LangGraph + Neo4j Crash Course for Beginners: AI Agent Development using Knowldege Graph | Hands-on! - How to Build Intelligent RAG Systems with Pinecone & LangGraph

Github Repo: https://github.com/homayounsrp/agentic-rag-using-pinecone Get your API from here: - Pinecone: https://www.pinecone.io/ - Tavily: https://www.tavily.com/ - OpenAI: https://platform.openai.com/ In this project, I built a sophisticated Retrieval-Augmented Generation (RAG) system that uses multiple AI agents working together to provide intelligent responses. Here's what I implemented: 🏗️ System Architecture: - Multi-Agent Framework: Built using LangGraph with three specialized agents working in coordination - Supervisor Agent: Acts as a router, intelligently deciding which agent - should handle each query - Researcher Agent: Performs real-time web searches using Tavily API for up-to-date information - Memory Agent: Manages long-term knowledge storage and retrieval using Pinecone vector database 🔧 Key Components: - Vector Database: Pinecone for semantic search and knowledge storage - Embeddings: OpenAI text-embedding-3-small for converting text to vectors - LLM: GPT-4o-mini for natural language processing - Search Engine: Tavily with advanced semantic ranking and reranking capabilities - State Management: LangGraph's state management for maintaining conversation context 💡 How It Works: 1. User asks a question 2. Supervisor agent analyzes the query and decides the best approach 3. Memory agent first searches stored knowledge in Pinecone 4. If complete information isn't found, Researcher agent performs web search 5. System provides comprehensive answers and can save new information to memory 6. All interactions maintain conversation context using checkpointing 🚀 Features: - Intelligent routing between memory retrieval and web research - Persistent memory storage with semantic search - Real-time web search with advanced ranking - Conversation state persistence - Automatic knowledge accumulation over time 🛠️ Tech Stack: - Python - LangGraph for multi-agent orchestration - Pinecone for vector storage - OpenAI GPT-4o-mini and embeddings - Tavily for web search - LangChain for tool integration This system demonstrates how to build production-ready agentic AI that can learn, remember, and research to provide accurate, up-to-date information while maintaining conversation context. #AI #RAG #LangGraph #Pinecone #OpenAI #MultiAgent #MachineLearning #Python