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| LLM Chat Application |
| ==================== |
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| 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. |
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| Features |
| -------- |
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| * **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. |
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| Routes |
| ------ |
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| ### `/chat` |
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| * **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. |
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| ### `/ask` |
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| * **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. |
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| Getting Started |
| --------------- |
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| ### Prerequisites |
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| * Python 3.7+ |
| * Docker (optional, for containerized deployment) |
| * A valid `GROQ_API_KEY` (from Groq API) |
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| ### Installation |
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| 1. **Clone the repository**: |
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| git clone https://github.com/your-username/llm-chat-app.git |
| cd llm-chat-app |
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| 2. **Create a virtual environment and activate it**: |
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| python -m venv venv |
| source venv/bin/activate # On Windows use `venv\Scripts\activate` |
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| 3. **Install the dependencies**: |
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| pip install -r requirements.txt |
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| 4. **Create a `.env` file and add your `GROQ_API_KEY`**: |
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| echo "GROQ_API_KEY=your_groq_api_key" > .env |
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| ### Running the Application |
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| 1. **Start the FastAPI server**: |
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| uvicorn main:app --reload |
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| 2. **The application will be available at** `http://127.0.0.1:8000`. |
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| ### Using the Application |
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| You can interact with the application using tools like `curl`, Postman, or directly from your frontend application. |
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| #### Example Requests |
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| **Chat with History**: |
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| curl -X POST "http://127.0.0.1:8000/chat" -H "Content-Type: application/json" -d '{"input": "Hello, how are you?"}' |
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| **Simple Q&A**: |
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| curl -X POST "http://127.0.0.1:8000/ask" -H "Content-Type: application/json" -d '{"input": "What is the capital of France?"}' |
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| ### Testing |
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| For testing purposes, you can use the base URL `https://llamachat-ipea.onrender.com/`. |
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| **Chat with History**: |
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| curl -X POST "https://llamachat-ipea.onrender.com/chat" -H "Content-Type: application/json" -d '{"input": "Hello, how are you?"}' |
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| **Simple Q&A**: |
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| curl -X POST "https://llamachat-ipea.onrender.com/ask" -H "Content-Type: application/json" -d '{"input": "What is the capital of France?"}' |
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| ### Docker Deployment (Optional) |
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| To run the application in a Docker container: |
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| 1. **Build the Docker image**: |
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| docker build -t llm-chat-app . |
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| 2. **Run the Docker container**: |
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| docker run -d -p 8000:8000 --env-file .env llm-chat-app |
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| The application will be available at `http://127.0.0.1:8000`. |
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