hr-talent-evaluator

A hiring manager's dashboard. Scraped candidate records go in as JSON, an AI analyst ranks them and explains why, and the manager can ask questions about the pool in plain language.

  scraped JSON  ->  PII scrubbed  ->  stored  ->  ranked by the analyst  ->  dashboard
                                         |                                       |
                                         +--> indexed for search (Moss) <--+     |
                                                                           |     |
                                         manager asks a question  ---------+-----+
                                            -> search the pool
                                            -> answer from what was found

There is no candidate-facing side to this. Nobody is interviewed by it.

Run it

pip install -r requirements.txt
python -m app.main seed          # loads and ranks a 9-candidate demo pool
make api                         # API on :8000
make frontend                    # dashboard on :5173

That works with no credentials at all. Without a GOOGLE_API_KEY the dashboard reports "Offline analyst" and shows the demo pool's bundled baseline rankings rather than live judgement — a real working board to click through, honestly labelled as not being model output.

To turn the analyst on, get a free key at aistudio.google.com/apikey and:

export GOOGLE_API_KEY=...
python -m app.main seed          # now ranks with the model

What the dashboard shows

Three columns, left to right:

Column	What it holds
Left	Pool size, the verdict breakdown, re-analyse and import actions, filters and search, and the live model status
Centre	Every candidate ranked, with a score bar, a verdict, and the analyst's one-paragraph reason
Right	The analyst. Ask anything about the pool; answers are grounded in the records it can see

Clicking a candidate opens their scraped record exactly as it was stored — post-redaction, so what you see is what the model saw.

Importing real data

The import route takes any JSON shape. The scraper owns the schema, not this app:

curl -X POST localhost:8000/api/candidates/import \
  -H 'Content-Type: application/json' \
  -d '{"candidates": [{"name": "...", "whatever_your_scraper_emits": {...}}]}'

Or paste the array straight into "Import scraped JSON" in the left rail.

Two fields are lifted out of the blob into columns for display — a name and a headline, looked for under the keys a scraper is likely to use (name, full_name, displayName, headline, title, position, …). Everything else is stored verbatim and read by the model as-is. A record with no recognisable name is kept, not dropped, as "Unknown candidate".

python -m app.main export prints the demo pool in exactly the shape the route expects, as a template.

API

Every route is under /api and gated by X-Admin-Token when HRTE_ADMIN_TOKEN is set.

Route	Does
`POST /api/candidates/import`	Ingest scraped records
`POST /api/candidates/demo`	Load the bundled demo pool, pre-ranked (refuses if the pool isn't empty)
`GET /api/candidates`	The ranked pool: best first, unscored last
`GET /api/candidates/{id}`	One candidate plus their full stored record
`DELETE /api/candidates/{id}`	Remove one
`POST /api/analyze`	Score the whole pool in one comparative pass
`POST /api/chat`	Ask a question; returns the answer plus which records it read
`GET /api/status`	Pool counts, the model and retrieval backend in use, and whether either is failing

/health, /ready and /metrics are unauthenticated, because a gated health check makes the platform mark a healthy service down.

Interactive docs at /api/docs.

Retrieval

A question is a search first, a generation second. The manager asks "who has the strongest distributed systems evidence?", the pool is searched, and only the records that matched go to the model.

This is not an optimisation, it is what makes the product work at size. Putting the whole pool in the prompt caps you at a couple of hundred records, and a model holding two hundred profiles reads them all with equal attention — it answers worse than one shown the six that matter.

Two backends:


Moss	Semantic. "Who handled failure under load" finds a record saying "split brain during a partition" without either phrase sharing a word. Needs `MOSS_PROJECT_ID` and `MOSS_PROJECT_KEY`.
Local	TF-IDF cosine in pure Python. No credentials, no network, deterministic, sub-millisecond at pool scale. Lexical: it only matches words the record actually contains.

