Fast GraphRAG is a Python framework for building interpretable, high-precision retrieval-augmented generation (RAG) systems. It targets developers and researchers seeking efficient, agent-driven knowledge retrieval without complex workflow setup. The framework leverages graph-based knowledge representation and PageRank for intelligent data exploration, offering significant cost savings over traditional RAG approaches.

How It Works

Fast GraphRAG constructs a knowledge graph from input text, identifying and categorizing entities. It then uses a PageRank-based algorithm to traverse this graph, prioritizing relevant information for query answering. This approach provides a human-navigable knowledge base, enabling debuggability and dynamic refinement, while the graph traversal aims for enhanced accuracy and dependability.

Quick Start & Requirements

• Install from source (poetry install) or PyPI (pip install fast-graphrag).
• Requires OPENAI_API_KEY environment variable.
• Optional: CONCURRENT_TASK_LIMIT for LLM concurrency.
• Quickstart example provided in the README.

Highlighted Details

• Claims 6x cost savings compared to graphrag ($0.08 vs. $0.48 per query).
• Supports generic entities and concepts, with IDF weighting planned.
• Benchmarks against LightRAG, GraphRAG, and VectorDBs are available.
• Fully asynchronous with complete type support.

Maintenance & Community

• Active development with recent updates and benchmarks.
• Community support available via Discord.
• Contribution guide provided.

Licensing & Compatibility

• MIT License.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

The framework currently relies on OpenAI's API for LLM and embedding functionalities, though custom LLM configurations are supported. Future work includes IDF weighting for entities.