Gemini File Search lets a Go application retrieve document passages without you provisioning and maintaining a separate vector database. Google hosts the store and handles document chunking, embedding, indexing, and retrieval. The “two calls” description applies to the core flow after a store exists—not to every request required for first-time setup.
What “no vector database” means here
File Search is a managed retrieval-augmented generation (RAG) feature in the Gemini API. You provide documents and a question; Google’s service indexes the documents and retrieves relevant content to ground a model response. Google describes the feature as one that “imports, chunks, and indexes your data to enable fast retrieval of relevant information based on a provided prompt.” Google AI for Developers’ File Search guide explains the hosted workflow.
You avoid operating a separate vector database for this implementation, not the underlying retrieval infrastructure: Google operates the hosted File Search store. That reduces the components you need to provision and maintain, while tying your workflow to Gemini’s API and its supported formats, models, and controls.
Is it really two calls?
Only if you define the scope. Once the store has been created and populated, the application’s useful runtime flow is to send a question with the store specified and receive a grounded model response. Store creation is an additional setup request. And Google’s documented Go ingestion example uses a Files API upload followed by an import into the store, then waits for that asynchronous import to finish. First-time setup therefore involves more than two API operations. The File Search Stores reference documents the store resource.
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Google also documents direct upload to a File Search store, which can reduce distinct ingestion steps compared with the Files API upload-and-import route. It does not eliminate the need to create a store. Check the current File Search guide and Go SDK documentation for the supported method signatures and configuration before implementing; the flow below describes the documented sequence rather than a drop-in, version-pinned program.
Build the Go workflow
Google’s Go example uses the google.golang.org/genai package. It creates a client, uploads a local file, creates a File Search store, imports the uploaded file, polls the long-running operation until it is done, and then makes a model request that names the store in file_search_store_names. The guide’s example uses models/gemini-embedding-2; verify the current model listing and SDK API before choosing a model for a new project.
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- Initialize the client. Add the Google Gen AI Go SDK to your project and configure the client with the credentials and API access required by your Gemini API setup.
- Create the hosted store. Call
FileSearchStores.Createand keep the returned store name. This is a setup step, not part of each question. - Upload and import a document. In the documented sample route, call
Files.UploadFromPath, then pass the uploaded file toFileSearchStores.ImportFilefor the store. Alternatively, consult the guide’s direct-upload flow if you want to avoid a separate raw-file upload step. - Wait for indexing to complete. Import is asynchronous in the sample. Poll the returned long-running operation while it is not done; do not query on the assumption that a newly imported document is already available.
- Ask a question using the store. Make a model interaction request and set
file_search_store_namesto the store name. The model can then use retrieved store content to ground its answer.
For the complete current Go sample and request shapes, use Google’s File Search guide. The distinction between a raw Files API object and a store matters for both lifecycle and architecture: the raw upload is an input to ingestion, while the hosted store is the persistent indexed collection queried by the application.
What it costs—and what “cheap” does not prove
Google’s File Search documentation says storage and embedding generation at query time are free. Charges apply when embeddings are created during a file’s initial indexing, as well as for normal Gemini model input and output tokens. Those are different cost components: indexing depends on what you ingest, while model charges depend on your interactions and selected model.
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The published information cited here does not establish a numeric total or prove that File Search costs less than a particular vector database. Estimate for your own document volume, how often you re-index, and expected query and response usage, then verify the current applicable rates. “No vector DB” is an operational simplification; it is not by itself a cost comparison.
Retention: raw uploads and indexed store data are different
Google says raw Files API objects are deleted after 48 hours. Imported File Search store data remains until you delete it or the model is deprecated; the guide says store embeddings have no time-to-live. Do not treat the temporary upload as the retained copy of your knowledge base. Plan store deletion and data lifecycle around the indexed store itself. See the File Search documentation for the product’s current retention descriptions.
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Supported content and model choice
For text, Google distinguishes the text embedding model gemini-embedding-001 from multimodal gemini-embedding-2. For image search, the guide specifies PNG or JPEG images up to 4K × 4K pixels and requires models/gemini-embedding-2 when the store is created. Audio and video are not currently supported according to the same guide. Confirm the current supported models and limits before designing a corpus that depends on a particular modality: Google AI for Developers: File Search.
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When this approach fits
- Consider File Search when you want a managed document-ingestion and retrieval path and do not want to run a separate vector database for the application.
- Check the format limits first if your corpus includes audio or video, or image types or dimensions outside the documented PNG/JPEG and 4K × 4K bounds.
- Evaluate lifecycle and coupling if you need a particular retention policy, retrieval control, or model flexibility. A hosted store is convenient, but the workflow depends on Google’s service and supported configuration.
- Compare workload costs rather than labels if choosing between hosted retrieval and self-managed infrastructure. The available billing description identifies free storage and query-time embedding generation plus billable initial indexing and model tokens, but does not decide which option will be cheaper for your workload.
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