GraphRAG App Project using Neo4j, Langchain, GPT-4o, and Streamlit

In this tutorial, I walk you through the development of Graphy v1, a real-time GraphRAG (Graph Retrieval-Augmented Generation) application. Using LangChain, Neo4j, and OpenAI's GPT-4 and text-ada-002 models, I'll show you how to extract knowledge from documents and enable natural language querying over a graph database.

What You'll Learn:
1. Setting up a modular app where users can input their own credentials.
2. Using LangChain's LLMGraphTransformer to convert documents into graph data.
3. Integrating with Neo4j to store and query graph data.
4. Implementing natural language querying using OpenAI's GPT-4.
5. Enhancing the app's UI with Streamlit, including adding a sidebar, logo, and interactive elements.

By the end of this video, you'll have a functional app that allows users to upload PDF documents, extract their content into a Neo4j graph database, and interact with the data using natural language queries.

Graphy-v1 GitHub: https://github.com/AIAnytime/Graphy-v1
Document Buddy App GitHub: https://github.com/AIAnytime/Document...
Discord Link:   / discord  
Document Buddy App Video:    • Real time RAG App using Llama 3.2 and Open...  

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