https://github.com/sureshbeekhani/rag_with_knowledge_graph
RAG_With_Knowledge_Graph enhances customer support using Retrieval-Augmented Generation (RAG) and a knowledge graph. It leverages Neo4j for structured data, LangChain for retrieval, and Google Generative AI for intelligent responses, ensuring efficient query resolution.
https://github.com/sureshbeekhani/rag_with_knowledge_graph
ai-customer-support chatbot chatbots fastapi fastapi-docker generativeai neo4j-database
Last synced: about 1 month ago
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RAG_With_Knowledge_Graph enhances customer support using Retrieval-Augmented Generation (RAG) and a knowledge graph. It leverages Neo4j for structured data, LangChain for retrieval, and Google Generative AI for intelligent responses, ensuring efficient query resolution.
- Host: GitHub
- URL: https://github.com/sureshbeekhani/rag_with_knowledge_graph
- Owner: SURESHBEEKHANI
- Created: 2025-01-22T16:58:09.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-02T09:04:17.000Z (over 1 year ago)
- Last Synced: 2025-03-29T10:15:25.451Z (about 1 year ago)
- Topics: ai-customer-support, chatbot, chatbots, fastapi, fastapi-docker, generativeai, neo4j-database
- Language: Jupyter Notebook
- Homepage:
- Size: 2.33 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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:
```sh
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:
```sh
python -m venv venv
source venv/bin/activate # Use `venv\Scripts\activate` on Windows
```
3. Install required dependencies:
```sh
pip install -r requirements.txt
```
4. Configure environment variables:
```sh
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:
```sh
cd backend
```
2. Launch the FastAPI application:
```sh
uvicorn backend:app --host 127.0.0.1 --port 9999
```
### Frontend
1. Navigate to the frontend directory:
```sh
cd ../frontend
```
2. Start the Streamlit application:
```sh
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](notebook/Customer%20Support%20LangChain.mp4)
## License
This project is licensed under the MIT License.