This project provides a comprehensive RAG (Retrieval-Augmented Generation) solution for building intelligent Q&A systems and agents. It integrates GraphRAG, DeepSearch, and multi-agent collaboration with knowledge graphs to offer explainable and inferential reasoning, targeting developers and researchers building advanced private domain RAG applications.

How It Works

The system combines GraphRAG for knowledge graph construction and search with DeepSearch for private domain reasoning. It employs a multi-agent architecture, allowing specialized agents (e.g., graph-based, hybrid, deep research) to collaborate and coordinate for complex query resolution. The core approach leverages Neo4j for knowledge graph management and integrates various search strategies, including local, global, and graph-enhanced context retrieval, to provide richer and more accurate responses.

Quick Start & Requirements

• Install/Run: Execute Python scripts within the project structure. A demo is available via python test/search_with_stream.py.
• Prerequisites: Python, Neo4j database, and potentially specific LLM dependencies.
• Resources: Requires a Neo4j instance and LLM API access. Setup time varies based on Neo4j configuration and data loading.
• Links: deepwiki.com for project overview.

Highlighted Details

• Implements GraphRAG from scratch, representing knowledge as graph structures.
• Innovatively fuses DeepSearch with knowledge graphs, moving beyond purely vector-based approaches.
• Features a multi-agent collaborative architecture for enhanced problem-solving.
• Includes a custom evaluation system with over 20 metrics for comprehensive performance assessment.
• Supports incremental knowledge graph updates with intelligent deduplication.
• Offers visualization of AI reasoning processes for improved explainability.

Maintenance & Community

The project is noted as being included in deepwiki.com. Further community or maintenance details are not explicitly provided in the README.

Licensing & Compatibility

The README does not specify a license. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The project notes a potential issue where "excellent student" might be conflated with "national scholarship" due to embedding similarities, requiring further embedding fine-tuning. Future plans include domain-specific embeddings to address such semantic distinctions.