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Version: 0.1.21

Manual Import Extractor

Manual import lets users add jobs that automated scrapers miss.

Big picture​

User pastes raw description, AI infers structure, user reviews edits, then import saves and scores the job.

1) Input​

Manual import accepts:

  • plain text job descriptions
  • raw HTML job descriptions
  • job links/URLs

When a URL is provided, backend fetch attempts depend on whether the page can be resolved with curl. Some job sites block or heavily script content, so certain links will not resolve cleanly.

2) AI inference​

Endpoint:

  • POST /api/manual-jobs/infer

Service:

  • orchestrator/src/server/services/manualJob.ts

Behavior:

  • Converts the provided input into text context and sends it to the configured LLM
  • Extracts structured fields (title, employer, location, salary, etc.)
  • Returns inferred JSON for user review

Practical limit:

  • The inference quality ceiling is mostly the configured model capability and context behavior. Better model quality generally yields better field extraction.

If no LLM key is configured, inference is skipped and user can fill fields manually.

3) Review and edit​

User reviews inferred fields and corrects missing/wrong values.

4) Storage and scoring​

Import endpoint:

  • POST /api/manual-jobs/import

On import:

  • Generates unique job ID if URL absent
  • Stores source as manual
  • Triggers async suitability scoring
  • Persists score and reason