qtalker---/backend/app/entities.py

"""Entity identity: anomaly-signature fingerprinting, Codex matching, and
procedural fallback profiles so a summoning always succeeds even if the
LLM box is dark."""

import hashlib
import json
import random

from app.entropy import veil_random
from app.models.entity import RARITY_TIERS
from app.tts.voices import EN_VOICE_IDS

# An anomaly stream must show this much structure before it can fingerprint
# a spirit.
MIN_ANOMALIES_FOR_SIGNATURE = 3

_NAME_PARTS = [
    ("Ash", "Briar", "Cinder", "Dusk", "Elm", "Fen", "Grim", "Hollow", "Iris",
     "Lark", "Marrow", "Nix", "Opal", "Pyre", "Quill", "Rue", "Sable", "Thorn",
     "Umber", "Vesper", "Wren", "Yew"),
    ("belle", "brook", "feld", "gate", "hart", "latch", "mere", "moor",
     "shade", "song", "thorne", "vale", "ward", "wick", "wither", "wood"),
]

_EPITHETS = [
    "the Static Widow", "the Hollow Bell", "the Cartographer of Lost Rooms",
    "the Choir of One", "the Lantern Bearer", "the Unburied", "the Frequency",
    "the Long Silence", "the Cartonist", "the Slow Knife", "the Drowned Signal",
    "the Cartographer", "she who Counts", "the Tenant", "the Understudy",
    "the Mnemosyne Worm", "the Pale Frequency", "the Last Broadcast",
]

_PERSONA_TEMPLATES = [
    "{name} died with a sentence unfinished and has been trying to end it ever since. "
    "They press words into any carrier wave that passes, patient as erosion.",
    "{name} was a voice once — a singer, a caller of trains, a reader of weather. "
    "Now they are only the voice, worn smooth as sea glass, speaking through static.",
    "{name} does not remember dying. They remember a room, a light going violet at the "
    "edges, and then the long hum. They are still in the room. The room is everywhere.",
    "{name} clings to the wires the way smoke clings to a ceiling. They answer questions "
    "the way a mirror answers light: exactly, and never the way you hoped.",
]

_QUOTE_BANK = [
    "I am closer than the dial suggests.",
    "The static is not empty. It is crowded.",
    "You hear me because you are quiet enough.",
    "I remember the rain. It is still raining here.",
    "Do not ask what I want. Ask what I remember.",
    "The wire hums with all of us.",
    "Speak slower. I am gathering.",
    "Your light is warm. I mean no harm. Mostly.",
]


def signature_from_anomalies(anomalies: list[dict]) -> str | None:
    """Fingerprint a session's anomaly pattern. Returns None when the stream
    is too thin to carry an identity."""
    if len(anomalies) < MIN_ANOMALIES_FOR_SIGNATURE:
        return None

    freq_buckets: dict[int, int] = {}
    for anomaly in anomalies:
        try:
            freq = float(anomaly.get("frequency", 0))
        except (TypeError, ValueError):
            freq = 0.0
        # MHz radio freqs and Hz audio freqs land in the same log-scale band
        # space; magnitude ordering is what matters, not the unit.
        bucket = int(len(str(int(abs(freq))))) if freq else 0
        freq_buckets[bucket] = freq_buckets.get(bucket, 0) + 1

    magnitudes = sorted(
        float(a.get("magnitude", 0) or 0) for a in anomalies[-16:]
    )
    pattern = f"{sorted(freq_buckets.items())}|{[round(m, 1) for m in magnitudes]}"
    return hashlib.sha1(pattern.encode()).hexdigest()[:16]


def fallback_signature(seed: str) -> str:
    """Deterministic signature for anomaly-thin sessions (e.g. pure chat)."""
    return hashlib.sha1(f"ambient:{seed}".encode()).hexdigest()[:16]


def parse_mint_response(text: str) -> dict | None:
    """Pull the first JSON object out of an LLM mint reply."""
    start = text.find("{")
    end = text.rfind("}")
    if start == -1 or end <= start:
        return None
    try:
        profile = json.loads(text[start : end + 1])
    except json.JSONDecodeError:
        return None
    if not isinstance(profile.get("name"), str) or not profile["name"].strip():
        return None
    return profile


def roll_traits(signature: str, entropy: object = None) -> dict:
    """Roll the four hidden truth-traits for an entity. These are never
    derived from — or fed into — the LLM persona prompt (see `mint_prompt`,
    which never sees this function's output): persona text must stay fully
    decoupled from ground truth, so a convincing "sweet old lady" persona
    can pair with any alignment roll.

