LangChain Documentation Helper

A Streamlit-based chatbot that helps users navigate and understand LangChain documentation by providing intelligent answers to questions using RAG (Retrieval-Augmented Generation) with Pinecone vector database.

🚀 Features

• Intelligent Q&A: Ask questions about LangChain documentation and get accurate answers
• Source Citations: Each answer includes links to the relevant documentation sources
• Chat Interface: Interactive chat-like interface built with Streamlit
• Vector Search: Uses Pinecone vector database for efficient document retrieval
• OpenAI Integration: Powered by OpenAI's GPT models for natural language understanding

🛠️ Tech Stack

• Frontend: Streamlit
• Backend: Python, LangChain
• Vector Database: Pinecone
• LLM: OpenAI GPT
• Document Processing: BeautifulSoup4, LangChain document loaders

📋 Prerequisites

Before running this project, you'll need:

1. Python 3.11+ installed on your system
2. OpenAI API Key - Get one from OpenAI Platform
3. Pinecone API Key - Get one from Pinecone Console
4. Pinecone Environment - Your Pinecone environment (e.g., us-east-1-aws)

🚀 Installation

Option 1: Using pip (Recommended)

1. Clone the repository

   git clone <your-repo-url>
   cd documentation-helper

2. Create a virtual environment

   python -m venv venv
   # On Windows
   venv\Scripts\activate
   # On macOS/Linux
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

Option 2: Using pipenv

1. Clone the repository

   git clone <your-repo-url>
   cd documentation-helper

2. Install dependencies

   pipenv install
   pipenv shell

⚙️ Configuration

1. Create environment file Create a .env file in the project root:

   OPENAI_API_KEY=your_openai_api_key_here
   PINECONE_API_KEY=your_pinecone_api_key_here
   PINECONE_ENVIRONMENT=your_pinecone_environment_here

2. Set up Pinecone Index

   • Create a new index in your Pinecone console
   • Use dimension: 1536 (for text-embedding-3-small)
   • Use metric: cosine

📚 Data Ingestion

Before using the chatbot, you need to ingest the LangChain documentation:

1. Download documentation (if not already present)

   # The ingestion script will download LangChain docs automatically

2. Run the ingestion script

   python ingestion.py

   This will:

   • Download LangChain documentation
   • Process and chunk the documents
   • Upload embeddings to Pinecone
   • Create the vector index

🎯 Usage

1. Start the Streamlit app

   streamlit run main.py

2. Open your browser Navigate to http://localhost:8501

3. Ask questions

   • Type your question about LangChain in the input field
   • Press Enter or click the button
   • Get answers with source citations

📁 Project Structure

documentation-helper/
├── backend/
│   ├── __init__.py
│   └── core.py              # Main LLM logic and RAG implementation
├── langchain-docs/          # Downloaded documentation (gitignored)
├── venv/                    # Virtual environment (gitignored)
├── .env                     # Environment variables (gitignored)
├── .gitignore              # Git ignore rules
├── LICENSE                 # Apache 2.0 license
├── Pipfile                 # Dependencies (pipenv)
├── Pipfile.lock            # Locked dependencies
├── requirements.txt        # Dependencies (pip)
├── README.md              # This file
├── ingestion.py           # Data ingestion script
└── main.py                # Streamlit application

🔧 Key Components

main.py

• Streamlit web interface
• Chat history management
• User interaction handling

backend/core.py

• RAG implementation using LangChain
• Pinecone vector store integration
• OpenAI LLM configuration

ingestion.py

• Document downloading and processing
• Text chunking and embedding
• Vector database population

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

• LangChain for the amazing framework
• OpenAI for the language models
• Pinecone for the vector database
• Streamlit for the web framework

📞 Support

If you encounter any issues or have questions:

1. Check the Issues page
2. Create a new issue with detailed information
3. Include error messages and steps to reproduce

---

Happy coding! 🚀