
When this happens...
New Document Created

Automatically do this!
Create a Container Registry
Create a Template
Find a Container Registry Auth
Delete a Container Registry Auth
Delete a Template
Create Registry Authentication
When this happensTriggers
A trigger is an event that starts a workflow.
New or updated document alert
Action is the task that follows automatically within your Dust integrations.
Send a message to a Dust a gent and retrieve the answer.
Upload document to data source
Search for agent configurations by name in the workspace.
Show all spaces in workspace
List data sources in a space
List documents in data source
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To start, connect both your Dust and RunPod accounts to viaSocket. Once connected, you can set up a workflow where an event in Dust triggers actions in RunPod (or vice versa).
Absolutely. You can customize how Dust data is recorded in RunPod. This includes choosing which data fields go into which fields of RunPod, setting up custom formats, and filtering out unwanted information.
The data sync between Dust and RunPod typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Dust and RunPod. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
Dust.tt is a comprehensive workflow automation platform designed to seamlessly integrate and manage data across various sources. It provides robust tools for automating document workflows, data management, and API integrations, helping businesses streamline their operations. Whether it's cloud storage, document handling, or real-time data syncing, Dust.tt allows users to efficiently manage data sources and automate processes to boost productivity.
Learn MoreRunPod is a platform that provides cloud-based computing resources for running and managing machine learning models, data processing tasks, and other computational workloads. It offers scalable and efficient solutions for developers and data scientists to deploy and execute their projects seamlessly.
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