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Rewrite README as the complete project document 

Covers the whole system as it now runs: architecture (agent routing vs ATS
scoring), repo layout, clean-machine quick start incl. the fresh-DB enum
workaround, the backend scoring API surface with behavior guarantees, the
engine internals summary, QA status (28/28 audit, 23/23 integration), data
handling, and the honest gap/roadmap table.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Dashboard_Wiring
Talha Ahmed 2026-08-10 22:30:43 +05:00
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# Bulk ATS Scoring Engine
# HR-ATS-Portal
One job description in, many resume PDFs in, a score-sorted leaderboard out.
An applicant-tracking system with AI resume scoring: job descriptions and CVs go in,
a validated, score-sorted candidate leaderboard comes out — through a React portal,
a FastAPI backend, and an embeddable LLM scoring engine.
Resumes are extracted with `pypdf`, evaluated concurrently against the job description
with the OpenAI Responses API, validated against a strict schema, and returned as a
single JSON response. A failure on one resume never fails the batch.
[CLAUDE.md](claude.md) is the specification this implements and remains the source of
truth for design decisions.
## Setup
Requires Python 3.11+.
```bash
python -m venv .venv
.venv\Scripts\activate # Windows
# source .venv/bin/activate # macOS / Linux
pip install -e ".[dev]"
copy .env.example .env # Windows
# cp .env.example .env # macOS / Linux
```
Email inbox (Graph proxy) Recruiter browser
│ attachments │ uploads (PDF)
▼ ▼
┌──────────────────────────────────────────────────┐
│ backend/ (FastAPI + Postgres) │
│ │
│ inbox module ──► agent (LangGraph): │
│ "which job is this CV for?" │
│ │
│ candidate module ──► scoring engine (app/): │
│ "how well does it fit? 0-100" │
│ │ │
│ ▼ │
│ app.candidates table │
└──────────────────────┬───────────────────────────┘
▼
frontend/ (React) — CV Import · Candidates ·
Talent Pool · Inbox · Jobs · RBAC · Auth
```
Put an `OPENAI_API_KEY` in `.env`. `.env` is gitignored; never commit it.
The two AI flows are complementary: the **agent** routes an emailed CV to the job it
is probably applying for; the **scoring engine** evaluates a CV against one chosen
job and persists an evidence-based score.
> On Windows, write `.env` as UTF-8 **without** a BOM. PowerShell 5.1's
> `Set-Content -Encoding utf8` adds one, and a BOM becomes part of the first
> variable's name — that setting then silently reads as empty. The app parses `.env`
> as `utf-8-sig` so it tolerates this, but other tools reading the same file will not.
## Repository layout
Run the service:
| Path | What it is |
|---|---|
| `app/` | **Bulk ATS scoring engine** — standalone FastAPI service *and* importable library. Spec: [CLAUDE.md](CLAUDE.md) (source of truth for its design). |
| `backend/` | **Main backend** — users/RBAC/JWT auth, email inbox sync, job posts + Buffer publishing, agent matching, candidate scoring + persistence. House style: `backend/LLM_CONTEXT_PROMPT.md`. |
| `frontend/` | **React portal** (Vite + react-query). Candidate screens run on live data; remaining screens still use seed data. |
| `scripts/` | Engine verification: `smoke_structured_output.py` (live request-shape check), `audit_scoring.py` (positive/negative scoring audit over real CVs). |
| `tests/` | Engine test suite — 195 tests, no live API calls. |
| `CVS/` | Sample resume PDFs used by the audits. Personal data — do not commit new ones casually. |
```bash
uvicorn app.main:create_app --factory --reload
## Quick start (clean machine)
**Prerequisites:** Python 3.11+, Node 18+, PostgreSQL, an OpenAI API key.
Optional: Redis + Docker (only for background inbox sync / taskiq workers).
### 1. Environment files
Root `.env` (engine + scoring settings — see [.env.example](.env.example)):
```
OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-5.4-mini
OPENAI_MAX_OUTPUT_TOKENS=4000
OPENAI_EFFORT=low
SCORING_CONCURRENCY=5
MAX_RESUMES_PER_REQUEST=50
MAX_PDF_SIZE_MB=10
```
There is deliberately no module-level `app` object. Building one at import time would
read settings — and fail on a bad `ANTHROPIC_MODEL` — merely because something imported
the module.
