https://github.com/vansh-khaneja/userdata-rag-knowledge-graph-langchain
https://github.com/vansh-khaneja/userdata-rag-knowledge-graph-langchain
chatbot knowledge-graph neo4j rag
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/vansh-khaneja/userdata-rag-knowledge-graph-langchain
- Owner: vansh-khaneja
- Created: 2024-08-22T09:15:54.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-25T05:12:08.000Z (almost 2 years ago)
- Last Synced: 2025-06-23T01:05:18.290Z (about 1 year ago)
- Topics: chatbot, knowledge-graph, neo4j, rag
- Language: Jupyter Notebook
- Homepage:
- Size: 352 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Userdata RAG Using Knowledge Graph and LangChain
This project implements Retrieval Augmented Generation using Neo4j knowledge grphs and Langhcain framework. Using Llama 3 as the language model for beter graphs and response generation. To learn more about the project please refer this [article](...).
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Execution](#execution)
- [Contact](#contact)
## Introduction
This repository will guide you in buiding a user ineractive RAG application with the help of ```knowledge graphs``` and ```langchain```. Using ```Llama 3``` as the language model and ```streamlit``` to create a user interative web inteface.
## Features
- Fast and efficient way for data retrieval
- Supports `llama 3` and other models with groq
- Better Graphs Visualtization
- Scalable and high-performance retrieval system
## Installation
1. Clone the repository:
```sh
git clone https://github.com/vansh-khaneja/Userdata-RAG-Knowledge-Graph-Langchain
cd Userdata-RAG-Knowledge-Graph-Langchain
```
2. Set up the Python environment and install dependencies:
```sh
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
3. Set up Neo4j for knowledge graph:
Follow the [Neo4j documentation](https://console.neo4j.io/) to setup the instance.
4. Set up Groq API key:
Access the groq api key [GroqCloud](https://console.groq.com/keys) to setup the api key.
## Execution
1.Create a .env file and store all the credentials in it.
```sh
NEO4J_URI="YOUR_NEO4J_URL"
NEO4J_USERNAME="YOUR_NEO4J_USERNAME"
NEO4J_PASSWORD="YOUR_NEO4J_PASSWORD"
GROQ_API_KEY="YOUR_GROQ_API_KEY"
```
2.Download the dataset for this project [here](https://www.kaggle.com/datasets/arnavsmayan/amazon-prime-userbase-dataset) or you can try with your own dataset. Just upload it over github and use the repo link.
```sh
LOAD CSV WITH HEADERS FROM 'https://raw.githubusercontent.com/vansh-khaneja/test5/main/amazon_prime_users.csv' AS row
```
3.Execute the ```main.py``` file by running this command in terminal.
```sh
streamlit run main.py
```
## Contact
For any questions or issues, feel free to open an issue on this repository or contact me at vanshkhaneja2004@gmail.com.
Happy coding!