aiondtech 2.4.0

Official Python SDK for AiondTech Resume Analyser API

AiondTech Resume Analyser SDK

Official Python SDK for the AiondTech Resume Analyser API.

Installation

pip install aiondtech

Quick Start

from aiondtech import ResumeAnalyser

# Initialize client
client = ResumeAnalyser(api_key="your-api-key")

# Or use environment variable
# export AIONDTECH_API_KEY="your-api-key"
client = ResumeAnalyser()

# Production mode
client = ResumeAnalyser(api_key="your-api-key", production=True)

Constructor Parameters

Parameter	Type	Default	Description
api_key	str	None	API key (or set AIONDTECH_API_KEY env var)
base_url	str	None	Custom API base URL
timeout	int	120	Request timeout in seconds
production	bool	False	Use production URL (https://api.aiondtech.com)

---

API Endpoints

Credit usage varies depending on resume size and complexity. Every response includes the actual credits_used — always check it for accurate tracking.

Method	Credits	Description
resumes.upload()	Varies	Upload PDF resume
resumes.upload_and_analyze()	Varies	Upload + AI parsing
resumes.upload_analyze_compare()	Varies	Upload + parse + compare to job
resumes.analyze()	Varies	Parse existing resume by ID
resumes.compare_resumes()	Varies	Compare resume to job
resumes.list()	0	List all resumes
jobs.create()	Varies	Create job posting
jobs.list()	0	List all jobs
credits.balance()	0	Check credit balance
credits.usage()	0	View usage history

---

Detailed Usage

1. Upload Resume

Upload a PDF resume without parsing. Returns a resume_id for later use.

result = client.resumes.upload("path/to/resume.pdf")

print(f"Resume ID: {result.resume_id}")
print(f"Message: {result.message}")
print(f"Credits Used: {result.credits_used}")

Output:

Resume ID: 450
Message: Resume uploaded successfully
Credits Used: 1

---

2. Upload and Analyze Resume

Upload a PDF and immediately extract structured data (AI-powered parsing).

result = client.resumes.upload_and_analyze("path/to/resume.pdf")

print(f"Resume ID: {result.resume_id}")
print(f"Full Name: {result.full_name}")
print(f"Email: {result.email}")
print(f"Skills: {result.skills}")
print(f"Job Titles: {result.job_titles}")
print(f"Total Experience: {result.total_experience}")
print(f"Credits Used: {result.credits_used}")
print(f"Parsed Data: {result.parsed_data}")

Output:

Resume ID: 450
Full Name: John Doe
Email: [e-mail]
Skills: ['Python', 'Django', 'AWS']
Job Titles: ['Senior Developer', 'Tech Lead']
Total Experience: 8 years
Credits Used: 5
Parsed Data: {'full_name': 'John Doe', 'email': '[e-mail]', ...}

Full parsed_data structure:

{
    "full_name": "John Doe",
    "email": "[e-mail]",
    "contact_number": "[phone]",
    "linkedin": "linkedin.com/in/johndoe",
    "location": "New York, NY",
    "skills": ["Python", "Django", "AWS"],
    "job_titles": ["Senior Developer", "Tech Lead"],
    "companies": ["Google", "Meta"],
    "education": ["BSc Computer Science - MIT"],
    "total_experience": "8 years",
    "certifications": ["AWS Certified Solutions Architect"],
    "summary": "Experienced software engineer..."
}

---

3. Upload, Analyze, and Compare

Upload a resume, parse it, and compare against a job posting in one call.

result = client.resumes.upload_analyze_compare("resume.pdf", job_id=56)

print(f"Resume ID: {result.resume_id}")
print(f"Job ID: {result.job_id}")
print(f"Score: {result.comparison_score}")
print(f"Reason: {result.comparison_reason}")
print(f"Parsed Data: {result.parsed_data}")
print(f"Credits Used: {result.credits_used}")

Output:

Resume ID: 449
Job ID: 56
Score: 30
Reason: The candidate has a strong technical background...
Parsed Data: {'full_name': 'Salem O. Ba Atya', 'email': '[e-mail]', ...}
Credits Used: 9

---

4. Analyze Resume by ID

Parse an already-uploaded resume by its ID.

result = client.resumes.analyze(resume_id=444)

print(f"Resume ID: {result.resume_id}")
print(f"Partner: {result.partner}")
print(f"Full Name: {result.full_name}")
print(f"Email: {result.email}")
print(f"Phone: {result.phone}")
print(f"LinkedIn: {result.linkedin}")
print(f"Location: {result.location}")
print(f"Skills: {result.skills}")
print(f"Job Titles: {result.job_titles}")
print(f"Companies: {result.companies}")
print(f"Education: {result.education}")
print(f"Total Experience: {result.total_experience}")
print(f"Certifications: {result.certifications}")
print(f"Credits Used: {result.credits_used}")
print(f"Parsed Data: {result.parsed_data}")

