eb6ded5b3d
The sift was feeding Opus a magnitude-blind, under-trusted, truncated shortlist — only 68% of the genuinely-best jobs reached the curator. Fixes: - embed.py: embed the FULL description (was desc[:600]) + role_category + industry; richer profile text (current_role + experience summary + seniority). The best semantic signal, fed real content. - sift.py: magnitude-preserving min-max fusion (was rank-position, which flattened cosine 0.95 vs 0.72) and embedding-LED weights (W_VIBE 0.6 / W_WHITEBOX 0.4). Embed runs FIRST so the white-box f_semantic reuses the real cosine (threaded as job["_vibe_cosine"]) instead of token overlap. - config.py: SIFT_TOP_K 18 → 28 (the gate was cutting good jobs before Opus). - curate.py: richer Opus briefs — full responsibilities (was desc[:500]) + role_category/industry/ seniority/pay, so even the shortlist is fully described. Regression (120 pairs): sift membership recall 0.68 → 0.92, score MAE 24.7 → 18.4. 31 tests pass.
matchmaking-v2 (Scout)
Fresh, on-demand matchmaking service replacing the dead nightly aggregator. Same agent-mesh mold as the other GrowQR services (FastAPI · a2a card · /a2a/tasks · orchestrator-routed). Not a drop-in — it keeps the existing 4 skills working and adds new actions from scratch.
Layout
app/
main.py FastAPI app (card + /a2a/tasks + /api/v1/health), lifespan worker
config.py lean settings (no corpus DB, no scrape schedule)
a2a/ card.py (discovery), auth.py (bearer), tasks.py (orchestrator entry)
agent/session.py Session: on_session_start / on_user_action dispatch ← the brain
adk/worker.py Redis-Streams worker (graceful no-op without Redis)
api/v1/health.py /api/v1/health
engine/
board_adapters/ ScoutPrefs → Apify actor inputs (Naukri-first, verified maps)
... the §3 cascade (normalize→filter→utility→fusion→rerank) lands here
contracts/ Pydantic contracts (user_context, transport, …) — added per slice
research/ docs/ (ENGINE_DESIGN, SIGNAL_AUDIT_V2, ENGINE_INPUTS, …) + poc/ (auto-apply)
Contract (how it connects)
Frontend useAgentSession (page "job-matching") → orchestrator (routes by card name
= matchmaking-service) → POST /a2a/tasks {action, params, user_context} → Session pushes
agent_data{action,data} → orchestrator → frontend latestData[action].
Skills: existing get_feed · sync_preferences · record_feedback · get_opportunity_detail;
new run_search · tailor_resume · submit_application · get_apply_proof (stubbed). Each new action
needs: card skill (here) + orchestrator action-map entry + frontend sendAction wiring.
Run (local)
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8006
# card: GET http://localhost:8006/.well-known/agent-card.json
# health: GET http://localhost:8006/api/v1/health
# action: POST http://localhost:8006/a2a/tasks (Bearer dev-a2a-key)
Build order (full-stack slices)
- ✅ scaffold (this) — bootable skeleton, contract wired, handlers stubbed.
- on-demand
run_search— board_adapters → Apify → engine cascade → ranked feed (+ frontend wire). - feedback labels +
record_feedback. 3.tailor_resume. 4.submit_application+get_apply_proof. - cut over from old
:8006, decommission corpus.