This project is a Retrieval-Augmented Generation (RAG) system designed for providing customer support using an A.I. model. The system integrates with Pinecone for vector search and utilizes a language model (LLaMA 13B) for generating responses based on user queries and relevant context.

• Features
• Installation
• Usage
• Configuration
• API Endpoints
• Project Structure
• Contributing
• License

• Retrieval-Augmented Generation (RAG): Combines query and relevant context to generate accurate responses.
• Vector Search: Uses Pinecone for efficient vector search and retrieval of relevant information.
• Language Model Integration: Leverages LLaMA 13B for natural language processing and response generation.
• Flask API: Provides a simple Flask-based API for interacting with the system.

1. Clone the repository:

   git clone https://github.com/DaniyalAhm/A.I-Customer-Support.git
   cd A.I-Customer-Support

2. Create and activate a virtual environment:

   python3 -m venv env
   source env/bin/activate

3. Install the required dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   Create a .env file in the root directory and add the following environment variables:

   PINECONE_API_KEY=your-pinecone-api-key
   LLAMA_API_KEY=your-llama-api-key
   

5. Run the application:

   python app.py

Once the application is running, you can interact with it via HTTP requests to the Flask API. The main endpoint is /response, where you can send a query and receive a generated response.

curl "http://localhost:5000/response?value=How do I reset my password?"

{
    "response": "To reset your password, go to the login page and click on 'Forgot Password'. Follow the instructions provided to reset your password."
}

• Pinecone Configuration: Ensure that your Pinecone index is properly configured with the required vectors and metadata.
• LLaMA API Configuration: Make sure you have access to the LLaMA API and that your API key is set in the .env file.

• GET /response:
  • Description: Takes a user query and returns a generated response based on the query and retrieved context.
  • Parameters:
    • value (string): The user query.
  • Response: JSON object with the generated response.

A.I-Customer-Support/
│
├── app.py                  # Main Flask application file
├── requirements.txt        # List of Python dependencies
├── .env                    # Environment variables file (not included in the repository)
├── README.md               # Project README file
└── <additional-files>      # Other scripts, modules, or assets

Contributions are welcome! If you'd like to contribute, please fork the repository, make your changes, and submit a pull request. Make sure to follow the project's coding standards and include relevant tests.

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