Knowledge Graphs for RAG

Project Overview

This project demonstrates how to build and use Knowledge Graphs for Retrieval-Augmented Generation (RAG) using LangChain, Neo4j, and Groq LLM. It features text processing, knowledge graph extraction, and RAG implementation.

Key Features

• Text processing and chunking
• Knowledge graph extraction
• Neo4j integration
• RAG with knowledge graph
• Groq LLM query processing

Technical Details

Integrates Neo4j for graph storage and Groq LLM for high-speed inference in a RAG pipeline.

Challenges & Solutions

Efficiently extracting knowledge graphs from unstructured text and optimizing retrieval latency.