chore: upload app/llm/factory.py

app/llm/factory.py

@@ -0,0 +1,104 @@
+"""LLM 工厂: 根据 settings.llm_provider 选择 provider.
+
+新增 provider 的步骤:
+1. 在 base.py 实现一个新类 (或复用 OpenAICompatibleLLM)
+2. 在本文件 _PROVIDERS 加一行
+3. 在 .env.example 暴露新 env
+"""
+from __future__ import annotations
+
+import logging
+from functools import lru_cache
+
+from app.config import settings
+from app.core.errors import LLMUnavailableError
+from app.llm.base import AbstractLLM
+from app.llm.minimax import OpenAICompatibleLLM
+
+logger = logging.getLogger(__name__)
+
+
+def _build_minimax() -> OpenAICompatibleLLM:
+    key = settings.minimax_api_key.get_secret_value()
+    if not key:
+        raise LLMUnavailableError(
+            "MINIMAX_API_KEY is empty. Set it in .env or HF Space secrets.",
+            code="llm_api_key_missing",
+        )
+    return OpenAICompatibleLLM(
+        api_key=key,
+        base_url=settings.minimax_base_url,
+        model=settings.minimax_model,
+        provider_name="minimax",
+    )
+
+
+def _build_openai() -> OpenAICompatibleLLM:
+    key = settings.openai_api_key.get_secret_value()
+    if not key:
+        raise LLMUnavailableError(
+            "OPENAI_API_KEY is empty.",
+            code="llm_api_key_missing",
+        )
+    return OpenAICompatibleLLM(
+        api_key=key,
+        base_url="https://api.openai.com/v1",
+        # 允许用 env 覆盖 model (openai_model), 没设则用 gpt-4o-mini
+        model=getattr(settings, "openai_model", "gpt-4o-mini"),
+        provider_name="openai",
+    )
+
+
+def _build_qwen() -> OpenAICompatibleLLM:
+    key = settings.qwen_api_key.get_secret_value()
+    if not key:
+        raise LLMUnavailableError(
+            "QWEN_API_KEY is empty.",
+            code="llm_api_key_missing",
+        )
+    return OpenAICompatibleLLM(
+        api_key=key,
+        # 阿里百炼 OpenAI 兼容端点
+        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
+        model=getattr(settings, "qwen_model", "qwen-max"),
+        provider_name="qwen",
+    )
+
+
+def _build_anthropic() -> AbstractLLM:
+    """占位: Anthropic SDK 原生协议, 暂未实现, 留 hook."""
+    raise NotImplementedError(
+        "Anthropic provider not yet implemented. "
+        "Use openai-compatible via OpenRouter or implement anthropic.AsyncAnthropic wrapper."
+    )
+
+
+_PROVIDERS = {
+    "minimax": _build_minimax,
+    "openai": _build_openai,
+    "qwen": _build_qwen,
+    "anthropic": _build_anthropic,
+}
+
+
+@lru_cache(maxsize=1)
+def get_llm() -> AbstractLLM:
+    """单例 LLM. 在 lifespan 中调用 + 缓存."""
+    builder = _PROVIDERS.get(settings.llm_provider)
+    if builder is None:
+        raise LLMUnavailableError(
+            f"Unknown llm_provider: {settings.llm_provider}",
+            code="llm_provider_unknown",
+        )
+    logger.info("Initializing LLM provider: %s", settings.llm_provider)
+    return builder()
+
+
+async def close_llm() -> None:
+    """关闭 LLM 客户端. lifespan shutdown 时调用."""
+    try:
+        llm = get_llm()
+    except Exception:  # noqa: BLE001
+        return
+    await llm.aclose()
+    get_llm.cache_clear()