Google has officially launched the "File Search Tool" in the Gemini API, a fully managed RAG system. This tool seamlessly transforms private files into Gemini's knowledge base, eliminating the need for users to handle tedious steps such as data chunking, embedding generation, or vector storage. It enables efficient retrieval and generation directly through API integration.

Core Features of the Tool: One-stop RAG Process for Files

The search tool's core lies in its end-to-end integrated design. It automatically handles file upload, indexing, and retrieval processes, using Google's Gemini Embedding model (gemini-embedding-001) to generate high-quality vector representations, supporting semantic search rather than simple keyword matching. This means developers can focus on application logic instead of maintaining underlying infrastructure.

According to the official Google blog, the tool supports multiple common file formats, including PDF, DOCX, TXT, JSON, and various programming language files (such as Python, Java, etc.). Users can simply call the generateContent interface of the Gemini API to import private documents into the knowledge base. The system intelligently chunks the data to ensure contextual coherence in retrieval results and automatically generates citation links in the response, directly pointing to specific parts of the document, thereby enhancing the transparency and verifiability of the output.

This design is particularly suitable for enterprise-level scenarios, such as internal knowledge assistants, intelligent support bots, or content discovery platforms. Google emphasizes that for applications with large volumes of data, frequent updates, repeated queries, or strict traceability requirements, this tool significantly reduces the development barrier and provides scalable performance support.

Innovative Billing Model: Free Queries, First Index Starting at $0.15 per Million Tokens