langchain[patch]: Use async memory in Chain when needed (langchain-ai…

…#19429)

libs/langchain/langchain/chains/base.py

@@ -181,7 +181,7 @@ async def ainvoke(
         include_run_info = kwargs.get("include_run_info", False)
         return_only_outputs = kwargs.get("return_only_outputs", False)
 
-        inputs = self.prep_inputs(input)
+        inputs = await self.aprep_inputs(input)
         callback_manager = AsyncCallbackManager.configure(
             callbacks,
             self.callbacks,
@@ -482,6 +482,30 @@ def prep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]:
             inputs = dict(inputs, **external_context)
         return inputs
 
+    async def aprep_inputs(self, inputs: Union[Dict[str, Any], Any]) -> Dict[str, str]:
+        """Prepare chain inputs, including adding inputs from memory.
+
+        Args:
+            inputs: Dictionary of raw inputs, or single input if chain expects
+                only one param. Should contain all inputs specified in
+                `Chain.input_keys` except for inputs that will be set by the chain's
+                memory.
+
+        Returns:
+            A dictionary of all inputs, including those added by the chain's memory.
+        """
+        if not isinstance(inputs, dict):
+            _input_keys = set(self.input_keys)
+            if self.memory is not None:
+                # If there are multiple input keys, but some get set by memory so that
+                # only one is not set, we can still figure out which key it is.
+                _input_keys = _input_keys.difference(self.memory.memory_variables)
+            inputs = {list(_input_keys)[0]: inputs}
+        if self.memory is not None:
+            external_context = await self.memory.aload_memory_variables(inputs)
+            inputs = dict(inputs, **external_context)
+        return inputs
+
     @property
     def _run_output_key(self) -> str:
         if len(self.output_keys) != 1:

libs/langchain/tests/unit_tests/chains/test_conversation.py

@@ -1,5 +1,9 @@
 """Test conversation chain and memory."""
+from typing import Any, List, Optional
+
 import pytest
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models import LLM
 from langchain_core.memory import BaseMemory
 from langchain_core.prompts.prompt import PromptTemplate
 
@@ -10,6 +14,27 @@
 from tests.unit_tests.llms.fake_llm import FakeLLM
 
 
+class DummyLLM(LLM):
+    last_prompt = ""
+
+    def __init__(self, **kwargs: Any):
+        super().__init__(**kwargs)
+
+    @property
+    def _llm_type(self) -> str:
+        return "dummy"
+
+    def _call(
+        self,
+        prompt: str,
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any,
+    ) -> str:
+        self.last_prompt = prompt
+        return "dummy"
+
+
 def test_memory_ai_prefix() -> None:
     """Test that ai_prefix in the memory component works."""
     memory = ConversationBufferMemory(memory_key="foo", ai_prefix="Assistant")
@@ -32,13 +57,18 @@ async def test_memory_async() -> None:
     }
 
 
-def test_conversation_chain_works() -> None:
+async def test_conversation_chain_works() -> None:
     """Test that conversation chain works in basic setting."""
-    llm = FakeLLM()
+    llm = DummyLLM()
     prompt = PromptTemplate(input_variables=["foo", "bar"], template="{foo} {bar}")
     memory = ConversationBufferMemory(memory_key="foo")
     chain = ConversationChain(llm=llm, prompt=prompt, memory=memory, input_key="bar")
-    chain.run("foo")
+    chain.run("aaa")
+    assert llm.last_prompt == " aaa"
+    chain.run("bbb")
+    assert llm.last_prompt == "Human: aaa\nAI: dummy bbb"
+    await chain.arun("ccc")
+    assert llm.last_prompt == "Human: aaa\nAI: dummy\nHuman: bbb\nAI: dummy ccc"
 
 
 def test_conversation_chain_errors_bad_prompt() -> None: