From 66eab897d47850051a3875060a27c9f0244b4161 Mon Sep 17 00:00:00 2001 From: Joel Alexander Date: Wed, 27 Mar 2024 10:00:41 -0500 Subject: [PATCH 1/3] demo rag implementation --- evals/eval.py | 14 + ingest.py | 8 +- poetry.lock | 4219 ++++++++++++++++++++++-------------------- pyproject.toml | 1 + rag/chain.py | 127 +- rag/dataset_chain.py | 32 + rag/set_up.py | 64 + 7 files changed, 2334 insertions(+), 2131 deletions(-) create mode 100644 rag/dataset_chain.py create mode 100644 rag/set_up.py diff --git a/evals/eval.py b/evals/eval.py index fb82029..8162f7e 100644 --- a/evals/eval.py +++ b/evals/eval.py @@ -25,6 +25,20 @@ ), ] +questions = [ + "From the NIKE 2023 10k, Which operating segment contributed least to total Nike brand revenue in fiscal 2023?", + "From the NIKE 2023 10k, Which operating segment contributed most to total Nike brand revenue in fiscal 2023?", + "From the NIKE 2023 10k, Based on the North America revenue table for fiscal 2023, which segment contributed least to North America revenue?", + "From the NIKE 2023 10k, Based on the North America revenue table for fiscal 2023, which segment contributed most to North America revenue?", +] +target_answers = [ + "Global Brand Divisions", + "North America", + "Equipment", + "Footwear", +] +dataset = [{"question": q, "target": t} for q, t in zip(questions, target_answers)] + def run_evals( trace_id: str, diff --git a/ingest.py b/ingest.py index b385a0f..b2ee2b6 100644 --- a/ingest.py +++ b/ingest.py @@ -2,11 +2,11 @@ from dotenv import load_dotenv from langchain.document_loaders import UnstructuredFileLoader -from langchain.embeddings import HuggingFaceEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores import Redis +from langchain_community.embeddings import HuggingFaceEmbeddings -from rag.config import EMBED_MODEL, REDIS_URL, INDEX_NAME, INDEX_SCHEMA +from rag.config import REDIS_URL, INDEX_NAME, INDEX_SCHEMA, EMBED_MODEL load_dotenv() @@ -44,3 +44,7 @@ def ingest_documents(): redis_url=REDIS_URL, ) print("Finished.") + + +if __name__ == "__main__": + ingest_documents() diff --git a/poetry.lock b/poetry.lock index f38f601..18097c4 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,88 +1,88 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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"pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [metadata] lock-version = "2.0" python-versions = "^3.11" -content-hash = "a212ff246547314e70e264465c0c771b932effa3d0a42a4e3c69e493d9d79dc9" +content-hash = "a6e9d467242b8f3f0d0d4585fdef9bc26b7d698e3d9d6729e04f4eeda79ce2e3" diff --git a/pyproject.toml b/pyproject.toml index 050b359..8eb9211 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,6 +16,7 @@ sse-starlette = "^1.8.2" sentence-transformers = "^2.2.2" parea-ai = "^0.2.25" unstructured = {extras = ["pdf"], version = "^0.11.6"} +kay = "^0.1.2" [tool.poetry.group.dev.dependencies] langchain-cli = "^0.0.20" diff --git a/rag/chain.py b/rag/chain.py index f399a97..d130ce6 100644 --- a/rag/chain.py +++ b/rag/chain.py @@ -1,123 +1,40 @@ -import os - from dotenv import load_dotenv -from langchain.chat_models import ChatOpenAI -from langchain.embeddings import HuggingFaceEmbeddings -from langchain.prompts import ChatPromptTemplate -from langchain.vectorstores import Redis -from langchain_core.output_parsers import StrOutputParser -from langchain_core.runnables import RunnableParallel, RunnablePassthrough -from parea import Parea +from parea import trace_insert, trace +from parea.evals.general import answer_matches_target_llm_grader_factory +from parea.evals.rag import percent_target_supported_by_context_factory from parea.utils.trace_integrations.langchain import PareaAILangchainTracer -from evals.eval import run_evals -from rag.config import INDEX_NAME, INDEX_SCHEMA, REDIS_URL, EMBED_MODEL +from evals.eval import dataset +from rag.set_up import DocumentationChain, p load_dotenv() -# Need to instantiate Parea for tracing and evals -p = Parea(api_key=os.getenv("PAREA_API_KEY")) - # Init Tracer which will send logs to Parea AI parea_tracer = PareaAILangchainTracer() -# Init Embeddings -embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL) - -# Connect to preloaded vectorstore ( After you run ingest.py ) -vectorstore = Redis.from_existing_index( - embedding=embedder, index_name=INDEX_NAME, schema=INDEX_SCHEMA, redis_url=REDIS_URL -) - -# Init retriever -retriever = vectorstore.as_retriever(search_type="mmr") - -# Init global variable to store context from retriever -context_from_retriever = "" - - -def _format_docs(docs) -> str: - """ - Format the docs retrieved from the retriever - :param docs: list of documents retrieved from the