LangGraph + Neo4j Crash Course for Beginners: AI Agent Development using Knowldege Graph | Hands-on! - Hybrid RAG: Mastering Context By Combining GraphRAG and VectorRAG using LangGraph, NEO4J & PineCone

GitHub Repo: https://github.com/homayounsrp/hybrid_rag Pinecone: https://www.pinecone.io/ Neo4j: https://neo4j.com/ - A hybrid RAG system that merges Pinecone's vector embeddings with Neo4j's graph relationships - Instead of just finding similar text, it understands CONTEXT and RELATIONSHIPS - Real-time semantic search + knowledge graph navigation = Mind-blowing results! ⚡ Why This is Game-Changing: Traditional RAG systems only do vector similarity. Mine does that PLUS understands how information connects. It's like having Google + Wikipedia + a mind map all in one! 🧠 The Tech Stack: - OpenAI embeddings for understanding meaning - Pinecone for lightning-fast vector search - Neo4j for mapping relationships - LangChain for seamless AI integration 💡 Real-World Impact: This could transform research, customer support, research document analysis, and any field where understanding context matters more than just finding keywords. 🔥 The Result: Information retrieval that doesn't just find what you're looking for—it finds what you NEED to know, even if you didn't know to ask for it. Timestamps: 0:00 - Intro 0:30- System Design 2:21- Implementation hybrid rag,pinecone,neo4j python,pinecone vector database,graph database,vector database,neo4j cypher,pinecone embeddings,naive rag,advanced rag,rag implementation,langgraph,langchain,langchain openai,neo4j langchain,knowledge graph rag,neo4j,HybridRAG: Ultimate RAG Engine - Knowledge Graphs + Vector Retrieval! Better Than GraphRAG!,Hybrid Search RAG With Langchain And Pinecone Vector DB,This Hybrid RAG Trick Makes Your AI Agents More Reliable (n8n)