This guide will walk you through how to use these schemas for job indexing, retrieval, and editing.

A. Why Use Job Schemas?

Instead of managing job data by handling JSON objects directly and meticulously following the job object documentation to avoid errors, you can validate job objects locally using job schemas before making API calls. This approach catches errors early, streamlines the process, and reduces delays in identifying mistakes.

Benefits of Using Job Schemas

• Early Error Detection: Validate job data locally to catch errors before making API calls.
• Enhanced Data Integrity: Ensure that job objects conform to the expected structure and types.
• Streamlined Workflow: Simplify the process of managing job data with robust validation.

B. Step-by-Step Guide

B.1. Initialize the HrFlow Client

📘

Prerequisites

• ✨ Create a Workspace
• 🔑 Get your API Key
• HrFlow.ai Python SDK version 4.0.0 or above: Install it via pip with pip install -U hrflow>=4.0.0 or conda with conda install hrflow>=4.0.0 -c conda-forge.

First, initialize the HrFlow client with your API credentials.

from hrflow import Hrflow
from hrflow.schemas import HrFlowJob, Location

client = Hrflow(api_secret="your_api_secret", api_user="your_api_user")

B.2. Define a Job Using Job Schemas

Create a job object using the provided schemas. This ensures that the job data adheres to the expected structure and types.

from hrflow.schemas import HrFlowJob, Location

# Minimal job with only required fields
job = HrFlowJob(
    name="Data Scientist",
    board_key="YOUR_BOARD_KEY",
    location=Location(
        text="Paris, France"
    ),
)

B.3. Index a Job

Use the client.job.storing.add_json method to index the job. The job schema ensures that the job data is valid before it is sent to the API.

response = client.job.storing.add_json(board_key="YOUR_BOARD_KEY", job_json=job.dict())

print(response)
# Output : {'code': 201, 'message': 'Job created', 'data': {... job data ...}}

For more details, refer to the Index a Job documentation.

C. Additional Use Cases

C.1. Retrieve a Job

To retrieve a job, use the client.job.indexing.get method. The job data returned can be validated against the schema.

response = client.job.indexing.get(board_key="YOUR_BOARD_KEY", key="YOUR_JOB_KEY")
job = HrFlowJob.parse_obj(response["data"])

print(job)
# Output: HrFlowJob object with the job data

For more details, refer to the Get a Job documentation.

C.2. Edit a Job

To edit a job, modify the job object and use the client.job.indexing.edit method to update it.

job.name = "Senior Data Scientist"
response = client.job.indexing.edit(board_key="YOUR_BOARD_KEY", key="YOUR_JOB_KEY", job_json=job.dict())

print(response)
# Output : {'code': 200, 'message': 'Job edited', 'data': {... job data ...}}

For more details, refer to the Edit a Job documentation.

D. Additional Resources

• HrFlow.ai Python SDK on PyPI
• HrFlow Cookbook: Repository containing helpful notebooks by the HrFlow.ai team
• Job Object Documentation
• API Authentication Documentation
• Index a Job documentation
• Get a Job documentation
• Edit a Job documentation