wip transform

libs/core/langchain_core/messages/utils.py

@@ -28,7 +28,7 @@ from typing import (
     Type,
     Union,
     cast,
-    overload,
+    overload, Iterator,
 )
 
 from langchain_core.messages.ai import AIMessage, AIMessageChunk
@@ -41,6 +41,7 @@ from langchain_core.messages.system import SystemMessage, SystemMessageChunk
 from langchain_core.messages.tool import ToolMessage, ToolMessageChunk
 from langchain_core.messages.tool import (
     tool_call as create_tool_call,
+    tool_call_chunk as create_tool_call_chunk
 )
 
 if TYPE_CHECKING:
@@ -334,10 +335,7 @@ def convert_to_messages(
     return [_convert_to_message(m, copy=copy) for m in messages]
 
 
-def _runnable_support(*args: Callable, supports_single: bool = False) -> Callable:
+def _runnable_generator(func: Callable) -> Callable:
-    if supports_single:
-
-        def runnable_support(func: Callable) -> Callable:
     @overload
     def wrapped(
         messages: Literal[None] = None, **kwargs: Any
@@ -352,9 +350,7 @@ def _runnable_support(*args: Callable, supports_single: bool = False) -> Callabl
     ) -> List[BaseMessage]: ...
 
     @overload
-            def wrapped(
+    def wrapped(messages: MessageLikeRepresentation, **kwargs: Any) -> BaseMessage: ...
-                messages: MessageLikeRepresentation, **kwargs: Any
-            ) -> BaseMessage: ...
 
     def wrapped(
         messages: Union[
@@ -365,25 +361,25 @@ def _runnable_support(*args: Callable, supports_single: bool = False) -> Callabl
         BaseMessage,
         List[BaseMessage],
         Runnable[
-                    Union[
+            Union[MessageLikeRepresentation, Sequence[MessageLikeRepresentation]],
-                        MessageLikeRepresentation, Sequence[MessageLikeRepresentation]
-                    ],
             Union[BaseMessage, List[BaseMessage]],
         ],
     ]:
-                from langchain_core.runnables.base import RunnableLambda
+        from langchain_core.runnables.base import RunnableGenerator
 
         if messages is not None:
             return func(messages, **kwargs)
         else:
-                    return RunnableLambda(partial(func, **kwargs), name=func.__name__)
+            def transform(input_: Iterator, **kwargs: Any) -> Iterator:
+                for x in input_:
+                    yield func(x, **kwargs)
+            return RunnableGenerator(partial(transform, **kwargs), name=func.__name__)
 
     wrapped.__doc__ = func.__doc__
     return wrapped
 
-    else:
 
-        def runnable_support(func: Callable) -> Callable:
+def _runnable_support(func: Callable) -> Callable:
     @overload
     def wrapped(
         messages: Literal[None] = None, **kwargs: Any
@@ -411,8 +407,6 @@ def _runnable_support(*args: Callable, supports_single: bool = False) -> Callabl
     wrapped.__doc__ = func.__doc__
     return wrapped
 
-    return runnable_support(*args) if args else cast(Callable, runnable_support)
-
 
 @_runnable_support
 def filter_messages(
@@ -909,13 +903,13 @@ def trim_messages(
         )
 
 
-@_runnable_support(supports_single=True)
+@_runnable_generator
 def format_messages_as(
-    messages: Union[MessageLikeRepresentation, Iterable[MessageLikeRepresentation]],
+    messages: Union[MessageLikeRepresentation, Iterator[MessageLikeRepresentation]],
     *,
     format: Literal["openai", "anthropic"],
     text: Literal["string", "block"],
-) -> Union[BaseMessage, List[BaseMessage]]:
+) -> Union[BaseMessage, Iterator[BaseMessage]]:
     """Convert message contents into a standard format.
 
     .. versionadded:: 0.2.36
@@ -1123,7 +1117,6 @@ def _format_contents_as_openai(
                             )
                         # Bedrock converse
                         elif image := block.get("image"):
-                            raise ValueError("1064")
                             if missing := [
                                 k for k in ("source", "format") if k not in image
                             ]:
@@ -1153,6 +1146,7 @@ def _format_contents_as_openai(
                                 f"content block:\n\n{block}"
                             )
                     elif block.get("type") == "tool_use":
+                        if not isinstance(message, BaseMessageChunk):
                             if missing := [
                                 k for k in ("id", "name", "input") if k not in block
                             ]:
@@ -1173,6 +1167,9 @@ def _format_contents_as_openai(
                                         args=block["input"],
                                     )
                                 )
+                        else:
+                            if not message.tool_call_chunks:
+                                message.tool_call_chunks = [create_tool_call_chunk(id=block.get("id"), index=block.get("index"), args=block.get("partial_json"), name=block.get("name"))]
                     elif block.get("type") == "tool_result":
                         if missing := [
                             k for k in ("content", "tool_use_id") if k not in block

libs/core/langchain_core/runnables/base.py

@@ -3882,6 +3882,7 @@ class RunnableGenerator(Runnable[Input, Output]):
         atransform: Optional[
             Callable[[AsyncIterator[Input]], AsyncIterator[Output]]
         ] = None,
+        name: Optional[str] = None,
     ) -> None:
         """Initialize a RunnableGenerator.
 
@@ -3909,6 +3910,9 @@ class RunnableGenerator(Runnable[Input, Output]):
             )
 
         try:
+            if name:
+                self.name = name
+            else:
                 self.name = func_for_name.__name__
         except AttributeError:
             pass

libs/partners/anthropic/langchain_anthropic/chat_models.py

@@ -1210,12 +1210,12 @@ def _make_message_chunk_from_anthropic_event(
             content_block = event.delta.model_dump()
             content_block["index"] = event.index
             content_block["type"] = "tool_use"
-            tool_call_chunk = {
+            tool_call_chunk = create_tool_call_chunk(**{
                 "index": event.index,
                 "id": None,
                 "name": None,
                 "args": event.delta.partial_json,
-            }
+            })
             message_chunk = AIMessageChunk(
                 content=[content_block],
                 tool_call_chunks=[tool_call_chunk],  # type: ignore