Graph RAG

This guide provides an introduction to Graph RAG. For detailed documentation of all supported features and configurations, refer to the Graph RAG Project Page.

Overview

The GraphRetriever from the langchain-graph-retriever package provides a LangChain retriever that combines unstructured similarity search on vectors with structured traversal of metadata properties. This enables graph-based retrieval over an existing vector store.

Integration details

Retriever	Source	PyPI Package	Latest	Project Page
GraphRetriever	github.com/datastax/graph-rag	langchain-graph-retriever		Graph RAG

Benefits

• Link based on existing metadata: Use existing metadata fields without additional processing. Retrieve more from an existing vector store!

• Change links on demand: Edges can be specified on-the-fly, allowing different relationships to be traversed based on the question.

• Pluggable Traversal Strategies: Use built-in traversal strategies like Eager or MMR, or define custom logic to select which nodes to explore.

• Broad compatibility: Adapters are available for a variety of vector stores with support for additional stores easily added.

Setup

Installation

This retriever lives in the langchain-graph-retriever package.

pip install -qU langchain-graph-retriever

Instantiation

The following examples will show how to perform graph traversal over some sample Documents about animals.

Prerequisites

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1. Ensure you have Python 3.10+ installed

2. Install the following package that provides sample data.

   pip install -qU graph_rag_example_helpers

3. Download the test documents:

   from graph_rag_example_helpers.datasets.animals import fetch_documents
   animals = fetch_documents()
4. Select embeddings model:
5. OpenAI
6. Azure
7. Google
8. AWS
9. HuggingFace
10. Ollama
11. Cohere
12. MistralAI
13. Nomic
14. NVIDIA
15. Voyage AI
16. IBM watsonx
17. Fake

pip install -qU langchain-openai

import getpass
import os

if not os.environ.get("OPENAI_API_KEY"):
  os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter API key for OpenAI: ")

from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")