Add TOC to GraphVectorStore doc

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

@@ -1,4 +1,7 @@
-"""**Graph Vector Store**
+""".. title:: Graph Vector Store
+
+Graph Vector Store
+==================
 
 Sometimes embedding models don’t capture all the important relationships between
 documents.
@@ -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
-***********
+.. contents:: Contents
+    :local:
 
-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
@@ -46,9 +47,7 @@
 For this guide, we'll use the
 :class:`~langchain_community.graph_vectorstores.extractors.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 +57,12 @@
     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
+from the documents and an :class:`~langchain_openai.OpenAIEmbeddings` model.::
 
     import cassio
     from langchain_community.graph_vectorstores import CassandraGraphVectorStore
@@ -80,45 +76,37 @@
         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`
 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`
 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 +115,12 @@
         )
     ]
 
-****************************
 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?")