LangChain Graph Retriever

LangChain Graph Retriever is a Python library that supports traversing a document graph on top of vector-based similarity search. It works seamlessly with LangChain's retriever framework and supports various graph traversal strategies for efficient document discovery.

Features

• Vector Search: Perform similarity searches using vector embeddings.
• Graph Traversal: Apply traversal strategies such as breadth-first (Eager) or Maximal Marginal Relevance (MMR) to explore document relationships.
• Customizable Strategies: Easily extend and configure traversal strategies to meet your specific use case.
• Multiple Adapters: Support for various vector stores, including AstraDB, Cassandra, Chroma, OpenSearch, and in-memory storage.
• Synchronous and Asynchronous Retrieval: Supports both sync and async workflows for flexibility in different applications.

Installation

Install the library via pip:

pip install langchain-graph-retriever

Getting Started

Here is an example of how to use LangChain Graph Retriever:

from langchain_graph_retriever import GraphRetriever
from langchain_core.vectorstores import Chroma

# Initialize the vector store (Chroma in this example)
vector_store = Chroma(embedding_function=your_embedding_function)

# Create the Graph Retriever
retriever = GraphRetriever(
    store=vector_store,
    # Define edges based on document metadata
    edges=[("keywords", "keywords")],
)

# Perform a retrieval
documents = retriever.retrieve("What is the capital of France?")

# Print the results
for doc in documents:
    print(doc.page_content)

License

This project is licensed under the Apache 2 License. See the LICENSE file for more details.