Gemini File Search lets a Go application retrieve document passages without you provisioning and maintaining a separate vector database: Google manages the hosted store, chunking, embeddings, indexing and retrieval. The “two calls” shorthand applies to the core flow after a store exists—not to every request in a first-time setup. Google’s guide describes the feature as importing, chunking and indexing data for fast retrieval based on a prompt: Google AI for Developers: File Search.
What “two calls” means—and what it leaves out
For an application with a provisioned store and indexed documents, think of the ongoing RAG path as two stages: submit a prompt that references the store, then receive a model response grounded in retrieved material. Provisioning the store is an additional setup request. And Google’s Go example for first-time ingestion performs more than two operations: it uploads a file through the Files API, creates a store, imports the file into that store, waits for the import operation to finish, and then makes a model interaction.
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Google’s File Search Stores reference describes the store as a distinct resource: File Search Stores API reference. Direct upload to a File Search store is also documented in the feature guide, and can reduce the separate ingestion steps. It does not eliminate the need to create a store.
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Build the Go ingestion and query flow
The official Go example uses the google.golang.org/genai package. The outline below preserves its order while omitting version-sensitive method signatures: use the current guide for exact signatures and configuration, since SDK interfaces can change.
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- Initialize the client. Create a GenAI client using the configuration for your Gemini API access.
- Upload a source file. In the example’s Files API route, call
Files.UploadFromPathwith the local document path. - Create the hosted store. Call
FileSearchStores.Createand retain the returned store name for import and query requests. - Import the uploaded file. Call
FileSearchStores.ImportFile, associating the uploaded file with the store. - Wait for indexing to finish. Import is a long-running operation in the sample. Poll the operation until it is done before allowing queries that depend on the new document.
- Query with the store attached. Make a model interaction request with
file_search_store_namesset to the store name. The model can then use retrieved content to ground its response.
Google’s guide shows models/gemini-embedding-2 in its Go/store examples. Treat that as a configuration to verify against the current model listing and feature documentation when implementing, rather than assuming a model name or capability will remain unchanged.
Do you need a vector database?
Not a separate one that you operate for this integration path. File Search is a managed retrieval service, so Google handles the indexing infrastructure behind the hosted store. That reduces the infrastructure your application team must run; it does not mean there is no index or vector infrastructure behind the service.
This is an operational choice, not a demonstrated performance or cost winner over self-managed retrieval. The available documentation does not establish a workload-level comparison. If you are choosing between approaches, assess retrieval controls, supported content, data lifecycle, API and model coupling, operational responsibility, and your own measured costs before deciding.
What it costs—and what “cheap” can safely mean
Google’s billing description says storage and embedding generation at query time are free. Charges apply to generating embeddings when files are first indexed, as well as ordinary Gemini model input and output tokens. This explains why File Search can avoid some infrastructure management costs, but it does not establish a guaranteed low bill or savings against a vector database. No numeric total or comparative cost is provided by the cited documentation.
Estimate spending from the volume of documents you initially index and the model tokens used by your actual query patterns, then check current applicable rates in Google’s documentation before deploying. Do not treat free storage or query-time embeddings as meaning that the whole RAG workflow is free.
Understand file retention before importing data
The Files API upload object and the imported store content have different lifecycles. Google says raw Files API files are deleted after 48 hours. Once imported, File Search store data remains until you delete it or the model is deprecated; the guide says store embeddings have no TTL. Plan cleanup around the retained store contents, not just the temporary upload.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check supported content and embedding needs
- Audio and video: Google’s guide says these formats are not currently supported.
- Images: Multimodal image search supports PNG and JPEG images up to 4K × 4K pixels, and requires
models/gemini-embedding-2when creating the store. - Embedding choice: The guide distinguishes text embeddings with
gemini-embedding-001from multimodal embeddings withgemini-embedding-2. Confirm current model availability and requirements for the content you intend to index.
For implementation details and current capability limits, consult Google’s File Search guide.
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