The arrival of File Search to the ecosystem of Gemini API It has changed the game for those who want to create RAG applications without infrastructure conflicts. With this managed service, Google automates file storage, chunking, embedding, and dynamic context injection in generation time, so you can focus on building product and not on setting up pipelines.
Beyond document ingestion, File Search provides advanced semantic searchCompatibility with various formats, automatic citations, and a simple pricing structureAnd if you need up-to-date information from the web, the API also offers that. google_search for grounding with Google Search, with very useful verification metadata for interfaces with traceable appointments.
What exactly is File Search in the Gemini API?
File Search is a fully managed RAG solution Integrated into the Gemini API. In practice, you upload your files (or import them from the file service), and the system takes care of the rest. break them into fragments, generate embeddings, index them in a File Search Store and use them as a basis for responding to user queries through vector search.
The goal is that you don't have to deal with Vector bases, indexing queues, or chunking strategies on your own. The tool is based on Google's embedding model (for example, gemini-embedding-001) and integrates natively with generateContentwhere you declare the File Search tool and the store to check.
How it works: from document to contextual response
The conceptual flow is simple, although there's a lot of substance behind it. First, your documents become numerical representations (embeddings) that capture the meaning. These vectors are stored in a specialized store. Then, when you send a query, the API converts the prompt into another embedding and executes a semantic search to recover the most relevant pieces.
Finally, in the call to generateContent with the tool of FileSearch, you add one FileSearchRetrievalResource which points to the specific store. With that, the model knows that it must recover context from your store and use it to support your response. All of this happens without you having to schedule manual recovery or use external services.
Uploading and importing documents: two compatible paths
To bring data to your store, you have two options. If you want to get straight to the point, use the API. uploadToFileSearchStore to upload the file directly and index it in a single operation. If you prefer to separate steps, you can upload the file using the Files API and then import it to importFile to the File Search Store.
When you choose to upload and import all at once, a File temporary as a reference to the raw document; that The object is removed after 48 hours.However, the indexed data remains in the store until you decide to delete it. If You use Files API And then you import; the pipeline goes through file storage before the embeddings phase.
Chunking control: precision and overlap
By default, the API decides on a strategy of intelligent slicingBut if you need to fine-tune, you can specify chunking_config with parameters such as maximum tokens per shard y overlapping tokensWith fewer tokens per chunk you'll gain granularity in the search; with more, you'll retain more context by fragment.
This fine control is useful in cases such as Source code, extensive papers, or technical manualswhere it is advisable to adjust the balance between retrieval accuracy and contextual continuity.
File Search Stores: Persistence, Scope, and Management
A File Search Store is the persistent container where the File Search Stores reside. processed embeddingsUnlike raw Files API files (which disappear after 48 hours), content imported into the store It is retained until explicitly deletedYou can create multiple stores to organize your knowledge domains, and their names are globally unique.
The API of FileSearchStore it allows you create, list, get and delete storesAdditionally, there is a Documents API for managing content within each store, and you can attach custom metadata (key-value pairs) to your files to filter searches by subsets. For the filter, the syntax is used of filter lists described in google.aip.dev/160.
Detailed API usage flow
In operational terms, the typical process follows three steps. First, You create the File Search Store. After, upload and import files (Or you upload and then import). Finally, questions to the model with generateContent indicating the tool FileSearch and the destination store through FileSearchRetrievalResource.
In JavaScript/TypeScript environments, a common practice is to use concurrent operations (for example, Promise.all) to load multiple files at once and monitor operation.done Before continuing. It is also common to search for the store by its displayName (human-readable) if you don't remember the identifier fileSearchStores/....
Document management: find, update and delete
Within a store, it is sometimes useful to locate a specific document by its displayName to manage it. One important detail: the documents are immutable after indexing. If you need to update them, the recommended pattern is delete and upload again the new version.
As an operational practice, many workflows automate this cycle: search → delete → uploadAnd when you're done with the resources (for example, in development), remember that there's a limit to 10 stores per projectSo it's a good idea to clean them if you no longer need them.
Citations and verification: grounding in your documents
A key advantage of File Search is that the model's responses can include automatic citations which indicate the fragments of your documents used to support the output. This traceability appears in the attribute grounding_metadata of the answer and is crucial for audit, verification and trust in business environments.
This way, when the assistant responds, you'll be able to show precise references to the relevant parts of your files, making it easier to review claims and build interfaces with quotes online.
Supported file formats
File Search supports a wide variety of formatsAmong the most common are application/pdf, application/vnd.openxmlformats-officedocument.wordprocessingml.document (DOCX), application/vnd.openxmlformats-officedocument.spreadsheetml.sheet (XLSX), application/vnd.openxmlformats-officedocument.presentationml.presentation (PPTX), application/json, application/xml, application/zip, in addition to many languages of programming y text types , the text/plain, text/html, text/css, text/csv, text/markdown, text/javascript, text/yaml, etc.
It also covers numerous specific types (for example, code and scripts , the application/x-php, application/x-powershell, application/x-sh, application/x-tex, application/x-zsh, and textual , the text/x-python, text/x-java, text/x-ruby-script, text/x-rust, text/x-go, text/x-kotlin, text/x-sql, text/x-c, text/x-csharp, text/x-swift, text/x-tex, text/x-scss, text/x-tcl, text/x-asm(among many others). If your case requires an unusual type, it is most likely that it is contemplated in the official documentation.
