Stuart Hudson goulash1971

Converting MS Office files to PDF

(Description of the different solutions / alternatives)

This is the current, official, Microsoft endorsed/supported solution ("cloud based")
(2017 - present)

The user uploads their MS Office document (source.doc in our example snippet bellow) to their Microsoft OneDrive
The user then uses the Microsoft Graph REST API to send a HTTP GET Request to the Convert content endpoint:

The package linked to from here is now pure ESM. It cannot be require()'d from CommonJS.

This means you have the following choices:

Use ESM yourself. (preferred)
Use import foo from 'foo' instead of const foo = require('foo') to import the package. You also need to put "type": "module" in your package.json and more. Follow the below guide.
If the package is used in an async context, you could use await import(…) from CommonJS instead of require(…).
Stay on the existing version of the package until you can move to ESM.

The main difference between the two pages is the method of sending messages. Recieving messages is the same in both.

Send messages to iframe using iframeEl.contentWindow.postMessage Recieve messages using window.addEventListener('message')

	// Required Plugin: https://marketplace.visualstudio.com/items?itemName=drcika.apc-extension

	// settings.json
	{
	// Remove left-side icons
	"workbench.activityBar.location": "hidden",
	// Remove bottom status bar
	"workbench.statusBar.visible": false,
	// Remove position indicator in the editor's scrollbar
	"editor.hideCursorInOverviewRuler": true,

	// Requires the gpt library from https://github.com/hrishioa/socrate and the progress bar library.
	// Created by Hrishi Olickel ([email protected]) (@hrishioa). Reach out if you have trouble running this.

	import { ThunkQueue } from '../../utils/simplethrottler';
	import {
	AcceptedModels,
	Messages,
	askChatGPT,
	getMessagesTokenCount,
	getProperJSONFromGPT,

	# STEP 1: Load

	# Load documents using LangChain's DocumentLoaders
	# This is from https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/csv.html

	from langchain.document_loaders.csv_loader import CSVLoader
	loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv')
	data = loader.load()

	[{
	"Note": "The first two digits (ranging from 10–43) correspond to the province, while the last two digits correspond either to the city/delivery zone (range 01–50) or to the district/delivery zone (range 51–99). Afghanistan Postal code lookup",
	"Country": "Afghanistan",
	"ISO": "AF",
	"Format": "NNNN",
	"Regex": "^\\d{4}$"
	}, {
	"Note": "With Finland, first two numbers are 22.",
	"Country": "Åland Islands",
	"ISO": "AX",