Integrate the Libraria API with the Python API

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

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Popular Libraria and Python Actions#

Add Document with the Libraria API

Add a document. 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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Create Query with the Libraria API

Creates a query. See the documentation

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Overview of Libraria#

The Libraria API allows you to access a vast library of books and their metadata, including titles, authors, publication dates, and genres. With this API on Pipedream, you can craft serverless workflows to automate various tasks around book data management, enrichment, and sharing. Whether you're a librarian seeking to update catalog records, a retailer syncing inventory, or a book lover curating a reading list, Pipedream streamlines these tasks by connecting the Libraria API to other apps and services.

Connect Libraria#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    libraria: {
      type: "app",
      app: "libraria",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://api.libraria.dev/me`,
      headers: {
        Authorization: `Bearer ${this.libraria.$auth.api_key}`,
        "Content-Type": `application/json`,
      },
    })
  },
})

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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