Google’s new Files API is a quiet but very big deal for anyone who wants to use AI with their own documents, images, audio, or video. It takes a process that used to be messy and developer‑heavy and turns it into something much closer to “upload a file and ask questions about it.”
Below is a non‑technical walkthrough of what it is, why it changes how Retrieval‑Augmented Generation (RAG) works, and what it could mean in business, government, and nonprofit settings. I will note that it is in public preview phase and does have a time limitation to it’s usefulness so far. I am hoping this is worked around.
What the Files API is
The Files API is a new way to upload and manage files for Google’s Gemini AI models so the model can “see” and work with your actual content. Instead of pasting text or trying to cram large documents into a single prompt, you upload the file once and then reference it in your AI requests.
Google describes it simply: you use the Files API to upload media files—documents, images, audio, videos, and other supported types—and then you can include those files in prompts such as “summarize this report” or “describe this audio clip.” Technically, the file is stored in your project for up to 48 hours and can be used multiple times in that window, which means you are not paying or waiting to re‑upload it for every question.
The same interface works across multiple programming languages and tools (Python, JavaScript, Go, REST), but non‑technical users will most often encounter it inside products that “sit on top” of this API—dashboards, chatbots, or apps that your IT team or vendors build.
Why this matters for RAG
RAG (Retrieval‑Augmented Generation) is a fancy way of saying: “Let the AI read your own files first, then generate an answer.” Traditionally, doing this well required custom infrastructure: building an index of your files, managing storage, and carefully slicing up documents so they could fit into the model’s input limit.
The Files API changes the feel of that process in three key ways.
- You can upload larger files directly to Gemini when the total request (prompt + files) is bigger than 20 MB, instead of hitting limits every time you ask a question.
- Once a file is uploaded, you can reference it repeatedly by name or URI, so conversations can build on the same source materials over 48 hours without re‑uploading or re‑processing.
- The same mechanism works not only for text documents but also for images, audio, video, and other media, making RAG truly multimodal—“retrieve this PDF, these slides, and this audio recording, then answer my question.”
From a non‑technical perspective, this means a RAG workflow becomes: upload → ask questions → refine, instead of “copy‑paste, get an error, cut it down, repeat.” It lowers the cost and complexity of letting AI work with real‑world, messy content.
Practical behaviors of the Files API
There are a few practical details that shape how you will use this in real applications.
- Size and storage limits: You can store up to 20 GB of files per project, and each individual file can be up to 2 GB. That is enough for many realistic document collections, training decks, or audio archives in a short‑term workspace.
- Expiration and cleanup: Files are kept for 48 hours, after which they are automatically deleted; developers can also delete them sooner via the API. This time‑bounded storage is helpful for privacy and temporary “workspaces” built around a specific task or project.
- Cost and availability: Google notes that the Files API itself is available at no cost in all regions where the Gemini API is available, which makes experimentation and prototyping much easier.
Together, these behaviors make the Files API a good backbone for “session‑based” RAG: the system pulls in your files for a period of focused work and then cleans up after itself.
How developers actually use it (in plain language)
Under the hood, a developer (or tool vendor) does three things with the Files API.
- Upload files
They send a file—say, a PDF or MP3—using a simple command, and the API returns a file object with a name and URI. That file object becomes the handle the system uses later to refer to the content. - Refer to files in prompts
When they ask the model a question, they include that file object along with the text prompt, such as: “Describe this audio clip” plus the uploaded audio file. The model then uses the file content as part of its reasoning. - Manage files (list, inspect, delete)
The API lets them list all uploaded files, fetch metadata (to confirm that an upload succeeded), and delete files manually if needed. This allows building interfaces where you see what you’ve uploaded and what’s being used.
As a non‑technical user, the important takeaway is that this makes it much easier for teams to advance on projects that require temporary access to large stores of knowledge.
Business use case: smarter document workflows
In a business context, the Files API can quietly transform how staff interact with reports, proposals, and internal documentation. Think of a sales or operations team that constantly juggles large slide decks, contracts, policy PDFs, and recorded calls.
With a RAG‑style system built on the Files API, a typical workflow could look like this.
- A team member drags and drops a stack of files—last quarter’s sales report, a pricing spreadsheet, and a customer contract—into an internal AI assistant.
