Gemini File Search can index a persistent corpus and retrieve relevant content to ground model answers. Its documented multimodal retrieval support covers text and images—not audio or video. For text, Google documents gemini-embedding-001; for image indexing, configure models/gemini-embedding-2 for the store. The steps below show how to choose the right input path, prepare files, query a store, and trace citations.
What Gemini File Search does
File Search is a managed retrieval-augmented generation (RAG) workflow: it imports files, chunks and indexes their content, then retrieves relevant chunks as context for a Gemini response. Google describes semantic search as embedding both imported content and the query, then finding similar, relevant chunks. See the Gemini API File Search documentation for current API and SDK examples.
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The typical flow is to create a store, add files, wait for any asynchronous processing to finish, and make a model request with the File Search tool configured to use that store. The official page includes Python, JavaScript, Java, and REST examples. SDK syntax and API surfaces evolve, so follow the example matching your installed SDK and chosen API.
Choose between text retrieval, image retrieval, and direct file input
| Approach | Best fit | Key distinction |
|---|---|---|
| File Search with text embeddings | Repeated retrieval over a persistent corpus of text-based files | Google documents gemini-embedding-001 for text embeddings. |
| File Search with image-capable embeddings | Retrieval that needs indexed image content as well as text | Configure models/gemini-embedding-2; the documented image formats and resolution limit apply. |
| Direct file input | Supplying a file as part of a particular request rather than searching a persistent indexed collection | It is a separate input path, with constraints that vary by method, format, and endpoint. |
Google says the appropriate file-input method depends on file size, where data is stored, and how often it will be used. The Gemini API file-input guide discusses availability across Batch, Interactions, and Live API endpoints. Its example limit of 50 MB applies to reading a local PDF; do not treat that as a universal limit for other formats or methods.
Build a File Search workflow
- Create a File Search store. Use the documented text embedding setup for text-only retrieval. If the store must index images, configure its embedding model as
models/gemini-embedding-2rather than leaving the default text-only setup in place. - Add your files. Upload directly to the store or use a documented import workflow. For images, use PNG or JPEG files no larger than 4K × 4K pixels, as specified in Google’s File Search documentation.
- Wait for processing where required. Some upload or import methods return a long-running operation. Poll that operation until it completes before relying on the file in a query.
- Query the intended store. Make a Gemini request with the File Search tool pointed at the store containing the corpus. The File Search documentation presents both
generateContent-style examples and newer Interactions examples; use the API surface and SDK version your application actually targets. - Review the response annotations. Inspect file citations to identify the source material used. For image citations, a
media_idcan be used to download the referenced image chunk.
What “multimodal” means here—and what it does not
For File Search, the documented multimodal configuration is about indexing text and images. Google lists PNG and JPEG support, with a maximum image resolution of 4K × 4K pixels. The same File Search documentation explicitly says audio and video formats are not currently supported by File Search. A Gemini model or another Gemini input method may accept media without making that media searchable in a File Search store.
Citations and source checking
File citations help trace an answer to the uploaded source material; an image citation can include a media_id for retrieving its image chunk. Treat citations as a way to inspect provenance, not proof that a generated conclusion is correct. Check important claims against the original files, especially when the answer combines multiple retrieved chunks or interprets visual content.
Persistence and costs
Google’s current File Search documentation says raw File API objects are deleted after 48 hours, while indexed store data persists until you manually delete it or the model is deprecated. It also says File Search storage and embedding generation at query time are free; embedding generation is charged when files are first indexed, and normal Gemini model input and output token charges still apply. These are documentation statements, not a workload estimate; check the live File Search page for current billing and retention terms before deployment.
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Quick Recap
Implementation checks before deployment
- Confirm whether the corpus is text-only or includes images, and set the store’s embedding model accordingly.
- Validate image format and dimensions before import.
- Account for asynchronous processing; do not query on the assumption that an unfinished import is searchable.
- Choose File Search for recurring retrieval across an indexed corpus, and direct file input when the request-specific path better fits the file’s size, location, and usage pattern.
- Test the API surface and SDK version used by the application, then inspect returned annotations and verify consequential answers against the underlying files.
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