Merge Workstream A: entity hidden traits + migration
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@@ -96,6 +96,23 @@ def parse_mint_response(text: str) -> dict | None:
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return profile
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return profile
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def roll_traits(signature: str) -> dict:
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"""Roll the four hidden truth-traits for an entity, seeded from its
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signature. These are never derived from — or fed into — the LLM persona
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prompt (see `mint_prompt`, which never sees this function's output):
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persona text must stay fully decoupled from ground truth, so a
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convincing "sweet old lady" persona can pair with any alignment roll.
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A separate `random.Random` namespace (`"traits:"` vs. `normalize_profile`'s
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`"norm:"`) keeps this roll independent of the cosmetic-defaults rng."""
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rng = random.Random(f"traits:{signature}")
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return {
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"alignment": rng.uniform(0.0, 1.0),
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"power": rng.uniform(0.0, 1.0),
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"volatility": rng.uniform(0.0, 1.0),
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"deceptiveness": rng.uniform(0.0, 1.0),
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}
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def normalize_profile(profile: dict, signature: str) -> dict:
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def normalize_profile(profile: dict, signature: str) -> dict:
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"""Coerce an LLM (or fallback) profile into the exact shape the DB and
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"""Coerce an LLM (or fallback) profile into the exact shape the DB and
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frontend expect, filling gaps with signature-deterministic defaults."""
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frontend expect, filling gaps with signature-deterministic defaults."""
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@@ -138,6 +155,7 @@ def normalize_profile(profile: dict, signature: str) -> dict:
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},
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},
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"quotes": [str(q)[:200] for q in quotes[:4] if isinstance(q, str)]
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"quotes": [str(q)[:200] for q in quotes[:4] if isinstance(q, str)]
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or rng.sample(_QUOTE_BANK, 2),
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or rng.sample(_QUOTE_BANK, 2),
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"traits": roll_traits(signature),
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}
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}
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@@ -6,6 +6,7 @@ from pathlib import Path
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from fastapi import FastAPI, HTTPException
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from fastapi import FastAPI, HTTPException
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from fastapi.responses import FileResponse
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from fastapi.responses import FileResponse
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from fastapi.staticfiles import StaticFiles
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from fastapi.staticfiles import StaticFiles
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from sqlalchemy import text
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import app.models # noqa: F401 — registers models on Base.metadata before create_all
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import app.models # noqa: F401 — registers models on Base.metadata before create_all
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from app.config import settings
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from app.config import settings
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@@ -45,6 +46,12 @@ async def lifespan(app: FastAPI):
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AUDIO_DIR.mkdir(parents=True, exist_ok=True)
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AUDIO_DIR.mkdir(parents=True, exist_ok=True)
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async with engine.begin() as conn:
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async with engine.begin() as conn:
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await conn.run_sync(Base.metadata.create_all)
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await conn.run_sync(Base.metadata.create_all)
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# No Alembic in this repo — `create_all` never alters existing
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# tables, so columns added to live models need a manual, idempotent
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# migration here. Safe to run on every startup.
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await conn.execute(text(
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"ALTER TABLE entities ADD COLUMN IF NOT EXISTS traits JSONB NOT NULL DEFAULT '{}'::jsonb"
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))
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cleanup_task = asyncio.create_task(_session_cleanup_loop())
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cleanup_task = asyncio.create_task(_session_cleanup_loop())
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try:
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try:
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yield
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yield
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@@ -24,6 +24,7 @@ class Entity(Base):
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signature: Mapped[str] = mapped_column(String(64), unique=True, index=True)
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signature: Mapped[str] = mapped_column(String(64), unique=True, index=True)
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voice_profile: Mapped[dict] = mapped_column(JSONB, default=dict)
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voice_profile: Mapped[dict] = mapped_column(JSONB, default=dict)
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visual_profile: Mapped[dict] = mapped_column(JSONB, default=dict)
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visual_profile: Mapped[dict] = mapped_column(JSONB, default=dict)
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traits: Mapped[dict] = mapped_column(JSONB, default=dict)
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sample_quotes: Mapped[list] = mapped_column(JSONB, default=list)
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sample_quotes: Mapped[list] = mapped_column(JSONB, default=list)
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contact_count: Mapped[int] = mapped_column(Integer, default=0)
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contact_count: Mapped[int] = mapped_column(Integer, default=0)
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discovered_by: Mapped[uuid.UUID | None] = mapped_column(
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discovered_by: Mapped[uuid.UUID | None] = mapped_column(
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@@ -1,9 +1,15 @@
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import inspect
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from app.entities import (
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from app.entities import (