- Behind the scenes, the app uploads these via the Files API, gets back file identifiers, and passes them into Gemini with prompts like “Summarize the main risk factors mentioned in this contract, cross‑referenced against the pricing model in the spreadsheet.”
- The assistant then generates a plain‑language risk summary, plus a suggested email to the client, all grounded in the uploaded documents.
Because the Files API supports up to 2 GB per file and 20 GB per project, the assistant can handle collections of sizable PDFs, slide decks, or even meeting recordings within a focused window. And because files vanish after 48 hours unless intentionally re‑uploaded, the workspace can be designed to align with internal data handling policies.
The net effect for the business user: less time hunting through folders and more time asking direct questions like “What changed between last quarter’s and this quarter’s contract terms?” and getting answers that actually reference the right files.
Government use case: time‑boxed analysis of sensitive material
Government agencies often have to review sensitive documents, reports, and media in time‑bound projects, where long‑term storage in third‑party systems may be restricted. The Files API’s 48‑hour window and manual deletion options make it well‑suited for controlled “analysis sessions.”
Imagine a city department using an internal AI assistant to review public‑meeting transcripts, citizen feedback forms, and photos of infrastructure issues.
- Analysts upload meeting transcripts (text), scanned comment cards (images or PDFs), and photos of damaged sidewalks or signage (images).
- These are uploaded via the Files API, then Gemini is prompted to identify recurring citizen concerns, classify the types of issues, and draft a briefing memo for leadership.
- Once the analysis is complete, the system automatically deletes the files after 48 hours, or earlier if required, while preserving only high‑level insights in the agency’s internal systems.
Because the same interface works for text, images, and audio, teams can integrate diverse public input channels—comments, recordings, photos—without building different pipelines for each media type. For non‑technical staff, this can look like a simple “drop zone” and a chat window, while compliance staff benefit from predictable retention behavior.
Nonprofit use case: program insights from messy data
Nonprofits often live in spreadsheets, grant documents, training materials, and case notes that are spread across different formats and tools. The Files API can underpin an “insight assistant” that helps program staff and leaders understand their work more quickly.
Consider a nonprofit that runs family support programs and needs to prepare a grant report summarizing outcomes for the past six months.
- A staff member uploads a CSV or Excel export of attendance records, a PDF of the grant agreement, and a set of training slide decks for volunteers.
- Using the Files API, these are all stored for the duration of the work session and passed into Gemini with prompts like: “Compare our actual activities against the commitments in this grant agreement, and draft a narrative summary for funders.”
- The AI generates a draft report that highlights how program activities match (or diverge from) the agreed‑upon outputs and outcomes, pointing back to the relevant documents for verification.
Because the Files API is free to use where Gemini is available, small nonprofits can experiment without incurring separate file‑storage fees at the API level, which is especially important for organizations with tight budgets. And the 20 GB per‑project limit is often sufficient for the working set of documents involved in a single reporting or evaluation cycle.
Why this is a significant development
Taken together, these features make the Files API more than just “file upload” plumbing; it is a foundation for more grounded, practical AI systems that work with your actual content rather than generic internet training data. It simplifies multimodal RAG—across text, images, audio, and video—and it does so in a way that fits real organizational needs around storage limits, temporary workspaces, and cost.
For non‑technical users, the significance is simple: more AI tools will feel like working with a smart colleague who can read what you upload and stay “on the same page” with you for the duration of a project. As this public preview matures, expect to see more business, government, and nonprofit applications that quietly rely on the Files API to make your AI experiences feel both more powerful and more grounded in the documents that matter to you.
References
Google. (2023, January 11). Files API | Gemini API – Google AI for Developers. Google AI. https://ai.google.dev/gemini-api/docs/files
Google. (2025, November 5). Introducing the File Search Tool in Gemini API. The Keyword: Google Blog. https://blog.google/products/gemini/file-search-gemini-api
Google. (2025, September 7). Gemini API reference | Google AI for Developers. Google AI. https://ai.google.dev/api/gemini
Google. (2025, November 5). Using files | Gemini API – Google AI for Developers. Google AI. https://ai.google.dev/gemini-api/docs/using-files
Google. (2025, November 9). Gemini API | Google AI for Developers. Google AI. https://ai.google.dev/gemini-api
