ChatBotKit

ChatBotKit helps you create conversational AI chatbots with your own data to communicate naturally with users on your website, Slack, Discord and WhatsApp.

Integrate the ChatBotKit API with the Python API

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

Create Conversation with the ChatBotKit API

Creates a new conversation in the bot. 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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Import Dataset with the ChatBotKit API

Imports a specified file into the bot's dataset. See the documentation

 
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Send Conversation Message with the ChatBotKit API

Send and receive a conversation response. See the documentation

 
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Overview of ChatBotKit

ChatBotKit API empowers you to create and manage conversational experiences with ease. Within Pipedream, you can leverage this API to automate interactions, analyze message content, and enhance customer engagement by integrating with other apps. Think of ChatBotKit as the backbone of your chatbot logic, while Pipedream serves as the orchestrator, connecting your bot to a vast array of services, databases, and communication platforms.

Connect ChatBotKit

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    chatbotkit: {
      type: "app",
      app: "chatbotkit",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.chatbotkit.com/v1/usage/fetch`,
      headers: {
        "Authorization": `Token ${this.chatbotkit.$auth.token}`,
      },
    })
  },
})

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

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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}}