Integrate the FlowiseAI API with the Python API

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

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Popular FlowiseAI and Python Actions#

Make Prediction with the FlowiseAI API

Calculates an output based on your created flow in Flowise. 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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Overview of FlowiseAI#

The FlowiseAI API offers powerful AI-driven capabilities to enhance data with smart predictions and insights. In Pipedream, you can leverage this API to automate tasks, analyze large sets of data, and build intelligent workflows that respond to the AI's output. FlowiseAI can pair with various apps on Pipedream to enrich CRM data, optimize marketing campaigns, or streamline customer support processes.

Connect FlowiseAI#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    flowiseai: {
      type: "app",
      app: "flowiseai",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      method: "POST",
      url: `https://pipedreamtest.onrender.com/api/v1/prediction/{{your_chatflow_id}}`,
      headers: {
        Authorization: `Bearer ${this.flowiseai.$auth.api_key}`,
        "Content-Type": `application/json`,
      },
      data: {
        question: "{{your_prompt}}"
      }
    })
  },
})

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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