MoneyHub Financial AI Chatbot Assistant

A chatbot API built with Flask, integrating SambaNova OpenAI models for dynamic conversational AI experiences.

Features

• Dynamic Model Selection: Choose between multiple AI models like Meta-Llama-3.1-405B, 70B, and 8B for different use cases.
• Chat History Management: Maintain context with persistent chat history for a natural conversational flow.
• System Prompt Customization: Configure the chatbot's personality and responses using customizable system prompts.
• Thinking Time Analysis: Monitor AI response times with a thinking_budget feature.
• Error Handling: Provides robust error handling and logging for seamless user experience.
• Static File Hosting: Serves an interactive frontend from the static directory for easy deployment.

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Table of Contents

• Installation
• Usage
• API Endpoints
• Environment Variables
• File Structure
• Contributing
• License

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Installation

Prerequisites

• Python 3.8 or later
• Pip
• SambaNova OpenAI API Key

Steps

1. Clone the repository:

   git clone https://github.com/your-username/ripple-chatbot.git
   cd ripple-chatbot

2. Create a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   • Create a .env file in the project root:

     API_KEY=your_sambanova_api_key
     PORT=5000
     

   • Replace your_sambanova_api_key with your actual API key.

5. Run the application:

   python app.py

6. Access the application: Open your browser and navigate to http://localhost:5000.

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Usage

Starting the API

Run the Flask server with:

python app.py

Using the API

• The primary endpoint for generating responses is /api/generate.
• Use an HTTP client like Postman, Curl, or your own frontend to send POST requests.

Example Request

POST /api/generate
Content-Type: application/json

{
  "message": "Tell me a joke.",
  "chat_history": [
    {"user": "Who are you?", "assistant": "I am your chatbot."}
  ],
  "model": "Meta-Llama-3.1-70B-Instruct",
  "system_prompt": "You are a funny assistant.",
  "thinking_budget": 15
}

Example Response

{
  "response": "Why did the scarecrow win an award? Because he was outstanding in his field!",
  "thinking_time": 2.345
}

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API Endpoints

GET /

Serves the static HTML frontend (static/ripple.html).

POST /api/generate

Generates a chatbot response based on the user input and chat history.

Parameter	Type	Description
message	string	The user’s message for the chatbot.
chat_history	array	List of prior messages in the chat.
model	string	The model to use. Defaults to Meta-Llama-3.1-405B-Instruct.
system_prompt	string	A customizable prompt that sets the chatbot’s personality.
thinking_budget	number	Maximum response time for the AI (in seconds). Default: 10.
api_key	string	API key for SambaNova authentication (optional; defaults to .env key).

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Environment Variables

Variable	Description
API_KEY	Your SambaNova OpenAI API key.
PORT	Port to run the Flask server (default: 5000).

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File Structure

ripple-chatbot/
│
├── static/
│   ├── ripple.html           # Frontend HTML file
│   ├── styles.css            # Stylesheet for the frontend
│   └── ...                   # Additional frontend assets
│
├── app.py                    # Main Flask application
├── requirements.txt          # Python dependencies
├── .env                      # Environment variables
├── README.md                 # Project documentation

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Contributing

We welcome contributions! To contribute:

1. Fork the repository.
2. Create a feature branch:

   git checkout -b feature-name

3. Commit your changes:

   git commit -m "Add feature-name"

4. Push to the branch:

   git push origin feature-name

5. Create a pull request.

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License

This project is licensed under the MIT License.

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Feel free to reach out if you have questions or feature suggestions. Happy coding! 🚀

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