Connect Sulekha and GPT-trainer to Build Intelligent Automations
Choose a Trigger
When this happens...
New Leads
Choose an Action
Automatically do this!
Create QA Source
Create URL Source
Create Source Tag
Ready to use Sulekha and GPT-trainer automations
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Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
New Leads
Triggers when the New Lead is captured.
Do thisActions
Action is the task that follows automatically within your Sulekha integrations.
Create QA Source
Create a QA source for a chatbot specified by chatbot uuid
Create URL Source
Creates a URL source for a chatbot specified by chatbot uuid.
Create Source Tag
Create a source tag for a chatbot.
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Frequently Asked Questions
To start, connect both your Sulekha and GPT-trainer accounts to viaSocket. Once connected, you can set up a workflow where an event in Sulekha triggers actions in GPT-trainer (or vice versa).
Absolutely. You can customize how Sulekha data is recorded in GPT-trainer. This includes choosing which data fields go into which fields of GPT-trainer, setting up custom formats, and filtering out unwanted information.
The data sync between Sulekha and GPT-trainer 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 Sulekha and GPT-trainer. 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 Sulekha
Sulekha is a comprehensive online platform connecting users with local service providers across various categories, including home services, education, and events. It facilitates easy access to trusted professionals and businesses, enhancing convenience and efficiency for users seeking specific services.
Learn MoreAbout GPT-trainer
GPT Trainer is a platform designed to enhance and customize the training of AI models, particularly those based on the GPT architecture. It provides tools and resources for developers and researchers to fine-tune and optimize AI models for specific tasks and applications.
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