
When this happens...

Automatically do this!
Create an index
Generate vectors
Delete an Index
Fetch Index by Name
Enable Integrations or automations with these events of Adyen and Pinecone
Create an Adyen checkout session and return the session ID, expiry, and data for redirect or embedded checkout.
Create a new Adyen checkout order to start payment processing and return order details (provider reference, order reference, amount, expiry).
Cancel an existing Adyen order to stop further processing and, when supported, release reserved funds.
Generate an Adyen-hosted payment link for a specified amount and shopper so they can complete payment.
Create an index for vectors created with an external embedding model.
Generate vector embeddings for input data.
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To start, connect both your Adyen and Pinecone accounts to viaSocket. Once connected, you can set up a workflow where an event in Adyen triggers actions in Pinecone (or vice versa).
Absolutely. You can customize how Adyen 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 Adyen 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 Adyen 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.
Adyen is a global payment platform that enables businesses to accept payments online, in-app, and in-store through a single solution. With advanced fraud protection, seamless checkout experiences, and support for major payment methods worldwide, Adyen powers secure and scalable payment processing for leading enterprises.
Learn MorePinecone 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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