Airparser
Revolutionize data extraction with the GPT-powered document parser.
Integrate the Airparser API with the Python API
Setup the Airparser API trigger to run a workflow which integrates with the Python API. Pipedream's integration platform allows you to integrate Airparser and Python remarkably fast. Free for developers.
Popular Airparser and Python Triggers#
Emit new event when a document is parsed. See the documentation
Popular Airparser and Python Actions#
Extracts structured data based on a user-predefined extraction schema. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Uploads a document into the inbox for data extraction. See the documentation
Overview of Airparser#
The Airparser API simplifies the process of extracting data from emails, such as order confirmations, shipping notifications, or any structured email content. By transforming emails into structured data, it enables seamless integration of email data into various workflows. On Pipedream, you can leverage this API to create automations that act on the parsed data—like updating databases, triggering notifications, or integrating with other apps and services.
Connect Airparser#
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
airparser: {
type: "app",
app: "airparser",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://api.airparser.com/inboxes/`,
headers: {
"X-API-Key": `${this.airparser.$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#
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}}