89ca9ad647
On-demand Scout service (replaces the nightly aggregator): - FastAPI agent-mesh service; card name "matchmaking-service" (HTTP /a2a/tasks + Redis-stream worker matching the sibling-service pattern) - run_search: parallel multi-board sweep (Naukri/blackfalcondata + Foundit + LinkedIn), city-filtered at the board, cheapest-per-result first - per-board adapters + normalizers -> ScoutJob with rich read-only details and offsite apply links; recall mode + MVQ guard - result cache for dev replay ($0); per-board cost knobs; retry-on-5xx - research/: engine design, board cost economics, India-actor shortlist, POC
122 lines
5.4 KiB
Python
122 lines
5.4 KiB
Python
"""
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Input coercion layer — turns the frontend `ScoutPrefs` (JSON dict) into each board
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actor's exact input schema. See `docs/ENGINE_DESIGN.md` §1.2.
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All maps verified against the live Apify input schemas (2026-06-17).
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GOLDEN RULE: a value that doesn't resolve to a board enum/code is NEVER sent as an
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invalid enum — it degrades to a keyword fold (or post-filter) and is flagged.
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Coverage gaps lose precision, never the call.
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"""
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from __future__ import annotations
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import re
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from typing import Any, Optional
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Prefs = dict[str, Any]
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# ── lookup maps (verified codes/enums) ───────────────────────────────────────
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COUNTRY_ISO2 = { # Indeed `country`
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"India": "IN", "United States": "US", "United Kingdom": "GB", "Canada": "CA",
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"Australia": "AU", "Germany": "DE", "Singapore": "SG", "UAE": "AE",
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}
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WORKMODE_NAUKRI = {"Remote": "remote", "Hybrid": "hybrid", "On-site": "office"}
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WORKMODE_LINKEDIN = {"Remote": "remote", "Hybrid": "hybrid", "On-site": "office"}
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SENIORITY_YEARS = {"Fresher": 0, "Junior": 2, "Mid-level": 5, "Senior": 9, "Lead": 13}
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SENIORITY_LINKEDIN = { # LinkedIn `experienceLevel`
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"Fresher": "entry", "Junior": "associate", "Mid-level": "mid-senior",
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"Senior": "director", "Lead": "executive",
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}
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COMP_NAUKRI_SALARY = { # ₹ band → Naukri `salaryRange` (lakhs bucket)
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"₹10–14L": "10to15", "₹14–18L": "15to25", "₹16–22L": "15to25",
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"₹22–30L": "25to50", "₹30L+": "50to75",
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}
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COMPANY_TYPE_NAUKRI = { # loose — funding stage ≠ company type
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"Seed / early": "Startup", "Growth-stage": "Startup",
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"Late / pre-IPO": "Corporate", "Enterprise": "MNC",
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}
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# Naukri numeric codes (labels are display-only — the actor wants the code)
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ROLE_NAUKRI_DEPT = { # role area → `department`
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"Product": 10, "Engineering": 5, "Design": 15, "Data": 3,
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"Marketing": 9, "Sales": 14, "Ops": 2, # "Open to any" → omit
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}
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INDUSTRY_NAUKRI = { # → `industry`
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"Fintech": 114, "SaaS": 110, "E-commerce": 108, "EdTech": 133,
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"HealthTech": 131, "Dev tools": 110, "Consulting": 126,
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}
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CITY_NAUKRI = { # → `cities` (1→N for metro regions)
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"Bengaluru": [97], "Bangalore": [97], "Mumbai": [134], "New Delhi": [6], "Delhi": [6],
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"Delhi NCR": [6, 73, 220, 350], "Gurgaon": [73], "Gurugram": [73], "Noida": [220],
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"Hyderabad": [17], "Chennai": [183], "Pune": [139], "Kolkata": [232],
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}
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# LinkedIn industry IDs — best-effort, TODO verify vs LinkedIn taxonomy; unmapped → keyword fold
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INDUSTRY_LINKEDIN_ID = {
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"Fintech": "1742", "SaaS": "4", "E-commerce": "6", "EdTech": "68",
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"HealthTech": "14", "Dev tools": "4", "Consulting": "11",
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}
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# ── parsing + accessors ──────────────────────────────────────────────────────
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def parse_location(loc: str) -> dict:
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"""'Bengaluru · India' → {city, country, remote, anywhere}."""
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s = (loc or "").strip()
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low = s.lower()
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if low == "remote":
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return {"city": None, "country": None, "remote": True, "anywhere": False}
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if low in ("anywhere", ""):
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return {"city": None, "country": None, "remote": False, "anywhere": True}
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parts = [p.strip() for p in re.split(r"[·,]", s) if p.strip()]
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return {
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"city": parts[0] if parts else None,
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"country": parts[1] if len(parts) > 1 else None,
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"remote": False, "anywhere": False,
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}
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def parsed_locations(prefs: Prefs) -> list[dict]:
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locs = [parse_location(l) for l in (prefs.get("location") or [])]
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return locs or [{"city": None, "country": None, "remote": False, "anywhere": True}]
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def is_remote(prefs: Prefs) -> bool:
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return any(l["remote"] for l in parsed_locations(prefs)) or "Remote" in (prefs.get("workMode") or [])
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def title_of(prefs: Prefs) -> str:
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"""Keyword precedence: specific fine-tune pick → typed title → first concrete role."""
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tt = prefs.get("targetTitles") or []
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if tt:
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return tt[0]
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if (prefs.get("title") or "").strip():
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return prefs["title"].strip()
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for r in (prefs.get("role") or []):
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if r and r != "Open to any":
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return r
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return ""
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def seniority_band(prefs: Prefs) -> Optional[str]:
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exp = prefs.get("experience") or []
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return exp[0] if exp else None
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def years_of(prefs: Prefs) -> Optional[int]:
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"""Numeric years: explicit `years` → else derived from the seniority band."""
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y = prefs.get("years")
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if isinstance(y, (int, float)):
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return max(0, min(30, int(y)))
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b = seniority_band(prefs)
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return SENIORITY_YEARS.get(b) if b else None
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def build_keyword(prefs: Prefs, *, seniority: bool = False, industry: bool = False) -> str:
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"""Composite keyword for boards lacking department/industry filters."""
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parts: list[str] = []
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if seniority and seniority_band(prefs) in ("Senior", "Lead"):
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parts.append(seniority_band(prefs)) # prefix only the high bands
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parts.append(title_of(prefs))
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if industry:
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inds = prefs.get("industry") or []
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if inds and inds[0] not in ("Any", None):
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parts.append(inds[0])
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return " ".join(p for p in parts if p).strip()
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def has_mvq(prefs: Prefs) -> bool:
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"""Minimum Viable Query = a title + a resolvable location (city/country/remote)."""
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if not title_of(prefs):
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return False
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return any(l["city"] or l["country"] or l["remote"] for l in parsed_locations(prefs))
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