Connect Conversion and TimescaleDB to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Conversion
Choose an Action
Automatically do this!
- Request a new Action for TimescaleDB
Enable Integrations or automations with these events of Conversion and TimescaleDB
Enable Integrations or automations with these events of Conversion and TimescaleDB
Actions
Extract and Text from Document/Image URL
Extracts and converts text from PDF, DOC, or image URLs into readable text content.
Generate Image from text prompt
Generates Image from given text prompt.
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Frequently Asked Questions
To start, connect both your Conversion and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Conversion triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Conversion 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 Conversion 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 Conversion 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.
About Conversion
Conversion enables seamless conversion of various document formats (PDF, DOC) and images into text. Simply provide a URL for your PDF, DOC, or image file, and the plug will extract and convert the content into readable text. Ideal for users looking to quickly transform content from documents and images into an editable format for further processing or analysis.
Learn MoreAbout TimescaleDB
TimescaleDB 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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