mirror of
https://github.com/computerim/impactflow-discovery.git
synced 2026-08-27 07:20:36 +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."""
|
|
from datetime import datetime
|
|
from typing import Optional
|
|
|
|
from pydantic import BaseModel
|
|
|
|
|
|
class StartResponse(BaseModel):
|
|
conversation_id: str
|
|
|
|
|
|
class RespondRequest(BaseModel):
|
|
prompt_alive: str = ""
|
|
prompt_friction: str = ""
|
|
prompt_pull: str = ""
|
|
prompt_recognition: str = ""
|
|
prompt_future: str = ""
|
|
prompt_goals_short: str = ""
|
|
prompt_goals_long: str = ""
|
|
|
|
|
|
class RespondResponse(BaseModel):
|
|
conversation_id: str
|
|
status: str
|
|
|
|
|
|
class Confidence(BaseModel):
|
|
triad: Optional[str] = None
|
|
type: Optional[str] = None
|
|
variant: Optional[str] = None
|
|
ikigai: Optional[str] = None
|
|
|
|
|
|
class ProfileResponse(BaseModel):
|
|
id: str
|
|
user_id: str
|
|
conversation_id: str
|
|
generated_at: datetime
|
|
triad: Optional[str] = None
|
|
probable_type: Optional[int] = None
|
|
wing: Optional[int] = None
|
|
instinctual_variant: Optional[str] = None
|
|
instinctual_stack: Optional[str] = None
|
|
love_summary: Optional[str] = None
|
|
strength_summary: Optional[str] = None
|
|
mission_summary: Optional[str] = None
|
|
vocation_summary: Optional[str] = None
|
|
overlap_narrative: Optional[str] = None
|
|
short_term_goals: Optional[str] = None
|
|
long_term_goals: Optional[str] = None
|
|
confidence: Optional[Confidence] = None
|
|
locked: bool = False
|
|
extraction_notes: Optional[str] = None
|
|
|
|
|
|
class ProfileUpdate(BaseModel):
|
|
"""Partial edit of a profile's prose. Only fields explicitly provided are
|
|
updated (see exclude_unset in the router). The AI's structural inference
|
|
(triad/type/wing/variant) and confidence are not editable here."""
|
|
|
|
love_summary: Optional[str] = None
|
|
strength_summary: Optional[str] = None
|
|
mission_summary: Optional[str] = None
|
|
vocation_summary: Optional[str] = None
|
|
overlap_narrative: Optional[str] = None
|
|
short_term_goals: Optional[str] = None
|
|
long_term_goals: Optional[str] = None
|
|
|
|
|
|
class ConfirmResponse(BaseModel):
|
|
status: str
|
|
|
|
|
|
# -- Phase 2: AI coach reflection loop ---------------------------------------
|
|
|
|
|
|
class ReflectRequest(BaseModel):
|
|
# Empty/omitted starts the loop (the coach's opening reflection).
|
|
message: str = ""
|
|
|
|
|
|
class ReflectionMessageOut(BaseModel):
|
|
role: str # "coach" or "person"
|
|
content: str
|
|
sequence: int
|
|
created_at: datetime
|
|
|
|
|
|
class ReflectTurnResponse(BaseModel):
|
|
"""One coach turn, plus the (possibly revised) profile."""
|
|
|
|
message: ReflectionMessageOut
|
|
profile: ProfileResponse
|
|
revised: bool = False
|
|
revision_note: Optional[str] = None
|
|
|
|
|
|
class ReflectionThreadResponse(BaseModel):
|
|
messages: list[ReflectionMessageOut]
|
|
profile: ProfileResponse
|
|
|
|
|
|
class ProfileRevisionOut(BaseModel):
|
|
id: str
|
|
source: str # "extraction" | "reflection" | "manual_edit"
|
|
fields: dict
|
|
note: Optional[str] = None
|
|
created_at: datetime
|
|
|
|
|
|
# -- Phase 3: coaching preferences + check-ins -------------------------------
|
|
|
|
|
|
class CoachingPreferencesOut(BaseModel):
|
|
coaching_frequency: str
|
|
coaching_style: str
|
|
misalignment_threshold: str
|
|
friction_tolerance: str
|
|
prefer_questions_over_directives: bool
|
|
time_of_day_preference: str
|
|
auto_generated: bool
|
|
updated_at: datetime
|
|
|
|
|
|
class CoachingPreferencesUpdate(BaseModel):
|
|
coaching_frequency: Optional[str] = None
|
|
coaching_style: Optional[str] = None
|
|
misalignment_threshold: Optional[str] = None
|
|
friction_tolerance: Optional[str] = None
|
|
prefer_questions_over_directives: Optional[bool] = None
|
|
time_of_day_preference: Optional[str] = None
|
|
|
|
|
|
class CheckinOut(BaseModel):
|
|
id: str
|
|
body: str
|
|
created_at: datetime
|
|
still_valid: Optional[bool] = None
|
|
response_note: Optional[str] = None
|
|
acknowledged_at: Optional[datetime] = None
|
|
|
|
|
|
class CheckinRespondRequest(BaseModel):
|
|
still_valid: bool
|
|
note: str = ""
|
|
|
|
|
|
class RunCheckinsResponse(BaseModel):
|
|
considered: int
|
|
generated: int
|
|
|
|
|
|
class ConversationResponse(BaseModel):
|
|
id: str
|
|
user_id: str
|
|
started_at: datetime
|
|
completed_at: Optional[datetime] = None
|
|
prompt_alive: Optional[str] = None
|
|
prompt_friction: Optional[str] = None
|
|
prompt_pull: Optional[str] = None
|
|
prompt_recognition: Optional[str] = None
|
|
prompt_future: Optional[str] = None
|
|
prompt_goals_short: Optional[str] = None
|
|
prompt_goals_long: Optional[str] = None
|
|
|
|
model_config = {"from_attributes": True}
|