[OpenSpec 与项目骨架]：完成实施型变更与 Python 基础骨架，包含 OpenSpec 工件、核心模型、配置边界和基础测试

src/owner_voice_pet/config.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+from pathlib import Path
+
+from .models import ErrorCode, ProviderError
+
+
+@dataclass(frozen=True, slots=True)
+class AppConfig:
+    wake_word: str = "小杰小杰"
+    sample_rate: int = 16000
+    channels: int = 1
+    llm_base_url: str = "https://api.openai.com"
+    llm_api_key: str | None = None
+    llm_model: str = "gpt-4o-mini"
+    llm_api_style: str = "chat_completions"
+    audio_input_device: str | None = None
+    audio_output_device: str | None = None
+    asset_dir: Path = Path("assets/pet")
+    log_dir: Path = Path("logs")
+    context_max_messages: int = 12
+    context_max_chars: int = 12000
+
+    @classmethod
+    def from_env(cls, prefix: str = "OWNER_") -> "AppConfig":
+        def get(name: str, default: str | None = None) -> str | None:
+            value = os.environ.get(f"{prefix}{name}")
+            return default if value is None or value == "" else value
+
+        return cls(
+            wake_word=get("WAKE_WORD", "小杰小杰") or "小杰小杰",
+            sample_rate=int(get("SAMPLE_RATE", "16000") or "16000"),
+            channels=int(get("CHANNELS", "1") or "1"),
+            llm_base_url=(get("LLM_BASE_URL", "https://api.openai.com") or "").rstrip("/"),
+            llm_api_key=get("LLM_API_KEY"),
+            llm_model=get("LLM_MODEL", "gpt-4o-mini") or "gpt-4o-mini",
+            llm_api_style=get("LLM_API_STYLE", "chat_completions") or "chat_completions",
+            audio_input_device=get("AUDIO_INPUT_DEVICE"),
+            audio_output_device=get("AUDIO_OUTPUT_DEVICE"),
+            asset_dir=Path(get("ASSET_DIR", "assets/pet") or "assets/pet"),
+            log_dir=Path(get("LOG_DIR", "logs") or "logs"),
+            context_max_messages=int(get("CONTEXT_MAX_MESSAGES", "12") or "12"),
+            context_max_chars=int(get("CONTEXT_MAX_CHARS", "12000") or "12000"),
+        )
+
+    def require_llm_credentials(self) -> None:
+        if not self.llm_api_key:
+            raise ProviderError(
+                code=ErrorCode.LLM_API_KEY_MISSING,
+                message="OWNER_LLM_API_KEY is required for cloud LLM calls",
+                retryable=False,
+                provider="openai-compatible",
+                stage="llm",
+            )
+
+    def validate_basic(self) -> list[ProviderError]:
+        errors: list[ProviderError] = []
+        if self.sample_rate <= 0:
+            errors.append(
+                ProviderError(
+                    ErrorCode.CONFIG_MISSING_VALUE,
+                    "sample_rate must be positive",
+                    False,
+                    "config",
+                    "startup",
+                )
+            )
+        if self.channels <= 0:
+            errors.append(
+                ProviderError(
+                    ErrorCode.CONFIG_MISSING_VALUE,
+                    "channels must be positive",
+                    False,
+                    "config",
+                    "startup",
+                )
+            )
+        if self.llm_api_style not in {"chat_completions", "responses"}:
+            errors.append(
+                ProviderError(
+                    ErrorCode.CONFIG_MISSING_VALUE,
+                    "OWNER_LLM_API_STYLE must be chat_completions or responses",
+                    False,
+                    "config",
+                    "startup",
+                )
+            )
+        return errors