feat：简化部分代码

app/clients/llm.py

@@ -1,10 +1,8 @@
 """统一获取普通模型或绑定结构化输出的模型"""
 
 from functools import cache
-from typing import Literal, TypeVar, cast, overload
+from typing import Any, TypeVar, overload
 
-from langchain_core.language_models import LanguageModelInput
-from langchain_core.language_models.chat_models import BaseChatModel
 from langchain_core.runnables import Runnable
 from langchain_openai import ChatOpenAI
 from pydantic import BaseModel
@@ -12,7 +10,6 @@ from pydantic import BaseModel
 from app.config import settings
 
 OutputModel = TypeVar("OutputModel", bound=BaseModel)
-StructuredMethod = Literal["function_calling", "json_schema", "json_mode"]
 
 
 @cache
@@ -29,64 +26,22 @@ def _get_base_llm() -> ChatOpenAI:
     )
 
 
+# 下面两个 @overload 只给类型检查器和编辑器看，运行时不会执行
+# 不传 schema：返回普通聊天模型，输出是自由文本
 @overload
-def get_llm(
-    schema: type[OutputModel],
-    *,
-    method: StructuredMethod = "function_calling",
-    strict: bool | None = False,
-    client: BaseChatModel | None = None,
-) -> Runnable[LanguageModelInput, OutputModel]: ...
+def get_llm() -> ChatOpenAI: ...
 
 
+# 传入 Pydantic 类：返回结构化调用器，ainvoke 的结果就是该类的实例
+# OutputModel 让返回类型跟着传入的类走，例如 get_llm(Requirement) 得到 Runnable[..., Requirement]
 @overload
-def get_llm(
-    schema: None = None,
-    *,
-    method: StructuredMethod = "function_calling",
-    strict: bool | None = False,
-    client: None = None,
-) -> ChatOpenAI: ...
+def get_llm(schema: type[OutputModel]) -> Runnable[Any, OutputModel]: ...
 
 
-@overload
-def get_llm(
-    schema: None = None,
-    *,
-    method: StructuredMethod = "function_calling",
-    strict: bool | None = False,
-    client: BaseChatModel,
-) -> BaseChatModel: ...
-
-
-def get_llm(
-    schema: type[OutputModel] | None = None,
-    *,
-    method: StructuredMethod = "function_calling",
-    strict: bool | None = False,
-    client: BaseChatModel | None = None,
-) -> BaseChatModel | Runnable[LanguageModelInput, OutputModel]:
-    """不传 schema 返回普通模型，传入 Pydantic 类则返回结构化调用器
-
-    client 用于注入模型，省略时使用缓存客户端
-    strict 控制服务端 schema 约束，本地仍由 Pydantic 校验
-    """
-    if schema is not None and (
-        not isinstance(schema, type) or not issubclass(schema, BaseModel)
-    ):
-        raise TypeError("schema 必须是 Pydantic BaseModel 类")
-    if method not in ("function_calling", "json_schema", "json_mode"):
-        raise ValueError(f"不支持的结构化输出方式: {method}")
-    if schema is None and (method != "function_calling" or strict not in (False, None)):
-        raise ValueError("设置结构化输出参数时必须提供 schema")
-    if method == "json_mode" and strict is True:
-        raise ValueError("json_mode 不支持 strict=True")
-
-    llm = client if client is not None else _get_base_llm()
+# 真正的实现：schema 用 type[BaseModel] | None 覆盖上面两种调用
+# 返回类型由 overload 决定，这里不再重复标注
+def get_llm(schema: type[BaseModel] | None = None) -> Any:
+    llm = _get_base_llm()
     if schema is None:
         return llm
-    # json_mode 不接受 strict 参数，提示词还需要明确要求 JSON 及其结构
-    structured = llm.with_structured_output(
-        schema, method=method, strict=None if method == "json_mode" else strict
-    )
-    return cast(Runnable[LanguageModelInput, OutputModel], structured)
+    return llm.with_structured_output(schema, method="function_calling", strict=False)