Clean up api docs

.github/workflows/ci.yml

@@ -86,7 +86,7 @@ jobs:
         with:
           enable-cache: true
           python-version: 3.9
-      - run: uv run sphinx-build -T -b html docs docs/_build/html
+      - run: uv run sphinx-build -T -W -b html docs docs/_build/html
 
   ci_success:
     name: "CI Success"

docs/conf.py

@@ -52,7 +52,7 @@ def skip_private_members(app, what, name, obj, skip, options):
         return True
     if hasattr(obj, "__doc__") and obj.__doc__ and ":private:" in obj.__doc__:
         return True
-    if name == "__init__" and obj.__objclass__ is object:
+    if name == "__init__" and getattr(obj, "__objclass__", None) is object:
         # dont document default init
         return True
     return None
@@ -76,10 +76,12 @@ def skip_private_members(app, what, name, obj, skip, options):
 extensions = [
     "sphinx.ext.autodoc",
     "sphinx.ext.autodoc.typehints",
+    "sphinx.ext.mathjax",
     "sphinx.ext.autosummary",
     "sphinx.ext.napoleon",
     "sphinx.ext.viewcode",
     "sphinxcontrib.autodoc_pydantic",
+    "IPython.sphinxext.ipython_console_highlighting",
     "myst_parser",
     "_extensions.gallery_directive",
     "sphinx_design",

docs/create_api_rst.py

@@ -88,6 +88,8 @@ def _load_module_members(module_path: str, namespace: str) -> ModuleMembers:
             continue
         if ":private:" in (type_.__doc__ or ""):
             continue
+        if hasattr(module, "__all__") and name not in module.__all__:
+            continue
 
         if inspect.isclass(type_):
             # The type of the class is used to select a template

libs/langchain-mongodb/langchain_mongodb/pipelines.py

@@ -116,7 +116,7 @@ def reciprocal_rank_stage(
         extra_fields: Any fields other than text_field that one wishes to keep.
 
     Returns:
-        RRF score := \frac{1}{rank + penalty} with rank in [1,2,..,n]
+        RRF score
     """
 
     rrf_pipeline = [

libs/langchain-mongodb/langchain_mongodb/vectorstores.py

@@ -61,12 +61,12 @@ class MongoDBAtlasVectorSearch(VectorStore):
 
     Setup:
         * Set up a MongoDB Atlas cluster. The free tier M0 will allow you to start.
-        Search Indexes are only available on Atlas, the fully managed cloud service,
-        not the self-managed MongoDB.
-        Follow [this guide](https://www.mongodb.com/basics/mongodb-atlas-tutorial)
+          Search Indexes are only available on Atlas, the fully managed cloud service,
+          not the self-managed MongoDB.
+          Follow [this guide](https://www.mongodb.com/basics/mongodb-atlas-tutorial)
 
         * Create a Collection and a Vector Search Index.The procedure is described
-        [here](https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#procedure).
+          [here](https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/#procedure).
 
         * Install ``langchain-mongodb``
 
@@ -189,6 +189,7 @@ class MongoDBAtlasVectorSearch(VectorStore):
             * [SIM=0.916096] foo [{'_id': '1', 'baz': 'bar'}]
 
     Use as Retriever:
+
         .. code-block:: python
 
             retriever = vector_store.as_retriever(