self.extract_msg(llm_result, response)
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/graph/modules/thought.html b/docs/_build/html/_modules/ldp/graph/modules/thought.html new file mode 100644 index 00000000..93b49fba --- /dev/null +++ b/docs/_build/html/_modules/ldp/graph/modules/thought.html @@ -0,0 +1,146 @@ + + + + + + + ldp.graph.modules.thought — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.graph.modules.thought

    +from collections.abc import Iterable, Mapping
+from typing import Any
+
+from aviary.message import Message
+from aviary.tools import ToolRequestMessage
+
+from ldp.graph import FxnOp, OpResult, PromptOp, compute_graph
+from ldp.llms import prepend_sys_and_append_sys
+
+from .llm_call import ParsedLLMCallModule
+
+
+
    
+[docs]
+class ThoughtModule:
+    @staticmethod
+    def _downcast_to_message(message: Message | ToolRequestMessage) -> Message:
+        if isinstance(message, ToolRequestMessage):
+            # Downcast into a normal Message if the LLM tried to call tools
+            return Message(role=message.role, content=message.content)
+        return message
+
+    def __init__(
+        self, llm_model: dict[str, Any], first_sys_prompt: str, second_sys_prompt: str
+    ):
+        self.first_sys_prompt_op = PromptOp(first_sys_prompt)
+        self.second_sys_prompt_op = PromptOp(second_sys_prompt)
+        self.package_msg_op = FxnOp(prepend_sys_and_append_sys)
+        self.llm_call = ParsedLLMCallModule[Message](
+            llm_model, parser=self._downcast_to_message
+        )
+
+    @compute_graph()
+    async def __call__(
+        self,
+        messages: Iterable[Message],
+        first_prompt_kwargs: Mapping[str, Any],
+        second_prompt_kwargs: Mapping[str, Any],
+    ) -> OpResult[Message]:
+        packaged_msgs = await self.package_msg_op(
+            messages,
+            initial_sys_content=await self.first_sys_prompt_op(**first_prompt_kwargs),
+            final_sys_content=await self.second_sys_prompt_op(**second_prompt_kwargs),
+        )
+        return (await self.llm_call(packaged_msgs))[0]  # type: ignore[arg-type]
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/graph/op_utils.html b/docs/_build/html/_modules/ldp/graph/op_utils.html new file mode 100644 index 00000000..03149253 --- /dev/null +++ b/docs/_build/html/_modules/ldp/graph/op_utils.html @@ -0,0 +1,295 @@ + + + + + + + ldp.graph.op_utils — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.graph.op_utils

    +import contextvars
+from collections.abc import AsyncIterator
+from contextlib import asynccontextmanager
+from uuid import UUID, uuid4
+
+from aviary.utils import is_coroutine_callable
+from pydantic import BaseModel, field_serializer, field_validator
+
+
+
    
+[docs]
+class CallID(BaseModel):
+    run_id: UUID
+    fwd_id: UUID
+
+    def __repr__(self) -> str:
+        return f"{self.run_id}:{self.fwd_id}"
+
+    def __hash__(self) -> int:
+        return hash((self.run_id, self.fwd_id))
+
+
    
+[docs]
+    @field_validator("run_id", "fwd_id", mode="before")
+    @classmethod
+    def validate_uuid(cls, value: UUID | str) -> UUID:
+        if isinstance(value, str):
+            return UUID(value)
+        return value
    
+
+
+
    
+[docs]
+    @field_serializer("run_id", "fwd_id")
+    def serialize_uuid(self, value: UUID) -> str:
+        return str(value)
    
+
+
+    def __init__(self, run_id: str | UUID, fwd_id: str | UUID):
+        # Convenience so we can use positional arguments
+        super().__init__(run_id=run_id, fwd_id=fwd_id)
    
+
+
+
+_RUN_ID = contextvars.ContextVar[UUID]("run_id")
+_CALL_ID = contextvars.ContextVar[CallID]("call_id")
+
+
+
    
+[docs]
+@asynccontextmanager
+async def compute_graph() -> AsyncIterator[UUID]:
+    """Initialize a compute graph by setting a run ID.
+
+    If a run ID is already set (i.e. we are already inside a
+    get_run_id() context), then the existing run ID is returned.
+    Otherwise, a new UUID is created.
+    """
+    try:
+        # If a run ID is set, return it.
+        run_id = _RUN_ID.get()
+        token: contextvars.Token | None = None
+    except LookupError:
+        # If not, make a new run ID.
+        run_id = uuid4()
+        token = _RUN_ID.set(run_id)
+
+    try:
+        yield run_id
+    finally:
+        if token is not None:
+            # token is not None if we made a new run ID. In that case,
+            # reset the context to its previous state.
+            _RUN_ID.reset(token)
    
+
+
+
+
    
+[docs]
+def get_run_id() -> UUID:
+    """Get the current run ID."""
+    try:
+        return _RUN_ID.get()
+    except LookupError:
+        raise RuntimeError(
+            "Attempting to access run ID, but not inside compute graph context."
+        ) from None
    
+
+
+
+
    
+[docs]
+@asynccontextmanager
+async def op_call() -> AsyncIterator[CallID]:
+    """Decorate an op call with a call ID.
+
+    If a call ID is already set (i.e. we are already inside an op call),
+    then the existing call ID is returned.
+    Otherwise, a new UUID is created.
+    """
+    # Get run_id in case we need to construct a CallID, but this also serves
+    # as a check that we're inside compute_graph()
+    run_id = get_run_id()
+
+    try:
+        call_id = _CALL_ID.get()
+        token: contextvars.Token | None = None
+    except LookupError:
+        fwd_id = uuid4()
+        call_id = CallID(run_id, fwd_id)
+        token = _CALL_ID.set(call_id)
+
+    try:
+        yield call_id
+    finally:
+        if token is not None:
+            # token is not None if we made a new call ID. In that case,
+            # reset the context to its previous state.
+            _CALL_ID.reset(token)
    
+
+
+
+
    
+[docs]
+def get_call_id() -> CallID:
+    """Get the current call ID."""
+    try:
+        return _CALL_ID.get()
+    except LookupError:
+        raise RuntimeError(
+            "Attempting to access call ID, but not inside op call context."
+        ) from None
    
+
+
+
+_TRAINING_MODE = contextvars.ContextVar[bool]("training_mode", default=True)
+
+
+
    
+[docs]
+def get_training_mode() -> bool:
+    """Get the current training mode."""
+    return _TRAINING_MODE.get()
    
+
+
+
+
    
+[docs]
+def set_training_mode(training_mode: bool) -> None:
+    """Set the training mode."""
+    _TRAINING_MODE.set(training_mode)
    
+
+
+
+class _TrainingModeContext:
+    """Automatically set and reset the training_mode with a context manager."""
+
+    def __init__(self, training_mode: bool):
+        self.training_mode = training_mode
+        self.prev_training_mode = get_training_mode()
+
+    def __call__(self, fn=None):
+        if fn is None:
+            return self
+
+        if is_coroutine_callable(fn):
+
+            async def wrapper(*args, **kwargs):
+                async with self:
+                    return await fn(*args, **kwargs)
+
+        else:
+
+            def wrapper(*args, **kwargs):
+                with self:
+                    return fn(*args, **kwargs)
+
+        return wrapper
+
+    def __enter__(self) -> None:
+        self.prev_training_mode = get_training_mode()
+        set_training_mode(self.training_mode)
+
+    def __exit__(self, exc_type, exc_value, traceback) -> None:
+        set_training_mode(self.prev_training_mode)
+
+    async def __aenter__(self) -> None:
+        self.__enter__()
+
+    async def __aexit__(self, exc_type, exc_value, traceback) -> None:
+        self.__exit__(exc_type, exc_value, traceback)
+
+
+train_mode = _TrainingModeContext(training_mode=True)
+eval_mode = _TrainingModeContext(training_mode=False)
+
    + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/graph/ops.html b/docs/_build/html/_modules/ldp/graph/ops.html new file mode 100644 index 00000000..c7f319c1 --- /dev/null +++ b/docs/_build/html/_modules/ldp/graph/ops.html @@ -0,0 +1,702 @@ + + + + + + + ldp.graph.ops — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.graph.ops

    +"""This module defines the Op class and its helper classes."""
+
+from __future__ import annotations
+
+import inspect
+import itertools
+import logging
+from abc import ABC, abstractmethod
+from collections import defaultdict
+from collections.abc import Callable, Collection, Iterable, Iterator, Mapping, Sequence
+from typing import Any, ClassVar, Generic, TypeAlias, TypeVar
+from uuid import UUID
+
+import networkx as nx
+import tree
+from pydantic import BaseModel, Field
+
+from .op_utils import CallID, compute_graph, get_call_id, get_training_mode, op_call
+
+logger = logging.getLogger(__name__)
+
+
+GradOutType: TypeAlias = tree.Structure | None  # None means the gradient has terminated
+GradInType: TypeAlias = tuple[Sequence[GradOutType], Mapping[str, GradOutType]]
+BackwardsType: TypeAlias = Callable[
+    # Call signature of Op.backward or GradientEstimator.backward
+    ["OpCtx", list, dict, tree.Structure, "CallID"], GradInType
+]
+TOutput = TypeVar("TOutput")
+
+
+
    
+[docs]
+class OpResult(Generic[TOutput]):
+    """Result of a forward pass, used in the compute graph."""
+
+    def __init__(
+        self, call_id: CallID | Any, op_name: str, op_class_name: str, value: TOutput
+    ):
+        """
+        Initialize an OpResult instance.
+
+        Args:
+            call_id: The unique identifier for the call.
+            op_name: Name of the Op instance (i.e. op.name) that produced this OpResult.
+            op_class_name: Fully qualified name of the class of the Op that produced this OpResult.
+            value: The output of the call.
+        """
+        self.call_id = CallID.model_validate(call_id)
+        self.op_name = op_name
+        self.op_class_name = op_class_name
+        self.value = value
+
+
    
+[docs]
+    def to_dict(self) -> dict[str, Any]:
+        value_dump = (
+            self.value.model_dump() if isinstance(self.value, BaseModel) else self.value
+        )
+
+        return {
+            "call_id": self.call_id.model_dump(),
+            "op_name": self.op_name,
+            "op_class_name": self.op_class_name,
+            "value": value_dump,
+        }
    
+
+
+
    
+[docs]
+    @classmethod
+    def from_dict(
+        cls, t_output: type[TOutput], dump: dict[str, Any]
+    ) -> OpResult[TOutput]:
+        value = dump.pop("value")
+        if issubclass(t_output, BaseModel):
+            value = t_output.model_validate(value)
+
+        return cls[t_output](**dump, value=value)  # type: ignore[index]
    
+
+
+    def __hash__(self) -> int:
+        return hash(self.call_id)
+
+
    
+[docs]
+    def compute_grads(
+        self,
+        grad_output: tree.Structure | None = None,
+        backward_fns: Mapping[str | type, BackwardsType] | None = None,
+    ) -> None:
+        """
+        Compute the gradient of the backward graph in-place.
+
+        This executes topological traversal.
+        It is up to the Op to:
+            (a) define the backward computation
+            (b) store internal gradients for optimizer updates.
+        """
+        # call ID -> [d op(x) / d x] for each op that consumes x
+        grad_outputs: dict[CallID, list[tree.Structure]] = defaultdict(list)
+
+        # grad_outputs stores a list of output grads (corresponding to each consuming op call).
+        # Since the root node is not consumed by any other node, we create a singleton list here.
+        # If None was passed, set it to 0 so that we don't prune the compute graph here.
+        grad_outputs[self.call_id] = [grad_output] if grad_output is not None else [0.0]
+
+        # We will traverse the graph in reverse topological order
+        for node in self.traverse():
+            # get output gradients
+            grad_output = grad_outputs[node.call_id]
+            if not grad_output:
+                # compute graph terminated
+                continue
+            # Make sure structure of grads match before summing
+            try:
+                [tree.assert_same_structure(grad_output[0], g) for g in grad_output[1:]]
+            except ValueError as e:
+                raise ValueError(
+                    f"Mismatched gradient structures in compute graph for at Op: {self.op_name}."
+                ) from e
+            aggregated_grad_output = tree.map_structure(lambda *x: sum(x), *grad_output)  # noqa: FURB111
+
+            input_args, input_kwargs = node.inputs
+            arg_grads, kwarg_grads = node._run_backward(
+                input_args,
+                input_kwargs,
+                aggregated_grad_output,
+                node._resolve_backward_impl(backward_fns),
+            )
+
+            for a, g in zip(input_args, arg_grads, strict=True):
+                # Must have exact match between input_args and arg_grads
+                # Only propagate gradients to input OpResults if grad is not None
+                if g is not None and isinstance(a, OpResult):
+                    grad_outputs[a.call_id].append(g)
+
+            if kwarg_grads.keys() != input_kwargs.keys():
+                raise ValueError(
+                    f"Mismatch between grads returned in Op.backward and its input kwargs. "
+                    f"Expected {input_kwargs.keys()}, got {kwarg_grads.keys()}."
+                )
+            for k, a in input_kwargs.items():
+                # input_kwargs.keys() may be a subset of kwarg_grads.keys() if defaults
+                # are specified
+                if (g := kwarg_grads[k]) is not None and isinstance(a, OpResult):
+                    grad_outputs[a.call_id].append(g)
    
+
+
+    def _resolve_backward_impl(
+        self, backward_fns: Mapping[str | type, BackwardsType] | None
+    ) -> BackwardsType:
+        backward_fns = backward_fns or {}
+        for key in (self.ctx.op_name, self.op_class_name, self.op_class):
+            if key in backward_fns:
+                return backward_fns[key]
+        return self.op_class.backward
+
+    def _run_backward(
+        self,
+        input_args: list[ResultOrValue],
+        input_kwargs: dict[str, ResultOrValue],
+        grad_output: tree.Structure,
+        backward_fn: BackwardsType,
+    ) -> GradInType:
+        self._update_ctx("grad_output", grad_output)
+        unwrapped_input_args = [OpResult.unwrap_value(a) for a in input_args]
+        unwrapped_input_kwargs = {
+            k: OpResult.unwrap_value(v) for k, v in input_kwargs.items()
+        }
+        input_grads = backward_fn(
+            self.ctx,
+            unwrapped_input_args,
+            unwrapped_input_kwargs,
+            grad_output,
+            self.call_id,
+        )
+        self._update_ctx("grad_input", input_grads)
+        return input_grads
+
+    @property
+    def op_class(self) -> type[Op]:
+        return _OP_CLASS_REGISTRY[self.op_class_name]
+
+    @property
+    def ctx(self) -> OpCtx:
+        # This is a property to avoid serialization of the context. There are two reasons:
+        # 1. Contexts have their own persist() mechanism for serialization
+        # 2. We'd prefer contexts to be created via get_or_create(). Allowing for arbitrary
+        #    deserialization makes it hard to enforce that.
+        return OpCtx.get_or_create(self.op_name)
+
+
    
+[docs]
+    def get_compute_graph(self, backward: bool = True) -> nx.DiGraph:
+        """Construct a directed graph of the compute graph that led to this OpResult.
+
+        Args:
+            backward: If True (default), constructs the backwards graph in which outputs
+                point to inputs. If False, constructs the forward call graph.
+                For most cases (e.g. backprop), backward=True is desirable.
+
+        Returns:
+            A digraph in which nodes are OpResults.
+        """
+
+        def add_edges(graph: nx.DiGraph, node: OpResult) -> None:
+            """Recursively add edges to the input graph."""
+            input_args, input_kwargs = node.inputs
+            for x in itertools.chain(input_args, input_kwargs.values()):
+                if isinstance(x, OpResult):
+                    edge = (node, x) if backward else (x, node)
+                    graph.add_edge(*edge)
+                    add_edges(graph, x)
+
+        graph = nx.DiGraph()
+        graph.add_node(self)
+        add_edges(graph, self)
+
+        return graph
    
+
+
+
    
+[docs]
+    def traverse(
+        self,
+        topological_order: bool = True,
+        filter_fn: Callable[[OpResult], bool] = lambda _: True,
+    ) -> Iterator[OpResult]:
+        """Traverse the compute graph that led to this OpResult.
+
+        Args:
+            topological_order: If True, traverse the backwards graph in topological
+                order. This requires having the whole graph in memory. If False,
+                traverse the backwards graph in depth-first order. This can be done
+                lazily and is useful if we are trying to hydrate the graph node-by-node.
+                Most user-facing cases can leave this as True. Defaults to True.
+            filter_fn: Will only yield nodes that pass this filter function. Note that
+                nodes that fail will still be traversed.
+
+        Yields:
+            An iterator over the nodes of this graph.
+        """
+        if topological_order:
+            G = self.get_compute_graph()
+            for node in nx.topological_sort(G):
+                if filter_fn(node):
+                    yield node
+
+        else:
+            # If not topological order, do a recursive depth-first traversal.
+            # Note that, when traversing a node, its children do not need to be available
+            # yet. This allows us to lazily load nodes when hydrating from a ctx backend.
+            if filter_fn(self):
+                yield self
+            input_args, input_kwargs = self.inputs
+            for a in itertools.chain(input_args, input_kwargs.values()):
+                if isinstance(a, OpResult):
+                    # Recursively apply depth-first traversal on each node
+                    yield from a.traverse(topological_order=False)
    
+
+
+    @property
+    def inputs(self) -> tuple[list[ResultOrValue], dict[str, ResultOrValue]]:
+        return self._get_from_ctx("input")
+
+    @property
+    def logprob(self) -> float | None:
+        return self._get_from_ctx("logprob", default=None)
+
+    @property
+    def grad(self) -> tree.Structure | None:
+        """Returns `d ln(P_{compute_graph}) / d self` or None if gradients have not been computed."""
+        return self._get_from_ctx("grad_output")
+
+    @property
+    def run_id(self) -> UUID:
+        return self.call_id.run_id
+
+
    
+[docs]
+    @staticmethod
+    def unwrap_value(result: ResultOrValue[TOutput]) -> TOutput:
+        if isinstance(result, OpResult):
+            return result.value
+        return result
    
+
+
+    def __repr__(self) -> str:
+        return (
+            f"OpResult(op={self.op_class_name}:{self.op_name}, "
+            f"call_id={self.call_id}, value={self.value!r})"
+        )
+
+    def __str__(self) -> str:
+        return str(self.value)
+
+    def _get_from_ctx(self, key: str, **kwargs):
+        if self.call_id is None:
+            raise ValueError(
+                "Attempting to access context but compute graph "
+                "is not available for this OpResult."
+            )
+
+        return self.ctx.get(call_id=self.call_id, key=key, **kwargs)
+
+    def _update_ctx(self, key: str, value: Any):
+        if self.call_id is None:
+            raise RuntimeError(
+                "Attempting to update context but compute graph "
+                "is not available for this OpResult."
+            )
+
+        self.ctx.update(call_id=self.call_id, key=key, value=value)
    
+
+
+
+ResultOrValue: TypeAlias = OpResult[TOutput] | TOutput
+
+# Sentinel value for get() default
+NOT_FOUND = object()
+
+
+
    
+[docs]
+class OpCtx(BaseModel):
+    # A global registry of contexts. We'd prefer to use an existing context
+    # for an Op if it already has been created. Also useful for persist_all()
+    _CTX_REGISTRY: ClassVar[dict[str, OpCtx]] = {}
+
+    op_name: str
+
+    data: dict = Field(
+        default_factory=lambda: defaultdict(dict),
+        exclude=True,
+        description="Maps run_id -> (fwd_id, key) -> value. "
+        "data is excluded from model_dump() etc because we do "
+        "not use Pydantic to persist context information. That "
+        "should be done via the DB backend instead. OpCtx will "
+        "serialize op_name, which is enough to rehydrate "
+        "from the DB.",
+    )
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._CTX_REGISTRY[self.op_name] = self
+
+
    
+[docs]
+    @classmethod
+    def get_or_create(cls, op_name: str) -> OpCtx:
+        """Return an OpCtx corresponding to the Op with the given name."""
+        try:
+            return cls._CTX_REGISTRY[op_name]  # Get
+        except KeyError:
+            return cls(op_name=op_name)  # Create
    
+
+
+
    
+[docs]
+    @classmethod
+    def clear_contexts(cls, op_names: Iterable[str] | None = None) -> None:
+        """Clear the data in all contexts. If op_names is provided, only clear those contexts."""
+        if op_names is None:
+            op_names = cls._CTX_REGISTRY.keys()
+        for op_name in op_names:
+            try:
+                cls._CTX_REGISTRY[op_name].data.clear()
+            except KeyError:
+                logger.warning(f"Op with name={op_name} not found in context registry.")
    
+
+
+
    
+[docs]
+    def get(self, call_id: CallID, key: str, default: Any = NOT_FOUND) -> Any:
+        """Get an attribute with an optional default, emulating dict.get."""
+        value = self.data.get(call_id.run_id, {}).get((call_id.fwd_id, key), default)
+        if value is NOT_FOUND:
+            raise KeyError(f"call_id={call_id}, key='{key}' not found in context")
+        return value
    
+
+
+
    
+[docs]
+    def update(self, call_id: CallID, key: str, value: Any):
+        self.data[call_id.run_id][(call_id.fwd_id, key)] = value
    
+
+
+
    
+[docs]
+    def get_input_grads(self, call_id: CallID) -> GradInType:
+        # TODO: this function name is confusing. Let's deprecate it. We only use it
+        # in tests as far as I can tell.
+        try:
+            return self.get(call_id, "grad_input")
+        except KeyError as exc:
+            raise ValueError(
+                f"No gradients have been computed for call_id={call_id}."
+            ) from exc
    
+
    
+
+
+
+
    
+[docs]
+def resolve_fully_qualified_name(cls: type) -> str:
+    return f"{cls.__module__}.{cls.__name__}"
    
+
+
+
+# A global registry of Op classes, so we can look up backward() implementations
+# without needing an instantiated Op.
+_OP_CLASS_REGISTRY: dict[str, type[Op]] = {}
+
+
+
    
+[docs]
+class Op(ABC, Generic[TOutput]):
+    """
+    An operation that is 'differentiable' and can be used in an optimizer.
+
+    Think torch.autograd.Function that can also be applied to non-differentiable
+    operations like prompt template formatting or Python function calls.
+
+    These form a forward computation graph when composed with other Ops via
+    __call__. In training mode, this graph is constructed dynamically.
+    """
+
+    # Name is not guaranteed to be unique. Reasons:
+    # 1. We definitely don't want it to be unique when recreating a compute graph
+    #    for training on previously-collected data. In that case, we want the Op's
+    #    name to be the same as it was before, to match up contexts/OpResults
+    # 2. Uniqueness could make some DB lookups faster, but I don't think we run the
+    #    risk of OpCtx clobbers as long as call_id (which is guaranteed to be unique)
+    #    is always used as part of the key.
+    name: str
+    ctx: OpCtx
+    _fwd_args: list[inspect.Parameter]
+
+
    
+[docs]
+    def clear_ctx(self) -> None:
+        self.ctx.data.clear()
    
+
+
+    def __init_subclass__(cls, **kwargs):
+        super().__init_subclass__(**kwargs)
+        _OP_CLASS_REGISTRY[resolve_fully_qualified_name(cls)] = cls
+
+    def __new__(cls, *args, **kwargs):
+        instance = super().__new__(cls)
+
+        # Needs to be overridden by caller if this Op is to have
+        # a unique name in the compute graph. c.f. Agent.__init_subclass__
+        # for an example of how to do this.
+        instance.set_name(cls.__name__)
+
+        # Set an attribute to help us map positional forward arguments to parameter
+        # names, for the backward pass. We do this on the instance and not cls b/c
+        # some instancees may override (e.g FxnOp).
+        fwd_sig = inspect.signature(instance.forward)
+        instance._fwd_args = list(fwd_sig.parameters.values())
+
+        return instance
+
+
    
+[docs]
+    def set_name(self, name: str) -> None:
+        self.name = name
+        self.ctx = OpCtx.get_or_create(name)
    
+
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__} (name={self.name}, id={id(self)})"
+
+
    
+[docs]
+    @abstractmethod
+    async def forward(self, *args, **kwargs) -> TOutput:
+        """
+        Forward pass of the Op. Must accept call_id as an argument.
+
+        Returns:
+            Depending on this Op's purpose, the return may be considered an action
+                (e.g. a tool call) or it may not (e.g. a loss calculation).
+        """
    
+
+
+
    
+[docs]
+    @classmethod
+    @abstractmethod
+    def backward(
+        cls,
+        ctx: OpCtx,
+        input_args: list[ResultOrValue],
+        input_kwargs,
+        grad_output: tree.Structure,
+        call_id: CallID,
+    ) -> GradInType:
+        """
+        Backward pass of the Op.
+
+        Args:
+            ctx: Context that was used during the forward pass.
+            input_args: Variable-length input arguments passed to forward, i.e.
+                via *args.
+            input_kwargs: All other arguments passed to forward pass.
+            grad_output: A list of backpropagated gradients from each consumer
+                of the output of the forward pass. It is up to the implementation
+                to decide how to aggregate these gradients (e.g. in most cases summing).
+            call_id: Call ID of the forward pass.
+
+        Returns:
+            grad_input: `d log(p) / d input` for each input to the forward pass.
+                It should include gradients for all input positional and keyword
+                arguments. Set to None for gradients that should terminate.
+        """
    
+
+
+
    
+[docs]
+    def get_call_ids(self, run_ids: Collection[UUID] | None = None) -> set[CallID]:
+        ctx = self.ctx
+        if run_ids is None:
+            run_ids = ctx.data.keys()
+
+        # de-duplicate before constructing CallIDs
+        ids = {(run_id, fwd_id) for run_id in run_ids for fwd_id, _ in ctx.data[run_id]}
+        return set(itertools.starmap(CallID, ids))
    
+
+
+    # This compute_graph() decoration will do nothing if we are already inside a compute graph.
+    # We add it here in case we are calling a bare op(), in which case we want a graph
+    # with a single node.
+    @compute_graph()
+    @op_call()
+    async def __call__(self, *args, **kwargs) -> OpResult[TOutput]:
+        call_id = get_call_id()
+
+        if not all(
+            arg.call_id.run_id == call_id.run_id
+            for arg in itertools.chain(args, kwargs.values())
+            if isinstance(arg, OpResult)
+        ):
+            raise RuntimeError(
+                "All args and kwargs must have the same run_id as the call_id's run_id. "
+                "Consider using @compute_graph() decorator to ensure this."
+            )
+
+        # we're over-saving here - can explore later if memory usage is high
+        # unpack the args and kwargs from the result holders
+        unpacked_args = [(a.value if isinstance(a, OpResult) else a) for a in args]
+        unpacked_kwargs = {
+            k: v.value if isinstance(v, OpResult) else v for k, v in kwargs.items()
+        }
+
+        if get_training_mode():
+            # If training, save the inputs for the backward pass
+            # Map positional arguments to keyword arguments to make backward pass easier
+            for i_arg, (arg, param) in enumerate(
+                # strict=False b/c not all params in _fwd_args will be in args (i.e. defaults and **kwargs)
+                zip(args, self._fwd_args, strict=False)
+            ):
+                # Don't need to check for too many args or collisions with kwargs, since forward()
+                # will raise an exception anyway
+                if param.kind == inspect.Parameter.VAR_POSITIONAL:
+                    # *args, so scoop up the rest of the arg tuple.
+                    var_args = list(args[i_arg:])
+                    break
+
+                # Normal positional arg
+                kwargs[param.name] = arg
+            else:
+                var_args = []  # there were no *args if we got here
+
+            self.ctx.update(call_id, "input", (var_args, kwargs))
+
+        # actually call forward pass with unpacked args and kwargs
+        result = await self.forward(*unpacked_args, **unpacked_kwargs)
+        t_output: type[TOutput] = type(result)
+
+        # Now package up my result so it can be consumed by other calls.
+        # Explicitly specify t_output. OpResult[TOutput] returns a generic object
+        op_result = OpResult[t_output](  # type: ignore[valid-type]
+            value=result,
+            call_id=call_id,
+            op_name=self.name,
+            op_class_name=resolve_fully_qualified_name(type(self)),
+        )
+
+        if get_training_mode():
+            self.ctx.update(call_id, "output", op_result)
+
+        return op_result
+
+
    
+[docs]
+    def get_input_grads(self, call_id: CallID) -> GradInType:
+        return self.ctx.get_input_grads(call_id)
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/graph/torch_ops.html b/docs/_build/html/_modules/ldp/graph/torch_ops.html new file mode 100644 index 00000000..22a7cfa9 --- /dev/null +++ b/docs/_build/html/_modules/ldp/graph/torch_ops.html @@ -0,0 +1,254 @@ + + + + + + + ldp.graph.torch_ops — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.graph.torch_ops

    +import inspect
+from collections.abc import Mapping, Sequence
+from typing import Any, ClassVar
+
+try:
+    import torch
+    from torch import nn
+except ImportError:
+    raise ImportError(
+        "ldp.graph.torch_ops requires PyTorch as a dependency. "
+        "Please run `pip install ldp[nn]`."
+    ) from None
+
+from .async_torch import async_protect_torch_call
+from .op_utils import CallID, get_call_id, get_training_mode
+from .ops import GradInType, Op, OpCtx, ResultOrValue
+
+
+
    
+[docs]
+class TorchOp(Op[torch.Tensor]):
+    """An operation that wraps a PyTorch module."""
+
+    CTX_TENSOR_INPUT_KEY: ClassVar[str] = "tensor_input"
+
+    def __init__(self, module: nn.Module):
+        super().__init__()
+        self.module = module
+
+        # override forward args with the signature of the function
+        fwd_sig = inspect.signature(self.module.forward)
+        self._fwd_args = list(fwd_sig.parameters.values())
+
+    def __str__(self) -> str:
+        return f"{type(self).__name__} {type(self.module).__name__} ({id(self)})"
+
+
    
+[docs]
+    async def forward(self, *args, **kwargs: Any) -> torch.Tensor:
+        tensor_args = [
+            arg
+            if isinstance(arg, torch.Tensor)
+            else torch.tensor(arg, requires_grad=True)
+            for arg in args
+        ]
+        tensor_kwargs = {
+            k: v if isinstance(v, torch.Tensor) else torch.tensor(v, requires_grad=True)
+            for k, v in kwargs.items()
+        }
+
+        is_training = get_training_mode()
+
+        if is_training:
+            store_tensor_inputs(
+                ctx=self.ctx,
+                key=self.CTX_TENSOR_INPUT_KEY,
+                tensor_args=tensor_args,
+                tensor_kwargs=tensor_kwargs,
+                fwd_args=self._fwd_args,
+            )
+
+        return await async_protect_torch_call(self.module, no_grad=not is_training)(
+            *tensor_args, **tensor_kwargs
+        )
    
+
+
+
    
+[docs]
+    @classmethod
+    def backward(
+        cls,
+        ctx: OpCtx,
+        input_args: list[ResultOrValue],
+        input_kwargs: dict[str, ResultOrValue],
+        grad_output: float | torch.Tensor,
+        call_id: CallID,
+    ) -> GradInType:
+        tensor_args, tensor_kwargs = ctx.get(call_id, cls.CTX_TENSOR_INPUT_KEY)
+        n_pos_args = len(tensor_args)
+        output = ctx.get(call_id, "output").value
+
+        if not isinstance(grad_output, torch.Tensor):
+            grad_output = torch.tensor(grad_output, dtype=output.dtype)
+        grad_output = grad_output.to(output.device)
+
+        while grad_output.ndim < output.ndim:
+            # Assume we can broadcast, so expand dims
+            # e.g. if output.shape = (2, 1, 1) and grad_output is a scalar
+            # then we want to expand to (1, 1, 1) and then broadcast
+            grad_output = grad_output.unsqueeze(-1)
+
+        if output.shape != grad_output.shape:
+            raise RuntimeError(
+                f"Output shape {output.shape} does not match grad_output shape {grad_output.shape}"
+            )
+
+        gradients = torch.autograd.grad(
+            output,
+            [*tensor_args, *tensor_kwargs.values()],
+            grad_outputs=grad_output,
+            allow_unused=True,
+            retain_graph=True,
+        )
+
+        grad_args = [grad.detach().cpu().float() for grad in gradients[:n_pos_args]]
+        grad_kwargs = {
+            k: grad.detach().cpu().float()
+            for k, grad in zip(
+                tensor_kwargs.keys(), gradients[n_pos_args:], strict=True
+            )
+        }
+
+        return grad_args, grad_kwargs
    
+
    
+
+
+
+
    
+[docs]
+def store_tensor_inputs(
+    ctx: OpCtx,
+    key: str,
+    tensor_args: Sequence[torch.Tensor],
+    tensor_kwargs: Mapping[str, torch.Tensor],
+    fwd_args: Sequence[inspect.Parameter],
+    detach: bool = False,
+) -> None:
+    call_id = get_call_id()
+    # Save tensor inputs for backward pass. Do not clobber "input", since
+    # that is needed for compute graph. Map positional args to kwargs
+    # Copying so we don't modify tensor_kwargs in-place
+    ctx_kwargs = tensor_kwargs.copy()  # type: ignore[attr-defined]
+
+    # See Op.__call__ for some notes on what this is doing.
+    for i_arg, (arg, param) in enumerate(
+        # strict=False b/c not all params in _fwd_args will be in args (i.e. defaults and **kwargs)
+        zip(tensor_args, fwd_args, strict=False)
+    ):
+        if param.kind == inspect.Parameter.VAR_POSITIONAL:
+            ctx_args = list(tensor_args[i_arg:])
+            break
+
+        # Normal positional arg
+        ctx_kwargs[param.name] = arg
+    else:
+        ctx_args = []  # if we got here, there were no *args
+
+    if detach:
+        # Detach the tensors from the compute graph and move to CPU
+        ctx_args = [arg.detach().cpu() for arg in ctx_args]
+        ctx_kwargs = {k: v.detach().cpu() for k, v in ctx_kwargs.items()}
+
+    ctx.update(call_id, key, (ctx_args, ctx_kwargs))
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/llms/chat.html b/docs/_build/html/_modules/ldp/llms/chat.html new file mode 100644 index 00000000..f05dca82 --- /dev/null +++ b/docs/_build/html/_modules/ldp/llms/chat.html @@ -0,0 +1,469 @@ + + + + + + + ldp.llms.chat — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.llms.chat

    +import asyncio
+import json
+from collections.abc import AsyncGenerator, Callable, Iterable
+from datetime import datetime
+from typing import Any, ClassVar, Self, cast
+from uuid import UUID, uuid4
+
+import litellm
+from aviary.message import Message
+from aviary.tools import Tool, ToolRequestMessage, ToolsAdapter
+from aviary.utils import is_coroutine_callable
+from pydantic import BaseModel, ConfigDict, Field, ValidationError, model_validator
+
+
+
    
+[docs]
+class JSONSchemaValidationError(ValueError):
+    """Raised when the completion does not match the specified schema."""
    
+
+
+
+
    
+[docs]
+class LLMResult(BaseModel):
+    """A class to hold the result of a LLM completion."""
+
+    id: UUID = Field(default_factory=uuid4)
+    config: dict | None = None
+    prompt: list[Message] | None = Field(
+        default=None, description="Messages sent to the LLM."
+    )
+    messages: list[Message] | None = Field(
+        default=None, description="Messages received from the LLM."
+    )
+    prompt_count: int = Field(default=0, description="Count of prompt tokens.")
+    completion_count: int = Field(default=0, description="Count of completion tokens.")
+    model: str
+    date: str = Field(default_factory=datetime.now().isoformat)
+    seconds_to_first_token: float | None = None
+    seconds_to_last_token: float = 0
+    logprob: float | None = Field(
+        default=None, description="Sum of logprobs in the completion."
+    )
+    system_fingerprint: str | None = Field(
+        default=None, description="System fingerprint received from the LLM."
+    )
+
+    @property
+    def prompt_and_completion_costs(self) -> tuple[float, float]:
+        """Get a two-tuple of prompt tokens cost and completion tokens cost, in USD."""
+        return litellm.cost_per_token(
+            self.model,
+            prompt_tokens=self.prompt_count,
+            completion_tokens=self.completion_count,
+        )
+
+    @property
+    def provider(self) -> str:
+        """Get the model provider's name (e.g. "openai", "mistral")."""
+        return litellm.get_llm_provider(self.model)[1]
+
+
    
+[docs]
+    def get_supported_openai_params(self) -> list[str] | None:
+        """Get the supported OpenAI parameters for the model."""
+        return litellm.get_supported_openai_params(self.model)
    
+
    
+
+
+
+
    
+[docs]
+def sum_logprobs(choice: litellm.utils.Choices) -> float | None:
+    """Calculate the sum of the log probabilities of an LLM completion (a Choices object).
+
+    Args:
+        choice: A sequence of choices from the completion.
+
+    Returns:
+        The sum of the log probabilities of the choice.
+    """
+    try:
+        logprob_obj = choice.logprobs
+    except AttributeError:
+        return None
+    if isinstance(logprob_obj, dict):
+        if logprob_obj.get("content"):
+            return sum(
+                logprob_info["logprob"] for logprob_info in logprob_obj["content"]
+            )
+    elif choice.logprobs.content:
+        return sum(logprob_info.logprob for logprob_info in choice.logprobs.content)
+    return None
    
+
+
+
+
    
+[docs]
+def validate_json_completion(
+    completion: litellm.ModelResponse, output_type: type[BaseModel]
+) -> None:
+    """Validate a completion against a JSON schema.
+
+    Args:
+        completion: The completion to validate.
+        output_type: The Pydantic model to validate the completion against.
+    """
+    try:
+        for choice in completion.choices:
+            output_type.model_validate_json(choice.message.content or "")  # type: ignore[union-attr]
+    except ValidationError as err:
+        raise JSONSchemaValidationError(
+            "The completion does not match the specified schema."
+        ) from err
    
+
+
+
+
    
+[docs]
+class MultipleCompletionLLMModel(BaseModel):
+    """Run n completions at once, all starting from the same messages."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    # this should keep the original model
+    # if fine-tuned, this should still refer to the base model
+    name: str = "unknown"
+    config: dict = Field(
+        default={
+            "model": "gpt-3.5-turbo",  # Default model should have cheap input/output for testing
+            "temperature": 0.1,
+        }
+    )
+    encoding: Any | None = None
+
+    def __str__(self) -> str:
+        return f"{type(self).__name__} {self.name}"
+
+
    
+[docs]
+    @model_validator(mode="after")
+    def set_model_name(self) -> Self:
+        if (
+            self.config.get("model") in {"gpt-3.5-turbo", None}
+            and self.name != "unknown"
+            or self.name != "unknown"
+            and "model" not in self.config
+        ):
+            self.config["model"] = self.name
+        elif "model" in self.config and self.name == "unknown":
+            self.name = self.config["model"]
+        # note we do not consider case where both are set
+        # because that could be true if the model is fine-tuned
+        return self
    
+
+
+
    
+[docs]
+    async def achat(
+        self, messages: Iterable[Message], **kwargs
+    ) -> litellm.ModelResponse:
+        return await litellm.acompletion(
+            messages=[m.model_dump(by_alias=True) for m in messages],
+            **(self.config | kwargs),
+        )
    
+
+
+
    
+[docs]
+    async def achat_iter(self, messages: Iterable[Message], **kwargs) -> AsyncGenerator:
+        return cast(
+            AsyncGenerator,
+            await litellm.acompletion(
+                messages=[m.model_dump(by_alias=True) for m in messages],
+                stream=True,
+                stream_options={
+                    "include_usage": True,  # Included to get prompt token counts
+                },
+                **(self.config | kwargs),
+            ),
+        )
    
+
+
+    # SEE: https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice
+    # > `required` means the model must call one or more tools.
+    TOOL_CHOICE_REQUIRED: ClassVar[str] = "required"
+
+
    
+[docs]
+    async def call(  # noqa: C901, PLR0915
+        self,
+        messages: list[Message],
+        callbacks: list[Callable] | None = None,
+        output_type: type[BaseModel] | None = None,
+        tools: list[Tool] | None = None,
+        tool_choice: Tool | str | None = TOOL_CHOICE_REQUIRED,
+        **chat_kwargs,
+    ) -> list[LLMResult]:
+        start_clock = asyncio.get_running_loop().time()
+
+        # Deal with tools. OpenAI throws an error if tool list is empty,
+        # so skip this block if tools in (None, [])
+        if tools:
+            chat_kwargs["tools"] = ToolsAdapter.dump_python(
+                tools, exclude_none=True, by_alias=True
+            )
+            if tool_choice is not None:
+                chat_kwargs["tool_choice"] = (
+                    {
+                        "type": "function",
+                        "function": {"name": tool_choice.info.name},
+                    }
+                    if isinstance(tool_choice, Tool)
+                    else tool_choice
+                )
+
+        # deal with specifying output type
+        if output_type is not None:
+            schema = json.dumps(
+                output_type.model_json_schema(mode="serialization"), indent=2
+            )
+            schema_msg = f"Respond following this JSON schema:\n\n{schema}"
+            # Get the system prompt and its index, or the index to add it
+            i, system_prompt = next(
+                ((i, m) for i, m in enumerate(messages) if m.role == "system"),
+                (0, None),
+            )
+            messages = [
+                *messages[:i],
+                system_prompt.append_text(schema_msg, inplace=False)
+                if system_prompt
+                else Message(role="system", content=schema_msg),
+                *messages[i + 1 if system_prompt else i :],
+            ]
+            chat_kwargs["response_format"] = {"type": "json_object"}
+
+        # add static configuration to kwargs
+        chat_kwargs = self.config | chat_kwargs
+        n = chat_kwargs.get("n", 1)  # number of completions
+        if n < 1:
+            raise ValueError("Number of completions (n) must be >= 1.")
+
+        prompt = [
+            m
+            if not isinstance(m, ToolRequestMessage) or m.tool_calls
+            # OpenAI doesn't allow for empty tool_calls lists, so downcast empty
+            # ToolRequestMessage to Message here
+            else Message(role=m.role, content=m.content)
+            for m in messages
+        ]
+        results: list[LLMResult] = []
+
+        if callbacks is None:
+            completion: litellm.ModelResponse = await self.achat(prompt, **chat_kwargs)
+            if output_type is not None:
+                validate_json_completion(completion, output_type)
+
+            for choice in completion.choices:
+                if isinstance(choice, litellm.utils.StreamingChoices):
+                    raise NotImplementedError("Streaming is not yet supported.")
+
+                if (
+                    tools is not None  # Allows for empty tools list
+                    or choice.finish_reason == "tool_calls"
+                    or (getattr(choice.message, "tool_calls", None) is not None)
+                ):
+                    serialized_choice_message = choice.message.model_dump()
+                    serialized_choice_message["tool_calls"] = (
+                        serialized_choice_message.get("tool_calls") or []
+                    )
+                    output_messages: list[Message | ToolRequestMessage] = [
+                        ToolRequestMessage(**serialized_choice_message)
+                    ]
+                else:
+                    output_messages = [Message(**choice.message.model_dump())]
+
+                results.append(
+                    LLMResult(
+                        model=self.name,
+                        config=chat_kwargs,
+                        prompt=prompt,
+                        messages=output_messages,
+                        logprob=sum_logprobs(choice),
+                        system_fingerprint=completion.system_fingerprint,
+                        # Note that these counts are aggregated over all choices
+                        completion_count=completion.usage.completion_tokens,  # type: ignore[attr-defined,unused-ignore]
+                        prompt_count=completion.usage.prompt_tokens,  # type: ignore[attr-defined,unused-ignore]
+                    )
+                )
+        else:
+            if tools:
+                raise NotImplementedError("Using tools with callbacks is not supported")
+            if n > 1:
+                raise NotImplementedError(
+                    "Multiple completions with callbacks is not supported"
+                )
+            result = LLMResult(model=self.name, config=chat_kwargs, prompt=prompt)
+
+            sync_callbacks = [f for f in callbacks if not is_coroutine_callable(f)]
+            async_callbacks = [f for f in callbacks if is_coroutine_callable(f)]
+            stream_completion = await self.achat_iter(messages, **chat_kwargs)
+            text_result = []
+            role = "assistant"
+
+            async for chunk in stream_completion:
+                delta = chunk.choices[0].delta
+                role = delta.role or role
+                if delta.content:
+                    s = delta.content
+                    if result.seconds_to_first_token == 0:
+                        result.seconds_to_first_token = (
+                            asyncio.get_running_loop().time() - start_clock
+                        )
+                    text_result.append(s)
+                    [await f(s) for f in async_callbacks]
+                    [f(s) for f in sync_callbacks]
+                if hasattr(chunk, "usage"):
+                    result.prompt_count = chunk.usage.prompt_tokens
+
+            output = "".join(text_result)
+            result.completion_count = litellm.token_counter(
+                model=self.name,
+                text=output,
+            )
+            # TODO: figure out how tools stream, and log probs
+            result.messages = [Message(role=role, content=output)]
+            results.append(result)
+
+        if not results:
+            # This happens in unit tests. We should probably not keep this block around
+            # long-term. Previously, we would emit an empty ToolRequestMessage if
+            # completion.choices were empty, so  I am replicating that here.
+            results.append(
+                LLMResult(
+                    model=self.name,
+                    config=chat_kwargs,
+                    prompt=prompt,
+                    messages=[ToolRequestMessage(tool_calls=[])],
+                )
+            )
+
+        end_clock = asyncio.get_running_loop().time()
+
+        for result in results:
+            # Manually update prompt count if not set, which can
+            # happen if the target model doesn't support 'include_usage'
+            if not result.prompt_count:
+                result.prompt_count = litellm.token_counter(
+                    model=self.name,
+                    messages=[m.model_dump() for m in result.messages],  # type: ignore[union-attr]
+                )
+
+            # update with server-side counts
+            result.seconds_to_last_token = end_clock - start_clock
+
+        return results
    
+
    
+
+
+
+
    
+[docs]
+class LLMModel(MultipleCompletionLLMModel):
+
    
+[docs]
+    async def call(self, *args, **kwargs) -> LLMResult:  # type: ignore[override]
+        return (await super().call(*args, **kwargs))[0]
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/llms/embeddings.html b/docs/_build/html/_modules/ldp/llms/embeddings.html new file mode 100644 index 00000000..874ebae9 --- /dev/null +++ b/docs/_build/html/_modules/ldp/llms/embeddings.html @@ -0,0 +1,276 @@ + + + + + + + ldp.llms.embeddings — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.llms.embeddings

    +import asyncio
+from abc import ABC, abstractmethod
+from enum import StrEnum
+from typing import Any
+
+import litellm
+import numpy as np
+import tiktoken
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+
+
    
+[docs]
+class EmbeddingModes(StrEnum):
+    """Enum representing the different modes of an embedding model."""
+
+    DOCUMENT = "document"
+    QUERY = "query"
    
+
+
+
+
    
+[docs]
+class EmbeddingModel(ABC, BaseModel):
+    name: str
+    dimensions: int | None = None
+
+
    
+[docs]
+    def set_mode(self, mode: EmbeddingModes) -> None:
+        """Several embedding models have a 'mode' or prompt which affects output."""
    
