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RAG_With_Knowledge_Graph

RAG_With_Knowledge_Graph is an advanced AI-driven customer support system that integrates LangChain, Neo4j, and Google Generative AI to deliver efficient and dependable customer assistance. The application features a FastAPI backend and a Streamlit frontend.

Key Features

  • AI-Powered Assistance
  • 24/7 Support Availability
  • Comprehensive Customer Query Resolution

Installation

  1. Clone the repository:

    git clone https://github.com/SURESHBEEKHANI/RAG_With_Knowledge_Graph.git
cd RAG_With_Knowledge_Graph

  2. Set up a virtual environment and activate it:

    python -m venv venv
source venv/bin/activate  # Use `venv\Scripts\activate` on Windows

  3. Install required dependencies:

    pip install -r requirements.txt

  4. Configure environment variables:

    export NEO4J_URI="your_neo4j_uri"
export NEO4J_USERNAME="your_neo4j_username"
export NEO4J_PASSWORD="your_neo4j_password"
export GROQ_API_KEY="your_groq_api_key"
export GEMINI_API_KEY="your_gemini_api_key"

Running the Application

Backend

  1. Navigate to the backend directory:

    cd backend

  2. Launch the FastAPI application:

    uvicorn backend:app --host 127.0.0.1 --port 9999

Frontend

  1. Navigate to the frontend directory:

    cd ../frontend

  2. Start the Streamlit application:

    streamlit run app.py

Usage

  1. Open your web browser and go to http://127.0.0.1:8501 to access the Streamlit frontend.
  2. Interact with the chatbot by entering your queries into the input box.
  3. The chatbot will respond with AI-generated answers based on context and data retrieved from the Neo4j graph database.

Project Structure

  • backend.py: Implementation of the FastAPI backend.
  • app.py: Implementation of the Streamlit frontend.
  • Graprag.py: Core logic for query processing and data retrieval.

Video Demonstration

Check out the video demonstration of the project:

Video Demonstration

License

This project is licensed under the MIT License.