bot9

AI chatbot builder for customer support.

Integrate the bot9 API with the Python API

Setup the bot9 API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate bot9 and Python remarkably fast. Free for developers.

Initiate Chat with the bot9 API

Initiates a new conversation with a Bot9 chatbot. 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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Send Chat Message with the bot9 API

Send a chat message to a Bot9 chatbot. See the documentation

 
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Overview of bot9

The Bot9 API enables automated interactions with trading systems, allowing users to execute, manage, and analyze trades through a programmatic interface. In Pipedream, you can leverage this API to craft serverless workflows that handle trading tasks, notifications, and analyses without needing to build a full backend system. This can speed up trade execution, improve response times to market changes, and enable complex trading strategies that adjust to live market data.

Connect bot9

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import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    bot9: {
      type: "app",
      app: "bot9",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://apiv1.bot9.ai/api/auth/account`,
      headers: {
        Authorization: `Bearer ${this.bot9.$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}}