Fix links in GraphVectorStore pydoc

docs/api_reference/scripts/custom_formatter.py

@@ -17,15 +17,16 @@ def process_toc_h3_elements(html_content: str) -> str:
 
     # Process each element
     for element in toc_h3_elements:
-        element = element.a.code.span
-        # Get the text content of the element
-        content = element.get_text()
+        if element.a.code:
+            element = element.a.code.span
+            # Get the text content of the element
+            content = element.get_text()
 
-        # Apply the regex substitution
-        modified_content = content.split(".")[-1]
+            # Apply the regex substitution
+            modified_content = content.split(".")[-1]
 
-        # Update the element's content
-        element.string = modified_content
+            # Update the element's content
+            element.string = modified_content
 
     # Return the modified HTML
     return str(soup)

libs/community/langchain_community/graph_vectorstores/__init__.py

@@ -1,6 +1,9 @@
-"""**Graph Vector Store**
+""".. title:: Graph Vector Store
 
-Sometimes embedding models don’t capture all the important relationships between
+Graph Vector Store
+==================
+
+Sometimes embedding models don't capture all the important relationships between
 documents.
 Graph Vector Stores are an extension to both vector stores and retrievers that allow
 documents to be explicitly connected to each other.
@@ -15,23 +18,21 @@
 
 Link extractors can be used to extract links from documents.
 
-Example:
-
-.. code-block:: python
+Example::
 
     graph_vector_store = CassandraGraphVectorStore()
     link_extractor = HtmlLinkExtractor()
     links = link_extractor.extract_one(HtmlInput(document.page_content, "http://mysite"))
     add_links(document, links)
     graph_vector_store.add_document(document)
 
-***********
-Get started
-***********
+.. seealso::
+    :class:`How to create links between documents <langchain_core.graph_vectorstores.links.Link>`
 
-We chunk the State of the Union text and split it into documents.
+Get started
+-----------
 
-.. code-block:: python
+We chunk the State of the Union text and split it into documents::
 
     from langchain_community.document_loaders import TextLoader
     from langchain_text_splitters import CharacterTextSplitter
@@ -41,14 +42,12 @@
     documents = text_splitter.split_documents(raw_documents)
 
 Links can be added to documents manually but it's easier to use a
-:class:`~langchain_community.graph_vectorstores.extractors.LinkExtractor`.
+:class:`~langchain_community.graph_vectorstores.extractors.link_extractor.LinkExtractor`.
 Several common link extractors are available and you can build your own.
 For this guide, we'll use the
-:class:`~langchain_community.graph_vectorstores.extractors.KeybertLinkExtractor`
+:class:`~langchain_community.graph_vectorstores.extractors.keybert_link_extractor.KeybertLinkExtractor`
 which uses the KeyBERT model to tag documents with keywords and uses these keywords to
-create links between documents.
-
-.. code-block:: python
+create links between documents.::
 
     from langchain_community.graph_vectorstores.extractors import KeybertLinkExtractor
     from langchain_community.graph_vectorstores.links import add_links
@@ -58,15 +57,14 @@
     for doc in documents:
         add_links(doc, extractor.extract_one(doc))
 
-***********************************************
 Create the graph vector store and add documents
-***********************************************
+-----------------------------------------------
 
 We'll use an Apache Cassandra or Astra DB database as an example.
-We create a :class:`~langchain_community.graph_vectorstores.CassandraGraphVectorStore`
-from the documents and an :class:`~langchain_openai.OpenAIEmbeddings` model.
-
-.. code-block:: python
+We create a
+:class:`~langchain_community.graph_vectorstores.cassandra.CassandraGraphVectorStore`
+from the documents and an :class:`~langchain_openai.embeddings.base.OpenAIEmbeddings`
+model.::
 
     import cassio
     from langchain_community.graph_vectorstores import CassandraGraphVectorStore
@@ -80,45 +78,37 @@
         documents=documents,
     )
 
-*****************
+
 Similarity search
-*****************
+-----------------
 
 If we don't traverse the graph, a graph vector store behaves like a regular vector
 store.
 So all methods available in a vector store are also available in a graph vector store.
-The :meth:`~langchain_community.graph_vectorstores.base.GraphVectorStore.similarity_search`
+The :meth:`~langchain_core.graph_vectorstores.base.GraphVectorStore.similarity_search`
 method returns documents similar to a query without considering
-the links between documents.
-
-.. code-block:: python
+the links between documents.::
 
     docs = store.similarity_search(
         "What did the president say about Ketanji Brown Jackson?"
     )
 
-****************
 Traversal search
-****************
+----------------
 
-The :meth:`~langchain_community.graph_vectorstores.base.GraphVectorStore.traversal_search`
+The :meth:`~langchain_core.graph_vectorstores.base.GraphVectorStore.traversal_search`
 method returns documents similar to a query considering the links
 between documents. It first does a similarity search and then traverses the graph to
-find linked documents.
-
-.. code-block:: python
+find linked documents.::
 
     docs = list(
         store.traversal_search("What did the president say about Ketanji Brown Jackson?")
     )
 
-*************
 Async methods
-*************
+-------------
 
-The graph vector store has async versions of the methods prefixed with ``a``.
-
-.. code-block:: python
+The graph vector store has async versions of the methods prefixed with ``a``.::
 
     docs = [
         doc
@@ -127,15 +117,12 @@
         )
     ]
 
-****************************
 Graph vector store retriever
-****************************
+----------------------------
 
 The graph vector store can be converted to a retriever.
 It is similar to the vector store retriever but it also has traversal search methods
-such as ``traversal`` and ``mmr_traversal``.
-
-.. code-block:: python
+such as ``traversal`` and ``mmr_traversal``.::
 
     retriever = store.as_retriever(search_type="mmr_traversal")
     docs = retriever.invoke("What did the president say about Ketanji Brown Jackson?")