Integrate the Metatext.AI Inference API API with the Python API

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

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Popular Metatext.AI Inference API and Python Actions#

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 Metatext.AI Inference API#

The Metatext.AI Inference API allows developers to leverage advanced machine learning models for natural language understanding tasks such as classification, sentiment analysis, named entity recognition, and more. In Pipedream, you can easily integrate this API into your serverless workflows to automate text analysis and enrichment across various data sources and applications.

Connect Metatext.AI Inference API#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    metatext_ai_inference_api: {
      type: "app",
      app: "metatext_ai_inference_api",
    }
  },
  async run({steps, $}) {
    const data = {
      "text": `{your_text}`,
    }
    return await axios($, {
      method: "post",
      url: `https://api.metatext.ai/v1/inference/${this.metatext_ai_inference_api.$auth.model_id}`,
      headers: {
        "Content-Type": `application/json`,
        "x-api-key": `${this.metatext_ai_inference_api.$auth.api_key}`,
      },
      data,
    })
  },
})

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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