The original title is about building a RAG system with tool use for autonomous LLM searching. Let me rewrite this for a mobile feed.

Short summary

Tool Use enables LLMs to autonomously decide when to search a vector database within RAG pipelines, rather than always calling search before answer generation. This tutorial demonstrates practical implementation using Google Gemini API and PostgreSQL vectors, showing how the LLM correctly decides when retrieval is needed versus answering directly. Code examples included demonstrate both single-tool and multi-tool scenarios.

• •LLM receives tool definitions and decides whether to call search or answer directly
• •Includes runnable Python code with Google Gemini API and vector database integration
• •Demonstrates multi-tool scenarios where LLM picks the right search strategy

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