Chatlayer

Power your business with #AI | Build voice- and #chatbots in 100+ languages, no coding skills needed

Integrate the Chatlayer API with the Python API

Setup the Chatlayer API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Chatlayer 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 Chatlayer

The Chatlayer API by Sinch allows developers to integrate advanced chatbot functionalities into their applications, enabling AI-driven conversations across various channels like web, mobile, and social media platforms. With Pipedream, you can automate workflows involving the Chatlayer API to facilitate seamless interactions, gather user insights, and streamline communications. This integration can help in automating responses, analyzing sentiment, and managing user data in real-time, thus enhancing user engagement through personalized and context-aware dialogues.

Connect Chatlayer

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    chatlayer: {
      type: "app",
      app: "chatlayer",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `${this.chatlayer.$auth.url}/v1/bots`,
      headers: {
        Authorization: `Bearer ${this.chatlayer.$auth.access_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}}