Introducing RAG 2.0: Agentic RAG + Knowledge Graphs (FREE Template)

Traditional RAG systems only scratch the surface of what's possible. This advanced AI agent combines vector search with knowledge graphs to create a system that understand relationships, track changes over time, and reason about complex connections. Built with PostgreSQL + pgvector and Neo4j + Graphiti, it automatically chooses between vector search, graph traversal, or hybrid approaches based on what will answer your question the best. I built the full package here - a complete implementation including semantic chunking, a vector database/knowledge graph pipeline, a FastAPI backend with streaming responses, and a CLI tool to chat with the agent. Full source code (linked below!) included with support for multiple LLM providers. This is production-ready RAG. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Neon's free tier is more than enough to cover what you'll need in this guide! But if you do decide that you need to upgrade, you can sign up through this link and get a $100 credit: https://get.neon.com/2scm ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Code and instructions for this Agentic RAG Agent here: https://github.com/coleam00/ottomator-agents/tree/main/agentic-rag-knowledge-graph