> For the complete documentation index, see [llms.txt](https://docs.diaflow.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.diaflow.io/workflow-builder/nodes/public-ai-llm-models/google-gemini-cloud.md). # Google Gemini Cloud The **Google Gemini Cloud** node lets you use Gemini inside a Diaflow workflow with your own API key. It is a good fit when your team wants a flexible text model for summaries, answers, and business content based on larger amounts of context.
You can use this node for tasks such as reviewing long documents, summarizing meeting content, answering questions from business material, and producing structured outputs for the next workflow step. {% hint style="info" %} Diaflow runs the workflow. Google charges usage to your own Gemini account. {% endhint %} ## What this node does The node takes your instruction, sends it to the selected Gemini model, and returns the result to the next step in your workflow. In most business workflows, that result is text. You can also pass content from earlier steps into this node. For example, you can send large notes, source documents, retrieved knowledge, or user input and ask Gemini to turn them into a concise business answer. ## When to use it Use **Google Gemini Cloud** when you want Diaflow to work with your own Gemini setup. This is useful when your business needs a provider that can work well across everyday writing, summarization, and context-heavy business tasks. Common use cases include: * Summarize long reports, transcripts, or meeting notes. * Answer questions from internal business content. * Turn large inputs into clean summaries, categories, or structured output. ## Before you start Make sure you have: * An active Google Gemini account. * A valid Gemini API key. * Permission to use the model you want. ## How to set it up Set up the node in this order: 1. In **Credentials**, choose your saved Gemini key. 2. In **Model**, choose the Gemini model that fits your task. 3. In **Prompt**, describe what you want the model to do. 4. Run the node and review the result.
If you want the model to work from earlier workflow data, insert that data into the prompt with `@`. ## What each field means ### Credentials This is where you select the Gemini API key your team wants to use. If the correct key is not listed, add it first, then refresh the list if needed. ### Model This is where you choose the Gemini model. The list depends on the key and account you connected. For most business users, the choice is simple: * Choose a general text model for writing, summaries, and business Q\&A. * Choose a stronger model when you need better output quality or deeper analysis. * Test with a real business document before using the node at scale. If you are unsure, start with a general-purpose model and compare results using real workflow data. ### Prompt This is the instruction you give the model. Write it in plain language and focus on the result you want. A strong prompt usually includes: * the task * the context * the expected format For example: ``` Summarize this meeting transcript for department leads. Highlight the main decisions, blockers, and next steps. Return the result as 3 sections with bullet points. ``` If your workflow already has data from previous steps, insert that data into the prompt with `@` so the model uses that content directly. ### Enable caching Caching reuses a previous result when the same input runs again. This can help save time and reduce repeated calls. Use caching when the same request is likely to run more than once and the answer does not need to change every time. ### Caching time This controls how long the cached result stays available. Use a shorter time when the source content changes often. Use a longer time when the same request repeats across multiple runs. ### Max token This limits how long the model response can be. Use a lower value for short answers. Increase it when you need fuller summaries, longer drafts, or more detailed analysis. ## What you get back The node returns the model’s result to the next step in your workflow. In most cases, that result is text. You can send it to an output node, store it, transform it, or pass it into another business step. ## Tips for business users * Start with one clear task per prompt. * Ask for a specific structure such as summary, decisions, actions, or bullet points. * Use real long-form content during testing. * If the answer is too generic, give more context and a stricter output format. ### Next steps * Compare other providers in [Public AI/LLM Models](/workflow-builder/nodes/public-ai-llm-models.md). * Learn the workflow basics in [Overview](/workflow-builder/overview.md). * Browse more nodes in [Component List](/workflow-builder/component-list.md). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.diaflow.io/workflow-builder/nodes/public-ai-llm-models/google-gemini-cloud.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.