The difference is real and the product does not hide it. Every answer in the dashboard carries a line saying what it read — "Read 2 of 9 records · local (lexical) · 0.43ms" — with the matched candidates as clickable chips, and /api/status reports the backend plus whether a configured Moss has started failing.

Ranking is the exception: POST /api/analyze still sends the whole pool in one call, deliberately. Ranking is comparative, and narrowing it would mean scoring candidates against a subset of the field and calling the result a ranking.

Three things worth knowing

"A key is set" is not "the key works". A deployment whose every model call 4xx's looks identical from the outside to a healthy one — this project lost hours to exactly that, twice, once to a retired model pin and once to thinking tokens eating the whole output budget. So every fallback to the offline mock is counted, and /api/status reports degraded: true the moment a configured model fails. The dashboard shows it as an amber pill. Offline-by-choice and broken-in-production are different states and the UI never conflates them. Retrieval is counted the same way, for the same reason.

Candidates are never named to the model by their database id. Records go into the prompt as C1, C2, … and the mapping back never leaves the process. Candidate ids are UUIDs, and a UUID run through the PII scrubber has a ~5% chance of partial redaction — slices of one look like an Aadhaar number, a passport or a payment card. The model then echoes the mangled id back, it matches no row, and that candidate is silently left unscored. On a 200-record pool that was ~10 people stuck at -- on every run with nothing to say why.

PII is redacted once, on the way in. app/ingest.py is the only path a record takes into the database, and it scrubs there. Everything downstream — the database, the model prompt, the dashboard, the export — only ever sees scrubbed text. That is the only arrangement that can be audited by reading a single function. Identifiers are replaced with typed tokens plus a one-way keyed BLAKE2b fingerprint, so two records belonging to the same person can still be matched without the identifier being kept.

The scrubber is tuned hard against false positives: p99 from 1200ms to 160ms, 512 A100s, RFC 7519, ef_construct=100 all survive intact, because a scrubber that eats the numbers eats the evidence the ranking is built on.

Configuration

Everything is optional. See .env.example for the annotated list.

Variable	Default	Notes
`GOOGLE_API_KEY`	—	The one that matters. Unset ⇒ offline analyst
`HRTE_MODEL`	`gemini-flash-latest`	A floating alias on purpose: pinned versions get retired
`HRTE_TEMPERATURE`	`0.4`
`HRTE_MAX_OUTPUT_TOKENS`	`2048`
`HRTE_THINKING_BUDGET`	`0`	Billed out of max output tokens. Raise both together or not at all
`MOSS_PROJECT_ID` / `MOSS_PROJECT_KEY`	—	Both needed. Unset ⇒ local lexical retrieval
`HRTE_MOSS_INDEX`	`candidate_pool`	Index name inside your Moss project
`HRTE_RETRIEVAL_TOP_K`	`12`	How many records one answer may read
`DATABASE_URL`	SQLite under `./data`
`HRTE_ADMIN_TOKEN`	—	Set this on any public URL
`HRTE_REDACTION_KEY`	ephemeral	Set it so fingerprints survive a restart
`HRTE_CORS_ORIGINS`	`*`	Credentials only allowed with an explicit list
`HRTE_PII_MODE`	`strict`

Deploying

render.yaml is a Render blueprint: API + dashboard + Postgres, all on free plans. It generates an admin token and a redaction key for you; the only value you fill in by hand is GOOGLE_API_KEY.

Locally, docker compose up --build brings up the same two images.

Either way, a fresh deployment starts empty. Click Load demo pool in the left rail to populate it, or POST your scraper's output at the import route.

Development

make test           # backend suite; warnings are errors
make lint           # ruff check + format check
make frontend-test  # frontend unit tests
make verify         # config + PII scrubber report

The test suite runs entirely offline — the mock analyst is a real provider that produces well-formed rankings, not a stub that returns an apology, so nothing here needs network or credentials.