+
+
+
    
+[docs]
+    @abstractmethod
+    async def embed_texts(self, texts: list[str]) -> list[np.ndarray]:
+        pass
    
+
+
+
    
+[docs]
+    async def embed_text(self, text: str) -> np.ndarray:
+        return (await self.embed_texts([text]))[0]
    
+
+
+
    
+[docs]
+    @staticmethod
+    def from_name(embedding: str, **kwargs) -> "EmbeddingModel":
+        if embedding.startswith("hybrid"):
+            dense_model = LiteEmbeddingModel(name="-".join(embedding.split("-")[1:]))
+            return HybridEmbeddingModel(
+                name=embedding, models=[dense_model, SparseEmbeddingModel(**kwargs)]
+            )
+        if embedding == "sparse":
+            return SparseEmbeddingModel(**kwargs)
+        return LiteEmbeddingModel(name=embedding, **kwargs)
    
+
    
+
+
+
+
    
+[docs]
+class LiteEmbeddingModel(EmbeddingModel):
+    name: str = Field(default="text-embedding-3-small")
+    dimensions: int | None = Field(
+        default=None,
+        description=(
+            "The length an embedding will have. If left unspecified, we attempt to"
+            " infer an un-truncated length via LiteLLM's internal model map. If this"
+            " inference fails, the embedding will be un-truncated."
+        ),
+    )
+    batch_size: int = 16
+    embed_kwargs: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Extra kwargs to pass to litellm.aembedding.",
+    )
+
+
    
+[docs]
+    @model_validator(mode="before")
+    @classmethod
+    def infer_dimensions(cls, data: dict[str, Any]) -> dict[str, Any]:
+        if data.get("dimensions") is not None:
+            return data
+        # Let's infer the dimensions
+        config: dict[str, dict[str, Any]] = litellm.get_model_cost_map(
+            url="https://raw.githubusercontent.com/BerriAI/litellm/main/litellm/model_prices_and_context_window_backup.json"
+        )
+        output_vector_size: int | None = config.get(data.get("name", ""), {}).get(  # noqa: FURB184
+            "output_vector_size"
+        )
+        if output_vector_size:
+            data["dimensions"] = output_vector_size
+        return data
    
+
+
+
    
+[docs]
+    async def embed_texts(self, texts: list[str]) -> list[np.ndarray]:
+        embeddings = []
+        # Before you get excited to asyncio.gather this:
+        # The point of this is to not hit the API rate limit
+        for i in range(0, len(texts), self.batch_size):
+            response = await litellm.aembedding(
+                model=self.name,
+                input=texts[i : i + self.batch_size],
+                encoding_format="float",
+                dimensions=self.dimensions,
+                **self.embed_kwargs,
+            )
+            embeddings.extend([
+                np.array(e["embedding"], dtype=np.float32) for e in response.data
+            ])
+        return embeddings
    
+
    
+
+
+
+
    
+[docs]
+class SparseEmbeddingModel(EmbeddingModel):
+    """This is a very simple keyword search model - probably best to be mixed with others."""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    name: str = "sparse"
+    dimensions: int = 256
+    enc: tiktoken.Encoding = Field(
+        default_factory=lambda: tiktoken.get_encoding("cl100k_base")
+    )
+
+
    
+[docs]
+    async def embed_texts(self, texts) -> list[np.ndarray]:
+        enc_batch = self.enc.encode_ordinary_batch(texts)
+        # now get frequency of each token rel to length
+        return [
+            np.bincount(
+                [xi % self.dimensions for xi in x], minlength=self.dimensions
+            ).astype(np.float32)
+            / len(x)
+            for x in enc_batch
+        ]
    
+
    
+
+
+
+
    
+[docs]
+class HybridEmbeddingModel(EmbeddingModel):
+    name: str = "hybrid-embed"
+    models: list[EmbeddingModel]
+
+
    
+[docs]
+    @model_validator(mode="before")
+    @classmethod
+    def infer_dimensions(cls, data: dict[str, Any]) -> dict[str, Any]:
+        if data.get("dimensions") is not None:
+            raise ValueError(f"Don't specify dimensions to {cls.__name__}.")
+        if not data.get("models") or any(m.dimensions is None for m in data["models"]):
+            return data
+        data["dimensions"] = sum(m.dimensions for m in data["models"])
+        return data
    
+
+
+
    
+[docs]
+    async def embed_texts(self, texts):
+        all_embeds = await asyncio.gather(*[m.embed_texts(texts) for m in self.models])
+        return np.concatenate(all_embeds, axis=1)
    
+
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/llms/prompts.html b/docs/_build/html/_modules/ldp/llms/prompts.html new file mode 100644 index 00000000..4905f6f1 --- /dev/null +++ b/docs/_build/html/_modules/ldp/llms/prompts.html @@ -0,0 +1,215 @@ + + + + + + + ldp.llms.prompts — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.llms.prompts

    +"""This module provides utility functions for appending and prepending system messages."""
+
+from collections.abc import Collection, Iterable
+
+from aviary.message import Message
+
+
+
    
+[docs]
+def append_to_messages(messages: list[Message], new_message: Message) -> list[Message]:
+    """Appends a message to a list of messages, returning that in-place modified list.
+
+    Examples:
+        >>> messages = [Message(content="Hello")]
+        >>> modified_messages = append_to_messages(messages, Message(content="New"))
+        >>> modified_messages
+        [Message(role='user', content='Hello'), Message(role='user', content='New')]
+        >>> id(messages) == id(modified_messages)
+        True
+    """
+    messages.append(new_message)
+    return messages
    
+
+
+
+
    
+[docs]
+def append_to_sys(user_content: str, sys_content: str | None = None) -> list[Message]:
+    """Appends a user message to a list of messages, optionally including a system message.
+
+    Args:
+        user_content: The content of the user message.
+        sys_content: Optional content for the system message. Defaults to None.
+
+    Returns:
+        A list of messages including the optional system message and the user message.
+
+    Examples:
+        >>> append_to_sys("Hello, world!")
+        [Message(role='user', content='Hello, world!')]
+
+        >>> append_to_sys("Hello, world!", "System initialized.")
+        [Message(role='system', content='System initialized.'), Message(role='user', content='Hello, world!')]
+    """
+    sys = [Message(role="system", content=sys_content)] if sys_content else []
+    return [*sys, Message(content=user_content)]
    
+
+
+
+
    
+[docs]
+def prepend_sys(messages: Collection, sys_content: str) -> list[Message]:
+    """Prepends a system message to a list of messages.
+
+    Args:
+        messages: The list of existing messages.
+        sys_content: The content of the system message to be prepended.
+
+    Returns:
+        A new list of messages with the system message prepended.
+
+    Examples:
+        >>> messages = [Message(role="user", content="Hello!")]
+        >>> prepend_sys(messages, "System initialized.")
+        [Message(role='system', content='System initialized.'), Message(role='user', content='Hello!')]
+    """
+    return [Message(role="system", content=sys_content), *messages]
    
+
+
+
+
    
+[docs]
+def indent_xml(xml_string, indent_size=2):
+    output = []
+    indent_level = 0
+
+    # Split the input XML into parts by tags
+    parts = xml_string.replace(">", ">\n").replace("<", "\n<").split("\n")
+    parts = [part for part in parts if part.strip()]  # Remove empty parts
+
+    for part in parts:
+        if part.startswith("</"):
+            # Closing tag, decrease indent
+            indent_level -= indent_size
+            output.append(" " * indent_level + part)
+        elif part.startswith("<") and not part.endswith("/>") and ">" in part:
+            # Opening tag, maintain then increase indent
+            output.append(" " * indent_level + part)
+            indent_level += indent_size
+        elif part.endswith("/>"):
+            # Self-closing tag, just append
+            output.append(" " * indent_level + part)
+        else:
+            # Text or other data, maintain current indent
+            # Handle multiple lines within text nodes
+            text_lines = part.split("\n")
+            output.extend([
+                " " * indent_level + line.strip() for line in text_lines if line.strip()
+            ])
+
+    return "\n".join(output)
    
+
+
+
+
    
+[docs]
+def prepend_sys_and_append_sys(
+    messages: Iterable[Message], initial_sys_content: str, final_sys_content: str
+) -> list[Message]:
+    return [
+        Message(role="system", content=initial_sys_content),
+        *messages,
+        Message(role="system", content=final_sys_content),
+    ]
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/main.html b/docs/_build/html/_modules/ldp/main.html new file mode 100644 index 00000000..b12be050 --- /dev/null +++ b/docs/_build/html/_modules/ldp/main.html @@ -0,0 +1,179 @@ + + + + + + + ldp.main — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.main

    +import argparse
+import asyncio
+import pickle
+from contextlib import suppress
+from os import PathLike
+from pathlib import Path
+
+from aviary.env import Environment
+
+from ldp.agent import Agent
+from ldp.alg.callbacks import TerminalPrintingCallback
+from ldp.alg.rollout import RolloutManager
+
+
+
    
+[docs]
+def get_or_make_agent(agent: Agent | str | PathLike) -> Agent:
+    if isinstance(agent, Agent):
+        return agent
+
+    if isinstance(agent, str):
+        with suppress(KeyError):
+            return Agent.from_name(agent)
+
+    path = Path(agent)
+    if not path.exists():
+        raise ValueError(f"Could not resolve agent: {agent}")
+
+    with path.open("rb") as f:
+        return pickle.load(f)  # noqa: S301
    
+
+
+
+
    
+[docs]
+def get_or_make_environment(environment: Environment | str, task: str) -> Environment:
+    if isinstance(environment, Environment):
+        return environment
+
+    if isinstance(environment, str):
+        with suppress(KeyError):
+            return Environment.from_name(environment, task=task)
+
+    raise ValueError(
+        f"Could not resolve environment: {environment}. Available environments: {Environment.available()}"
+    )
    
+
+
+
+
    
+[docs]
+async def main(
+    task: str,
+    environment: Environment | str,
+    agent: Agent | str | PathLike = "SimpleAgent",
+):
+    agent = get_or_make_agent(agent)
+
+    callback = TerminalPrintingCallback()
+    rollout_manager = RolloutManager(agent=agent, callbacks=[callback])
+
+    _ = await rollout_manager.sample_trajectories(
+        environment_factory=lambda: get_or_make_environment(environment, task)
+    )
    
+
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("task", help="Task to prompt environment with.")
+    parser.add_argument(
+        "--env", required=True, help="Environment to sample trajectories from."
+    )
+    parser.add_argument(
+        "--agent", default="SimpleAgent", help="Agent to sample trajectories with."
+    )
+    args = parser.parse_args()
+
+    asyncio.run(main(args.task, args.env, args.agent))
+
    + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/ldp/utils.html b/docs/_build/html/_modules/ldp/utils.html new file mode 100644 index 00000000..404f07b9 --- /dev/null +++ b/docs/_build/html/_modules/ldp/utils.html @@ -0,0 +1,215 @@ + + + + + + + ldp.utils — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for ldp.utils

    +import logging
+import logging.config
+import os
+from typing import Any
+
+import litellm
+
+
+
    
+[docs]
+def configure_log_levels() -> None:
+    """Configure log levels."""
+    # Set sane default LiteLLM logging configuration
+    # SEE: https://docs.litellm.ai/docs/observability/telemetry
+    litellm.telemetry = False
+    if (
+        logging.getLevelNamesMapping().get(
+            os.environ.get("LITELLM_LOG", ""), logging.WARNING
+        )
+        < logging.WARNING
+    ):
+        # If LITELLM_LOG is DEBUG or INFO, don't change the LiteLLM log levels
+        litellm_loggers_config: dict[str, Any] = {}
+    else:
+        litellm_loggers_config = {
+            "LiteLLM": {"level": "WARNING"},
+            "LiteLLM Proxy": {"level": "WARNING"},
+            "LiteLLM Router": {"level": "WARNING"},
+        }
+
+    logging.config.dictConfig({
+        "version": 1,
+        "disable_existing_loggers": False,
+        # Lower level for httpx and LiteLLM
+        "loggers": {"httpx": {"level": "WARNING"}} | litellm_loggers_config,
+    })
    
+
+
+
+
    
+[docs]
+def configure_stdout_logs(
+    level: int | str = logging.INFO,
+    fmt: str = "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+) -> None:
+    """Configure root logger to log to stdout.
+
+    Args:
+        level: Log level to be emitted to stdout.
+        fmt: Optional format string.
+    """
+    logging.config.dictConfig({
+        "version": 1,
+        "disable_existing_loggers": False,
+        "formatters": {"standard": {"format": fmt}},
+        "handlers": {
+            "stdout": {
+                "level": "INFO",
+                "formatter": "standard",
+                "class": "logging.StreamHandler",
+                "stream": "ext://sys.stdout",
+            },
+        },
+        "root": {"level": level, "handlers": ["stdout"]},
+    })
    
+
+
+
+
    
+[docs]
+def discounted_returns(
+    rewards: list[float], terminated: list[bool], discount: float = 1.0
+) -> list[float]:
+    r"""
+    Calculate the discounted returns for a list of rewards, considering termination flags and a discount factor.
+
+    The discounted return represents the future discounted rewards from each time step onwards, taking into account
+    whether an episode has terminated at each step.
+
+    The discounted return \( G_t \) is given by:
+
+    .. math::
+        G_t = \sum_{k=1}^{\infty} \gamma^{k-1} R_{t+k}
+
+        where:
+        - \( G_t \) is the discounted return starting from time step \( t \).
+        - \( \gamma \) is the discount factor.
+        - \( R_{t+k} \) is the reward received at time step \( t+k \).
+
+    NOTE: this could live in ldp.alg, but it's here to avoid circular imports.
+
+    Args:
+        rewards: A list of rewards at each time step.
+        terminated: A list of boolean flags indicating whether the episode terminated at each time step.
+        discount: Discount factor to apply to future rewards. Defaults to 1.0 which means no discounting is applied.
+
+    Returns:
+        A list of discounted returns (rewards to go), with each element representing the
+            total discounted reward from that step onwards.
+
+    Example:
+        >>> rewards = [1.0, 2.0, 3.0]
+        >>> terminated = [False, False, True]
+        >>> discounted_returns(rewards, terminated, discount=0.9)
+        [5.23, 4.7, 3.0]
+    """
+    returns = []
+    r = 0.0
+    for reward, term in zip(reversed(rewards), reversed(terminated), strict=False):
+        # 1 - term is 0 if the episode has terminated
+        r = reward + discount * r * (1 - term)
+        returns.append(r)
+    returns.reverse()
+    return returns
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/tqdm/std.html b/docs/_build/html/_modules/tqdm/std.html new file mode 100644 index 00000000..57aede77 --- /dev/null +++ b/docs/_build/html/_modules/tqdm/std.html @@ -0,0 +1,1699 @@ + + + + + + + tqdm.std — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +

    Source code for tqdm.std

    +"""
+Customisable progressbar decorator for iterators.
+Includes a default `range` iterator printing to `stderr`.
+
+Usage:
+>>> from tqdm import trange, tqdm
+>>> for i in trange(10):
+...     ...
+"""
+import sys
+from collections import OrderedDict, defaultdict
+from contextlib import contextmanager
+from datetime import datetime, timedelta, timezone
+from numbers import Number
+from time import time
+from warnings import warn
+from weakref import WeakSet
+
+from ._monitor import TMonitor
+from .utils import (
+    CallbackIOWrapper, Comparable, DisableOnWriteError, FormatReplace, SimpleTextIOWrapper,
+    _is_ascii, _screen_shape_wrapper, _supports_unicode, _term_move_up, disp_len, disp_trim,
+    envwrap)
+
+__author__ = "https://github.com/tqdm/tqdm#contributions"
+__all__ = ['tqdm', 'trange',
+           'TqdmTypeError', 'TqdmKeyError', 'TqdmWarning',
+           'TqdmExperimentalWarning', 'TqdmDeprecationWarning',
+           'TqdmMonitorWarning']
+
+
+class TqdmTypeError(TypeError):
+    pass
+
+
+class TqdmKeyError(KeyError):
+    pass
+
+
+class TqdmWarning(Warning):
+    """base class for all tqdm warnings.
+
+    Used for non-external-code-breaking errors, such as garbled printing.
+    """
+    def __init__(self, msg, fp_write=None, *a, **k):
+        if fp_write is not None:
+            fp_write("\n" + self.__class__.__name__ + ": " + str(msg).rstrip() + '\n')
+        else:
+            super().__init__(msg, *a, **k)
+
+
+class TqdmExperimentalWarning(TqdmWarning, FutureWarning):
+    """beta feature, unstable API and behaviour"""
+    pass
+
+
+class TqdmDeprecationWarning(TqdmWarning, DeprecationWarning):
+    # not suppressed if raised
+    pass
+
+
+class TqdmMonitorWarning(TqdmWarning, RuntimeWarning):
+    """tqdm monitor errors which do not affect external functionality"""
+    pass
+
+
+def TRLock(*args, **kwargs):
+    """threading RLock"""
+    try:
+        from threading import RLock
+        return RLock(*args, **kwargs)
+    except (ImportError, OSError):  # pragma: no cover
+        pass
+
+
+class TqdmDefaultWriteLock(object):
+    """
+    Provide a default write lock for thread and multiprocessing safety.
+    Works only on platforms supporting `fork` (so Windows is excluded).
+    You must initialise a `tqdm` or `TqdmDefaultWriteLock` instance
+    before forking in order for the write lock to work.
+    On Windows, you need to supply the lock from the parent to the children as
+    an argument to joblib or the parallelism lib you use.
+    """
+    # global thread lock so no setup required for multithreading.
+    # NB: Do not create multiprocessing lock as it sets the multiprocessing
+    # context, disallowing `spawn()`/`forkserver()`
+    th_lock = TRLock()
+
+    def __init__(self):
+        # Create global parallelism locks to avoid racing issues with parallel
+        # bars works only if fork available (Linux/MacOSX, but not Windows)
+        cls = type(self)
+        root_lock = cls.th_lock
+        if root_lock is not None:
+            root_lock.acquire()
+        cls.create_mp_lock()
+        self.locks = [lk for lk in [cls.mp_lock, cls.th_lock] if lk is not None]
+        if root_lock is not None:
+            root_lock.release()
+
+    def acquire(self, *a, **k):
+        for lock in self.locks:
+            lock.acquire(*a, **k)
+
+    def release(self):
+        for lock in self.locks[::-1]:  # Release in inverse order of acquisition
+            lock.release()
+
+    def __enter__(self):
+        self.acquire()
+
+    def __exit__(self, *exc):
+        self.release()
+
+    @classmethod
+    def create_mp_lock(cls):
+        if not hasattr(cls, 'mp_lock'):
+            try:
+                from multiprocessing import RLock
+                cls.mp_lock = RLock()
+            except (ImportError, OSError):  # pragma: no cover
+                cls.mp_lock = None
+
+    @classmethod
+    def create_th_lock(cls):
+        assert hasattr(cls, 'th_lock')
+        warn("create_th_lock not needed anymore", TqdmDeprecationWarning, stacklevel=2)
+
+
+class Bar(object):
+    """
+    `str.format`-able bar with format specifiers: `[width][type]`
+
+    - `width`
+      + unspecified (default): use `self.default_len`
+      + `int >= 0`: overrides `self.default_len`
+      + `int < 0`: subtract from `self.default_len`
+    - `type`
+      + `a`: ascii (`charset=self.ASCII` override)
+      + `u`: unicode (`charset=self.UTF` override)
+      + `b`: blank (`charset="  "` override)
+    """
+    ASCII = " 123456789#"
+    UTF = u" " + u''.join(map(chr, range(0x258F, 0x2587, -1)))
+    BLANK = "  "
+    COLOUR_RESET = '\x1b[0m'
+    COLOUR_RGB = '\x1b[38;2;%d;%d;%dm'
+    COLOURS = {'BLACK': '\x1b[30m', 'RED': '\x1b[31m', 'GREEN': '\x1b[32m',
+               'YELLOW': '\x1b[33m', 'BLUE': '\x1b[34m', 'MAGENTA': '\x1b[35m',
+               'CYAN': '\x1b[36m', 'WHITE': '\x1b[37m'}
+
+    def __init__(self, frac, default_len=10, charset=UTF, colour=None):
+        if not 0 <= frac <= 1:
+            warn("clamping frac to range [0, 1]", TqdmWarning, stacklevel=2)
+            frac = max(0, min(1, frac))
+        assert default_len > 0
+        self.frac = frac
+        self.default_len = default_len
+        self.charset = charset
+        self.colour = colour
+
+    @property
+    def colour(self):
+        return self._colour
+
+    @colour.setter
+    def colour(self, value):
+        if not value:
+            self._colour = None
+            return
+        try:
+            if value.upper() in self.COLOURS:
+                self._colour = self.COLOURS[value.upper()]
+            elif value[0] == '#' and len(value) == 7:
+                self._colour = self.COLOUR_RGB % tuple(
+                    int(i, 16) for i in (value[1:3], value[3:5], value[5:7]))
+            else:
+                raise KeyError
+        except (KeyError, AttributeError):
+            warn("Unknown colour (%s); valid choices: [hex (#00ff00), %s]" % (
+                 value, ", ".join(self.COLOURS)),
+                 TqdmWarning, stacklevel=2)
+            self._colour = None
+
+    def __format__(self, format_spec):
+        if format_spec:
+            _type = format_spec[-1].lower()
+            try:
+                charset = {'a': self.ASCII, 'u': self.UTF, 'b': self.BLANK}[_type]
+            except KeyError:
+                charset = self.charset
+            else:
+                format_spec = format_spec[:-1]
+            if format_spec:
+                N_BARS = int(format_spec)
+                if N_BARS < 0:
+                    N_BARS += self.default_len
+            else:
+                N_BARS = self.default_len
+        else:
+            charset = self.charset
+            N_BARS = self.default_len
+
+        nsyms = len(charset) - 1
+        bar_length, frac_bar_length = divmod(int(self.frac * N_BARS * nsyms), nsyms)
+
+        res = charset[-1] * bar_length
+        if bar_length < N_BARS:  # whitespace padding
+            res = res + charset[frac_bar_length] + charset[0] * (N_BARS - bar_length - 1)
+        return self.colour + res + self.COLOUR_RESET if self.colour else res
+
+
+class EMA(object):
+    """
+    Exponential moving average: smoothing to give progressively lower
+    weights to older values.
+
+    Parameters
+    ----------
+    smoothing  : float, optional
+        Smoothing factor in range [0, 1], [default: 0.3].
+        Increase to give more weight to recent values.
+        Ranges from 0 (yields old value) to 1 (yields new value).
+    """
+    def __init__(self, smoothing=0.3):
+        self.alpha = smoothing
+        self.last = 0
+        self.calls = 0
+
+    def __call__(self, x=None):
+        """
+        Parameters
+        ----------
+        x  : float
+            New value to include in EMA.
+        """
+        beta = 1 - self.alpha
+        if x is not None:
+            self.last = self.alpha * x + beta * self.last
+            self.calls += 1
+        return self.last / (1 - beta ** self.calls) if self.calls else self.last
+
+
+
    
+[docs]
+class tqdm(Comparable):
+    """
+    Decorate an iterable object, returning an iterator which acts exactly
+    like the original iterable, but prints a dynamically updating
+    progressbar every time a value is requested.
+
+    Parameters
+    ----------
+    iterable  : iterable, optional
+        Iterable to decorate with a progressbar.
+        Leave blank to manually manage the updates.
+    desc  : str, optional
+        Prefix for the progressbar.
+    total  : int or float, optional
+        The number of expected iterations. If unspecified,
+        len(iterable) is used if possible. If float("inf") or as a last
+        resort, only basic progress statistics are displayed
+        (no ETA, no progressbar).
+        If `gui` is True and this parameter needs subsequent updating,
+        specify an initial arbitrary large positive number,
+        e.g. 9e9.
+    leave  : bool, optional
+        If [default: True], keeps all traces of the progressbar
+        upon termination of iteration.
+        If `None`, will leave only if `position` is `0`.
+    file  : `io.TextIOWrapper` or `io.StringIO`, optional
+        Specifies where to output the progress messages
+        (default: sys.stderr). Uses `file.write(str)` and `file.flush()`
+        methods.  For encoding, see `write_bytes`.
+    ncols  : int, optional
+        The width of the entire output message. If specified,
+        dynamically resizes the progressbar to stay within this bound.
+        If unspecified, attempts to use environment width. The
+        fallback is a meter width of 10 and no limit for the counter and
+        statistics. If 0, will not print any meter (only stats).
+    mininterval  : float, optional
+        Minimum progress display update interval [default: 0.1] seconds.
+    maxinterval  : float, optional
+        Maximum progress display update interval [default: 10] seconds.
+        Automatically adjusts `miniters` to correspond to `mininterval`
+        after long display update lag. Only works if `dynamic_miniters`
+        or monitor thread is enabled.
+    miniters  : int or float, optional
+        Minimum progress display update interval, in iterations.
+        If 0 and `dynamic_miniters`, will automatically adjust to equal
+        `mininterval` (more CPU efficient, good for tight loops).
+        If > 0, will skip display of specified number of iterations.
+        Tweak this and `mininterval` to get very efficient loops.
+        If your progress is erratic with both fast and slow iterations
+        (network, skipping items, etc) you should set miniters=1.
+    ascii  : bool or str, optional
+        If unspecified or False, use unicode (smooth blocks) to fill
+        the meter. The fallback is to use ASCII characters " 123456789#".
+    disable  : bool, optional
+        Whether to disable the entire progressbar wrapper
+        [default: False]. If set to None, disable on non-TTY.
+    unit  : str, optional
+        String that will be used to define the unit of each iteration
+        [default: it].
+    unit_scale  : bool or int or float, optional
+        If 1 or True, the number of iterations will be reduced/scaled
+        automatically and a metric prefix following the
+        International System of Units standard will be added
+        (kilo, mega, etc.) [default: False]. If any other non-zero
+        number, will scale `total` and `n`.
+    dynamic_ncols  : bool, optional
+        If set, constantly alters `ncols` and `nrows` to the
+        environment (allowing for window resizes) [default: False].
+    smoothing  : float, optional
+        Exponential moving average smoothing factor for speed estimates
+        (ignored in GUI mode). Ranges from 0 (average speed) to 1
+        (current/instantaneous speed) [default: 0.3].
+    bar_format  : str, optional
+        Specify a custom bar string formatting. May impact performance.
+        [default: '{l_bar}{bar}{r_bar}'], where
+        l_bar='{desc}: {percentage:3.0f}%|' and
+        r_bar='| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, '
+            '{rate_fmt}{postfix}]'
+        Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,
+            percentage, elapsed, elapsed_s, ncols, nrows, desc, unit,
+            rate, rate_fmt, rate_noinv, rate_noinv_fmt,
+            rate_inv, rate_inv_fmt, postfix, unit_divisor,
+            remaining, remaining_s, eta.
+        Note that a trailing ": " is automatically removed after {desc}
+        if the latter is empty.
+    initial  : int or float, optional
+        The initial counter value. Useful when restarting a progress
+        bar [default: 0]. If using float, consider specifying `{n:.3f}`
+        or similar in `bar_format`, or specifying `unit_scale`.
+    position  : int, optional
+        Specify the line offset to print this bar (starting from 0)
+        Automatic if unspecified.
+        Useful to manage multiple bars at once (eg, from threads).
+    postfix  : dict or *, optional
+        Specify additional stats to display at the end of the bar.
+        Calls `set_postfix(**postfix)` if possible (dict).
+    unit_divisor  : float, optional
+        [default: 1000], ignored unless `unit_scale` is True.
+    write_bytes  : bool, optional
+        Whether to write bytes. If (default: False) will write unicode.
+    lock_args  : tuple, optional
+        Passed to `refresh` for intermediate output
+        (initialisation, iterating, and updating).
+    nrows  : int, optional
+        The screen height. If specified, hides nested bars outside this
+        bound. If unspecified, attempts to use environment height.
+        The fallback is 20.
+    colour  : str, optional
+        Bar colour (e.g. 'green', '#00ff00').
+    delay  : float, optional
+        Don't display until [default: 0] seconds have elapsed.
+    gui  : bool, optional
+        WARNING: internal parameter - do not use.
+        Use tqdm.gui.tqdm(...) instead. If set, will attempt to use
+        matplotlib animations for a graphical output [default: False].
+
+    Returns
+    -------
+    out  : decorated iterator.
+    """
+
+    monitor_interval = 10  # set to 0 to disable the thread
+    monitor = None
+    _instances = WeakSet()
+
+
    
+[docs]
+    @staticmethod
+    def format_sizeof(num, suffix='', divisor=1000):
+        """
+        Formats a number (greater than unity) with SI Order of Magnitude
+        prefixes.
+
+        Parameters
+        ----------
+        num  : float
+            Number ( >= 1) to format.
+        suffix  : str, optional
+            Post-postfix [default: ''].
+        divisor  : float, optional
+            Divisor between prefixes [default: 1000].
+
+        Returns
+        -------
+        out  : str
+            Number with Order of Magnitude SI unit postfix.
+        """
+        for unit in ['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z']:
+            if abs(num) < 999.5:
+                if abs(num) < 99.95:
+                    if abs(num) < 9.995:
+                        return f'{num:1.2f}{unit}{suffix}'
+                    return f'{num:2.1f}{unit}{suffix}'
+                return f'{num:3.0f}{unit}{suffix}'
+            num /= divisor
+        return f'{num:3.1f}Y{suffix}'
    
+
+
+
    
+[docs]
+    @staticmethod
+    def format_interval(t):
+        """
+        Formats a number of seconds as a clock time, [H:]MM:SS
+
+        Parameters
+        ----------
+        t  : int
+            Number of seconds.
+
+        Returns
+        -------
+        out  : str
+            [H:]MM:SS
+        """
+        mins, s = divmod(int(t), 60)
+        h, m = divmod(mins, 60)
+        return f'{h:d}:{m:02d}:{s:02d}' if h else f'{m:02d}:{s:02d}'
    
+
+
+
    
+[docs]
+    @staticmethod
+    def format_num(n):
+        """
+        Intelligent scientific notation (.3g).
+
+        Parameters
+        ----------
+        n  : int or float or Numeric
+            A Number.
+
+        Returns
+        -------
+        out  : str
+            Formatted number.
+        """
+        f = f'{n:.3g}'.replace('e+0', 'e+').replace('e-0', 'e-')
+        n = str(n)
+        return f if len(f) < len(n) else n
    
+
+
+
    
+[docs]
+    @staticmethod
+    def status_printer(file):
+        """
+        Manage the printing and in-place updating of a line of characters.
+        Note that if the string is longer than a line, then in-place
+        updating may not work (it will print a new line at each refresh).
+        """
+        fp = file
+        fp_flush = getattr(fp, 'flush', lambda: None)  # pragma: no cover
+        if fp in (sys.stderr, sys.stdout):
+            getattr(sys.stderr, 'flush', lambda: None)()
+            getattr(sys.stdout, 'flush', lambda: None)()
+
+        def fp_write(s):
+            fp.write(str(s))
+            fp_flush()
+
+        last_len = [0]
+
+        def print_status(s):
+            len_s = disp_len(s)
+            fp_write('\r' + s + (' ' * max(last_len[0] - len_s, 0)))
+            last_len[0] = len_s
+
+        return print_status
    
+
+
+
    
+[docs]
+    @staticmethod
+    def format_meter(n, total, elapsed, ncols=None, prefix='', ascii=False, unit='it',
+                     unit_scale=False, rate=None, bar_format=None, postfix=None,
+                     unit_divisor=1000, initial=0, colour=None, **extra_kwargs):
+        """
+        Return a string-based progress bar given some parameters
+
+        Parameters
+        ----------
+        n  : int or float
+            Number of finished iterations.
+        total  : int or float
+            The expected total number of iterations. If meaningless (None),
+            only basic progress statistics are displayed (no ETA).
+        elapsed  : float
+            Number of seconds passed since start.
+        ncols  : int, optional
+            The width of the entire output message. If specified,
+            dynamically resizes `{bar}` to stay within this bound
+            [default: None]. If `0`, will not print any bar (only stats).
+            The fallback is `{bar:10}`.
+        prefix  : str, optional
+            Prefix message (included in total width) [default: ''].
+            Use as {desc} in bar_format string.
+        ascii  : bool, optional or str, optional
+            If not set, use unicode (smooth blocks) to fill the meter
+            [default: False]. The fallback is to use ASCII characters
+            " 123456789#".
+        unit  : str, optional
+            The iteration unit [default: 'it'].
+        unit_scale  : bool or int or float, optional
+            If 1 or True, the number of iterations will be printed with an
+            appropriate SI metric prefix (k = 10^3, M = 10^6, etc.)
+            [default: False]. If any other non-zero number, will scale
+            `total` and `n`.
+        rate  : float, optional
+            Manual override for iteration rate.
+            If [default: None], uses n/elapsed.
+        bar_format  : str, optional
+            Specify a custom bar string formatting. May impact performance.
+            [default: '{l_bar}{bar}{r_bar}'], where
+            l_bar='{desc}: {percentage:3.0f}%|' and
+            r_bar='| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, '
+              '{rate_fmt}{postfix}]'
+            Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,
+              percentage, elapsed, elapsed_s, ncols, nrows, desc, unit,
+              rate, rate_fmt, rate_noinv, rate_noinv_fmt,
+              rate_inv, rate_inv_fmt, postfix, unit_divisor,
+              remaining, remaining_s, eta.
+            Note that a trailing ": " is automatically removed after {desc}
+            if the latter is empty.
+        postfix  : *, optional
+            Similar to `prefix`, but placed at the end
+            (e.g. for additional stats).
+            Note: postfix is usually a string (not a dict) for this method,
+            and will if possible be set to postfix = ', ' + postfix.
+            However other types are supported (#382).
+        unit_divisor  : float, optional
+            [default: 1000], ignored unless `unit_scale` is True.
+        initial  : int or float, optional
+            The initial counter value [default: 0].
+        colour  : str, optional
+            Bar colour (e.g. 'green', '#00ff00').
+
+        Returns
+        -------
+        out  : Formatted meter and stats, ready to display.
+        """
+
+        # sanity check: total
+        if total and n >= (total + 0.5):  # allow float imprecision (#849)
+            total = None
+
+        # apply custom scale if necessary
+        if unit_scale and unit_scale not in (True, 1):
+            if total:
+                total *= unit_scale
+            n *= unit_scale
+            if rate:
+                rate *= unit_scale  # by default rate = self.avg_dn / self.avg_dt
+            unit_scale = False
+
+        elapsed_str = tqdm.format_interval(elapsed)
+
+        # if unspecified, attempt to use rate = average speed
+        # (we allow manual override since predicting time is an arcane art)
+        if rate is None and elapsed:
+            rate = (n - initial) / elapsed
+        inv_rate = 1 / rate if rate else None
+        format_sizeof = tqdm.format_sizeof
+        rate_noinv_fmt = ((format_sizeof(rate) if unit_scale else f'{rate:5.2f}')
+                          if rate else '?') + unit + '/s'
+        rate_inv_fmt = (
+            (format_sizeof(inv_rate) if unit_scale else f'{inv_rate:5.2f}')
+            if inv_rate else '?') + 's/' + unit
+        rate_fmt = rate_inv_fmt if inv_rate and inv_rate > 1 else rate_noinv_fmt
+
+        if unit_scale:
+            n_fmt = format_sizeof(n, divisor=unit_divisor)
+            total_fmt = format_sizeof(total, divisor=unit_divisor) if total is not None else '?'
+        else:
+            n_fmt = str(n)
+            total_fmt = str(total) if total is not None else '?'
+
+        try:
+            postfix = ', ' + postfix if postfix else ''
+        except TypeError:
+            pass
+
+        remaining = (total - n) / rate if rate and total else 0
+        remaining_str = tqdm.format_interval(remaining) if rate else '?'
+        try:
+            eta_dt = (datetime.now() + timedelta(seconds=remaining)
+                      if rate and total else datetime.fromtimestamp(0, timezone.utc))
+        except OverflowError:
+            eta_dt = datetime.max
+
+        # format the stats displayed to the left and right sides of the bar
+        if prefix:
+            # old prefix setup work around
+            bool_prefix_colon_already = (prefix[-2:] == ": ")
+            l_bar = prefix if bool_prefix_colon_already else prefix + ": "
+        else:
+            l_bar = ''
+
+        r_bar = f'| {n_fmt}/{total_fmt} [{elapsed_str}<{remaining_str}, {rate_fmt}{postfix}]'
+
+        # Custom bar formatting
+        # Populate a dict with all available progress indicators
+        format_dict = {
+            # slight extension of self.format_dict
+            'n': n, 'n_fmt': n_fmt, 'total': total, 'total_fmt': total_fmt,
+            'elapsed': elapsed_str, 'elapsed_s': elapsed,
+            'ncols': ncols, 'desc': prefix or '', 'unit': unit,
+            'rate': inv_rate if inv_rate and inv_rate > 1 else rate,
+            'rate_fmt': rate_fmt, 'rate_noinv': rate,
+            'rate_noinv_fmt': rate_noinv_fmt, 'rate_inv': inv_rate,
+            'rate_inv_fmt': rate_inv_fmt,
+            'postfix': postfix, 'unit_divisor': unit_divisor,
+            'colour': colour,
+            # plus more useful definitions
+            'remaining': remaining_str, 'remaining_s': remaining,
+            'l_bar': l_bar, 'r_bar': r_bar, 'eta': eta_dt,
+            **extra_kwargs}
+
+        # total is known: we can predict some stats
+        if total:
+            # fractional and percentage progress
+            frac = n / total
+            percentage = frac * 100
+
+            l_bar += f'{percentage:3.0f}%|'
+
+            if ncols == 0:
+                return l_bar[:-1] + r_bar[1:]
+
+            format_dict.update(l_bar=l_bar)
+            if bar_format:
+                format_dict.update(percentage=percentage)
+
+                # auto-remove colon for empty `{desc}`
+                if not prefix:
+                    bar_format = bar_format.replace("{desc}: ", '')
+            else:
+                bar_format = "{l_bar}{bar}{r_bar}"
+
+            full_bar = FormatReplace()
+            nobar = bar_format.format(bar=full_bar, **format_dict)
+            if not full_bar.format_called:
+                return nobar  # no `{bar}`; nothing else to do
+
+            # Formatting progress bar space available for bar's display
+            full_bar = Bar(frac,
+                           max(1, ncols - disp_len(nobar)) if ncols else 10,
+                           charset=Bar.ASCII if ascii is True else ascii or Bar.UTF,
+                           colour=colour)
+            if not _is_ascii(full_bar.charset) and _is_ascii(bar_format):
+                bar_format = str(bar_format)
+            res = bar_format.format(bar=full_bar, **format_dict)
+            return disp_trim(res, ncols) if ncols else res
+
+        elif bar_format:
+            # user-specified bar_format but no total
+            l_bar += '|'
+            format_dict.update(l_bar=l_bar, percentage=0)
+            full_bar = FormatReplace()
+            nobar = bar_format.format(bar=full_bar, **format_dict)
+            if not full_bar.format_called:
+                return nobar
+            full_bar = Bar(0,
+                           max(1, ncols - disp_len(nobar)) if ncols else 10,
+                           charset=Bar.BLANK, colour=colour)
+            res = bar_format.format(bar=full_bar, **format_dict)
+            return disp_trim(res, ncols) if ncols else res
+        else:
+            # no total: no progressbar, ETA, just progress stats
+            return (f'{(prefix + ": ") if prefix else ""}'
+                    f'{n_fmt}{unit} [{elapsed_str}, {rate_fmt}{postfix}]')
    
+
+
+    def __new__(cls, *_, **__):
+        instance = object.__new__(cls)
+        with cls.get_lock():  # also constructs lock if non-existent
+            cls._instances.add(instance)
+            # create monitoring thread
+            if cls.monitor_interval and (cls.monitor is None
+                                         or not cls.monitor.report()):
+                try:
+                    cls.monitor = TMonitor(cls, cls.monitor_interval)
+                except Exception as e:  # pragma: nocover
+                    warn("tqdm:disabling monitor support"
+                         " (monitor_interval = 0) due to:\n" + str(e),
+                         TqdmMonitorWarning, stacklevel=2)
+                    cls.monitor_interval = 0
+        return instance
+
+    @classmethod
+    def _get_free_pos(cls, instance=None):
+        """Skips specified instance."""
+        positions = {abs(inst.pos) for inst in cls._instances
+                     if inst is not instance and hasattr(inst, "pos")}
+        return min(set(range(len(positions) + 1)).difference(positions))
+
+    @classmethod
+    def _decr_instances(cls, instance):
+        """
+        Remove from list and reposition another unfixed bar
+        to fill the new gap.
+
+        This means that by default (where all nested bars are unfixed),
+        order is not maintained but screen flicker/blank space is minimised.
+        (tqdm<=4.44.1 moved ALL subsequent unfixed bars up.)
+        """
+        with cls._lock:
+            try:
+                cls._instances.remove(instance)
+            except KeyError:
+                # if not instance.gui:  # pragma: no cover
+                #     raise
+                pass  # py2: maybe magically removed already
+            # else:
+            if not instance.gui:
+                last = (instance.nrows or 20) - 1
+                # find unfixed (`pos >= 0`) overflow (`pos >= nrows - 1`)
+                instances = list(filter(
+                    lambda i: hasattr(i, "pos") and last <= i.pos,
+                    cls._instances))
+                # set first found to current `pos`
+                if instances:
+                    inst = min(instances, key=lambda i: i.pos)
+                    inst.clear(nolock=True)
+                    inst.pos = abs(instance.pos)
+
+
    
