Connect touchSMS 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 SMS Received
Triggers when your touchSMS account receives a new SMS and provides the message details (sender, text, time).
Do thisActions
Action is the task that follows automatically within your touchSMS integrations.
Create Contact
Add a new contact in touchSMS using a mobile number and optional details (first name, surname, email, group). Can update the contact if the number already exists.
Send SMS
Send a text message to the specified phone number using your approved sender ID.
Send Verification SMS
Send an SMS verification code to a recipient using the touchSMS verification template.
Add Opt-Out Number
Adds a mobile number to the touchSMS opt-out list to stop SMS delivery.
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 touchSMS and Pinecone accounts to viaSocket. Once connected, you can set up a workflow where an event in touchSMS triggers actions in Pinecone (or vice versa).
Absolutely. You can customize how touchSMS 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 touchSMS 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 touchSMS 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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