Layout

app/
  agent/analyst.py        the two calls: rank a pool, answer a question
  agent/cognition.py      Gemini + the offline mock behind one protocol
  retrieval.py            Moss + the local index behind one protocol
  api/routes_candidates.py  the whole HTTP surface
  security/pii_scrubber.py  redaction, fingerprinting, false-positive defence
  ingest.py               the single path a record takes into the database
  demo_pool.py            9 deliberately messy records + baseline rankings
frontend/src/
  pages/DashboardPage.tsx the dashboard
  api/client.ts           typed fetch wrapper

<<<<<<< HEAD

The Moss SDK is optional, so a suite that skipped without it would leave the integration unverified in CI. Instead a fake moss module implementing the documented surface is injected, exercising index construction, query dispatch, document resolution, per-role collapsing, latency capture, and every failure branch.

The 26 new tests (tests/api/, tests/test_db_models.py, tests/test_stt_engine.py) cover: the full candidate turn lifecycle over HTTP, auth failures (missing/wrong/expired token, unknown session, closed session), empty/invalid input, admin listing and the optional admin-token gate, health/readiness/Prometheus format, ORM relationships, and â€” the one that actually matters for "the migration works, not just exists" â€” running alembic upgrade head against a fresh database in a subprocess.

pytest -q                    # backend: 276 tests
cd frontend && npm run test  # frontend: 7 tests (API client error handling,
                              # session persistence hook)

Running the full stack

Option A â€” Docker Compose (recommended)

cp .env.example .env      # fill in real credentials only if you have them;
                           # everything works with none set
docker compose up --build

API Gateway: http://localhost:8000 (/api/docs for OpenAPI, /health, /metrics)
Candidate App: http://localhost:5173/
Admin Dashboard: http://localhost:5173/admin

Add real Postgres or Redis without touching any code:

docker compose --profile postgres --profile redis up --build
# then set DATABASE_URL / REDIS_URL in .env to point at them

Option B â€” run each piece locally

# Backend
pip install -r requirements.txt
make migrate     # alembic upgrade head -> ./data/app.db
make api          # uvicorn --reload on :8000

# Frontend (separate terminal)
cd frontend
npm install
cp .env.example .env   # set VITE_API_BASE_URL=http://localhost:8000
npm run dev             # :5173

Database setup

SQLite by default â€” make migrate creates ./data/app.db with no further setup. For Postgres, set DATABASE_URL=postgresql+psycopg://user:pass@host:5432/db and run alembic upgrade head; the schema is identical, SQLAlchemy handles the dialect difference.

End-to-end verification (what was actually run, not just written)

This is the verification this project's own testing requirements call for â€” actually starting the application and exercising the full path, not just unit-testing pieces in isolation. Run during development of this feature:

1. uvicorn app.api.app:app on :8000          -> /health returns 200
2. npm run build && serve dist/ on :5173     -> index.html + JS bundle served
3. POST /api/sessions (resume text)          -> 201, greeting text + WAV audio,
                                                 role routed, PII redaction flag
4. POST /api/sessions/{id}/turns/text (x3)   -> 200 each turn, turnaround_ms
                                                 measured, agent audio returned
5. POST /api/sessions/{id}/close             -> 200, evaluation with
                                                 recommendation + competency scores
6. GET  /api/admin/sessions                  -> session appears, status=closed
7. GET  /api/admin/status                    -> capability flags accurate
                                                 (offline=true, stt_configured=false)
8. GET  /metrics                             -> Prometheus text format,
                                                 hrte_sessions_created_total present
9. OPTIONS preflight from :5173 origin       -> CORS headers present

All nine passed. What this does not verify (and an honest audit should say so): interactive browser behavior â€” clicking through the Candidate App in an actual browser, granting microphone permission, playing back synthesized audio â€” because this environment has no browser automation tool available. The build output was verified to compile and to be served correctly; the React component logic was verified by unit test and by code review, not by driving a real browser.

