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The bank used to be write-only: a CV uploaded with no job carried only its
full text, so nothing could search or rank it. Now the employment agent's
extraction (title, company, education, plus new skills and years_experience,
both clamped to what the resume actually states) is stored on the row, and
the bank is ranked against a job the moment that job opens.
- New CV Bank screen at /cvbank replaces Talent Pool; the inline bank card
moves out of CV Import. One table, two populations: speculative uploads,
and silver medalists (rejected applicants scoring >= CV_BANK_SILVER_FLOOR,
read live from their application rather than copied).
- matching/ranking.py: the tier-1 keyword ranker moves out of
talent/plugins.py so Find Talent and the bank share one implementation;
talent/plugins.py re-exports it and its numbers are unchanged.
- Taskiq tasks in job.candidate.bank_tasks: backfill profiles for CVs
banked before extraction existed, and rank the bank when a job opens so
recruiters are told about matches above CV_BANK_SUGGEST_THRESHOLD.
Retention (CV_BANK_RETENTION_MONTHS) is stamped on the row at upload; the
sweep flags expired rows and never deletes.
- Migrations 029 (bank profile columns) and 030 (per-job bank matches).
- Routes: POST /candidate/cv-bank/score, GET /candidate/cv-bank/suggestions.
- README: The CV Bank, plus the retention and deletion policy.
Also in this change:
- Inbox, Sheet Forms: has_linkedin / has_resume filters, tri-valued so
"no link" is a real filter and NULL rows are kept in it; tab badge counts
now narrow with the list and the search box.
- Hiring-manager candidate rows carry the ATS score and band.
- Tests: analytics dashboard merge logic, employment extraction clamps,
form-data filters, manager candidate serializer, CV Bank mapper.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
- Added methods to retrieve application history by email across various sources (inbox, manual uploads, form data).
- Introduced new serializers for application history items and overall history.
- Updated candidate and inbox views to include application history in responses.
- Enhanced frontend components to display reapplication badges and previous application details.
- Adjusted API endpoints to support fetching application history based on email input.
Reported as "LinkedIn is not showing though the resume has it". The LinkedIn
was never the problem.
Traced on the live application (Mohammad Raza, inbox row 2f81cebc). Its stored
resume_text is 4,555 characters over 2,278 lines, and every one of those lines
is exactly one character long. The CV really does say
LinkedIn:linkedin.com/in/mohammad-raza-digital-marketer
but it is stored as forty separate lines, so nothing that looks for a substring
can find it. Not a link annotation, not an image, not OCR: pypdf's default mode
breaks after every glyph on PDFs whose author positioned each one separately,
which design tools do routinely.
It survived review because a model reads that text fine. The candidate was
classified, matched and scored normally. What fails, silently, is every check
that asks "does this string appear in the resume":
- slugs_from_text finds no profile, so linkedin_slug is stored empty
- _clean_skills drops every skill, since each must appear in the text
- the company and education clamps drop theirs for the same reason
- verify_matched_keywords drops every matched keyword in the ATS engine
despace_line could not help: it rebuilds glyphs padded *within* a line, and
here there is nothing left on a line to rebuild.
is_glyph_fragmented measures the giveaway — the share of non-empty lines that
are a single character — and extract_pdf_text re-extracts with pypdf's layout
mode when it trips. Layout mode is the fallback, never the default: it is
slower and pads ordinary documents with alignment whitespace, so a CV that
extracts cleanly today is untouched. The fallback is checked before it is
trusted; fragmented text still scores a candidate, empty text fails them.
Both extractors had the defect, so the helpers live in app/services/pdf.py,
which owns PDF handling and is already imported by the recruiting path.
Measured against that real CV, before and after:
slugs_from_text [] -> ['mohammad-raza-digital-marketer']
profile_url_from_text None -> https://www.linkedin.com/in/...
lines 2278 -> 61
single-char lines 2278 -> 0
'performance' found False -> True
'google ads' found False -> True
Existing rows keep their broken text; extraction runs at ingest. Re-running
the match on affected rows is what backfills them.
.gitignore had `tests/**` twice and `/backend/tests/**` once. Both suites are
tracked and both run in CI, so the rules were inert for existing files and did
nothing but swallow new ones — this test was invisible to `git status` until
they went. That is also why they are removed rather than negated.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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The recruiter table was user-centric (GET /candidate/fetch/users), so it could
only ever render account fields - name, email, created date. Everything a
recruiter actually triages on lives on the application, not the user.
Point the table at GET /candidate/fetch and map application rows through a new
toApplicationListView, adding Job, ATS (score + band chip), Stage and Recruiter
columns. Stage and band become real filters; the dead Department facet is gone.
Manual uploads came back unscored because the list path never joined the ATS
results, so attach scores there and expose ai_score/recommendation from the
manager serializer, deriving the band from the score when the model omitted it.
Co-authored-by: Cursor <cursoragent@cursor.com>
The recruiter table was user-centric (GET /candidate/fetch/users), so it could
only ever render account fields - name, email, created date. Everything a
recruiter actually triages on lives on the application, not the user.
Point the table at GET /candidate/fetch and map application rows through a new
toApplicationListView, adding Job, ATS (score + band chip), Stage and Recruiter
columns. Stage and band become real filters; the dead Department facet is gone.
Manual uploads came back unscored because the list path never joined the ATS
results, so attach scores there and expose ai_score/recommendation from the
manager serializer, deriving the band from the score when the model omitted it.
Co-authored-by: Cursor <cursoragent@cursor.com>
ahmed.mujtaba
Talha Ahmed