Tutorial: How to Let an LLM Autonomously Decide When to Search in a RAG System

A new developer tutorial explains how to implement Tool Use in a Retrieval-Augmented Generation (RAG) pipeline, enabling a large language model to decide when and what to search rather than following a hardcoded retrieval flow. In traditional RAG setups, a search function is always called before generating an answer, but Tool Use allows the LLM to determine whether retrieval is necessary at all. The LLM is provided with descriptions of available functions and can respond with either a function call or a direct text answer based on its judgment. The tutorial uses Google's Gemini API alongside a PostgreSQL vector database, walking through a working Python implementation called 06_tool_basic.py. This approach improves response quality in cases where the user's question may already be answerable, or where multiple targeted searches with different queries would yield better results.