Connect EKM and Pinecone to Build Intelligent Automations
Choose a Trigger
When this happens...
New Customer
New Order
New Product
Choose an Action
Automatically do this!
Create an index
Generate vectors
Delete an Index
Fetch Index by Name
Ready to use EKM and Pinecone automations
Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
New Customer
Runs when a new customer is created.
New Order
Trigger when new order is created in your account.
New Product
Trigger when new product is created in your account.
Do thisActions
Action is the task that follows automatically within your EKM integrations.
Create Customer
Add a new customer to the EKM account.
Create Product
Add a new Product to the EKM account.
Update Product
Update the details of an existing product.
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.
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Frequently Asked Questions
To start, connect both your EKM and Pinecone accounts to viaSocket. Once connected, you can set up a workflow where an event in EKM triggers actions in Pinecone (or vice versa).
Absolutely. You can customize how EKM 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 EKM 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 EKM 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 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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