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.