Google has just simplified RAG (Retrieval-Augmented Generation) like never before.

Google has just simplified Retrieval-Augmented Generation (RAG) like never before.

Today they launched the File Search Tool, a fully managed layer within the Gemini API that eliminates the complexity of the retrieval pipeline. In other words, you can connect your own data to Gemini without having to build the entire infrastructure for embedding, chunking, or vector search.

The interesting features:

• Everything is integrated into the same generateContent API.
• Generating embeddings at the time of the query is free—you only pay for indexing the files (at $0.15 per million tokens).
• It supports PDFs, DOCX, TXT, JSON, and even code files.
• And the responses include automatic citations, showing where each piece of information comes from.

This makes RAG a plug-and-play service, ideal for those who want to build experiences with their own data without getting bogged down in the architecture.

At Solcre, we've been exploring different grounding strategies for generative AI, and this kind of progress shows how the ecosystem is moving toward simpler, more accessible, and scalable solutions for developers.

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