RAG Chatbot Application

A powerful Retrieval-Augmented Generation (RAG) chatbot that answers questions based on your documents using OpenAI embeddings and comprehensive logging capabilities.

📁 Project Structure

Features

• AI-Powered Q&A: Get intelligent answers from your documents
• OpenAI Integration: Uses state-of-the-art OpenAI embeddings and language models
• Comprehensive Logging: Detailed logging with multiple log files for monitoring
• Colored Terminal Output: Beautiful, color-coded responses and status messages
• Similarity Scoring: Intelligent document retrieval with relevance scores
• Source Attribution: Always shows which documents were used for answers
• Error Handling: Robust error handling with detailed error messages
• Multiple Document Formats: Supports PDF, Markdown, and other formats

📁 Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)
• OpenAI API key (for embeddings and language model)

Step 1: Clone or Download the Project

# If using git:
git clone <your-repository-url>
cd RAG-Chatbot

# Or download and extract the ZIP file to your desired location

Step 2: Create Virtual Environment

# Create a virtual environment
python -m venv rag_env

# Activate the virtual environment
# On Windows:
rag_env\Scripts\activate

# On macOS/Linux:
source rag_env/bin/activate

Step 3: Install Dependencies

# Install all required packages
pip install -r requirements.txt

Note: This will install all dependencies including:

• langchain and related packages for RAG functionality
• openai for API integration
• chromadb for vector database
• loguru for advanced logging
• colorama for colored terminal output
• sentence-transformers for local embeddings
• And many other supporting packages

Step 4: Set Up Environment Variables

Create a .env file in the project root:

# On Windows:
echo OPENAI_API_KEY=your_api_key_here > .env

# On macOS/Linux:
echo "OPENAI_API_KEY=your_api_key_here" > .env

Or manually create .env file with:

OPENAI_API_KEY=your_openai_api_key_here

Step 5: Verify Installation

# Test logging functionality
python test_logging.py

You should see colored log output indicating successful setup.

📚 Usage

1. Creating a Vector Database

First, you need to create a vector database from your documents:

# Create database from sample data
python openai_create_database.py

This will:

• Load documents from ./sample_data/
• Split them into chunks
• Create embeddings using OpenAI
• Store them in ./chroma_db_openai/

Expected Output:

2025-06-29 18:XX:XX | INFO | === Starting OpenAI RAG Chatbot Database Creation ===
2025-06-29 18:XX:XX | INFO | Loading documents from directory: ./sample_data/aws_lambda
2025-06-29 18:XX:XX | INFO | Starting document chunking process
2025-06-29 18:XX:XX | INFO | Starting Chroma database creation
2025-06-29 18:XX:XX | INFO | Saved X chunks to ./chroma_db_openai.
2025-06-29 18:XX:XX | INFO | === Database Creation Completed Successfully ===

2. Querying the Database

Now you can ask questions about your documents:

# Ask a question
python openai_query_data.py "What is AWS Lambda?"

Example Queries for AWS Lambda:

python openai_query_data.py "How do I create my first Lambda function?"
python openai_query_data.py "What are the benefits of serverless computing?"
python openai_query_data.py "How does Lambda handle concurrent executions?"
python openai_query_data.py "What security best practices should I follow?"

Example Queries for Alice in Wonderland:

python openai_query_data.py "What is Alice's adventure about?"
python openai_query_data.py "Who is the White Rabbit?"
python openai_query_data.py "What happens when Alice drinks the potion?"

3. Understanding the Output

The application provides rich, colored output:

Search Results:
  1. Score: 0.856 - AWS Lambda is a serverless compute service that runs your code...
  2. Score: 0.743 - Lambda functions are event-driven and automatically scale...
  3. Score: 0.689 - You can use Lambda to run code without provisioning servers...

================================================================================
🤖 AI RESPONSE 
================================================================================
AWS Lambda is a serverless compute service that allows you to run code without 
provisioning or managing servers. It automatically scales your applications and 
you only pay for the compute time you consume.

--------------------------------------------------------------------------------
📚 Sources:
  1. sample_data/aws_lambda/lambda-dg.pdf
  2. sample_data/aws_lambda/lambda-dg.pdf
================================================================================

⚙️ Configuration

Logging Configuration

The application uses a sophisticated logging system with multiple log files:

• logs/app.log: General application logs
• logs/requests.log: Request tracking
• logs/responses.log: Response tracking
• logs/payloads.log: Detailed payload information
• logs/errors.log: Error tracking

Database Configuration

• Database Path: ./chroma_db_openai/
• Sample Data: ./sample_data/

Customizing Document Sources

To use your own documents:

1. Add your documents to the sample_data/ directory
2. Update the DATA_PATH in openai_create_database.py:

   DATA_PATH = "./sample_data/your_documents"

3. Run the database creation script again:

   python openai_create_database.py

🐛 Troubleshooting

Common Issues

1. OpenAI API Key Error

Error: No API key found. Please set OPENAI_API_KEY environment variable.

Solution:

• Add your OpenAI API key to the .env file
• Make sure the .env file is in the project root directory

2. Database Not Found

Error: Database directory not found

Solution:

• Run the database creation script first: python openai_create_database.py
• Check if chroma_db_openai/ directory exists

3. Low Similarity Scores

Warning: Best match has low similarity score: 0.45

Solutions:

• Try rephrasing your question
• Add more relevant documents to the database
• Lower the similarity threshold in the code (currently 0.7)

4. Missing Dependencies

ModuleNotFoundError: No module named 'langchain'

Solution:

• Install dependencies: pip install -r requirements.txt
• Make sure your virtual environment is activated

5. Permission Errors

PermissionError: [Errno 13] Permission denied

Solution:

• Run as administrator (Windows)
• Check file permissions
• Make sure you have write access to the project directory

Debug Mode

Enable debug logging by modifying logger_config.py:

# Change log level to DEBUG
logger.add(sys.stdout, level="DEBUG")

🔒 Security Considerations

• API Keys: Never commit your .env file to version control
• Log Files: Review log files for sensitive information
• Network: Ensure secure connections when using OpenAI API
• Documents: Be careful with sensitive documents in your database

📈 Performance Optimization

For Large Document Collections

1. Increase chunk size for better context
2. Adjust overlap for better document coverage
3. Use batch processing for large files
4. Monitor memory usage during database creation

For Faster Queries

1. Reduce k value in similarity search (currently 3)
2. Optimize prompt length
3. Cache frequently asked questions

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📄 License

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

📈 Acknowledgments

• LangChain for the RAG framework
• OpenAI for the embedding and language models
• Chroma for the vector database
• Loguru for advanced logging capabilities

📞 Support

If you encounter any issues:

1. Check the troubleshooting section above
2. Review the log files in the logs/ directory
3. Create an issue in the repository
4. Contact the development team

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Happy Querying! 🚀