Integrate the Chatbot Builder API with the Python API

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

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Popular Chatbot Builder and Python Actions#

Create Tag with the Chatbot Builder API

Creates a new tag in Chatbot Builder. 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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Delete Tag with the Chatbot Builder API

Deletes a tag from Chatbot Builder. See the documentation

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List Tags with the Chatbot Builder API

Lists all tags in Chatbot Builder. See the documentation

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Overview of Chatbot Builder#

The Chatbot Builder API allows you to create and manage customized chatbots that can interact with users in real-time. Within Pipedream's serverless platform, this API can be harnessed to build powerful workflows that react to messages, automate responses, and integrate with a plethora of other services. With Pipedream, you can set up event-driven processes, making it easy to manage chatbot activities, analyze conversations, and trigger actions in other apps based on chatbot interactions.

Connect Chatbot Builder#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    chatbot_builder: {
      type: "app",
      app: "chatbot_builder",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://app.chatgptbuilder.io/api/accounts/me`,
      headers: {
        "X-ACCESS-TOKEN": `${this.chatbot_builder.$auth.api_access_token}`,
        "Accept": `application/json`,
      },
    })
  },
})

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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