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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