GraphRAG Toolkit - FalkorDB Docs

AWS GraphRAG Toolkit is an open-source framework for building knowledge graph applications with Large Language Models (LLMs). FalkorDB is supported as a graph store backend, enabling you to leverage FalkorDB’s high-performance graph database capabilities in your GraphRAG applications.

Overview

The GraphRAG Toolkit provides tools and patterns for building retrieval-augmented generation (RAG) applications that use knowledge graphs. With FalkorDB as the graph store, you can:

Build and query knowledge graphs efficiently
Use semantic-guided search for intelligent retrieval
Connect to FalkorDB Cloud or local instances
Integrate with LLM-powered applications

Installation

The FalkorDB graph store is contained in a separate contributor package. Install it using:

pip install https://github.com/awslabs/graphrag-toolkit/archive/refs/tags/v3.13.3.zip#subdirectory=lexical-graph-contrib/falkordb

Quick Start

1. Register FalkorDB as a Graph Store

Before creating a FalkorDB graph store, register the FalkorDBGraphStoreFactory with the GraphStoreFactory:

from graphrag_toolkit.lexical_graph.storage import GraphStoreFactory
from graphrag_toolkit_contrib.lexical_graph.storage.graph.falkordb import FalkorDBGraphStoreFactory

GraphStoreFactory.register(FalkorDBGraphStoreFactory)

2. Create a FalkorDB Graph Store

You can use the GraphStoreFactory.for_graph_store() static factory method to create an instance of a FalkorDB graph store.

Using FalkorDB Cloud

To create a FalkorDB Cloud graph store, supply a connection string that begins with falkordb://, followed by your FalkorDB endpoint:

from graphrag_toolkit.lexical_graph.storage import GraphStoreFactory
from graphrag_toolkit_contrib.lexical_graph.storage.graph.falkordb import FalkorDBGraphStoreFactory

falkordb_connection_info = 'falkordb://your-falkordb-endpoint'

GraphStoreFactory.register(FalkorDBGraphStoreFactory)

with GraphStoreFactory.for_graph_store(falkordb_connection_info) as graph_store:
    # Your code here
    pass

You may need to pass credentials and SSL configuration:

from graphrag_toolkit.lexical_graph.storage import GraphStoreFactory
from graphrag_toolkit_contrib.lexical_graph.storage.graph.falkordb import FalkorDBGraphStoreFactory

falkordb_connection_info = 'falkordb://<your-falkordb-endpoint>'

GraphStoreFactory.register(FalkorDBGraphStoreFactory)

with GraphStoreFactory.for_graph_store(
    falkordb_connection_info,
    username='<username>',
    password='<password>',
    ssl=True
) as graph_store:
    # Your code here
    pass

Using Local FalkorDB

To create a local FalkorDB graph store, supply a connection string with only falkordb://:

from graphrag_toolkit.lexical_graph.storage import GraphStoreFactory
from graphrag_toolkit_contrib.lexical_graph.storage.graph.falkordb import FalkorDBGraphStoreFactory

falkordb_connection_info = 'falkordb://'

GraphStoreFactory.register(FalkorDBGraphStoreFactory)

with GraphStoreFactory.for_graph_store(falkordb_connection_info) as graph_store:
    # Your code here
    pass

3. Start FalkorDB (Local Setup)

If you’re using a local instance, start FalkorDB with Docker:

docker run -p 6379:6379 -p 3000:3000 -it --rm falkordb/falkordb:edge

Or sign up for FalkorDB Cloud for a managed solution.

Features

Semantic-Guided Search

The FalkorDB graph store supports semantic-guided search, enabling intelligent retrieval based on meaning and context rather than just keyword matching. Note: The FalkorDB graph store currently does not support traversal-based search.

Resources

🔗 AWS GraphRAG Toolkit GitHub Repository
📖 GraphRAG Toolkit Documentation
📓 FalkorDB Graph Store Documentation
☁️ FalkorDB Cloud

Use Cases

Knowledge Graph Construction: Build structured knowledge graphs from unstructured data
Semantic Search: Implement context-aware search using graph-based retrieval
Question Answering: Combine LLMs with graph data for accurate responses
Document Understanding: Extract and organize information in a knowledge graph

GraphRAG-SDK: FalkorDB’s native GraphRAG solution
LangChain: Build AI agents with graph memory
LlamaIndex: LLM application framework with FalkorDB support

Frequently Asked Questions

What is the AWS GraphRAG Toolkit?

The AWS GraphRAG Toolkit is an open-source framework from AWS Labs for building knowledge graph applications with LLMs. FalkorDB is supported as a graph store backend through a separate contributor package, enabling high-performance graph database capabilities in GraphRAG applications.

How do I install FalkorDB support for the GraphRAG Toolkit?

Install the FalkorDB graph store contributor package using:

pip install https://github.com/awslabs/graphrag-toolkit/archive/refs/tags/v3.13.3.zip#subdirectory=lexical-graph-contrib/falkordb

Can I use FalkorDB Cloud with the GraphRAG Toolkit?

Yes. Use a connection string starting with falkordb:// followed by your FalkorDB Cloud endpoint. You can pass username, password, and ssl=True parameters to GraphStoreFactory.for_graph_store() for secure authentication.

Does the toolkit support traversal-based search?

No, the FalkorDB graph store currently supports semantic-guided search only, enabling intelligent retrieval based on meaning and context. Traversal-based search is not yet supported.

How does this differ from the FalkorDB GraphRAG-SDK?

The AWS GraphRAG Toolkit is a broader AWS framework where FalkorDB is one of several possible graph stores. The FalkorDB GraphRAG-SDK is FalkorDB’s native solution with tighter integration, ontology extraction, and conversational sessions built specifically for FalkorDB.

​Overview

​Installation

​Quick Start

​1. Register FalkorDB as a Graph Store

​2. Create a FalkorDB Graph Store

​Using FalkorDB Cloud

​Using Local FalkorDB

​3. Start FalkorDB (Local Setup)

​Features

​Semantic-Guided Search

​Resources

​Use Cases

​Related Tools

​Frequently Asked Questions