+[docs]
+    @classmethod
+    def write(cls, s, file=None, end="\n", nolock=False):
+        """Print a message via tqdm (without overlap with bars)."""
+        fp = file if file is not None else sys.stdout
+        with cls.external_write_mode(file=file, nolock=nolock):
+            # Write the message
+            fp.write(s)
+            fp.write(end)
    
+
+
+
    
+[docs]
+    @classmethod
+    @contextmanager
+    def external_write_mode(cls, file=None, nolock=False):
+        """
+        Disable tqdm within context and refresh tqdm when exits.
+        Useful when writing to standard output stream
+        """
+        fp = file if file is not None else sys.stdout
+
+        try:
+            if not nolock:
+                cls.get_lock().acquire()
+            # Clear all bars
+            inst_cleared = []
+            for inst in getattr(cls, '_instances', []):
+                # Clear instance if in the target output file
+                # or if write output + tqdm output are both either
+                # sys.stdout or sys.stderr (because both are mixed in terminal)
+                if hasattr(inst, "start_t") and (inst.fp == fp or all(
+                        f in (sys.stdout, sys.stderr) for f in (fp, inst.fp))):
+                    inst.clear(nolock=True)
+                    inst_cleared.append(inst)
+            yield
+            # Force refresh display of bars we cleared
+            for inst in inst_cleared:
+                inst.refresh(nolock=True)
+        finally:
+            if not nolock:
+                cls._lock.release()
    
+
+
+
    
+[docs]
+    @classmethod
+    def set_lock(cls, lock):
+        """Set the global lock."""
+        cls._lock = lock
    
+
+
+
    
+[docs]
+    @classmethod
+    def get_lock(cls):
+        """Get the global lock. Construct it if it does not exist."""
+        if not hasattr(cls, '_lock'):
+            cls._lock = TqdmDefaultWriteLock()
+        return cls._lock
    
+
+
+
    
+[docs]
+    @classmethod
+    def pandas(cls, **tqdm_kwargs):
+        """
+        Registers the current `tqdm` class with
+            pandas.core.
+            ( frame.DataFrame
+            | series.Series
+            | groupby.(generic.)DataFrameGroupBy
+            | groupby.(generic.)SeriesGroupBy
+            ).progress_apply
+
+        A new instance will be created every time `progress_apply` is called,
+        and each instance will automatically `close()` upon completion.
+
+        Parameters
+        ----------
+        tqdm_kwargs  : arguments for the tqdm instance
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> import numpy as np
+        >>> from tqdm import tqdm
+        >>> from tqdm.gui import tqdm as tqdm_gui
+        >>>
+        >>> df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))
+        >>> tqdm.pandas(ncols=50)  # can use tqdm_gui, optional kwargs, etc
+        >>> # Now you can use `progress_apply` instead of `apply`
+        >>> df.groupby(0).progress_apply(lambda x: x**2)
+
+        References
+        ----------
+        <https://stackoverflow.com/questions/18603270/\
+        progress-indicator-during-pandas-operations-python>
+        """
+        from warnings import catch_warnings, simplefilter
+
+        from pandas.core.frame import DataFrame
+        from pandas.core.series import Series
+        try:
+            with catch_warnings():
+                simplefilter("ignore", category=FutureWarning)
+                from pandas import Panel
+        except ImportError:  # pandas>=1.2.0
+            Panel = None
+        Rolling, Expanding = None, None
+        try:  # pandas>=1.0.0
+            from pandas.core.window.rolling import _Rolling_and_Expanding
+        except ImportError:
+            try:  # pandas>=0.18.0
+                from pandas.core.window import _Rolling_and_Expanding
+            except ImportError:  # pandas>=1.2.0
+                try:  # pandas>=1.2.0
+                    from pandas.core.window.expanding import Expanding
+                    from pandas.core.window.rolling import Rolling
+                    _Rolling_and_Expanding = Rolling, Expanding
+                except ImportError:  # pragma: no cover
+                    _Rolling_and_Expanding = None
+        try:  # pandas>=0.25.0
+            from pandas.core.groupby.generic import SeriesGroupBy  # , NDFrameGroupBy
+            from pandas.core.groupby.generic import DataFrameGroupBy
+        except ImportError:  # pragma: no cover
+            try:  # pandas>=0.23.0
+                from pandas.core.groupby.groupby import DataFrameGroupBy, SeriesGroupBy
+            except ImportError:
+                from pandas.core.groupby import DataFrameGroupBy, SeriesGroupBy
+        try:  # pandas>=0.23.0
+            from pandas.core.groupby.groupby import GroupBy
+        except ImportError:  # pragma: no cover
+            from pandas.core.groupby import GroupBy
+
+        try:  # pandas>=0.23.0
+            from pandas.core.groupby.groupby import PanelGroupBy
+        except ImportError:
+            try:
+                from pandas.core.groupby import PanelGroupBy
+            except ImportError:  # pandas>=0.25.0
+                PanelGroupBy = None
+
+        tqdm_kwargs = tqdm_kwargs.copy()
+        deprecated_t = [tqdm_kwargs.pop('deprecated_t', None)]
+
+        def inner_generator(df_function='apply'):
+            def inner(df, func, *args, **kwargs):
+                """
+                Parameters
+                ----------
+                df  : (DataFrame|Series)[GroupBy]
+                    Data (may be grouped).
+                func  : function
+                    To be applied on the (grouped) data.
+                **kwargs  : optional
+                    Transmitted to `df.apply()`.
+                """
+
+                # Precompute total iterations
+                total = tqdm_kwargs.pop("total", getattr(df, 'ngroups', None))
+                if total is None:  # not grouped
+                    if df_function == 'applymap':
+                        total = df.size
+                    elif isinstance(df, Series):
+                        total = len(df)
+                    elif (_Rolling_and_Expanding is None or
+                          not isinstance(df, _Rolling_and_Expanding)):
+                        # DataFrame or Panel
+                        axis = kwargs.get('axis', 0)
+                        if axis == 'index':
+                            axis = 0
+                        elif axis == 'columns':
+                            axis = 1
+                        # when axis=0, total is shape[axis1]
+                        total = df.size // df.shape[axis]
+
+                # Init bar
+                if deprecated_t[0] is not None:
+                    t = deprecated_t[0]
+                    deprecated_t[0] = None
+                else:
+                    t = cls(total=total, **tqdm_kwargs)
+
+                if len(args) > 0:
+                    # *args intentionally not supported (see #244, #299)
+                    TqdmDeprecationWarning(
+                        "Except func, normal arguments are intentionally" +
+                        " not supported by" +
+                        " `(DataFrame|Series|GroupBy).progress_apply`." +
+                        " Use keyword arguments instead.",
+                        fp_write=getattr(t.fp, 'write', sys.stderr.write))
+
+                try:  # pandas>=1.3.0
+                    from pandas.core.common import is_builtin_func
+                except ImportError:
+                    is_builtin_func = df._is_builtin_func
+                try:
+                    func = is_builtin_func(func)
+                except TypeError:
+                    pass
+
+                # Define bar updating wrapper
+                def wrapper(*args, **kwargs):
+                    # update tbar correctly
+                    # it seems `pandas apply` calls `func` twice
+                    # on the first column/row to decide whether it can
+                    # take a fast or slow code path; so stop when t.total==t.n
+                    t.update(n=1 if not t.total or t.n < t.total else 0)
+                    return func(*args, **kwargs)
+
+                # Apply the provided function (in **kwargs)
+                # on the df using our wrapper (which provides bar updating)
+                try:
+                    return getattr(df, df_function)(wrapper, **kwargs)
+                finally:
+                    t.close()
+
+            return inner
+
+        # Monkeypatch pandas to provide easy methods
+        # Enable custom tqdm progress in pandas!
+        Series.progress_apply = inner_generator()
+        SeriesGroupBy.progress_apply = inner_generator()
+        Series.progress_map = inner_generator('map')
+        SeriesGroupBy.progress_map = inner_generator('map')
+
+        DataFrame.progress_apply = inner_generator()
+        DataFrameGroupBy.progress_apply = inner_generator()
+        DataFrame.progress_applymap = inner_generator('applymap')
+        DataFrame.progress_map = inner_generator('map')
+        DataFrameGroupBy.progress_map = inner_generator('map')
+
+        if Panel is not None:
+            Panel.progress_apply = inner_generator()
+        if PanelGroupBy is not None:
+            PanelGroupBy.progress_apply = inner_generator()
+
+        GroupBy.progress_apply = inner_generator()
+        GroupBy.progress_aggregate = inner_generator('aggregate')
+        GroupBy.progress_transform = inner_generator('transform')
+
+        if Rolling is not None and Expanding is not None:
+            Rolling.progress_apply = inner_generator()
+            Expanding.progress_apply = inner_generator()
+        elif _Rolling_and_Expanding is not None:
+            _Rolling_and_Expanding.progress_apply = inner_generator()
    
+
+
+    # override defaults via env vars
+    @envwrap("TQDM_", is_method=True, types={'total': float, 'ncols': int, 'miniters': float,
+                                             'position': int, 'nrows': int})
+    def __init__(self, iterable=None, desc=None, total=None, leave=True, file=None,
+                 ncols=None, mininterval=0.1, maxinterval=10.0, miniters=None,
+                 ascii=None, disable=False, unit='it', unit_scale=False,
+                 dynamic_ncols=False, smoothing=0.3, bar_format=None, initial=0,
+                 position=None, postfix=None, unit_divisor=1000, write_bytes=False,
+                 lock_args=None, nrows=None, colour=None, delay=0.0, gui=False,
+                 **kwargs):
+        """see tqdm.tqdm for arguments"""
+        if file is None:
+            file = sys.stderr
+
+        if write_bytes:
+            # Despite coercing unicode into bytes, py2 sys.std* streams
+            # should have bytes written to them.
+            file = SimpleTextIOWrapper(
+                file, encoding=getattr(file, 'encoding', None) or 'utf-8')
+
+        file = DisableOnWriteError(file, tqdm_instance=self)
+
+        if disable is None and hasattr(file, "isatty") and not file.isatty():
+            disable = True
+
+        if total is None and iterable is not None:
+            try:
+                total = len(iterable)
+            except (TypeError, AttributeError):
+                total = None
+        if total == float("inf"):
+            # Infinite iterations, behave same as unknown
+            total = None
+
+        if disable:
+            self.iterable = iterable
+            self.disable = disable
+            with self._lock:
+                self.pos = self._get_free_pos(self)
+                self._instances.remove(self)
+            self.n = initial
+            self.total = total
+            self.leave = leave
+            return
+
+        if kwargs:
+            self.disable = True
+            with self._lock:
+                self.pos = self._get_free_pos(self)
+                self._instances.remove(self)
+            raise (
+                TqdmDeprecationWarning(
+                    "`nested` is deprecated and automated.\n"
+                    "Use `position` instead for manual control.\n",
+                    fp_write=getattr(file, 'write', sys.stderr.write))
+                if "nested" in kwargs else
+                TqdmKeyError("Unknown argument(s): " + str(kwargs)))
+
+        # Preprocess the arguments
+        if (
+            (ncols is None or nrows is None) and (file in (sys.stderr, sys.stdout))
+        ) or dynamic_ncols:  # pragma: no cover
+            if dynamic_ncols:
+                dynamic_ncols = _screen_shape_wrapper()
+                if dynamic_ncols:
+                    ncols, nrows = dynamic_ncols(file)
+            else:
+                _dynamic_ncols = _screen_shape_wrapper()
+                if _dynamic_ncols:
+                    _ncols, _nrows = _dynamic_ncols(file)
+                    if ncols is None:
+                        ncols = _ncols
+                    if nrows is None:
+                        nrows = _nrows
+
+        if miniters is None:
+            miniters = 0
+            dynamic_miniters = True
+        else:
+            dynamic_miniters = False
+
+        if mininterval is None:
+            mininterval = 0
+
+        if maxinterval is None:
+            maxinterval = 0
+
+        if ascii is None:
+            ascii = not _supports_unicode(file)
+
+        if bar_format and ascii is not True and not _is_ascii(ascii):
+            # Convert bar format into unicode since terminal uses unicode
+            bar_format = str(bar_format)
+
+        if smoothing is None:
+            smoothing = 0
+
+        # Store the arguments
+        self.iterable = iterable
+        self.desc = desc or ''
+        self.total = total
+        self.leave = leave
+        self.fp = file
+        self.ncols = ncols
+        self.nrows = nrows
+        self.mininterval = mininterval
+        self.maxinterval = maxinterval
+        self.miniters = miniters
+        self.dynamic_miniters = dynamic_miniters
+        self.ascii = ascii
+        self.disable = disable
+        self.unit = unit
+        self.unit_scale = unit_scale
+        self.unit_divisor = unit_divisor
+        self.initial = initial
+        self.lock_args = lock_args
+        self.delay = delay
+        self.gui = gui
+        self.dynamic_ncols = dynamic_ncols
+        self.smoothing = smoothing
+        self._ema_dn = EMA(smoothing)
+        self._ema_dt = EMA(smoothing)
+        self._ema_miniters = EMA(smoothing)
+        self.bar_format = bar_format
+        self.postfix = None
+        self.colour = colour
+        self._time = time
+        if postfix:
+            try:
+                self.set_postfix(refresh=False, **postfix)
+            except TypeError:
+                self.postfix = postfix
+
+        # Init the iterations counters
+        self.last_print_n = initial
+        self.n = initial
+
+        # if nested, at initial sp() call we replace '\r' by '\n' to
+        # not overwrite the outer progress bar
+        with self._lock:
+            # mark fixed positions as negative
+            self.pos = self._get_free_pos(self) if position is None else -position
+
+        if not gui:
+            # Initialize the screen printer
+            self.sp = self.status_printer(self.fp)
+            if delay <= 0:
+                self.refresh(lock_args=self.lock_args)
+
+        # Init the time counter
+        self.last_print_t = self._time()
+        # NB: Avoid race conditions by setting start_t at the very end of init
+        self.start_t = self.last_print_t
+
+    def __bool__(self):
+        if self.total is not None:
+            return self.total > 0
+        if self.iterable is None:
+            raise TypeError('bool() undefined when iterable == total == None')
+        return bool(self.iterable)
+
+    def __len__(self):
+        return (
+            self.total if self.iterable is None
+            else self.iterable.shape[0] if hasattr(self.iterable, "shape")
+            else len(self.iterable) if hasattr(self.iterable, "__len__")
+            else self.iterable.__length_hint__() if hasattr(self.iterable, "__length_hint__")
+            else getattr(self, "total", None))
+
+    def __reversed__(self):
+        try:
+            orig = self.iterable
+        except AttributeError:
+            raise TypeError("'tqdm' object is not reversible")
+        else:
+            self.iterable = reversed(self.iterable)
+            return self.__iter__()
+        finally:
+            self.iterable = orig
+
+    def __contains__(self, item):
+        contains = getattr(self.iterable, '__contains__', None)
+        return contains(item) if contains is not None else item in self.__iter__()
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        try:
+            self.close()
+        except AttributeError:
+            # maybe eager thread cleanup upon external error
+            if (exc_type, exc_value, traceback) == (None, None, None):
+                raise
+            warn("AttributeError ignored", TqdmWarning, stacklevel=2)
+
+    def __del__(self):
+        self.close()
+
+    def __str__(self):
+        return self.format_meter(**self.format_dict)
+
+    @property
+    def _comparable(self):
+        return abs(getattr(self, "pos", 1 << 31))
+
+    def __hash__(self):
+        return id(self)
+
+    def __iter__(self):
+        """Backward-compatibility to use: for x in tqdm(iterable)"""
+
+        # Inlining instance variables as locals (speed optimisation)
+        iterable = self.iterable
+
+        # If the bar is disabled, then just walk the iterable
+        # (note: keep this check outside the loop for performance)
+        if self.disable:
+            for obj in iterable:
+                yield obj
+            return
+
+        mininterval = self.mininterval
+        last_print_t = self.last_print_t
+        last_print_n = self.last_print_n
+        min_start_t = self.start_t + self.delay
+        n = self.n
+        time = self._time
+
+        try:
+            for obj in iterable:
+                yield obj
+                # Update and possibly print the progressbar.
+                # Note: does not call self.update(1) for speed optimisation.
+                n += 1
+
+                if n - last_print_n >= self.miniters:
+                    cur_t = time()
+                    dt = cur_t - last_print_t
+                    if dt >= mininterval and cur_t >= min_start_t:
+                        self.update(n - last_print_n)
+                        last_print_n = self.last_print_n
+                        last_print_t = self.last_print_t
+        finally:
+            self.n = n
+            self.close()
+
+
    
+[docs]
+    def update(self, n=1):
+        """
+        Manually update the progress bar, useful for streams
+        such as reading files.
+        E.g.:
+        >>> t = tqdm(total=filesize) # Initialise
+        >>> for current_buffer in stream:
+        ...    ...
+        ...    t.update(len(current_buffer))
+        >>> t.close()
+        The last line is highly recommended, but possibly not necessary if
+        `t.update()` will be called in such a way that `filesize` will be
+        exactly reached and printed.
+
+        Parameters
+        ----------
+        n  : int or float, optional
+            Increment to add to the internal counter of iterations
+            [default: 1]. If using float, consider specifying `{n:.3f}`
+            or similar in `bar_format`, or specifying `unit_scale`.
+
+        Returns
+        -------
+        out  : bool or None
+            True if a `display()` was triggered.
+        """
+        if self.disable:
+            return
+
+        if n < 0:
+            self.last_print_n += n  # for auto-refresh logic to work
+        self.n += n
+
+        # check counter first to reduce calls to time()
+        if self.n - self.last_print_n >= self.miniters:
+            cur_t = self._time()
+            dt = cur_t - self.last_print_t
+            if dt >= self.mininterval and cur_t >= self.start_t + self.delay:
+                cur_t = self._time()
+                dn = self.n - self.last_print_n  # >= n
+                if self.smoothing and dt and dn:
+                    # EMA (not just overall average)
+                    self._ema_dn(dn)
+                    self._ema_dt(dt)
+                self.refresh(lock_args=self.lock_args)
+                if self.dynamic_miniters:
+                    # If no `miniters` was specified, adjust automatically to the
+                    # maximum iteration rate seen so far between two prints.
+                    # e.g.: After running `tqdm.update(5)`, subsequent
+                    # calls to `tqdm.update()` will only cause an update after
+                    # at least 5 more iterations.
+                    if self.maxinterval and dt >= self.maxinterval:
+                        self.miniters = dn * (self.mininterval or self.maxinterval) / dt
+                    elif self.smoothing:
+                        # EMA miniters update
+                        self.miniters = self._ema_miniters(
+                            dn * (self.mininterval / dt if self.mininterval and dt
+                                  else 1))
+                    else:
+                        # max iters between two prints
+                        self.miniters = max(self.miniters, dn)
+
+                # Store old values for next call
+                self.last_print_n = self.n
+                self.last_print_t = cur_t
+                return True
    
+
+
+
    
+[docs]
+    def close(self):
+        """Cleanup and (if leave=False) close the progressbar."""
+        if self.disable:
+            return
+
+        # Prevent multiple closures
+        self.disable = True
+
+        # decrement instance pos and remove from internal set
+        pos = abs(self.pos)
+        self._decr_instances(self)
+
+        if self.last_print_t < self.start_t + self.delay:
+            # haven't ever displayed; nothing to clear
+            return
+
+        # GUI mode
+        if getattr(self, 'sp', None) is None:
+            return
+
+        # annoyingly, _supports_unicode isn't good enough
+        def fp_write(s):
+            self.fp.write(str(s))
+
+        try:
+            fp_write('')
+        except ValueError as e:
+            if 'closed' in str(e):
+                return
+            raise  # pragma: no cover
+
+        leave = pos == 0 if self.leave is None else self.leave
+
+        with self._lock:
+            if leave:
+                # stats for overall rate (no weighted average)
+                self._ema_dt = lambda: None
+                self.display(pos=0)
+                fp_write('\n')
+            else:
+                # clear previous display
+                if self.display(msg='', pos=pos) and not pos:
+                    fp_write('\r')
    
+
+
+
    
+[docs]
+    def clear(self, nolock=False):
+        """Clear current bar display."""
+        if self.disable:
+            return
+
+        if not nolock:
+            self._lock.acquire()
+        pos = abs(self.pos)
+        if pos < (self.nrows or 20):
+            self.moveto(pos)
+            self.sp('')
+            self.fp.write('\r')  # place cursor back at the beginning of line
+            self.moveto(-pos)
+        if not nolock:
+            self._lock.release()
    
+
+
+
    
+[docs]
+    def refresh(self, nolock=False, lock_args=None):
+        """
+        Force refresh the display of this bar.
+
+        Parameters
+        ----------
+        nolock  : bool, optional
+            If `True`, does not lock.
+            If [default: `False`]: calls `acquire()` on internal lock.
+        lock_args  : tuple, optional
+            Passed to internal lock's `acquire()`.
+            If specified, will only `display()` if `acquire()` returns `True`.
+        """
+        if self.disable:
+            return
+
+        if not nolock:
+            if lock_args:
+                if not self._lock.acquire(*lock_args):
+                    return False
+            else:
+                self._lock.acquire()
+        self.display()
+        if not nolock:
+            self._lock.release()
+        return True
    
+
+
+
    
+[docs]
+    def unpause(self):
+        """Restart tqdm timer from last print time."""
+        if self.disable:
+            return
+        cur_t = self._time()
+        self.start_t += cur_t - self.last_print_t
+        self.last_print_t = cur_t
    
+
+
+
    
+[docs]
+    def reset(self, total=None):
+        """
+        Resets to 0 iterations for repeated use.
+
+        Consider combining with `leave=True`.
+
+        Parameters
+        ----------
+        total  : int or float, optional. Total to use for the new bar.
+        """
+        self.n = 0
+        if total is not None:
+            self.total = total
+        if self.disable:
+            return
+        self.last_print_n = 0
+        self.last_print_t = self.start_t = self._time()
+        self._ema_dn = EMA(self.smoothing)
+        self._ema_dt = EMA(self.smoothing)
+        self._ema_miniters = EMA(self.smoothing)
+        self.refresh()
    
+
+
+
    
+[docs]
+    def set_description(self, desc=None, refresh=True):
+        """
+        Set/modify description of the progress bar.
+
+        Parameters
+        ----------
+        desc  : str, optional
+        refresh  : bool, optional
+            Forces refresh [default: True].
+        """
+        self.desc = desc + ': ' if desc else ''
+        if refresh:
+            self.refresh()
    
+
+
+
    
+[docs]
+    def set_description_str(self, desc=None, refresh=True):
+        """Set/modify description without ': ' appended."""
+        self.desc = desc or ''
+        if refresh:
+            self.refresh()
    
+
+
+
    
+[docs]
+    def set_postfix(self, ordered_dict=None, refresh=True, **kwargs):
+        """
+        Set/modify postfix (additional stats)
+        with automatic formatting based on datatype.
+
+        Parameters
+        ----------
+        ordered_dict  : dict or OrderedDict, optional
+        refresh  : bool, optional
+            Forces refresh [default: True].
+        kwargs  : dict, optional
+        """
+        # Sort in alphabetical order to be more deterministic
+        postfix = OrderedDict([] if ordered_dict is None else ordered_dict)
+        for key in sorted(kwargs.keys()):
+            postfix[key] = kwargs[key]
+        # Preprocess stats according to datatype
+        for key in postfix.keys():
+            # Number: limit the length of the string
+            if isinstance(postfix[key], Number):
+                postfix[key] = self.format_num(postfix[key])
+            # Else for any other type, try to get the string conversion
+            elif not isinstance(postfix[key], str):
+                postfix[key] = str(postfix[key])
+            # Else if it's a string, don't need to preprocess anything
+        # Stitch together to get the final postfix
+        self.postfix = ', '.join(key + '=' + postfix[key].strip()
+                                 for key in postfix.keys())
+        if refresh:
+            self.refresh()
    
+
+
+
    
+[docs]
+    def set_postfix_str(self, s='', refresh=True):
+        """
+        Postfix without dictionary expansion, similar to prefix handling.
+        """
+        self.postfix = str(s)
+        if refresh:
+            self.refresh()
    
+
+
+
    
+[docs]
+    def moveto(self, n):
+        # TODO: private method
+        self.fp.write('\n' * n + _term_move_up() * -n)
+        getattr(self.fp, 'flush', lambda: None)()
    
+
+
+    @property
+    def format_dict(self):
+        """Public API for read-only member access."""
+        if self.disable and not hasattr(self, 'unit'):
+            return defaultdict(lambda: None, {
+                'n': self.n, 'total': self.total, 'elapsed': 0, 'unit': 'it'})
+        if self.dynamic_ncols:
+            self.ncols, self.nrows = self.dynamic_ncols(self.fp)
+        return {
+            'n': self.n, 'total': self.total,
+            'elapsed': self._time() - self.start_t if hasattr(self, 'start_t') else 0,
+            'ncols': self.ncols, 'nrows': self.nrows, 'prefix': self.desc,
+            'ascii': self.ascii, 'unit': self.unit, 'unit_scale': self.unit_scale,
+            'rate': self._ema_dn() / self._ema_dt() if self._ema_dt() else None,
+            'bar_format': self.bar_format, 'postfix': self.postfix,
+            'unit_divisor': self.unit_divisor, 'initial': self.initial,
+            'colour': self.colour}
+
+
    
+[docs]
+    def display(self, msg=None, pos=None):
+        """
+        Use `self.sp` to display `msg` in the specified `pos`.
+
+        Consider overloading this function when inheriting to use e.g.:
+        `self.some_frontend(**self.format_dict)` instead of `self.sp`.
+
+        Parameters
+        ----------
+        msg  : str, optional. What to display (default: `repr(self)`).
+        pos  : int, optional. Position to `moveto`
+          (default: `abs(self.pos)`).
+        """
+        if pos is None:
+            pos = abs(self.pos)
+
+        nrows = self.nrows or 20
+        if pos >= nrows - 1:
+            if pos >= nrows:
+                return False
+            if msg or msg is None:  # override at `nrows - 1`
+                msg = " ... (more hidden) ..."
+
+        if not hasattr(self, "sp"):
+            raise TqdmDeprecationWarning(
+                "Please use `tqdm.gui.tqdm(...)`"
+                " instead of `tqdm(..., gui=True)`\n",
+                fp_write=getattr(self.fp, 'write', sys.stderr.write))
+
+        if pos:
+            self.moveto(pos)
+        self.sp(self.__str__() if msg is None else msg)
+        if pos:
+            self.moveto(-pos)
+        return True
    
+
+
+
    
+[docs]
+    @classmethod
+    @contextmanager
+    def wrapattr(cls, stream, method, total=None, bytes=True, **tqdm_kwargs):
+        """
+        stream  : file-like object.
+        method  : str, "read" or "write". The result of `read()` and
+            the first argument of `write()` should have a `len()`.
+
+        >>> with tqdm.wrapattr(file_obj, "read", total=file_obj.size) as fobj:
+        ...     while True:
+        ...         chunk = fobj.read(chunk_size)
+        ...         if not chunk:
+        ...             break
+        """
+        with cls(total=total, **tqdm_kwargs) as t:
+            if bytes:
+                t.unit = "B"
+                t.unit_scale = True
+                t.unit_divisor = 1024
+            yield CallbackIOWrapper(t.update, stream, method)
    
+
    
+
+
+
+
    
+[docs]
+def trange(*args, **kwargs):
+    """Shortcut for tqdm(range(*args), **kwargs)."""
+    return tqdm(range(*args), **kwargs)
    
