This library automates the creation of knowledge graphs and document networks from text, enhancing Retrieval Augmented Generation (RAG) performance. It is designed for developers and researchers working with large document sets who need to improve LLM context retrieval and understanding of complex relationships within the data.

How It Works

The library leverages a graph-based approach to represent relationships between entities and documents. It processes input documents to extract entities and their connections, building a knowledge graph. A separate document graph maps relationships between documents based on shared entities or concepts. This structured representation allows for more targeted and contextually rich retrieval for LLM prompts.

Quick Start & Requirements

• Primary install: pip install knowledge_graph_rag
• Prerequisites: Python 3.x. No specific hardware or GPU requirements are mentioned.
• Official Docs: Not explicitly linked, but the README provides usage examples.

Highlighted Details

• Automatically creates knowledge graphs and document networks.
• Enables searching knowledge graph entities for LLM context.
• Facilitates finding interconnected documents based on search results.
• Includes plotting capabilities for visualizing the graphs.

Maintenance & Community

• The repository is maintained by sarthakrastogi.
• No community links (Discord, Slack) or roadmap are provided in the README.

Licensing & Compatibility

• The license is not specified in the README.
• Compatibility for commercial use or closed-source linking is unknown.

Limitations & Caveats

The README does not specify the underlying LLM or embedding models used, nor does it provide performance benchmarks or details on the graph construction algorithms. The lack of explicit licensing information may pose a barrier to commercial adoption.