mkbk
139 lines
5.1 KiB
Python
139 lines
5.1 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from pathlib import Path
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from .models import ErrorCode, ProviderError
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@dataclass(frozen=True, slots=True)
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class AppConfig:
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wake_word: str = "小杰小杰"
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sample_rate: int = 16000
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channels: int = 1
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llm_base_url: str = "https://newapi.mkbk.shop"
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llm_api_key: str | None = None
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llm_model: str = "gpt-5.4-mini"
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llm_api_style: str = "chat_completions"
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llm_stream: bool = True
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audio_input_device: str | None = None
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audio_output_device: str | None = None
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asset_dir: Path = Path("assets/pet")
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log_dir: Path = Path("logs")
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speech_models_dir: Path = Path("models")
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context_max_messages: int = 12
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context_max_chars: int = 12000
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@classmethod
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def from_dotenv(cls, path: str | Path = ".env", prefix: str = "OWNER_") -> "AppConfig":
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values = parse_dotenv(Path(path))
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def get(name: str, default: str | None = None) -> str | None:
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value = values.get(f"{prefix}{name}")
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return default if value is None or value == "" else value
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return cls(
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wake_word=get("WAKE_WORD", "小杰小杰") or "小杰小杰",
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sample_rate=int(get("SAMPLE_RATE", "16000") or "16000"),
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channels=int(get("CHANNELS", "1") or "1"),
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llm_base_url=(get("LLM_BASE_URL", "https://newapi.mkbk.shop") or "").rstrip("/"),
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llm_api_key=get("LLM_API_KEY"),
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llm_model=get("LLM_MODEL", "gpt-5.4-mini") or "gpt-5.4-mini",
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llm_api_style=get("LLM_API_STYLE", "chat_completions") or "chat_completions",
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llm_stream=(get("LLM_STREAM", "1") or "1").lower() not in {"0", "false", "no"},
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audio_input_device=get("AUDIO_INPUT_DEVICE"),
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audio_output_device=get("AUDIO_OUTPUT_DEVICE"),
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asset_dir=Path(get("ASSET_DIR", "assets/pet") or "assets/pet"),
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log_dir=Path(get("LOG_DIR", "logs") or "logs"),
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speech_models_dir=Path(get("SPEECH_MODELS_DIR", "models") or "models"),
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context_max_messages=int(get("CONTEXT_MAX_MESSAGES", "12") or "12"),
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context_max_chars=int(get("CONTEXT_MAX_CHARS", "12000") or "12000"),
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)
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@classmethod
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def from_env(cls, prefix: str = "OWNER_") -> "AppConfig":
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return cls.from_dotenv(".env", prefix=prefix)
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def require_llm_credentials(self) -> None:
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if not self.llm_api_key:
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raise ProviderError(
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code=ErrorCode.LLM_API_KEY_MISSING,
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message="OWNER_LLM_API_KEY is required for cloud LLM calls",
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retryable=False,
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provider="openai-compatible",
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stage="llm",
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)
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def validate_basic(self) -> list[ProviderError]:
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errors: list[ProviderError] = []
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if self.sample_rate <= 0:
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errors.append(
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ProviderError(
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ErrorCode.CONFIG_MISSING_VALUE,
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"sample_rate must be positive",
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False,
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"config",
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"startup",
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)
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)
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if self.channels <= 0:
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errors.append(
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ProviderError(
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ErrorCode.CONFIG_MISSING_VALUE,
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"channels must be positive",
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False,
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"config",
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"startup",
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)
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)
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if self.llm_api_style not in {"chat_completions", "responses"}:
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errors.append(
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ProviderError(
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ErrorCode.CONFIG_MISSING_VALUE,
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"OWNER_LLM_API_STYLE must be chat_completions or responses",
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False,
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"config",
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"startup",
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)
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)
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if not self.llm_base_url.startswith(("http://", "https://")):
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errors.append(
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ProviderError(
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ErrorCode.CONFIG_MISSING_VALUE,
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"OWNER_LLM_BASE_URL must start with http:// or https://",
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False,
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"config",
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"startup",
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)
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)
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return errors
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def parse_dotenv(path: Path) -> dict[str, str]:
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if not path.exists():
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return {}
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values: dict[str, str] = {}
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for line_no, raw_line in enumerate(path.read_text(encoding="utf-8").splitlines(), start=1):
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line = raw_line.strip()
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if not line or line.startswith("#"):
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continue
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if line.startswith("export "):
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line = line.removeprefix("export ").strip()
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if "=" not in line:
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raise ValueError(f"invalid .env line {line_no}: missing '='")
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key, value = line.split("=", 1)
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key = key.strip()
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value = _strip_dotenv_value(value.strip())
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if not key:
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raise ValueError(f"invalid .env line {line_no}: empty key")
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values[key] = value
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return values
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def _strip_dotenv_value(value: str) -> str:
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if len(value) >= 2 and value[0] == value[-1] and value[0] in {"'", '"'}:
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return value[1:-1]
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if " #" in value:
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return value.split(" #", 1)[0].rstrip()
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return value
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