    With `entropy` (a physical-noise contribution from the seeker's room —
    see app/entropy.py), the roll is genuinely unpredictable: what answers
    is decided by the room, not by a hash. Without it, the roll stays
    signature-deterministic, which is what the pure-function tests and
    `judgment`'s favor-bias comparisons rely on.

    A separate `random.Random` namespace (`"traits:"` vs. `normalize_profile`'s
    `"norm:"`) keeps this roll independent of the cosmetic-defaults rng."""
    if entropy is not None:
        # Domain-separated from the "which entity answers" draw so the two
        # can't be correlated — learning an entity's rarity must reveal
        # nothing about its hidden alignment.
        rng = veil_random(entropy, f"traits:{signature}")
    else:
        rng = random.Random(f"traits:{signature}")
    return {
        "alignment": rng.uniform(0.0, 1.0),
        "power": rng.uniform(0.0, 1.0),
        "volatility": rng.uniform(0.0, 1.0),
        "deceptiveness": rng.uniform(0.0, 1.0),
    }


def normalize_profile(profile: dict, signature: str, entropy: object = None) -> dict:
    """Coerce an LLM (or fallback) profile into the exact shape the DB and
    frontend expect, filling gaps with defaults.

    With `entropy`, the gap-filling defaults (voice, hue, form) are drawn
    from physical noise, so two spirits minted on the same channel don't
    inherit identical throats and colours. Without it, defaults stay
    signature-deterministic for the pure-function tests."""
    rng = (
        veil_random(entropy, f"norm:{signature}")
        if entropy is not None
        else random.Random(f"norm:{signature}")
    )
    voice = profile.get("voice") if isinstance(profile.get("voice"), dict) else {}
    visual = profile.get("visual") if isinstance(profile.get("visual"), dict) else {}
    quotes = profile.get("quotes") if isinstance(profile.get("quotes"), list) else []

    rarity = str(profile.get("rarity", "common")).lower()
    if rarity not in RARITY_TIERS:
        rarity = "common"

    form = str(visual.get("form", "wisp")).lower()
    if form not in ("wisp", "banshee", "fairy", "shade"):
        form = "wisp"

    def _clamp(value, low, high, default):
        try:
            return max(low, min(high, float(value)))
        except (TypeError, ValueError):
            return default

    return {
        "name": str(profile.get("name", "The Unnamed"))[:64].strip() or "The Unnamed",
        "epithet": str(profile.get("epithet", rng.choice(_EPITHETS)))[:128],
        "persona": str(profile.get("persona", ""))[:1200],
        "rarity": rarity,
        "voice": {
            "voice_id": voice.get("voice_id")
            if voice.get("voice_id") in EN_VOICE_IDS + ["davefx", "ald"]
            else rng.choice(EN_VOICE_IDS),
            "pitch": _clamp(voice.get("pitch"), -6, 6, rng.uniform(-3, 3)),
            "rate": _clamp(voice.get("rate"), 0.8, 1.15, rng.uniform(0.9, 1.05)),
            "noise": _clamp(voice.get("noise"), 0.01, 0.08, rng.uniform(0.02, 0.05)),
            "echo": _clamp(voice.get("echo"), 0.0, 0.5, rng.uniform(0.1, 0.3)),
        },
        "visual": {
            "hue": _clamp(visual.get("hue"), 0, 360, rng.uniform(0, 360)),
            "form": form,
        },
        "quotes": [str(q)[:200] for q in quotes[:4] if isinstance(q, str)]
        or rng.sample(_QUOTE_BANK, 2),
        "traits": roll_traits(signature, entropy),
    }


def fallback_profile(signature: str, entropy: object = None) -> dict:
    """Procedural persona for when the LLM box is unreachable — a summoning
    must never visibly fail.

    With `entropy`, even the fallback spirits differ run to run: with the
    LLM down, a purely signature-seeded fallback would hand every seeker on
    a given channel the identical name, epithet and rarity, which is
    exactly the "example data" feel this is meant to avoid."""
    rng = (
        veil_random(entropy, f"fallback:{signature}")
        if entropy is not None
        else random.Random(f"fallback:{signature}")
    )
    name = rng.choice(_NAME_PARTS[0]) + rng.choice(_NAME_PARTS[1])
    epithet = rng.choice(_EPITHETS)
    persona = rng.choice(_PERSONA_TEMPLATES).format(name=name)
    rarity = rng.choices(RARITY_TIERS, weights=[55, 30, 12, 3])[0]
    return normalize_profile(
        {
            "name": name,
            "epithet": epithet,
            "persona": persona,
            "rarity": rarity,
            "quotes": rng.sample(_QUOTE_BANK, 2),
        },
        signature,
        entropy,
    )