community: Fix links in GraphVectorStore pydoc (#25959)

Co-authored-by: Erick Friis <erick@langchain.dev>

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.
@@ -13,11 +16,10 @@ Each document identifies tags that link to and from it.
 For example, a paragraph of text may be linked to URLs based on the anchor tags in
 it's content and linked from the URL(s) it is published at.
 
-Link extractors can be used to extract links from documents.
+`Link extractors <langchain_community.graph_vectorstores.extractors.link_extractor.LinkExtractor>`
+can be used to extract links from documents.
 
-Example:
+Example::
-
-.. code-block:: python
 
     graph_vector_store = CassandraGraphVectorStore()
     link_extractor = HtmlLinkExtractor()
@@ -25,13 +27,18 @@ Example:
     add_links(document, links)
     graph_vector_store.add_document(document)
 
-***********
+.. seealso::
+
+    - :class:`How to use a graph vector store as a retriever <langchain_community.graph_vectorstores.base.GraphVectorStoreRetriever>`
+    - :class:`How to create links between documents <langchain_community.graph_vectorstores.links.Link>`
+    - :class:`How to link Documents on hyperlinks in HTML <langchain_community.graph_vectorstores.extractors.html_link_extractor.HtmlLinkExtractor>`
+    - :class:`How to link Documents on common keywords (using KeyBERT) <langchain_community.graph_vectorstores.extractors.keybert_link_extractor.KeybertLinkExtractor>`
+    - :class:`How to link Documents on common named entities (using GliNER) <langchain_community.graph_vectorstores.extractors.gliner_link_extractor.GLiNERLinkExtractor>`
+
 Get started
-***********
+-----------
 
-We chunk the State of the Union text and split it into documents.
+We chunk the State of the Union text and split it into documents::
-
-.. code-block:: python
 
     from langchain_community.document_loaders import TextLoader
     from langchain_text_splitters import CharacterTextSplitter
@@ -41,14 +48,12 @@ We chunk the State of the Union text and split it into documents.
     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.
+create links between documents::
-
-.. code-block:: python
 
     from langchain_community.graph_vectorstores.extractors import KeybertLinkExtractor
     from langchain_community.graph_vectorstores.links import add_links
@@ -58,15 +63,14 @@ create links between documents.
     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`
+We create a
-from the documents and an :class:`~langchain_openai.OpenAIEmbeddings` model.
+:class:`~langchain_community.graph_vectorstores.cassandra.CassandraGraphVectorStore`
-
+from the documents and an :class:`~langchain_openai.embeddings.base.OpenAIEmbeddings`
-.. code-block:: python
+model::
 
     import cassio
     from langchain_community.graph_vectorstores import CassandraGraphVectorStore
@@ -80,45 +84,37 @@ from the documents and an :class:`~langchain_openai.OpenAIEmbeddings` model.
         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.
+the links between documents::
-
-.. code-block:: python
 
     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.
+find linked documents::
-
-.. code-block:: python
 
     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``.
+The graph vector store has async versions of the methods prefixed with ``a``::
-
-.. code-block:: python
 
     docs = [
         doc
@@ -127,15 +123,12 @@ The graph vector store has async versions of the methods prefixed with ``a``.
         )
     ]
 
-****************************
 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``.
+such as ``traversal`` and ``mmr_traversal``::
-
-.. code-block:: python
 
     retriever = store.as_retriever(search_type="mmr_traversal")
     docs = retriever.invoke("What did the president say about Ketanji Brown Jackson?")