core: fix batch race condition in FakeListChatModel

libs/core/langchain_core/language_models/fake_chat_models.py

@@ -13,6 +13,7 @@
 from langchain_core.language_models.chat_models import BaseChatModel, SimpleChatModel
 from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
+from langchain_core.runnables import RunnableConfig
 
 
 class FakeMessagesListChatModel(BaseChatModel):
@@ -128,6 +129,33 @@ async def _astream(
     def _identifying_params(self) -> dict[str, Any]:
         return {"responses": self.responses}
 
+    # manually override batch to preserve batch ordering with no concurrency
+    def batch(
+        self,
+        inputs: list[Any],
+        config: Optional[Union[RunnableConfig, list[RunnableConfig]]] = None,
+        *,
+        return_exceptions: bool = False,
+        **kwargs: Any,
+    ) -> list[BaseMessage]:
+        if isinstance(config, list):
+            return [self.invoke(m, c, **kwargs) for m, c in zip(inputs, config)]
+        return [self.invoke(m, config, **kwargs) for m in inputs]
+
+    async def abatch(
+        self,
+        inputs: list[Any],
+        config: Optional[Union[RunnableConfig, list[RunnableConfig]]] = None,
+        *,
+        return_exceptions: bool = False,
+        **kwargs: Any,
+    ) -> list[BaseMessage]:
+        if isinstance(config, list):
+            # do Not use an async iterator here because need explicit ordering
+            return [await self.ainvoke(m, c, **kwargs) for m, c in zip(inputs, config)]
+        # do Not use an async iterator here because need explicit ordering
+        return [await self.ainvoke(m, config, **kwargs) for m in inputs]
+
 
 class FakeChatModel(SimpleChatModel):
     """Fake Chat Model wrapper for testing purposes."""

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

@@ -5,7 +5,11 @@
 from uuid import UUID
 
 from langchain_core.callbacks.base import AsyncCallbackHandler
-from langchain_core.language_models import GenericFakeChatModel, ParrotFakeChatModel
+from langchain_core.language_models import (
+    FakeListChatModel,
+    GenericFakeChatModel,
+    ParrotFakeChatModel,
+)
 from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage
 from langchain_core.outputs import ChatGenerationChunk, GenerationChunk
 from tests.unit_tests.stubs import (
@@ -205,3 +209,16 @@ def test_chat_model_inputs() -> None:
     assert fake.invoke([AIMessage(content="blah")]) == _any_id_ai_message(
         content="blah"
     )
+
+
+def test_fake_list_chat_model_batch() -> None:
+    expected = [
+        _any_id_ai_message(content="a"),
+        _any_id_ai_message(content="b"),
+        _any_id_ai_message(content="c"),
+    ]
+    for _ in range(20):
+        # run this 20 times to test race condition in batch
+        fake = FakeListChatModel(responses=["a", "b", "c"])
+        resp = fake.batch(["1", "2", "3"])
+        assert resp == expected

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

@@ -199,19 +199,13 @@ async def test_global_cache_abatch() -> None:
         assert results[0].content == "hello"
         assert results[1].content == "hello"
 
-        ## RACE CONDITION -- note behavior is different from sync
-        # Now, reset cache and test the race condition
-        # For now we just hard-code the result, if this changes
-        # we can investigate further
         global_cache = InMemoryCache()
         set_llm_cache(global_cache)
         assert global_cache._cache == {}
         results = await chat_model.abatch(["prompt", "prompt"])
-        # suspecting that tasks will be scheduled and executed in order
-        # if this ever fails, we can relax to a set comparison
-        # Cache misses likely guaranteed?
+
         assert results[0].content == "meow"
-        assert results[1].content == "woof"
+        assert results[1].content == "meow"
     finally:
         set_llm_cache(None)