
When this happens...

Automatically do this!
Enable Integrations or automations with these events of Luma AI and TimescaleDB
Retrieve a list of generations with optional filtering and sorting.
Initiate a new generation with the provided prompt
Upscale a generation by its ID.
Add audio to a generation by its ID
Remove a specific generation by its ID
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Gain insights into how viaSocket functions through our detailed guide. Understand its key features and benefits to maximize your experience and efficiency.

Unlock your team's potential with 5 straightforward automation hacks designed to streamline processes and free up valuable time for more important work.

Workflow automation is the process of using technology to execute repetitive tasks with minimal human intervention, creating a seamless flow of activities.
To start, connect both your Luma AI and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Luma AI triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Luma AI data is recorded in TimescaleDB. This includes choosing which data fields go into which fields of TimescaleDB, setting up custom formats, and filtering out unwanted information.
The data sync between Luma AI and TimescaleDB 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 Luma AI and TimescaleDB. 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.
Lumalabs AI is a cutting-edge platform that leverages artificial intelligence to provide innovative solutions for various industries. It focuses on enhancing productivity, automating processes, and delivering intelligent insights to drive business growth.
Learn MoreTimescaleDB is a powerful time-series database designed for fast ingest and complex queries, making it ideal for handling time-series data efficiently. It extends PostgreSQL, providing scalability and performance enhancements specifically for time-series workloads.
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