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fallback_profile,
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fallback_profile,
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normalize_profile,
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normalize_profile,
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parse_mint_response,
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parse_mint_response,
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roll_traits,
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signature_from_anomalies,
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signature_from_anomalies,
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)
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)
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from app.llm import prompts as llm_prompts
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TRAIT_KEYS = ("alignment", "power", "volatility", "deceptiveness")
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def _anomaly(freq, mag):
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def _anomaly(freq, mag):
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@@ -60,3 +66,88 @@ def test_fallback_profile_is_deterministic_and_valid():
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assert one["name"]
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assert one["name"]
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assert one["rarity"] in ("common", "uncommon", "rare", "mythic")
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assert one["rarity"] in ("common", "uncommon", "rare", "mythic")
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assert one["quotes"]
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assert one["quotes"]
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# --- hidden traits (Workstream A) -------------------------------------
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def test_roll_traits_values_are_in_range():
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traits = roll_traits("some-signature-1")
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assert set(traits.keys()) == set(TRAIT_KEYS)
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for key in TRAIT_KEYS:
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assert isinstance(traits[key], float)
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assert 0.0 <= traits[key] <= 1.0
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def test_roll_traits_is_deterministic_for_same_signature():
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one = roll_traits("repeatable-signature")
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two = roll_traits("repeatable-signature")
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assert one == two
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def test_roll_traits_varies_by_signature():
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values = {tuple(roll_traits(f"sig-{i}").values()) for i in range(20)}
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# 20 distinct signatures should not collapse onto a single roll.
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assert len(values) > 1
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def test_normalize_profile_includes_traits_in_range():
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profile = normalize_profile({"name": "Hollow Briar"}, "traits-norm-signature")
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traits = profile["traits"]
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assert set(traits.keys()) == set(TRAIT_KEYS)
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for key in TRAIT_KEYS:
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assert 0.0 <= traits[key] <= 1.0
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def test_normalize_profile_traits_match_roll_traits_for_signature():
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signature = "cross-check-signature"
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profile = normalize_profile({"name": "X"}, signature)
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assert profile["traits"] == roll_traits(signature)
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def test_normalize_profile_traits_are_signature_deterministic():
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one = normalize_profile({"name": "A"}, "det-signature")
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two = normalize_profile({"name": "B", "rarity": "rare"}, "det-signature")
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# Same signature -> same traits every call, regardless of the rest of
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# the (LLM-supplied or fallback) profile passed in.
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assert one["traits"] == two["traits"]
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def test_fallback_profile_includes_traits_in_range():
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profile = fallback_profile("fallback-traits-signature")
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traits = profile["traits"]
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assert set(traits.keys()) == set(TRAIT_KEYS)
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for key in TRAIT_KEYS:
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assert 0.0 <= traits[key] <= 1.0
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def test_fallback_profile_traits_are_signature_deterministic():
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one = fallback_profile("fallback-det-signature")
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two = fallback_profile("fallback-det-signature")
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assert one["traits"] == two["traits"]
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def test_persona_is_independent_of_rolled_traits():
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"""Persona text must stay fully decoupled from hidden truth: pairing the
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exact same persona string with many different signatures should surface
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a spread of alignment rolls, not a single value tied to the persona."""
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fixed_persona = "A sweet old lady who just wants to chat about her garden."
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alignments = set()
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for i in range(40):
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profile = normalize_profile(
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{"name": "Nana", "persona": fixed_persona}, f"persona-independence-{i}"
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)
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assert profile["persona"] == fixed_persona
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alignments.add(round(profile["traits"]["alignment"], 3))
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# A meaningful spread (not clustered on one or two values) shows
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# alignment isn't derived from — or gated by — the persona text.
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assert len(alignments) > 20
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def test_mint_prompt_never_receives_traits():
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"""`mint_prompt` builds the LLM system/user prompt; traits must never be
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one of its inputs, so hidden ground truth can't leak into persona
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generation."""
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params = set(inspect.signature(llm_prompts.mint_prompt).parameters)
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assert "traits" not in params
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assert params == {"signature", "channel", "anomaly_summary", "voice_ids"}
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