Connect Figma and Pinecone to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Figma
Choose an Action
Automatically do this!
Create an index
Generate vectors
Delete an Index
Fetch Index by Name
Enable Integrations or automations with these events of Figma and Pinecone
Enable Integrations or automations with these events of Figma and Pinecone
Actions
Create Comment
Create comment on a file.
Add Comment Reaction
Create a new comment reaction on a file comment.
Delete Comment Reaction
Deletes a specific comment reaction.
Delete Comment
Deletes a specific comment
Create an index
Create an index for vectors created with an external embedding model.
Generate vectors
Generate vector embeddings for input data.
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Frequently Asked Questions
To start, connect both your Figma and Pinecone accounts to viaSocket. Once connected, you can set up a workflow where an event in Figma triggers actions in Pinecone (or vice versa).
Absolutely. You can customize how Figma 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 Figma 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 Figma 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 Figma
Figma is a collaborative interface design tool that allows teams to create, prototype, and share designs seamlessly. It is widely used by designers and developers to work together in real-time, making the design process more efficient and effective.
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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