Connect PostgreSQL and Pinecone to Build Intelligent Automations
Choose a Trigger
When this happens...
Postgresql (mutiple table)
Choose an Action
Automatically do this!
Create an index
Generate vectors
Delete an Index
Fetch Index by Name
Ready to use PostgreSQL and Pinecone automations
Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
Postgresql (mutiple table)
Get all the data, which was updated in last 15 min, from all the table provided.
Do thisActions
Action is the task that follows automatically within your PostgreSQL integrations.
Execute a Query (advanced)
You can select, create, update or delete row(s) of table via custom SQL query.
Insert row
Adds a new row into the table.
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 PostgreSQL and Pinecone accounts to viaSocket. Once connected, you can set up a workflow where an event in PostgreSQL triggers actions in Pinecone (or vice versa).
Absolutely. You can customize how PostgreSQL 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 PostgreSQL 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 PostgreSQL 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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