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-Buddy-App Discord Link: https://discord.com/invite/aianytime Document Buddy App Video: https://youtu.be/7hnZ6SFnXXA If you found this video helpful, please like, comment, and subscribe to my channel for more tutorials like this! Your support helps me create more content to help you in your development journey. Join this channel to get access to perks: https://www.youtube.com/channel/UC-zVytOQB62OwMhKRi0TDvg/join To further support the channel, you can contribute via the following methods: Bitcoin Address: 32zhmo5T9jvu8gJDGW3LTuKBM1KPMHoCsW UPI: sonu1000raw@ybl #graphrag #rag #ai