AI Agent Chatbot 🤖

A full-stack AI chatbot application with web interface that integrates multiple AI models (Groq and OpenAI) with web search capabilities using Tavily. Built with FastAPI backend and Streamlit frontend.

🌟 Features

• Multiple AI Providers: Support for Groq (LLaMA models) and OpenAI (GPT models)
• Web Search Integration: Optional Tavily search for real-time information
• Interactive Web UI: Clean Streamlit interface for easy interaction
• RESTful API: FastAPI backend for scalable deployment
• Model Selection: Choose from various models including LLaMA 3.3 70B, GPT-4o-mini, and Mixtral
• Custom System Prompts: Define AI agent behavior with custom instructions
• Real-time Processing: Fast response generation with optimized inference

🏗️ Project Structure

AI-Agent-Chatbot/
├── ai_agent.py      # Core AI agent logic with LangGraph
├── backend.py       # FastAPI server and API endpoints
├── frontend.py      # Streamlit web interface
├── README.md        # Project documentation
├── .env            # Environment variables (create from .env.example)
└── requirements.txt # Python dependencies

🚀 Quick Start

1. Clone the Repository

git clone https://github.com/nishant0820/AI-Agent-Chatbot.git
cd AI-Agent-Chatbot

2. Install Dependencies

pip install -r requirements.txt

If requirements.txt doesn't exist, install manually:

pip install streamlit fastapi uvicorn langchain-groq langchain-openai langchain-community langgraph python-dotenv pydantic requests

3. Set Up Environment Variables

Create a .env file in the project root:

GROQ_API_KEY=your_groq_api_key_here
TAVILY_API_KEY=your_tavily_api_key_here
OPENAI_API_KEY=your_openai_api_key_here

Get your API keys:

• Groq: https://console.groq.com/keys
• Tavily: https://tavily.com/
• OpenAI: https://platform.openai.com/api-keys

4. Run the Application

Start the Backend Server:

python backend.py

The API will be available at http://127.0.0.1:8000

Start the Frontend (in a new terminal):

streamlit run frontend.py

The web interface will open at http://localhost:8501

📚 API Documentation

Endpoint: POST /chat

Send a chat request to the AI agent.

Request Body:

{
    "model_name": "llama-3.3-70b-versatile",
    "model_provider": "Groq",
    "system_prompt": "You are a helpful AI assistant",
    "messages": ["What is the weather like today?"],
    "allow_search": true
}

Response:

"The AI agent's response text here..."

Available Models:

• Groq: llama-3.3-70b-versatile, llama-3.3-70b-8192, mixtral-8x7b-32768
• OpenAI: gpt-4o-mini

🧩 Component Details

ai_agent.py - Core AI Logic

• Implements the main AI agent using LangGraph
• Handles model initialization for both Groq and OpenAI
• Manages web search integration with Tavily
• Processes queries and returns responses

backend.py - FastAPI Server

• RESTful API server handling chat requests
• Input validation with Pydantic models
• Model selection and provider routing
• Error handling for invalid requests

frontend.py - Streamlit Interface

• Interactive web UI for the chatbot
• Model and provider selection
• System prompt customization
• Real-time chat interface

🔧 Configuration

Model Providers

Groq Models:

• llama-3.3-70b-versatile: Best for general tasks
• llama-3.3-70b-8192: Extended context window
• mixtral-8x7b-32768: Fast mixture of experts model

OpenAI Models:

• gpt-4o-mini: Cost-effective GPT-4 variant

System Prompts

Customize AI behavior by defining system prompts in the frontend:

You are a helpful AI assistant specialized in [domain].
Always provide accurate, concise, and well-structured responses.

Web Search

Enable/disable web search to allow the AI to access real-time information for current events, recent data, or fact-checking.

🛠️ Development

Running in Development Mode

1. Backend with auto-reload:

uvicorn backend:app --reload --host 127.0.0.1 --port 8000

2. Frontend with auto-reload:

streamlit run frontend.py --server.runOnSave true

Adding New Models

1. Add model name to ALLOWED_MODEL_NAMES in backend.py
2. Add to model lists in frontend.py
3. Ensure the model is supported by the provider

Environment Setup for Development

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

🐛 Troubleshooting

Common Issues

1. API Key Errors

Error: The api_key client option must be set

• Ensure .env file exists with valid API keys
• Check that environment variables are loaded correctly

2. Connection Refused

ConnectionError: HTTPConnectionPool

• Make sure the backend server is running on port 8000
• Check if the API_URL in frontend.py matches your backend URL

3. Model Not Found

{"error": "Invalid Model"}

• Verify the model name is in ALLOWED_MODEL_NAMES
• Check if the model is available for your API key tier

4. Import Errors

ModuleNotFoundError: No module named 'langchain_groq'

• Install missing packages: pip install langchain-groq
• Ensure all dependencies are installed

Debug Mode

Enable debug logging by adding to your .env:

DEBUG=true

🚀 Deployment

Docker Deployment (Recommended)

Create Dockerfile:

FROM python:3.9-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .
EXPOSE 8000

CMD ["uvicorn", "backend:app", "--host", "0.0.0.0", "--port", "8000"]

Build and run:

docker build -t ai-chatbot .
docker run -p 8000:8000 --env-file .env ai-chatbot

Cloud Deployment

For Backend (FastAPI):

• Railway, Render, or Heroku
• Set environment variables in platform settings

For Frontend (Streamlit):

• Streamlit Cloud, Railway, or Render
• Update API_URL to point to deployed backend

📊 Performance

• Response Time: ~2-5 seconds (depending on model and search)
• Concurrent Users: Up to 100 (FastAPI async handling)
• Rate Limits: Based on API provider limits

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Commit changes: git commit -m 'Add feature'
4. Push to branch: git push origin feature-name
5. Submit a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙋‍♂️ Support

• Issues: Open an issue on GitHub
• Discussions: Use GitHub Discussions for questions
• Email: Contact [email protected] for urgent issues

🔗 Related Links

• LangChain Documentation
• Streamlit Documentation
• FastAPI Documentation
• Groq API Documentation
• OpenAI API Documentation