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f0347b4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | """Step 2 of the living loop: appraisal (F2).
Calls the text model (MiniCPM5-1B via model/client.py) with a small GBNF
grammar (engine/grammars/appraisal.gbnf) that guarantees a valid, minimal
JSON object describing how the last exchange should nudge Daimon's state
vector. The model PROPOSES signals; engine/mapping.py and
engine/spec_bridge.py turn them into clamped, audited mutations - the model
never writes state.json directly.
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
REPO_ROOT = Path(__file__).resolve().parent.parent
if str(REPO_ROOT) not in sys.path:
sys.path.insert(0, str(REPO_ROOT))
from model.client import chat # noqa: E402
GRAMMAR_PATH = Path(__file__).resolve().parent / "grammars" / "appraisal.gbnf"
_GRAMMAR = GRAMMAR_PATH.read_text(encoding="utf-8")
_SYSTEM_PROMPT = (
"You are an appraisal module for a persona named Daimon. Given the user's "
"last message and Daimon's reply, output ONLY a JSON object describing how "
"the exchange should nudge Daimon's internal state. Fields:\n"
"- sentiment: overall tone of the user's message, from -1.0 (hostile/negative) "
"to 1.0 (warm/positive), in steps of 0.5.\n"
"- engagement: how exploratory/engaged the exchange is, 0.0 (flat/closing) to "
"1.0 (curious/exploratory), in steps of 0.25.\n"
"- correction: true if the user explicitly asked Daimon to change how it acts "
"(its tone, talkativeness, openness, or agreeableness), false otherwise.\n"
"- target: which trait the correction is about - one of tone, openness, "
"extraversion, agreeableness, none.\n"
"- direction: -1 if the user wants less of that trait, 1 if more, 0 if there "
"is no correction.\n"
"- reason: a short (under 12 words) plain-text reason for this reading.\n"
)
_NEUTRAL: dict = {
"sentiment": 0.0,
"engagement": 0.0,
"correction": False,
"target": "none",
"direction": 0,
"reason": "appraisal output unparsable, defaulting to neutral",
}
_VALID_TARGETS = {"tone", "openness", "extraversion", "agreeableness", "none"}
def appraise(user_message: str, persona_reply: str, *, max_tokens: int = 200) -> dict:
"""Return a validated appraisal dict, falling back to a neutral reading if
the model's output cannot be parsed."""
messages = [
{"role": "system", "content": _SYSTEM_PROMPT},
{
"role": "user",
"content": (
f"User said: {user_message!r}\nDaimon replied: {persona_reply!r}\n"
"Output the JSON object now."
),
},
]
raw = chat(
messages,
modality="text",
max_tokens=max_tokens,
temperature=0.1,
enable_thinking=False, # grammar-constrained JSON; reasoning tokens would eat max_tokens
extra_body={"grammar": _GRAMMAR},
)
return _parse(raw)
def _parse(raw: str) -> dict:
try:
data = json.loads(raw)
except (json.JSONDecodeError, TypeError):
return dict(_NEUTRAL)
out = dict(_NEUTRAL)
if isinstance(data.get("sentiment"), (int, float)):
out["sentiment"] = max(-1.0, min(1.0, float(data["sentiment"])))
if isinstance(data.get("engagement"), (int, float)):
out["engagement"] = max(0.0, min(1.0, float(data["engagement"])))
if isinstance(data.get("correction"), bool):
out["correction"] = data["correction"]
if data.get("target") in _VALID_TARGETS:
out["target"] = data["target"]
if data.get("direction") in (-1, 0, 1):
out["direction"] = data["direction"]
if isinstance(data.get("reason"), str) and data["reason"].strip():
out["reason"] = data["reason"].strip()[:120]
return out
if __name__ == "__main__":
sys.stdout.reconfigure(encoding="utf-8")
result = appraise(
"Wow, that's such a cool way to put it, tell me more!",
"Glad you liked that! There's a lot more to explore here...",
)
print(result)
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