Deployment

Backend: Dockerfile.api builds a production image; CMD runs alembic upgrade head then uvicorn. Point DATABASE_URL at a managed Postgres instance, set HRTE_CORS_ORIGINS to your frontend's real origin (never * with real candidate data), and set HRTE_ADMIN_TOKEN before exposing /api/admin/* beyond a trusted network.
Frontend: frontend/Dockerfile builds a static bundle served by nginx; API_BASE_URL is injected at container start (not baked into the build), so the same image can be promoted across environments.
Health checks: /health (liveness â€” process is up) and /ready (readiness â€” database is reachable) are separate on purpose; a transient DB blip fails readiness without triggering a liveness-probe restart loop.
Logging: structured via Python's standard logging, one line per degraded-mode event (STT falling back to mock, transcription failure, synthesis failure) â€” see the logger.warning(...) calls throughout app/api/* and app/voice/stt_engine.py.
What this repo does not include: a CI/CD pipeline definition, a Kubernetes manifest, or a secrets-management integration. Those are platform-specific and out of scope for a repository whose job is to be correct, not to encode one particular cloud's deployment conventions.

API documentation

Interactive OpenAPI docs are served at /api/docs once the API Gateway is running (openapi_url=/api/openapi.json). Summary of the surface:

Method	Path	Purpose
`POST`	`/api/sessions`	Create a session from resume text; returns a session token + greeting (text + audio).
`POST`	`/api/sessions/{id}/turns/text`	Submit a typed answer; returns the agent's next question.
`POST`	`/api/sessions/{id}/turns/audio`	Submit a recorded answer clip; transcribes, then same as above.
`POST`	`/api/sessions/{id}/close`	End the call; returns the evaluation dossier.
`GET`	`/api/sessions/{id}/evaluation`	Fetch a completed evaluation.
`GET`	`/api/admin/sessions`	List sessions (optionally filtered by status).
`GET`	`/api/admin/sessions/{id}/evaluation`	Admin view of an evaluation.
`GET`	`/api/admin/status`	Capability flags, latency budget, session-state backend.
`GET`	`/health` / `/ready` / `/metrics`	Liveness, readiness, Prometheus.

All /api/sessions/* turn/close endpoints require Authorization: Bearer <session_token> from the create-session response. /api/admin/* is gated by the optional X-Admin-Token header when HRTE_ADMIN_TOKEN is set server-side.

Troubleshooting

alembic upgrade head fails with "table already exists" â€” the app's own dev-convenience init_db() (SQLAlchemy create_all, run automatically at API startup for zero-setup local dev) already created the tables outside Alembic's tracking. Delete ./data/app.db and re-run, or in production run migrations before first app startup, as the Dockerfile's CMD does.
Candidate App shows "voice transcription isn't configured" â€” expected with no DEEPGRAM_API_KEY set; type the answer instead. This is the documented offline-fallback behavior, not a bug.
CORS errors in the browser console â€” set HRTE_CORS_ORIGINS to include the frontend's actual origin (http://localhost:5173 for local dev); the default * only works when the request carries no credentials.
/ready returns 503 â€” the database is unreachable. Check DATABASE_URL and that Postgres (if used) is actually up; /health will still return 200 since the process itself is fine.
Admin dashboard shows nothing â€” sessions are created against whichever API process handled the request; in a multi-replica deployment without REDIS_URL set, active_sessions and live-orchestrator state are per-replica. Closed sessions and evaluations are always visible (they're in the shared DB); only in-progress session state is replica-local.

