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Demo data setup in ERPNext (Frappe app)

This document explains how ERPNext wires demo data into the install/setup experience, how the data is produced, and how you can replicate the pattern in another Frappe app.

Where the demo-data option shows up

ERPNext adds a “Generate Demo Data for Exploration” checkbox to the setup wizard. This is defined in the setup wizard front-end slide configuration (setup_wizard.js) under the organization slide as the setup_demo field. When checked, its value is included in the setup wizard payload. 【F:erpnext/public/js/setup_wizard.js†L30-L74】

How the setup wizard triggers demo data

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YouTube Shorts API – Architecture & File Layout (Frappe)

Goal

• Avoid slow/blocking HTTP requests
• Move heavy work to background jobs
• Add safe parallelism
• Enable caching + polling
• Keep frontend API fast (<100 ms)

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Adding pip Dependencies in a Frappe App (Correct Way)

This is the recommended and production-safe way to add Python dependencies to a Frappe app such as google_services.

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Preferred Method: pyproject.toml

Open the file:

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Realtime websocket-based listeners are implemented in Frappe CRM

🔌 Realtime WebSocket Implementation in Frappe CRM

1. Socket Initialization (src/socket.js)

The app initializes a Socket.IO connection that connects to the Frappe realtime server:

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Frappe Studio Architecture Documentation

Github Url: https://github.com/frappe/studio

Overview

Frappe Studio is a visual application builder for the Frappe Framework that enables developers to create applications through a drag-and-drop interface. It combines Vue.js frontend technology with Frappe Framework's backend capabilities to provide a comprehensive low-code development environment.

Architecture Overview

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A preview of the code in action:

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WhatsApp Bulk Sender Setup

1. Initialize the Project:

   npm init -y

2. Install Dependencies:

npm install axios csv-parser dotenv

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Multi-Stage PDF Processing Pipeline (Open-Source Tools)

Goal: Build a Python pipeline on Linux/macOS that processes PDFs in stages: removing headers/footers, detecting signatures (digital and handwritten), assessing page quality, performing OCR (skipping certain regions), and optionally falling back to cloud vision models for very complex pages. Below are recommended open-source tools/models for each stage, with setup/usage notes and integration tips.

Stage 1: Header/Footer Detection and Removal

Repetitive headers, footers, or page numbers can interfere with content extraction. The pipeline’s first step is to detect and strip these recurring elements:

• PDF Text Parsing: Use a PDF parser like PyMuPDF (Python binding fitz) or pdfplumber to extract text with coordinates. These libraries let you analyze text positions on each page. For example, with PyMuPDF you can iterate through pages and retrieve text blocks or individual text spans with their bounding boxes. By analyzing top/bottom

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Conversational AI API Reference

List Conversations

Endpoint: GET /v1/convai/conversations

Description: Retrieve all conversations of agents owned by the user. Optionally filter by agent.

cURL: