vh
0b1d9e2b15
The affect console now reconstructs + displays the complete affect-context block
Worldtree assembles into the agent's system prompt — never on any wire, hidden from
regular consumers, surfaced here as the reference-impl's privileged dev view.
- extend build_persona_canon.py to emit mood_directive {occ_directives (15),
pad_band_fallback, salience, pad_band_cutoff, full_only} into the browser canon
(strings were already in the pinned d2-mood-render-canon; regen via Worldtree loader)
- canonPadFallback(pad) + canonEmotionDirective(type): byte-exact mirrors of Worldtree
core/persona/renderer._pad_band_fallback + derive_directive
- renderDirective -> a "CONTEXT INJECTION · reconstructed · hidden from consumers" panel:
mood descriptor [exact] + mood directive [candidate] + relationship directive [exact]
- honest-partial (affect-egress-reference sec 3): affect.emit is type-only (no
intensity), so the salience gate can't be evaluated -> show BOTH the OCC emotion
directive AND the PAD-band fallback with the "injected if intensity >= 0.2" caveat,
never asserting which fires; fallback alone is exact when no dominant_emotion
- vendor + pin affect-egress-consumer-reference.md (tolerate_drift; worldtree-dev
co-signs + pings on change). drift 6/6 green
- contract amended for the new reconstruction fns + honest-partial provenance
Verified: pytest tests/test_web_* (84) + node Playwright (sindra dominant_emotion=joy
-> joy OCC directive candidate + PAD-band fallback both render with exact/candidate tags).
65 lines
3.4 KiB
Python
65 lines
3.4 KiB
Python
#!/usr/bin/env python3
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"""Regenerate src/ratatoskr/web/static/persona_render_canon.json from the vendored
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Worldtree d2 render canons (docs/vendor/worldtree-persona-canon/).
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The web persona pane renders the CANONICAL affect->NL (mood word + relationship
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directive) BYTE-EXACT to what Worldtree injects into the agent's context. That render
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needs the relation canon parsed into per-band phrase maps; this script reparses the
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vendored raw canons into the flat form the browser JS consumes.
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Uses Worldtree's OWN loader (core.persona.stance_render.load_canon) as the authoritative
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parser, so the flat form can never drift from Worldtree's parsing semantics. Requires
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Worldtree's venv (pydantic etc.).
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Run when scripts/canonical_drift.py flags a canon bump:
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PYTHONPATH=~/development/Worldtree ~/development/Worldtree/.venv/bin/python \
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scripts/build_persona_canon.py
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"""
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import json
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from pathlib import Path
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from core.persona.stance_render import load_canon # Worldtree (authoritative parser)
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ROOT = Path(__file__).resolve().parent.parent
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VENDOR = ROOT / "docs" / "vendor" / "worldtree-persona-canon"
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OUT = ROOT / "src" / "ratatoskr" / "web" / "static" / "persona_render_canon.json"
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canon = load_canon(str(VENDOR / "d2-render-canon-v1.json"))
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mood = json.loads((VENDOR / "d2-mood-render-canon-v1.json").read_text())
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out = {
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"_source": "vendored from Worldtree core/persona/canon/{d2-mood-render-canon-v1,d2-render-canon-v1}.json",
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"_generated_by": "scripts/build_persona_canon.py (regen on canonical_drift flag)",
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"_render_path": "deterministic, no LLM; mirrors Worldtree describe_pad + render_d2_canonical byte-exact",
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"mood_grid": mood["describe_pad"]["valence_arousal_grid"],
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# Context-injection reconstruction (affect-egress-consumer-reference §2b/2c): the
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# hidden mood DIRECTIVE. occ_directives = per-OCC-type behavioral string + tier;
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# pad_band_fallback = the P×A-quadrant default when no emotion is salient. The
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# salience gate (emotion_salience) + full_only tiers drive which one fires — but
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# affect.emit is type-only (no intensity), so the consumer shows BOTH candidates.
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"mood_directive": {
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"salience": mood["thresholds"]["emotion_salience"],
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"pad_band_cutoff": mood["thresholds"]["pad_band_cutoff"],
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"full_only": mood["emotion_tiers"]["full_only"],
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"occ_directives": {
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t: {"directive": e["directive"], "tier": e["tier"]}
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for t, e in mood["derive_directive"]["occ_directives"].items()
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},
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"pad_band_fallback": mood["pad_band_fallback"],
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},
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"relation": {
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"trust_cuts": [list(c) for c in canon.trust_cuts],
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"warmth_cuts": [list(c) for c in canon.warmth_cuts],
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"agency_cuts": [list(c) for c in canon.agency_cuts],
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"warmth_phrase": canon.warmth_phrase, "warmth_beh": canon.warmth_beh,
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"agency_phrase": canon.agency_phrase, "agency_beh": canon.agency_beh,
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"history": canon.history,
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"prefix": "Use this graded relationship state: toward target, warmth is ",
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"tbeh": {"low_trust": "verify important claims before relying on them",
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"cold_warmth": "protect boundaries while staying useful",
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"default": "work from ordinary good faith"},
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"cold_warmth_bands": ["distant", "cold", "hostile"], "high_conf_floor": 0.55,
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},
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}
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OUT.write_text(json.dumps(out, indent=1) + "\n")
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print(f"wrote {OUT.relative_to(ROOT)}")
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