DeepLightRAG: High-performance Document Indexing and Retrieval System (use with any LLM)
Project description
DeepLightRAG
DeepLightRAG is a high-performance document indexing and retrieval system designed to work with any Large Language Model (LLM). It features a dual-layer graph architecture (Visual-Spatial and Entity-Relationship) to provide context-aware and visually-grounded retrieval.
Features
- Dual-Layer Graph: Combines visual layout awareness with semantic entity relationships.
- Visual-Grounded Retrieval: Retrieves not just text, but visual regions and their spatial context.
- Robust OCR: Integrated with DeepSeek-OCR and EasyOCR fallback for reliable text extraction.
- Advanced NER: Uses GLiNER for zero-shot entity recognition.
- Flexible LLM Support: Compatible with OpenAI, Google Gemini, Anthropic, and local LLMs via MLX/Ollama.
Installation
pip install deeplightrag
Usage
Index a document:
deeplightrag index document.pdf
Query the index:
deeplightrag query "What is the main topic?"
License
MIT License
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