Youtu-GraphRAG is a vertically unified agentic framework for complex reasoning, particularly multi-hop and knowledge-intensive tasks. It targets users needing to leverage large, structured, or private knowledge bases, offering significant accuracy and efficiency gains over SOTA baselines. Its primary benefit is remarkable domain transferability with minimal schema intervention.

How It Works

The framework employs a vertically unified agentic paradigm centered on a graph schema. It constructs a four-level hierarchical knowledge tree (Attributes, Relations, Keywords, Communities) via schema-guided extraction and expansion. A novel dually-perceived community detection algorithm fuses structural topology with subgraph semantics. Agentic retrieval uses schema-aware query decomposition and Iterative Retrieval Chain of Thought (IRCoT) for advanced reasoning, enabling seamless domain transfer and performance improvements.

Quick Start & Requirements

Installation is recommended via Docker, requiring a Docker environment and LLM API configuration in a .env file. Alternatively, Python 3.10 and pip can be used. Key dependencies include LLM API access (OpenAI format) and potentially Neo4j for visualization. The project provides commands for Docker build/run and Python environment setup, including launching a web UI.

Highlighted Details

• Achieves 33.6% lower token cost and 16.62% higher accuracy over SOTA baselines.
• Features a four-level knowledge tree: Attributes, Relations, Keywords, Communities.
• Employs a novel community detection algorithm combining structural topology and subgraph semantics.
• Supports agentic retrieval via schema-aware query decomposition and IRCoT.
• Offers user-friendly visualization compatible with Neo4j.
• Includes the 'AnonyRAG' dataset on Hugging Face for performance evaluation.
• Provides unified configuration management via a single YAML file with runtime overrides.

Maintenance & Community

The project welcomes community contributions via pull requests. It offers links to a Discord community (https://discord.gg/QjqhkHQVVM) and WeChat community. Contact information for key contributors is provided.

Licensing & Compatibility

Released under the MIT License, permitting broad usage, including commercial applications and integration into closed-source projects.

Limitations & Caveats

The README does not detail specific limitations or known bugs. The framework's effectiveness hinges on the quality of the initial graph schema, though the process for this initial setup is not elaborated upon.