Traditional RAG is like a flip phone in 2025—it's broken. 🧠 We're revealing a game-changing "RAG 2.0" system that gives your AI a photographic memory and the ability to connect the dots, transforming it from a simple search tool into a true research assistant.

We’ll talk about:

The complete guide to building a "RAG 2.0" agent that combines Agentic RAG with Knowledge Graphs for superior reasoning.
Why traditional RAG fails because "mathematically similar" doesn't always mean "contextually relevant," and how this new system fixes it.
A deep dive into the powerful open-source tech stack used to build this agent: Pydantic AI, Graphiti, Neo4j, and PostgreSQL with pgvector.
A step-by-step technical guide to setting up the entire system, from configuring databases on Neon and Neo4j to running the ingestion script.
How the agent intelligently chooses the right tool for your query—a vector search for facts or a knowledge graph search for relationships.

Keywords: RAG, RAG 2.0, Retrieval-Augmented Generation, Knowledge Graphs, AI Agents, n8n, Vector Database, Neo4j, pgvector, Pydantic AI, Graphiti, OpenAI, AI Data Understanding

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#59 Max: RAG 2.0 is Here – Build an AI Agent That Understands Your Data with Knowledge Graphs

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#59 Max: RAG 2.0 is Here – Build an AI Agent That Understands Your Data with Knowledge Graphs