LangGraph-based AI Agent Chatbot

This project is a LangGraph-based AI Agent Chatbot deployed on Hugging Face Spaces. It uses FastAPI for the backend and Streamlit for the frontend.

Features

• Define custom AI agents with system prompts.
• Select from a list of Groq models.
• Optionally allow the agent to perform web searches.
• Interact with the AI agent through a user-friendly interface.

Requirements

• Docker
• Python 3.9+

Setup

1. Clone the Repository

git clone https://github.com/yourusername/LangGraph-based-AI-Agent-Chatbot.git
cd LangGraph-based-AI-Agent-Chatbot

2. Create a .env File

Create a .env file in the root directory and add your API keys:

GROQ_API_KEY=your_groq_api_key
TAVILY_API_KEY=your_tavily_api_key

3. Build and Run the Docker Container

docker build -t langgraph-ai-agent .
docker run -p 8501:8501 -p 9999:9999 langgraph-ai-agent

4. Access the Application

Open your browser and go to http://localhost:8501 to access the Streamlit frontend.

File Structure

• frontend.py: Streamlit app for the user interface.
• backend.py: FastAPI app for handling API requests.
• ai_agent.py: Logic for interacting with the AI agent.
• Dockerfile: Docker configuration for containerizing the application.
• requirements.txt: List of Python dependencies.

Usage

1. Define your AI agent by entering a system prompt.
2. Select a Groq model from the list.
3. Optionally enable web search.
4. Enter your query and click "Ask Agent!" to get a response.

License

This project is licensed under the MIT License.