Service limits and architectural recommendations
To maintain service stability, the API establishes clear limits: the The maximum file size is 100 MBThe total size added in stores depends on the user level: 1 GB Free, Tier 1 10 GB, Tier 2 100 GB y Tier 3 1 TB.
As a performance guideline, it is recommended keep each store under 20 GB To ensure optimal latency. Note that the size of a store is calculated in the backend as the input size. plus the embeddingswhich they usually multiply approximately by three the original data volume.
Prices: simple and predictable
The payment model is direct: You only pay for the creation of embeddings in the indexing.at a price of $0,15 per 1 million tokens (depending on the applicable embedding model cost). storage and generation of embeddings in query They have no additional cost, and the recovered tokens They are billed as normal context tokens upon generation.
This scheme makes budgeting easier: the major cost is concentrated in the initial intake (and in subsequent document updates, if any), minimizing the surprise in consumption by volume of inquiries.
Compatible models for File Search
The tool works with current models in the Gemini family. The reference materials highlight its availability in Gemini 2.5 Pro y Gemini 2.5 Flash for File Search, with support for grounding, metadata filters, and citations in order to build verifiable experiences.
Regarding grounding with Google Search (discussed below), there is a wider range of supported models, and it is specified that the Experimental and Preview Models They are not included.
Grounding with Google Search: current information and web citations
If in addition to your documents you need real-time web contentYou can activate the tool google_searchThe model manages the entire flow: decide whether it is appropriate to search, generate and execute queries, process results, synthesize and return a response with grounding metadata (consultations, results and citations).
When grounding is done correctly, the response includes groundingMetadata with fields like webSearchQueries (consultations used), searchEntryPoint (HTML and CSS required for search suggestions, with usage requirements detailed in the Terms of Service), grounding Chunks (web sources with uri y title) and groundingSupports (fragments that link text segments of the model with indexes of groundingChunks to construct online citations).
Prices and models for grounding using Google Search
The use of google_search invoice for each request that activates that tool, even if the model launches several internal queries for the same request; all of this counts as single billable useThe models compatible with this tool are listed below. Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 2.5 Flash-Lite, Gemini 2.0 Flash, Gemini 1.5 Pro y Gemini 1.5 Flash.
For models prior to version 1.5, there is a legacy tool. google_search_retrieval with mode dynamicwhere you configure a dynamic_threshold (0.0–1.0) and the model decides whether or not to perform the search based on its confidence. where you need up-to-date information.
Use cases: from internal assistants to customer support
File Search is geared towards business environments where privacy, accuracy, and traceability matter. It is useful for knowledge workers who interrogate proprietary documentation, for automate support with answers cited from manuals and policies, or to accelerate search and review in information-intensive sectors (legal, health, financial).
It also accelerates flows of development and debugging by allowing interaction with codebases or technical specifications. Its combination with other Gemini capabilities (such as code or function execution) enables rich integrations in compliance, auditing, or analytics processes.
Operations and status: working with the API
The REST surface exposes methods such as fileSearchStores.create (creates an empty store), fileSearchStores.delete (removes a store), fileSearchStores.get (obtains information from a store), fileSearchStores.list (lists user stores) and importFile (it matters a File (from file service to a store). The endpoints of operations allow you to check the status of long-term operations, with application forms that in several cases go empty according to the specification.
On the direct uphill route (uploadToFileSearchStoreA set of operations Specifically designed to gauge progress. This pattern is typical: spears the operation, you check periodically if it has finished and, when complete, you continue with the flow (for example, launching RAG queries).
Best practices: filters, labeling, and latency
If you anticipate many inquiries, label your documents with metadata and uses metadataFilter to restrict retrieval to relevant subsets. Keep the stores within the size recommendation It helps with latency and stability. And if you're going to charge full foldersIt takes advantage of the concurrent rise to reduce ingestion times.
In technical scenarios, define chunking strategies appropriate to the content: for example, shorter chunks with moderate overlap for bar code (better precision in functions/classes), and somewhat longer chunks in narrative documentation to preserve semantic context.
Compatibility with other Gemini tools
Grounding with Google Search can be combined with URL context (to provide specific URLs) and with execution code or additional tool functions depending on the use case. This allows you to mix and match. insider knowledge File Search with updated information from the web, always maintaining citations and traces of sources.
When designing the UX, take advantage of groundingSupports y grounding Chunks to offer clickable citations alongside the model text. This is the clearest way for users to verify the source of each statement.
File Search in the Gemini API offers a very direct way to equip your applications with well-reasoned answers in your own data, minimizing operational friction. Its combination of Predictable indexing costs, support for various formats, metadata filters, automatic citations, and grounding with Google Search It forms a production-ready stack that takes the pressure off RAG deployment in teams of all sizes.
Passionate writer about the world of bytes and technology in general. I love sharing my knowledge through writing, and that's what I'll do on this blog, show you all the most interesting things about gadgets, software, hardware, tech trends, and more. My goal is to help you navigate the digital world in a simple and entertaining way.