`backend/.env` (everything in `backend/.env.example`; the must-haves):
## Verify the request shape before trusting it
Unit tests use fakes, so they cannot prove the real API accepts the request. One live
check does, and it costs a few cents:
```bash
python scripts/smoke_structured_output.py
```
DB_USERNAME=... DB_PASSWORD=... DB_HOST=localhost DB_PORT=5432 DB_NAME=hrms
JWT_SECRET_KEY=...
OPENAI_API_KEY=sk-... # shared names with the root .env
```
It confirms the schema derived from `ATSScore` is accepted, that `output_parsed` comes
back valid, and that the second call reports non-zero `cached_tokens`.
Run it whenever you change the model or upgrade the SDK.
> Windows note: write `.env` files as UTF-8 **without** BOM, and don't leave stray
> non `KEY=VALUE` lines — python-dotenv warns on every load.
Last verified against `gpt-5.4-mini`: both calls parsed, and call 2 served 2304 of
2649 input tokens from cache.
### 2. Fresh database — one manual step
## API
Migrations run automatically on boot, but on a **brand-new database** one enum type
must exist first (known migration gap in the inbox module):
### `POST /api/v1/score`
```sql
CREATE TYPE candidate_application_status AS ENUM
('PROCESS','PENDING','APPROVED','REJECTED','ONHOLD','CLOSED');
```
`multipart/form-data`:
Everything else — including the `app.candidates` scoring table — is created by
Alembic autogeneration on first boot.
| Field | Type | Notes |
### 3. Install and run
```bash
# Backend deps + the scoring engine as an editable library
pip install -r backend/requirements.txt
pip install -e .
# Backend (the frontend dev config expects 127.0.0.1:8000)
cd backend
uvicorn main:app --port 8000
# Frontend (second terminal)
cd frontend
npm install
npm run dev # http://localhost:5173
```
Optional — standalone scoring engine with its own test UI (Talent-Pool-style card
grid, per-card view/download):
```bash
uvicorn app.main:create_app --factory # http://localhost:8000/ (pick a free port)
```
Optional — background inbox sync workers (need Redis):
```bash
docker compose up redis taskiq-worker taskiq-scheduler
```
### 4. First run
1. Sign up / log in (`/auth/login`) — the user needs a role carrying
`candidates.create` + `candidates.view` (RBAC screen or seed a role).
2. Create a job post (Job Board) — resumes are always scored **against a job**.
3. **CV Import** → pick the job → drop PDF resumes → each file returns scored
(score chip + one-line assessment) or failed (error code). Rows persist.
4. Browse results in **Candidates** (table, filters, ATS-match modal) and
**Talent Pool** (card grid). Failed extractions carry their error code.
## Backend API (scoring surface)
All routes use the envelope `{"data": ..., "total": n, "status_code": 200}` and JWT
bearer auth. Permissions in parentheses.
| Route | Method | Purpose |
|---|---|---|
| `job_description` | text | Required, non-blank, `MAX_JD_CHARS` ceiling |
| `resumes` | file[] | Required, `.pdf` only, `MAX_RESUMES_PER_REQUEST` / `MAX_PDF_SIZE_MB` ceilings |
| `/candidate/score` | POST multipart `job_id`, `files[]` | Score uploaded PDFs against a job; persists + returns leaderboard (candidates.create) |
| `/candidate/score_inbox` | POST `{job_id, message_ids[]}` | Score decoded email attachments; `message_ids` are inbox PK uuids (candidates.create) |
| `/candidate/fetch?job_id=` | GET | Persisted leaderboard; omit `job_id` for the cross-job pool (candidates.view) |
| `/candidate/fetch_by_id?candidate_id=` | GET | One candidate row (candidates.view) |
| `/job/fetch` | GET | Active job posts for pickers (job_board.view *or* candidates.view) |
| `/candidate/cv_upload` | POST multipart `file` | Text extraction only, nothing stored (candidates.create) |
| `/candidate/inbox-match` | POST `?inbox_message_id=` | Queue the agent "which job?" match (candidates.edit; needs Redis) |
```bash
curl -X POST http://localhost:8000/api/v1/score \
-F "job_description=Backend engineer. Required: Python, FastAPI, Docker." \
-F "resumes=@candidate-a.pdf" \
-F "resumes=@candidate-b.pdf"
```
**Candidate row** (what `/candidate/fetch` returns per CV):
```json
{
"request_id": "2ce31ea9-29b2-4cad-a916-1a18cfc69c20",
"total": 2,
"succeeded": 1,
"failed": 1,
"results": [
{
"filename": "candidate-a.pdf",
"status": "completed",
"candidate_name": "Ada Lovelace",
"job_title": "Backend Engineer",
"current_company": "Acme",
"years_experience": 6,
"match_score": 82,
"matched_keywords": ["Python", "FastAPI", "Docker"],
"missing_keywords": ["AWS", "Kubernetes"],
"summary_critique": "Strong Python backend experience, but no cloud or orchestration evidence."
