GraphDB with LangChain, Neo4j, and Groq LLM 📌 Overview

This project demonstrates how to build a Graph-based Question Answering (QA) system using:

Neo4j as the Graph Database

LangChain for orchestration

Groq LLM (ChatGroq) for large language model capabilities

LLM Graph Transformer for converting unstructured text into structured graph data

GraphCypherQAChain for natural language querying over Neo4j

The notebook walks through:

Connecting to a Neo4j database

Converting text into graph-structured data

Loading a movie dataset into Neo4j

Querying the graph using natural language

(Optional) Preparing for vector + hybrid graph retrieval

🚀 Features

✅ Connect to Neo4j using LangChain

✅ Convert unstructured text into graph documents

✅ Automatically extract nodes and relationships

✅ Insert structured data into Neo4j

✅ Load external CSV movie dataset

✅ Natural language → Cypher query generation

✅ Graph-based Question Answering

✅ Optional vector database setup (FAISS / Neo4jVector ready)

🛠️ Tech Stack

Python

Neo4j

LangChain

LangChain-Neo4j

LangChain-Groq

LangChain-Experimental

FAISS (optional vector store)

HuggingFace Embeddings (optional)