Chatforma

The biggest CIS chatbots platform. Build, connect and manage Telegram, Facebook, VK and Viber chatbots without coding.

Integrate the Chatforma API with the Python API

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

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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Overview of Chatforma

Chatforma is an API that opens up a world of possibilities in chatbot creation and management, allowing businesses to automate interactions with customers across various platforms. With Chatforma, you can design, deploy, and track chatbots that handle customer service, feedback collection, and more, streamlining your communication processes and providing valuable analytical insights. Leveraging Pipedream’s integration capabilities, you can efficiently tie Chatforma into a multitude of systems and services, creating custom workflows that enhance your chatbot's utility and extend its reach.

Connect Chatforma

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    chatforma: {
      type: "app",
      app: "chatforma",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.pro.chatforma.com/public/v1/auth-test`,
      params: {
        api_key: `${this.chatforma.$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

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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}}