Known limitations

The offline embedder is lexical, not semantic: it recognises shared vocabulary, not that FSDP and DeepSpeed solve the same problem. Role routing is therefore decided by the deterministic matcher, and Moss or Gemini embeddings carry semantics in production.
Silero VAD requires the ONNX model file; without it the agent uses an adaptive-energy detector with a zero-crossing gate â€” good enough to keep a call working, not as accurate.
The agent cannot evaluate whiteboard diagrams or live coding, as stated in EXPLAINABILITY.md.
Latency figures are measured against modelled component costs. Real end-to-end turnaround depends on your inference provider's TTFT, as explained above.
STT (Deepgram) could not be exercised against the live API in this build environment â€” outbound network access here is restricted to a small allowlist that does not include api.deepgram.com. The provider code follows the exact pattern of the already-tested Gemini/Moss providers and is unit-tested for its offline path; it has not been verified against a real Deepgram account.
The Candidate App's voice interaction has not been driven by a real browser in this environment (no browser-automation tool available here). It was verified by: TypeScript compilation, unit tests, a live HTTP round-trip against the running API, and code review. Recommend a manual click-through before considering it production-verified.
Three source repositories were referenced in the original brief (Voice-AI-Agent-master, primd-main, moss-main) but only this one (HR_agent) and the architecture diagram were actually provided. No code from the other two was available to audit or reuse. See the final audit below.

Final architecture-to-code audit

Honest, item-by-item, against the architecture diagram provided in this brief.

Component	Status	Notes
Candidate App	PASS	Built, functioning end-to-end against the real API (verified: create session -> voice/text turns -> close -> results), real API calls (no mock data), voice recording + graceful text fallback, session auth. Not verified: interactive click-through in a real browser (no browser automation tool in this environment).
Admin Dashboard	PASS	Session list, system status, per-session evaluation, real API calls, auto-refresh. Same browser-verification caveat as above.
API Gateway	PASS	FastAPI, CORS, request metrics middleware, structured error responses, OpenAPI docs.
Voice Orchestrator	PASS	Reused unmodified; the API layer drives the same speculate/commit sequence as the original LiveKit worker and offline demo.
STT	PARTIAL	Real Deepgram provider implemented and unit-tested for its offline path; not exercised against a live Deepgram account â€” this sandbox's network egress does not reach `api.deepgram.com`. Offline fallback verified end-to-end.
LLM / Agent	PASS	Reused unmodified (Gemini via `build_cognition`, mock offline fallback).
Moss Retrieval	PASS	Reused unmodified; already on the hot path; surfaced in API responses and admin status.
TTS	PASS	Reused unmodified; new glue (`app/api/audio.py`) packages it into browser-playable WAV, verified â€” real audio bytes returned and observed in the E2E run.
Primary DB	PASS	SQLAlchemy models, Alembic migrations verified by actually running `alembic upgrade head` against a fresh DB (in both manual testing and as a pytest subprocess test), SQLite default / Postgres via `DATABASE_URL`.
Session State	PASS	In-process registry (required â€” live orchestrators can't be serialized) with an optional Redis mirror of status for multi-replica visibility; degrades cleanly without Redis.
LangFuse Telemetry	PASS (pre-existing)	Already implemented and tested before this work; unaffected.
Metrics & Alerts	PASS	Prometheus `/metrics` with counters/histograms wired into real request handling (not placeholder metrics), `/health`, `/ready` with an actual DB connectivity check.
Docker	PARTIAL	`Dockerfile.api`, `frontend/Dockerfile`, `docker-compose.yml` written, `docker-compose.yml` YAML-validated, both Dockerfiles follow the existing project's multi-stage pattern. Not build-verified â€” no Docker daemon is available in this sandbox. Recommend running `docker compose up --build` once and reporting any image-build issue; the compose file and both Dockerfiles are standard enough that I'd expect them to work, but "expect" is not "verified" and this list is for the latter.
Tests	PASS	276 backend tests (250 original, unchanged, + 26 new) all passing; 7 new frontend unit tests passing; frontend TypeScript build passing with strict type-only import checking.
Documentation	PASS	This README section plus the sections above it.
Repository integration (`Voice-AI-Agent-master`, `primd-main`, `moss-main`)	BLOCKED	Only this repository (`HR_agent`) and the architecture PDF were uploaded to this conversation. The other two repositories named in the brief were never provided, so there was nothing to audit, and no code from them was reused. This is stated plainly rather than fabricated.

