bvandeusen
9eba6ac107
Deletes ~760 lines of legacy briefing code: format_task, compute_task_hash, upsert_task_snapshots, _gather_internal, _gather_weekly_review, _llm_synthesise, and the unified prompt helpers. run_compilation and run_slot_injection are now agentic-tool-use-loop only. briefing_scheduler and user_profile migrated from the deleted helper to services.llm.generate_completion (retry + keep_alive baked in). routes/briefing.manual_trigger now persists agentic tool-call receipts via _persist_agentic_messages (previously silently dropped them) and adds POST /api/briefing/reset-today to wipe today's briefing messages. BREAKING: briefing_mode setting no longer honored; no legacy fallback. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
214 lines
8.0 KiB
Python
214 lines
8.0 KiB
Python
"""User profile service — structured per-user preferences for LLM context."""
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import asyncio
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import logging
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from datetime import date, datetime, timedelta, timezone
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from sqlalchemy import select
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from fabledassistant.models import async_session
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from fabledassistant.models.user_profile import UserProfile
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logger = logging.getLogger(__name__)
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VALID_EXPERTISE = {"novice", "intermediate", "expert"}
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VALID_STYLES = {"concise", "balanced", "detailed"}
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VALID_TONES = {"casual", "professional", "technical"}
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# Trigger consolidation when raw observations reach this count
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_CONSOLIDATION_THRESHOLD = 14
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async def get_profile(user_id: int) -> UserProfile:
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"""Get or create the profile row for a user."""
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async with async_session() as session:
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result = await session.execute(
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select(UserProfile).where(UserProfile.user_id == user_id)
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)
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profile = result.scalar_one_or_none()
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if profile is None:
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profile = UserProfile(user_id=user_id)
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session.add(profile)
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await session.commit()
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await session.refresh(profile)
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return profile
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async def update_profile(user_id: int, data: dict) -> UserProfile:
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"""Upsert structured profile fields from a validated dict."""
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allowed = {
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"display_name", "job_title", "industry", "expertise_level",
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"response_style", "tone", "interests", "work_schedule",
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}
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async with async_session() as session:
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result = await session.execute(
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select(UserProfile).where(UserProfile.user_id == user_id)
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)
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profile = result.scalar_one_or_none()
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if profile is None:
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profile = UserProfile(user_id=user_id)
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session.add(profile)
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for key, value in data.items():
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if key in allowed:
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setattr(profile, key, value)
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await session.commit()
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await session.refresh(profile)
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return profile
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async def append_observations(user_id: int, bullets: str) -> None:
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"""
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Append a new dated observation entry from the day's briefing closeout.
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Automatically triggers consolidation when the raw list grows large.
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"""
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if not bullets.strip():
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return
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async with async_session() as session:
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result = await session.execute(
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select(UserProfile).where(UserProfile.user_id == user_id)
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)
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profile = result.scalar_one_or_none()
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if profile is None:
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profile = UserProfile(user_id=user_id)
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session.add(profile)
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existing: list = list(profile.observations_raw or [])
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existing.append({
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"date": date.today().isoformat(),
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"bullets": bullets.strip(),
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})
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# Keep at most 60 raw entries as a rolling window
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profile.observations_raw = existing[-60:]
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profile.observations_updated_at = datetime.now(timezone.utc)
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await session.commit()
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raw_count = len(profile.observations_raw or [])
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logger.info("Appended observations for user %d (%d raw entries)", user_id, raw_count)
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if raw_count >= _CONSOLIDATION_THRESHOLD:
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asyncio.create_task(_consolidate_observations(user_id))
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async def consolidate_observations(user_id: int) -> str:
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"""Public entry point to manually trigger observation consolidation."""
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return await _consolidate_observations(user_id)
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async def clear_learned_data(user_id: int) -> None:
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"""Reset all learned observations and summary for a user."""
