LLM Chat Application

Features

• Chat with history: Interact with the model while maintaining a conversation history.
• Simple Q&A: Ask questions and get answers without maintaining a conversation history.
• Streaming Responses: Receive responses in a streaming fashion.

Routes

/chat

• Method: POST
• Description: Chat with the model while maintaining conversation history.
• Request Body:
  • input (string): The input message from the user.
• Response: Streaming response of the model's reply.

/ask

• Method: POST
• Description: Ask a question and get a response without maintaining conversation history.
• Request Body:
  • input (string): The input question from the user.
• Response: Streaming response of the model's answer.

Getting Started

Prerequisites

• Python 3.7+
• Docker (optional, for containerized deployment)
• A valid GROQ_API_KEY (from Groq API)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/llm-chat-app.git
   cd llm-chat-app

2. Create a virtual environment and activate it:

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

3. Install the dependencies:
   pip install -r requirements.txt
4. Create a .env file and add your GROQ_API_KEY:
   echo "GROQ_API_KEY=your_groq_api_key" > .env

Running the Application

1. Start the FastAPI server:
   uvicorn main:app --reload
2. The application will be available at http://127.0.0.1:8000.

Using the Application

Example Requests

Testing

Docker Deployment (Optional)

1. Build the Docker image:
   docker build -t llm-chat-app .
2. Run the Docker container:
   docker run -d -p 8000:8000 --env-file .env llm-chat-app