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📝 SDK Notebook examples
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‎examples/job/job_endpoints_examples.ipynb‎

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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Setup the config and authentication"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from getpass import getpass\n",
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"import os\n",
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"import json \n",
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"from hrflow import Hrflow\n",
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"\n",
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"# # Get API credentials from environment variables\n",
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"api_secret = getpass(\"Enter your API secret: \")\n",
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"board_key = getpass(\"Enter your board key: \")\n",
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"user_email = getpass(\"Enter your user email: \")"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Setup HrFlow.ai Client "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"client = Hrflow(api_secret=api_secret, api_user=user_email)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 1 - Read a Job in a Board in HrFlow.ai "
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### a - Using external ID set : `reference` in HrFlow.ai"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"reference = \"a2n1j000000g0AAAAY\" # <-- Replace with your job reference\n",
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"\n",
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"# Get job by reference\n",
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"response = client.job.storing.get(board_key=board_key, reference=reference)\n",
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"\n",
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"response"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### b - Using profile key (internal set by HrFlow.ai) : `key` in HrFlow.ai"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"key = \"FILL THIS\" # <-- Replace with your job key\n",
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"\n",
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"# Get job by reference\n",
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"response = client.job.storing.get(board_key=board_key, key=key)\n",
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"\n",
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"response"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 2 - Write a job in a board from a Structured data "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'code': 201,\n",
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" 'message': 'Job created',\n",
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" 'data': {'id': 1245507,\n",
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" 'key': '4f2e11266a11e728e794dc5f0867462fa60321ba',\n",
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" 'reference': 'my_custom_reference',\n",
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" 'board_key': '7fce016712fa373456ef279c297da5009a2020d9',\n",
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" 'board': {'key': '7fce016712fa373456ef279c297da5009a2020d9',\n",
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" 'name': 'vulcain_test',\n",
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" 'type': 'api',\n",
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" 'subtype': 'python',\n",
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" 'environment': 'production'},\n",
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" 'name': 'Data Engineer',\n",
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" 'url': 'https://www.pole-emploi.ai/jobs/data_engineer',\n",
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" 'picture': None,\n",
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" 'summary': 'As an engineer for the Data Engineering Infrastructure team, you will design, build, scale, and evolve our data engineering platform, services and tooling. Your work will have a critical impact on all areas of business:supporting detailed internal analytics, calculating customer usage, securing our platform, and much more.',\n",
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" 'location': {'text': 'Dampierre en Burly (45)',\n",
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" 'lat': None,\n",
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" 'lng': None,\n",
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" 'gmaps': None,\n",
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" 'fields': None},\n",
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" 'archive': None,\n",
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" 'archived_at': None,\n",
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" 'updated_at': '2023-06-20T15:50:32+0000',\n",
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" 'created_at': '2023-06-20T15:50:32+0000',\n",
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" 'sections': [{'name': 'section 1',\n",
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" 'title': 'title section 1',\n",
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" 'description': 'text section 1'}],\n",
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" 'culture': 'FILL THIS WITH A TEXT ABOUT YOUR COMPANY CULTURE',\n",
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" 'responsibilities': 'FILL THIS WITH A TEXT ABOUT THE RESPONSIBILITIES OF THE JOB',\n",
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" 'requirements': 'FILL THIS WITH A TEXT ABOUT THE REQUIREMENTS OF THE JOB',\n",
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" 'benefits': 'FILL THIS WITH A TEXT ABOUT THE BENEFITS OF THE JOB',\n",
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" 'interviews': 'FILL THIS WITH A TEXT ABOUT THE INTERVIEWS OF THE JOB',\n",
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" 'skills': [{'name': 'python', 'value': None, 'type': 'hard'},\n",
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" {'name': 'spark', 'value': 0.9, 'type': 'hard'}],\n",
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" 'languages': [{'name': 'english', 'value': None},\n",
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" {'name': 'french', 'value': None}],\n",
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" 'certifications': None,\n",
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" 'courses': None,\n",
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" 'tasks': None,\n",
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" 'tags': [{'name': 'company', 'value': 'Google'}],\n",
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" 'metadatas': [{'name': 'metadata example', 'value': 'metadata'}],\n",
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" 'ranges_float': [{'name': 'salary',\n",
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" 'value_min': 45000,\n",
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" 'value_max': 50000,\n",
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" 'unit': 'eur'}],\n",
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" 'ranges_date': [{'name': 'interview_dates',\n",
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" 'value_min': '2023-05-18T21:59',\n",
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" 'value_max': '2023-09-15T21:59'}]}}"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"input_data = {\n",
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" \"name\": \"Data Engineer\",\n",
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" \"reference\": \"my_custom_reference\", # <-- Replace with your job reference\n",
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" \"url\": \"https://www.pole-emploi.ai/jobs/data_engineer\",\n",
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" \"summary\": \"As an engineer for the Data Engineering Infrastructure team, you will design, build, scale, and evolve our data engineering platform, services and tooling. Your work will have a critical impact on all areas of business:supporting detailed internal analytics, calculating customer usage, securing our platform, and much more.\",\n",
