ArjunSoniK/llama_chat_plus_ask_api

LLM Chat Application

This repository contains a FastAPI application for chatting and question answering using a Large Language Model (LLM). The application supports conversational interactions with memory as well as simple Q&A without conversation history.

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

You can interact with the application using tools like curl, Postman, or directly from your frontend application.

Example Requests

Chat with History:

curl -X POST "http://127.0.0.1:8000/chat" -H "Content-Type: application/json" -d '{"input": "Hello, how are you?"}'

Simple Q&A:

curl -X POST "http://127.0.0.1:8000/ask" -H "Content-Type: application/json" -d '{"input": "What is the capital of France?"}'

Testing

For testing purposes, you can use the base URL https://llamachat-ipea.onrender.com/.

Chat with History:

curl -X POST "https://llamachat-ipea.onrender.com/chat" -H "Content-Type: application/json" -d '{"input": "Hello, how are you?"}'

Simple Q&A:

curl -X POST "https://llamachat-ipea.onrender.com/ask" -H "Content-Type: application/json" -d '{"input": "What is the capital of France?"}'

Docker Deployment (Optional)

To run the application in a Docker container:

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
   

The application will be available at http://127.0.0.1:8000.