railseek6/LightRAG-main

Production-Grade RAG System with LightRAG

A high-performance Retrieval-Augmented Generation (RAG) system built on LightRAG framework with multi-format document processing, GPU-accelerated OCR, and production-grade database storage.

System Architecture

Storage Backends

• KV Storage: Redis (Memurai) - redis://localhost:6379
• Graph Storage: Neo4j - bolt://localhost:7687 (username: neo4j, password: jleu1212)
• Vector Storage: Qdrant - http://localhost:6333/
• Document Status Storage: PostgreSQL - postgresql://jleu3482:jleu1212@localhost:5432/rag_anything

AI Model Configuration

• LLM: DeepSeek API (API Key: sk-55f6e57f1d834b0e93ceaf98cc2cb715)
• Embeddings: Jina AI
• Entity Extraction: spaCy models for fast indexing
• Reranker: Disabled for performance optimization
• OCR: PaddleOCR with GPU acceleration

Performance Settings

• Multi-core processing: Parallel document processing
• GPU acceleration: NVIDIA RTX 4070 Super
• Chunking: 1200 tokens with 100 token overlap
• Max graph nodes: 1000
• Max parallel insert: 4 concurrent operations

Installation

Prerequisites

• Python 3.9+
• Redis (Memurai) running on port 6379
• Neo4j running on bolt://localhost:7687
• Qdrant running on http://localhost:6333
• PostgreSQL running on port 5432 with database rag_anything

Install Dependencies

cd LightRAG-main
pip install -r requirements.txt

Download spaCy Model

python -m spacy download en_core_web_lg

Environment Setup

Set the following environment variables:

# For Jina embeddings (optional)
set JINA_API_KEY=your_jina_api_key_here

# For DeepSeek LLM
set DEEPSEEK_API_KEY=sk-55f6e57f1d834b0e93ceaf98cc2cb715

# For Windows terminal encoding
set PYTHONIOENCODING=utf-8

Running the System

Start the Server

python -m lightrag.api.lightrag_server --port 3015 --working-dir rag_storage --input-dir inputs --key jleu1212 --auto-scan-at-startup --llm-binding openai --embedding-binding jina --rerank-binding null

Server Parameters

• --port 3015: Web server port
• --working-dir rag_storage: Storage directory for processed data
• --input-dir inputs: Directory for document uploads
• --key jleu1212: Authentication key
• --auto-scan-at-startup: Automatically process documents in input directory
• --llm-binding openai: Use OpenAI-compatible API (DeepSeek)
• --embedding-binding jina: Use Jina embeddings
• --rerank-binding null: Disable reranker for performance

Document Processing Capabilities

Supported Formats

• DOCX/DOC: python-docx + custom table parser
• XLSX/XLS: pandas + openpyxl
• PDF (Text-based): pymupdf (fitz)
• PDF (Image-based): paddleocr + layout detection
• PPTX/PPT: python-pptx
• Images: MobileOne classification → PaddleOCR (Fast filter, OCR only when needed)
• TXT/CSV: Direct read
• HTML: beautifulsoup4

OCR Processing

• Uses PaddleOCR with GPU acceleration
• MobileOne-S1 model for image classification
• Automatic detection of scanned pages and images
• Fast filtering to only OCR when necessary

API Endpoints

Authentication

POST /login
Content-Type: application/x-www-form-urlencoded

username=admin&password=jleu1212

Document Management

POST /upload
Content-Type: multipart/form-data
Authorization: Bearer {token}

POST /documents/status
GET /documents

Search

POST /search
Content-Type: application/json
Authorization: Bearer {token}

{
  "query": "search query",
  "top_k": 10
}

System Health

GET /health

Web UI

The system includes a web interface running on port 3015. Access it at:

http://localhost:3015

Web UI Features

• Document upload and management
• Real-time search interface
• Document processing status
• System health monitoring
• Authentication-protected access

Testing

Test Script

Run the comprehensive test script:

python test_lightrag_webui.py

Test Documents

Place test documents in the test_documents/ directory:

• Text files (.txt)
• PDF documents (.pdf)
• Word documents (.docx)
• Excel files (.xlsx)
• PowerPoint files (.pptx)
• Images with text (.jpg, .png)

Health Check

curl http://localhost:3015/health

Performance Optimization

GPU Configuration

• Uses NVIDIA RTX 4070 Super for OCR and model inference
• CUDA device: cuda:0
• Batch processing for optimal throughput

Memory Management

• Parallel processing with configurable worker count
• Chunked document processing
• Efficient entity extraction with spaCy

Database Optimization

• Connection pooling for all databases
• Batch insert operations
• Indexed queries for fast retrieval

Troubleshooting

Common Issues

1. Authentication 401 Errors

   • Ensure login uses form data, not JSON
   • Check password matches the --key parameter

2. OCR Initialization Failures

   • Verify PaddleOCR version 3.3.0+
   • Check GPU availability and CUDA installation

3. Database Connection Issues

   • Verify all databases are running
   • Check connection strings in config.ini

4. Unicode Encoding Problems

   • Set PYTHONIOENCODING=utf-8 environment variable

Logs and Monitoring

• Server logs output to console
• Document processing status available via API
• System health endpoint for monitoring

Configuration

Edit config.ini for custom settings:

• Database connection strings
• Performance parameters
• OCR and processing settings
• Server configuration

License

This project is built on LightRAG framework. See individual component licenses for details.