Gemini API adds File Search Tool supporting PDF, DOCX, TXT, JSON, code files
Gemini’s newest API adds a File Search Tool that seems to let developers read straight from everyday docs without having to write their own parsers. In practice, it can scan a mixed bag of PDFs, Word files, plain-text logs, JSON configs, even source code, and turn it into a searchable knowledge base. The neat part?
You probably won’t need separate pipelines for each format - a single call can pull relevant passages from the whole collection. The demo linked in the release notes shows snippets being extracted from a multi-language repo and a handful of policy PDFs in just a few seconds. Still, it’s unclear how far the tool goes beyond those examples; the range of supported file types will dictate how complete the index can become.
The next section lists the exact formats that are covered, which should help anyone thinking about building a unified reference layer over existing assets.
- Support for a wide range of formats: You can build a comprehensive knowledge base using a vast array of file formats, including PDF, DOCX, TXT, JSON and many common programming language file types (see the full list of supported formats in the docs) You can see the File Search Tool in action through one of our new demo app in Google AI Studio (needs a paid API key). Ask the Manual demo app powered by the new File Search tool in Gemini API How developers are using File Search Developers in our early access program are already using it to build incredible things from intelligent support bots, to internal knowledge assistants and creative content discovery platforms.
Google just added a File Search Tool to Gemini API, wrapping a fully managed RAG system into the service. The idea is to hide the retrieval pipeline so developers don’t have to juggle storage or generate embeddings themselves. According to the announcement, the tool should return answers that are more accurate, relevant and easier to verify, and it supposedly scales without extra ops work.
It handles PDFs, DOCX, TXT, JSON and a bunch of code files, which could let teams build fairly rich knowledge bases. Google pitches the storage and embedding part as “simple and affordable,” but the pricing details are still vague and it’s hard to say how costs will grow with heavy use. The docs mention a few more formats, yet the preview gives no numbers on latency or cost per query.
So while the integration looks handy, I’m not convinced it will meet the speed or cost expectations of demanding workloads. A short demo is available, but we’ll probably have to wait for real-world feedback to see if the convenience outweighs any performance trade-offs.
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Common Questions Answered
What file formats does the Gemini API File Search Tool support?
The File Search Tool supports a wide range of formats, including PDF, DOCX, TXT, JSON, and many common programming language file types. This allows developers to build a unified knowledge base without writing separate parsers for each format.
How does the new File Search Tool simplify retrieval‑augmented generation (RAG) for developers?
Gemini API now bundles a fully managed RAG system directly into its service, eliminating the need to set up storage or generate embeddings manually. The tool handles indexing and retrieval behind the scenes, delivering more accurate and verifiable responses.
Can developers test the File Search Tool without building their own integration?
Yes, Google AI Studio offers a demo app called the Manual demo, which showcases the File Search Tool in action. Access requires a paid API key, but it provides a ready‑made example of searching across mixed document types.
What are the claimed benefits of using the Gemini File Search Tool for mixed document folders?
The tool enables a single API call to surface relevant passages from PDFs, Word documents, plain‑text logs, JSON configs, and source code files. This reduces pipeline complexity, improves response relevance, and scales without developers managing the underlying retrieval infrastructure.