Output:

Resume ID: 444
Partner: your-partner-id
Full Name: John Doe
Email: [e-mail]
Phone: [phone]
LinkedIn: linkedin.com/in/johndoe
Location: New York, NY
Skills: ['Python', 'Django', 'AWS']
Job Titles: ['Senior Developer', 'Tech Lead']
Companies: ['Google', 'Meta']
Education: ['BSc Computer Science - MIT']
Total Experience: 8 years
Certifications: ['AWS Certified Solutions Architect']
Credits Used: 4
Parsed Data: {'full_name': 'John Doe', ...}

---

5. Create Job Posting

Create a new job posting to compare resumes against.

job = client.jobs.create(
    title="Senior Python Developer",
    description="We are looking for an experienced Python developer..."
)

print(f"Job ID: {job.job_id}")
print(f"Title: {job.title}")
print(f"Description: {job.description}")
print(f"Created By: {job.created_by}")
print(f"Created At: {job.created_at}")
print(f"Credits Used: {job.credits_used}")

Output:

Job ID: 57
Title: Senior Python Developer
Description: We are looking for an experienced Python developer...
Created By: your-partner-id
Created At: 2026-02-06T10:30:00Z
Credits Used: 1

---

6. Compare Resume to Job

Compare an existing resume against an existing job posting.

result = client.resumes.compare_resumes(resume_id=445, job_id=56)

print(f"Resume ID: {result.resume_id}")
print(f"Job ID: {result.job_id}")
print(f"Score: {result.comparison_score}")
print(f"Reason: {result.comparison_reason}")
print(f"Language: {result.language}")
print(f"Credits Used: {result.credits_used}")

Output:

Resume ID: 445
Job ID: 56
Score: 75
Reason: The candidate demonstrates strong alignment with the role requirements...
Language: en
Credits Used: 5

---

7. List Resumes

List all uploaded resumes with pagination.

result = client.resumes.list(page=1, limit=50)

print(f"Total: {result.total}")
print(f"Page: {result.page}")
print(f"Limit: {result.limit}")
print(f"Has More: {result.has_more}")
print(f"Count: {len(result)}")

for resume in result:
    print(f"  ID: {resume['id']}, Name: {resume.get('full_name')}, File: {resume.get('filename')}")

Output:

Total: 120
Page: 1
Limit: 50
Has More: True
Count: 50
  ID: 450, Name: John Doe, File: resume.pdf
  ID: 449, Name: Salem O. Ba Atya, File: Salem_20220524.pdf
  ...

---

8. List Jobs

List all job postings with pagination.

result = client.jobs.list(page=1, limit=50)

print(f"Total: {result.total}")
print(f"Page: {result.page}")
print(f"Limit: {result.limit}")
print(f"Has More: {result.has_more}")
print(f"Count: {len(result)}")

for job in result:
    print(f"  ID: {job['id']}, Title: {job['title']}")

Output:

Total: 10
Page: 1
Limit: 50
Has More: False
Count: 10
  ID: 56, Title: Senior Python Developer
  ID: 55, Title: Data Scientist
  ...

---

9. Check Credit Balance

balance = client.credits.balance()

print(f"Total Credits: {balance.get('total_credits')}")
print(f"Used Credits: {balance.get('used_credits')}")
print(f"Remaining Credits: {balance.get('remaining_credits')}")
print(f"Daily Limit: {balance.get('daily_limit')}")
print(f"Used Today: {balance.get('used_today')}")
print(f"Credits Today: {balance.get('credits_today')}")
print(f"Period Start: {balance.get('period_start')}")
print(f"Period End: {balance.get('period_end')}")

Output:

Total Credits: 3500.0
Used Credits: 123
Remaining Credits: 3377.0
Daily Limit: 300.0
Used Today: 36
Credits Today: 70
Period Start: 2026-02-01
Period End: 2026-02-28

---

10. Credit Usage History

usage = client.credits.usage()

print(f"Total Credits Used: {usage.get('total_credits')}")
print(f"Base Credits: {usage.get('base_credits')}")
print(f"GPT Credits: {usage.get('gpt_credits')}")
print(f"Record Count: {usage.get('record_count')}")

