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Overview

A built-in tool that provides a fully managed Retrieval-Augmented Generation (RAG) system. It handles file storage, chunking, embedding generation, and context injection into prompts, allowing your agent to search through uploaded files using vector search.

Usage

Advanced Example

Parameters

  • file_store_ids (Sequence[str], required): The file store IDs to search through (max 2 for OpenAI)
    • For OpenAI: Vector store IDs created via the OpenAI API
    • For Google: File search store names uploaded and processed via the Gemini Files API

Provider Support

  • OpenAI Responses: ✅ Full support
  • Google (Gemini): ✅ Full support
  • Anthropic: ❌ Not supported

Characteristics

  • Fully managed RAG pipeline (chunking, embedding, retrieval)
  • Vector-based semantic search across uploaded documents
  • Provider-managed file storage and indexing
  • Automatic context injection into model prompts