+
+
    + +
    + +
    +
    + +
    +
    +
    + ©2024, FutureHouse. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt new file mode 100644 index 00000000..75b5c770 --- /dev/null +++ b/docs/_build/html/_sources/index.rst.txt @@ -0,0 +1,23 @@ +.. ldp documentation master file, created by + sphinx-quickstart on Thu Oct 24 14:50:30 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to ldp's documentation! +=============================== + +.. toctree:: + :maxdepth: 4 + :caption: Contents: + + modules + + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/_build/html/_sources/ldp.agent.rst.txt b/docs/_build/html/_sources/ldp.agent.rst.txt new file mode 100644 index 00000000..146fe429 --- /dev/null +++ b/docs/_build/html/_sources/ldp.agent.rst.txt @@ -0,0 +1,69 @@ +ldp.agent package +================= + +Submodules +---------- + +ldp.agent.agent module +---------------------- + +.. automodule:: ldp.agent.agent + :members: + :undoc-members: + :show-inheritance: + +ldp.agent.agent\_client module +------------------------------ + +.. automodule:: ldp.agent.agent_client + :members: + :undoc-members: + :show-inheritance: + +ldp.agent.interactive\_agent module +----------------------------------- + +.. automodule:: ldp.agent.interactive_agent + :members: + :undoc-members: + :show-inheritance: + +ldp.agent.memory\_agent module +------------------------------ + +.. automodule:: ldp.agent.memory_agent + :members: + :undoc-members: + :show-inheritance: + +ldp.agent.react\_agent module +----------------------------- + +.. automodule:: ldp.agent.react_agent + :members: + :undoc-members: + :show-inheritance: + +ldp.agent.simple\_agent module +------------------------------ + +.. automodule:: ldp.agent.simple_agent + :members: + :undoc-members: + :show-inheritance: + +ldp.agent.tree\_of\_thoughts\_agent module +------------------------------------------ + +.. automodule:: ldp.agent.tree_of_thoughts_agent + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp.agent + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/ldp.alg.optimizer.rst.txt b/docs/_build/html/_sources/ldp.alg.optimizer.rst.txt new file mode 100644 index 00000000..f05f2c58 --- /dev/null +++ b/docs/_build/html/_sources/ldp.alg.optimizer.rst.txt @@ -0,0 +1,45 @@ +ldp.alg.optimizer package +========================= + +Submodules +---------- + +ldp.alg.optimizer.ape module +---------------------------- + +.. automodule:: ldp.alg.optimizer.ape + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.optimizer.memory module +------------------------------- + +.. automodule:: ldp.alg.optimizer.memory + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.optimizer.opt module +---------------------------- + +.. automodule:: ldp.alg.optimizer.opt + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.optimizer.replay\_buffers module +---------------------------------------- + +.. automodule:: ldp.alg.optimizer.replay_buffers + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp.alg.optimizer + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/ldp.alg.rst.txt b/docs/_build/html/_sources/ldp.alg.rst.txt new file mode 100644 index 00000000..621e6817 --- /dev/null +++ b/docs/_build/html/_sources/ldp.alg.rst.txt @@ -0,0 +1,77 @@ +ldp.alg package +=============== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + ldp.alg.optimizer + +Submodules +---------- + +ldp.alg.algorithms module +------------------------- + +.. automodule:: ldp.alg.algorithms + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.beam\_search module +--------------------------- + +.. automodule:: ldp.alg.beam_search + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.callbacks module +------------------------ + +.. automodule:: ldp.alg.callbacks + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.datasets module +----------------------- + +.. automodule:: ldp.alg.datasets + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.rollout module +---------------------- + +.. automodule:: ldp.alg.rollout + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.runners module +---------------------- + +.. automodule:: ldp.alg.runners + :members: + :undoc-members: + :show-inheritance: + +ldp.alg.tree\_search module +--------------------------- + +.. automodule:: ldp.alg.tree_search + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp.alg + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/ldp.graph.modules.rst.txt b/docs/_build/html/_sources/ldp.graph.modules.rst.txt new file mode 100644 index 00000000..28b36540 --- /dev/null +++ b/docs/_build/html/_sources/ldp.graph.modules.rst.txt @@ -0,0 +1,45 @@ +ldp.graph.modules package +========================= + +Submodules +---------- + +ldp.graph.modules.llm\_call module +---------------------------------- + +.. automodule:: ldp.graph.modules.llm_call + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.modules.react module +------------------------------ + +.. automodule:: ldp.graph.modules.react + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.modules.reflect module +-------------------------------- + +.. automodule:: ldp.graph.modules.reflect + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.modules.thought module +-------------------------------- + +.. automodule:: ldp.graph.modules.thought + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp.graph.modules + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/ldp.graph.rst.txt b/docs/_build/html/_sources/ldp.graph.rst.txt new file mode 100644 index 00000000..9fc73995 --- /dev/null +++ b/docs/_build/html/_sources/ldp.graph.rst.txt @@ -0,0 +1,85 @@ +ldp.graph package +================= + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + ldp.graph.modules + +Submodules +---------- + +ldp.graph.async\_torch module +----------------------------- + +.. automodule:: ldp.graph.async_torch + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.common\_ops module +---------------------------- + +.. automodule:: ldp.graph.common_ops + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.gradient\_estimators module +------------------------------------- + +.. automodule:: ldp.graph.gradient_estimators + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.loss\_ops module +-------------------------- + +.. automodule:: ldp.graph.loss_ops + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.memory module +----------------------- + +.. automodule:: ldp.graph.memory + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.op\_utils module +-------------------------- + +.. automodule:: ldp.graph.op_utils + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.ops module +-------------------- + +.. automodule:: ldp.graph.ops + :members: + :undoc-members: + :show-inheritance: + +ldp.graph.torch\_ops module +--------------------------- + +.. automodule:: ldp.graph.torch_ops + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp.graph + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/ldp.llms.rst.txt b/docs/_build/html/_sources/ldp.llms.rst.txt new file mode 100644 index 00000000..31d1b865 --- /dev/null +++ b/docs/_build/html/_sources/ldp.llms.rst.txt @@ -0,0 +1,37 @@ +ldp.llms package +================ + +Submodules +---------- + +ldp.llms.chat module +-------------------- + +.. automodule:: ldp.llms.chat + :members: + :undoc-members: + :show-inheritance: + +ldp.llms.embeddings module +-------------------------- + +.. automodule:: ldp.llms.embeddings + :members: + :undoc-members: + :show-inheritance: + +ldp.llms.prompts module +----------------------- + +.. automodule:: ldp.llms.prompts + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp.llms + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/ldp.rst.txt b/docs/_build/html/_sources/ldp.rst.txt new file mode 100644 index 00000000..df50fc31 --- /dev/null +++ b/docs/_build/html/_sources/ldp.rst.txt @@ -0,0 +1,56 @@ +ldp package +=========== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + ldp.agent + ldp.alg + ldp.graph + ldp.llms + +Submodules +---------- + +ldp.data\_structures module +--------------------------- + +.. automodule:: ldp.data_structures + :members: + :undoc-members: + :show-inheritance: + +ldp.main module +--------------- + +.. automodule:: ldp.main + :members: + :undoc-members: + :show-inheritance: + +ldp.shims module +---------------- + +.. automodule:: ldp.shims + :members: + :undoc-members: + :show-inheritance: + +ldp.utils module +---------------- + +.. automodule:: ldp.utils + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: ldp + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/modules.rst.txt b/docs/_build/html/_sources/modules.rst.txt new file mode 100644 index 00000000..f473f842 --- /dev/null +++ b/docs/_build/html/_sources/modules.rst.txt @@ -0,0 +1,7 @@ +ldp +=== + +.. toctree:: + :maxdepth: 4 + + ldp diff --git a/docs/_build/html/_static/alabaster.css b/docs/_build/html/_static/alabaster.css new file mode 100644 index 00000000..e3174bf9 --- /dev/null +++ b/docs/_build/html/_static/alabaster.css @@ -0,0 +1,708 @@ +@import url("basic.css"); + +/* -- page layout ----------------------------------------------------------- */ + +body { + font-family: Georgia, serif; + font-size: 17px; + background-color: #fff; + color: #000; + margin: 0; + padding: 0; +} + + +div.document { + width: 940px; + margin: 30px auto 0 auto; +} + +div.documentwrapper { + float: left; + width: 100%; +} + +div.bodywrapper { + margin: 0 0 0 220px; +} + +div.sphinxsidebar { + width: 220px; + font-size: 14px; + line-height: 1.5; +} + +hr { + border: 1px solid #B1B4B6; +} + +div.body { + background-color: #fff; + color: #3E4349; + padding: 0 30px 0 30px; +} + +div.body > .section { + text-align: left; +} + +div.footer { + width: 940px; + margin: 20px auto 30px auto; + font-size: 14px; + color: #888; + text-align: right; +} + +div.footer a { + color: #888; +} + +p.caption { + font-family: inherit; + font-size: inherit; +} + + +div.relations { + display: none; +} + + +div.sphinxsidebar { + max-height: 100%; + overflow-y: auto; +} + +div.sphinxsidebar a { + color: #444; + text-decoration: none; + border-bottom: 1px dotted #999; +} + +div.sphinxsidebar a:hover { + border-bottom: 1px solid #999; +} + +div.sphinxsidebarwrapper { + padding: 18px 10px; +} + +div.sphinxsidebarwrapper p.logo { + padding: 0; + margin: -10px 0 0 0px; + text-align: center; +} + +div.sphinxsidebarwrapper h1.logo { + margin-top: -10px; + text-align: center; + margin-bottom: 5px; + text-align: left; +} + +div.sphinxsidebarwrapper h1.logo-name { + margin-top: 0px; +} + +div.sphinxsidebarwrapper p.blurb { + margin-top: 0; + font-style: normal; +} + +div.sphinxsidebar h3, +div.sphinxsidebar h4 { + font-family: Georgia, serif; + color: #444; + font-size: 24px; + font-weight: normal; + margin: 0 0 5px 0; + padding: 0; +} + +div.sphinxsidebar h4 { + font-size: 20px; +} + +div.sphinxsidebar h3 a { + color: #444; +} + +div.sphinxsidebar p.logo a, +div.sphinxsidebar h3 a, +div.sphinxsidebar p.logo a:hover, +div.sphinxsidebar h3 a:hover { + border: none; +} + +div.sphinxsidebar p { + color: #555; + margin: 10px 0; +} + +div.sphinxsidebar ul { + margin: 10px 0; + padding: 0; + color: #000; +} + +div.sphinxsidebar ul li.toctree-l1 > a { + font-size: 120%; +} + +div.sphinxsidebar ul li.toctree-l2 > a { + font-size: 110%; +} + +div.sphinxsidebar input { + border: 1px solid #CCC; + font-family: Georgia, serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox input[type="text"] { + width: 160px; +} + +div.sphinxsidebar .search > div { + display: table-cell; +} + +div.sphinxsidebar hr { + border: none; + height: 1px; + color: #AAA; + background: #AAA; + + text-align: left; + margin-left: 0; + width: 50%; +} + +div.sphinxsidebar .badge { + border-bottom: none; +} + +div.sphinxsidebar .badge:hover { + border-bottom: none; +} + +/* To address an issue with donation coming after search */ +div.sphinxsidebar h3.donation { + margin-top: 10px; +} + +/* -- body styles ----------------------------------------------------------- */ + +a { + color: #004B6B; + text-decoration: underline; +} + +a:hover { + color: #6D4100; + text-decoration: underline; +} + +div.body h1, +div.body h2, +div.body h3, +div.body h4, +div.body h5, +div.body h6 { + font-family: Georgia, serif; + font-weight: normal; + margin: 30px 0px 10px 0px; + padding: 0; +} + +div.body h1 { margin-top: 0; padding-top: 0; font-size: 240%; } +div.body h2 { font-size: 180%; } +div.body h3 { font-size: 150%; } +div.body h4 { font-size: 130%; } +div.body h5 { font-size: 100%; } +div.body h6 { font-size: 100%; } + +a.headerlink { + color: #DDD; + padding: 0 4px; + text-decoration: none; +} + +a.headerlink:hover { + color: #444; + background: #EAEAEA; +} + +div.body p, div.body dd, div.body li { + line-height: 1.4em; +} + +div.admonition { + margin: 20px 0px; + padding: 10px 30px; + background-color: #EEE; + border: 1px solid #CCC; +} + +div.admonition tt.xref, div.admonition code.xref, div.admonition a tt { + background-color: #FBFBFB; + border-bottom: 1px solid #fafafa; +} + +div.admonition p.admonition-title { + font-family: Georgia, serif; + font-weight: normal; + font-size: 24px; + margin: 0 0 10px 0; + padding: 0; + line-height: 1; +} + +div.admonition p.last { + margin-bottom: 0; +} + +div.highlight { + background-color: #fff; +} + +dt:target, .highlight { + background: #FAF3E8; +} + +div.warning { + background-color: #FCC; + border: 1px solid #FAA; +} + +div.danger { + background-color: #FCC; + border: 1px solid #FAA; + -moz-box-shadow: 2px 2px 4px #D52C2C; + -webkit-box-shadow: 2px 2px 4px #D52C2C; + box-shadow: 2px 2px 4px #D52C2C; +} + +div.error { + background-color: #FCC; + border: 1px solid #FAA; + -moz-box-shadow: 2px 2px 4px #D52C2C; + -webkit-box-shadow: 2px 2px 4px #D52C2C; + box-shadow: 2px 2px 4px #D52C2C; +} + +div.caution { + background-color: #FCC; + border: 1px solid #FAA; +} + +div.attention { + background-color: #FCC; + border: 1px solid #FAA; +} + +div.important { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.note { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.tip { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.hint { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.seealso { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.topic { + background-color: #EEE; +} + +p.admonition-title { + display: inline; +} + +p.admonition-title:after { + content: ":"; +} + +pre, tt, code { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; + font-size: 0.9em; +} + +.hll { + background-color: #FFC; + margin: 0 -12px; + padding: 0 12px; + display: block; +} + +img.screenshot { +} + +tt.descname, tt.descclassname, code.descname, code.descclassname { + font-size: 0.95em; +} + +tt.descname, code.descname { + padding-right: 0.08em; +} + +img.screenshot { + -moz-box-shadow: 2px 2px 4px #EEE; + -webkit-box-shadow: 2px 2px 4px #EEE; + box-shadow: 2px 2px 4px #EEE; +} + +table.docutils { + border: 1px solid #888; + -moz-box-shadow: 2px 2px 4px #EEE; + -webkit-box-shadow: 2px 2px 4px #EEE; + box-shadow: 2px 2px 4px #EEE; +} + +table.docutils td, table.docutils th { + border: 1px solid #888; + padding: 0.25em 0.7em; +} + +table.field-list, table.footnote { + border: none; + -moz-box-shadow: none; + -webkit-box-shadow: none; + box-shadow: none; +} + +table.footnote { + margin: 15px 0; + width: 100%; + border: 1px solid #EEE; + background: #FDFDFD; + font-size: 0.9em; +} + +table.footnote + table.footnote { + margin-top: -15px; + border-top: none; +} + +table.field-list th { + padding: 0 0.8em 0 0; +} + +table.field-list td { + padding: 0; +} + +table.field-list p { + margin-bottom: 0.8em; +} + +/* Cloned from + * https://github.com/sphinx-doc/sphinx/commit/ef60dbfce09286b20b7385333d63a60321784e68 + */ +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +table.footnote td.label { + width: .1px; + padding: 0.3em 0 0.3em 0.5em; +} + +table.footnote td { + padding: 0.3em 0.5em; +} + +dl { + margin-left: 0; + margin-right: 0; + margin-top: 0; + padding: 0; +} + +dl dd { + margin-left: 30px; +} + +blockquote { + margin: 0 0 0 30px; + padding: 0; +} + +ul, ol { + /* Matches the 30px from the narrow-screen "li > ul" selector below */ + margin: 10px 0 10px 30px; + padding: 0; +} + +pre { + background: #EEE; + padding: 7px 30px; + margin: 15px 0px; + line-height: 1.3em; +} + +div.viewcode-block:target { + background: #ffd; +} + +dl pre, blockquote pre, li pre { + margin-left: 0; + padding-left: 30px; +} + +tt, code { + background-color: #ecf0f3; + color: #222; + /* padding: 1px 2px; */ +} + +tt.xref, code.xref, a tt { + background-color: #FBFBFB; + border-bottom: 1px solid #fff; +} + +a.reference { + text-decoration: none; + border-bottom: 1px dotted #004B6B; +} + +/* Don't put an underline on images */ +a.image-reference, a.image-reference:hover { + border-bottom: none; +} + +a.reference:hover { + border-bottom: 1px solid #6D4100; +} + +a.footnote-reference { + text-decoration: none; + font-size: 0.7em; + vertical-align: top; + border-bottom: 1px dotted #004B6B; +} + +a.footnote-reference:hover { + border-bottom: 1px solid #6D4100; +} + +a:hover tt, a:hover code { + background: #EEE; +} + + +@media screen and (max-width: 870px) { + + div.sphinxsidebar { + display: none; + } + + div.document { + width: 100%; + + } + + div.documentwrapper { + margin-left: 0; + margin-top: 0; + margin-right: 0; + margin-bottom: 0; + } + + div.bodywrapper { + margin-top: 0; + margin-right: 0; + margin-bottom: 0; + margin-left: 0; + } + + ul { + margin-left: 0; + } + + li > ul { + /* Matches the 30px from the "ul, ol" selector above */ + margin-left: 30px; + } + + .document { + width: auto; + } + + .footer { + width: auto; + } + + .bodywrapper { + margin: 0; + } + + .footer { + width: auto; + } + + .github { + display: none; + } + + + +} + + + +@media screen and (max-width: 875px) { + + body { + margin: 0; + padding: 20px 30px; + } + + div.documentwrapper { + float: none; + background: #fff; + } + + div.sphinxsidebar { + display: block; + float: none; + width: 102.5%; + margin: 50px -30px -20px -30px; + padding: 10px 20px; + background: #333; + color: #FFF; + } + + div.sphinxsidebar h3, div.sphinxsidebar h4, div.sphinxsidebar p, + div.sphinxsidebar h3 a { + color: #fff; + } + + div.sphinxsidebar a { + color: #AAA; + } + + div.sphinxsidebar p.logo { + display: none; + } + + div.document { + width: 100%; + margin: 0; + } + + div.footer { + display: none; + } + + div.bodywrapper { + margin: 0; + } + + div.body { + min-height: 0; + padding: 0; + } + + .rtd_doc_footer { + display: none; + } + + .document { + width: auto; + } + + .footer { + width: auto; + } + + .footer { + width: auto; + } + + .github { + display: none; + } +} + + +/* misc. */ + +.revsys-inline { + display: none!important; +} + +/* Hide ugly table cell borders in ..bibliography:: directive output */ +table.docutils.citation, table.docutils.citation td, table.docutils.citation th { + border: none; + /* Below needed in some edge cases; if not applied, bottom shadows appear */ + -moz-box-shadow: none; + -webkit-box-shadow: none; + box-shadow: none; +} + + +/* relbar */ + +.related { + line-height: 30px; + width: 100%; + font-size: 0.9rem; +} + +.related.top { + border-bottom: 1px solid #EEE; + margin-bottom: 20px; +} + +.related.bottom { + border-top: 1px solid #EEE; +} + +.related ul { + padding: 0; + margin: 0; + list-style: none; +} + +.related li { + display: inline; +} + +nav#rellinks { + float: right; +} + +nav#rellinks li+li:before { + content: "|"; +} + +nav#breadcrumbs li+li:before { + content: "\00BB"; +} + +/* Hide certain items when printing */ +@media print { + div.related { + display: none; + } +} \ No newline at end of file diff --git a/docs/_build/html/_static/basic.css b/docs/_build/html/_static/basic.css new file mode 100644 index 00000000..e5179b7a --- /dev/null +++ b/docs/_build/html/_static/basic.css @@ -0,0 +1,925 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: inherit; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +a:visited { + color: #551A8B; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; + float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} + +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ + +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + + +/* -- other body styles ----------------------------------------------------- */ + +ol.arabic { + list-style: decimal; +} + +ol.loweralpha { + list-style: lower-alpha; +} + +ol.upperalpha { + list-style: upper-alpha; +} + +ol.lowerroman { + list-style: lower-roman; +} + +ol.upperroman { + list-style: upper-roman; +} + +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { + margin-top: 0px; +} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block .line-block { + margin-top: 0; + margin-bottom: 0; + margin-left: 1.5em; +} + +.guilabel, .menuselection { + font-family: sans-serif; +} + +.accelerator { + text-decoration: underline; +} + +.classifier { + font-style: oblique; +} + +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + +abbr, acronym { + border-bottom: dotted 1px; + cursor: help; +} + +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + +/* -- code displays --------------------------------------------------------- */ + +pre { + overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + +div.code-block-caption code { + background-color: transparent; +} + +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ +} + +div.code-block-caption span.caption-number { + padding: 0.1em 0.3em; + font-style: italic; +} + +div.code-block-caption span.caption-text { +} + +div.literal-block-wrapper { + margin: 1em 0; +} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/docs/_build/html/_static/custom.css b/docs/_build/html/_static/custom.css new file mode 100644 index 00000000..2a924f1d --- /dev/null +++ b/docs/_build/html/_static/custom.css @@ -0,0 +1 @@ +/* This file intentionally left blank. */ diff --git a/docs/_build/html/_static/doctools.js b/docs/_build/html/_static/doctools.js new file mode 100644 index 00000000..4d67807d --- /dev/null +++ b/docs/_build/html/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/docs/_build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js new file mode 100644 index 00000000..7e4c114f --- /dev/null +++ b/docs/_build/html/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/docs/_build/html/_static/file.png b/docs/_build/html/_static/file.png new file mode 100644 index 0000000000000000000000000000000000000000..a858a410e4faa62ce324d814e4b816fff83a6fb3 GIT binary patch literal 286 zcmV+(0pb3MP)s`hMrGg#P~ix$^RISR_I47Y|r1 z_CyJOe}D1){SET-^Amu_i71Lt6eYfZjRyw@I6OQAIXXHDfiX^GbOlHe=Ae4>0m)d(f|Me07*qoM6N<$f}vM^LjV8( literal 0 HcmV?d00001 diff --git a/docs/_build/html/_static/groundwork.css b/docs/_build/html/_static/groundwork.css new file mode 100644 index 00000000..57363052 --- /dev/null +++ b/docs/_build/html/_static/groundwork.css @@ -0,0 +1,720 @@ +/* + * flasky.css_t + * ~~~~~~~~~~~~ + * + * :copyright: Copyright 2010 by Armin Ronacher. Modifications by Kenneth Reitz. + * :license: Flask Design License, see LICENSE for details. + */ + + + + + +@import url("basic.css"); + +/* -- page layout ----------------------------------------------------------- */ + +body { + font: 400 16px Lato, 'Helvetica Neue', Helvetica, Arial, sans-serif; + /*font-family: 'goudy old style', 'minion pro', 'bell mt', Georgia, 'Hiragino Mincho Pro'; + font-size: 17px; */ + line-height: normal; + background-color: #333; + color: #333; + margin: 0; + padding: 0; + text-shadow: 0 1px 3px rgba(0,0,0,0.5); +} + +strong { + font-weight: bold; +} + +div.document { + width: 1024px; + margin: 30px auto 0 auto; +} + +div.documentwrapper { + float: left; + width: 100%; +} + +div.bodywrapper { + margin: 0 0 0 220px; +} + +div.sphinxsidebar { + width: 220px; +} + +hr { + border: 1px solid #B1B4B6; +} + +div.body { + background-color: #444; + border-radius: 5px; + color: #ddd; + padding: 15px 30px 15px 30px; + box-shadow: 0 3px 10px rgba(0,0,0,0.3); +} + +img.floatingflask { + padding: 0 0 10px 10px; + float: right; +} + +div.footer { + width: 1024px; + margin: 20px auto 30px auto; + font-size: 14px; + color: #888; + text-align: right; +} + +div.footer a { + color: #888; +} + +div.related { + display: none; +} + +div.sphinxsidebar a { + color: #FFCC00; + text-decoration: none; + border-bottom: none; +} +div.sphinxsidebar a:hover { + color: #ff9900; +} + +div.sphinxsidebar a:hover, +div.sphinxsidebar a:visited, +div.sphinxsidebar a:focus { + border-bottom: none; +} + +div.sphinxsidebar { + margin: 0; + position: fixed; + overflow: auto; + height: auto; + font-size: 14px; + line-height: 1.5; +} + +div.sphinxsidebarwrapper { + padding: 18px 0 18px 10px; +} + +div.sphinxsidebarwrapper p.logo { + padding: 0; + margin: -10px 0 0 -20px; + text-align: center; +} + +div.sphinxsidebar h3, +div.sphinxsidebar h4 { + /*font-family: 'Garamond', 'Georgia', serif;*/ + font-family: Lato, 'Helvetica Neue', Helvetica, Arial, sans-serif; + color: #ddd; + font-size: 20px; + font-weight: normal; + padding: 0; + /*border-bottom: 1px solid #fff;*/ + + background-color: #444; + border-radius: 5px 0 0 0px; + padding-left: 5px; + border: 1px solid #444; + margin-top: 10px; + box-shadow: 0 3px 10px rgba(0,0,0,0.3); + margin-left: -10px; + /* Needed, if siebar is not sticky. Otherwise shadows moves over body element*/ + clip-path: inset(-10px -0px -10px -10px); +} + +div.sphinxsidebar h4 { + font-size: 20px; +} + +div.sphinxsidebar h3 a { + color: #ddd; +} + +div.sphinxsidebar p.logo a, +div.sphinxsidebar h3 a, +div.sphinxsidebar p.logo a:hover, +div.sphinxsidebar h3 a:hover { + border: none; +} + +div.sphinxsidebar p { + color: #555; + margin: 10px 0; +} + +div.sphinxsidebar ul { + margin: 0; + padding: 0; + color: #ddd; +} + +div.sphinxsidebar input { + /*border: 1px solid #ccc;*/ + border: 1px solid #333; + font-family: 'Georgia', serif; + font-size: 1em; + color: #ddd; + width: 170px; + border-radius: 5px; + padding: 6px 12px; + box-shadow: inset 0 1px 10px rgba(0,0,0,0.3), 0 1px 0 rgba(255,255,255,0.1), 0 0px 0 rgba(0,0,0,0.5); + background-color: #2b2b2b; +} + +div.need-tobediscussed { + background-color: #0a4a63 !important; +} + +/*span.needs-type {*/ + /*border: 1px solid #444;*/ + /*border-radius: 5px;*/ + /*padding: 3px 5px;*/ +/*}*/ + +/*div.need-implementation span.needs-type {*/ + /*!*background-color: #ffdd00 !important;*!*/ + /*border: 1px solid #ffdd00*/ +/*}*/ + +div.need-status-closed span.needs-status { + background-color:darkgreen; +} + +div.need-status-open span.needs-status { + background-color:firebrick; +} + +div.need-status-tbd span.needs-status { + background-color:orange; +} + +div.need-technicaldebt span.needs-type { + background-color:red; +} + +/* need part table layout */ +tr.need_part td { + background-color: #555; + padding-left: 30px !important; +} + +/* using more space for whole page */ +div.document { + width: auto; + margin: 30px auto 0 auto; + max-width: 98%; +} +div.body { + min-width: 450px; + max-width: 100%; +} + +span.need-part a { + font-size: 0.75em; +} +span.need-part { + border-bottom: 1px solid #888; +} + +/* Code block design inside needs */ +div.need pre { + margin: 0; +} + +/* Better need title and ID layout*/ +span.needs-id { + font-size: 120% !important; + color: #ffcc00 !important; + float:right +} +span.needs-title { + font-weight: bold; + font-size: 1.3em; +} + +div.needs_meta div.headline { + margin-bottom: 5px; + border-bottom: 1px solid #555; +} +div.needs_meta.line-block{ + margin: 0; +} + +/* Need headline color design*/ +div.needs_meta div.headline { + margin: -5px -10px 5px -10px; + border-bottom: 1px solid #555; + border-radius: 3px 3px 0 0; + padding: 5px 10px 0px 10px; +} + +div.story div.headline { + background-color: #39024e80; +} + +div.spec div.headline { + background-color: #00beff33; +} + +div.impl div.headline { + background-color: #ff000a33; +} + +div.test div.headline { + background-color: #2ed15233; +} +/* -- body styles ----------------------------------------------------------- */ + +a { + color: #FFCC00; + text-decoration: none; +} + +a:hover { + color: #ff9900; + text-decoration: none; +} + +div.body h1, +div.body h2, +div.body h3, +div.body h4, +div.body h5, +div.body h6 { + font-family: Lato, 'Helvetica Neue', Helvetica, Arial, sans-serif; + /*font-family: 'Garamond', 'Georgia', serif;*/ + font-weight: normal; + /* margin: 30px 0px 10px 0px; */ + margin: 10px 0px 00px 0px; + padding: 0; +} + +div.body h1 { margin-top: 0; padding-top: 0; font-size: 240%; } +div.body h2 { font-size: 180%; } +div.body h3 { font-size: 150%; } +div.body h4 { font-size: 130%; } +div.body h5 { font-size: 100%; } +div.body h6 { font-size: 100%; } + +a.headerlink { + color: #ffcc00; + padding: 0 4px; + text-decoration: none; + text-shadow: none; +} + +a.headerlink:hover { + color: #ff9900; + background: none; + text-shadow: none; +} + +div.body p, div.body dd, div.body li { + line-height: 1.4em; +} + +div.admonition { + background: none; + margin: 20px -30px; + padding: 10px 30px; +} + +div.admonition tt.xref, div.admonition a tt { + border-bottom: 1px solid #fafafa; +} + +dd div.admonition { + margin-left: -60px; + padding-left: 60px; +} + +div.admonition p.admonition-title { + font-family: Lato, 'Helvetica Neue', Helvetica, Arial, sans-serif; + /* font-family: 'Garamond', 'Georgia', serif; */ + /* font-weight: normal;/* + font-weight: bold; + /* font-size: 24px; */ + font-size: 18px; + margin: 0 0 10px 0; + padding: 0; + line-height: 1; +} + +div.admonition p.last { + margin-bottom: 0; +} + +div.highlight { + /*background-color: white;*/ + background-color: #444; + text-shadow: none; + color: #fff; +} + +dt:target, .highlight { + background: #333; +} + +div.note { + background-color: #555; + border: none; +} + +div.warning { + background-color: #7f1212; + border: none; +} + +div.seealso { + background-color: #ffc; + border: 1px solid #ff6; +} + +div.topic { + background-color: inherit; + border: none; +} + +p.admonition-title { + display: inline; +} + +p.admonition-title:after { + content: ":"; +} + +pre, tt { + font-family: 'Consolas', 'Menlo', 'Deja Vu Sans Mono', 'Bitstream Vera Sans Mono', monospace; + font-size: 0.9em; +} + +img.screenshot { +} + +tt.descname, tt.descclassname { + font-size: 0.95em; 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"are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/docs/_build/html/_static/minus.png b/docs/_build/html/_static/minus.png new 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DBA onefinestay + * :license: MIT, see LICENSE for details. + * + */ + + + +@import url('basic.css'); + +/* -- page layout --------------------------------------------------------- */ + +body { + font-family: "Lato","proxima-nova","Helvetica Neue",Arial,sans-serif; + margin: 0; + padding: 0; + background-color: rgb(252, 252, 252); +} + +h1, h2, h3, h4 { + color: #2a2730; + font-weight: 700; + font-family: "Lora","Times",serif; + clear: both; +} + +div.document { + margin: 10px auto 0 auto; + max-width: 940px; +} + +div.documentwrapper { + float: left; + width: 100%; +} + +div.bodywrapper { + margin: 0 0 0 230px; +} + +div.body { + background-color: rgb(252, 252, 252); + color: #3e4349; + padding: 0 30px 30px 30px; +} + +div.footer { + color: #555; + font-size: 14px; + margin: 20px auto 30px auto; + text-align: right; + max-width: 880px; +} + +div.footer a { + color: #444; + text-decoration: underline; +} + +div.related { + padding: 10px 10px; + width: auto; +} + +div.sphinxsidebar { + float: left; + font-size: 14px; + line-height: 1.5em; + margin-left: -100%; + width: 230px; +} + +div.sphinxsidebarwrapper { + font-size: 14px; + line-height: 1.5em; + padding: 10px 0 10px 10px; +} + +div.sphinxsidebar h3, +div.sphinxsidebar h4 { + color: #333; + font-size: 24px; + font-weight: normal; + margin: 0 0 5px 0; + padding: 0; +} + +div.sphinxsidebar h4 { + font-size: 1.1em; +} + +div.sphinxsidebar h3 a { + color: #333; +} + +div.sphinxsidebar p { + color: #888; +} + +div.sphinxsidebar p.searchtip { + line-height: 1.4em; +} + +div.sphinxsidebar ul { + color: #000; + margin: 10px 0 20px; + padding: 0; +} + +div.sphinxsidebar a { + color: #444; +} + +div.sphinxsidebar a:hover { + color: #d00; +} + +div.sphinxsidebar input { + border: 1px solid #ccc; + font-family: sans-serif; + font-size: 1em; +} + +/* -- body styles --------------------------------------------------------- */ + +a { + color: #900; + text-decoration: none; +} + +a:visited { + color: #700; +} + +a:hover { + color: #d00; + text-decoration: underline; +} + +hr { + border: 1px solid #b1b4b6; +} + +div.body h1, +div.body h2, +div.body h3, +div.body h4, +div.body h5, +div.body h6 { font-weight: normal; } + +div.body h1, +div.body h2, +div.body h3, +div.body h4 { color: #212224; } +div.body h5 { color: #000; } +div.body h6 { color: #777; } + +div.body h1 { margin: 0 0 10px 0; } +div.body h2, +div.body h3 { margin: 30px 0px 10px 0px; } +div.body h4, +div.body h5, +div.body h6 { margin: 20px 0px 10px 0px; } + +div.body h1 { padding: 0 0 10px 0; } +div.body h2, +div.body h3 { padding: 10px 0 10px 0; } +div.body h4 { padding: 10px 0 10px 0; } +div.body h5, +div.body h6 { padding: 10px 0 0 0; } + +div.body h1, +div.body h2, +div.body h3 { border-bottom: 1px solid #ddd; } +div.body h4 { border-bottom: 1px solid #e5e5e5; } + +div.body h1 { font-size: 230%; } +div.body h2 { font-size: 180%; } +div.body h3 { font-size: 130%; } +div.body h4 { font-size: 110%; } +div.body h5 { font-size: 105%; } +div.body h6 { font-size: 100%; } + +a.headerlink { + color: #c60f0f; + font-size: 0.8em; + padding: 0 4px 0 4px; + text-decoration: none; +} + +a.headerlink:hover { + background-color: #c60f0f; + color: #fff; +} + +div.body ul { + list-style: disc; + margin: 1em 0; + padding-left: 1.3em; +} + +div.body ul ul, div.body ol ul { + margin: .2em 0; + padding-left: 1.2em; +} + +div.body ul li { + padding: 2px 0; +} + +div.body ul.search li { + padding: 5px 0 5px 20px; +} + +div.body ol { + counter-reset: li; + margin-left: 0; + padding-left: 0; +} + +div.body ol ol { + margin: .2em 0; +} + +div.body ol > li { + list-style: none; + margin: 0 0 0 1.9em; + padding: 2px 1px; + position: relative; +} + +div.body ol > li:before { + content: counter(li) "."; + counter-increment: li; + top: -2px; + left: -1.9em; + width: 1.9em; + padding: 4px 0; + position: absolute; + text-align: left; +} + +div.body p, +div.body dd, +div.body li { + line-height: 1.4em; +} + +div.sidebar, div.topic { + background-color: #f6f7f9; + border: 1px solid #cdcdd1; + padding: 0; + margin: 0 0 1em 1.5em; +} + +div.topic { + margin: 0; +} + +div.sidebar p.sidebar-title, div.topic p.topic-title { + background-color: #cdcdd1; + padding: 8px; +} + +div.sidebar p, div.topic p { + font-size: 0.9em; + padding: 10px 20px 0px 20px; +} + +div.admonition { + background-color: #f6f7f9; + border: 1px solid #cdcdd1; + padding: 0px 12px 12px 12px; + font-size: 0.9em; +} + +p.admonition-title { + display: block; + background-color: #cdcdd1; + margin: -12px -12px 12px -12px; + padding: 6px 12px; +} + +div.admonition p.admonition-title + p { + display: inline; +} + +div.attention, div.caution, div.seealso { + background-color: #fef9e9; + border: 1px solid #fbe091; +} + +div.attention p.admonition-title, +div.caution p.admonition-title, +div.seealso p.admonition-title { + background-color: #fbe091; +} + +div.danger, div.error, div.warning { + border: 1px solid #FEA1A2; + background-color: #FEE4E4; +} + +div.danger p.admonition-title, +div.error p.admonition-title, +div.warning p.admonition-title { + background-color: #FEA1A2; +} + +div.note, div.important, div.hint, div.tip { + border: 1px solid #daebea; + background-color: #F7FAFA; +} + +div.note p.admonition-title, +div.important p.admonition-title, +div.hint p.admonition-title, +div.tip p.admonition-title { + background-color: #daebea; +} + +div.highlight { + background-color: #fff; +} + +pre { + background-color: #f6f7f9; + border: 1px solid #9aa2ab; + color: #222; + font-size: 0.75em; + line-height: 1.5em; + margin: 1.5em 0 1.5em 0; + padding: 10px; +} + +pre, tt, code { + font-family: 'Consolas', 'Menlo', 'Deja Vu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +tt, code, code.xref { + padding: 2px; + background-color: #fff; + border: 1px solid #daebea; + font-size: 0.75em; +} + +tt.descname, code.descname { + font-size: 0.95em; +} + +tt.xref, a tt, code.xref, a code { + font-weight: normal; +} + +dl.class, dl.function { + margin-bottom: 50px; +} + +dl.function > dt, +dl.attribute > dt, +dl.classmethod > dt, +dl.method > dt, +dl.class > dt, +dl.exception > dt { + background-color: #f5f5f5; + margin: 25px 0 10px 0; + padding: 1px 10px; +} + +dl.function code.descname, +dl.attribute code.descname, +dl.classmethod code.descname, +dl.method code.descname, +dl.class code.descname, +dl.exception code.descname { + border: none; +} + +table.docutils { + width: 100%; + font-size: 0.9em; +} + +table.docutils.footnote { + width: auto; +} + +table.docutils thead, +table.docutils tfoot { + background: #f5f5f5; +} + +table.docutils thead tr th { + color: #000; + font-weight: normal; + padding: 7px 5px; + vertical-align: middle; +} + +table.docutils tbody tr th, +table.docutils tbody tr td { + border-bottom: 0; + border-top: solid 1px #ddd; + padding: 7px 5px; + vertical-align: top; +} +table.docutils tbody tr:last-child th, +table.docutils tbody tr:last-child td { + border-bottom: solid 1px #ddd; +} + +table.docutils thead tr td p, +table.docutils tfoot tr td p, +table.docutils tbody tr td p, +table.docutils thead tr td ul, +table.docutils tfoot tr td ul, +table.docutils tbody tr td ul, +table.docutils thead tr td ol, +table.docutils tfoot tr td ol, +table.docutils tbody tr td ol { + margin: 0 0 .5em; 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+ +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlinks", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/docs/_build/html/_static/small_groundwork.css b/docs/_build/html/_static/small_groundwork.css new file mode 100644 index 00000000..298de27e --- /dev/null +++ b/docs/_build/html/_static/small_groundwork.css @@ -0,0 +1,102 @@ +/* + * small_flask.css_t + * ~~~~~~~~~~~~~~~~~ + * + * :copyright: Copyright 2010 by Armin Ronacher. + * :license: Flask Design License, see LICENSE for details. + */ + +body { + margin: 0; + padding: 0 10px 0 10px; +} + +.github_fork { + display: none; +} + +div.document { + margin: 10px; +} + +div.documentwrapper { + float: none; +} + +div.sphinxsidebar { + display: block; + float: none; + width: 102.5%; + margin: 50px -30px -20px -30px; + padding: 10px 20px; + background: #333; + color: white; +} + +div.sphinxsidebar h3, div.sphinxsidebar h4, div.sphinxsidebar p, +div.sphinxsidebar h3 a { + color: white; +} + +div.sphinxsidebar a { + color: #aaa; +} + +div.sphinxsidebar p.logo { + display: none; +} + +div.document { + width: 100%; + margin: 0; +} + +div.related { + display: block; + margin: 0; + padding: 10px 0 20px 0; +} + +div.related ul, +div.related ul li { + margin: 0; + padding: 0; +} + +div.footer { + display: none; +} + +div.bodywrapper { + margin: 0; +} + +div.body { + min-height: 0; + padding: 10px; +} + +.rtd_doc_footer { + display: none; +} + +.document { + width: auto; +} + +.footer { + width: auto; +} + +.footer { + width: auto; +} + +pre { + margin: 15px -10px !important; +} + +div.admonition { + margin: 20px -10px !important; +} + diff --git a/docs/_build/html/_static/sphinx_highlight.js b/docs/_build/html/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/docs/_build/html/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

    ' + + '' + + _("Hide Search Matches") + + "

    " + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html new file mode 100644 index 00000000..d800722b --- /dev/null +++ b/docs/_build/html/genindex.html @@ -0,0 +1,3106 @@ + + + + + + + Index — ldp documentation + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + + +

    Index

    + +
    + A + | B + | C + | D + | E + | F + | G + | H + | I + | J + | L + | M + | N + | O + | P + | Q + | R + | S + | T + | U + | V + | W + | X + | Z + +
    +

    A

    + + + +
    + +

    B

    + + + +
    + +

    C

    + + + +
    + +

    D

    + + + +
    + +

    E

    + + + +
    + +

    F

    + + + +
    + +

    G

    + + + +
    + +

    H

    + + + +
    + +

    I

    + + + +
    + +

    J

    + + + +
    + +

    L

    + + + +
      +
    • + ldp + +
      • +
    • + ldp.agent + +
      • +
    • + ldp.agent.agent + +
      • +
    • + ldp.agent.agent_client + +
      • +
    • + ldp.agent.interactive_agent + +
      • +
    • + ldp.agent.memory_agent + +
      • +
    • + ldp.agent.react_agent + +
      • +
    • + ldp.agent.simple_agent + +
      • +
    • + ldp.agent.tree_of_thoughts_agent + +
      • +
    • + ldp.alg + +
      • +
    • + ldp.alg.algorithms + +
      • +
    • + ldp.alg.beam_search + +
      • +
    • + ldp.alg.callbacks + +
      • +
    • + ldp.alg.datasets + +
      • +
    • + ldp.alg.optimizer + +
      • +
    • + ldp.alg.optimizer.ape + +
      • +
    • + ldp.alg.optimizer.memory + +
      • +
    • + ldp.alg.optimizer.opt + +
      • +
    • + ldp.alg.optimizer.replay_buffers + +
      • +
    • + ldp.alg.rollout + +
      • +
    • + ldp.alg.runners + +
      • +
    • + ldp.alg.tree_search + +
      • +
    • + ldp.data_structures + +
      • +
    • + ldp.graph + +
      • +
    • + ldp.graph.async_torch + +
      • +
    • + ldp.graph.common_ops + +
      • +
    • + ldp.graph.gradient_estimators + +
      • +
    • + ldp.graph.loss_ops + +
      • +
    • + ldp.graph.memory + +
      • +
    + +

    M

    + + + +
    + +

    N

    + + + +
    + +

    O

    + + + +
    + +

    P

    + + + +
    + +

    Q

    + + + +
    + +

    R

    + + + +
    + +

    S

    + + + +
    + +

    T

    + + + +
    + +

    U

    + + + +
    + +

    V

    + + + +
    + +

    W

    + + + +
    + +

    X

    + + +
    + +

    Z

    + + +
    + + + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html new file mode 100644 index 00000000..d21f6c0a --- /dev/null +++ b/docs/_build/html/index.html @@ -0,0 +1,158 @@ + + + + + + + + Welcome to ldp’s documentation! — ldp documentation + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    Welcome to ldp’s documentation!

    +
    +

    Contents:

    + +
    +
    +
    +

    Indices and tables

    + +
    + + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/ldp.agent.html b/docs/_build/html/ldp.agent.html new file mode 100644 index 00000000..0b2db7d8 --- /dev/null +++ b/docs/_build/html/ldp.agent.html @@ -0,0 +1,1378 @@ + + + + + + + + ldp.agent package — ldp documentation + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    ldp.agent package

    +
    +

    Submodules

    +
    +
    +

    ldp.agent.agent module

    +
    +
    +class ldp.agent.agent.Agent[source]
    +

    Bases: ABC, Generic[TAgentState]

    +
    +
    +classmethod from_name(name: str, **kwargs) → Agent[source]
    +
    + +
    +
    +abstract async get_asv(agent_state: TAgentState, obs: list[Message]) → tuple[OpResult[ToolRequestMessage], TAgentState, float][source]
    +

    Get new action, state, and value given state and observation messages.

    +

    NOTE: the method’s name has action listed before state to help you realize it’s +a new state.

    +
    +
    Parameters:
    +
      +

    • agent_state – Optional current agent state, pass None if irrelevant. +This can be something like agent memory.

      • +

    • obs – Most recent list of observation messages from the environment’s steps. +If more observations than the most recent list are necessary, track them +in the agent state.

      • +
    +
    +
    Returns:
    +

    +
    Three-tuple of new action, new agent state, and estimated value. The

    agent_state is returned as a copy so that you can safely mutate it +without affecting the original. The estimated value is the agent’s +estimate of the future rewards given the input state and observations, +and is used for RL training. If estimated value doesn’t matter, just +return 0. The value could also come from a Q-value evaluated at the +action chosen by the agent.

    +
    +
    +

    +
    +
    +
    + +
    +
    +abstract async init_state(tools: list[Tool]) → TAgentState[source]
    +

    Initializes the first agent state with the provided tools.

    +
    + +
    +
    +named_ops() → Iterable[tuple[str, Op]][source]
    +

    Analogous to torch.nn.Module.named_parameters().

    +
    + +
    + +
    +
    +class ldp.agent.agent.AgentConfig(*, agent_type: str, agent_kwargs: dict[str, JsonValue] = None)[source]
    +

    Bases: BaseModel

    +

    Configuration for specifying the type of agent i.e. the subclass of Agent above.

    +
    +
    +agent_kwargs: dict[str, JsonValue]
    +
    + +
    +
    +agent_type: str
    +
    + +
    +
    +construct_agent() → Agent[source]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'agent_kwargs': FieldInfo(annotation=dict[str, JsonValue], required=False, default_factory=dict, description="Keyword arguments to pass to the agent's constructor."), 'agent_type': FieldInfo(annotation=str, required=True, description='The type of agent to be used. This should be a subclass of Agent above.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    + +
    +
    +

    ldp.agent.agent_client module

    +
    +
    +class ldp.agent.agent_client.HTTPAgentClient(*args, **kwargs)[source]
    +

    Bases: Agent[TSerializableAgentState]

    +

    Interact with an Agent running in a server via POST requests.

    +
    +
    +async get_asv(agent_state: TSerializableAgentState, obs: list[Message]) → tuple[OpResult[ToolRequestMessage], TSerializableAgentState, float][source]
    +

    Get new action, state, and value given state and observation messages.

    +

    NOTE: the method’s name has action listed before state to help you realize it’s +a new state.

    +
    +
    Parameters:
    +
      +

    • agent_state – Optional current agent state, pass None if irrelevant. +This can be something like agent memory.

      • +

    • obs – Most recent list of observation messages from the environment’s steps. +If more observations than the most recent list are necessary, track them +in the agent state.

      • +
    +
    +
    Returns:
    +

    +
    Three-tuple of new action, new agent state, and estimated value. The

    agent_state is returned as a copy so that you can safely mutate it +without affecting the original. The estimated value is the agent’s +estimate of the future rewards given the input state and observations, +and is used for RL training. If estimated value doesn’t matter, just +return 0. The value could also come from a Q-value evaluated at the +action chosen by the agent.

    +
    +
    +

    +
    +
    +
    + +
    +
    +async init_state(tools: list[Tool]) → TSerializableAgentState[source]
    +

    Initializes the first agent state with the provided tools.

    +
    + +
    + +
    +
    +ldp.agent.agent_client.make_simple_agent_server(agent: Agent[SimpleAgentState], render_docs: bool = False) → FastAPI[source]
    +

    Make a FastAPI app designed to work with the above HTTPAgentClient.

    +

    Here’s how this works: +1. There is an entity orchestrating an Agent’s interactions with an Environment.

    +
    +

    A simple example of this is an integration test that sequentially calls +Agent.get_asv and Environment.step.

    +
    +
      +

    1. That entity is given the above HTTPAgentClient. Any Agent.init_state or +Agent.get_asv calls the orchestration entity makes are actually +POST requests under the hood. The agent’s “brains” aren’t local.

      2. +

    2. Remotely, this server code is running, and is where the actual Agent logic lives. +An example of this is a remote server containing GPU(s).

      3. +
    +
    + +
    +
    +

    ldp.agent.interactive_agent module

    +
    +
    +class ldp.agent.interactive_agent.InteractiveAgent(*args, **kwargs)[source]
    +

    Bases: Agent[SimpleAgentState]

    +

    An “agent” that provides an interface for human users to interact with environments.

    +
    +
    +async get_asv(agent_state: SimpleAgentState, obs: list[Message]) → tuple[OpResult[ToolRequestMessage], SimpleAgentState, float][source]
    +

    Get new action, state, and value given state and observation messages.

    +

    NOTE: the method’s name has action listed before state to help you realize it’s +a new state.

    +
    +
    Parameters:
    +
      +

    • agent_state – Optional current agent state, pass None if irrelevant. +This can be something like agent memory.

      • +

    • obs – Most recent list of observation messages from the environment’s steps. +If more observations than the most recent list are necessary, track them +in the agent state.

      • +
    +
    +
    Returns:
    +

    +
    Three-tuple of new action, new agent state, and estimated value. The

    agent_state is returned as a copy so that you can safely mutate it +without affecting the original. The estimated value is the agent’s +estimate of the future rewards given the input state and observations, +and is used for RL training. If estimated value doesn’t matter, just +return 0. The value could also come from a Q-value evaluated at the +action chosen by the agent.

    +
    +
    +

    +
    +
    +
    + +
    +
    +async init_state(tools: list[Tool]) → SimpleAgentState[source]
    +

    Initializes the first agent state with the provided tools.

    +
    + +
    + +
    +
    +

    ldp.agent.memory_agent module

    +

    This module defines the MemoryAgent class, which extends a base agent model with memory.

    +

    capabilities. The MemoryAgent can pick and invoke tools based on the stored and retrieved +memories, formatted using specified prompts. A memory is typically a set of previous trajectories

    +
    +
    +class ldp.agent.memory_agent.MemoryAgent(*args, llm_model: dict[str, ~typing.Any] = {'model': 'gpt-4o-2024-08-06', 'temperature': 0.1}, sys_prompt: str | None = None, prompt: str = '<episode-memories>\n<description>\nThese are relevant memories from previous attempts at similar tasks, along with the action taken and the discounted cumulative reward from that action. A negative reward is failure, a positive reward is success.\n</description>{memories}</episode-memories>\n\nConsidering the memories, choose the next action.', query_factory: ~collections.abc.Callable[[~collections.abc.Iterable[~aviary.message.Message]], ~collections.abc.Awaitable[str]] = <function _default_query_factory>, memory_prompt: str = '<memory><obs>{input}</obs><action>{output}</action><reward>{value}</reward></memory>', num_memories: int = 3)[source]
    +

    Bases: SimpleAgent

    +

    Simple agent that can pick and invoke tools with memory.

    +

    NOTE: the MemoryAgent does not maintain an explicit value estimate, +it simply supplies previous trajectories via the prompt. +As such, the value estimate vhat will always be zero.

    +
    +
    +async default_query_factory() → str
    +
    + +
    +
    +async get_asv(agent_state: SimpleAgentState, obs: list[Message]) → tuple[OpResult[ToolRequestMessage], SimpleAgentState, float][source]
    +

    Get new action, state, and value given state and observation messages.

    +

    NOTE: the method’s name has action listed before state to help you realize it’s +a new state.

    +
    +
    Parameters:
    +
      +

    • agent_state – Optional current agent state, pass None if irrelevant. +This can be something like agent memory.

      • +

    • obs – Most recent list of observation messages from the environment’s steps. +If more observations than the most recent list are necessary, track them +in the agent state.

      • +
    +
    +
    Returns:
    +

    +
    Three-tuple of new action, new agent state, and estimated value. The

    agent_state is returned as a copy so that you can safely mutate it +without affecting the original. The estimated value is the agent’s +estimate of the future rewards given the input state and observations, +and is used for RL training. If estimated value doesn’t matter, just +return 0. The value could also come from a Q-value evaluated at the +action chosen by the agent.

    +
    +
    +

    +
    +
    +
    + +
    +
    +memory_prompt: str
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'frozen': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'llm_model': FieldInfo(annotation=dict[str, Any], required=False, default={'model': 'gpt-4o-2024-08-06', 'temperature': 0.1}, description='Starting configuration for the LLM model. Trainable.'), 'memory_prompt': FieldInfo(annotation=str, required=False, default='<memory><obs>{input}</obs><action>{output}</action><reward>{value}</reward></memory>', description='Prompt for formatting an individual memory. Use XML instead of JSON to avoid potential escaping issues.'), 'num_memories': FieldInfo(annotation=int, required=False, default=3, description='Number of memories to retrieve from MemoryOp'), 'prompt': FieldInfo(annotation=str, required=False, default='<episode-memories>\n<description>\nThese are relevant memories from previous attempts at similar tasks, along with the action taken and the discounted cumulative reward from that action. A negative reward is failure, a positive reward is success.\n</description>{memories}</episode-memories>\n\nConsidering the memories, choose the next action.', description='Prompt that includes the memories.'), 'query_factory': FieldInfo(annotation=Callable[list, Awaitable[str]], required=False, default=<function _default_query_factory>, description="Async function to generate a Memory query string from messages. It's async so this can involve an LLM completion if desired.", exclude=True), 'sys_prompt': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Opt-in system prompt. If one is passed, the system prompt is not set up to be trainable, because this class is meant to be quite simple as far as possible hyperparameters.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +num_memories: int
    +
    + +
    +
    +prompt: str
    +
    + +
    +
    +query_factory: Callable[[Iterable[Message]], Awaitable[str]]
    +
    + +
    + +
    +
    +

    ldp.agent.react_agent module

    +
    +
    +class ldp.agent.react_agent.ReActAgent(*args, llm_model: dict[str, Any] = {'logprobs': True, 'model': 'gpt-4o-2024-08-06', 'temperature': 0.1, 'top_logprobs': 1}, sys_prompt: str = 'Answer the following questions as best you can. You have access to the following tools:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: comma separated list of inputs to action as python tuple\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\n\nExample:\n\nThought: I need to use the get_weather tool\nAction: get_weather\nAction Input: "New York", 7\nObservation: The 7 day forecast for New York is [...]', tool_description_method: ToolDescriptionMethods = ToolDescriptionMethods.STR)[source]
    +

    Bases: BaseModel, Agent[SimpleAgentState]

    +

    An Act or ReAct Agent built to work with chat models.

    +

    Paper: https://arxiv.org/abs/2210.03629

    +

    The ReAct style is like so, and note Act style has no ‘Thought: ‘ entries: +System:

    +
    +

    Answer the following questions as best you can. You have access to the following tools:

    +
    +

    {tools}

    +

    Use the following format:

    +

    Thought: you should always think about what to do +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +… (this Thought/Action/Action Input/Observation can repeat N times)

    +
    +
    +
    +
    User:

    {questions}

    +
    +
    Assistant:

    Thought: +Action: +Action Input:

    +
    +
    User:

    Observation:

    +
    +
    Assistant:

    Thought: +Action: +Action Input:

    +
    +
    +

    …

    +

    One notable design decision is that ReAct’s state does not necessarily +track ToolRequestMessage. Recall that aviary is in a partially observable +domain, meaning we don’t need to have perfect symmetry with Environments. +Instead, ReActAgent’s state stores a ReAct-style message history, where the +messages are plain Message (and not a ToolRequestMessage).

    +
    +
    +static after_retry_failure_log(retry_state: RetryCallState)[source]
    +
    + +
    +
    +async get_asv(agent_state: SimpleAgentState, obs: list[Message]) → tuple[OpResult[ToolRequestMessage], SimpleAgentState, float][source]
    +

    Get new action, state, and value given state and observation messages.

    +

    NOTE: the method’s name has action listed before state to help you realize it’s +a new state.

    +
    +
    Parameters:
    +
      +

    • agent_state – Optional current agent state, pass None if irrelevant. +This can be something like agent memory.

      • +

    • obs – Most recent list of observation messages from the environment’s steps. +If more observations than the most recent list are necessary, track them +in the agent state.

      • +
    +
    +
    Returns:
    +

    +
    Three-tuple of new action, new agent state, and estimated value. The

    agent_state is returned as a copy so that you can safely mutate it +without affecting the original. The estimated value is the agent’s +estimate of the future rewards given the input state and observations, +and is used for RL training. If estimated value doesn’t matter, just +return 0. The value could also come from a Q-value evaluated at the +action chosen by the agent.

    +
    +
    +

    +
    +
    +
    + +
    +
    +async init_state(tools: list[Tool]) → SimpleAgentState[source]
    +

    Initializes the first agent state with the provided tools.

    +
    + +
    +
    +llm_model: dict[str, Any]
    +
    + +
    +
    +classmethod make_act_agent(**kwargs) → Self[source]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'frozen': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'llm_model': FieldInfo(annotation=dict[str, Any], required=False, default={'model': 'gpt-4o-2024-08-06', 'temperature': 0.1, 'logprobs': True, 'top_logprobs': 1}, description='Starting configuration for the LLM model.'), 'sys_prompt': FieldInfo(annotation=str, required=False, default='Answer the following questions as best you can. You have access to the following tools:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: comma separated list of inputs to action as python tuple\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\n\nExample:\n\nThought: I need to use the get_weather tool\nAction: get_weather\nAction Input: "New York", 7\nObservation: The 7 day forecast for New York is [...]', description='Learnable system prompt template, defaults to ReAct.'), 'tool_description_method': FieldInfo(annotation=ToolDescriptionMethods, required=False, default=<ToolDescriptionMethods.STR: 'describe_str'>, description="Method used to describe the tools, defaults to 'str' description.")}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +sys_prompt: str
    +
    + +
    +
    +tool_description_method: ToolDescriptionMethods
    +
    + +
    + +
    +
    +

    ldp.agent.simple_agent module

    +
    +
    +class ldp.agent.simple_agent.SimpleAgent(*args, llm_model: dict[str, Any] = {'model': 'gpt-4o-2024-08-06', 'temperature': 0.1}, sys_prompt: str | None = None)[source]
    +

    Bases: BaseModel, Agent[SimpleAgentState]

    +

    Simple agent that can pick and invoke tools with a language model.

    +

    It does not have a system prompt because it’s meant to be lightweight.

    +
    +
    +async get_asv(agent_state: SimpleAgentState, obs: list[Message]) → tuple[OpResult[ToolRequestMessage], SimpleAgentState, float][source]
    +

    Get new action, state, and value given state and observation messages.

    +

    NOTE: the method’s name has action listed before state to help you realize it’s +a new state.

    +
    +
    Parameters:
    +
      +

    • agent_state – Optional current agent state, pass None if irrelevant. +This can be something like agent memory.

      • +

    • obs – Most recent list of observation messages from the environment’s steps. +If more observations than the most recent list are necessary, track them +in the agent state.