# Print last 5 entries
for entry in usage.get('history', [])[:5]:
    print(f"  {entry['timestamp']} | {entry['endpoint']} | {entry['credits_used']} credits (base: {entry['base_credits']}, gpt: {entry['gpt_credits']})")

Output:

Total Credits Used: 123
Base Credits: 75
GPT Credits: 48
Record Count: 69
  2026-02-05T23:14:52 | /external/credits/balance | 0 credits (base: 0, gpt: 0)
  2026-02-05T23:03:57 | /external/upload-resume-analyze-compare | 9 credits (base: 5, gpt: 4)
  2026-02-05T23:02:20 | /external/upload-resume-analyze | 5 credits (base: 3, gpt: 2)
  2026-02-05T23:01:21 | /external/upload-resume | 1 credits (base: 1, gpt: 0)
  2026-02-05T23:06:23 | /external/create-job | 1 credits (base: 1, gpt: 0)

With date filters:

usage = client.credits.usage(start_date="2026-02-01", end_date="2026-02-28")
print(f"Total Credits Used: {usage.get('total_credits')}")

---

Error Handling

from aiondtech import (
    ResumeAnalyser,
    APIError,
    AuthenticationError,
    InsufficientCreditsError,
    ValidationError,
    NotFoundError,
    RateLimitError,
)

client = ResumeAnalyser(api_key="your-api-key")

try:
    result = client.resumes.upload_and_analyze("resume.pdf")
except AuthenticationError as e:
    print(f"Invalid API key: {e}")
except InsufficientCreditsError as e:
    print(f"Not enough credits: {e}")
    print(f"Remaining: {e.credits_remaining}")
    print(f"Required: {e.credits_required}")
except ValidationError as e:
    print(f"Invalid input: {e}")
except NotFoundError as e:
    print(f"Resource not found: {e}")
except RateLimitError as e:
    print(f"Rate limited. Retry after: {e.retry_after} seconds")
except APIError as e:
    print(f"API error [{e.status_code}]: {e.message}")

---

Complete Workflow Example

from aiondtech import ResumeAnalyser

client = ResumeAnalyser(api_key="your-api-key")

# Step 1: Create a job posting
job = client.jobs.create(
    title="Senior Python Developer",
    description="5+ years Python, Django, AWS, PostgreSQL required..."
)
print(f"Created job #{job.job_id}: {job.title} ({job.credits_used} credits)")

# Step 2: Upload and analyze a resume
analysis = client.resumes.upload_and_analyze("candidate_resume.pdf")
print(f"Candidate: {analysis.full_name}")
print(f"Skills: {', '.join(analysis.skills)}")
print(f"Experience: {analysis.total_experience}")
print(f"Credits used: {analysis.credits_used}")

# Step 3: Compare resume to job
comparison = client.resumes.compare_resumes(
    resume_id=analysis.resume_id,
    job_id=job.job_id
)
print(f"Score: {comparison.comparison_score}%")
print(f"Reasoning: {comparison.comparison_reason}")
print(f"Credits used: {comparison.credits_used}")

# Step 4: Check remaining credits
balance = client.credits.balance()
print(f"Credits remaining: {balance['remaining_credits']}")

One-Step Workflow (Upload + Analyze + Compare)

result = client.resumes.upload_analyze_compare("resume.pdf", job_id=job.job_id)
print(f"Score: {result.comparison_score}% — {result.comparison_reason}")
print(f"Credits used: {result.credits_used}")

Batch Processing

import os

job = client.jobs.create("Data Scientist", "ML, Python, statistics...")

total_credits = job.credits_used
results = []

for pdf in os.listdir("resumes/"):
    if pdf.endswith(".pdf"):
        r = client.resumes.upload_analyze_compare(
            f"resumes/{pdf}", job_id=job.job_id
        )
        results.append(r)
        total_credits += r.credits_used
        print(f"{r.comparison_score:5.1f}% — {pdf} ({r.credits_used} credits)")

# Sort by score
results.sort(key=lambda x: x.comparison_score, reverse=True)
print(f"\nTop candidate: resume_id={results[0].resume_id} ({results[0].comparison_score}%)")
print(f"Total credits used: {total_credits}")

---

Environment Variables

# Set your API key
export AIONDTECH_API_KEY="your-api-key"

# Optional: Custom base URL
export AIONDTECH_BASE_URL="https://api.aiondtech.com"

from aiondtech import ResumeAnalyser

# Client automatically uses environment variables
client = ResumeAnalyser()

---

Support

• Documentation: https://docs.aiondtech.com
• API Reference: https://api.aiondtech.com/docs
• Email: [e-mail]
• Issues: GitHub Issues

License

MIT License - see LICENSE for details.