Building agentic RAG with PostgreSQL and n8n

Building agentic RAG with PostgreSQL and n8n involves enhancing traditional Retrieval-Augmented Generation (RAG) pipelines by incorporating a reasoning loop instead of a linear process, using PostgreSQL and n8n for orchestration and storage. Unlike standard RAG, which handles every query identically, agentic RAG employs an AI agent to dynamically evaluate queries and select the appropriate tools for data retrieval, improving accuracy. This approach consolidates vector storage, chat memory, and SQL data within PostgreSQL, reducing infrastructure complexity and enabling a seamless transition between semantic searches and precise database queries. The system leverages PostgreSQL's pgvector extension for vector storage, while also using tables for document metadata and content to manage and query large texts effectively. The integration of n8n provides orchestration capabilities, allowing the system to function as a chatbot or API backend, and the flexibility to add various tools for document and vector data handling.

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