DeepLightRAG
High-performance document indexing and retrieval system. Works with any LLM.
Features
- Dual-Layer Graph: Visual-Spatial + Entity-Relationship architecture
- GLiNER2 NER: Zero-shot entity extraction with fastino/gliner2-base-v1
- DeepSeek-OCR: Visual token compression for efficient document processing
- TOON Format: Token-efficient context formatting via toon-python
- Any LLM: Works with OpenAI, Gemini, Claude, Ollama, MLX
Installation
pip install deeplightrag
With GPU (CUDA):
pip install "deeplightrag[gpu]"
macOS (Apple Silicon):
pip install "deeplightrag[macos]"
Quick Start
Python API
from deeplightrag import DeepLightRAG
# Initialize
rag = DeepLightRAG()
# Index document
rag.index_document("document.pdf", document_id="doc_001")
# Retrieve
result = rag.retrieve("What are the key findings?")
print(result["context"])
CLI
# Index
deeplightrag index document.pdf
# Query
deeplightrag retrieve "What is the main topic?"
Architecture
┌─────────────────────────────────────────────┐
│ DeepLightRAG │
├─────────────────────────────────────────────┤
│ DeepSeek-OCR → Visual Token Compression │
├─────────────────────────────────────────────┤
│ Dual-Layer Graph │
│ ├── Layer 1: Visual-Spatial (WHERE) │
│ └── Layer 2: Entity-Relationship (WHAT) │
├─────────────────────────────────────────────┤
│ GLiNER2 → Entity Extraction │
├─────────────────────────────────────────────┤
│ Adaptive Retriever → Context Generation │
└─────────────────────────────────────────────┘
Configuration
# config.yaml
ocr:
model_name: "deepseek-ai/DeepSeek-OCR"
resolution: "gundam"
ner:
model_name: "fastino/gliner2-base-v1"
confidence_threshold: 0.3
retrieval:
top_k: 5
Requirements
- Python >= 3.9
- CUDA GPU recommended for DeepSeek-OCR
- Dependencies:
gliner2,toon-python,networkx,torch
License
MIT License
1 maintainer
Metadata
1 maintainer
Release files for deeplightrag 1.0.22
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