Retriever for GraphVectorStore.

A graph vector store retriever is a retriever that uses a graph vector store to retrieve documents. It is similar to a vector store retriever, except that it uses both vector similarity and graph connections to retrieve documents. It uses the search methods implemented by a graph vector store, like traversal search and MMR traversal search, to query the texts in the graph vector store.

Example::

store = CassandraGraphVectorStore(...)
retriever = store.as_retriever()
retriever.invoke("What is ...")

.. seealso::

:mod:`How to use a graph vector store <langchain_community.graph_vectorstores>`

How to use a graph vector store as a retriever

Creating a retriever from a graph vector store

You can build a retriever from a graph vector store using its :meth:~langchain_community.graph_vectorstores.base.GraphVectorStore.as_retriever method.

First we instantiate a graph vector store. We will use a store backed by Cassandra :class:~langchain_community.graph_vectorstores.cassandra.CassandraGraphVectorStore graph vector store::

from langchain_community.document_loaders import TextLoader
from langchain_community.graph_vectorstores import CassandraGraphVectorStore
from langchain_community.graph_vectorstores.extractors import (
    KeybertLinkExtractor,
    LinkExtractorTransformer,
)
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

loader = TextLoader("state_of_the_union.txt")
documents = loader.load()

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

pipeline = LinkExtractorTransformer([KeybertLinkExtractor()])
pipeline.transform_documents(texts)
embeddings = OpenAIEmbeddings()
graph_vectorstore = CassandraGraphVectorStore.from_documents(texts, embeddings)

We can then instantiate a retriever::

retriever = graph_vectorstore.as_retriever()

This creates a retriever (specifically a GraphVectorStoreRetriever), which we can use in the usual way::

docs = retriever.invoke("what did the president say about ketanji brown jackson?")

Maximum marginal relevance traversal retrieval

By default, the graph vector store retriever uses similarity search, then expands the retrieved set by following a fixed number of graph edges. If the underlying graph vector store supports maximum marginal relevance traversal, you can specify that as the search type.

MMR-traversal is a retrieval method combining MMR and graph traversal. The strategy first retrieves the top fetch_k results by similarity to the question. It then iteratively expands the set of fetched documents by following adjacent_k graph edges and selects the top k results based on maximum-marginal relevance using the given lambda_mult::

retriever = graph_vectorstore.as_retriever(search_type="mmr_traversal")

Passing search parameters

We can pass parameters to the underlying graph vector store's search methods using search_kwargs.

Specifying graph traversal depth ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

For example, we can set the graph traversal depth to only return documents reachable through a given number of graph edges::

retriever = graph_vectorstore.as_retriever(search_kwargs={"depth": 3})

Specifying MMR parameters ^^^^^^^^^^^^^^^^^^^^^^^^^

When using search type mmr_traversal, several parameters of the MMR algorithm can be configured.

The fetch_k parameter determines how many documents are fetched using vector similarity and adjacent_k parameter determines how many documents are fetched using graph edges. The lambda_mult parameter controls how the MMR re-ranking weights similarity to the query string vs diversity among the retrieved documents as fetched documents are selected for the set of k final results::

retriever = graph_vectorstore.as_retriever(
    search_type="mmr",
    search_kwargs={"fetch_k": 20, "adjacent_k": 20, "lambda_mult": 0.25},
)

Specifying top k ^^^^^^^^^^^^^^^^

We can also limit the number of documents k returned by the retriever.

Note that if depth is greater than zero, the retriever may return more documents than is specified by k, since both the original k documents retrieved using vector similarity and any documents connected via graph edges will be returned::

retriever = graph_vectorstore.as_retriever(search_kwargs={"k": 1})

Similarity score threshold retrieval ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

For example, we can set a similarity score threshold and only return documents with a score above that threshold::

retriever = graph_vectorstore.as_retriever(search_kwargs={"score_threshold": 0.5})