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)
134 lines
7.1 KiB
Python
134 lines
7.1 KiB
Python
"""Matchmaking-v2 agent session.
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Mirrors the mesh contract: the orchestrator calls /a2a/tasks, which drives
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on_session_start / on_user_action; handlers `push` messages back (agent_thinking,
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agent_data{action,data}, agent_error) that the orchestrator forwards to the frontend.
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Step-0 scaffold: the dispatch + contract are wired; handlers are honest stubs that
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say "not implemented yet" so we can fill them in one full-stack slice at a time.
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"""
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from __future__ import annotations
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import asyncio
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import logging
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logger = logging.getLogger(__name__)
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class MatchmakingAgentSession:
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def __init__(self, websocket, user_id: str):
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self.ws = websocket
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self.user_id = user_id
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self.context: dict = {}
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async def push(self, msg_type: str, **kwargs):
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await self.ws.send_json({"type": msg_type, **kwargs})
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async def _stub(self, action: str, note: str):
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await self.push("agent_data", action=action, data={"status": "not_implemented", "note": note})
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async def on_session_start(self, params: dict):
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self.context["user_context"] = params.get("user_context")
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await self.push("agent_thinking", message="Scout is warming up…")
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# Real flow lands in a later slice: resolve prefs → run_search → emit feed.
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await self.push("agent_data", action="session_ready", data={"service": "matchmaking-v2", "version": "2.0.0"})
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async def on_user_action(self, action: str, params: dict):
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handlers = {
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# existing contract
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"sync_preferences": self.handle_sync_preferences,
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"get_scout_feed": self.handle_get_feed, # unique name (avoids the course-service "get_feed" collision)
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"record_feedback": self.handle_record_feedback,
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"get_opportunity_detail": self.handle_get_opportunity_detail,
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# new in v2
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"run_search": self.handle_run_search,
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"suggest_bubbles": self.handle_suggest_bubbles, # the Fine-tune "pathway finder" engine
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"tailor_resume": self.handle_tailor_resume,
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"submit_application": self.handle_submit_application,
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"get_apply_proof": self.handle_get_apply_proof,
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}
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handler = handlers.get(action)
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if not handler:
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await self.push("agent_error", message=f"Unknown action: {action}")
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return
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try:
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await handler(params)
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except Exception as e: # noqa: BLE001 — surface to the client, log the trace
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logger.error("action '%s' failed: %s", action, e, exc_info=True)
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await self.push("agent_error", message=f"Failed to {action}: {e}")
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# ── handlers (stubs — replaced one full-stack slice at a time) ──
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async def handle_sync_preferences(self, params: dict):
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await self._stub("preferences_synced", "persist ScoutPrefs → label store")
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async def handle_get_feed(self, params: dict):
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"""Replay the user's last persisted feed so matches survive navigation (no re-search)."""
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from app.db.repo import get_feed
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feed = await get_feed(self.user_id)
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await self.push("agent_data", action="feed_loaded", data=feed or {"opportunities": [], "cached": False})
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async def handle_record_feedback(self, params: dict):
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await self._stub("feedback_recorded", "store SAVE/DISMISS/APPLY label")
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async def handle_get_opportunity_detail(self, params: dict):
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await self._stub("opportunity_detail", "return match-score breakdown")
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async def handle_run_search(self, params: dict):
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"""Slice-1a: ScoutPrefs → live multi-board fetch → cards. No engine, no user-arm."""
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from app.engine import search
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prefs = params # the frontend's ScoutPrefs arrive as params (user_context, if present, is ignored)
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fresh = bool(prefs.get("_fresh")) # dev toggle: force a live sweep instead of replaying cache
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if not search.has_mvq(prefs):
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await self.push("agent_data", action="search_complete",
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data={"opportunities": [], "needs": ["title", "location"]})
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return
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await self.push("agent_thinking",
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message="Fetching fresh roles across job boards…" if fresh
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else "Scanning live roles across job boards…")
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sweep = await search.run_sweep(prefs, fresh=fresh)
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# Engine: assemble FULL context (profile + prefs). Cheap rankers SIFT 90 → top ~18 (white-box ‖
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# embedding vibe); then Opus reads only those and curates the honest shortlist + report cards.
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from app.engine import curate as _curate
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from app.engine import rank as _rank
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from app.engine import sift as _sift
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user_context = params.get("user_context") # resume skills / experience / education / QScore
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await self.push("agent_thinking", message="Scoring roles against your profile…")
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# Run the blocking LLM work (embeddings + Opus) OFF the event loop, or the long sync OpenAI
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# call freezes the loop and the Redis response can't publish (→ the loader hangs forever).
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top, dbg = await asyncio.to_thread(_sift.sift, prefs, user_context, sweep["opportunities"])
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await self.push("agent_thinking", message="Scout is reading your top roles…")
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curated = await asyncio.to_thread(_curate.curate, prefs, user_context, top) # Opus's call is final
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if curated is None: # safety net: sift + templated cards
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curated = _rank.select(top, prefs.get("stretch", "balanced"))
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engine = f"fallback:{dbg['mode']}"
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else:
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engine = "opus"
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result = {"opportunities": curated, "sources": sweep["sources"],
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"shortlisted": len(curated), "scanned": dbg["scored"], "engine": engine,
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"has_profile": bool(user_context and user_context.get("skills"))}
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await self.push("agent_data", action="search_complete", data=result)
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# Persist the feed so it survives navigation/refresh (replayed by handle_get_feed).
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from app.db.repo import save_feed
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clean_prefs = {k: v for k, v in prefs.items() if k not in ("user_context", "_fresh")}
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await save_feed(self.user_id, clean_prefs, result)
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async def handle_suggest_bubbles(self, params: dict):
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"""Fine-tune bubbling — stage-aware (broad→narrow→role) bubbles from the picks so far."""
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from app.engine import suggest
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stage = params.get("stage", "role")
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items = await asyncio.to_thread(
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suggest.suggest, stage, params.get("picks") or {}, params.get("profile") or {}
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)
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await self.push("agent_data", action="bubbles_suggested",
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data={"stage": stage, "items": items or []})
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async def handle_tailor_resume(self, params: dict):
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await self._stub("resume_tailored", "resume-builder: tailor resume to the chosen role")
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async def handle_submit_application(self, params: dict):
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await self._stub("application_submitted", "master-key auto-apply (tier-aware)")
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async def handle_get_apply_proof(self, params: dict):
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await self._stub("apply_proof", "return captured confirmation screenshots")
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