      • +
    +
    +
    Returns:
    +

    +
    Three-tuple of new action, new agent state, and estimated value. The

    agent_state is returned as a copy so that you can safely mutate it +without affecting the original. The estimated value is the agent’s +estimate of the future rewards given the input state and observations, +and is used for RL training. If estimated value doesn’t matter, just +return 0. The value could also come from a Q-value evaluated at the +action chosen by the agent.

    +
    +
    +

    +
    +
    +
    + +
    +
    +async init_state(tools: list[Tool]) → SimpleAgentState[source]
    +

    Initializes the first agent state with the provided tools.

    +
    + +
    +
    +llm_model: dict[str, Any]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'frozen': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'llm_model': FieldInfo(annotation=dict[str, Any], required=False, default={'model': 'gpt-4o-2024-08-06', 'temperature': 0.1}, description='Starting configuration for the LLM model. Trainable.'), 'sys_prompt': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Opt-in system prompt. If one is passed, the system prompt is not set up to be trainable, because this class is meant to be quite simple as far as possible hyperparameters.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +sys_prompt: str | None
    +
    + +
    + +
    +
    +class ldp.agent.simple_agent.SimpleAgentState(*, tools: list[Tool] = None, messages: list[ToolRequestMessage | ToolResponseMessage | Message] = None)[source]
    +

    Bases: BaseModel

    +

    Simple bucket for an Agent to access tools and store messages.

    +
    +
    +get_next_state(obs: list[Message] | None = None, tools: list[Tool] | None = None, **kwargs) → Self[source]
    +

    Get the next agent state without mutating the optional prior state.

    +

    Do not mutate self here, just read from it.

    +
    +
    Parameters:
    +
      +

    • obs – Optional observation messages to use in creating the next state.

      • +

    • tools – Optional list of tools available to the agent. If unspecified, these +should be pulled from the prior_state.

      • +

    • kwargs – Additional keyword arguments to pass to this class’s constructor.

      • +
    +
    +
    Returns:
    +

    The next agent state (which is not an in-place change to self).

    +
    +
    +
    + +
    +
    +messages: list[ToolRequestMessage | ToolResponseMessage | Message]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'messages': FieldInfo(annotation=list[Union[ToolRequestMessage, ToolResponseMessage, Message]], required=False, default_factory=list), 'tools': FieldInfo(annotation=list[Tool], required=False, default_factory=list)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +tools: list[Tool]
    +
    + +
    + +
    +
    +

    ldp.agent.tree_of_thoughts_agent module

    +

    Module for the Tree of Thoughts agent.

    +

    This module defines the Tree of Thoughts agent which uses a language model to generate and evaluate possible +steps in a puzzle or problem-solving environment. The agent employs a tree search mechanism to explore different +solutions and selects the most promising ones based on evaluations.

    +

    This module is based on the following paper: https://openreview.net/forum?id=5Xc1ecxO1h

    +

    Note: TreeofThoughtsAgent is currently tested as a baseline agent for Game of 24. It does not yet support tool calls +that operate on the intermediate reasoning steps. This would probably entail a redefinition of the POMDP to +undertake intermediate reasoning steps as environment steps.

    +
    +
    +class ldp.agent.tree_of_thoughts_agent.TreeofThoughtsAgent(*args, llm_model: dict[str, ~typing.Any] = {'model': 'gpt-4o-2024-08-06', 'temperature': 0.1}, value_prompt_func: ~collections.abc.Callable[[str, str], str] = <function TreeofThoughtsAgent.<lambda>>, proposal_prompt_func: ~collections.abc.Callable[[str, str], str] = <function TreeofThoughtsAgent.<lambda>>)[source]
    +

    Bases: BaseModel, Agent[SimpleAgentState]

    +

    Tree of Thoughts Agent.

    +

    This agent uses a tree search mechanism combined with an LLM to generate and evaluate +possible steps in a problem-solving environment. It is designed to explore different solutions +and select the most promising ones based on a heuristic evaluation function.

    +
    +
    +async get_asv(agent_state: SimpleAgentState, obs: list[Message], eval_function: Callable[[str, list[str]], float], n_steps: int = 0, n_select_samples: int = 0) → tuple[OpResult[ToolRequestMessage], SimpleAgentState, float][source]
    +

    Generate and evaluate possible steps in the problem-solving process.

    +
    +
    Parameters:
    +
      +

    • agent_state – The current state of the agent.

      • +

    • obs – The observations provided to the agent.

      • +

    • eval_function – Function to evaluate the generated paths in the tree.

      • +

    • n_steps – Number of steps to generate. Defaults to 0. Dictated by the environment.

      • +

    • n_select_samples – Number of tree nodes to select to explore in each step. Defaults to 0.

      • +
    +
    +
    Returns:
    +

    The result of the operation, the new state of the agent, and the number representing the value (0).

    +
    +
    +
    + +
    +
    +async init_state(tools: list[Tool]) → SimpleAgentState[source]
    +

    Initializes the first agent state with the provided tools.

    +
    + +
    +
    +llm_model: dict[str, Any]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'frozen': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'llm_model': FieldInfo(annotation=dict[str, Any], required=False, default={'model': 'gpt-4o-2024-08-06', 'temperature': 0.1}, description='Starting configuration for the LLM model.'), 'proposal_prompt_func': FieldInfo(annotation=Callable[list, str], required=False, default=<function TreeofThoughtsAgent.<lambda>>, description='Function to format proposal prompt template.'), 'value_prompt_func': FieldInfo(annotation=Callable[list, str], required=False, default=<function TreeofThoughtsAgent.<lambda>>, description='Function to format value prompt template.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +proposal_prompt_func: Callable[[str, str], str]
    +
    + +
    +
    +value_prompt_func: Callable[[str, str], str]
    +
    + +
    + +
    +
    +

    Module contents

    +
    +
    +class ldp.agent.Agent[source]
    +

    Bases: ABC, Generic[TAgentState]

    +
    +
    +classmethod from_name(name: str, **kwargs) → Agent[source]
    +
    + +
    +
    +abstract async get_asv(agent_state: TAgentState, obs: list[Message]) → tuple[OpResult[ToolRequestMessage], TAgentState, float][source]
    +

    Get new action, state, and value given state and observation messages.

    +

    NOTE: the method’s name has action listed before state to help you realize it’s +a new state.

    +
    +
    Parameters:
    +
      +

    • agent_state – Optional current agent state, pass None if irrelevant. +This can be something like agent memory.

      • +

    • obs – Most recent list of observation messages from the environment’s steps. +If more observations than the most recent list are necessary, track them +in the agent state.

      • +
    +
    +
    Returns:
    +

    +
    Three-tuple of new action, new agent state, and estimated value. The

    agent_state is returned as a copy so that you can safely mutate it +without affecting the original. The estimated value is the agent’s +estimate of the future rewards given the input state and observations, +and is used for RL training. If estimated value doesn’t matter, just +return 0. The value could also come from a Q-value evaluated at the +action chosen by the agent.

    +
    +
    +

    +
    +
    +
    + +
    +
    +abstract async init_state(tools: list[Tool]) → TAgentState[source]
    +

    Initializes the first agent state with the provided tools.

    +
    + +
    +
    +named_ops() → Iterable[tuple[str, Op]][source]
    +

    Analogous to torch.nn.Module.named_parameters().

    +
    + +
    + +
    +
    +class ldp.agent.AgentConfig(*, agent_type: str, agent_kwargs: dict[str, JsonValue] = None)[source]
    +

    Bases: BaseModel

    +

    Configuration for specifying the type of agent i.e. the subclass of Agent above.

    +
    +
    +agent_kwargs: dict[str, JsonValue]
    +
    + +
    +
    +agent_type: str
    +
    + +
    +
    +construct_agent() → Agent[source]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'agent_kwargs': FieldInfo(annotation=dict[str, JsonValue], required=False, default_factory=dict, description="Keyword arguments to pass to the agent's constructor."), 'agent_type': FieldInfo(annotation=str, required=True, description='The type of agent to be used. This should be a subclass of Agent above.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    + +
    +
    +class ldp.agent.DefaultLLMModelNames(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]
    +

    Bases: StrEnum

    +

    Defaults for LLM models, pin exact versions for performance stability.

    +
    +
    +OPENAI = 'gpt-4o-2024-08-06'
    +
    + +
    + +
    +
    +class ldp.agent.HTTPAgentClient(*args, **kwargs)[source]
    +

    Bases: Agent[TSerializableAgentState]

    +

    Interact with an Agent running in a server via POST requests.

    +
    +
    +async get_asv(agent_state: TSerializableAgentState, obs: list[Message]) → tuple[OpResult[ToolRequestMessage], TSerializableAgentState, float][source]
    +

    Get new action, state, and value given state and observation messages.

    +

    NOTE: the method’s name has action listed before state to help you realize it’s +a new state.

    +
    +
    Parameters:
    +
      +

    • agent_state – Optional current agent state, pass None if irrelevant. +This can be something like agent memory.

      • +

    • obs – Most recent list of observation messages from the environment’s steps. +If more observations than the most recent list are necessary, track them +in the agent state.

      • +
    +
    +
    Returns:
    +

    +
    Three-tuple of new action, new agent state, and estimated value. The

    agent_state is returned as a copy so that you can safely mutate it +without affecting the original. The estimated value is the agent’s +estimate of the future rewards given the input state and observations, +and is used for RL training. If estimated value doesn’t matter, just +return 0. The value could also come from a Q-value evaluated at the +action chosen by the agent.

    +
    +
    +

    +
    +
    +
    + +
    +
    +async init_state(tools: list[Tool]) → TSerializableAgentState[source]
    +

    Initializes the first agent state with the provided tools.

    +
    + +
    + +
    +
    +class ldp.agent.MemoryAgent(*args, llm_model: dict[str, ~typing.Any] = {'model': 'gpt-4o-2024-08-06', 'temperature': 0.1}, sys_prompt: str | None = None, prompt: str = '<episode-memories>\n<description>\nThese are relevant memories from previous attempts at similar tasks, along with the action taken and the discounted cumulative reward from that action. A negative reward is failure, a positive reward is success.\n</description>{memories}</episode-memories>\n\nConsidering the memories, choose the next action.', query_factory: ~collections.abc.Callable[[~collections.abc.Iterable[~aviary.message.Message]], ~collections.abc.Awaitable[str]] = <function _default_query_factory>, memory_prompt: str = '<memory><obs>{input}</obs><action>{output}</action><reward>{value}</reward></memory>', num_memories: int = 3)[source]
    +

    Bases: SimpleAgent

    +

    Simple agent that can pick and invoke tools with memory.

    +

    NOTE: the MemoryAgent does not maintain an explicit value estimate, +it simply supplies previous trajectories via the prompt. +As such, the value estimate vhat will always be zero.

    +
    +
    +async default_query_factory() → str
    +
    + +
    +
    +async get_asv(agent_state: SimpleAgentState, obs: list[Message]) → tuple[OpResult[ToolRequestMessage], SimpleAgentState, float][source]
    +

    Get new action, state, and value given state and observation messages.

    +

    NOTE: the method’s name has action listed before state to help you realize it’s +a new state.

    +
    +
    Parameters:
    +
      +

    • agent_state – Optional current agent state, pass None if irrelevant. +This can be something like agent memory.

      • +

    • obs – Most recent list of observation messages from the environment’s steps. +If more observations than the most recent list are necessary, track them +in the agent state.

      • +
    +
    +
    Returns:
    +

    +
    Three-tuple of new action, new agent state, and estimated value. The

    agent_state is returned as a copy so that you can safely mutate it +without affecting the original. The estimated value is the agent’s +estimate of the future rewards given the input state and observations, +and is used for RL training. If estimated value doesn’t matter, just +return 0. The value could also come from a Q-value evaluated at the +action chosen by the agent.

    +
    +
    +

    +
    +
    +
    + +
    +
    +llm_model: dict[str, Any]
    +
    + +
    +
    +memory_prompt: str
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'frozen': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'llm_model': FieldInfo(annotation=dict[str, Any], required=False, default={'model': 'gpt-4o-2024-08-06', 'temperature': 0.1}, description='Starting configuration for the LLM model. Trainable.'), 'memory_prompt': FieldInfo(annotation=str, required=False, default='<memory><obs>{input}</obs><action>{output}</action><reward>{value}</reward></memory>', description='Prompt for formatting an individual memory. Use XML instead of JSON to avoid potential escaping issues.'), 'num_memories': FieldInfo(annotation=int, required=False, default=3, description='Number of memories to retrieve from MemoryOp'), 'prompt': FieldInfo(annotation=str, required=False, default='<episode-memories>\n<description>\nThese are relevant memories from previous attempts at similar tasks, along with the action taken and the discounted cumulative reward from that action. A negative reward is failure, a positive reward is success.\n</description>{memories}</episode-memories>\n\nConsidering the memories, choose the next action.', description='Prompt that includes the memories.'), 'query_factory': FieldInfo(annotation=Callable[list, Awaitable[str]], required=False, default=<function _default_query_factory>, description="Async function to generate a Memory query string from messages. It's async so this can involve an LLM completion if desired.", exclude=True), 'sys_prompt': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Opt-in system prompt. If one is passed, the system prompt is not set up to be trainable, because this class is meant to be quite simple as far as possible hyperparameters.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +num_memories: int
    +
    + +
    +
    +prompt: str
    +
    + +
    +
    +query_factory: Callable[[Iterable[Message]], Awaitable[str]]
    +
    + +
    +
    +sys_prompt: str | None
    +
    + +
    + +
    +
    +class ldp.agent.ReActAgent(*args, llm_model: dict[str, Any] = {'logprobs': True, 'model': 'gpt-4o-2024-08-06', 'temperature': 0.1, 'top_logprobs': 1}, sys_prompt: str = 'Answer the following questions as best you can. You have access to the following tools:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: comma separated list of inputs to action as python tuple\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\n\nExample:\n\nThought: I need to use the get_weather tool\nAction: get_weather\nAction Input: "New York", 7\nObservation: The 7 day forecast for New York is [...]', tool_description_method: ToolDescriptionMethods = ToolDescriptionMethods.STR)[source]
    +

    Bases: BaseModel, Agent[SimpleAgentState]

    +

    An Act or ReAct Agent built to work with chat models.

    +

    Paper: https://arxiv.org/abs/2210.03629

    +

    The ReAct style is like so, and note Act style has no ‘Thought: ‘ entries: +System:

    +
    +

    Answer the following questions as best you can. You have access to the following tools:

    +
    +

    {tools}

    +

    Use the following format:

    +

    Thought: you should always think about what to do +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +… (this Thought/Action/Action Input/Observation can repeat N times)

    +
    +
    +
    +
    User:

    {questions}

    +
    +
    Assistant:

    Thought: +Action: +Action Input:

    +
    +
    User:

    Observation:

    +
    +
    Assistant:

    Thought: +Action: +Action Input:

    +
    +
    +

    …

    +

    One notable design decision is that ReAct’s state does not necessarily +track ToolRequestMessage. Recall that aviary is in a partially observable +domain, meaning we don’t need to have perfect symmetry with Environments. +Instead, ReActAgent’s state stores a ReAct-style message history, where the +messages are plain Message (and not a ToolRequestMessage).

    +
    +
    +static after_retry_failure_log(retry_state: RetryCallState)[source]
    +
    + +
    +
    +async get_asv(agent_state: SimpleAgentState, obs: list[Message]) → tuple[OpResult[ToolRequestMessage], SimpleAgentState, float][source]
    +

    Get new action, state, and value given state and observation messages.

    +

    NOTE: the method’s name has action listed before state to help you realize it’s +a new state.

    +
    +
    Parameters:
    +
      +

    • agent_state – Optional current agent state, pass None if irrelevant. +This can be something like agent memory.

      • +

    • obs – Most recent list of observation messages from the environment’s steps. +If more observations than the most recent list are necessary, track them +in the agent state.

      • +
    +
    +
    Returns:
    +

    +
    Three-tuple of new action, new agent state, and estimated value. The

    agent_state is returned as a copy so that you can safely mutate it +without affecting the original. The estimated value is the agent’s +estimate of the future rewards given the input state and observations, +and is used for RL training. If estimated value doesn’t matter, just +return 0. The value could also come from a Q-value evaluated at the +action chosen by the agent.

    +
    +
    +

    +
    +
    +
    + +
    +
    +async init_state(tools: list[Tool]) → SimpleAgentState[source]
    +

    Initializes the first agent state with the provided tools.

    +
    + +
    +
    +llm_model: dict[str, Any]
    +
    + +
    +
    +classmethod make_act_agent(**kwargs) → Self[source]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'frozen': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'llm_model': FieldInfo(annotation=dict[str, Any], required=False, default={'model': 'gpt-4o-2024-08-06', 'temperature': 0.1, 'logprobs': True, 'top_logprobs': 1}, description='Starting configuration for the LLM model.'), 'sys_prompt': FieldInfo(annotation=str, required=False, default='Answer the following questions as best you can. You have access to the following tools:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: comma separated list of inputs to action as python tuple\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\n\nExample:\n\nThought: I need to use the get_weather tool\nAction: get_weather\nAction Input: "New York", 7\nObservation: The 7 day forecast for New York is [...]', description='Learnable system prompt template, defaults to ReAct.'), 'tool_description_method': FieldInfo(annotation=ToolDescriptionMethods, required=False, default=<ToolDescriptionMethods.STR: 'describe_str'>, description="Method used to describe the tools, defaults to 'str' description.")}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +sys_prompt: str
    +
    + +
    +
    +tool_description_method: ToolDescriptionMethods
    +
    + +
    + +
    +
    +class ldp.agent.SimpleAgent(*args, llm_model: dict[str, Any] = {'model': 'gpt-4o-2024-08-06', 'temperature': 0.1}, sys_prompt: str | None = None)[source]
    +

    Bases: BaseModel, Agent[SimpleAgentState]

    +

    Simple agent that can pick and invoke tools with a language model.

    +

    It does not have a system prompt because it’s meant to be lightweight.

    +
    +
    +async get_asv(agent_state: SimpleAgentState, obs: list[Message]) → tuple[OpResult[ToolRequestMessage], SimpleAgentState, float][source]
    +

    Get new action, state, and value given state and observation messages.

    +

    NOTE: the method’s name has action listed before state to help you realize it’s +a new state.

    +
    +
    Parameters:
    +
      +

    • agent_state – Optional current agent state, pass None if irrelevant. +This can be something like agent memory.

      • +

    • obs – Most recent list of observation messages from the environment’s steps. +If more observations than the most recent list are necessary, track them +in the agent state.

      • +
    +
    +
    Returns:
    +

    +
    Three-tuple of new action, new agent state, and estimated value. The

    agent_state is returned as a copy so that you can safely mutate it +without affecting the original. The estimated value is the agent’s +estimate of the future rewards given the input state and observations, +and is used for RL training. If estimated value doesn’t matter, just +return 0. The value could also come from a Q-value evaluated at the +action chosen by the agent.

    +
    +
    +

    +
    +
    +
    + +
    +
    +async init_state(tools: list[Tool]) → SimpleAgentState[source]
    +

    Initializes the first agent state with the provided tools.

    +
    + +
    +
    +llm_model: dict[str, Any]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'frozen': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'llm_model': FieldInfo(annotation=dict[str, Any], required=False, default={'model': 'gpt-4o-2024-08-06', 'temperature': 0.1}, description='Starting configuration for the LLM model. Trainable.'), 'sys_prompt': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Opt-in system prompt. If one is passed, the system prompt is not set up to be trainable, because this class is meant to be quite simple as far as possible hyperparameters.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +sys_prompt: str | None
    +
    + +
    + +
    +
    +class ldp.agent.SimpleAgentState(*, tools: list[Tool] = None, messages: list[ToolRequestMessage | ToolResponseMessage | Message] = None)[source]
    +

    Bases: BaseModel

    +

    Simple bucket for an Agent to access tools and store messages.

    +
    +
    +get_next_state(obs: list[Message] | None = None, tools: list[Tool] | None = None, **kwargs) → Self[source]
    +

    Get the next agent state without mutating the optional prior state.

    +

    Do not mutate self here, just read from it.

    +
    +
    Parameters:
    +
      +

    • obs – Optional observation messages to use in creating the next state.

      • +

    • tools – Optional list of tools available to the agent. If unspecified, these +should be pulled from the prior_state.

      • +

    • kwargs – Additional keyword arguments to pass to this class’s constructor.

      • +
    +
    +
    Returns:
    +

    The next agent state (which is not an in-place change to self).

    +
    +
    +
    + +
    +
    +messages: list[ToolRequestMessage | ToolResponseMessage | Message]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'messages': FieldInfo(annotation=list[Union[ToolRequestMessage, ToolResponseMessage, Message]], required=False, default_factory=list), 'tools': FieldInfo(annotation=list[Tool], required=False, default_factory=list)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +tools: list[Tool]
    +
    + +
    + +
    +
    +class ldp.agent.TreeofThoughtsAgent(*args, llm_model: dict[str, ~typing.Any] = {'model': 'gpt-4o-2024-08-06', 'temperature': 0.1}, value_prompt_func: ~collections.abc.Callable[[str, str], str] = <function TreeofThoughtsAgent.<lambda>>, proposal_prompt_func: ~collections.abc.Callable[[str, str], str] = <function TreeofThoughtsAgent.<lambda>>)[source]
    +

    Bases: BaseModel, Agent[SimpleAgentState]

    +

    Tree of Thoughts Agent.

    +

    This agent uses a tree search mechanism combined with an LLM to generate and evaluate +possible steps in a problem-solving environment. It is designed to explore different solutions +and select the most promising ones based on a heuristic evaluation function.

    +
    +
    +async get_asv(agent_state: SimpleAgentState, obs: list[Message], eval_function: Callable[[str, list[str]], float], n_steps: int = 0, n_select_samples: int = 0) → tuple[OpResult[ToolRequestMessage], SimpleAgentState, float][source]
    +

    Generate and evaluate possible steps in the problem-solving process.

    +
    +
    Parameters:
    +
      +

    • agent_state – The current state of the agent.

      • +

    • obs – The observations provided to the agent.

      • +

    • eval_function – Function to evaluate the generated paths in the tree.

      • +

    • n_steps – Number of steps to generate. Defaults to 0. Dictated by the environment.

      • +

    • n_select_samples – Number of tree nodes to select to explore in each step. Defaults to 0.

      • +
    +
    +
    Returns:
    +

    The result of the operation, the new state of the agent, and the number representing the value (0).

    +
    +
    +
    + +
    +
    +async init_state(tools: list[Tool]) → SimpleAgentState[source]
    +

    Initializes the first agent state with the provided tools.

    +
    + +
    +
    +llm_model: dict[str, Any]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'frozen': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'llm_model': FieldInfo(annotation=dict[str, Any], required=False, default={'model': 'gpt-4o-2024-08-06', 'temperature': 0.1}, description='Starting configuration for the LLM model.'), 'proposal_prompt_func': FieldInfo(annotation=Callable[list, str], required=False, default=<function TreeofThoughtsAgent.<lambda>>, description='Function to format proposal prompt template.'), 'value_prompt_func': FieldInfo(annotation=Callable[list, str], required=False, default=<function TreeofThoughtsAgent.<lambda>>, description='Function to format value prompt template.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +proposal_prompt_func: Callable[[str, str], str]
    +
    + +
    +
    +value_prompt_func: Callable[[str, str], str]
    +
    + +
    + +
    +
    +ldp.agent.make_simple_agent_server(agent: Agent[SimpleAgentState], render_docs: bool = False) → FastAPI[source]
    +

    Make a FastAPI app designed to work with the above HTTPAgentClient.

    +

    Here’s how this works: +1. There is an entity orchestrating an Agent’s interactions with an Environment.

    +
    +

    A simple example of this is an integration test that sequentially calls +Agent.get_asv and Environment.step.

    +
    +
      +

    1. That entity is given the above HTTPAgentClient. Any Agent.init_state or +Agent.get_asv calls the orchestration entity makes are actually +POST requests under the hood. The agent’s “brains” aren’t local.

      2. +

    2. Remotely, this server code is running, and is where the actual Agent logic lives. +An example of this is a remote server containing GPU(s).

      3. +
    +
    + +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/ldp.alg.html b/docs/_build/html/ldp.alg.html new file mode 100644 index 00000000..e958d537 --- /dev/null +++ b/docs/_build/html/ldp.alg.html @@ -0,0 +1,1627 @@ + + + + + + + + ldp.alg package — ldp documentation + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    ldp.alg package

    +
    +

    Subpackages

    +
    + +
    +
    +
    +

    Submodules

    +
    +
    +

    ldp.alg.algorithms module

    +
    +
    +ldp.alg.algorithms.to_network(op_result: OpResult, max_label_height: int | None = None, max_label_width: int | None = None, G: MultiDiGraph | None = None) → MultiDiGraph[source]
    +

    Populate a NetworkX graph from the input op result’s computation graph.

    +

    How to export Graphviz .dot file: nx.drawing.nx_pydot.write_dot(G, “file.dot”) +How to render with Graphviz: nx.drawing.nx_pydot.to_pydot(G).write_png(“file.png”) +Online Graphviz renderer: https://dreampuf.github.io/GraphvizOnline/

    +
    +
    Parameters:
    +
      +

    • op_result – Starting op result to recurse parent op calls and results.

      • +

    • max_label_height – Optional max label height (lines).

      • +

    • max_label_width – Optional max label width (chars).

      • +

    • G – Optional graph to add nodes/edges to. Allows this to be a recursive function.

      • +
    +
    +
    Returns:
    +

    Populated a NetworkX multi-edge directed graph.

    +
    +
    +
    + +
    + +
    +

    ldp.alg.callbacks module

    +
    +
    +class ldp.alg.callbacks.Callback[source]
    +

    Bases: object

    +

    Base class for callbacks used by RolloutManager/Evaluator/OnlineTrainer.

    +

    Pseudocode to demonstrate how callback methods are invoked (marked as *):

    +
    +
    RolloutManager.sample_trajectories():

    env.reset() +callback.after_env_reset() * +agent.init_state() +callback.after_agent_init_state() * +while not done:

    +
    +

    callback.before_transition() * +agent.get_asv() +callback.after_agent_get_asv() * +env.step() +callback.after_env_step() * +callback.after_transition() *

    +
    +
    +
    Evaluator.evaluate / OnlineTrainer._eval_loop():

    callback.before_eval_loop() * +for batch in eval_dataset:

    +
    +

    rollout_manager.sample_trajectories() +callback.after_eval_step() *

    +
    +

    callback.after_eval_loop() *

    +
    +
    OfflineTrainer / OnlineTrainer.train():
    +
    for batch in train_dataset:

    rollout_manager.sample_trajectories() # if online +optimizer.aggregate() +if updating_optimizer:

    +
    +

    optimizer.update() +callback.after_update() *

    +
    +

    callback.after_train_step() *

    +
    +
    +
    +
    +
    +
    +async after_agent_get_asv(traj_id: str, action: OpResult[ToolRequestMessage], next_agent_state: Any, value: float) → None[source]
    +

    Invoked by runners after agent.get_asv().

    +
    + +
    +
    +async after_agent_init_state(traj_id: str, init_state: Any) → None[source]
    +

    Invoked by runners after agent.init_state().

    +
    + +
    +
    +async after_env_reset(traj_id: str, obs: list[Message], tools: list[Tool]) → None[source]
    +

    Invoked by runners after env.reset().

    +
    + +
    +
    +async after_env_step(traj_id: str, obs: list[Message], reward: float, done: bool, trunc: bool) → None[source]
    +

    Invoked by runners after env.step().

    +
    + +
    +
    +async after_eval_loop() → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after the evaluation loop.

    +
    + +
    +
    +async after_eval_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after each evaluation step.

    +
    + +
    +
    +async after_train_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by OnlineTrainer after each training step.

    +
    + +
    +
    +async after_transition(traj_id: str, agent: Agent, env: Environment, transition: Transition) → None[source]
    +

    Invoked by runners after each transition.

    +
    + +
    +
    +async after_update() → None[source]
    +

    Invoked by OnlineTrainer after each optimizer.update() call.

    +
    + +
    +
    +async before_eval_loop() → None[source]
    +

    Invoked by Evaluator and OnlineTrainer before the evaluation loop.

    +
    + +
    +
    +async before_transition(traj_id: str, agent: Agent, env: Environment, agent_state: Any, obs: list[Message]) → None[source]
    +

    Invoked by runners before each transition and after agent and env reset.

    +
    + +
    + +
    +
    +class ldp.alg.callbacks.ClearContextCallback(op_names: Iterable[str] | None = None)[source]
    +

    Bases: Callback

    +
    +
    +async after_eval_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after each evaluation step.

    +
    + +
    +
    +async after_update() → None[source]
    +

    Invoked by OnlineTrainer after each optimizer.update() call.

    +
    + +
    + +
    +
    +class ldp.alg.callbacks.ComputeTrajectoryMetricsMixin[source]
    +

    Bases: object

    +

    Mixin for TaskDataset classes to enable them to compute metrics.

    +
    +
    +compute_trajectory_metrics(trajectories: Sequence[Trajectory]) → dict[str, list[float]][source]
    +
    + +
    +
    +tools_to_track: Collection[str | Tool] = {}
    +
    + +
    + +
    +
    +class ldp.alg.callbacks.LoggingCallback(train_dataset: TaskDataset | None = None, eval_dataset: TaskDataset | None = None, metrics_to_log: Collection[str] | None = None)[source]
    +

    Bases: MeanMetricsCallback

    +

    Custom callback for logging filtered metrics (e.g., pass rates) to the console.

    +

    This callback extends the MeanMetricsCallback and allows logging of user-specified metrics +after each training step and after the evaluation loop. It calculates the specified metrics +(e.g., pass rates) from the trajectories and logs the results.

    +
    +
    +async after_eval_loop() → None[source]
    +

    Log metrics and pass rate after the evaluation loop.

    +

    This method is called after the evaluation loop finishes, calculating and logging +the evaluation metrics and pass rate.

    +
    + +
    +
    +async after_train_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Log metrics and pass rate after each training step.

    +

    This method is called after every training step, calculating and logging +the training metrics and pass rate.

    +
    +
    Parameters:
    +

    trajectories – A sequence of trajectories from the training step.

    +
    +
    +
    + +
    + +
    +
    +class ldp.alg.callbacks.MeanMetricsCallback(*args, **kwargs)[source]
    +

    Bases: TrajectoryMetricsCallback

    +

    Take a mean of all metrics.

    +
    +
    +async after_eval_loop() → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after the evaluation loop.

    +
    + +
    +
    +async after_train_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by OnlineTrainer after each training step.

    +
    + +
    +
    +property eval_means: dict[str, float]
    +
    + +
    +
    +property train_means: dict[str, float]
    +
    + +
    + +
    +
    +class ldp.alg.callbacks.RolloutDebugDumpCallback(output_dir: PathLike | str)[source]
    +

    Bases: Callback

    +

    Dump JSONL files for each agent and environment step to a directory.

    +
    +
    +async after_agent_get_asv(traj_id: str, action: OpResult[ToolRequestMessage], next_agent_state: Any, value: float) → None[source]
    +

    Invoked by runners after agent.get_asv().

    +
    + +
    +
    +async after_env_step(traj_id: str, obs: list[Message], reward: float, done: bool, trunc: bool) → None[source]
    +

    Invoked by runners after env.step().

    +
    + +
    +
    +async before_transition(traj_id: str, agent: Agent, env: Environment, agent_state, obs: list[Message]) → None[source]
    +

    Invoked by runners before each transition and after agent and env reset.

    +
    + +
    + +
    +
    +class ldp.alg.callbacks.TerminalPrintingCallback[source]
    +

    Bases: Callback

    +

    Callback that prints action, observation, and timing information to the terminal.

    +
    +
    +async after_agent_get_asv(traj_id: str, action: OpResult[ToolRequestMessage], next_agent_state: Any, value: float) → None[source]
    +

    Invoked by runners after agent.get_asv().

    +
    + +
    +
    +async after_env_step(traj_id: str, obs: list[Message], reward: float, done: bool, trunc: bool) → None[source]
    +

    Invoked by runners after env.step().

    +
    + +
    +
    +async before_transition(traj_id: str, agent: Agent, env: Environment, agent_state: Any, obs: list[Message]) → None[source]
    +

    Start the timer before each transition.

    +
    + +
    + +
    +
    +class ldp.alg.callbacks.TrajectoryFileCallback(output_dir: PathLike | str)[source]
    +

    Bases: Callback

    +

    Callback that writes trajectories to a file.

    +
    +
    +async after_transition(traj_id: str, agent: Agent, env: Environment, transition: Transition) → None[source]
    +

    Invoked by runners after each transition.

    +
    + +
    +
    +async before_transition(traj_id: str, agent: Agent, env: Environment, agent_state: Any, obs: list[Message]) → None[source]
    +

    Invoked by runners before each transition and after agent and env reset.

    +
    + +
    +
    +cleanup() → None[source]
    +
    + +
    + +
    +
    +class ldp.alg.callbacks.TrajectoryMetricsCallback(train_dataset: TaskDataset | None = None, eval_dataset: TaskDataset | None = None, track_tool_usage: bool = False)[source]
    +

    Bases: Callback

    +

    Compute metrics that are defined by task datasets.

    +

    NOTE: evaluation portion’s after_eval_step/loop() is not concurrency safe because +trajectories should be stored in the order of after_eval_step() calls.

    +
    +
    +async after_env_reset(traj_id: str, obs: list[Message], tools: list[Tool]) → None[source]
    +

    Invoked by runners after env.reset().

    +
    + +
    +
    +async after_eval_loop() → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after the evaluation loop.

    +
    + +
    +
    +async after_eval_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after each evaluation step.

    +
    + +
    +
    +async after_train_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by OnlineTrainer after each training step.

    +
    + +
    + +
    +
    +class ldp.alg.callbacks.WandBLoggingCallback(*args, **kwargs)[source]
    +

    Bases: TrajectoryMetricsCallback

    +
    +
    +async after_eval_loop() → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after the evaluation loop.

    +
    + +
    +
    +async after_train_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by OnlineTrainer after each training step.

    +
    + +
    + +
    +
    +

    ldp.alg.datasets module

    +
    +
    +class ldp.alg.datasets.DummyTaskDataset[source]
    +

    Bases: DummyTaskDataset, ComputeTrajectoryMetricsMixin

    +
    + +
    +
    +

    ldp.alg.rollout module

    +
    +
    +exception ldp.alg.rollout.AgentError(original_exc: Exception)[source]
    +

    Bases: CaughtError

    +
    +
    +exc_type = 'agent'
    +
    + +
    + +
    +
    +exception ldp.alg.rollout.CaughtError(original_exc: Exception)[source]
    +

    Bases: Exception

    +

    Base class for reraised exceptions when catching is enabled.

    +
    +
    +exc_type = 'undefined'
    +
    + +
    + +
    +
    +exception ldp.alg.rollout.EnvError(original_exc: Exception)[source]
    +

    Bases: CaughtError

    +
    +
    +exc_type = 'env'
    +
    + +
    + +
    +
    +class ldp.alg.rollout.RolloutManager(agent: Agent, catch_agent_failures: bool = True, catch_env_failures: bool = True, callbacks: Sequence[Callback] | None = None, concurrency_limit: int | None = None)[source]
    +

    Bases: object

    +
    +
    +async sample_trajectories(environment_factory: Callable[[], TEnv], batch_size: int = 1, max_steps: int | None = None) → list[tuple[Trajectory, TEnv]][source]
    +
    +async sample_trajectories(environments: Sequence[Environment], max_steps: int | None = None) → list[Trajectory]
    +
    + +
    + +
    +
    +ldp.alg.rollout.reraise_exc_as(reraise: type[CaughtError], enabled: bool) → Iterator[None][source]
    +
    + +
    +
    +

    ldp.alg.runners module

    +
    +
    +class ldp.alg.runners.Evaluator(config: EvaluatorConfig, agent: Agent, dataset: TaskDataset, callbacks: Sequence[Callback] | None = None)[source]
    +

    Bases: object

    +
    +
    +async evaluate(**kwargs)
    +
    + +
    +
    +async run(**kwargs) → None[source]
    +

    Run the agent over the provided dataset.

    +

    This method does not set training mode, so it can be used to collect +trajectories for offline training.

    +
    + +
    + +
    +
    +class ldp.alg.runners.EvaluatorConfig(*, batch_size: int = 1, num_eval_iterations: int | None = None, max_rollout_steps: int | None = None, catch_agent_failures: bool = True, catch_env_failures: bool = True, clear_ctx_at_each_iter: bool = False)[source]
    +

    Bases: BaseModel

    +
    +
    +batch_size: int
    +
    + +
    +
    +catch_agent_failures: bool
    +
    + +
    +
    +catch_env_failures: bool
    +
    + +
    +
    +clear_ctx_at_each_iter: bool
    +
    + +
    +
    +make_rollout_manager(agent: Agent, callbacks: Sequence[Callback]) → RolloutManager[source]
    +
    + +
    +
    +max_rollout_steps: int | None
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'batch_size': FieldInfo(annotation=int, required=False, default=1), 'catch_agent_failures': FieldInfo(annotation=bool, required=False, default=True), 'catch_env_failures': FieldInfo(annotation=bool, required=False, default=True), 'clear_ctx_at_each_iter': FieldInfo(annotation=bool, required=False, default=False), 'max_rollout_steps': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'num_eval_iterations': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='Number of eval iterations. If not provided, will exhaust the dataset. If 0, will not run the eval loop. ')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +num_eval_iterations: int | None
    +
    + +
    + +
    +
    +class ldp.alg.runners.OfflineTrainer(config: OfflineTrainerConfig, agent: Agent, optimizer: Optimizer, train_trajectories: list[Trajectory], callbacks: Sequence[Callback] | None = None)[source]
    +

    Bases: object

    +
    +
    +async train() → None[source]
    +
    + +
    + +
    +
    +class ldp.alg.runners.OfflineTrainerConfig(*, batch_size: int, update_every: int = 1, clear_ctx_at_each_iter: bool = False)[source]
    +

    Bases: BaseModel

    +
    +
    +batch_size: int
    +
    + +
    +
    +clear_ctx_at_each_iter: bool
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'batch_size': FieldInfo(annotation=int, required=True), 'clear_ctx_at_each_iter': FieldInfo(annotation=bool, required=False, default=False), 'update_every': FieldInfo(annotation=int, required=False, default=1, description='Number of training iterations to run before updating the model.', metadata=[Ge(ge=1)])}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +update_every: int
    +
    + +
    + +
    +
    +class ldp.alg.runners.OnlineTrainer(config: OnlineTrainerConfig, agent: Agent, optimizer: Optimizer, train_dataset: TaskDataset, eval_dataset: TaskDataset | None = None, callbacks: Sequence[Callback] | None = None)[source]
    +

    Bases: object

    +
    +
    +async evaluate(**kwargs)
    +
    + +
    +
    +async train() → None[source]
    +
    + +
    + +
    +
    +class ldp.alg.runners.OnlineTrainerConfig(*, batch_size: int, num_eval_iterations: int | None = None, max_rollout_steps: int | None = None, catch_agent_failures: bool = True, catch_env_failures: bool = True, clear_ctx_at_each_iter: bool = False, num_train_iterations: int, num_rollouts_per_env: int = 1, update_every: int = 1, eval_every: int | None = None, eval_before: bool = True)[source]
    +

    Bases: EvaluatorConfig

    +
    +
    +batch_size: int
    +
    + +
    +
    +clear_ctx_at_each_iter: bool
    +
    + +
    +
    +eval_before: bool
    +
    + +
    +
    +eval_every: int | None
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'batch_size': FieldInfo(annotation=int, required=True), 'catch_agent_failures': FieldInfo(annotation=bool, required=False, default=True), 'catch_env_failures': FieldInfo(annotation=bool, required=False, default=True), 'clear_ctx_at_each_iter': FieldInfo(annotation=bool, required=False, default=False), 'eval_before': FieldInfo(annotation=bool, required=False, default=True, description='If True (default), evaluate on the validation set before kicking off training.'), 'eval_every': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='If set, will repeatedly evaluate on the validation set after this many iterations. If unset (default), no evaluation is performed.'), 'max_rollout_steps': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'num_eval_iterations': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='Number of eval iterations. If not provided, will exhaust the dataset. If 0, will not run the eval loop. '), 'num_rollouts_per_env': FieldInfo(annotation=int, required=False, default=1, description='Number of rollouts to execute for each environment per training iteration.'), 'num_train_iterations': FieldInfo(annotation=int, required=True, description='Number of iterations (at one batch per iteration) to process during training, and setting to 0 skips training.', metadata=[Ge(ge=0)]), 'update_every': FieldInfo(annotation=int, required=False, default=1, description='Number of training iterations between optimizer update calls.', metadata=[Ge(ge=1)])}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +num_rollouts_per_env: int
    +
    + +
    +
    +num_train_iterations: int
    +
    + +
    +
    +update_every: int
    +
    + +
    + +
    + +
    +

    Module contents

    +
    +
    +class ldp.alg.Beam(traj, env)[source]
    +

    Bases: NamedTuple

    +
    +
    +env: Environment
    +

    Alias for field number 1

    +
    + +
    +
    +traj: Trajectory
    +

    Alias for field number 0

    +
    + +
    + +
    +
    +class ldp.alg.BeamSearchRollout(agent: Agent, beam_width: int, samples_per_beam: int, env_clone_fn: Callable[[TEnv], Awaitable[TEnv]], agent_clone_fn: Callable[[TAgentState], TAgentState], scoring_fn: Callable[[Trajectory], Awaitable[float]], replay_actions_on_clone: bool = False, callbacks: Sequence[Callback] | None = None, catch_agent_failures: bool = True, catch_env_failures: bool = True, verbose: bool = False)[source]
    +

    Bases: object

    +
    +
    +async sample_trajectories(environments: Sequence[Environment], max_steps: int | None = None) → list[Trajectory][source]
    +
    + +
    + +
    +
    +class ldp.alg.Callback[source]
    +

    Bases: object

    +

    Base class for callbacks used by RolloutManager/Evaluator/OnlineTrainer.

    +

    Pseudocode to demonstrate how callback methods are invoked (marked as *):

    +
    +
    RolloutManager.sample_trajectories():

    env.reset() +callback.after_env_reset() * +agent.init_state() +callback.after_agent_init_state() * +while not done:

    +
    +

    callback.before_transition() * +agent.get_asv() +callback.after_agent_get_asv() * +env.step() +callback.after_env_step() * +callback.after_transition() *

    +
    +
    +
    Evaluator.evaluate / OnlineTrainer._eval_loop():

    callback.before_eval_loop() * +for batch in eval_dataset:

    +
    +

    rollout_manager.sample_trajectories() +callback.after_eval_step() *

    +
    +

    callback.after_eval_loop() *

    +
    +
    OfflineTrainer / OnlineTrainer.train():
    +
    for batch in train_dataset:

    rollout_manager.sample_trajectories() # if online +optimizer.aggregate() +if updating_optimizer:

    +
    +

    optimizer.update() +callback.after_update() *

    +
    +

    callback.after_train_step() *

    +
    +
    +
    +
    +
    +
    +async after_agent_get_asv(traj_id: str, action: OpResult[ToolRequestMessage], next_agent_state: Any, value: float) → None[source]
    +

    Invoked by runners after agent.get_asv().

    +
    + +
    +
    +async after_agent_init_state(traj_id: str, init_state: Any) → None[source]
    +

    Invoked by runners after agent.init_state().

    +
    + +
    +
    +async after_env_reset(traj_id: str, obs: list[Message], tools: list[Tool]) → None[source]
    +

    Invoked by runners after env.reset().

    +
    + +
    +
    +async after_env_step(traj_id: str, obs: list[Message], reward: float, done: bool, trunc: bool) → None[source]
    +

    Invoked by runners after env.step().

    +
    + +
    +
    +async after_eval_loop() → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after the evaluation loop.

    +
    + +
    +
    +async after_eval_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after each evaluation step.

    +
    + +
    +
    +async after_train_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by OnlineTrainer after each training step.

