Qdrant-LangChainReActAgents-Groq

Article Link : https://medium.com/@yash9439/using-langchain-react-agents-with-qdrant-and-llama3-for-intelligent-information-retrieval-b181ce7a5962

Code implementation for building an intelligent information retrieval system using LangChain ReAct agents, Qdrant Vector Database, and the llama3 large language model (LLM) from the Groq Inference endpoint.

Introduction

This project demonstrates how to combine the power of LangChain's ReAct agents, Qdrant's efficient vector storage and retrieval capabilities, and the lightning-fast inference of llama3 from Groq to create a robust and intelligent information retrieval system.

Key Features

• Data Extraction and Processing: Extracts text from PDF documents and prepares it for storage in Qdrant.
• Qdrant Vector Database Integration: Utilizes Qdrant to store and retrieve text chunks as high-dimensional vectors using custom embeddings.
• LangChain ReAct Agents: Employs ReAct agents to orchestrate complex query handling, breaking down queries into manageable steps.
• llama3 LLM from Groq Endpoint: Leverages the llama3 LLM for advanced language understanding and response generation.
• Customizable Tools and Functions: Includes customizable tools and functions for retrieving specific information, such as age and health status.
• Gradio UI: Provides a user-friendly interface for interacting with the system.

Getting Started

Prerequisites

• Python 3.7 or higher
• Groq API key (obtain from https://console.groq.com/keys)
• Qdrant account (sign up at https://cloud.qdrant.io/login)

How to use

Follow the Article : https://link.medium.com/1eAtV8X29Jb