Agentic RAG using LangGraph

This project implements a Agentic RAG application using LangGraph and Qdrant. The embeddings are stored and queried using the Qdrant vector database. To learn more about the project please refer this article.

Table of Contents

• Introduction
• Features
• Installation
• Execution
• Contact

Introduction

In this project we are building a RAG application that uses agents to answer the question based on the query given by the user.

Features

• Fast and efficient way for data retrieval
• Wide queries support
• Multi agentic RAG
• Scalable and high-performance retrieval system

Installation

1. Clone the repository:

   git clone https://github.com/vansh-khaneja/RAG-using-LangGraph-Agents
   cd RAG-using-LangGraph-Agents

2. Set up the Python environment and install dependencies:

   python3 -m venv venv
   source venv/bin/activate
   pip install -r requirements.txt

3. Set up Qdrant:

   Follow the Qdrant documentation to install and configure Qdrant on your system.

Execution

1.Download the dataset for this project here or you can try with your own dataset. Just change the path of the file here.

    file_path = '/content/Airline Dataset.csv'

2.Execute the main.py file by running this command in terminal.

    python 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!