mirror of
https://github.com/computerim/impactflow-discovery.git
synced 2026-08-27 06:00:35 +00:00
Joel Salmon
50453901b3
Add coaching preferences (auto-derived from the profile, user-overridable) and a periodic check-in engine that quotes the person's own words and asks whether their direction still feels valid — mirror, not compass. - Preferences are deterministic: a documented triad mapping (gut → direct/ higher-friction, heart → warm/drift-sensitive, head → reflective/question-led) produces defaults for the six fields (coaching_frequency, coaching_style, misalignment_threshold, friction_tolerance, prefer_questions_over_directives, time_of_day_preference). PUT overrides; regenerate re-derives. - CheckinCoach (app/services/coaching.py): Anthropic-backed; writes a check-in that quotes the person's goals back and asks if the direction still holds. - Endpoints (app/routers/coaching.py): GET/PUT/regenerate preferences; GET/POST checkins; respond (records still_valid); admin POST /run is the weekly batch (due = cadence elapsed + locked profile), intended for a cron. - Models + migration 005: coaching_preferences (per user) and coaching_checkin. - Frontend: coaching.html (preferences form + check-in feed); linked from profile.html. Tests: 68 passing (added deterministic-preference unit tests and coaching endpoint/batch tests; run in-container). README updated for Phase 3. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
167 lines
4.3 KiB
Python
167 lines
4.3 KiB
Python
"""Pydantic request/response models for the discovery API."""
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from datetime import datetime
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from typing import Optional
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from pydantic import BaseModel
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class StartResponse(BaseModel):
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conversation_id: str
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class RespondRequest(BaseModel):
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prompt_alive: str = ""
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prompt_friction: str = ""
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prompt_pull: str = ""
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prompt_recognition: str = ""
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prompt_future: str = ""
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prompt_goals_short: str = ""
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prompt_goals_long: str = ""
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class RespondResponse(BaseModel):
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conversation_id: str
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status: str
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class Confidence(BaseModel):
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triad: Optional[str] = None
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type: Optional[str] = None
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variant: Optional[str] = None
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ikigai: Optional[str] = None
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class ProfileResponse(BaseModel):
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id: str
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user_id: str
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conversation_id: str
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generated_at: datetime
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triad: Optional[str] = None
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probable_type: Optional[int] = None
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wing: Optional[int] = None
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instinctual_variant: Optional[str] = None
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instinctual_stack: Optional[str] = None
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love_summary: Optional[str] = None
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strength_summary: Optional[str] = None
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mission_summary: Optional[str] = None
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vocation_summary: Optional[str] = None
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overlap_narrative: Optional[str] = None
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short_term_goals: Optional[str] = None
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long_term_goals: Optional[str] = None
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confidence: Optional[Confidence] = None
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locked: bool = False
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extraction_notes: Optional[str] = None
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class ProfileUpdate(BaseModel):
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"""Partial edit of a profile's prose. Only fields explicitly provided are
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updated (see exclude_unset in the router). The AI's structural inference
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(triad/type/wing/variant) and confidence are not editable here."""
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love_summary: Optional[str] = None
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strength_summary: Optional[str] = None
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mission_summary: Optional[str] = None
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vocation_summary: Optional[str] = None
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overlap_narrative: Optional[str] = None
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short_term_goals: Optional[str] = None
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long_term_goals: Optional[str] = None
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class ConfirmResponse(BaseModel):
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status: str
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# -- Phase 2: AI coach reflection loop ---------------------------------------
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class ReflectRequest(BaseModel):
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# Empty/omitted starts the loop (the coach's opening reflection).
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message: str = ""
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class ReflectionMessageOut(BaseModel):
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role: str # "coach" or "person"
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content: str
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sequence: int
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created_at: datetime
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class ReflectTurnResponse(BaseModel):
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"""One coach turn, plus the (possibly revised) profile."""
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message: ReflectionMessageOut
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profile: ProfileResponse
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revised: bool = False
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revision_note: Optional[str] = None
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class ReflectionThreadResponse(BaseModel):
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messages: list[ReflectionMessageOut]
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profile: ProfileResponse
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class ProfileRevisionOut(BaseModel):
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id: str
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source: str # "extraction" | "reflection" | "manual_edit"
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fields: dict
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note: Optional[str] = None
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created_at: datetime
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# -- Phase 3: coaching preferences + check-ins -------------------------------
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class CoachingPreferencesOut(BaseModel):
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coaching_frequency: str
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coaching_style: str
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misalignment_threshold: str
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friction_tolerance: str
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prefer_questions_over_directives: bool
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time_of_day_preference: str
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auto_generated: bool
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updated_at: datetime
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class CoachingPreferencesUpdate(BaseModel):
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coaching_frequency: Optional[str] = None
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coaching_style: Optional[str] = None
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misalignment_threshold: Optional[str] = None
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friction_tolerance: Optional[str] = None
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prefer_questions_over_directives: Optional[bool] = None
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time_of_day_preference: Optional[str] = None
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class CheckinOut(BaseModel):
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id: str
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body: str
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created_at: datetime
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still_valid: Optional[bool] = None
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response_note: Optional[str] = None
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acknowledged_at: Optional[datetime] = None
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class CheckinRespondRequest(BaseModel):
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still_valid: bool
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note: str = ""
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class RunCheckinsResponse(BaseModel):
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considered: int
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generated: int
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class ConversationResponse(BaseModel):
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id: str
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user_id: str
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started_at: datetime
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completed_at: Optional[datetime] = None
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prompt_alive: Optional[str] = None
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prompt_friction: Optional[str] = None
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prompt_pull: Optional[str] = None
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prompt_recognition: Optional[str] = None
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prompt_future: Optional[str] = None
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prompt_goals_short: Optional[str] = None
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prompt_goals_long: Optional[str] = None
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model_config = {"from_attributes": True}
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