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Impersonator 🤖

A powerful document-based chatbot powered by LangChain and OpenAI that lets you have intelligent conversations with your documents. Upload PDFs and interact with their content through natural language queries.

Python License LangChain Streamlit

✨ Features

  • Document Intelligence: Upload PDFs and have natural conversations about their content
  • Smart Text Processing: Advanced chunking algorithms for optimal context preservation
  • Vector Database Integration: Efficient document embedding storage with Supabase
  • Flexible Architecture: Support for multiple LLMs (OpenAI, HuggingFace) and Vector DBs (Supabase, Pinecone)
  • Containerized Deployment: Ready for production with Docker support
  • Interactive UI: Built with Streamlit for a seamless user experience

🚀 Quick Start

Using Docker (Recommended)

  1. Clone the repository

  2. Open a terminal in the impersonator folder

  3. Create a .env file in the src/ directory with the following format:

OPENAI_API_KEY=your_openai_key
SUPABASE_API_KEY=your_supabase_key
SUPABASE_PROJ_URL=your_project_url
PINECONE_API_KEY=your_pinecone_key
  1. Build the docker image
docker build -t impersonator .
  1. Run with docker binding the docker containers ports to the host machines port!
docker run -p 8501:8501 impersonator
  1. Visit http://localhost:8501 in your browser

Manual Setup

  1. Navigate to source directory:
cd src/
  1. Install all dependencies
pip install -r requirements.txt
  1. Run the app!
streamlit run script.py
  1. Go to the local host link provided in the CLI!

To-Do

  • Validate the uploaded files
  • Pre-train using the text
  • Add a chat feature with history

🛠️ Technical Stack

  • Frontend: Streamlit with streamlit-chat for UI components
  • Backend: Python with LangChain for LLM operations
  • Document Processing: PyPDF for PDF parsing
  • Vector Embeddings: OpenAI's embedding model (with HuggingFace support)
  • Vector Storage: Supabase (with Pinecone integration ready)
  • Containerization: Docker for consistent deployment
  • Environment Management: python-dotenv for configuration

💡 How It Works

  1. Document Upload: Users upload PDF documents through the Streamlit interface
  2. Text Processing: Documents are parsed and split into manageable chunks
  3. Vector Embedding: Text chunks are converted to vector embeddings
  4. Storage: Embeddings are stored in Supabase's vector database
  5. Query Processing: User questions are processed using conversational AI
  6. Response Generation: LLM generates contextual responses based on retrieved information

🔜 Roadmap

  • File validation and error handling
  • Pre-training capabilities
  • Enhanced chat history with conversation memory
  • Support for additional document formats
  • Alternative LLM integrations
  • Advanced vector database options

📄 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please feel free to submit issues and pull requests.

Built with ❤️ by jaysqvl