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" \"location\": {\n",
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" \"text\": \"Dampierre en Burly (45)\",\n",
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" \"geopoint\": {\n",
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" \"lat\": 47.7667,\n",
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" \"lon\": 2.5167\n",
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" }\n",
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" },\n",
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" \"culture\": \"FILL THIS WITH A TEXT ABOUT YOUR COMPANY CULTURE\",\n",
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" \"responsibilities\": \"FILL THIS WITH A TEXT ABOUT THE RESPONSIBILITIES OF THE JOB\",\n",
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" \"requirements\": \"FILL THIS WITH A TEXT ABOUT THE REQUIREMENTS OF THE JOB\",\n",
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" \"benefits\": \"FILL THIS WITH A TEXT ABOUT THE BENEFITS OF THE JOB\",\n",
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" \"interviews\": \"FILL THIS WITH A TEXT ABOUT THE INTERVIEWS OF THE JOB\",\n",
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" \"sections\": [{ # <-- Sections are mandatory, leave as empty list if no section is provided\n",
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" \"name\": \"section 1\",\n",
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" \"title\": \"title section 1\",\n",
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" \"description\": \"text section 1\"\n",
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" }\n",
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" ],\n",
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" \"skills\": [{\n",
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" \"name\": \"python\",\n",
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" \"value\": None, # <-- Leave None if no assessment to skills is provided\n",
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" \"type\": \"hard\"\n",
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" },\n",
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" {\n",
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" \"name\": \"spark\",\n",
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" \"value\": 0.9,\n",
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" \"type\": \"hard\"\n",
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" }\n",
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" ],\n",
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" \"languages\": [{\n",
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" \"name\": \"english\",\n",
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" \"value\": None\n",
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" },\n",
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" { \n",
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" \"name\": \"french\",\n",
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" \"value\": None\n",
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" }\n",
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" ],\n",
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" \"tags\": [{\n",
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" \"name\": \"company\", # <-- Other custom fields can be added as tags\n",
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" \"value\": \"Google\"\n",
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" }\n",
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" ],\n",
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" \"ranges_date\": [{\n",
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" \"name\": \"interview_dates\",\n",
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" \"value_min\": \"2023-05-18T21:59\",\n",
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" \"value_max\": \"2023-09-15T21:59\"\n",
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" }\n",
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" ],\n",
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" \"ranges_float\": [{\n",
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" \"name\": \"salary\",\n",
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" \"value_min\": 45000,\n",
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" \"value_max\": 50000,\n",
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" \"unit\": \"eur\"\n",
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" }\n",
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" ],\n",
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" \"metadatas\": [{\n",
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" \"name\": \"metadata example\",\n",
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" \"value\": \"metadata\"\n",
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" }\n",
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" ]\n",
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" }\n",
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"\n",
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"response = client.job.storing.add_json(board_key=board_key, job_json=input_data)\n",
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"\n",
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"response"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 3 - Archive a Job already in a Board "
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### a - Archive a Job from a Board"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'code': 200,\n",
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" 'message': 'Job archived',\n",
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" 'data': {'key': '4f2e11266a11e728e794dc5f0867462fa60321ba'}}"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Let's archive the job with reference \"my_custom_reference\"\n",
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"reference = \"my_custom_reference\" # <-- Replace with your job reference\n",
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"\n",
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"response = client.job.storing.archive(board_key=board_key,\n",
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" reference=reference)\n",
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"\n",
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"response"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 5 - Edit a Job already in a Board "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Edit a job : edit is a Put not a Patch so you need to provide all the fields\n",
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"\n",
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"# let's get the job by reference\n",
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"reference = \"a2n1j000000g0AAAAY\" # <-- Replace with your job reference\n",
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"\n",
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"response = client.job.storing.get(board_key=board_key, reference=reference)\n",
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"\n",
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"job = response['data']\n",
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"\n",
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"# edit a field or more \n",
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"job['culture'] = \"This is a New CULTURE !!!\"\n",
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"\n",
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"# edit the job\n",
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"response = client.job.storing.edit(board_key=board_key,\n",
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" key=job['key'],\n",
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" job_json=job)\n",
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"\n",
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"response"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "python-hrflow-api-S_mz3ndj-py3.8",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.6"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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