[pull] master from langchain-ai:master

libs/core/langchain_core/language_models/base.py

@@ -233,7 +233,7 @@ async def agenerate_prompt(
         """
 
     def with_structured_output(
-        self, schema: Union[dict, type[BaseModel]], **kwargs: Any
+        self, schema: Union[dict, type], **kwargs: Any
     ) -> Runnable[LanguageModelInput, Union[dict, BaseModel]]:
         """Not implemented on this class."""
         # Implement this on child class if there is a way of steering the model to

libs/core/langchain_core/language_models/chat_models.py

@@ -1128,7 +1128,7 @@ def with_structured_output(
                 The output schema. Can be passed in as:
                     - an OpenAI function/tool schema,
                     - a JSON Schema,
-                    - a TypedDict class (support added in 0.2.26),
+                    - a TypedDict class,
                     - or a Pydantic class.
                 If ``schema`` is a Pydantic class then the model output will be a
                 Pydantic instance of that class, and the model-generated fields will be
@@ -1137,10 +1137,6 @@ def with_structured_output(
                 for more on how to properly specify types and descriptions of
                 schema fields when specifying a Pydantic or TypedDict class.
 
-                .. versionchanged:: 0.2.26
-
-                        Added support for TypedDict class.
-
             include_raw:
                 If False then only the parsed structured output is returned. If
                 an error occurs during model output parsing it will be raised. If True
@@ -1222,6 +1218,10 @@ class AnswerWithJustification(BaseModel):
                 #     'answer': 'They weigh the same',
                 #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
                 # }
+
+        .. versionchanged:: 0.2.26
+
+                Added support for TypedDict class.
         """  # noqa: E501
         if kwargs:
             msg = f"Received unsupported arguments {kwargs}"

libs/core/langchain_core/prompts/structured.py

@@ -28,7 +28,7 @@
 class StructuredPrompt(ChatPromptTemplate):
     """Structured prompt template for a language model."""
 
-    schema_: Union[dict, type[BaseModel]]
+    schema_: Union[dict, type]
     """Schema for the structured prompt."""
     structured_output_kwargs: dict[str, Any] = Field(default_factory=dict)
 
@@ -66,7 +66,7 @@ def get_lc_namespace(cls) -> list[str]:
     def from_messages_and_schema(
         cls,
         messages: Sequence[MessageLikeRepresentation],
-        schema: Union[dict, type[BaseModel]],
+        schema: Union[dict, type],
         **kwargs: Any,
     ) -> ChatPromptTemplate:
         """Create a chat prompt template from a variety of message formats.

libs/partners/anthropic/langchain_anthropic/chat_models.py

@@ -16,7 +16,6 @@
     Sequence,
     Tuple,
     Type,
-    TypedDict,
     Union,
     cast,
 )
@@ -72,7 +71,7 @@
     SecretStr,
     model_validator,
 )
-from typing_extensions import NotRequired
+from typing_extensions import NotRequired, TypedDict
 
 from langchain_anthropic.output_parsers import extract_tool_calls
 
@@ -973,7 +972,7 @@ class GetPrice(BaseModel):
 
     def with_structured_output(
         self,
-        schema: Union[Dict, Type[BaseModel]],
+        schema: Union[Dict, type],
         *,
         include_raw: bool = False,
         **kwargs: Any,