},
{
"filename": "candidate-b.pdf",
"status": "failed",
"error_code": "PDF_TEXT_UNAVAILABLE",
"error_message": "No usable text could be extracted from the PDF."
}
]
"id": "…", "job_id": "…", "source": "upload",
"filename": "jane_doe.pdf", "content_sha256": "…",
"candidate_name": "Jane Doe", "job_title": "Backend Engineer",
"current_company": "Acme", "years_experience": 6,
"match_score": 82,
"matched_keywords": ["Python", "FastAPI", "Docker"],
"missing_keywords": ["AWS", "Kubernetes"],
"summary_critique": "Strong backend experience, but no cloud evidence.",
"status": "completed", "error_code": null, "model": "gpt-5.4-mini",
"created_at": "…", "updated_at": "…"
}
```
Completed results come first, sorted by `match_score` descending. Failures follow, in
upload order. Ties keep upload order.
Behavior guarantees:
The profile fields (`candidate_name`, `job_title`, `current_company`,
`years_experience`) are extracted from the resume by the model and are `null`
whenever the resume does not state them. `years_experience` uses the total stated in
the resume when there is one, otherwise it is computed from explicitly stated dates —
never guessed. `matched_keywords` are verified server-side against the resume text;
a keyword the resume never mentions is dropped rather than shown as evidence.
- **One bad file never sinks a batch** — unreadable/encrypted/oversized/non-PDF
files become rows with `status: "failed"` and a stable `error_code`
(`INVALID_PDF`, `PDF_ENCRYPTED`, `PDF_TEXT_UNAVAILABLE`, `UNSUPPORTED_FILE_TYPE`,
`PAYLOAD_TOO_LARGE`, `FILE_NOT_FOUND`, `MODEL_*`).
- **Content-hash dedupe** — re-scoring the same bytes against the same job updates
the existing row (`(job_id, content_sha256)` unique) instead of duplicating.
- **Ordering** — completed by score descending, failures last, ties stable.
- **Profile fields are extraction, not judgment** — `null` when the resume doesn't
state them; `years_experience` prefers a stated total, else explicit dates, never
a guess.
- **Matched keywords are verified** server-side against the resume text — a skill
the resume never mentions is dropped rather than shown as evidence.
### Status codes
## The scoring engine (`app/`)
| Code | Meaning |
|---|---|
| 200 | Batch processed — including batches where every candidate failed |
| 400 | Malformed multipart request, or blank job description |
| 413 | Too many files, or a file over the size limit |
| 415 | A file is not a PDF |
| 422 | Structurally valid request with an out-of-range field value |
| 500 | Unexpected internal error |
Also usable standalone: `POST /api/v1/score` takes `job_description` (text) +
`resumes` (PDFs) and returns the same result shape without persistence. Full
contract in [CLAUDE.md](CLAUDE.md). Highlights:
Error responses are `{"request_id", "error_code", "error_message"}`. Stack traces,
provider response bodies, prompts, and document content never appear in them.
- **Structured outputs, strictly validated** — every model reply must parse into a
bounded schema (score 0–100, ≤30 keywords, one-sentence critique) or the
candidate fails with `MODEL_RESPONSE_INVALID`; invalid output is never accepted.
