> 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/perplexity-cloud.md). # Perplexity Cloud The **Perplexity Cloud** node lets you use Perplexity inside a Diaflow workflow with your own API key. It is a strong fit when your team wants fast answers, summaries, and research support inside an automated business process.
You can use this node for tasks such as summarizing long content, answering business questions, extracting key points, and turning raw information into concise outputs your team can act on. {% hint style="info" %} Diaflow runs the workflow. Perplexity charges usage to your own Perplexity account. {% endhint %} ## What this node does The node takes your instruction, sends it to the selected Perplexity model, and returns the result to the next step in your workflow. In most business workflows, that result is a text answer, summary, or structured response. You can also pass content from earlier steps into this node. For example, you can send customer requests, uploaded text, internal notes, or knowledge results and ask Perplexity to turn them into a clear business answer. ## When to use it Use **Perplexity Cloud** when you want Diaflow to work with your own Perplexity setup. This is useful when your company already has a Perplexity account, wants direct control over model usage, or needs a provider that fits research-oriented tasks. Common use cases include: * Summarize reports, articles, and long text into key takeaways. * Answer business questions from uploaded or retrieved content. * Turn research material into short insights, action points, or structured output. ## Before you start Make sure you have: * An active Perplexity account. * A valid Perplexity 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 Perplexity key. 2. In **Model**, choose the Perplexity 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 Perplexity API key your team wants to use. If the correct key is not listed, use **Manage** to add it, then use **Refresh** to reload the list. ### Model This is where you choose the Perplexity 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 summaries, answers, and everyday content tasks. * Choose a stronger model when quality matters more than speed or cost. * Test with a real business example before using the node at scale. If you are unsure, start with a general-purpose model and compare the result with your real workflow content. ### Prompt This is the instruction you give the model. Write it in plain language and focus on the outcome you want. A strong prompt usually includes: * the task * the context * the expected format For example: ``` Review this market research summary and list 5 key insights for a sales team. Keep the language simple. Return the result as bullet points. ``` If your workflow already has data from previous steps, insert it into the prompt with `@` so the model uses that content directly. ## Advanced configurations Open **Show advanced configurations** when you need more control. ### 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 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 more detailed summaries or longer written output. ### Output format If you see an output format field such as **Formatted**, use it to guide how the answer should come back. This helps when another node needs a predictable structure. ## 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 question or task per prompt. * Ask for a clear format such as bullets, short summary, table-ready text, or JSON. * Use real documents and real questions during testing. * If the answer feels too broad, make the prompt narrower and more specific. ### 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/perplexity-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.