MOSDAC Chatbot Agent

A modular, intelligent chatbot designed to provide weather information and answer queries about ISRO/MOSDAC using RAG (Retrieval-Augmented Generation). Built with LangGraph, LangChain, and Streamlit.

🚀 Features

• Weather Forecast: Fetches real-time 7-day weather forecasts for Indian cities using the MOSDAC API.
• Knowledge Base (RAG): Answers questions about ISRO, satellites, and technical documents by retrieving information from a local vector database.
• Intelligent Routing: Automatically decides whether to use the Weather tool or the RAG tool based on the user's query.
• Modular Architecture: Clean, scalable code structure separated into core, services, tools, and config.

🛠️ Tech Stack

• Frontend: Streamlit
• Orchestration: LangGraph, LangChain
• LLM: Google Gemini (via langchain-google-genai)
• Vector Store: ChromaDB
• Data Processing: BeautifulSoup4 (for scraping), RecursiveCharacterTextSplitter

📂 Project Structure

/src
  /config       # Configuration and environment variables
  /core         # Core logic (LLM, Graph, Router, Vector Store)
  /services     # External services (Weather API, Scraper)
  /tools        # LangChain tools
  /utils        # Utility functions (Data loading)
  /data         # Data files (JSON, etc.)
  app.py        # Main Streamlit application

⚙️ Installation

1. Clone the repository:

   git clone https://github.com/nikhilongo/Agentic-Ai-for-mosdac.gov.in.git
   cd Agentic-Ai-for-mosdac.gov.in

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   # Windows
   .\venv\Scripts\activate
   # Mac/Linux
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

   (Note: Ensure you have streamlit, langchain, langgraph, langchain-google-genai, chromadb, beautifulsoup4, geopy, python-dotenv installed)

4. Set up Environment Variables: Create a .env file in the root directory and add your Google API Key:

   GOOGLE_API_KEY=your_google_api_key_here

🏃‍♂️ Usage

Run the application using the provided batch script (Windows) or directly via Streamlit:

Option 1: Batch Script Double-click run_app.bat or run:

.\run_app.bat

Option 2: Manual Command

streamlit run src/app.py

🧠 How it Works

1. Input: User enters a query in the Streamlit UI.
2. Routing: The Router node analyzes the intent (Weather vs. General Info).
3. Execution:
   • Weather: Calls WeatherService -> MOSDAC API -> Formats response.
   • RAG: Embeds query -> Searches ChromaDB -> Generates answer using retrieved context.
4. Response: The final answer is displayed in the chat interface.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.