This SDK simplifies building Graph Retrieval-Augmented Generation (GraphRAG) systems by integrating knowledge graphs, ontology management, and LLMs. It targets developers building scalable GenAI applications, offering accurate, efficient, and customizable RAG workflows.

How It Works

The SDK leverages FalkorDB for high-performance knowledge graph storage and querying. It automates ontology creation from unstructured data or allows manual definition. Users can then process data sources (URLs, PDFs, etc.) to build a knowledge graph, which can be queried via a chat session for RAG applications. It also supports multi-agent systems, where specialized agents interact with different knowledge graphs, orchestrated for complex tasks.

Quick Start & Requirements

• Install: pip install graphrag_sdk[all] (or [litellm] for specific providers).
• Dependencies: FalkorDB (Docker image provided), Python, LLM API credentials (e.g., OpenAI, Gemini, Ollama).
• Setup: Requires FalkorDB setup and LLM provider configuration.
• Docs: [Not explicitly linked, but examples provided in README.]

Highlighted Details

• Supports automated ontology creation from various data sources (URLs, PDFs, etc.).
• Enables multi-agent systems with an orchestrator for complex task management.
• Allows customization of LLM prompts for agents and RAG steps.
• Optimized for FalkorDB, supporting multi-graph querying and low-latency operations.

Maintenance & Community

• Community support via GitHub Issues and Discord.
• Encourages project starring for visibility.

Licensing & Compatibility

• MIT License. Permissive for commercial use and closed-source linking.

Limitations & Caveats

The SDK is optimized for FalkorDB; other graph databases may require custom adapters. Ollama models are currently supported only for the Q&A step, not for knowledge graph creation.