LangChain Q&A System

A question-answering system built with LangChain and ChromaDB that provides accurate answers based on a knowledge base.

Features

• Document loading and text splitting
• Vector embeddings using OpenAI
• Persistent vector storage with ChromaDB
• Interactive question-answering interface
• Cross-platform compatibility

Project Structure

project/
├── data/                  # Knowledge base files
│   └── knowledge.txt
├── db/                    # Vector database storage
│   └── chroma_db/
├── src/                   # Source code
│   └── qa_system.py
├── .env                   # Environment variables
├── .gitignore            # Git ignore rules
├── requirements.txt      # Python dependencies
└── README.md            # This file

Prerequisites

• Python 3.8 or higher
• OpenAI API key with sufficient credits
• Required Python packages:
  • langchain>=0.1.0
  • openai>=1.0.0
  • chromadb>=0.4.0

Setup

1. Clone the repository:

   git clone <repository-url>
   cd <project-directory>

2. Create and activate a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file in the project root with your API key:

   OPENAI_API_KEY=your-api-key-here
   

5. Add your knowledge base file to the data/ directory:

   • Place your text file in data/knowledge.txt
   • The system will automatically split and process the content

Knowledge Base

The system comes with a pre-configured knowledge base about migratory birds in data/knowledge.txt. You can:

• Replace the content with your own knowledge base
• Ensure the text is well-structured with clear headings and sections
• Keep the file size reasonable for optimal performance

Usage

1. Run the Q&A system:

   python src/qa_system.py

2. Enter your questions when prompted

3. Type 'quit' to exit the program

Example Questions

The system is optimized for questions about:

• Basic migration concepts and navigation methods
• Different types of migration (Latitudinal, Longitudinal, Altitudinal)
• Notable migratory species and their records
• Conservation challenges and efforts
• Migration timing and seasons

How It Works

1. The system loads your knowledge base from data/knowledge.txt
2. Documents are split into chunks for processing
3. OpenAI embeddings are generated for each chunk
4. ChromaDB stores the embeddings persistently
5. When you ask a question:
   • The question is embedded
   • Similar chunks are retrieved
   • An answer is generated using the context

Maintenance

• The vector database is stored in db/chroma_db/
• To force a fresh start, delete the db/chroma_db/ directory
• The system will automatically recreate the database on next run

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

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

Security Setup

1. Create a .env file in the project root
2. Add your OpenAI API key to the .env file:

   OPENAI_API_KEY=your-api-key-here
   

3. Ensure .env is listed in your .gitignore
4. Never commit the .env file or share it publicly

API Usage and Limits

• Maximum query length: 500 characters
• Rate limits: Follow OpenAI's API rate limits
• Error handling: The system provides clear error messages without exposing sensitive information

Error Recovery

If you encounter errors:

1. Check your API key is valid
2. Ensure the knowledge.txt file exists
3. Verify you have sufficient API credits
4. Check your internet connection

Version

Current version: 1.0.0