DocuChat AI

A self-hosted, privacy-focused RAG (Retrieval-Augmented Generation) interface for intelligent document interaction. Turn any document into a knowledge base you can chat with.

A powerful and secure document interaction system that transforms any document into an interactive knowledge base. Using advanced AI models that run entirely on-premises, DocuChat allows you to have natural conversations with your documents while maintaining complete data privacy and security.

Security & Privacy

• Complete Data Isolation: All documents and conversations stay within your network
• On-Premises Processing: AI models run locally, ensuring no data leaves your secure environment
• Local Vector Storage: Document embeddings are stored in your local Milvus instance
• Network Control: No external API dependencies for core functionality

Local AI models

The system uses the following model configurations by default:

• LLM Model: ibm-granite/granite-3.1-1b-a400m-instruct
• Embedding Model: ibm-granite/granite-embedding-30m-english

You can configure different models based on your needs:

• Smaller models for faster responses and lower resource usage
• Larger models for higher quality responses when compute resources are available
• Balance between model size and performance based on your hardware capabilities

Demo

Features

• Fully on-premises deployment for maximum security and privacy
• All documents and embeddings stored locally in your secure environment
• No external API calls - all processing happens within your network
• Self-contained AI models running locally
• Interactive web interface for document Q&A
• Support for loading content from:
  • Local files
  • Local directories (recursive scanning)
  • URLs
• Support for multiple document formats:
  • PDF documents
  • HTML pages
  • Markdown files
  • Plain text files
• Flexible model selection to balance performance and resource usage
• Configurable AI models to match your hardware capabilities

Prerequisites

• Python 3.8+
• GPU (recommended) or CPU for model inference

Installation

1. Clone the repository:

git clone https://github.com/yaacov/rag-chat-interface.git
cd rag-chat-interface

2. Install dependencies:

# Optional: set a virtual env
python3.10 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Usage

1. Start the server:

python main.py \
    [--source INITIAL_SOURCE] \
    [--host HOST] \
    [--port PORT] \
    [--db-path DB_PATH] \
    [--models-cache-dir CACHE_DIR] \
    [--downloads-dir DOWNLOADS_DIR] \
    [--chunk_size CHUNK_SIZE] \
    [--chunk_overlap CHUNK_OVERLAP]

Arguments:

• --source: Optional initial document or directory to load
• --host: Host to bind the server to (default: 0.0.0.0)
• --port: Port to bind the server to (default: 8000)
• --db-path: Path to the Milvus database file (default: ./rag_milvus.db)
• --models-cache-dir: Directory to store downloaded models (default: ./models_cache)
• --downloads-dir: Directory to store downloaded files (default: ./downloads)
• --chunk_size: Maximum size of each document chunk (default: 1000 characters)
• --chunk_overlap: Overlap between chunks (default: 200 characters)

2. Open your browser and navigate to http://localhost:8000

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.