graph_vectorstores#
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.
Graph vector store retrievers use both vector similarity and links to find documents related to an unstructured query.
Graphs allow linking between documents. 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.
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#
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
raw_documents = TextLoader("state_of_the_union.txt").load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)
Links can be added to documents manually but it’s easier to use a
LinkExtractor.
Several common link extractors are available and you can build your own.
For this guide, we’ll use the
KeybertLinkExtractor
which uses the KeyBERT model to tag documents with keywords and uses these keywords to
create links between documents.
from langchain_community.graph_vectorstores.extractors import KeybertLinkExtractor
from langchain_community.graph_vectorstores.links import add_links
extractor = KeybertLinkExtractor()
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 CassandraGraphVectorStore
from the documents and an OpenAIEmbeddings model.
import cassio
from langchain_community.graph_vectorstores import CassandraGraphVectorStore
from langchain_openai import OpenAIEmbeddings
# Initialize cassio and the Cassandra session from the environment variables
cassio.init(auto=True)
store = CassandraGraphVectorStore.from_documents(
embedding=OpenAIEmbeddings(),
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 similarity_search()
method returns documents similar to a query without considering
the links between documents.
docs = store.similarity_search(
"What did the president say about Ketanji Brown Jackson?"
)
Traversal search#
The 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.
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.
docs = [
doc
async for doc in store.atraversal_search(
"What did the president say about Ketanji Brown Jackson?"
)
]
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.
retriever = store.as_retriever(search_type="mmr_traversal")
docs = retriever.invoke("What did the president say about Ketanji Brown Jackson?")
Classes