    +
    + +
    +
    +async after_transition(traj_id: str, agent: Agent, env: Environment, transition: Transition) → None[source]
    +

    Invoked by runners after each transition.

    +
    + +
    +
    +async after_update() → None[source]
    +

    Invoked by OnlineTrainer after each optimizer.update() call.

    +
    + +
    +
    +async before_eval_loop() → None[source]
    +

    Invoked by Evaluator and OnlineTrainer before the evaluation loop.

    +
    + +
    +
    +async before_transition(traj_id: str, agent: Agent, env: Environment, agent_state: Any, obs: list[Message]) → None[source]
    +

    Invoked by runners before each transition and after agent and env reset.

    +
    + +
    + +
    +
    +class ldp.alg.ClearContextCallback(op_names: Iterable[str] | None = None)[source]
    +

    Bases: Callback

    +
    +
    +async after_eval_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after each evaluation step.

    +
    + +
    +
    +async after_update() → None[source]
    +

    Invoked by OnlineTrainer after each optimizer.update() call.

    +
    + +
    + +
    +
    +class ldp.alg.ComputeTrajectoryMetricsMixin[source]
    +

    Bases: object

    +

    Mixin for TaskDataset classes to enable them to compute metrics.

    +
    +
    +compute_trajectory_metrics(trajectories: Sequence[Trajectory]) → dict[str, list[float]][source]
    +
    + +
    +
    +tools_to_track: Collection[str | Tool] = {}
    +
    + +
    + +
    +
    +class ldp.alg.Evaluator(config: EvaluatorConfig, agent: Agent, dataset: TaskDataset, callbacks: Sequence[Callback] | None = None)[source]
    +

    Bases: object

    +
    +
    +async evaluate(**kwargs)
    +
    + +
    +
    +async run(**kwargs) → None[source]
    +

    Run the agent over the provided dataset.

    +

    This method does not set training mode, so it can be used to collect +trajectories for offline training.

    +
    + +
    + +
    +
    +class ldp.alg.EvaluatorConfig(*, batch_size: int = 1, num_eval_iterations: int | None = None, max_rollout_steps: int | None = None, catch_agent_failures: bool = True, catch_env_failures: bool = True, clear_ctx_at_each_iter: bool = False)[source]
    +

    Bases: BaseModel

    +
    +
    +batch_size: int
    +
    + +
    +
    +catch_agent_failures: bool
    +
    + +
    +
    +catch_env_failures: bool
    +
    + +
    +
    +clear_ctx_at_each_iter: bool
    +
    + +
    +
    +make_rollout_manager(agent: Agent, callbacks: Sequence[Callback]) → RolloutManager[source]
    +
    + +
    +
    +max_rollout_steps: int | None
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'batch_size': FieldInfo(annotation=int, required=False, default=1), 'catch_agent_failures': FieldInfo(annotation=bool, required=False, default=True), 'catch_env_failures': FieldInfo(annotation=bool, required=False, default=True), 'clear_ctx_at_each_iter': FieldInfo(annotation=bool, required=False, default=False), 'max_rollout_steps': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'num_eval_iterations': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='Number of eval iterations. If not provided, will exhaust the dataset. If 0, will not run the eval loop. ')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +num_eval_iterations: int | None
    +
    + +
    + +
    +
    +class ldp.alg.LoggingCallback(train_dataset: TaskDataset | None = None, eval_dataset: TaskDataset | None = None, metrics_to_log: Collection[str] | None = None)[source]
    +

    Bases: MeanMetricsCallback

    +

    Custom callback for logging filtered metrics (e.g., pass rates) to the console.

    +

    This callback extends the MeanMetricsCallback and allows logging of user-specified metrics +after each training step and after the evaluation loop. It calculates the specified metrics +(e.g., pass rates) from the trajectories and logs the results.

    +
    +
    +async after_eval_loop() → None[source]
    +

    Log metrics and pass rate after the evaluation loop.

    +

    This method is called after the evaluation loop finishes, calculating and logging +the evaluation metrics and pass rate.

    +
    + +
    +
    +async after_train_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Log metrics and pass rate after each training step.

    +

    This method is called after every training step, calculating and logging +the training metrics and pass rate.

    +
    +
    Parameters:
    +

    trajectories – A sequence of trajectories from the training step.

    +
    +
    +
    + +
    + +
    +
    +class ldp.alg.MeanMetricsCallback(*args, **kwargs)[source]
    +

    Bases: TrajectoryMetricsCallback

    +

    Take a mean of all metrics.

    +
    +
    +async after_eval_loop() → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after the evaluation loop.

    +
    + +
    +
    +async after_train_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by OnlineTrainer after each training step.

    +
    + +
    +
    +property eval_means: dict[str, float]
    +
    + +
    +
    +property train_means: dict[str, float]
    +
    + +
    + +
    +
    +class ldp.alg.OfflineTrainer(config: OfflineTrainerConfig, agent: Agent, optimizer: Optimizer, train_trajectories: list[Trajectory], callbacks: Sequence[Callback] | None = None)[source]
    +

    Bases: object

    +
    +
    +async train() → None[source]
    +
    + +
    + +
    +
    +class ldp.alg.OfflineTrainerConfig(*, batch_size: int, update_every: int = 1, clear_ctx_at_each_iter: bool = False)[source]
    +

    Bases: BaseModel

    +
    +
    +batch_size: int
    +
    + +
    +
    +clear_ctx_at_each_iter: bool
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'batch_size': FieldInfo(annotation=int, required=True), 'clear_ctx_at_each_iter': FieldInfo(annotation=bool, required=False, default=False), 'update_every': FieldInfo(annotation=int, required=False, default=1, description='Number of training iterations to run before updating the model.', metadata=[Ge(ge=1)])}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +update_every: int
    +
    + +
    + +
    +
    +class ldp.alg.OnlineTrainer(config: OnlineTrainerConfig, agent: Agent, optimizer: Optimizer, train_dataset: TaskDataset, eval_dataset: TaskDataset | None = None, callbacks: Sequence[Callback] | None = None)[source]
    +

    Bases: object

    +
    +
    +async evaluate(**kwargs)
    +
    + +
    +
    +async train() → None[source]
    +
    + +
    + +
    +
    +class ldp.alg.OnlineTrainerConfig(*, batch_size: int, num_eval_iterations: int | None = None, max_rollout_steps: int | None = None, catch_agent_failures: bool = True, catch_env_failures: bool = True, clear_ctx_at_each_iter: bool = False, num_train_iterations: int, num_rollouts_per_env: int = 1, update_every: int = 1, eval_every: int | None = None, eval_before: bool = True)[source]
    +

    Bases: EvaluatorConfig

    +
    +
    +batch_size: int
    +
    + +
    +
    +catch_agent_failures: bool
    +
    + +
    +
    +catch_env_failures: bool
    +
    + +
    +
    +clear_ctx_at_each_iter: bool
    +
    + +
    +
    +eval_before: bool
    +
    + +
    +
    +eval_every: int | None
    +
    + +
    +
    +max_rollout_steps: int | None
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'batch_size': FieldInfo(annotation=int, required=True), 'catch_agent_failures': FieldInfo(annotation=bool, required=False, default=True), 'catch_env_failures': FieldInfo(annotation=bool, required=False, default=True), 'clear_ctx_at_each_iter': FieldInfo(annotation=bool, required=False, default=False), 'eval_before': FieldInfo(annotation=bool, required=False, default=True, description='If True (default), evaluate on the validation set before kicking off training.'), 'eval_every': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='If set, will repeatedly evaluate on the validation set after this many iterations. If unset (default), no evaluation is performed.'), 'max_rollout_steps': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'num_eval_iterations': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description='Number of eval iterations. If not provided, will exhaust the dataset. If 0, will not run the eval loop. '), 'num_rollouts_per_env': FieldInfo(annotation=int, required=False, default=1, description='Number of rollouts to execute for each environment per training iteration.'), 'num_train_iterations': FieldInfo(annotation=int, required=True, description='Number of iterations (at one batch per iteration) to process during training, and setting to 0 skips training.', metadata=[Ge(ge=0)]), 'update_every': FieldInfo(annotation=int, required=False, default=1, description='Number of training iterations between optimizer update calls.', metadata=[Ge(ge=1)])}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +num_eval_iterations: int | None
    +
    + +
    +
    +num_rollouts_per_env: int
    +
    + +
    +
    +num_train_iterations: int
    +
    + +
    +
    +update_every: int
    +
    + +
    + +
    +
    +class ldp.alg.RolloutDebugDumpCallback(output_dir: PathLike | str)[source]
    +

    Bases: Callback

    +

    Dump JSONL files for each agent and environment step to a directory.

    +
    +
    +async after_agent_get_asv(traj_id: str, action: OpResult[ToolRequestMessage], next_agent_state: Any, value: float) → None[source]
    +

    Invoked by runners after agent.get_asv().

    +
    + +
    +
    +async after_env_step(traj_id: str, obs: list[Message], reward: float, done: bool, trunc: bool) → None[source]
    +

    Invoked by runners after env.step().

    +
    + +
    +
    +async before_transition(traj_id: str, agent: Agent, env: Environment, agent_state, obs: list[Message]) → None[source]
    +

    Invoked by runners before each transition and after agent and env reset.

    +
    + +
    + +
    +
    +class ldp.alg.RolloutManager(agent: Agent, catch_agent_failures: bool = True, catch_env_failures: bool = True, callbacks: Sequence[Callback] | None = None, concurrency_limit: int | None = None)[source]
    +

    Bases: object

    +
    +
    +async sample_trajectories(environment_factory: Callable[[], TEnv], batch_size: int = 1, max_steps: int | None = None) → list[tuple[Trajectory, TEnv]][source]
    +
    +async sample_trajectories(environments: Sequence[Environment], max_steps: int | None = None) → list[Trajectory]
    +
    + +
    + +
    +
    +class ldp.alg.TrajectoryFileCallback(output_dir: PathLike | str)[source]
    +

    Bases: Callback

    +

    Callback that writes trajectories to a file.

    +
    +
    +async after_transition(traj_id: str, agent: Agent, env: Environment, transition: Transition) → None[source]
    +

    Invoked by runners after each transition.

    +
    + +
    +
    +async before_transition(traj_id: str, agent: Agent, env: Environment, agent_state: Any, obs: list[Message]) → None[source]
    +

    Invoked by runners before each transition and after agent and env reset.

    +
    + +
    +
    +cleanup() → None[source]
    +
    + +
    + +
    +
    +class ldp.alg.TrajectoryMetricsCallback(train_dataset: TaskDataset | None = None, eval_dataset: TaskDataset | None = None, track_tool_usage: bool = False)[source]
    +

    Bases: Callback

    +

    Compute metrics that are defined by task datasets.

    +

    NOTE: evaluation portion’s after_eval_step/loop() is not concurrency safe because +trajectories should be stored in the order of after_eval_step() calls.

    +
    +
    +async after_env_reset(traj_id: str, obs: list[Message], tools: list[Tool]) → None[source]
    +

    Invoked by runners after env.reset().

    +
    + +
    +
    +async after_eval_loop() → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after the evaluation loop.

    +
    + +
    +
    +async after_eval_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after each evaluation step.

    +
    + +
    +
    +async after_train_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by OnlineTrainer after each training step.

    +
    + +
    + +
    +
    +class ldp.alg.TreeSearchRollout(agent: Agent, branching_factor: int, env_clone_fn: Callable[[TEnv], Awaitable[TEnv]] | Callable[[TEnv], TEnv], catch_agent_failures: bool = True, catch_env_failures: bool = True, callbacks: Sequence[Callback] | None = None, concurrency_limit: int | None = None, target_reward: float | None = None)[source]
    +

    Bases: RolloutManager

    +
    +
    +async sample_tree(env: TEnv, max_depth: int | None) → TransitionTree[source]
    +
    + +
    +
    +async sample_trees(environments: Sequence[TEnv], max_depth: int | None = None) → list[TransitionTree][source]
    +
    + +
    + +
    +
    +class ldp.alg.WandBLoggingCallback(*args, **kwargs)[source]
    +

    Bases: TrajectoryMetricsCallback

    +
    +
    +async after_eval_loop() → None[source]
    +

    Invoked by Evaluator and OnlineTrainer after the evaluation loop.

    +
    + +
    +
    +async after_train_step(trajectories: Sequence[Trajectory]) → None[source]
    +

    Invoked by OnlineTrainer after each training step.

    +
    + +
    + +
    +
    +ldp.alg.to_network(op_result: OpResult, max_label_height: int | None = None, max_label_width: int | None = None, G: MultiDiGraph | None = None) → MultiDiGraph[source]
    +

    Populate a NetworkX graph from the input op result’s computation graph.

    +

    How to export Graphviz .dot file: nx.drawing.nx_pydot.write_dot(G, “file.dot”) +How to render with Graphviz: nx.drawing.nx_pydot.to_pydot(G).write_png(“file.png”) +Online Graphviz renderer: https://dreampuf.github.io/GraphvizOnline/

    +
    +
    Parameters:
    +
      +

    • op_result – Starting op result to recurse parent op calls and results.

      • +

    • max_label_height – Optional max label height (lines).

      • +

    • max_label_width – Optional max label width (chars).

      • +

    • G – Optional graph to add nodes/edges to. Allows this to be a recursive function.

      • +
    +
    +
    Returns:
    +

    Populated a NetworkX multi-edge directed graph.

    +
    +
    +
    + +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/ldp.alg.optimizer.html b/docs/_build/html/ldp.alg.optimizer.html new file mode 100644 index 00000000..ba4cb2ad --- /dev/null +++ b/docs/_build/html/ldp.alg.optimizer.html @@ -0,0 +1,968 @@ + + + + + + + + ldp.alg.optimizer package — ldp documentation + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    ldp.alg.optimizer package

    +
    +

    Submodules

    +
    +
    +

    ldp.alg.optimizer.ape module

    +
    +
    +class ldp.alg.optimizer.ape.APEOpt(*, max_examples: int | None = 50, system_prompt: str = 'We are optimizing prompts for a language model. The model sees a prompt, an input, and then generates an output.', query_prompt: str = 'Here are correct example outputs that the language model and prompt should produce:\n{good_examples}\n\nThe current prompt is: "{prompt}"\n\nWhich resulted in the following incorrect input, output, and {score}:\n{examples}\n\nRevise the current prompt to improve the outputs. Your proposed prompt should be concise, correct, and specify the desired output format.', llm: LLMModel = None, prompt_op: PromptOp, llm_call_op: LLMCallOp, score_fn: APEScoreFn = APEScoreFn.REWARD, good_reward_threshold: float | None = None, reward_discount: float = 1.0, examples: list[Example] = None, good_examples: list[Example] = None, steps: int = 0, trace: list[str] = None)[source]
    +

    Bases: BaseModel, Optimizer

    +

    Basic optimizer that acts as an Automatic Prompt Engineer (APE).

    +

    Paper: https://openreview.net/pdf?id=92gvk82DE-

    +

    Details: +- This implements the “forward mode generation” strategy. +- The score function used is the gradient (float) at the output of the

    +
    +

    PromptOp being optimized. A zero gradient means the prompt was “good”, +and a non-zero gradient means we can learn from the prompt.

    +
    +
      +
    • +
      Possible improvements include:
        +

      • Extending the score function to the LLM result’s logprobs

        • +

      • Iterating with Monte Carlo Search

        • +

      • Use of memory for further example augmentation

        • +
      +
      +
      +
      • +
    +
    +
    +aggregate_trajectory(trajectory: Trajectory) → None[source]
    +

    Aggregate transitions from a single trajectory to construct training samples.

    +
    + +
    +
    +examples: list[Example]
    +
    + +
    +
    +classmethod from_agent(agent: ReActAgent, **kwargs) → Self[source]
    +
    + +
    +
    +good_examples: list[Example]
    +
    + +
    +
    +good_reward_threshold: float | None
    +
    + +
    +
    +llm: LLMModel
    +
    + +
    +
    +llm_call_op: LLMCallOp
    +
    + +
    +
    +max_examples: int | None
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'examples': FieldInfo(annotation=list[Example], required=False, default_factory=list), 'good_examples': FieldInfo(annotation=list[Example], required=False, default_factory=list), 'good_reward_threshold': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='If using reward as the score_fn, then a good example is defined by reward>=good_reward_threshold.'), 'llm': FieldInfo(annotation=LLMModel, required=False, default_factory=LLMModel, description='LLM used to update the prompt inside the PromptOp. The paper mentions that larger models produce better prompt proposals.'), 'llm_call_op': FieldInfo(annotation=LLMCallOp, required=True, description='LLMCallOp to be optimized.'), 'max_examples': FieldInfo(annotation=Union[int, NoneType], required=False, default=50, description='Max number of examples to include in the below query_prompt, or None for no limit. The paper mentions that more examples produce better prompt proposals.'), 'prompt_op': FieldInfo(annotation=PromptOp, required=True, description='PromptOp to be optimized.'), 'query_prompt': FieldInfo(annotation=str, required=False, default='Here are correct example outputs that the language model and prompt should produce:\n{good_examples}\n\nThe current prompt is: "{prompt}"\n\nWhich resulted in the following incorrect input, output, and {score}:\n{examples}\n\nRevise the current prompt to improve the outputs. Your proposed prompt should be concise, correct, and specify the desired output format.'), 'reward_discount': FieldInfo(annotation=float, required=False, default=1.0), 'score_fn': FieldInfo(annotation=APEScoreFn, required=False, default=<APEScoreFn.REWARD: 'reward'>), 'steps': FieldInfo(annotation=int, required=False, default=0), 'system_prompt': FieldInfo(annotation=str, required=False, default='We are optimizing prompts for a language model. The model sees a prompt, an input, and then generates an output.'), 'trace': FieldInfo(annotation=list[str], required=False, default_factory=list, description='History of prompts used.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +model_post_init(_APEOpt__context: Any) → None[source]
    +

    Override this method to perform additional initialization after __init__ and model_construct. +This is useful if you want to do some validation that requires the entire model to be initialized.

    +
    + +
    +
    +prompt_op: PromptOp
    +
    + +
    +
    +query_prompt: str
    +
    + +
    +
    +reward_discount: float
    +
    + +
    +
    +score_fn: APEScoreFn
    +
    + +
    +
    +steps: int
    +
    + +
    +
    +system_prompt: str
    +
    + +
    +
    +trace: list[str]
    +
    + +
    +
    +async update() → None[source]
    +

    Update the model based on the aggregated samples.

    +
    + +
    +
    +validate_score_fn()[source]
    +
    + +
    + +
    +
    +class ldp.alg.optimizer.ape.APEScoreFn(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]
    +

    Bases: StrEnum

    +
    +
    +GRADIENT = 'gradient'
    +
    + +
    +
    +REWARD = 'reward'
    +
    + +
    + +
    +
    +class ldp.alg.optimizer.ape.Example(*, input: JsonValue, output: JsonValue, score: float)[source]
    +

    Bases: BaseModel

    +
    +
    +input: JsonValue
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'input': FieldInfo(annotation=JsonValue, required=True), 'output': FieldInfo(annotation=JsonValue, required=True), 'score': FieldInfo(annotation=float, required=True)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +output: JsonValue
    +
    + +
    +
    +score: float
    +
    + +
    + +
    +
    +class ldp.alg.optimizer.ape.OutputPrompt(*, prompt: str)[source]
    +

    Bases: BaseModel

    +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'prompt': FieldInfo(annotation=str, required=True, description='Prompt for language model')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +prompt: str
    +
    + +
    + +
    +
    +ldp.alg.optimizer.ape.get_formatted_variables(s: str) → set[str][source]
    +

    Returns the set of variables implied by the format string.

    +
    + +
    +
    +

    ldp.alg.optimizer.memory module

    +
    +
    +class ldp.alg.optimizer.memory.MemoryFactory(*args, **kwargs)[source]
    +

    Bases: Protocol

    +
    + +
    +
    +class ldp.alg.optimizer.memory.MemoryOpt(*, memory_op: ~ldp.graph.common_ops.MemoryOp, output_op: ~ldp.graph.ops.Op, reward_discount: float = 1.0, memory_factory: ~ldp.alg.optimizer.memory.MemoryFactory = <function _default_memory_factory>, memory_template: str = 'Input: {input}\nOutput: {output}\nReward: {value}', steps: int = 0, example_buffer: list[tuple[~ldp.graph.op_utils.CallID, ~ldp.graph.op_utils.CallID, float, JsonValue]] = None)[source]
    +

    Bases: BaseModel, Optimizer

    +

    Trainer for memory agents. By default it is a minimizer.

    +

    This optimizer simply adds memories to the MemoryOp using a memory factory.

    +
    +
    +aggregate_trajectory(trajectory: Trajectory) → None[source]
    +

    Aggregate transitions from a single trajectory to construct training samples.

    +
    + +
    +
    +async default_memory_factory(output_op: Op[TOutput], memory_template: str, example_buffer: Iterable[tuple[CallID, CallID, float, JsonValue]]) → list[Memory]
    +
    + +
    +
    +example_buffer: list[tuple[CallID, CallID, float, JsonValue]]
    +
    + +
    +
    +classmethod from_agent(agent: MemoryAgent, **kwargs) → Self[source]
    +
    + +
    +
    +memory_factory: MemoryFactory
    +
    + +
    +
    +memory_op: MemoryOp
    +
    + +
    +
    +memory_template: str
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'example_buffer': FieldInfo(annotation=list[tuple[CallID, CallID, float, JsonValue]], required=False, default_factory=list), 'memory_factory': FieldInfo(annotation=MemoryFactory, required=False, default=<function _default_memory_factory>, description="Async function to make Memories from an example buffer. It's async so this can involve an LLM completion if desired.", exclude=True), 'memory_op': FieldInfo(annotation=MemoryOp, required=True), 'memory_template': FieldInfo(annotation=str, required=False, default='Input: {input}\nOutput: {output}\nReward: {value}', description="Template for a Memory's string representation."), 'output_op': FieldInfo(annotation=Op, required=True), 'reward_discount': FieldInfo(annotation=float, required=False, default=1.0), 'steps': FieldInfo(annotation=int, required=False, default=0)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +output_op: Op
    +
    + +
    +
    +reward_discount: float
    +
    + +
    +
    +steps: int
    +
    + +
    +
    +async update() → None[source]
    +

    Create new memories from the example buffer and add them to MemoryOp.

    +
    + +
    + +
    +
    +class ldp.alg.optimizer.memory.PositiveMemoryOpt(*, memory_op: ~ldp.graph.common_ops.MemoryOp, output_op: ~ldp.graph.ops.Op, reward_discount: float = 1.0, memory_factory: ~ldp.alg.optimizer.memory.MemoryFactory = <function _default_memory_factory>, memory_template: str = 'Input: {input}\nOutput: {output}\nReward: {value}', steps: int = 0, example_buffer: list[tuple[~ldp.graph.op_utils.CallID, ~ldp.graph.op_utils.CallID, float, JsonValue]] = None)[source]
    +

    Bases: MemoryOpt

    +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'example_buffer': FieldInfo(annotation=list[tuple[CallID, CallID, float, JsonValue]], required=False, default_factory=list), 'memory_factory': FieldInfo(annotation=MemoryFactory, required=False, default=<function _default_memory_factory>, description="Async function to make Memories from an example buffer. It's async so this can involve an LLM completion if desired.", exclude=True), 'memory_op': FieldInfo(annotation=MemoryOp, required=True), 'memory_template': FieldInfo(annotation=str, required=False, default='Input: {input}\nOutput: {output}\nReward: {value}', description="Template for a Memory's string representation."), 'output_op': FieldInfo(annotation=Op, required=True), 'reward_discount': FieldInfo(annotation=float, required=False, default=1.0), 'steps': FieldInfo(annotation=int, required=False, default=0)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    + +
    +
    +

    ldp.alg.optimizer.opt module

    +
    +
    +class ldp.alg.optimizer.opt.ChainedOptimizer(*optimizers: Optimizer)[source]
    +

    Bases: Optimizer

    +

    An optimizer that runs a sequence of sub-optimizers in the order they are provided.

    +
    +
    +aggregate(trajectories: Iterable[Trajectory], show_pbar: bool = False) → None[source]
    +

    Aggregate trajectories to construct training samples.

    +
    + +
    +
    +async update() → None[source]
    +

    Update the model based on the aggregated samples.

    +
    + +
    + +
    +
    +class ldp.alg.optimizer.opt.Optimizer[source]
    +

    Bases: ABC

    +

    Base class for all optimizers.

    +
    +
    +aggregate(trajectories: Iterable[Trajectory], show_pbar: bool = False) → None[source]
    +

    Aggregate trajectories to construct training samples.

    +
    + +
    +
    +abstract aggregate_trajectory(trajectory: Trajectory) → None[source]
    +

    Aggregate transitions from a single trajectory to construct training samples.

    +
    + +
    +
    +abstract async update() → None[source]
    +

    Update the model based on the aggregated samples.

    +
    + +
    + +
    +
    +

    ldp.alg.optimizer.replay_buffers module

    +
    +
    +class ldp.alg.optimizer.replay_buffers.CircularReplayBuffer(initlist=None)[source]
    +

    Bases: UserList[dict]

    +
    +
    +batched_iter(batch_size: int, shuffle: bool = True, infinite: bool = False)[source]
    +
    + +
    +
    +resize(size: int)[source]
    +
    + +
    + +
    +
    +

    Module contents

    +
    +
    +class ldp.alg.optimizer.APEOpt(*, max_examples: int | None = 50, system_prompt: str = 'We are optimizing prompts for a language model. The model sees a prompt, an input, and then generates an output.', query_prompt: str = 'Here are correct example outputs that the language model and prompt should produce:\n{good_examples}\n\nThe current prompt is: "{prompt}"\n\nWhich resulted in the following incorrect input, output, and {score}:\n{examples}\n\nRevise the current prompt to improve the outputs. Your proposed prompt should be concise, correct, and specify the desired output format.', llm: LLMModel = None, prompt_op: PromptOp, llm_call_op: LLMCallOp, score_fn: APEScoreFn = APEScoreFn.REWARD, good_reward_threshold: float | None = None, reward_discount: float = 1.0, examples: list[Example] = None, good_examples: list[Example] = None, steps: int = 0, trace: list[str] = None)[source]
    +

    Bases: BaseModel, Optimizer

    +

    Basic optimizer that acts as an Automatic Prompt Engineer (APE).

    +

    Paper: https://openreview.net/pdf?id=92gvk82DE-

    +

    Details: +- This implements the “forward mode generation” strategy. +- The score function used is the gradient (float) at the output of the

    +
    +

    PromptOp being optimized. A zero gradient means the prompt was “good”, +and a non-zero gradient means we can learn from the prompt.

    +
    +
      +
    • +
      Possible improvements include:
        +

      • Extending the score function to the LLM result’s logprobs

        • +

      • Iterating with Monte Carlo Search

        • +

      • Use of memory for further example augmentation

        • +
      +
      +
      +
      • +
    +
    +
    +aggregate_trajectory(trajectory: Trajectory) → None[source]
    +

    Aggregate transitions from a single trajectory to construct training samples.

    +
    + +
    +
    +examples: list[Example]
    +
    + +
    +
    +classmethod from_agent(agent: ReActAgent, **kwargs) → Self[source]
    +
    + +
    +
    +good_examples: list[Example]
    +
    + +
    +
    +good_reward_threshold: float | None
    +
    + +
    +
    +llm: LLMModel
    +
    + +
    +
    +llm_call_op: LLMCallOp
    +
    + +
    +
    +max_examples: int | None
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'examples': FieldInfo(annotation=list[Example], required=False, default_factory=list), 'good_examples': FieldInfo(annotation=list[Example], required=False, default_factory=list), 'good_reward_threshold': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='If using reward as the score_fn, then a good example is defined by reward>=good_reward_threshold.'), 'llm': FieldInfo(annotation=LLMModel, required=False, default_factory=LLMModel, description='LLM used to update the prompt inside the PromptOp. The paper mentions that larger models produce better prompt proposals.'), 'llm_call_op': FieldInfo(annotation=LLMCallOp, required=True, description='LLMCallOp to be optimized.'), 'max_examples': FieldInfo(annotation=Union[int, NoneType], required=False, default=50, description='Max number of examples to include in the below query_prompt, or None for no limit. The paper mentions that more examples produce better prompt proposals.'), 'prompt_op': FieldInfo(annotation=PromptOp, required=True, description='PromptOp to be optimized.'), 'query_prompt': FieldInfo(annotation=str, required=False, default='Here are correct example outputs that the language model and prompt should produce:\n{good_examples}\n\nThe current prompt is: "{prompt}"\n\nWhich resulted in the following incorrect input, output, and {score}:\n{examples}\n\nRevise the current prompt to improve the outputs. Your proposed prompt should be concise, correct, and specify the desired output format.'), 'reward_discount': FieldInfo(annotation=float, required=False, default=1.0), 'score_fn': FieldInfo(annotation=APEScoreFn, required=False, default=<APEScoreFn.REWARD: 'reward'>), 'steps': FieldInfo(annotation=int, required=False, default=0), 'system_prompt': FieldInfo(annotation=str, required=False, default='We are optimizing prompts for a language model. The model sees a prompt, an input, and then generates an output.'), 'trace': FieldInfo(annotation=list[str], required=False, default_factory=list, description='History of prompts used.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +model_post_init(_APEOpt__context: Any) → None[source]
    +

    Override this method to perform additional initialization after __init__ and model_construct. +This is useful if you want to do some validation that requires the entire model to be initialized.

    +
    + +
    +
    +prompt_op: PromptOp
    +
    + +
    +
    +query_prompt: str
    +
    + +
    +
    +reward_discount: float
    +
    + +
    +
    +score_fn: APEScoreFn
    +
    + +
    +
    +steps: int
    +
    + +
    +
    +system_prompt: str
    +
    + +
    +
    +trace: list[str]
    +
    + +
    +
    +async update() → None[source]
    +

    Update the model based on the aggregated samples.

    +
    + +
    +
    +validate_score_fn()[source]
    +
    + +
    + +
    +
    +class ldp.alg.optimizer.APEScoreFn(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]
    +

    Bases: StrEnum

    +
    +
    +GRADIENT = 'gradient'
    +
    + +
    +
    +REWARD = 'reward'
    +
    + +
    + +
    +
    +class ldp.alg.optimizer.ChainedOptimizer(*optimizers: Optimizer)[source]
    +

    Bases: Optimizer

    +

    An optimizer that runs a sequence of sub-optimizers in the order they are provided.

    +
    +
    +aggregate(trajectories: Iterable[Trajectory], show_pbar: bool = False) → None[source]
    +

    Aggregate trajectories to construct training samples.

    +
    + +
    +
    +async update() → None[source]
    +

    Update the model based on the aggregated samples.

    +
    + +
    + +
    +
    +class ldp.alg.optimizer.MemoryFactory(*args, **kwargs)[source]
    +

    Bases: Protocol

    +
    + +
    +
    +class ldp.alg.optimizer.MemoryOpt(*, memory_op: ~ldp.graph.common_ops.MemoryOp, output_op: ~ldp.graph.ops.Op, reward_discount: float = 1.0, memory_factory: ~ldp.alg.optimizer.memory.MemoryFactory = <function _default_memory_factory>, memory_template: str = 'Input: {input}\nOutput: {output}\nReward: {value}', steps: int = 0, example_buffer: list[tuple[~ldp.graph.op_utils.CallID, ~ldp.graph.op_utils.CallID, float, JsonValue]] = None)[source]
    +

    Bases: BaseModel, Optimizer

    +

    Trainer for memory agents. By default it is a minimizer.

    +

    This optimizer simply adds memories to the MemoryOp using a memory factory.

    +
    +
    +aggregate_trajectory(trajectory: Trajectory) → None[source]
    +

    Aggregate transitions from a single trajectory to construct training samples.

    +
    + +
    +
    +async default_memory_factory(output_op: Op[TOutput], memory_template: str, example_buffer: Iterable[tuple[CallID, CallID, float, JsonValue]]) → list[Memory]
    +
    + +
    +
    +example_buffer: list[tuple[CallID, CallID, float, JsonValue]]
    +
    + +
    +
    +classmethod from_agent(agent: MemoryAgent, **kwargs) → Self[source]
    +
    + +
    +
    +memory_factory: MemoryFactory
    +
    + +
    +
    +memory_op: MemoryOp
    +
    + +
    +
    +memory_template: str
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'example_buffer': FieldInfo(annotation=list[tuple[CallID, CallID, float, JsonValue]], required=False, default_factory=list), 'memory_factory': FieldInfo(annotation=MemoryFactory, required=False, default=<function _default_memory_factory>, description="Async function to make Memories from an example buffer. It's async so this can involve an LLM completion if desired.", exclude=True), 'memory_op': FieldInfo(annotation=MemoryOp, required=True), 'memory_template': FieldInfo(annotation=str, required=False, default='Input: {input}\nOutput: {output}\nReward: {value}', description="Template for a Memory's string representation."), 'output_op': FieldInfo(annotation=Op, required=True), 'reward_discount': FieldInfo(annotation=float, required=False, default=1.0), 'steps': FieldInfo(annotation=int, required=False, default=0)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +output_op: Op
    +
    + +
    +
    +reward_discount: float
    +
    + +
    +
    +steps: int
    +
    + +
    +
    +async update() → None[source]
    +

    Create new memories from the example buffer and add them to MemoryOp.

    +
    + +
    + +
    +
    +class ldp.alg.optimizer.Optimizer[source]
    +

    Bases: ABC

    +

    Base class for all optimizers.

    +
    +
    +aggregate(trajectories: Iterable[Trajectory], show_pbar: bool = False) → None[source]
    +

    Aggregate trajectories to construct training samples.

    +
    + +
    +
    +abstract aggregate_trajectory(trajectory: Trajectory) → None[source]
    +

    Aggregate transitions from a single trajectory to construct training samples.

    +
    + +
    +
    +abstract async update() → None[source]
    +

    Update the model based on the aggregated samples.

    +
    + +
    + +
    +
    +class ldp.alg.optimizer.OptimizerConfig(*, optimizer_type: str | None = None, optimizer_kwargs: dict[str, Any] = None)[source]
    +

    Bases: BaseModel

    +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'optimizer_kwargs': FieldInfo(annotation=dict[str, Any], required=False, default_factory=dict), 'optimizer_type': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +optimizer_kwargs: dict[str, Any]
    +
    + +
    +
    +optimizer_type: str | None
    +
    + +
    + +
    +
    +class ldp.alg.optimizer.PositiveMemoryOpt(*, memory_op: ~ldp.graph.common_ops.MemoryOp, output_op: ~ldp.graph.ops.Op, reward_discount: float = 1.0, memory_factory: ~ldp.alg.optimizer.memory.MemoryFactory = <function _default_memory_factory>, memory_template: str = 'Input: {input}\nOutput: {output}\nReward: {value}', steps: int = 0, example_buffer: list[tuple[~ldp.graph.op_utils.CallID, ~ldp.graph.op_utils.CallID, float, JsonValue]] = None)[source]
    +

    Bases: MemoryOpt

    +
    +
    +example_buffer: list[tuple[CallID, CallID, float, JsonValue]]
    +
    + +
    +
    +memory_factory: MemoryFactory
    +
    + +
    +
    +memory_op: MemoryOp
    +
    + +
    +
    +memory_template: str
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'example_buffer': FieldInfo(annotation=list[tuple[CallID, CallID, float, JsonValue]], required=False, default_factory=list), 'memory_factory': FieldInfo(annotation=MemoryFactory, required=False, default=<function _default_memory_factory>, description="Async function to make Memories from an example buffer. It's async so this can involve an LLM completion if desired.", exclude=True), 'memory_op': FieldInfo(annotation=MemoryOp, required=True), 'memory_template': FieldInfo(annotation=str, required=False, default='Input: {input}\nOutput: {output}\nReward: {value}', description="Template for a Memory's string representation."), 'output_op': FieldInfo(annotation=Op, required=True), 'reward_discount': FieldInfo(annotation=float, required=False, default=1.0), 'steps': FieldInfo(annotation=int, required=False, default=0)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +output_op: Op
    +
    + +
    +
    +reward_discount: float
    +
    + +
    +
    +steps: int
    +
    + +
    + +
    +
    +ldp.alg.optimizer.default_optimizer_factory(agent: Agent, optimizer_cls: str | type[Optimizer] | None = None, **optimizer_kwargs) → Optimizer[source]
    +

    A method that constructs a default optimizer for commonly-used agents.

    +
    +
    Parameters:
    +
      +

    • agent – Agent to construct the optimizer for.

      • +

    • optimizer_cls – The optimizer class to use. If not specified, we will try a +default based on the provided agent.

      • +

    • optimizer_kwargs – Arguments forwarded to optimizer_cls.

      • +
    +
    +
    Returns:
    +

    Instantiated optimizer.

    +
    +
    +
    + +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/ldp.graph.html b/docs/_build/html/ldp.graph.html new file mode 100644 index 00000000..6f382f9d --- /dev/null +++ b/docs/_build/html/ldp.graph.html @@ -0,0 +1,2324 @@ + + + + + + + + ldp.graph package — ldp documentation + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    ldp.graph package

    +
    +

    Subpackages

    +
    + +
    +
    +
    +

    Submodules

    +
    +
    +

    ldp.graph.async_torch module

    +
    +
    +class ldp.graph.async_torch.AsyncTorchModule(module: ~torch.nn.modules.module.Module, batch_size: int, max_wait_interval: float, collate_fn: ~collections.abc.Callable = <function default_collate>, decollate_fn: ~collections.abc.Callable = <class 'list'>, module_call_fn: ~collections.abc.Callable = <function AsyncTorchModule.<lambda>>)[source]
    +

    Bases: AsyncBufferedWorker

    +
    + +
    +
    +ldp.graph.async_torch.async_protect_torch_call(module: ~torch.nn.modules.module.Module, module_call_fn: ~collections.abc.Callable = <function <lambda>>, no_grad: bool = False, autocast_dtype: ~torch.dtype | None = None, autocast_device_type=None) → Callable[source]
    +
    + +
    +
    +

    ldp.graph.common_ops module

    +

    This module contains commonly-used Op implementations.

    +
    +
    +class ldp.graph.common_ops.Cacheable(co: Awaitable[TResult])[source]
    +

    Bases: Generic[TResult]

    +
    +
    +async get_result() → TResult | None[source]
    +
    + +
    + +
    +
    +class ldp.graph.common_ops.ConfigOp(*args, **kwargs)[source]
    +

    Bases: Op[TConfig], Generic[TConfig]

    +

    An operation that contains a configuration object.

    +
    +
    +classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async forward() → TConfig[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    + +
    +
    +class ldp.graph.common_ops.EmbeddingOp(*args, **kwargs)[source]
    +

    Bases: Op

    +

    A general operation for embedding text using LiteLLM.

    +
    +
    +classmethod backward(ctx: OpCtx, input_args, input_kwargs, grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async forward(string_input: str) → ndarray[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    + +
    +
    +class ldp.graph.common_ops.FxnOp(*args, **kwargs)[source]
    +

    Bases: Op[TOutput]

    +

    Wrap a function for a straight through gradient approximation for all args/kwargs.

    +

    Basically, consider the fxn as a transform upon the inputs during the forward pass, +and propagating the same gradient for all inputs during the backward pass.

    +
    +
    +classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async forward(*args, **kwargs) → TOutput[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    + +
    +
    +class ldp.graph.common_ops.IdentityOp(*args, **kwargs)[source]
    +

    Bases: Op[TOutput]

    +

    An operation that simply returns the input value.

    +

    NOTE: this op is equivalent to FxnOp(lambda x: x).

    +
    +
    +classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async forward(value: TOutput) → TOutput[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    + +
    +
    +class ldp.graph.common_ops.LLMCallOp(*args, **kwargs)[source]
    +

    Bases: Op[Message]

    +

    An operation for LLM calls interaction.

    +
    +
    +classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async compute_logprob(raw_log_p: float | None, temperature: float, model: LLMModel, **model_kwargs) → float | None[source]
    +

    This method computes a Monte Carlo estimate of logprob for a given temperature.

    +

    It takes as input the logprob at T=1. The derivation is in Section 5.1 of the Aviary notes.

    +
    + +
    +
    +async forward(config: dict, msgs: list[Message], tools: list[Tool] = None, tool_choice: Tool | str | None = LLMModel.TOOL_CHOICE_REQUIRED) → ToolRequestMessage[source]
    +
    +async forward(config: dict, msgs: list[Message], tools: None = None, tool_choice: str | None = LLMModel.TOOL_CHOICE_REQUIRED) → Message
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    +
    +get_examples() → list[tuple[LLMResult, float]][source]
    +
    + +
    + +
    +
    +class ldp.graph.common_ops.MemoryOp(*args, **kwargs)[source]
    +

    Bases: Op[list[Memory]]

    +

    An operation for managing memory retrieval and storage.

    +
    +
    +classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass for memory retrieval - goes back to item.

    +
    + +
    +
    +async forward(query: str, input: str | None = None, matches: int = 3) → list[Memory][source]
    +

    Retrieve relevant memories based on a query.

    +
    + +
    + +
    +
    +class ldp.graph.common_ops.PromptOp(*args, **kwargs)[source]
    +

    Bases: FxnOp[str]

    +

    An operation that formats kwargs into a prompt string.

    +
    + +
    +
    +class ldp.graph.common_ops.StopGradOp(*args, **kwargs)[source]
    +

    Bases: IdentityOp[TOutput]

    +

    Pass through Op that terminates gradients in the backward pass.

    +
    +
    +classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    + +
    +
    +ldp.graph.common_ops.async_cache(func)[source]
    +
    + +
    +
    +ldp.graph.common_ops.logsumexp(a: ndarray | list[float]) → float[source]
    +
    + +
    +
    +

    ldp.graph.gradient_estimators module

    +

    This module defines various gradient estimators that can be patched in during backward passes.

    +
    +
    +class ldp.graph.gradient_estimators.TorchParamBackwardEstimator(module: Module)[source]
    +

    Bases: object

    +

    Gradient estimator for TorchOp internal parameters.

    +

    This estimator computes gradients with respect to the internal parameters of a +torch.nn.Module by calling the backward method of the estimator instead of the default +backward method of TorchOp. Computed gradients are stored in the context of the operation +under the key “grad_params”.

    +

    Examples

    +
    >>> torch_module = torch.nn.Sequential(
+...     torch.nn.Linear(4, 4),
+...     torch.nn.Linear(4, 1),
+... )
+>>> torch_op = TorchOp(torch_module)
+>>> estimator = TorchParamBackwardEstimator(torch_module)
+>>> result = await torch_op(torch.randn(4, requires_grad=True))
+>>> result.compute_grads(backward_fns={"TorchOp": estimator.backward})
+
    +
    +
    +

    Note

    +

    This estimator is only compatible with TorchOp operations.

    +
    +
    +
    +backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +
    + +
    + +
    +
    +ldp.graph.gradient_estimators.assign_constant_grads(input_args: list[OpResult[TOutput] | TOutput], input_kwargs: dict[str, OpResult[TOutput] | TOutput], value: Any, descend: bool = True)[source]
    +
    + +
    +
    +ldp.graph.gradient_estimators.assign_default_grads(input_grads: GradInType, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], default_grad_val: float = 0.0) → GradInType[source]
    +

    Sets a default value of default_grad_val for every element in input_grads.

    +

    Example: +- input_kwargs = {“a”: {“b”: 1, “c”: 2}}, +- input_grad_kwargs = {“a”: {“b”: 0.1}} +Output: input_grads[1] = {“a”: {“b”: 0.1, “c”: default_grad_val}}

    +
    +
    Returns:
    +

    +
    A tuple containing the updated input_grad_args and

    input_grad_kwargs with default values assigned where necessary.

    +
    +
    +

    +
    +
    Return type:
    +

    GradInType

    +
    +
    +
    + +
    +
    +ldp.graph.gradient_estimators.llm_straight_through_estimator(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Straight-through for an LLM: descend into the config, but not msgs/tools/tool_calls.