retriever - :return: str of concatenated documents - """ - global context_from_retriever - formatted_context = "\n\n".join(doc.page_content for doc in docs) - context_from_retriever = formatted_context - return formatted_context - - -# A sentinel value to indicate whether to add the source to the prompt -# When running evals such as exact match excluding the source could help the LLM judge the answer better -ADD_SOURCE = False -ADD_SOURCE_TEXT = """Include the 'source' and 'start_index from the metadata included in the context you used to -answer the question""" - -# Define our prompt template -template = """ -Use the following pieces of context from Nike's financial 10k filings -dataset to answer the question. Do not make up an answer if there is no -context provided to help answer it. {ADD_SOURCE_TEXT} - -Context: ---------- -{context} +dc = DocumentationChain() ---------- -Question: {question} ---------- -Answer: -""" - -prompt = ChatPromptTemplate.from_template(template).partial( - ADD_SOURCE_TEXT=ADD_SOURCE_TEXT if ADD_SOURCE else "" +@trace( + eval_funcs=[ + answer_matches_target_llm_grader_factory(), + percent_target_supported_by_context_factory(), + ] ) - - -# RAG Chain -model = ChatOpenAI(model_name="gpt-3.5-turbo-16k") -chain = ( - RunnableParallel( - {"context": retriever | _format_docs, "question": RunnablePassthrough()} - ) - | prompt - | model - | StrOutputParser() -) - - -def invoke(question: str) -> tuple[str, str]: - """ - Invoke the chain with the question - :param question: - :return: response and trace_id - """ - response = chain.invoke(question, config={"callbacks": [parea_tracer]}) - trace_id = parea_tracer.get_parent_trace_id() - return response, str(trace_id) - - -def run_chain(question: str, target: str, run_eval: bool): +def run_chain(question: str) -> str: """ Run the chain with the question and target answer and optionally run evals :param question: question to ask - :param target: target answer - :param run_eval: whether to run evals - :return: None + :return: str """ - response, trace_id = invoke(question) - print("Question: ", question, "\n") - print("Response: ", response, "\n") + output = dc.get_chain().invoke( + {"question": question}, + config={"callbacks": [parea_tracer]}, + ) + trace_insert({"inputs": {"context": dc.get_context()}}) + return output + - if run_eval: - print("Evals started in thread: \n") - run_evals( - trace_id=trace_id, - question=question, - context=context_from_retriever, - response=response, - target_answer=target, - ) +if __name__ == "__main__": + p.experiment(data=dataset, func=run_chain).run() diff --git a/rag/dataset_chain.py b/rag/dataset_chain.py new file mode 100644 index 0000000..392c082 --- /dev/null +++ b/rag/dataset_chain.py @@ -0,0 +1,32 @@ +from dotenv import load_dotenv +from parea import trace +from parea.evals.general import answer_matches_target_llm_grader_factory +from parea.evals.rag import percent_target_supported_by_context_factory +from parea.schemas import Completion + +from rag.set_up import p + +load_dotenv() + + +@trace( + eval_funcs=[ + answer_matches_target_llm_grader_factory(), + percent_target_supported_by_context_factory(), + ] +) +def run_dataset_chain(question: str, context: str) -> str: + output = p.completion( + Completion( + deployment_id="p-T8VBzfZnwlVOUeD0aOwdu", + llm_inputs={"context": context, "question": question}, + ) + ) + return output.content + + +if __name__ == "__main__": + p.experiment( + data="RAG2", + func=run_dataset_chain, + ).run(name="rag_original_chain") diff --git a/rag/set_up.py b/rag/set_up.py new file mode 100644 index 0000000..96f826d --- /dev/null +++ b/rag/set_up.py @@ -0,0 +1,64 @@ +import os +from operator import itemgetter + +from dotenv import load_dotenv +from langchain.chat_models import ChatOpenAI +from langchain.prompts import ChatPromptTemplate +from langchain_community.embeddings import HuggingFaceEmbeddings +from langchain_community.vectorstores.redis import Redis +from langchain_core.output_parsers import StrOutputParser +from parea import Parea +from parea.schemas.models import UseDeployedPrompt + +from rag.config import REDIS_URL, INDEX_SCHEMA, INDEX_NAME, EMBED_MODEL + +load_dotenv() + +# Need to instantiate Parea for tracing and evals +p = Parea(api_key=os.getenv("PAREA_API_KEY")) + + +class DocumentRetriever: + def __init__(self): + embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL) + vectorstore = Redis.from_existing_index( + embedding=embedder, + index_name=INDEX_NAME, + schema=INDEX_SCHEMA, + redis_url=REDIS_URL, + ) + self.retriever = vectorstore.as_retriever(search_type="mmr") + # self.retriever = KayAiRetriever.create( + # dataset_id="company", data_types=["10-K"], num_contexts=3 + # ) + + def get_retriever(self): + return self.retriever + + +class DocumentationChain: + def __init__(self): + retriever = DocumentRetriever().get_retriever() + fetched = p.get_prompt( + UseDeployedPrompt(deployment_id="p-T8VBzfZnwlVOUeD0aOwdu") + ) + template = fetched.prompt.raw_messages[0]["content"] + prompt = ChatPromptTemplate.from_template(template) + model = ChatOpenAI(model_name=fetched.model) + response_generator = prompt | model | StrOutputParser() + self.chain = { + "context": itemgetter("question") | retriever | self._format_docs, + "question": itemgetter("question"), + } | response_generator + + def get_context(self) -> str: + """Helper to get the context from a retrieval chain, so we can use it for evaluation metrics.""" + return self.context + + def _format_docs(self, docs) -> str: + context = "\n\n".join(doc.page_content for doc in docs) + self.context = context + return context + + def get_chain(self): + return self.chain From 140271f3d12e2bbe9c838e9ecc559d8329366200 Mon Sep 17 00:00:00 2001 From: Joel Alexander Date: Wed, 27 Mar 2024 10:48:03 -0500 Subject: [PATCH 2/3] demo --- rag/dataset_chain.py | 18 +++++++----------- rag/set_up.py | 25 +++++++++++-------------- 2 files changed, 18 insertions(+), 25 deletions(-) diff --git a/rag/dataset_chain.py b/rag/dataset_chain.py index 392c082..ac23bae 100644 --- a/rag/dataset_chain.py +++ b/rag/dataset_chain.py @@ -1,7 +1,9 @@ from dotenv import load_dotenv from parea import trace -from parea.evals.general import answer_matches_target_llm_grader_factory -from parea.evals.rag import percent_target_supported_by_context_factory +from parea.evals.general import answer_matches_target_llm_grader_factory as llm_grader +from parea.evals.rag import ( + percent_target_supported_by_context_factory as context_quality, +) from parea.schemas import Completion from rag.set_up import p @@ -9,20 +11,14 @@ load_dotenv() -@trace( - eval_funcs=[ - answer_matches_target_llm_grader_factory(), - percent_target_supported_by_context_factory(), - ] -) +@trace(eval_funcs=[llm_grader(), context_quality()]) def run_dataset_chain(question: str, context: str) -> str: - output = p.completion( + return p.completion( Completion( deployment_id="p-T8VBzfZnwlVOUeD0aOwdu", llm_inputs={"context": context, "question": question}, ) - ) - return output.content + ).content if __name__ == "__main__": diff --git a/rag/set_up.py b/rag/set_up.py index 96f826d..f38ba81 100644 --- a/rag/set_up.py +++ b/rag/set_up.py @@ -4,14 +4,11 @@ from dotenv import load_dotenv from langchain.chat_models import ChatOpenAI from langchain.prompts import ChatPromptTemplate -from langchain_community.embeddings import HuggingFaceEmbeddings -from langchain_community.vectorstores.redis import Redis +from langchain_community.retrievers import KayAiRetriever from langchain_core.output_parsers import StrOutputParser from parea import Parea from parea.schemas.models import UseDeployedPrompt -from rag.config import REDIS_URL, INDEX_SCHEMA, INDEX_NAME, EMBED_MODEL - load_dotenv() # Need to instantiate Parea for tracing and evals @@ -20,17 +17,17 @@ class DocumentRetriever: def __init__(self): - embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL) - vectorstore = Redis.from_existing_index( - embedding=embedder, - index_name=INDEX_NAME, - schema=INDEX_SCHEMA, - redis_url=REDIS_URL, - ) - self.retriever = vectorstore.as_retriever(search_type="mmr") - # self.retriever = KayAiRetriever.create( - # dataset_id="company", data_types=["10-K"], num_contexts=3 + # embedder = HuggingFaceEmbeddings(model_name=EMBED_MODEL) + # vectorstore = Redis.from_existing_index( + # embedding=embedder, + # index_name=INDEX_NAME, + # schema=INDEX_SCHEMA, + # redis_url=REDIS_URL, # ) + # self.retriever = vectorstore.as_retriever(search_type="mmr") + self.retriever = KayAiRetriever.create( + dataset_id="company", data_types=["10-K"], num_contexts=3 + ) def get_retriever(self): return self.retriever From d75198ffabf5d52542422df7145a8f3c29966375 Mon Sep 17 00:00:00 2001 From: Joel Alexander Date: Wed, 27 Mar 2024 15:57:24 -0500 Subject: [PATCH 3/3] demo --- rag/dataset_chain.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/rag/dataset_chain.py b/rag/dataset_chain.py index ac23bae..6fd390d 100644 --- a/rag/dataset_chain.py +++ b/rag/dataset_chain.py @@ -1,4 +1,3 @@ -from dotenv import load_dotenv from parea import trace from parea.evals.general import answer_matches_target_llm_grader_factory as llm_grader from parea.evals.rag import ( @@ -8,8 +7,6 @@ from rag.set_up import p -load_dotenv() - @trace(eval_funcs=[llm_grader(), context_quality()]) def run_dataset_chain(question: str, context: str) -> str: