Connect Uploadcare and Pinecone to Build Intelligent Automations
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Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
File Uploaded
Triggered when a new file is uploaded to Uploadcare.
Do thisActions
Action is the task that follows automatically within your Uploadcare integrations.
Upload File
Uploads a new file to Uploadcare.
Create an index
Create an index for vectors created with an external embedding model.
Generate vectors
Generate vector embeddings for input data.
Delete an Index
Removes a specified index by its ID or name.
Fetch Index by Name
Returns details and structure of a specified index.
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Frequently Asked Questions
To start, connect both your Uploadcare and Pinecone accounts to viaSocket. Once connected, you can set up a workflow where an event in Uploadcare triggers actions in Pinecone (or vice versa).
Absolutely. You can customize how Uploadcare data is recorded in Pinecone. This includes choosing which data fields go into which fields of Pinecone, setting up custom formats, and filtering out unwanted information.
The data sync between Uploadcare and Pinecone 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 Uploadcare and Pinecone. 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 Uploadcare
Uploadcare is a comprehensive file management and delivery platform that provides tools for uploading, processing, and delivering files efficiently. It offers a robust API and a suite of tools to handle file uploads, transformations, and optimizations, making it ideal for developers and businesses looking to streamline their media management processes.
Learn MoreAbout Pinecone
Pinecone is a vector database designed to provide fast and scalable similarity search and machine learning applications. It enables developers to build high-performance applications that require real-time vector search and retrieval, making it ideal for AI-driven solutions.
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