core[patch]: revert change to stream type hint (#31501)

https://github.com/langchain-ai/langchain/pull/31286 included an update
to the return type for `BaseChatModel.(a)stream`, from
`Iterator[BaseMessageChunk]` to `Iterator[BaseMessage]`.

This change is correct, because when streaming is disabled, the stream
methods return an iterator of `BaseMessage`, and the inheritance is such
that an `BaseMessage` is not a `BaseMessageChunk` (but the reverse is
true).

However, LangChain includes a pattern throughout its docs of [summing
BaseMessageChunks](https://python.langchain.com/docs/how_to/streaming/#llms-and-chat-models)
to accumulate a chat model stream. This pattern is implemented in tests
for most integration packages and appears in application code. So
https://github.com/langchain-ai/langchain/pull/31286 introduces mypy
errors throughout the ecosystem (or maybe more accurately, it reveals
that this pattern does not account for use of the `.stream` method when
streaming is disabled).

Here we revert just the change to the stream return type to unblock
things. A fix for this should address docs + integration packages (or if
we elect to just force people to update code, be explicit about that).

libs/core/langchain_core/language_models/chat_models.py

@@ -51,6 +51,7 @@ from langchain_core.messages import (
     AIMessage,
     AnyMessage,
     BaseMessage,
+    BaseMessageChunk,
     HumanMessage,
     convert_to_messages,
     convert_to_openai_image_block,
@@ -445,10 +446,13 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         *,
         stop: Optional[list[str]] = None,
         **kwargs: Any,
-    ) -> Iterator[BaseMessage]:
+    ) -> Iterator[BaseMessageChunk]:
         if not self._should_stream(async_api=False, **{**kwargs, "stream": True}):
             # model doesn't implement streaming, so use default implementation
-            yield self.invoke(input, config=config, stop=stop, **kwargs)
+            yield cast(
+                "BaseMessageChunk",
+                self.invoke(input, config=config, stop=stop, **kwargs),
+            )
         else:
             config = ensure_config(config)
             messages = self._convert_input(input).to_messages()
@@ -533,10 +537,13 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
         *,
         stop: Optional[list[str]] = None,
         **kwargs: Any,
-    ) -> AsyncIterator[BaseMessage]:
+    ) -> AsyncIterator[BaseMessageChunk]:
         if not self._should_stream(async_api=True, **{**kwargs, "stream": True}):
             # No async or sync stream is implemented, so fall back to ainvoke
-            yield await self.ainvoke(input, config=config, stop=stop, **kwargs)
+            yield cast(
+                "BaseMessageChunk",
+                await self.ainvoke(input, config=config, stop=stop, **kwargs),
+            )
             return
 
         config = ensure_config(config)

libs/core/tests/unit_tests/fake/test_fake_chat_model.py

@@ -1,7 +1,5 @@
 """Tests for verifying that testing utility code works as expected."""
 
-import operator
-from functools import reduce
 from itertools import cycle
 from typing import Any, Optional, Union
 from uuid import UUID
@@ -117,7 +115,12 @@ async def test_generic_fake_chat_model_stream() -> None:
     ]
     assert len({chunk.id for chunk in chunks}) == 1
 
-    accumulate_chunks = reduce(operator.add, chunks)
+    accumulate_chunks = None
+    for chunk in chunks:
+        if accumulate_chunks is None:
+            accumulate_chunks = chunk
+        else:
+            accumulate_chunks += chunk
 
     assert accumulate_chunks == AIMessageChunk(
         content="",

libs/core/tests/unit_tests/language_models/chat_models/test_base.py

@@ -163,10 +163,15 @@ async def test_astream_fallback_to_ainvoke() -> None:
 
     model = ModelWithGenerate()
     chunks = list(model.stream("anything"))
-    assert chunks == [_any_id_ai_message(content="hello")]
+    # BaseChatModel.stream is typed to return Iterator[BaseMessageChunk].
+    # When streaming is disabled, it returns Iterator[BaseMessage], so the type hint
+    # is not strictly correct.
+    # LangChain documents a pattern of adding BaseMessageChunks to accumulate a stream.
+    # This may be better done with `reduce(operator.add, chunks)`.
+    assert chunks == [_any_id_ai_message(content="hello")]  # type: ignore[comparison-overlap]
 
     chunks = [chunk async for chunk in model.astream("anything")]
-    assert chunks == [_any_id_ai_message(content="hello")]
+    assert chunks == [_any_id_ai_message(content="hello")]  # type: ignore[comparison-overlap]
 
 
 async def test_astream_implementation_fallback_to_stream() -> None:

libs/langchain/tests/unit_tests/llms/test_fake_chat_model.py

@@ -1,7 +1,5 @@
 """Tests for verifying that testing utility code works as expected."""
 
-import operator
-from functools import reduce
 from itertools import cycle
 from typing import Any, Optional, Union
 from uuid import UUID
@@ -109,7 +107,12 @@ async def test_generic_fake_chat_model_stream() -> None:
         ),
     ]
 
-    accumulate_chunks = reduce(operator.add, chunks)
+    accumulate_chunks = None
+    for chunk in chunks:
+        if accumulate_chunks is None:
+            accumulate_chunks = chunk
+        else:
+            accumulate_chunks += chunk
 
     assert accumulate_chunks == AIMessageChunk(
         id="a1",