mirror of
https://github.com/computerim/impactflow-discovery.git
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Joel Salmon
b9d7b0e22b
Final roadmap phase. No DB migration — it reads data already captured.
- Goal-evolution history: GET /discovery/profile/me/goal-history derives a
per-goal timeline from the profile_revision snapshots (pure aggregator in
app/services/profile_history.py).
- Smart tagging: POST /discovery/integration/suggest-foundation suggests which
foundation a task builds toward + rationale/confidence (FoundationTagger,
app/services/tagging.py). Suggestion only; the person confirms by posting the
task mapping.
- Deeper goal-refinement: the reflect loop accepts an optional focus ("goals")
that steers the coach toward sharpening goals — still mirror, not compass.
- Visualizations: visuals.html renders an Ikigai Venn and an Enneagram diagram
(plain-language callouts, not the raw type number) plus the goal-evolution
timeline; linked from profile.html.
Tests: 99 passing (added pure goal-history tests, goal-history + suggest
endpoint tests, reflect-focus passthrough; run in-container). README updated.
This completes the ImpactFlow Vision roadmap (Phases 1-5) on the Discovery side.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
"""Phase 5: derive how a person's goals evolved from the profile_revision
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snapshots already captured since Phase 2.
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Pure functions over revision rows — no DB access, fully testable.
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"""
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from typing import Any, Dict, List
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# The goal fields we track an evolution timeline for.
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GOAL_FIELDS = ("short_term_goals", "long_term_goals")
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def goal_history(revisions: List[Dict[str, Any]]) -> Dict[str, List[dict]]:
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"""Build a per-goal timeline of distinct values over time.
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Args:
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revisions: each ``{"fields": {<field>: value, ...}, "source": str,
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"created_at": datetime}``, in ascending chronological order.
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Returns:
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``{"short_term_goals": [{"value", "source", "at"}...],
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"long_term_goals": [...]}`` — one entry per *change*; consecutive
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identical values are collapsed so the timeline shows only when a goal
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actually moved.
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"""
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timelines: Dict[str, List[dict]] = {f: [] for f in GOAL_FIELDS}
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last: Dict[str, Any] = {f: _SENTINEL for f in GOAL_FIELDS}
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for rev in revisions:
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fields = rev.get("fields") or {}
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for field in GOAL_FIELDS:
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value = fields.get(field)
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if value == last[field]:
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continue
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last[field] = value
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timelines[field].append(
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{
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"value": value,
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"source": rev.get("source"),
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"at": rev.get("created_at"),
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}
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)
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return timelines
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class _Sentinel:
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pass
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# Distinct from None so a first snapshot whose goal is None still emits once.
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_SENTINEL = _Sentinel()
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