Скачать или смотреть Building Agentic RAG using Pinecone & Langgraph

Github Repo: https://github.com/homayounsrp/agenti...

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