Files
matchmaking-v2/app/engine/search.py
raulgupta b1e9cdd182 Engine v2: embeddings sift + Opus curator + Postgres feed persistence
- A1: fire the hard filter (Apify is precise); embedding vibe-ranker (embed.py)
  blends with the floor-free white-box (rank.py) to sift ~90 -> top 18
- A2: Opus curator (curate.py) reads the 18 -> honest <=count + Gemini-voiced
  report cards; graceful fallback to the white-box + templated cards
- LLM via opencode.ai/zen gateway (llm.py): Opus chat + direct-OpenAI embeddings
- Run LLM work off the event loop (asyncio.to_thread) so the Redis response publishes
- C1: dedicated Postgres (app/db/) persists the per-user feed; get_scout_feed
  replays it so matches survive navigation/refresh
- match contract + report-card fields (schema.py); skills.py; tests/
2026-06-19 15:51:16 +05:30

86 lines
3.8 KiB
Python

"""On-demand multi-board sweep: ScoutPrefs → all boards (parallel) → merge + dedup → ScoutJob[].
This is the retrieve layer (final shape). Slice-1a has NO engine — ranking is a placeholder
spread; the §3 cascade slots in here later without changing the output contract.
Degrade-don't-break: a board that errors/needs setup is skipped; the others still return.
"""
from __future__ import annotations
import asyncio
import logging
from app.config import get_settings
from app.engine.apify_client import run_actor
from app.engine.board_adapters import adapters as A
from app.engine.board_adapters import coerce as C
from app.engine import normalize as N
logger = logging.getLogger(__name__)
def _ats_companies() -> list[dict]:
return [{"company": c.strip()} for c in get_settings().ATS_COMPANIES.split(",") if c.strip()]
# board key → (actor_id, build_input, normalizer). "Balanced" India stack = naukri+foundit+linkedin,
# all city-filtered at the board. The rest stay registered (code ready) but off unless enabled.
BOARDS = {
"naukri": ("blackfalcondata~naukri-jobs-feed",
lambda p: A.build_naukri_feed_input(p, max_jobs=get_settings().NAUKRI_MAX_JOBS),
N.naukri_feed_to_scoutjob),
"foundit": ("shahidirfan~Foundit-Jobs-Scraper",
lambda p: A.build_foundit_input(p, results_wanted=get_settings().FOUNDIT_MAX_JOBS),
N.foundit_to_scoutjob),
"linkedin": ("harvestapi~linkedin-job-search",
lambda p: A.build_linkedin_input(p, recall=True, max_items=get_settings().LINKEDIN_MAX_JOBS),
N.linkedin_to_scoutjob),
# ── registered fallbacks / other lanes (off unless added to BOARDS_ENABLED) ──
"ats": ("bovi~greenhouse-lever-ashby-job-scraper",
lambda p: A.build_ats_input(p, recall=True, companies=_ats_companies(),
max_per_company=get_settings().ATS_MAX_PER_COMPANY),
N.ats_to_scoutjob),
"naukri_v1": ("muhammetakkurtt~naukri-job-scraper",
lambda p: A.build_naukri_input(p, recall=True, max_jobs=get_settings().NAUKRI_MAX_JOBS),
N.naukri_to_scoutjob),
"indeed": ("misceres~indeed-scraper",
lambda p: A.build_indeed_input(p, max_items=get_settings().INDEED_MAX_JOBS),
N.indeed_to_scoutjob),
}
async def _fetch_board(key: str, prefs: dict, cache_mode: str | None = None):
actor, build, norm = BOARDS[key]
items = await run_actor(actor, build(prefs), cache_mode=cache_mode)
jobs = [sj for it in items if (sj := norm(it))]
return key, jobs
async def run_sweep(prefs: dict, *, fresh: bool = False) -> dict:
# fresh=True forces a live fetch + cache overwrite (dev "fresh search" toggle);
# otherwise honor the configured cache mode (dev replays cached results for $0).
cache_mode = "refresh" if fresh else None
enabled = [b.strip() for b in get_settings().BOARDS_ENABLED.split(",") if b.strip() in BOARDS]
results = await asyncio.gather(*(_fetch_board(k, prefs, cache_mode) for k in enabled), return_exceptions=True)
merged: list[dict] = []
seen: set[str] = set()
sources: dict[str, int] = {}
for r in results:
if isinstance(r, Exception):
logger.warning("board fetch failed: %s", r)
continue
key, jobs = r
sources[key] = len(jobs)
for j in jobs:
dk = (j.get("apply_url") or "").strip() or f"{j['organization']}|{j['title']}|{j.get('location_city')}".lower()
if dk in seen:
continue
seen.add(dk)
merged.append(j)
# No ranking here — the engine (app/engine/rank.py) scores + selects after the sweep.
return {"opportunities": merged, "sources": sources}
def has_mvq(prefs: dict) -> bool:
return C.has_mvq(prefs)