README.md

Personal AI Assistant with RAG (Hugging Face Edition)

A powerful personal AI assistant built with LangChain, integrating Retrieval-Augmented Generation (RAG) with a vector database (Qdrant) for improved contextual awareness and memory. This version uses Hugging Face models and can be deployed to Hugging Face Spaces for free hosting.

Features

• Large Language Model integration using Hugging Face's free models
• RAG-based memory system with vector database storage
• Document ingestion pipeline for various file types
• Simple web UI built with Streamlit
• Conversation history tracking and retrieval
• Free deployment on Hugging Face Spaces

Project Structure

.
├── README.md
├── requirements.txt
├── .env.example
├── app.py                 # Main entry point for Hugging Face Spaces
├── space.py               # Hugging Face Spaces SDK integration
├── app/
│   ├── main.py            # FastAPI application entry point
│   ├── config.py          # Configuration settings
│   ├── ui/
│   │   └── streamlit_app.py # Streamlit web interface
│   ├── core/
│   │   ├── llm.py         # LLM integration (Hugging Face)
│   │   ├── memory.py      # RAG and vector store integration
│   │   ├── agent.py       # Agent orchestration
│   │   └── ingestion.py   # Document processing pipeline
│   └── utils/
│       └── helpers.py     # Utility functions
└── data/
    ├── documents/         # Store for uploaded documents
    └── vector_db/         # Local vector database storage

Setup

1. Clone this repository
2. Install dependencies:

   pip install -r requirements.txt
   

3. Copy .env.example to .env and fill in your Hugging Face API keys (optional)
4. Start the Streamlit UI:

   streamlit run app/ui/streamlit_app.py
   

Usage

1. Upload documents through the web interface
2. Chat with your assistant, which can now reference your documents
3. The assistant will automatically leverage your document knowledge to provide more personalized responses

Deployment to Hugging Face Spaces

This app can be easily deployed to Hugging Face Spaces for free hosting:

1. Create a Hugging Face account at huggingface.co
2. Set environment variables:

   export HF_USERNAME=your-username
   export HF_TOKEN=your-huggingface-token
   export SPACE_NAME=personal-rag-assistant  # optional
   

3. Run the deployment script:

   python space.py
   

4. Visit your deployed app at https://huggingface.co/spaces/{your-username}/{space-name}

Alternatively, you can manually create a new Space on Hugging Face and link it to your GitHub repository.

Models Used

This implementation uses the following free models from Hugging Face:

• LLM: google/flan-t5-large - A powerful instruction-tuned model
• Embeddings: sentence-transformers/all-MiniLM-L6-v2 - Efficient embedding model

You can change these in the .env file.

Extending

• Add more document loaders in ingestion.py
• Integrate additional tools in agent.py
• Customize the UI in streamlit_app.py
• Switch to a different LLM in llm.py and .env