Integrate the gpt-trainer API with the Python API

Setup the gpt-trainer API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate gpt-trainer and Python remarkably fast. Free for developers.

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Popular gpt-trainer and Python Actions#

Create Chat Session with the gpt-trainer API

Create a chat session for a chatbot specified by chatbot UUID. See the documentation

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Run Python Code with the Python API

Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.

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Create Chatbot with the gpt-trainer API

Creates a new chatbot that belongs to the authenticated user. See the documentation

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Create Message with the gpt-trainer API

Create a session message for a chatbot session specified by session UUID. See the documentation

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Overview of gpt-trainer#

The gpt-trainer API is a tool designed to train, run, and manage custom GPT-2 and GPT-3 models. It provides endpoints for submitting training data, starting the training process, and generating text from the trained model. With Pipedream's serverless integration platform, you can automate workflows that interact with the gpt-trainer API. You can trigger workflows using webhooks, schedule them, or even run them in response to events from other apps. Integrate the gpt-trainer API with other services on Pipedream to create powerful applications such as automated content creation, personalized messaging, or AI-driven data analysis.

Connect gpt-trainer#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    gpt_trainer: {
      type: "app",
      app: "gpt_trainer",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://app.gpt-trainer.com/api/v1/chatbots`,
      headers: {
        Authorization: `Bearer ${this.gpt_trainer.$auth.api_key}`,
      },
    })
  },
})

Overview of Python#

Develop, run and deploy your Python code in Pipedream workflows. Integrate seamlessly between no-code steps, with connected accounts, or integrate Data Stores and manipulate files within a workflow.

This includes installing PyPI packages, within your code without having to manage a requirements.txt file or running pip.

Below is an example of using Python to access data from the trigger of the workflow, and sharing it with subsequent workflow steps:

Connect Python#

def handler(pd: "pipedream"):
  # Reference data from previous steps
  print(pd.steps["trigger"]["context"]["id"])
  # Return data for use in future steps
  return {"foo": {"test":True}}

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