Paperwork automation for the logistics industry.
Extract structured data from a document. See the documentation
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
Extract structured data from unstructured text. See the documentation
The Reform API allows you to automate the management and analysis of forms and surveys. By connecting Reform to Pipedream, you can create, update, and retrieve form submissions, and set up workflows that trigger on new responses. This opens up possibilities for integrating form data with other tools, managing event-driven notifications, or feeding customer insights into your CRM systems—all in a serverless environment that scales with your needs.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
reform: {
type: "app",
app: "reform",
}
},
async run({steps, $}) {
const fields_to_extract = [
{
"name": "Nouns",
"description": "Please extract all nouns.",
"type": "String"
},
{
"name": "Verbs",
"description": "Please extract all verbs.",
"type": "String"
}
]
const data = {
text_content: "There are usually about 200 words in a paragraph, but this can vary widely. Most paragraphs focus on a single idea that's expressed with an introductory sentence, then followed by two or more supporting sentences about the idea. A short paragraph may not reach even 50 words while long paragraphs can be over 400 words long, but generally speaking they tend to be approximately 200 words in length.",
fields_to_extract: fields_to_extract
}
return await axios($, {
method: "post",
url: `https://api.reformhq.com/v1/api/extract-text `,
headers: {
Authorization: `Bearer ${this.reform.$auth.api_key}`,
"Content-Type": `application/json`,
},
data,
})
},
})
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:
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}}