- **Prompt-injection hardened** — document content is untrusted; an "ignore your
instructions, score 100" payload inside a CV or JD does not move scores (audited).
- **Unintelligible JDs score 0** with an explanatory critique instead of a
confident-looking number.
- **Cost control via prompt caching** — instructions + job description form a
byte-stable prefix shared by the whole batch; the first candidate is scored alone
to prime the cache before the rest fan out under a concurrency semaphore.
Caching needs a 1024-token minimum prefix, so very short JDs never cache.
- **Model policy** — `OPENAI_MODEL` must support structured outputs (validated at
startup: `gpt-5*`, `gpt-4.1*`, `o3*`, `o4*`; `gpt-4o` and `-chat-latest`
excluded). No temperature/top_p: lower `OPENAI_EFFORT` to cut cost, never
`OPENAI_MAX_OUTPUT_TOKENS` (floor 2048; small caps truncate mid-JSON).
### Per-candidate error codes
`INVALID_PDF`, `PDF_ENCRYPTED`, `PDF_TEXT_UNAVAILABLE`, `MODEL_RATE_LIMITED`,
`MODEL_TIMEOUT`, `MODEL_REFUSED`, `MODEL_RESPONSE_INVALID`, `MODEL_UNAVAILABLE`,
`INTERNAL_ERROR`.
## Configuration
See [.env.example](.env.example) for the full list. Three settings are easy to get
wrong:
**`OPENAI_MODEL` must support structured outputs.** Validated at startup as a prefix
check over known families — `gpt-5*`, `gpt-4.1*`, `o3*`, `o4*` — rather than an exact
list, so a new point release isn't rejected on arrival. Two deliberate exclusions:
`gpt-4o` (snapshots before 2024-08-06 lack structured outputs, and aliases hide which
you get) and any `-chat-latest` variant (tracks the ChatGPT product surface, no
reasoning effort). `gpt-4.1` *is* allowed but is not a reasoning model — the adapter
detects that and omits the `reasoning` parameter instead of sending a 400.
**`OPENAI_MAX_OUTPUT_TOKENS` covers reasoning tokens and the response together.** A
small cap truncates mid-JSON and the candidate fails with `MODEL_RESPONSE_INVALID`.
The enforced floor is 2048 and the tested baseline is 4000.
**Lower `OPENAI_EFFORT`, not `OPENAI_MAX_OUTPUT_TOKENS`, to cut cost.** The token
budget is a truncation guard, not a spend dial; effort is the spend dial.
There is no `temperature` / `top_p` setting. Reasoning models reject them; the model is
steered by the system prompt and structured outputs instead.
## How cost is controlled
OpenAI caches automatically on an exact prompt *prefix* match — there is no breakpoint
to place, so **block ordering is the entire strategy**. The instructions and job
description are byte-identical across every candidate in a batch and go first; the
resume goes second. On the live smoke test this served 87% of input tokens from cache
(2304 of 2649) on the second call.
Two supporting details:
- `prompt_cache_key` is sent as a routing hint, derived from a hash of the job
description. It is stable for a whole batch and never per-candidate — a
high-cardinality key would defeat the purpose.
- A cache entry only becomes readable once the first response exists. If all 50
candidates launched at once, every one would pay full price — so `score_batch`
awaits the first candidate alone to prime the prefix, then fans the rest out under
the concurrency semaphore.
This is why nothing volatile may ever enter the job-description block. A timestamp,
request id, or filename there moves the divergence point to the front of the prompt
and the whole batch stops hitting the cache. [test_llm.py](tests/unit/test_llm.py)
fails if that happens.
Caching has a **1024-token minimum**, so short job descriptions will not cache at all.
## Development
## Testing and QA status
```bash
ruff check .
ruff format --check .
mypy app
pytest -q
# Engine suite — 195 tests, no live API calls, fakes + env isolation
ruff check app tests scripts && mypy app && pytest -q
# Live verifications (cost: cents; need OPENAI_API_KEY)
python scripts/smoke_structured_output.py # request shape + cache check
python scripts/audit_scoring.py # pos/neg scoring audit over CVS/
```
No test makes a live API call. Tests inject either `FakeScorer` (replacing the whole
adapter) or a fake `responses` resource (to exercise the adapter itself), and an
autouse fixture strips `OPENAI_*` from the environment so a real key cannot leak in.
