sangaam / rag-llm-using-neo4j-graph-database Goto Github PK

Project Name: Chatbot with Neo4j and OpenAI Integration

Description: This project is a chatbot application that leverages Neo4j, a graph database, and OpenAI's natural language processing capabilities to provide intelligent responses to user queries. The chatbot interacts with users by answering questions based on the information stored in the Neo4j database and generating responses using OpenAI's GPT model.

Key Features:

1. Neo4j Integration: The application connects to a Neo4j graph database to store and retrieve structured data.
2. OpenAI Integration: It utilizes OpenAI's API to generate natural language responses based on user queries and database information.
3. Conversational Interface: Users can interact with the chatbot by asking questions, and the chatbot responds with relevant information from the database. 4)_ Dynamic Response Generation_: The chatbot dynamically generates responses based on user queries and the retrieved data from Neo4j.

Technologies Used:

1. Node.js: Backend server environment for running the application.
2. Express.js: Web framework for building RESTful APIs.
3. Neo4j: Graph database for storing and querying structured data.
4. OpenAI API: Natural language processing service for generating responses.
5. Axios: HTTP client for making API requests.
6. dotenv: Library for loading environment variables from a .env file.

Usage:

Install dependencies: Run npm install to install the required dependencies. Set up environment variables: Create a .env file and define variables like NEO4J_URI, NEO4J_USER, NEO4J_PASSWORD, and OPENAI_API_KEY. Start the server: Run npm start to start the server. Interact with the chatbot: Use a client application or tools like Postman to send POST requests to the chat endpoint (/chat) with questions in the request body.