Connect Google Chat 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.
New Message
Trigger when a new message received/sent in google chat space.
Do thisActions
Action is the task that follows automatically within your Google Chat integrations.
Send Message
Sends a message to a specified Google Chat space.
Create a Card Message
create a card message to a specified Google Chat space.
List all Messages
Retrieve a list of messages .
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 Google Chat and Pinecone accounts to viaSocket. Once connected, you can set up a workflow where an event in Google Chat triggers actions in Pinecone (or vice versa).
Absolutely. You can customize how Google Chat 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 Google Chat 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 Google Chat 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 Google Chat
Google Chat, bringing powerful features directly to your conversations. Whether you're working in a team, managing projects, or coordinating with clients, helps you stay organized, streamline communication, and boost productivity.
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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