    +

    See LLMCallOp.backward() for more details on this choice. +Don’t bother checking that input_args/input_kwargs have the right structure, +since compute_grads() will raise if not.

    +
    + +
    +
    +ldp.graph.gradient_estimators.stop_grad(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +
    + +
    +
    +ldp.graph.gradient_estimators.straight_through_estimator(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID, descend: bool = True) → GradInType[source]
    +
    + +
    +
    +ldp.graph.gradient_estimators.zero_estimator(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Sets the gradient of all inputs to zero.

    +

    Note that this is not the same as truncating the compute graph (stop_grad), +since upstream nodes can still optimize their logprobs. The zero estimator +the unbiased choice if we have no information about the gradient.

    +
    + +
    +
    +

    ldp.graph.loss_ops module

    +

    This module contains loss Op implementations.

    +
    +
    +class ldp.graph.loss_ops.MSELossOp(*args, **kwargs)[source]
    +

    Bases: Op

    +
    +
    +classmethod backward(ctx: OpCtx, input_args, input_kwargs, grad_output: Sequence[StructureKV[K, V]] | Mapping[str, StructureKV[K, V]] | V, call_id: CallID) → tuple[Sequence[Sequence[StructureKV[K, V]] | Mapping[str, StructureKV[K, V]] | V | None], Mapping[str, Sequence[StructureKV[K, V]] | Mapping[str, StructureKV[K, V]] | V | None]][source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async forward(prediction: npt.NDArray | torch.Tensor, target: npt.NDArray | torch.Tensor) → float | torch.Tensor[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    + +
    +
    +

    ldp.graph.memory module

    +
    +
    +class ldp.graph.memory.Memory(*, query: str, input: str | None = None, output: str, value: float | str, metadata: JsonValue = None, run_id: UUID | None = None, template: str = 'Input: {input}\nOutput: {output}\nValue: {value}')[source]
    +

    Bases: BaseModel

    +

    A single memory about an input, output, and value tuple.

    +

    A memory is a record of an input, output, and resulting value. Typically used +for prompting a language model. Or, it could be about a whole forward pass where +the input is the observation and the output is the action taken. +The query is optionally different and used for +retrieving the memory. For example, it could be much larger because it won’t +be formatted in the resulting prompt.

    +
    +
    +classmethod ensure_query(data: Any) → Any[source]
    +

    Copy input to match the query if input is None.

    +
    + +
    +
    +classmethod from_ops(mem_op: MemoryOp, mem_call_id: CallID, output_op: Op[TOutput], output_call_id: CallID, value: float, **kwargs) → Self[source]
    +

    Create from a MemoryOp, output Op, and their call IDs.

    +
    + +
    +
    +input: str | None
    +
    + +
    +
    +metadata: JsonValue
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'input': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Some input (e.g. prompt to LLM, observation). If None (default), the input is set to match the query.'), 'metadata': FieldInfo(annotation=JsonValue, required=False, default_factory=dict, description='Optional JSON metadata to store with the memory. An example is storing information an optimizer can use at training time.'), 'output': FieldInfo(annotation=str, required=True, description='Some output (e.g. tool selection).'), 'query': FieldInfo(annotation=str, required=True, description='String to be embedded into a retrieval key for a memory index.'), 'run_id': FieldInfo(annotation=Union[UUID, NoneType], required=False, default=None, description='Associate run_id for debugging purposes to trace which forward pass generated the memory.'), 'template': FieldInfo(annotation=str, required=False, default='Input: {input}\nOutput: {output}\nValue: {value}'), 'value': FieldInfo(annotation=Union[float, str], required=True, description="Measure of the output's quality (e.g. loss).")}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +output: str
    +
    + +
    +
    +query: str
    +
    + +
    +
    +run_id: UUID | None
    +
    + +
    +
    +template: str
    +
    + +
    +
    +value: float | str
    +
    + +
    + +
    +
    +class ldp.graph.memory.MemoryModel(*, embedding_model: EmbeddingModel = None, memories: dict[int, Memory] = None)[source]
    +

    Bases: BaseModel, Generic[TIndex], ABC

    +

    A collection of memories with retrieval.

    +
    +
    +DEFAULT_MEMORY_MATCHES: ClassVar[int] = 3
    +
    + +
    +
    +async add_memory(memory: Memory) → None[source]
    +
    + +
    +
    +embedding_model: EmbeddingModel
    +
    + +
    +
    +classmethod enforce_empty(v: dict) → dict[source]
    +
    + +
    +
    +async get_memory(query: str, matches: int = 3) → list[Memory][source]
    +
    + +
    +
    +memories: dict[int, Memory]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'embedding_model': FieldInfo(annotation=EmbeddingModel, required=False, default_factory=<lambda>), 'memories': FieldInfo(annotation=dict[int, Memory], required=False, default_factory=dict)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +model_post_init(context: Any, /) → None
    +

    This function is meant to behave like a BaseModel method to initialise private attributes.

    +

    It takes context as an argument since that’s what pydantic-core passes when calling it.

    +
    +
    Parameters:
    +
      +

    • self – The BaseModel instance.

      • +

    • context – The context.

      • +
    +
    +
    +
    + +
    +
    +safe_access_index() → AsyncIterator[TIndex][source]
    +

    Get the internal Index under the protection of an internal Lock.

    +
    + +
    + +
    +
    +class ldp.graph.memory.MemoryModel(*, embedding_model: EmbeddingModel = None, memories: dict[int, Memory] = None)[source]
    +

    Bases: BaseModel, Generic[TIndex], ABC

    +

    A collection of memories with retrieval.

    +
    +
    +DEFAULT_MEMORY_MATCHES: ClassVar[int] = 3
    +
    + +
    +
    +async add_memory(memory: Memory) → None[source]
    +
    + +
    +
    +embedding_model: EmbeddingModel
    +
    + +
    +
    +classmethod enforce_empty(v: dict) → dict[source]
    +
    + +
    +
    +async get_memory(query: str, matches: int = 3) → list[Memory][source]
    +
    + +
    +
    +memories: dict[int, Memory]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'embedding_model': FieldInfo(annotation=EmbeddingModel, required=False, default_factory=<lambda>), 'memories': FieldInfo(annotation=dict[int, Memory], required=False, default_factory=dict)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +model_post_init(context: Any, /) → None
    +

    This function is meant to behave like a BaseModel method to initialise private attributes.

    +

    It takes context as an argument since that’s what pydantic-core passes when calling it.

    +
    +
    Parameters:
    +
      +

    • self – The BaseModel instance.

      • +

    • context – The context.

      • +
    +
    +
    +
    + +
    +
    +safe_access_index() → AsyncIterator[TIndex][source]
    +

    Get the internal Index under the protection of an internal Lock.

    +
    + +
    + +
    +
    +class ldp.graph.memory.UIndexMemoryModel(*, embedding_model: EmbeddingModel = None, memories: dict[int, Memory] = None)[source]
    +

    Bases: MemoryModel[Index]

    +

    Memory model using a U-Search index.

    +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'embedding_model': FieldInfo(annotation=EmbeddingModel, required=False, default_factory=<lambda>), 'memories': FieldInfo(annotation=dict[int, Memory], required=False, default_factory=dict)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +model_post_init(context: Any, /) → None
    +

    We need to both initialize private attributes and call the user-defined model_post_init +method.

    +
    + +
    + +
    +
    +

    ldp.graph.op_utils module

    +
    +
    +class ldp.graph.op_utils.CallID(run_id: str | UUID, fwd_id: str | UUID)[source]
    +

    Bases: BaseModel

    +
    +
    +fwd_id: UUID
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'fwd_id': FieldInfo(annotation=UUID, required=True), 'run_id': FieldInfo(annotation=UUID, required=True)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +run_id: UUID
    +
    + +
    +
    +serialize_uuid(value: UUID) → str[source]
    +
    + +
    +
    +classmethod validate_uuid(value: UUID | str) → UUID[source]
    +
    + +
    + +
    +
    +ldp.graph.op_utils.compute_graph() → AsyncIterator[UUID][source]
    +

    Initialize a compute graph by setting a run ID.

    +

    If a run ID is already set (i.e. we are already inside a +get_run_id() context), then the existing run ID is returned. +Otherwise, a new UUID is created.

    +
    + +
    +
    +ldp.graph.op_utils.get_call_id() → CallID[source]
    +

    Get the current call ID.

    +
    + +
    +
    +ldp.graph.op_utils.get_run_id() → UUID[source]
    +

    Get the current run ID.

    +
    + +
    +
    +ldp.graph.op_utils.get_training_mode() → bool[source]
    +

    Get the current training mode.

    +
    + +
    +
    +ldp.graph.op_utils.op_call() → AsyncIterator[CallID][source]
    +

    Decorate an op call with a call ID.

    +

    If a call ID is already set (i.e. we are already inside an op call), +then the existing call ID is returned. +Otherwise, a new UUID is created.

    +
    + +
    +
    +ldp.graph.op_utils.set_training_mode(training_mode: bool) → None[source]
    +

    Set the training mode.

    +
    + +
    +
    +

    ldp.graph.ops module

    +

    This module defines the Op class and its helper classes.

    +
    +
    +class ldp.graph.ops.Op(*args, **kwargs)[source]
    +

    Bases: ABC, Generic[TOutput]

    +

    An operation that is ‘differentiable’ and can be used in an optimizer.

    +

    Think torch.autograd.Function that can also be applied to non-differentiable +operations like prompt template formatting or Python function calls.

    +

    These form a forward computation graph when composed with other Ops via +__call__. In training mode, this graph is constructed dynamically.

    +
    +
    +abstract classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs, grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +clear_ctx() → None[source]
    +
    + +
    +
    +ctx: OpCtx
    +
    + +
    +
    +abstract async forward(*args, **kwargs) → TOutput[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    +
    +get_call_ids(run_ids: Collection[UUID] | None = None) → set[CallID][source]
    +
    + +
    +
    +get_input_grads(call_id: CallID) → GradInType[source]
    +
    + +
    +
    +name: str
    +
    + +
    +
    +set_name(name: str) → None[source]
    +
    + +
    + +
    +
    +class ldp.graph.ops.OpCtx(*, op_name: str, data: dict = None)[source]
    +

    Bases: BaseModel

    +
    +
    +classmethod clear_contexts(op_names: Iterable[str] | None = None) → None[source]
    +

    Clear the data in all contexts. If op_names is provided, only clear those contexts.

    +
    + +
    +
    +data: dict
    +
    + +
    +
    +get(call_id: ~ldp.graph.op_utils.CallID, key: str, default: ~typing.Any = <object object>) → Any[source]
    +

    Get an attribute with an optional default, emulating dict.get.

    +
    + +
    +
    +get_input_grads(call_id: CallID) → GradInType[source]
    +
    + +
    +
    +classmethod get_or_create(op_name: str) → OpCtx[source]
    +

    Return an OpCtx corresponding to the Op with the given name.

    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'data': FieldInfo(annotation=dict, required=False, default_factory=<lambda>, description='Maps run_id -> (fwd_id, key) -> value. data is excluded from model_dump() etc because we do not use Pydantic to persist context information. That should be done via the DB backend instead. OpCtx will serialize op_name, which is enough to rehydrate from the DB.', exclude=True), 'op_name': FieldInfo(annotation=str, required=True)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +model_post_init(context: Any, /) → None
    +

    This function is meant to behave like a BaseModel method to initialise private attributes.

    +

    It takes context as an argument since that’s what pydantic-core passes when calling it.

    +
    +
    Parameters:
    +
      +

    • self – The BaseModel instance.

      • +

    • context – The context.

      • +
    +
    +
    +
    + +
    +
    +op_name: str
    +
    + +
    +
    +update(call_id: CallID, key: str, value: Any)[source]
    +
    + +
    + +
    +
    +class ldp.graph.ops.OpResult(call_id: CallID | Any, op_name: str, op_class_name: str, value: TOutput)[source]
    +

    Bases: Generic[TOutput]

    +

    Result of a forward pass, used in the compute graph.

    +
    +
    +compute_grads(grad_output: tree.Structure | None = None, backward_fns: Mapping[str | type, BackwardsType] | None = None) → None[source]
    +

    Compute the gradient of the backward graph in-place.

    +

    This executes topological traversal. +It is up to the Op to:

    +
    +
      +

    1. define the backward computation

      2. +

    2. store internal gradients for optimizer updates.

      3. +
    +
    +
    + +
    +
    +property ctx: OpCtx
    +
    + +
    +
    +classmethod from_dict(t_output: type[TOutput], dump: dict[str, Any]) → OpResult[TOutput][source]
    +
    + +
    +
    +get_compute_graph(backward: bool = True) → DiGraph[source]
    +

    Construct a directed graph of the compute graph that led to this OpResult.

    +
    +
    Parameters:
    +

    backward – If True (default), constructs the backwards graph in which outputs +point to inputs. If False, constructs the forward call graph. +For most cases (e.g. backprop), backward=True is desirable.

    +
    +
    Returns:
    +

    A digraph in which nodes are OpResults.

    +
    +
    +
    + +
    +
    +property grad: tree.Structure | None
    +

    Returns d ln(P_{compute_graph}) / d self or None if gradients have not been computed.

    +
    + +
    +
    +property inputs: tuple[list[OpResult[TOutput] | TOutput], dict[str, OpResult[TOutput] | TOutput]]
    +
    + +
    +
    +property logprob: float | None
    +
    + +
    +
    +property op_class: type[Op]
    +
    + +
    +
    +property run_id: UUID
    +
    + +
    +
    +to_dict() → dict[str, Any][source]
    +
    + +
    +
    +traverse(topological_order: bool = True, filter_fn: ~collections.abc.Callable[[~ldp.graph.ops.OpResult], bool] = <function OpResult.<lambda>>) → Iterator[OpResult][source]
    +

    Traverse the compute graph that led to this OpResult.

    +
    +
    Parameters:
    +
      +

    • topological_order – If True, traverse the backwards graph in topological +order. This requires having the whole graph in memory. If False, +traverse the backwards graph in depth-first order. This can be done +lazily and is useful if we are trying to hydrate the graph node-by-node. +Most user-facing cases can leave this as True. Defaults to True.

      • +

    • filter_fn – Will only yield nodes that pass this filter function. Note that +nodes that fail will still be traversed.

      • +
    +
    +
    Yields:
    +

    An iterator over the nodes of this graph.

    +
    +
    +
    + +
    +
    +static unwrap_value(result: OpResult[TOutput] | TOutput) → TOutput[source]
    +
    + +
    + +
    +
    +ldp.graph.ops.resolve_fully_qualified_name(cls: type) → str[source]
    +
    + +
    +
    +

    ldp.graph.torch_ops module

    +
    +
    +class ldp.graph.torch_ops.TorchOp(*args, **kwargs)[source]
    +

    Bases: Op[Tensor]

    +

    An operation that wraps a PyTorch module.

    +
    +
    +CTX_TENSOR_INPUT_KEY: ClassVar[str] = 'tensor_input'
    +
    + +
    +
    +classmethod backward(ctx: OpCtx, input_args: list[OpResult[TOutput] | TOutput], input_kwargs: dict[str, OpResult[TOutput] | TOutput], grad_output: float | Tensor, call_id: CallID) → tuple[Sequence[Sequence[StructureKV[K, V]] | Mapping[str, StructureKV[K, V]] | V | None], Mapping[str, Sequence[StructureKV[K, V]] | Mapping[str, StructureKV[K, V]] | V | None]][source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async forward(*args, **kwargs: Any) → Tensor[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    + +
    +
    +ldp.graph.torch_ops.store_tensor_inputs(ctx: OpCtx, key: str, tensor_args: Sequence[Tensor], tensor_kwargs: Mapping[str, Tensor], fwd_args: Sequence[Parameter], detach: bool = False) → None[source]
    +
    + +
    +
    +

    Module contents

    +
    +
    +class ldp.graph.CallID(run_id: str | UUID, fwd_id: str | UUID)[source]
    +

    Bases: BaseModel

    +
    +
    +fwd_id: UUID
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'fwd_id': FieldInfo(annotation=UUID, required=True), 'run_id': FieldInfo(annotation=UUID, required=True)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +run_id: UUID
    +
    + +
    +
    +serialize_uuid(value: UUID) → str[source]
    +
    + +
    +
    +classmethod validate_uuid(value: UUID | str) → UUID[source]
    +
    + +
    + +
    +
    +class ldp.graph.ConfigOp(*args, **kwargs)[source]
    +

    Bases: Op[TConfig], Generic[TConfig]

    +

    An operation that contains a configuration object.

    +
    +
    +classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async forward() → TConfig[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    + +
    +
    +class ldp.graph.EmbeddingOp(*args, **kwargs)[source]
    +

    Bases: Op

    +

    A general operation for embedding text using LiteLLM.

    +
    +
    +classmethod backward(ctx: OpCtx, input_args, input_kwargs, grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async forward(string_input: str) → ndarray[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    + +
    +
    +class ldp.graph.FxnOp(*args, **kwargs)[source]
    +

    Bases: Op[TOutput]

    +

    Wrap a function for a straight through gradient approximation for all args/kwargs.

    +

    Basically, consider the fxn as a transform upon the inputs during the forward pass, +and propagating the same gradient for all inputs during the backward pass.

    +
    +
    +classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async forward(*args, **kwargs) → TOutput[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    + +
    +
    +class ldp.graph.IdentityOp(*args, **kwargs)[source]
    +

    Bases: Op[TOutput]

    +

    An operation that simply returns the input value.

    +

    NOTE: this op is equivalent to FxnOp(lambda x: x).

    +
    +
    +classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async forward(value: TOutput) → TOutput[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    + +
    +
    +class ldp.graph.LLMCallOp(*args, **kwargs)[source]
    +

    Bases: Op[Message]

    +

    An operation for LLM calls interaction.

    +
    +
    +classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async compute_logprob(raw_log_p: float | None, temperature: float, model: LLMModel, **model_kwargs) → float | None[source]
    +

    This method computes a Monte Carlo estimate of logprob for a given temperature.

    +

    It takes as input the logprob at T=1. The derivation is in Section 5.1 of the Aviary notes.

    +
    + +
    +
    +async forward(config: dict, msgs: list[Message], tools: list[Tool] = None, tool_choice: Tool | str | None = LLMModel.TOOL_CHOICE_REQUIRED) → ToolRequestMessage[source]
    +
    +async forward(config: dict, msgs: list[Message], tools: None = None, tool_choice: str | None = LLMModel.TOOL_CHOICE_REQUIRED) → Message
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    +
    +get_examples() → list[tuple[LLMResult, float]][source]
    +
    + +
    + +
    +
    +class ldp.graph.MSELossOp(*args, **kwargs)[source]
    +

    Bases: Op

    +
    +
    +classmethod backward(ctx: OpCtx, input_args, input_kwargs, grad_output: Sequence[StructureKV[K, V]] | Mapping[str, StructureKV[K, V]] | V, call_id: CallID) → tuple[Sequence[Sequence[StructureKV[K, V]] | Mapping[str, StructureKV[K, V]] | V | None], Mapping[str, Sequence[StructureKV[K, V]] | Mapping[str, StructureKV[K, V]] | V | None]][source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +async forward(prediction: npt.NDArray | torch.Tensor, target: npt.NDArray | torch.Tensor) → float | torch.Tensor[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    + +
    +
    +class ldp.graph.Memory(*, query: str, input: str | None = None, output: str, value: float | str, metadata: JsonValue = None, run_id: UUID | None = None, template: str = 'Input: {input}\nOutput: {output}\nValue: {value}')[source]
    +

    Bases: BaseModel

    +

    A single memory about an input, output, and value tuple.

    +

    A memory is a record of an input, output, and resulting value. Typically used +for prompting a language model. Or, it could be about a whole forward pass where +the input is the observation and the output is the action taken. +The query is optionally different and used for +retrieving the memory. For example, it could be much larger because it won’t +be formatted in the resulting prompt.

    +
    +
    +classmethod ensure_query(data: Any) → Any[source]
    +

    Copy input to match the query if input is None.

    +
    + +
    +
    +classmethod from_ops(mem_op: MemoryOp, mem_call_id: CallID, output_op: Op[TOutput], output_call_id: CallID, value: float, **kwargs) → Self[source]
    +

    Create from a MemoryOp, output Op, and their call IDs.

    +
    + +
    +
    +input: str | None
    +
    + +
    +
    +metadata: JsonValue
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'input': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='Some input (e.g. prompt to LLM, observation). If None (default), the input is set to match the query.'), 'metadata': FieldInfo(annotation=JsonValue, required=False, default_factory=dict, description='Optional JSON metadata to store with the memory. An example is storing information an optimizer can use at training time.'), 'output': FieldInfo(annotation=str, required=True, description='Some output (e.g. tool selection).'), 'query': FieldInfo(annotation=str, required=True, description='String to be embedded into a retrieval key for a memory index.'), 'run_id': FieldInfo(annotation=Union[UUID, NoneType], required=False, default=None, description='Associate run_id for debugging purposes to trace which forward pass generated the memory.'), 'template': FieldInfo(annotation=str, required=False, default='Input: {input}\nOutput: {output}\nValue: {value}'), 'value': FieldInfo(annotation=Union[float, str], required=True, description="Measure of the output's quality (e.g. loss).")}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +output: str
    +
    + +
    +
    +query: str
    +
    + +
    +
    +run_id: UUID | None
    +
    + +
    +
    +template: str
    +
    + +
    +
    +value: float | str
    +
    + +
    + +
    +
    +class ldp.graph.MemoryModel(*, embedding_model: EmbeddingModel = None, memories: dict[int, Memory] = None)[source]
    +

    Bases: BaseModel, Generic[TIndex], ABC

    +

    A collection of memories with retrieval.

    +
    +
    +DEFAULT_MEMORY_MATCHES: ClassVar[int] = 3
    +
    + +
    +
    +async add_memory(memory: Memory) → None[source]
    +
    + +
    +
    +embedding_model: EmbeddingModel
    +
    + +
    +
    +classmethod enforce_empty(v: dict) → dict[source]
    +
    + +
    +
    +async get_memory(query: str, matches: int = 3) → list[Memory][source]
    +
    + +
    +
    +memories: dict[int, Memory]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'embedding_model': FieldInfo(annotation=EmbeddingModel, required=False, default_factory=<lambda>), 'memories': FieldInfo(annotation=dict[int, Memory], required=False, default_factory=dict)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +model_post_init(context: Any, /) → None
    +

    This function is meant to behave like a BaseModel method to initialise private attributes.

    +

    It takes context as an argument since that’s what pydantic-core passes when calling it.

    +
    +
    Parameters:
    +
      +

    • self – The BaseModel instance.

      • +

    • context – The context.

      • +
    +
    +
    +
    + +
    +
    +safe_access_index() → AsyncIterator[TIndex][source]
    +

    Get the internal Index under the protection of an internal Lock.

    +
    + +
    + +
    +
    +class ldp.graph.MemoryOp(*args, **kwargs)[source]
    +

    Bases: Op[list[Memory]]

    +

    An operation for managing memory retrieval and storage.

    +
    +
    +classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs: dict[str, ResultOrValue], grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass for memory retrieval - goes back to item.

    +
    + +
    +
    +async forward(query: str, input: str | None = None, matches: int = 3) → list[Memory][source]
    +

    Retrieve relevant memories based on a query.

    +
    + +
    + +
    +
    +class ldp.graph.Op(*args, **kwargs)[source]
    +

    Bases: ABC, Generic[TOutput]

    +

    An operation that is ‘differentiable’ and can be used in an optimizer.

    +

    Think torch.autograd.Function that can also be applied to non-differentiable +operations like prompt template formatting or Python function calls.

    +

    These form a forward computation graph when composed with other Ops via +__call__. In training mode, this graph is constructed dynamically.

    +
    +
    +abstract classmethod backward(ctx: OpCtx, input_args: list[ResultOrValue], input_kwargs, grad_output: tree.Structure, call_id: CallID) → GradInType[source]
    +

    Backward pass of the Op.

    +
    +
    Parameters:
    +
      +

    • ctx – Context that was used during the forward pass.

      • +

    • input_args – Variable-length input arguments passed to forward, i.e. +via *args.

      • +

    • input_kwargs – All other arguments passed to forward pass.

      • +

    • grad_output – A list of backpropagated gradients from each consumer +of the output of the forward pass. It is up to the implementation +to decide how to aggregate these gradients (e.g. in most cases summing).

      • +

    • call_id – Call ID of the forward pass.

      • +
    +
    +
    Returns:
    +

    +
    d log(p) / d input for each input to the forward pass.

    It should include gradients for all input positional and keyword +arguments. Set to None for gradients that should terminate.

    +
    +
    +

    +
    +
    Return type:
    +

    grad_input

    +
    +
    +
    + +
    +
    +clear_ctx() → None[source]
    +
    + +
    +
    +ctx: OpCtx
    +
    + +
    +
    +abstract async forward(*args, **kwargs) → TOutput[source]
    +

    Forward pass of the Op. Must accept call_id as an argument.

    +
    +
    Returns:
    +

    +
    Depending on this Op’s purpose, the return may be considered an action

    (e.g. a tool call) or it may not (e.g. a loss calculation).

    +
    +
    +

    +
    +
    +
    + +
    +
    +get_call_ids(run_ids: Collection[UUID] | None = None) → set[CallID][source]
    +
    + +
    +
    +get_input_grads(call_id: CallID) → GradInType[source]
    +
    + +
    +
    +name: str
    +
    + +
    +
    +set_name(name: str) → None[source]
    +
    + +
    + +
    +
    +class ldp.graph.OpCtx(*, op_name: str, data: dict = None)[source]
    +

    Bases: BaseModel

    +
    +
    +classmethod clear_contexts(op_names: Iterable[str] | None = None) → None[source]
    +

    Clear the data in all contexts. If op_names is provided, only clear those contexts.

    +
    + +
    +
    +data: dict
    +
    + +
    +
    +get(call_id: ~ldp.graph.op_utils.CallID, key: str, default: ~typing.Any = <object object>) → Any[source]
    +

    Get an attribute with an optional default, emulating dict.get.

    +
    + +
    +
    +get_input_grads(call_id: CallID) → GradInType[source]
    +
    + +
    +
    +classmethod get_or_create(op_name: str) → OpCtx[source]
    +

    Return an OpCtx corresponding to the Op with the given name.

    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'data': FieldInfo(annotation=dict, required=False, default_factory=<lambda>, description='Maps run_id -> (fwd_id, key) -> value. data is excluded from model_dump() etc because we do not use Pydantic to persist context information. That should be done via the DB backend instead. OpCtx will serialize op_name, which is enough to rehydrate from the DB.', exclude=True), 'op_name': FieldInfo(annotation=str, required=True)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +model_post_init(context: Any, /) → None
    +

    This function is meant to behave like a BaseModel method to initialise private attributes.

    +

    It takes context as an argument since that’s what pydantic-core passes when calling it.

    +
    +
    Parameters:
    +
      +

    • self – The BaseModel instance.

      • +

    • context – The context.

      • +
    +
    +
    +
    + +
    +
    +op_name: str
    +
    + +
    +
    +update(call_id: CallID, key: str, value: Any)[source]
    +
    + +
    + +
    +
    +class ldp.graph.OpResult(call_id: CallID | Any, op_name: str, op_class_name: str, value: TOutput)[source]
    +

    Bases: Generic[TOutput]

    +

    Result of a forward pass, used in the compute graph.

    +
    +
    +compute_grads(grad_output: tree.Structure | None = None, backward_fns: Mapping[str | type, BackwardsType] | None = None) → None[source]
    +

    Compute the gradient of the backward graph in-place.

    +

    This executes topological traversal. +It is up to the Op to:

    +
    +
      +

    1. define the backward computation

      2. +

    2. store internal gradients for optimizer updates.

      3. +
    +
    +
    + +
    +
    +property ctx: OpCtx
    +
    + +
    +
    +classmethod from_dict(t_output: type[TOutput], dump: dict[str, Any]) → OpResult[TOutput][source]
    +
    + +
    +
    +get_compute_graph(backward: bool = True) → DiGraph[source]
    +

    Construct a directed graph of the compute graph that led to this OpResult.

    +
    +
    Parameters:
    +

    backward – If True (default), constructs the backwards graph in which outputs +point to inputs. If False, constructs the forward call graph. +For most cases (e.g. backprop), backward=True is desirable.

    +
    +
    Returns:
    +

    A digraph in which nodes are OpResults.

    +
    +
    +
    + +
    +
    +property grad: tree.Structure | None
    +

    Returns d ln(P_{compute_graph}) / d self or None if gradients have not been computed.

    +
    + +
    +
    +property inputs: tuple[list[OpResult[TOutput] | TOutput], dict[str, OpResult[TOutput] | TOutput]]
    +
    + +
    +
    +property logprob: float | None
    +
    + +
    +
    +property op_class: type[Op]
    +
    + +
    +
    +property run_id: UUID
    +
    + +
    +
    +to_dict() → dict[str, Any][source]
    +
    + +
    +
    +traverse(topological_order: bool = True, filter_fn: ~collections.abc.Callable[[~ldp.graph.ops.OpResult], bool] = <function OpResult.<lambda>>) → Iterator[OpResult][source]
    +

    Traverse the compute graph that led to this OpResult.

    +
    +
    Parameters:
    +
      +

    • topological_order – If True, traverse the backwards graph in topological +order. This requires having the whole graph in memory. If False, +traverse the backwards graph in depth-first order. This can be done +lazily and is useful if we are trying to hydrate the graph node-by-node. +Most user-facing cases can leave this as True. Defaults to True.

      • +

    • filter_fn – Will only yield nodes that pass this filter function. Note that +nodes that fail will still be traversed.

      • +
    +
    +
    Yields:
    +

    An iterator over the nodes of this graph.

    +
    +
    +
    + +
    +
    +static unwrap_value(result: OpResult[TOutput] | TOutput) → TOutput[source]
    +
    + +
    + +
    +
    +class ldp.graph.PromptOp(*args, **kwargs)[source]
    +

    Bases: FxnOp[str]

    +

    An operation that formats kwargs into a prompt string.

    +
    + +
    +
    +ldp.graph.compute_graph() → AsyncIterator[UUID][source]
    +

    Initialize a compute graph by setting a run ID.

    +

    If a run ID is already set (i.e. we are already inside a +get_run_id() context), then the existing run ID is returned. +Otherwise, a new UUID is created.

    +
    + +
    +
    +ldp.graph.get_call_id() → CallID[source]
    +

    Get the current call ID.

    +
    + +
    +
    +ldp.graph.get_run_id() → UUID[source]
    +

    Get the current run ID.

    +
    + +
    +
    +ldp.graph.get_training_mode() → bool[source]
    +

    Get the current training mode.

    +
    + +
    +
    +ldp.graph.op_call() → AsyncIterator[CallID][source]
    +

    Decorate an op call with a call ID.

    +

    If a call ID is already set (i.e. we are already inside an op call), +then the existing call ID is returned. +Otherwise, a new UUID is created.

    +
    + +
    +
    +ldp.graph.set_training_mode(training_mode: bool) → None[source]
    +

    Set the training mode.

    +
    + +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/ldp.graph.modules.html b/docs/_build/html/ldp.graph.modules.html new file mode 100644 index 00000000..da4bbe4d --- /dev/null +++ b/docs/_build/html/ldp.graph.modules.html @@ -0,0 +1,430 @@ + + + + + + + + ldp.graph.modules package — ldp documentation + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    ldp.graph.modules package

    +
    +

    Submodules

    +
    +
    +

    ldp.graph.modules.llm_call module

    +
    +
    +class ldp.graph.modules.llm_call.ParsedLLMCallModule(llm_model: dict[str, Any], parser: Callable[[...], TParsedMessage])[source]
    +

    Bases: Generic[TParsedMessage]

    +

    Module for a processing-based tool selection, with a learnable configuration.

    +
    + +
    +
    +

    ldp.graph.modules.react module

    +
    +
    +class ldp.graph.modules.react.ReActModule(llm_model: dict[str, Any], sys_prompt: str = 'Answer the following questions as best you can. You have access to the following tools:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: comma separated list of inputs to action as python tuple\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\n\nExample:\n\nThought: I need to use the get_weather tool\nAction: get_weather\nAction Input: "New York", 7\nObservation: The 7 day forecast for New York is [...]', tool_description_method: ToolDescriptionMethods = ToolDescriptionMethods.STR)[source]
    +

    Bases: object

    +

    An Act or ReAct module built to work with chat models.

    +

    Paper: https://arxiv.org/abs/2210.03629

    +

    The ReAct style is like so, and note Act style has no ‘Thought: ‘ entries: +System:

    +
    +

    Answer the following questions as best you can. You have access to the following tools:

    +
    +

    {tools}

    +

    Use the following format:

    +

    Thought: you should always think about what to do +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +… (this Thought/Action/Action Input/Observation can repeat N times)

    +
    +
    +
    +
    User:

    {questions}

    +
    +
    Assistant:

    Thought: +Action: +Action Input:

    +
    +
    User:

    Observation:

    +
    +
    Assistant:

    Thought: +Action: +Action Input:

    +
    +
    +

    …

    +
    +
    +static parse_message(m: Message, tools: list[Tool]) → ToolRequestMessage[source]
    +
    + +
    + +
    +
    +class ldp.graph.modules.react.ToolDescriptionMethods(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]
    +

    Bases: StrEnum

    +

    Possible methods of describing the tools.

    +
    +
    +JSON = 'describe_json'
    +
    + +
    +
    +STR = 'describe_str'
    +
    + +
    +
    +XML = 'describe_xml'
    +
    + +
    +
    +get_prompt_prefix() → str[source]
    +

    Get the prefix to put in front of the prompt.

    +
    + +
    + +
    +
    +ldp.graph.modules.react.parse_message(m: Message, tools: list[Tool]) → ToolRequestMessage[source]
    +

    Parse an Act or ReAct Message into a ToolRequestMessage.

    +
    +
    Parameters:
    +
      +

    • m – Input raw message.

      • +

    • tools – Tools used to confirm a valid tool selection

      • +
    +
    +
    Returns:
    +

    Parsed ToolRequestMessage.

    +
    +
    +
    + +
    +
    +

    ldp.graph.modules.reflect module

    +
    +
    +class ldp.graph.modules.reflect.ReflectModule(start_config: ReflectModuleConfig)[source]
    +

    Bases: object

    +

    A module that simply gives an LLM to reflect on an input.

    +
    + +
    +
    +class ldp.graph.modules.reflect.ReflectModuleConfig(*, llm_model: dict[str, Any] = {'model': 'gpt-3.5-turbo'})[source]
    +

    Bases: BaseModel

    +

    Configuration for the ReflectModuleConfig.

    +
    +
    +llm_model: dict[str, Any]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'llm_model': FieldInfo(annotation=dict[str, Any], required=False, default={'model': 'gpt-3.5-turbo'}, description='Starting configuration for the LLM model.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    + +
    +
    +

    ldp.graph.modules.thought module

    +
    +
    +class ldp.graph.modules.thought.ThoughtModule(llm_model: dict[str, Any], first_sys_prompt: str, second_sys_prompt: str)[source]
    +

    Bases: object

    +
    + +
    +
    +

    Module contents

    +

    A module is a subgraph of a compute graph that can be exposed like a single node/op.

    +

    An analogous entity in PyTorch is torch.nn.Module.

    +
    +
    +class ldp.graph.modules.ParsedLLMCallModule(llm_model: dict[str, Any], parser: Callable[[...], TParsedMessage])[source]
    +

    Bases: Generic[TParsedMessage]

    +

    Module for a processing-based tool selection, with a learnable configuration.

    +
    + +
    +
    +class ldp.graph.modules.ReActModule(llm_model: dict[str, Any], sys_prompt: str = 'Answer the following questions as best you can. You have access to the following tools:\n\n{tools}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, should be one of [{tool_names}]\nAction Input: comma separated list of inputs to action as python tuple\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\n\nExample:\n\nThought: I need to use the get_weather tool\nAction: get_weather\nAction Input: "New York", 7\nObservation: The 7 day forecast for New York is [...]', tool_description_method: ToolDescriptionMethods = ToolDescriptionMethods.STR)[source]
    +

    Bases: object

    +

    An Act or ReAct module built to work with chat models.

    +

    Paper: https://arxiv.org/abs/2210.03629

    +

    The ReAct style is like so, and note Act style has no ‘Thought: ‘ entries: +System:

    +
    +

    Answer the following questions as best you can. You have access to the following tools:

    +
    +

    {tools}

    +

    Use the following format:

    +

    Thought: you should always think about what to do +Action: the action to take, should be one of [{tool_names}] +Action Input: the input to the action +Observation: the result of the action +… (this Thought/Action/Action Input/Observation can repeat N times)

    +
    +
    +
    +
    User:

    {questions}

    +
    +
    Assistant:

    Thought: +Action: +Action Input:

    +
    +
    User:

    Observation:

    +
    +
    Assistant:

    Thought: +Action: +Action Input:

    +
    +
    +

    …

    +
    +
    +static parse_message(m: Message, tools: list[Tool]) → ToolRequestMessage[source]
    +
    + +
    + +
    +
    +class ldp.graph.modules.ReflectModule(start_config: ReflectModuleConfig)[source]
    +

    Bases: object

    +

    A module that simply gives an LLM to reflect on an input.

    +
    + +
    +
    +class ldp.graph.modules.ReflectModuleConfig(*, llm_model: dict[str, Any] = {'model': 'gpt-3.5-turbo'})[source]
    +

    Bases: BaseModel

    +

    Configuration for the ReflectModuleConfig.

    +
    +
    +llm_model: dict[str, Any]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'llm_model': FieldInfo(annotation=dict[str, Any], required=False, default={'model': 'gpt-3.5-turbo'}, description='Starting configuration for the LLM model.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    + +
    +
    +class ldp.graph.modules.ThoughtModule(llm_model: dict[str, Any], first_sys_prompt: str, second_sys_prompt: str)[source]
    +

    Bases: object

    +
    + +
    +
    +class ldp.graph.modules.ToolDescriptionMethods(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]
    +

    Bases: StrEnum

    +

    Possible methods of describing the tools.

    +
    +
    +JSON = 'describe_json'
    +
    + +
    +
    +STR = 'describe_str'
    +
    + +
    +
    +XML = 'describe_xml'
    +
    + +
    +
    +get_prompt_prefix() → str[source]
    +

    Get the prefix to put in front of the prompt.

    +
    + +
    + +
    +
    +ldp.graph.modules.parse_message(m: Message, tools: list[Tool]) → ToolRequestMessage[source]
    +

    Parse an Act or ReAct Message into a ToolRequestMessage.

    +
    +
    Parameters:
    +
      +

    • m – Input raw message.

      • +

    • tools – Tools used to confirm a valid tool selection

      • +
    +
    +
    Returns:
    +

    Parsed ToolRequestMessage.

    +
    +
    +
    + +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/ldp.html b/docs/_build/html/ldp.html new file mode 100644 index 00000000..eba5d93c --- /dev/null +++ b/docs/_build/html/ldp.html @@ -0,0 +1,2054 @@ + + + + + + + + ldp package — ldp documentation + + + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    ldp package

    +
    +

    Subpackages

    +
    + +
    +
    +
    +

    Submodules

    +
    +
    +

    ldp.data_structures module

    +
    +
    +class ldp.data_structures.Trajectory(*, traj_id: str | None = None, steps: list[Transition] = None)[source]
    +

    Bases: BaseModel

    +
    +
    +compute_discounted_returns(discount: float = 1.0) → list[float][source]
    +

    Compute the discounted returns for each step in the trajectory.

    +
    + +
    +
    +property done: bool
    +
    + +
    +
    +property failed: bool
    +
    + +
    +
    +classmethod from_jsonl(filename: str | PathLike) → Self[source]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'steps': FieldInfo(annotation=list[Transition], required=False, default_factory=list), 'traj_id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +steps: list[Transition]
    +
    + +
    +
    +to_jsonl(filename: str | PathLike) → None[source]
    +
    + +
    +
    +traj_id: str | None
    +
    + +
    + +
    +
    +class ldp.data_structures.Transition(*, timestep: int, agent_state: Any, next_agent_state: Any, observation: list[ToolResponseMessage | Message], next_observation: list[ToolResponseMessage | Message], action: OpResult[ToolRequestMessage] | None = None, reward: float = 0.0, truncated: bool = False, done: bool = False, value: float = 0.0, metadata: dict[str, JsonValue] = None)[source]
    +

    Bases: BaseModel

    +
    +
    +NO_OBSERVATION: ClassVar[list[Message]] = []
    +
    + +
    +
    +action: OpResult[ToolRequestMessage] | None
    +
    + +
    +
    +agent_state: Any
    +
    + +
    +
    +classmethod construct_action(action: OpResult[ToolRequestMessage] | dict | None) → OpResult[ToolRequestMessage] | None[source]
    +
    + +
    +
    +done: bool
    +
    + +
    +
    +property failed: bool
    +

    Get if an exception was encountered during rollout, for convenience.

    +

    If True, this transition should not be trained on. +Failed transitions are for debugging purposes.

    +
    + +
    +
    +metadata: dict[str, JsonValue]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_dump_json(*, indent: int | None = None, **kwargs) → str[source]
    +

    Usage docs: https://docs.pydantic.dev/2.8/concepts/serialization/#modelmodel_dump_json

    +

    Generates a JSON representation of the model using Pydantic’s to_json method.

    +
    +
    Parameters:
    +
      +

    • indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

      • +

    • include – Field(s) to include in the JSON output.

      • +

    • exclude – Field(s) to exclude from the JSON output.

      • +

    • context – Additional context to pass to the serializer.

      • +

    • by_alias – Whether to serialize using field aliases.

      • +

    • exclude_unset – Whether to exclude fields that have not been explicitly set.

      • +

    • exclude_defaults – Whether to exclude fields that are set to their default value.

      • +

    • exclude_none – Whether to exclude fields that have a value of None.

      • +

    • round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

      • +

    • warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, +“error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

      • +

    • serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

      • +
    +
    +
    Returns:
    +

    A JSON string representation of the model.

    +
    +
    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'action': FieldInfo(annotation=Union[OpResult[ToolRequestMessage], NoneType], required=False, default=None, description='Agent.get_asv output. This is a_t in RL terms.'), 'agent_state': FieldInfo(annotation=Any, required=True, description="Agent.get_asv's input. This is `s_t` in RL terms. Note that `s_0` comes from `Agent.init_state()`"), 'done': FieldInfo(annotation=bool, required=False, default=False, description="timestep t's Environment.step output."), 'metadata': FieldInfo(annotation=dict[str, JsonValue], required=False, default_factory=dict), 'next_agent_state': FieldInfo(annotation=Any, required=True, description="Agent.get_asv's output. This is s_t+1 in RL terms."), 'next_observation': FieldInfo(annotation=list[Union[ToolResponseMessage, Message]], required=True, description='Environment.step output. This is o_t+1 in RL terms.'), 'observation': FieldInfo(annotation=list[Union[ToolResponseMessage, Message]], required=True, description="Agent.get_asv's input. This is o_t in RL terms."), 'reward': FieldInfo(annotation=float, required=False, default=0.0, description='Environment.step output. This is r_t in RL terms.'), 'timestep': FieldInfo(annotation=int, required=True, description='Zero-indexed MDP timestep t.'), 'truncated': FieldInfo(annotation=bool, required=False, default=False, description="timestep t's Environment.step output."), 'value': FieldInfo(annotation=float, required=False, default=0.0, description="Value estimate output from timestep t's Agent.get_asv. This is v(s_t) [state value function] or q(s_t, a_t) [state-action value].")}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +next_agent_state: Any
    +
    + +
    +
    +next_observation: list[ToolResponseMessage | Message]
    +
    + +
    +
    +observation: list[ToolResponseMessage | Message]
    +
    + +
    +
    +reward: float
    +
    + +
    +
    +timestep: int
    +
    + +
    +
    +truncated: bool
    +
    + +
    +
    +value: float
    +
    + +
    + +
    +
    +class ldp.data_structures.TransitionTree(root_id: str | UUID)[source]
    +

    Bases: object

    +
    +
    +add_transition(step_id: str, step: Transition, weight: float = 1.0) → None[source]
    +

    Add a transition to the tree.