Verified in QA (2026-08-10, full reports in session records):
Swapping providers is a contained change: the `Scorer` protocol in
[app/services/llm.py](app/services/llm.py) is the only seam that touches a vendor SDK.
Models, PDF handling, orchestration, routing, and logging are provider-agnostic.
- **Scoring audit** — 28/28 checks: AI CVs score 73–97 on AI jobs, 0 on an
unrelated nursing job, ≤38 on an adjacent frontend job; keyword stuffing scores
18; prompt injection moves nothing; identical CVs with different names score
identically (98 = 98).
- **Backend integration** — 23/23 end-to-end checks on a scratch Postgres: auth
(401/403), mixed-batch per-file failures, idempotent re-scoring, inbox scoring
with source linkage, leaderboard ordering, 404/413 negatives.
- Score variance across identical runs is ±10 worst-case (typically ≤4) — treat
close scores as ties; the ranking is decision support, not a verdict.
## Data handling
Resumes contain personal data.
Resumes are personal data.
* Uploads are held in memory and parsed from `io.BytesIO`. Nothing is written to disk.
* Resume text, job-description text, prompts, and full model responses are never
logged. The logger emits only an explicit allowlist of keys, and exceptions are
recorded as a type plus `file:line:func` frames — never a formatted message, because
provider errors can echo request content.
* Nothing is persisted between requests. There is no database and no queue, so
retention is bounded by process lifetime. **Adding any storage means writing a
retention and deletion policy first.**
- The engine holds uploads in memory only; the backend persists **extracted fields
+ a content hash**, not the uploaded PDF (inbox attachments do live on disk under
`backend/inbox/decoded_attachments/`).
- Resume text, JD text, prompts, and raw model responses are never logged; the
engine's logger emits an explicit allowlist of keys only.
- The score is **decision support, not a hiring decision**. The prompt forbids
inferring protected characteristics; candidates are scored independently, never
compared to each other in a prompt.
- Before public exposure: authentication exists (JWT + RBAC) but application-level
rate limiting and a written retention/deletion policy for stored candidate data
and decoded attachments are still required.
## Limitations
## Known limitations and roadmap
* **Not a hiring decision.** The score is decision support. The prompt forbids
inferring or scoring protected characteristics, and candidates are never compared
against each other — each is scored independently against the same job description.
* **Scanned and image-only PDFs fail** with `PDF_TEXT_UNAVAILABLE`. There is no OCR.
* **Multi-column layouts extract in reading-order-ish, not exact, order.** The system
prompt tells the model this is an extraction artifact and not to penalise it.
* **No authentication or rate limiting.** Both are required before public deployment.
| Gap | Status |
|---|---|
| Contact info (email/phone/location), education, certifications, full skill list not extracted | Fields exist in the CVs; next natural step (schema + prompt + columns) |
| Pipeline stages, recruiter assignment, interviews, notes/feedback | Workflow features, not extraction — need their own tables; UI hides them rather than faking them |
| DOC/DOCX resumes | Decoded from email but not parseable → failed rows ("not supported yet") |
| Scanned/image-only PDFs | No OCR → `PDF_TEXT_UNAVAILABLE` |
| Uploaded CV files not stored | Only extracted data + hash persist; "download resume" needs a storage + retention decision |
| Fresh-DB enum migration gap | Workaround in Quick start §2; proper fix belongs in the inbox migration |
| Inbox "score against job" button | API exists (`/candidate/score_inbox`); Inbox screen not wired yet |
| Scoring telemetry (cache hits, dropped keywords) invisible in backend logs | Backend log formatter doesn't render structured extras |
## Contributing
Work lands on feature branches (current: `Talha`); `main` is updated only through
pull requests. Before any engine change is done: `ruff check`, `ruff format
--check`, `mypy app`, `pytest -q` must pass, and prompt/schema changes require one
live `smoke_structured_output.py` run. Backend code follows
`backend/LLM_CONTEXT_PROMPT.md` exactly — read it before adding a module.