This project aims to develop an advanced AI assistant similar to Tony Stark's JARVIS. The assistant will leverage large language models and AI agent technologies to provide natural language conversation capabilities. The assistant will also feature text-to-speech (TTS) functionality to generate spoken responses.

• Natural Language Dialogue: Engage in text-based conversations with the AI assistant.
• Speech Synthesis: Convert text responses into spoken words using TTS technology.
• Information Retrieval: Use Retrieval-Augmented Generation (RAG) to fetch and generate responses based on a knowledge base.
• Large Model Support: Utilize models like GPT-3 or BERT for natural language understanding and generation.

• Natural Language Processing: GPT-3, BERT
• Speech Synthesis: Google Text-to-Speech (gTTS) or other TTS services
• Information Retrieval: ElasticSearch, Milvus + RAG technology
• Backend: Python (Flask or FastAPI)
• Frontend: HTML/CSS/JavaScript (optional for web interface)
• Database: Vector databases (Milvus) and relational databases (PostgreSQL)
• Deployment: Docker

• Python 3.8+
• Docker
• Git

1. Clone the Repository:

   git clone https://github.com/yourusername/ai-assistant-project.git
   cd ai-assistant-project

2. Set Up Virtual Environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Set Up Environment Variables: Create a .env file in the project root directory and add the necessary environment variables.

   OPENAI_API_KEY=your_openai_api_key
   ELASTICSEARCH_HOST=your_elasticsearch_host

5. Run Docker Containers:

   docker-compose up -d

6. Run the Application:

   python app.py

ai-assistant-project/
│
├── app.py                 # Main application file
├── requirements.txt       # Python dependencies
├── Dockerfile             # Docker configuration
├── docker-compose.yml     # Docker Compose configuration
├── .env                   # Environment variables
│
├── models/                # Pre-trained models and scripts
│   ├── gpt3.py
│   └── bert.py
│
├── services/              # Core services
│   ├── nlp_service.py     # Natural language processing
│   ├── tts_service.py     # Text-to-speech
│   ├── rag_service.py     # RAG technology implementation
│
├── static/                # Static files (for web interface)
│   └── css/
│   └── js/
│
├── templates/             # HTML templates (for web interface)
│   └── index.html
│
└── utils/                 # Utility scripts
    ├── data_preprocessing.py
    └── database_setup.py

You can start a conversation with the AI assistant by sending a POST request to the /chat endpoint with your query.

Example using curl:

curl -X POST http://localhost:5000/chat -H "Content-Type: application/json" -d '{"query": "Hello, how are you?"}'

The assistant can respond with speech if the tts parameter is set to true.

Example using curl:

curl -X POST http://localhost:5000/chat -H "Content-Type: application/json" -d '{"query": "Tell me a joke.", "tts": true}'

Contributions are welcome! Please create a new branch for your feature or bug fix and submit a pull request.

1. Fork the Repository
2. Create a New Branch

   git checkout -b feature/your-feature-name

3. Commit Your Changes

   git commit -m "Add your commit message here"

4. Push to Your Branch

   git push origin feature/your-feature-name

5. Open a Pull Request

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

• OpenAI for the GPT-3 model
• Google for the gTTS library
• The contributors to the ElasticSearch and Milvus projects