    +
    +
    Parameters:
    +
      +

    • step_id – A unique identifier for the root node of the tree. +The expected form of the step ID is “{parent step ID}:{step index}”.

      • +

    • step – The transition to add.

      • +

    • weight – Weight of the transition. Defaults to 1.0.

      • +
    +
    +
    +
    + +
    +
    +assign_mc_value_estimates(discount_factor: float = 1.0) → None[source]
    +

    Assign Monte Carlo state-action value estimates to each transition (in-place).

    +
    +
    Parameters:
    +

    discount_factor – The discount factor to use when computing cumulative +future rewards.

    +
    +
    +
    + +
    +
    +get_trajectories() → list[Trajectory][source]
    +

    Return a list of trajectories.

    +

    Since each path from the root node to a terminal node defines +a unique trajectory, N(terminal node) trajectories will be returned. +The trajectory ID will be set to the ID of the terminal step.

    +

    Note that we include failed and truncated trajectories; it is up to the +caller to decide what to do them.

    +
    +
    Returns:
    +

    All trajectories in this tree.

    +
    +
    +
    + +
    +
    +get_transition(step_id: str) → Transition[source]
    +
    + +
    +
    +get_weight(step_id: str) → float[source]
    +
    + +
    +
    +merge_identical_nodes(agent_state_hash_fn: ~collections.abc.Callable[[~typing.Any], ~collections.abc.Hashable], observation_hash_fn: ~collections.abc.Callable[[list[~aviary.tools.base.ToolResponseMessage | ~aviary.message.Message]], ~collections.abc.Hashable] = <function join>, next_observation_hash_fn: ~collections.abc.Callable[[list[~aviary.tools.base.ToolResponseMessage | ~aviary.message.Message]], ~collections.abc.Hashable] = <function join>) → TransitionTree[source]
    +

    Merge nodes with identical (state, observation, action)s. Returns a new tree.

    +
    +
    Parameters:
    +
      +

    • agent_state_hash_fn – A function that returns a hashable representation +of the agent state of a transition.

      • +

    • observation_hash_fn – A function that returns a hashable representation +of the observation messages of a transition.

      • +

    • next_observation_hash_fn – A function that returns a hashable representation +of the next observation messages of a transition.

      • +
    +
    +
    +
    + +
    + +
    +
    +

    ldp.main module

    +
    +
    +ldp.main.get_or_make_agent(agent: Agent | str | PathLike) → Agent[source]
    +
    + +
    +
    +ldp.main.get_or_make_environment(environment: Environment | str, task: str) → Environment[source]
    +
    + +
    +
    +async ldp.main.main(task: str, environment: Environment | str, agent: Agent | str | PathLike = 'SimpleAgent')[source]
    +
    + +
    +
    +

    ldp.shims module

    +
    +
    +class ldp.shims.tqdm(*_, **__)[source]
    +

    Bases: Comparable

    +

    Decorate an iterable object, returning an iterator which acts exactly +like the original iterable, but prints a dynamically updating +progressbar every time a value is requested.

    +
    +
    Parameters:
    +
      +

    • iterable (iterable, optional) – Iterable to decorate with a progressbar. +Leave blank to manually manage the updates.

      • +

    • desc (str, optional) – Prefix for the progressbar.

      • +

    • total (int or float, optional) – The number of expected iterations. If unspecified, +len(iterable) is used if possible. If float(“inf”) or as a last +resort, only basic progress statistics are displayed +(no ETA, no progressbar). +If gui is True and this parameter needs subsequent updating, +specify an initial arbitrary large positive number, +e.g. 9e9.

      • +

    • leave (bool, optional) – If [default: True], keeps all traces of the progressbar +upon termination of iteration. +If None, will leave only if position is 0.

      • +

    • file (io.TextIOWrapper or io.StringIO, optional) – Specifies where to output the progress messages +(default: sys.stderr). Uses file.write(str) and file.flush() +methods. For encoding, see write_bytes.

      • +

    • ncols (int, optional) – The width of the entire output message. If specified, +dynamically resizes the progressbar to stay within this bound. +If unspecified, attempts to use environment width. The +fallback is a meter width of 10 and no limit for the counter and +statistics. If 0, will not print any meter (only stats).

      • +

    • mininterval (float, optional) – Minimum progress display update interval [default: 0.1] seconds.

      • +

    • maxinterval (float, optional) – Maximum progress display update interval [default: 10] seconds. +Automatically adjusts miniters to correspond to mininterval +after long display update lag. Only works if dynamic_miniters +or monitor thread is enabled.

      • +

    • miniters (int or float, optional) – Minimum progress display update interval, in iterations. +If 0 and dynamic_miniters, will automatically adjust to equal +mininterval (more CPU efficient, good for tight loops). +If > 0, will skip display of specified number of iterations. +Tweak this and mininterval to get very efficient loops. +If your progress is erratic with both fast and slow iterations +(network, skipping items, etc) you should set miniters=1.

      • +

    • ascii (bool or str, optional) – If unspecified or False, use unicode (smooth blocks) to fill +the meter. The fallback is to use ASCII characters ” 123456789#”.

      • +

    • disable (bool, optional) – Whether to disable the entire progressbar wrapper +[default: False]. If set to None, disable on non-TTY.

      • +

    • unit (str, optional) – String that will be used to define the unit of each iteration +[default: it].

      • +

    • unit_scale (bool or int or float, optional) – If 1 or True, the number of iterations will be reduced/scaled +automatically and a metric prefix following the +International System of Units standard will be added +(kilo, mega, etc.) [default: False]. If any other non-zero +number, will scale total and n.

      • +

    • dynamic_ncols (bool, optional) – If set, constantly alters ncols and nrows to the +environment (allowing for window resizes) [default: False].

      • +

    • smoothing (float, optional) – Exponential moving average smoothing factor for speed estimates +(ignored in GUI mode). Ranges from 0 (average speed) to 1 +(current/instantaneous speed) [default: 0.3].

      • +

    • bar_format (str, optional) –

      Specify a custom bar string formatting. May impact performance. +[default: ‘{l_bar}{bar}{r_bar}’], where +l_bar=’{desc}: {percentage:3.0f}%|’ and +r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ‘

      +
      +

      ’{rate_fmt}{postfix}]’

      +
      +
      +
      Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,

      percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, +rate, rate_fmt, rate_noinv, rate_noinv_fmt, +rate_inv, rate_inv_fmt, postfix, unit_divisor, +remaining, remaining_s, eta.

      +
      +
      +

      Note that a trailing “: ” is automatically removed after {desc} +if the latter is empty.

      +

      • +

    • initial (int or float, optional) – The initial counter value. Useful when restarting a progress +bar [default: 0]. If using float, consider specifying {n:.3f} +or similar in bar_format, or specifying unit_scale.

      • +

    • position (int, optional) – Specify the line offset to print this bar (starting from 0) +Automatic if unspecified. +Useful to manage multiple bars at once (eg, from threads).

      • +

    • postfix (dict or *, optional) – Specify additional stats to display at the end of the bar. +Calls set_postfix(**postfix) if possible (dict).

      • +

    • unit_divisor (float, optional) – [default: 1000], ignored unless unit_scale is True.

      • +

    • write_bytes (bool, optional) – Whether to write bytes. If (default: False) will write unicode.

      • +

    • lock_args (tuple, optional) – Passed to refresh for intermediate output +(initialisation, iterating, and updating).

      • +

    • nrows (int, optional) – The screen height. If specified, hides nested bars outside this +bound. If unspecified, attempts to use environment height. +The fallback is 20.

      • +

    • colour (str, optional) – Bar colour (e.g. ‘green’, ‘#00ff00’).

      • +

    • delay (float, optional) – Don’t display until [default: 0] seconds have elapsed.

      • +

    • gui (bool, optional) – WARNING: internal parameter - do not use. +Use tqdm.gui.tqdm(…) instead. If set, will attempt to use +matplotlib animations for a graphical output [default: False].

      • +
    +
    +
    Returns:
    +

    out

    +
    +
    Return type:
    +

    decorated iterator.

    +
    +
    +
    +
    +clear(nolock=False)[source]
    +

    Clear current bar display.

    +
    + +
    +
    +close()[source]
    +

    Cleanup and (if leave=False) close the progressbar.

    +
    + +
    +
    +display(msg=None, pos=None)[source]
    +

    Use self.sp to display msg in the specified pos.

    +

    Consider overloading this function when inheriting to use e.g.: +self.some_frontend(**self.format_dict) instead of self.sp.

    +
    +
    Parameters:
    +
      +

    • msg (str, optional. What to display (default: repr(self)).)

      • +

    • pos (int, optional. Position to moveto) – (default: abs(self.pos)).

      • +
    +
    +
    +
    + +
    +
    +classmethod external_write_mode(file=None, nolock=False)[source]
    +

    Disable tqdm within context and refresh tqdm when exits. +Useful when writing to standard output stream

    +
    + +
    +
    +property format_dict
    +

    Public API for read-only member access.

    +
    + +
    +
    +static format_interval(t)[source]
    +

    Formats a number of seconds as a clock time, [H:]MM:SS

    +
    +
    Parameters:
    +

    t (int) – Number of seconds.

    +
    +
    Returns:
    +

    out – [H:]MM:SS

    +
    +
    Return type:
    +

    str

    +
    +
    +
    + +
    +
    +static format_meter(n, total, elapsed, ncols=None, prefix='', ascii=False, unit='it', unit_scale=False, rate=None, bar_format=None, postfix=None, unit_divisor=1000, initial=0, colour=None, **extra_kwargs)[source]
    +

    Return a string-based progress bar given some parameters

    +
    +
    Parameters:
    +
      +

    • n (int or float) – Number of finished iterations.

      • +

    • total (int or float) – The expected total number of iterations. If meaningless (None), +only basic progress statistics are displayed (no ETA).

      • +

    • elapsed (float) – Number of seconds passed since start.

      • +

    • ncols (int, optional) – The width of the entire output message. If specified, +dynamically resizes {bar} to stay within this bound +[default: None]. If 0, will not print any bar (only stats). +The fallback is {bar:10}.

      • +

    • prefix (str, optional) – Prefix message (included in total width) [default: ‘’]. +Use as {desc} in bar_format string.

      • +

    • ascii (bool, optional or str, optional) – If not set, use unicode (smooth blocks) to fill the meter +[default: False]. The fallback is to use ASCII characters +” 123456789#”.

      • +

    • unit (str, optional) – The iteration unit [default: ‘it’].

      • +

    • unit_scale (bool or int or float, optional) – If 1 or True, the number of iterations will be printed with an +appropriate SI metric prefix (k = 10^3, M = 10^6, etc.) +[default: False]. If any other non-zero number, will scale +total and n.

      • +

    • rate (float, optional) – Manual override for iteration rate. +If [default: None], uses n/elapsed.

      • +

    • bar_format (str, optional) –

      Specify a custom bar string formatting. May impact performance. +[default: ‘{l_bar}{bar}{r_bar}’], where +l_bar=’{desc}: {percentage:3.0f}%|’ and +r_bar=’| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ‘

      +
      +

      ’{rate_fmt}{postfix}]’

      +
      +
      +
      Possible vars: l_bar, bar, r_bar, n, n_fmt, total, total_fmt,

      percentage, elapsed, elapsed_s, ncols, nrows, desc, unit, +rate, rate_fmt, rate_noinv, rate_noinv_fmt, +rate_inv, rate_inv_fmt, postfix, unit_divisor, +remaining, remaining_s, eta.

      +
      +
      +

      Note that a trailing “: ” is automatically removed after {desc} +if the latter is empty.

      +

      • +

    • postfix (*, optional) – Similar to prefix, but placed at the end +(e.g. for additional stats). +Note: postfix is usually a string (not a dict) for this method, +and will if possible be set to postfix = ‘, ‘ + postfix. +However other types are supported (#382).

      • +

    • unit_divisor (float, optional) – [default: 1000], ignored unless unit_scale is True.

      • +

    • initial (int or float, optional) – The initial counter value [default: 0].

      • +

    • colour (str, optional) – Bar colour (e.g. ‘green’, ‘#00ff00’).

      • +
    +
    +
    Returns:
    +

    out

    +
    +
    Return type:
    +

    Formatted meter and stats, ready to display.

    +
    +
    +
    + +
    +
    +static format_num(n)[source]
    +

    Intelligent scientific notation (.3g).

    +
    +
    Parameters:
    +

    n (int or float or Numeric) – A Number.

    +
    +
    Returns:
    +

    out – Formatted number.

    +
    +
    Return type:
    +

    str

    +
    +
    +
    + +
    +
    +static format_sizeof(num, suffix='', divisor=1000)[source]
    +

    Formats a number (greater than unity) with SI Order of Magnitude +prefixes.

    +
    +
    Parameters:
    +
      +

    • num (float) – Number ( >= 1) to format.

      • +

    • suffix (str, optional) – Post-postfix [default: ‘’].

      • +

    • divisor (float, optional) – Divisor between prefixes [default: 1000].

      • +
    +
    +
    Returns:
    +

    out – Number with Order of Magnitude SI unit postfix.

    +
    +
    Return type:
    +

    str

    +
    +
    +
    + +
    +
    +classmethod get_lock()[source]
    +

    Get the global lock. Construct it if it does not exist.

    +
    + +
    +
    +monitor = None
    +
    + +
    +
    +monitor_interval = 10
    +
    + +
    +
    +moveto(n)[source]
    +
    + +
    +
    +classmethod pandas(**tqdm_kwargs)[source]
    +
    +
    Registers the current tqdm class with

    pandas.core. +( frame.DataFrame +| series.Series +| groupby.(generic.)DataFrameGroupBy +| groupby.(generic.)SeriesGroupBy +).progress_apply

    +
    +
    +

    A new instance will be created every time progress_apply is called, +and each instance will automatically close() upon completion.

    +
    +
    Parameters:
    +

    tqdm_kwargs (arguments for the tqdm instance)

    +
    +
    +

    Examples

    +
    >>> import pandas as pd
+>>> import numpy as np
+>>> from tqdm import tqdm
+>>> from tqdm.gui import tqdm as tqdm_gui
+>>>
+>>> df = pd.DataFrame(np.random.randint(0, 100, (100000, 6)))
+>>> tqdm.pandas(ncols=50)  # can use tqdm_gui, optional kwargs, etc
+>>> # Now you can use `progress_apply` instead of `apply`
+>>> df.groupby(0).progress_apply(lambda x: x**2)
+
    +
    +

    References

    +

    <https://stackoverflow.com/questions/18603270/ progress-indicator-during-pandas-operations-python>

    +
    + +
    +
    +refresh(nolock=False, lock_args=None)[source]
    +

    Force refresh the display of this bar.

    +
    +
    Parameters:
    +
      +

    • nolock (bool, optional) – If True, does not lock. +If [default: False]: calls acquire() on internal lock.

      • +

    • lock_args (tuple, optional) – Passed to internal lock’s acquire(). +If specified, will only display() if acquire() returns True.

      • +
    +
    +
    +
    + +
    +
    +reset(total=None)[source]
    +

    Resets to 0 iterations for repeated use.

    +

    Consider combining with leave=True.

    +
    +
    Parameters:
    +

    total (int or float, optional. Total to use for the new bar.)

    +
    +
    +
    + +
    +
    +set_description(desc=None, refresh=True)[source]
    +

    Set/modify description of the progress bar.

    +
    +
    Parameters:
    +
      +

    • desc (str, optional)

      • +

    • refresh (bool, optional) – Forces refresh [default: True].

      • +
    +
    +
    +
    + +
    +
    +set_description_str(desc=None, refresh=True)[source]
    +

    Set/modify description without ‘: ‘ appended.

    +
    + +
    +
    +classmethod set_lock(lock)[source]
    +

    Set the global lock.

    +
    + +
    +
    +set_postfix(ordered_dict=None, refresh=True, **kwargs)[source]
    +

    Set/modify postfix (additional stats) +with automatic formatting based on datatype.

    +
    +
    Parameters:
    +
      +

    • ordered_dict (dict or OrderedDict, optional)

      • +

    • refresh (bool, optional) – Forces refresh [default: True].

      • +

    • kwargs (dict, optional)

      • +
    +
    +
    +
    + +
    +
    +set_postfix_str(s='', refresh=True)[source]
    +

    Postfix without dictionary expansion, similar to prefix handling.

    +
    + +
    +
    +static status_printer(file)[source]
    +

    Manage the printing and in-place updating of a line of characters. +Note that if the string is longer than a line, then in-place +updating may not work (it will print a new line at each refresh).

    +
    + +
    +
    +unpause()[source]
    +

    Restart tqdm timer from last print time.

    +
    + +
    +
    +update(n=1)[source]
    +

    Manually update the progress bar, useful for streams +such as reading files. +E.g.: +>>> t = tqdm(total=filesize) # Initialise +>>> for current_buffer in stream: +… … +… t.update(len(current_buffer)) +>>> t.close() +The last line is highly recommended, but possibly not necessary if +t.update() will be called in such a way that filesize will be +exactly reached and printed.

    +
    +
    Parameters:
    +

    n (int or float, optional) – Increment to add to the internal counter of iterations +[default: 1]. If using float, consider specifying {n:.3f} +or similar in bar_format, or specifying unit_scale.

    +
    +
    Returns:
    +

    out – True if a display() was triggered.

    +
    +
    Return type:
    +

    bool or None

    +
    +
    +
    + +
    +
    +classmethod wrapattr(stream, method, total=None, bytes=True, **tqdm_kwargs)[source]
    +

    stream : file-like object. +method : str, “read” or “write”. The result of read() and

    +
    +

    the first argument of write() should have a len().

    +
    +
    >>> with tqdm.wrapattr(file_obj, "read", total=file_obj.size) as fobj:
+...     while True:
+...         chunk = fobj.read(chunk_size)
+...         if not chunk:
+...             break
+
    +
    +
    + +
    +
    +classmethod write(s, file=None, end='\n', nolock=False)[source]
    +

    Print a message via tqdm (without overlap with bars).

    +
    + +
    + +
    +
    +ldp.shims.trange(*args, **kwargs)[source]
    +

    Shortcut for tqdm(range(*args), **kwargs).

    +
    + +
    +
    +

    ldp.utils module

    +
    +
    +ldp.utils.configure_log_levels() → None[source]
    +

    Configure log levels.

    +
    + +
    +
    +ldp.utils.configure_stdout_logs(level: int | str = 20, fmt: str = '%(asctime)s - %(name)s - %(levelname)s - %(message)s') → None[source]
    +

    Configure root logger to log to stdout.

    +
    +
    Parameters:
    +
      +

    • level – Log level to be emitted to stdout.

      • +

    • fmt – Optional format string.

      • +
    +
    +
    +
    + +
    +
    +ldp.utils.discounted_returns(rewards: list[float], terminated: list[bool], discount: float = 1.0) → list[float][source]
    +

    Calculate the discounted returns for a list of rewards, considering termination flags and a discount factor.

    +

    The discounted return represents the future discounted rewards from each time step onwards, taking into account +whether an episode has terminated at each step.

    +

    The discounted return ( G_t ) is given by:

    +
    +\[ \begin{align}\begin{aligned}G_t = \sum_{k=1}^{\infty} \gamma^{k-1} R_{t+k}\\where: +- \( G_t \) is the discounted return starting from time step \( t \). +- \( \gamma \) is the discount factor. +- \( R_{t+k} \) is the reward received at time step \( t+k \).\end{aligned}\end{align} \]
    +

    NOTE: this could live in ldp.alg, but it’s here to avoid circular imports.

    +
    +
    Parameters:
    +
      +

    • rewards – A list of rewards at each time step.

      • +

    • terminated – A list of boolean flags indicating whether the episode terminated at each time step.

      • +

    • discount – Discount factor to apply to future rewards. Defaults to 1.0 which means no discounting is applied.

      • +
    +
    +
    Returns:
    +

    +
    A list of discounted returns (rewards to go), with each element representing the

    total discounted reward from that step onwards.

    +
    +
    +

    +
    +
    +

    Example

    +
    >>> rewards = [1.0, 2.0, 3.0]
+>>> terminated = [False, False, True]
+>>> discounted_returns(rewards, terminated, discount=0.9)
+[5.23, 4.7, 3.0]
+
    +
    +
    + +
    +
    +

    Module contents

    +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/ldp.llms.html b/docs/_build/html/ldp.llms.html new file mode 100644 index 00000000..1d5dd493 --- /dev/null +++ b/docs/_build/html/ldp.llms.html @@ -0,0 +1,1229 @@ + + + + + + + + ldp.llms package — ldp documentation + + + + + + + + + + + + + + + + + + +
    +
    +
    + + +
    + +
    +

    ldp.llms package

    +
    +

    Submodules

    +
    +
    +

    ldp.llms.chat module

    +
    +
    +exception ldp.llms.chat.JSONSchemaValidationError[source]
    +

    Bases: ValueError

    +

    Raised when the completion does not match the specified schema.

    +
    + +
    +
    +class ldp.llms.chat.LLMModel(*, name: str = 'unknown', config: dict = {'model': 'gpt-3.5-turbo', 'temperature': 0.1}, encoding: Any | None = None)[source]
    +

    Bases: MultipleCompletionLLMModel

    +
    +
    +async call(*args, **kwargs) → LLMResult[source]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'config': FieldInfo(annotation=dict, required=False, default={'model': 'gpt-3.5-turbo', 'temperature': 0.1}), 'encoding': FieldInfo(annotation=Union[Any, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=False, default='unknown')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    + +
    +
    +class ldp.llms.chat.LLMResult(*, id: UUID = None, config: dict | None = None, prompt: list[Message] | None = None, messages: list[Message] | None = None, prompt_count: int = 0, completion_count: int = 0, model: str, date: str = None, seconds_to_first_token: float | None = None, seconds_to_last_token: float = 0, logprob: float | None = None, system_fingerprint: str | None = None)[source]
    +

    Bases: BaseModel

    +

    A class to hold the result of a LLM completion.

    +
    +
    +completion_count: int
    +
    + +
    +
    +config: dict | None
    +
    + +
    +
    +date: str
    +
    + +
    +
    +get_supported_openai_params() → list[str] | None[source]
    +

    Get the supported OpenAI parameters for the model.

    +
    + +
    +
    +id: UUID
    +
    + +
    +
    +logprob: float | None
    +
    + +
    +
    +messages: list[Message] | None
    +
    + +
    +
    +model: str
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'completion_count': FieldInfo(annotation=int, required=False, default=0, description='Count of completion tokens.'), 'config': FieldInfo(annotation=Union[dict, NoneType], required=False, default=None), 'date': FieldInfo(annotation=str, required=False, default_factory=builtin_function_or_method), 'id': FieldInfo(annotation=UUID, required=False, default_factory=uuid4), 'logprob': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Sum of logprobs in the completion.'), 'messages': FieldInfo(annotation=Union[list[Message], NoneType], required=False, default=None, description='Messages received from the LLM.'), 'model': FieldInfo(annotation=str, required=True), 'prompt': FieldInfo(annotation=Union[list[Message], NoneType], required=False, default=None, description='Messages sent to the LLM.'), 'prompt_count': FieldInfo(annotation=int, required=False, default=0, description='Count of prompt tokens.'), 'seconds_to_first_token': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'seconds_to_last_token': FieldInfo(annotation=float, required=False, default=0), 'system_fingerprint': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='System fingerprint received from the LLM.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +prompt: list[Message] | None
    +
    + +
    +
    +property prompt_and_completion_costs: tuple[float, float]
    +

    Get a two-tuple of prompt tokens cost and completion tokens cost, in USD.

    +
    + +
    +
    +prompt_count: int
    +
    + +
    +
    +property provider: str
    +

    Get the model provider’s name (e.g. “openai”, “mistral”).

    +
    + +
    +
    +seconds_to_first_token: float | None
    +
    + +
    +
    +seconds_to_last_token: float
    +
    + +
    +
    +system_fingerprint: str | None
    +
    + +
    + +
    +
    +class ldp.llms.chat.MultipleCompletionLLMModel(*, name: str = 'unknown', config: dict = {'model': 'gpt-3.5-turbo', 'temperature': 0.1}, encoding: Any | None = None)[source]
    +

    Bases: BaseModel

    +

    Run n completions at once, all starting from the same messages.

    +
    +
    +TOOL_CHOICE_REQUIRED: ClassVar[str] = 'required'
    +
    + +
    +
    +async achat(messages: Iterable[Message], **kwargs) → ModelResponse[source]
    +
    + +
    +
    +async achat_iter(messages: Iterable[Message], **kwargs) → AsyncGenerator[source]
    +
    + +
    +
    +async call(messages: list[Message], callbacks: list[Callable] | None = None, output_type: type[BaseModel] | None = None, tools: list[Tool] | None = None, tool_choice: Tool | str | None = 'required', **chat_kwargs) → list[LLMResult][source]
    +
    + +
    +
    +config: dict
    +
    + +
    +
    +encoding: Any | None
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'config': FieldInfo(annotation=dict, required=False, default={'model': 'gpt-3.5-turbo', 'temperature': 0.1}), 'encoding': FieldInfo(annotation=Union[Any, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=False, default='unknown')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +name: str
    +
    + +
    +
    +set_model_name() → Self[source]
    +
    + +
    + +
    +
    +ldp.llms.chat.sum_logprobs(choice: Choices) → float | None[source]
    +

    Calculate the sum of the log probabilities of an LLM completion (a Choices object).

    +
    +
    Parameters:
    +

    choice – A sequence of choices from the completion.

    +
    +
    Returns:
    +

    The sum of the log probabilities of the choice.

    +
    +
    +
    + +
    +
    +ldp.llms.chat.validate_json_completion(completion: ModelResponse, output_type: type[BaseModel]) → None[source]
    +

    Validate a completion against a JSON schema.

    +
    +
    Parameters:
    +
      +

    • completion – The completion to validate.

      • +

    • output_type – The Pydantic model to validate the completion against.

      • +
    +
    +
    +
    + +
    +
    +

    ldp.llms.embeddings module

    +
    +
    +class ldp.llms.embeddings.EmbeddingModel(*, name: str, dimensions: int | None = None)[source]
    +

    Bases: ABC, BaseModel

    +
    +
    +dimensions: int | None
    +
    + +
    +
    +async embed_text(text: str) → ndarray[source]
    +
    + +
    +
    +abstract async embed_texts(texts: list[str]) → list[ndarray][source]
    +
    + +
    +
    +static from_name(embedding: str, **kwargs) → EmbeddingModel[source]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'dimensions': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +name: str
    +
    + +
    +
    +set_mode(mode: EmbeddingModes) → None[source]
    +

    Several embedding models have a ‘mode’ or prompt which affects output.

    +
    + +
    + +
    +
    +class ldp.llms.embeddings.EmbeddingModes(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]
    +

    Bases: StrEnum

    +

    Enum representing the different modes of an embedding model.

    +
    +
    +DOCUMENT = 'document'
    +
    + +
    +
    +QUERY = 'query'
    +
    + +
    + +
    +
    +class ldp.llms.embeddings.HybridEmbeddingModel(*, name: str = 'hybrid-embed', dimensions: int | None = None, models: list[EmbeddingModel])[source]
    +

    Bases: EmbeddingModel

    +
    +
    +async embed_texts(texts)[source]
    +
    + +
    +
    +classmethod infer_dimensions(data: dict[str, Any]) → dict[str, Any][source]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'dimensions': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'models': FieldInfo(annotation=list[EmbeddingModel], required=True), 'name': FieldInfo(annotation=str, required=False, default='hybrid-embed')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +models: list[EmbeddingModel]
    +
    + +
    +
    +name: str
    +
    + +
    + +
    +
    +class ldp.llms.embeddings.LiteEmbeddingModel(*, name: str = 'text-embedding-3-small', dimensions: int | None = None, batch_size: int = 16, embed_kwargs: dict[str, Any] = None)[source]
    +

    Bases: EmbeddingModel

    +
    +
    +batch_size: int
    +
    + +
    +
    +dimensions: int | None
    +
    + +
    +
    +embed_kwargs: dict[str, Any]
    +
    + +
    +
    +async embed_texts(texts: list[str]) → list[ndarray][source]
    +
    + +
    +
    +classmethod infer_dimensions(data: dict[str, Any]) → dict[str, Any][source]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'batch_size': FieldInfo(annotation=int, required=False, default=16), 'dimensions': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description="The length an embedding will have. If left unspecified, we attempt to infer an un-truncated length via LiteLLM's internal model map. If this inference fails, the embedding will be un-truncated."), 'embed_kwargs': FieldInfo(annotation=dict[str, Any], required=False, default_factory=dict, description='Extra kwargs to pass to litellm.aembedding.'), 'name': FieldInfo(annotation=str, required=False, default='text-embedding-3-small')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +name: str
    +
    + +
    + +
    +
    +class ldp.llms.embeddings.SparseEmbeddingModel(*, name: str = 'sparse', dimensions: int = 256, enc: Encoding = None)[source]
    +

    Bases: EmbeddingModel

    +

    This is a very simple keyword search model - probably best to be mixed with others.

    +
    +
    +dimensions: int
    +
    + +
    +
    +async embed_texts(texts) → list[ndarray][source]
    +
    + +
    +
    +enc: Encoding
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'dimensions': FieldInfo(annotation=int, required=False, default=256), 'enc': FieldInfo(annotation=Encoding, required=False, default_factory=<lambda>), 'name': FieldInfo(annotation=str, required=False, default='sparse')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +name: str
    +
    + +
    + +
    +
    +

    ldp.llms.prompts module

    +

    This module provides utility functions for appending and prepending system messages.

    +
    +
    +ldp.llms.prompts.append_to_messages(messages: list[Message], new_message: Message) → list[Message][source]
    +

    Appends a message to a list of messages, returning that in-place modified list.

    +

    Examples

    +
    >>> messages = [Message(content="Hello")]
+>>> modified_messages = append_to_messages(messages, Message(content="New"))
+>>> modified_messages
+[Message(role='user', content='Hello'), Message(role='user', content='New')]
+>>> id(messages) == id(modified_messages)
+True
+
    +
    +
    + +
    +
    +ldp.llms.prompts.append_to_sys(user_content: str, sys_content: str | None = None) → list[Message][source]
    +

    Appends a user message to a list of messages, optionally including a system message.

    +
    +
    Parameters:
    +
      +

    • user_content – The content of the user message.

      • +

    • sys_content – Optional content for the system message. Defaults to None.

      • +
    +
    +
    Returns:
    +

    A list of messages including the optional system message and the user message.

    +
    +
    +

    Examples

    +
    >>> append_to_sys("Hello, world!")
+[Message(role='user', content='Hello, world!')]
+
    +
    +
    >>> append_to_sys("Hello, world!", "System initialized.")
+[Message(role='system', content='System initialized.'), Message(role='user', content='Hello, world!')]
+
    +
    +
    + +
    +
    +ldp.llms.prompts.indent_xml(xml_string, indent_size=2)[source]
    +
    + +
    +
    +ldp.llms.prompts.prepend_sys(messages: Collection, sys_content: str) → list[Message][source]
    +

    Prepends a system message to a list of messages.

    +
    +
    Parameters:
    +
      +

    • messages – The list of existing messages.

      • +

    • sys_content – The content of the system message to be prepended.

      • +
    +
    +
    Returns:
    +

    A new list of messages with the system message prepended.

    +
    +
    +

    Examples

    +
    >>> messages = [Message(role="user", content="Hello!")]
+>>> prepend_sys(messages, "System initialized.")
+[Message(role='system', content='System initialized.'), Message(role='user', content='Hello!')]
+
    +
    +
    + +
    +
    +ldp.llms.prompts.prepend_sys_and_append_sys(messages: Iterable[Message], initial_sys_content: str, final_sys_content: str) → list[Message][source]
    +
    + +
    +
    +

    Module contents

    +
    +
    +class ldp.llms.EmbeddingModel(*, name: str, dimensions: int | None = None)[source]
    +

    Bases: ABC, BaseModel

    +
    +
    +dimensions: int | None
    +
    + +
    +
    +async embed_text(text: str) → ndarray[source]
    +
    + +
    +
    +abstract async embed_texts(texts: list[str]) → list[ndarray][source]
    +
    + +
    +
    +static from_name(embedding: str, **kwargs) → EmbeddingModel[source]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'dimensions': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True)}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +name: str
    +
    + +
    +
    +set_mode(mode: EmbeddingModes) → None[source]
    +

    Several embedding models have a ‘mode’ or prompt which affects output.

    +
    + +
    + +
    +
    +class ldp.llms.EmbeddingModes(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]
    +

    Bases: StrEnum

    +

    Enum representing the different modes of an embedding model.

    +
    +
    +DOCUMENT = 'document'
    +
    + +
    +
    +QUERY = 'query'
    +
    + +
    + +
    +
    +class ldp.llms.HybridEmbeddingModel(*, name: str = 'hybrid-embed', dimensions: int | None = None, models: list[EmbeddingModel])[source]
    +

    Bases: EmbeddingModel

    +
    +
    +dimensions: int | None
    +
    + +
    +
    +async embed_texts(texts)[source]
    +
    + +
    +
    +classmethod infer_dimensions(data: dict[str, Any]) → dict[str, Any][source]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'dimensions': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'models': FieldInfo(annotation=list[EmbeddingModel], required=True), 'name': FieldInfo(annotation=str, required=False, default='hybrid-embed')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +models: list[EmbeddingModel]
    +
    + +
    +
    +name: str
    +
    + +
    + +
    +
    +exception ldp.llms.JSONSchemaValidationError[source]
    +

    Bases: ValueError

    +

    Raised when the completion does not match the specified schema.

    +
    + +
    +
    +class ldp.llms.LLMModel(*, name: str = 'unknown', config: dict = {'model': 'gpt-3.5-turbo', 'temperature': 0.1}, encoding: Any | None = None)[source]
    +

    Bases: MultipleCompletionLLMModel

    +
    +
    +async call(*args, **kwargs) → LLMResult[source]
    +
    + +
    +
    +config: dict
    +
    + +
    +
    +encoding: Any | None
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'config': FieldInfo(annotation=dict, required=False, default={'model': 'gpt-3.5-turbo', 'temperature': 0.1}), 'encoding': FieldInfo(annotation=Union[Any, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=False, default='unknown')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +name: str
    +
    + +
    + +
    +
    +class ldp.llms.LLMResult(*, id: UUID = None, config: dict | None = None, prompt: list[Message] | None = None, messages: list[Message] | None = None, prompt_count: int = 0, completion_count: int = 0, model: str, date: str = None, seconds_to_first_token: float | None = None, seconds_to_last_token: float = 0, logprob: float | None = None, system_fingerprint: str | None = None)[source]
    +

    Bases: BaseModel

    +

    A class to hold the result of a LLM completion.

    +
    +
    +completion_count: int
    +
    + +
    +
    +config: dict | None
    +
    + +
    +
    +date: str
    +
    + +
    +
    +get_supported_openai_params() → list[str] | None[source]
    +

    Get the supported OpenAI parameters for the model.

    +
    + +
    +
    +id: UUID
    +
    + +
    +
    +logprob: float | None
    +
    + +
    +
    +messages: list[Message] | None
    +
    + +
    +
    +model: str
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'completion_count': FieldInfo(annotation=int, required=False, default=0, description='Count of completion tokens.'), 'config': FieldInfo(annotation=Union[dict, NoneType], required=False, default=None), 'date': FieldInfo(annotation=str, required=False, default_factory=builtin_function_or_method), 'id': FieldInfo(annotation=UUID, required=False, default_factory=uuid4), 'logprob': FieldInfo(annotation=Union[float, NoneType], required=False, default=None, description='Sum of logprobs in the completion.'), 'messages': FieldInfo(annotation=Union[list[Message], NoneType], required=False, default=None, description='Messages received from the LLM.'), 'model': FieldInfo(annotation=str, required=True), 'prompt': FieldInfo(annotation=Union[list[Message], NoneType], required=False, default=None, description='Messages sent to the LLM.'), 'prompt_count': FieldInfo(annotation=int, required=False, default=0, description='Count of prompt tokens.'), 'seconds_to_first_token': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'seconds_to_last_token': FieldInfo(annotation=float, required=False, default=0), 'system_fingerprint': FieldInfo(annotation=Union[str, NoneType], required=False, default=None, description='System fingerprint received from the LLM.')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +prompt: list[Message] | None
    +
    + +
    +
    +property prompt_and_completion_costs: tuple[float, float]
    +

    Get a two-tuple of prompt tokens cost and completion tokens cost, in USD.

    +
    + +
    +
    +prompt_count: int
    +
    + +
    +
    +property provider: str
    +

    Get the model provider’s name (e.g. “openai”, “mistral”).

    +
    + +
    +
    +seconds_to_first_token: float | None
    +
    + +
    +
    +seconds_to_last_token: float
    +
    + +
    +
    +system_fingerprint: str | None
    +
    + +
    + +
    +
    +class ldp.llms.LiteEmbeddingModel(*, name: str = 'text-embedding-3-small', dimensions: int | None = None, batch_size: int = 16, embed_kwargs: dict[str, Any] = None)[source]
    +

    Bases: EmbeddingModel

    +
    +
    +batch_size: int
    +
    + +
    +
    +dimensions: int | None
    +
    + +
    +
    +embed_kwargs: dict[str, Any]
    +
    + +
    +
    +async embed_texts(texts: list[str]) → list[ndarray][source]
    +
    + +
    +
    +classmethod infer_dimensions(data: dict[str, Any]) → dict[str, Any][source]
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'batch_size': FieldInfo(annotation=int, required=False, default=16), 'dimensions': FieldInfo(annotation=Union[int, NoneType], required=False, default=None, description="The length an embedding will have. If left unspecified, we attempt to infer an un-truncated length via LiteLLM's internal model map. If this inference fails, the embedding will be un-truncated."), 'embed_kwargs': FieldInfo(annotation=dict[str, Any], required=False, default_factory=dict, description='Extra kwargs to pass to litellm.aembedding.'), 'name': FieldInfo(annotation=str, required=False, default='text-embedding-3-small')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +name: str
    +
    + +
    + +
    +
    +class ldp.llms.MultipleCompletionLLMModel(*, name: str = 'unknown', config: dict = {'model': 'gpt-3.5-turbo', 'temperature': 0.1}, encoding: Any | None = None)[source]
    +

    Bases: BaseModel

    +

    Run n completions at once, all starting from the same messages.

    +
    +
    +TOOL_CHOICE_REQUIRED: ClassVar[str] = 'required'
    +
    + +
    +
    +async achat(messages: Iterable[Message], **kwargs) → ModelResponse[source]
    +
    + +
    +
    +async achat_iter(messages: Iterable[Message], **kwargs) → AsyncGenerator[source]
    +
    + +
    +
    +async call(messages: list[Message], callbacks: list[Callable] | None = None, output_type: type[BaseModel] | None = None, tools: list[Tool] | None = None, tool_choice: Tool | str | None = 'required', **chat_kwargs) → list[LLMResult][source]
    +
    + +
    +
    +config: dict
    +
    + +
    +
    +encoding: Any | None
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'config': FieldInfo(annotation=dict, required=False, default={'model': 'gpt-3.5-turbo', 'temperature': 0.1}), 'encoding': FieldInfo(annotation=Union[Any, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=False, default='unknown')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +name: str
    +
    + +
    +
    +set_model_name() → Self[source]
    +
    + +
    + +
    +
    +class ldp.llms.SparseEmbeddingModel(*, name: str = 'sparse', dimensions: int = 256, enc: Encoding = None)[source]
    +

    Bases: EmbeddingModel

    +

    This is a very simple keyword search model - probably best to be mixed with others.

    +
    +
    +dimensions: int
    +
    + +
    +
    +async embed_texts(texts) → list[ndarray][source]
    +
    + +
    +
    +enc: Encoding
    +
    + +
    +
    +model_computed_fields: ClassVar[dict[str, ComputedFieldInfo]] = {}
    +

    A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

    +
    + +
    +
    +model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True}
    +

    Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

    +
    + +
    +
    +model_fields: ClassVar[dict[str, FieldInfo]] = {'dimensions': FieldInfo(annotation=int, required=False, default=256), 'enc': FieldInfo(annotation=Encoding, required=False, default_factory=<lambda>), 'name': FieldInfo(annotation=str, required=False, default='sparse')}
    +

    Metadata about the fields defined on the model, +mapping of field names to [FieldInfo][pydantic.fields.FieldInfo].

    +

    This replaces Model.__fields__ from Pydantic V1.

    +
    + +
    +
    +name: str
    +
    + +
    + +
    +
    +ldp.llms.append_to_messages(messages: list[Message], new_message: Message) → list[Message][source]
    +

    Appends a message to a list of messages, returning that in-place modified list.

    +

    Examples

    +
    >>> messages = [Message(content="Hello")]
+>>> modified_messages = append_to_messages(messages, Message(content="New"))
+>>> modified_messages
+[Message(role='user', content='Hello'), Message(role='user', content='New')]
+>>> id(messages) == id(modified_messages)
+True
+
    +
    +
    + +
    +
    +ldp.llms.append_to_sys(user_content: str, sys_content: str | None = None) → list[Message][source]
    +

    Appends a user message to a list of messages, optionally including a system message.

    +
    +
    Parameters:
    +
      +

    • user_content – The content of the user message.

      • +

    • sys_content – Optional content for the system message. Defaults to None.

      • +
    +
    +
    Returns:
    +

    A list of messages including the optional system message and the user message.

    +
    +
    +

    Examples

    +
    >>> append_to_sys("Hello, world!")
+[Message(role='user', content='Hello, world!')]
+
    +
    +
    >>> append_to_sys("Hello, world!", "System initialized.")
+[Message(role='system', content='System initialized.'), Message(role='user', content='Hello, world!')]
+
    +
    +
    + +
    +
    +ldp.llms.prepend_sys(messages: Collection, sys_content: str) → list[Message][source]
    +

    Prepends a system message to a list of messages.

    +
    +
    Parameters:
    +
      +

    • messages – The list of existing messages.

      • +

    • sys_content – The content of the system message to be prepended.

      • +
    +
    +
    Returns:
    +

    A new list of messages with the system message prepended.

    +
    +
    +

    Examples

    +
    >>> messages = [Message(role="user", content="Hello!")]
+>>> prepend_sys(messages, "System initialized.")
+[Message(role='system', content='System initialized.'), Message(role='user', content='Hello!')]
+
    +
    +
    + +
    +
    +ldp.llms.prepend_sys_and_append_sys(messages: Iterable[Message], initial_sys_content: str, final_sys_content: str) → list[Message][source]
    +
    + +
    +
    +ldp.llms.sum_logprobs(choice: Choices) → float | None[source]
    +

    Calculate the sum of the log probabilities of an LLM completion (a Choices object).

    +
    +
    Parameters:
    +

    choice – A sequence of choices from the completion.

    +
    +
    Returns:
    +

    The sum of the log probabilities of the choice.

    +
    +
    +
    + +
    +
    +ldp.llms.validate_json_completion(completion: ModelResponse, output_type: type[BaseModel]) → None[source]
    +

    Validate a completion against a JSON schema.

    +
    +
    Parameters:
    +
      +

    • completion – The completion to validate.

      • +

    • output_type – The Pydantic model to validate the completion against.

      • +
    +
    +
    +
    + +
    +
    + + +
    + +
    +
    + +
    +
    +
    + ©. + + | + Powered by Sphinx 7.3.7 + & Alabaster 0.7.16 + + | + Page source +
    + + + + + + \ No newline at end of file diff --git a/docs/_build/html/modules.html b/docs/_build/html/modules.html new file mode 100644 index 00000000..6949d44f --- /dev/null +++ b/docs/_build/html/modules.html @@ -0,0 +1,262 @@ + + + + + + + + ldp — ldp documentation + + + + + + + + + + + + + + + + + + + + +
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    + +
    +

    ldp

    +
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