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async with async_session() as session:
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result = await session.execute(
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select(UserProfile).where(UserProfile.user_id == user_id)
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)
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profile = result.scalar_one_or_none()
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if profile:
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profile.learned_summary = None
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profile.observations_raw = []
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profile.observations_updated_at = None
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await session.commit()
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async def _consolidate_observations(user_id: int) -> str:
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"""
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LLM pass: synthesise all raw observation bullets into an updated
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learned_summary paragraph. Prunes raw entries older than 30 days afterwards.
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"""
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from fabledassistant.config import Config
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from fabledassistant.services.llm import generate_completion
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from fabledassistant.services.settings import get_setting
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async with async_session() as session:
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result = await session.execute(
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select(UserProfile).where(UserProfile.user_id == user_id)
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)
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profile = result.scalar_one_or_none()
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if not profile or not profile.observations_raw:
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return ""
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observations = list(profile.observations_raw)
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existing_summary = profile.learned_summary or ""
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obs_text = "\n\n".join(
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f"[{entry['date']}]\n{entry['bullets']}"
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for entry in observations
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)
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system = (
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"You are synthesising preference observations into a concise user profile summary. "
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"Consolidate the observations into 3-6 factual sentences describing the user's patterns, "
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"preferences, and habits. Be specific and useful for a personal assistant. "
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"Merge with any existing summary, removing duplicates and outdated information. "
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"Output only the consolidated summary paragraph — no preamble, no bullet points."
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)
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user_prompt = ""
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if existing_summary:
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user_prompt += f"Existing summary:\n{existing_summary}\n\n"
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user_prompt += f"New observations:\n{obs_text}"
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model = await get_setting(user_id, "default_model", Config.OLLAMA_MODEL)
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try:
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new_summary = (await generate_completion(
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[
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{"role": "system", "content": system},
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{"role": "user", "content": user_prompt},
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],
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model,
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)).strip()
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except Exception:
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logger.warning("Observation consolidation failed for user %d", user_id, exc_info=True)
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new_summary = ""
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if new_summary:
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cutoff = (date.today() - timedelta(days=30)).isoformat()
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async with async_session() as session:
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result = await session.execute(
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select(UserProfile).where(UserProfile.user_id == user_id)
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)
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profile = result.scalar_one_or_none()
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if profile:
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profile.learned_summary = new_summary
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profile.observations_raw = [
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o for o in (profile.observations_raw or [])
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if o.get("date", "") >= cutoff
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]
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await session.commit()
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logger.info("Consolidated observations for user %d", user_id)
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return new_summary
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async def build_profile_context(user_id: int) -> str:
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"""
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Build a formatted context string from the user's structured profile
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for injection into LLM system prompts (briefing and chat).
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Returns an empty string if no meaningful data is set.
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"""
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profile = await get_profile(user_id)
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parts: list[str] = []
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if profile.display_name:
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parts.append(f"User's name: {profile.display_name}")
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if profile.job_title or profile.industry:
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job = " in ".join(filter(None, [profile.job_title, profile.industry]))
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parts.append(f"Occupation: {job}")
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if profile.expertise_level and profile.expertise_level != "intermediate":
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parts.append(
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f"Expertise level: {profile.expertise_level} — calibrate explanation depth accordingly"
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)
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if profile.response_style or profile.tone:
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style = profile.response_style or "balanced"
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tone = profile.tone or "casual"
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parts.append(f"Preferred response style: {style}, tone: {tone}")
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if profile.interests:
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parts.append(f"Interests: {', '.join(profile.interests)}")
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if profile.work_schedule:
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sched = profile.work_schedule
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days = ", ".join(sched.get("days") or []) or "weekdays"
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start = sched.get("start", "9:00")
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end = sched.get("end", "17:00")
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parts.append(f"Work schedule: {days}, {start}–{end}")
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if profile.learned_summary:
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parts.append(f"What the assistant has learned about this user: {profile.learned_summary}")
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return "\n".join(parts)
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