Here’s the updated README.md for your backend project, integrating the changes for the testing section that recommends testing with the frontend:

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🎵 Music AI Bot Backend 🎶

This is the backend service for the Music AI Bot. It powers an AI-driven chatbot that learns from text data (txt and pdf), as well as YouTube video transcripts. The backend is built using Python with frameworks and libraries such as FastAPI, PyMongo, WebSockets, OpenAI API, and LangChain. It supports real-time, interactive conversations by processing and retrieving information from trained data.

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🛠️ Technology Stack

Languages & Frameworks:

• Python: The core language for the backend.
• FastAPI: High-performance, modern web framework for API development.

Integrations & Libraries:

• PyMongo: For seamless MongoDB database integration.
• OpenAI API: Powers the conversational AI model behind the chatbot.
• LangChain: For managing conversational context and complex AI workflows (e.g., chaining prompts and data sources).
• WebSockets: For real-time communication between server and frontend.

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🚀 Features

• Custom Data Training:
  • Process .txt and .pdf files for chatbot training.
  • Parse and process YouTube video transcripts to expand the chatbot's knowledge.
• Real-Time Chat:
  • Provides real-time chat responses using WebSocket communication.
• Intelligent Conversations:
  • Leverages OpenAI and LangChain to deliver meaningful, context-aware responses.
• Persistent Storage:
  • Stores all training data in MongoDB for efficient access and retrieval.

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📋 Prerequisites

Before you begin, ensure you have the following installed on your system:

• Python >= 3.8
• MongoDB (locally or a cloud instance, e.g., MongoDB Atlas)
• API Key for OpenAI API (sign up at OpenAI to obtain your key)

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📦 Installation and Setup

1. Clone the Repository

   git clone https://github.com/softdev629/AI-business-copliot-backend.git
   cd AI-business-copliot-backend

2. Set Up a Virtual Environment (Recommended)

   python -m venv venv
   source venv/bin/activate    # For Mac/Linux
   venv\Scripts\activate       # For Windows

3. Install Dependencies
   Install the required Python modules listed in requirements.txt:

   pip install -r requirements.txt

4. Set MongoDB Connection and OpenAI API Key
   In the project's root directory, create a .env file with the following variables:

   MONGO_URI=mongodb://localhost:27017
   OPENAI_API_KEY=your-openai-api-key

   Modify MONGO_URI if using a remote MongoDB instance (e.g., MongoDB Atlas).

5. Run the Development Server
   Start the backend server with FastAPI:

   uvicorn main:app --reload

   By default, the server will run at: http://127.0.0.1:8000

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🗂 Folder Structure

.
│── routes/                # API routes
│── schemas/             # Data models for MongoDB
│── core/           # Configuration settings, including environment variables
│── main.py             # Entry point for running the FastAPI app
├── requirements.txt        # Python dependencies
└── .env                    # Environment variables (add this manually)

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🧑‍🔧 How It Works

1. Training Data:
   Users upload .txt or .pdf files or submit YouTube transcripts. The uploaded data is processed and stored in MongoDB for retrieval.

2. Conversational AI:
   When users send a message via the frontend, the backend leverages OpenAI (GPT models) and LangChain's prompt workflows to fetch contextually relevant responses.

3. WebSocket Communication:
   The backend uses FastAPI WebSockets to facilitate real-time communication with the frontend.

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🧪 Testing

To test the backend functionality, we recommend testing it in conjunction with the frontend project to ensure seamless integration and real-time communication.

1. Run the Chatbot Server:
   Start the backend server using the following command:

   uvicorn main:app --reload

2. Connect the Frontend:
   Run the frontend React project and ensure the WebSocket connection is properly established with the backend.

3. Interact with the Bot:
   Use the chat interface from the frontend to test uploading files, training the bot with data, and chatting in real-time. Verify that both training and conversational responses work as expected.

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❤️ Future Enhancements

Here are some ideas for expanding the functionality of the backend:

• Add support for additional OpenAI models or fine-tune custom AI models.
• Improve error handling and input sanitization for safer uploads.
• Set up authentication/authorization for secure API usage.
• Enhance scalability with containerization (e.g., using Docker) and orchestration (e.g., Kubernetes).

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🤝 Contributing

We welcome contributions to improve this backend service! If you're interested, follow these steps:

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

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📄 License

This project is MIT Licensed. See the LICENSE file for more information.

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📞 Contact

For questions, feature requests, or improvement suggestions, feel free to reach out:

• GitHub: Bohdan
• Email: drozd.dev@outlook.com

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Happy Coding! 👨‍💻👩‍💻