The Unstructured Data ETL for Your LLM.
Write Python and use any of the 350k+ PyPi packages available. Refer to the Pipedream Python docs to learn more.
import { axios } from "@pipedream/platform"
import FormData from "form-data";
import request from "request";
export default defineComponent({
props: {
unstructured: {
type: "app",
app: "unstructured",
},
},
async run({ steps, $ }) {
const data = new FormData();
data.append("files", request("https://www.learningcontainer.com/wp-content/uploads/2019/09/sample-pdf-file.pdf"));
return await axios($,{
method: "POST",
url: `${this.unstructured.$auth.url}/general/v0/general`,
headers: {
"Content-Type": `multipart/form-data`,
"unstructured-api-key": `${this.unstructured.$auth.api_key}`
},
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}}