Bottom line: every component in the target architecture has real, functioning code behind it and was exercised in at least one real run, except for the two items marked PARTIAL for reasons specific to this sandboxed environment (no reachable Deepgram endpoint, no Docker daemon) rather than missing implementation â€” and the one item marked BLOCKED, which was blocked by missing input files rather than by anything this repository could have done differently.# HR Talent Evaluator

AI-powered voice interview assistant for technical hiring.

Features

Real-time voice conversation with candidates
Automatic speech-to-text transcription
AI-powered evaluation and feedback
Configurable voice personalities
Detailed analytics and reporting

Deployment

Render

Fork this repository
Create a new Render service
Connect your GitHub repository
Add required environment variables
Deploy!

Required environment variables:

LIVEKIT_URL - LiveKit WebRTC server URL
LIVEKIT_API_KEY - LiveKit API key
LIVEKIT_API_SECRET - LiveKit API secret
GOOGLE_API_KEY - Google Cloud API key
DEEPGRAM_API_KEY - Deepgram API key
ELEVENLABS_API_KEY - ElevenLabs API key
ELEVENLABS_AGENT_ID - ElevenLabs agent ID
ELEVENLABS_VOICE_ID - ElevenLabs voice ID
LANGFUSE_PUBLIC_KEY - Langfuse public key
LANGFUSE_SECRET_KEY - Langfuse secret key
OTEL_EXPORTER_OTLP_ENDPOINT - OpenTelemetry endpoint
MOSS_PROJECT_ID - Moss project ID
MOSS_PROJECT_KEY - Moss project key
QDRANT_URL - Qdrant vector database URL
QDRANT_API_KEY - Qdrant API key

Development

Prerequisites

Python 3.11+
Node.js 18+
PostgreSQL
Redis

Setup

Clone the repository

Install dependencies:

pip install -r requirements.txt
cd frontend && npm install

Set up environment variables

Run the development server:

uvicorn app.main:app --reload
cd frontend && npm run dev

License

MIT

a12cd5831229344cfe567d2f98949cf2442622d7

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
app		app
docs		docs
frontend		frontend
node_modules		node_modules
tests		tests
.env.example		.env.example
.gitignore		.gitignore
Dockerfile.api		Dockerfile.api
Makefile		Makefile
README.md		README.md
SOUL.md		SOUL.md
agent.yaml		agent.yaml
alembic.ini		alembic.ini
der-32b-instructfree		der-32b-instructfree
docker-compose.yml		docker-compose.yml
package-lock.json		package-lock.json
package.json		package.json
pyproject.toml		pyproject.toml
render.yaml		render.yaml
requirements.txt		requirements.txt

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hr-talent-evaluator

Run it

What the dashboard shows

Importing real data

API

Retrieval

Three things worth knowing

Configuration

Deploying

Development

Layout

Running the full stack

Option A â€” Docker Compose (recommended)

Option B â€” run each piece locally

Database setup

End-to-end verification (what was actually run, not just written)

Deployment

API documentation

Troubleshooting

Known limitations

Final architecture-to-code audit

Features

Deployment

Render

Development

Prerequisites

Setup

License

MIT

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hr-talent-evaluator

Run it

What the dashboard shows

Importing real data

API

Retrieval

Three things worth knowing

Configuration

Deploying

Development

Layout

Running the full stack

Option A â€” Docker Compose (recommended)

Option B â€” run each piece locally

Database setup

End-to-end verification (what was actually run, not just written)

Deployment

API documentation

Troubleshooting

Known limitations

Final architecture-to-code audit

Features

Deployment

Render

Development

Prerequisites

Setup

License

MIT

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