ecf5060acd
- suggest.py: stage-aware engine (broad -> narrow -> role) — LLM (claude-haiku-4-5) generates bubbles per stage from the picks so far. Profile-grounded, generalizes to any field (English professor -> Senior Instructional Designer, etc.); None on no input -> frontend keeps seeded bubbles - handle_suggest_bubbles action (run off the event loop) + SUGGEST_MODEL config - bundled: LLM work (sift/curate/suggest) now via asyncio.to_thread so the Redis response can publish (fixes the loader hang); get_feed handler -> get_scout_feed (avoids the course-service action collision)
91 lines
4.0 KiB
Python
91 lines
4.0 KiB
Python
"""The bubbling engine — the Fine-tune "pathway finder".
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One job: walk a user **broad → narrow → role** via three bubbler stages, generating each stage from
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the user's PICKS so far (so it genuinely narrows). Same journey for everyone — a résumé just warms up
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the first stage; a no-résumé user starts broad and funnels to the same concrete role.
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stage "role" → BROAD role areas / domains (input: profile, if any)
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stage "industry" → industries that fit the liked areas (input: role picks + profile)
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stage "relevant" → SPECIFIC pursuable job titles (input: role + industry picks + profile)
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Returns a list of short labels, or `None` on any failure / no usable input (the frontend then keeps
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its seeded bubbles). Uses a fast/cheap model (latency-sensitive UI).
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"""
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from __future__ import annotations
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import json
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import logging
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from app.config import get_settings
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from app.engine.curate import _parse_json # tolerant JSON parser
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from app.engine.llm import curate_enabled, gateway_client
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logger = logging.getLogger(__name__)
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_STAGE = {
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"role": (
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'Suggest BROAD role areas / domains a job-seeker could explore — wide buckets, NOT specific job '
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'titles (e.g. "Data & AI", "Platform Engineering", "Product", "Growth"). Tailor to their role and '
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'skills if given; otherwise span common tech + business areas so anyone can start broad.'
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),
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"industry": (
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'The job-seeker is drawn to these role areas: {role}. Suggest INDUSTRIES / domains that fit those '
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'areas and their profile (e.g. "AI/ML", "Fintech", "Cloud Infrastructure", "Healthcare").'
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),
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"relevant": (
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'The job-seeker likes these role areas: {role}; and these industries: {industry}. Suggest SPECIFIC, '
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'pursuable JOB TITLES (with seniority, e.g. "Senior ML Engineer", "Staff Data Engineer") they could '
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'realistically target. These are the payoff of the funnel — concrete roles to pursue.'
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),
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}
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_COUNT = {"role": 8, "industry": 7, "relevant": 10}
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_COMMON = (
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'\nRespond with ONLY a JSON object {{"items":[...]}} — short labels (max ~4 words each), best-fit '
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'first, deduplicated, no prose.'
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)
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def _profile_brief(profile: dict) -> dict:
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return {
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"current_role": profile.get("current_role") or profile.get("title"),
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"seniority": profile.get("seniority") or profile.get("experience"),
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"years": profile.get("years"),
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"industry": profile.get("industry"),
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"skills": (profile.get("skills") or [])[:20],
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}
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def suggest(stage: str, picks: dict | None, profile: dict | None) -> list[str] | None:
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client = gateway_client()
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if not (curate_enabled() and client) or stage not in _STAGE:
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return None
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picks = picks or {}
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role_picks = ", ".join(picks.get("role") or []) or "(none yet)"
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ind_picks = ", ".join(picks.get("industry") or []) or "(none yet)"
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brief = _profile_brief(profile or {})
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# "relevant" (the role payoff) needs SOMETHING to go on — picks or a profile; else bail to seeded.
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if stage == "relevant" and not (picks.get("role") or picks.get("industry") or brief.get("current_role")):
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return None
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system = _STAGE[stage].format(role=role_picks, industry=ind_picks) + _COMMON
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try:
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resp = client.chat.completions.create(
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model=get_settings().SUGGEST_MODEL,
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messages=[
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{"role": "system", "content": system},
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{"role": "user", "content": json.dumps({"profile": brief, "picks": picks}, ensure_ascii=False)},
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],
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max_tokens=400,
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)
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data = _parse_json(resp.choices[0].message.content)
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except Exception as e: # noqa: BLE001 — frontend keeps its seeded fallback
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logger.warning("suggest(%s) failed: %s", stage, e)
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return None
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out, seen = [], set()
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for x in data.get("items") or []:
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label = str(x).strip()
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if label and label.lower() not in seen:
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seen.add(label.lower())
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out.append(label)
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return out[: _COUNT[stage]] or None
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