Add convenience methods to ConversationBufferMemory and ConversationB… (#8981)

Add convenience methods to `ConversationBufferMemory` and
`ConversationBufferWindowMemory` to get buffer either as messages or as
string.

Helps when `return_messages` is set to `True` but you want access to the
messages as a string, and vice versa.

@hwchase17

One use case: Using a `MultiPromptRouter` where `default_chain` is
`ConversationChain`, but destination chains are `LLMChains`. Injecting
chat memory into prompts for destination chains prints a stringified
`List[Messages]` in the prompt, which creates a lot of noise. These
convenience methods allow caller to choose either as needed.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/langchain/langchain/memory/buffer.py

@@ -17,15 +17,22 @@ class ConversationBufferMemory(BaseChatMemory):
     @property
     def buffer(self) -> Any:
         """String buffer of memory."""
-        if self.return_messages:
-            return self.chat_memory.messages
-        else:
+        return self.buffer_as_messages if self.return_messages else self.buffer_as_str
+
+    @property
+    def buffer_as_str(self) -> str:
+        """Exposes the buffer as a string in case return_messages is True."""
         return get_buffer_string(
             self.chat_memory.messages,
             human_prefix=self.human_prefix,
             ai_prefix=self.ai_prefix,
         )
 
+    @property
+    def buffer_as_messages(self) -> List[Any]:
+        """Exposes the buffer as a list of messages in case return_messages is False."""
+        return self.chat_memory.messages
+
     @property
     def memory_variables(self) -> List[str]:
         """Will always return list of memory variables.

libs/langchain/langchain/memory/buffer_window.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Union
 
 from langchain.memory.chat_memory import BaseChatMemory
 from langchain.schema.messages import BaseMessage, get_buffer_string
@@ -14,9 +14,24 @@ class ConversationBufferWindowMemory(BaseChatMemory):
     """Number of messages to store in buffer."""
 
     @property
-    def buffer(self) -> List[BaseMessage]:
+    def buffer(self) -> Union[str, List[BaseMessage]]:
         """String buffer of memory."""
-        return self.chat_memory.messages
+        return self.buffer_as_messages if self.return_messages else self.buffer_as_str
+
+    @property
+    def buffer_as_str(self) -> str:
+        """Exposes the buffer as a string in case return_messages is True."""
+        messages = self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else []
+        return get_buffer_string(
+            messages,
+            human_prefix=self.human_prefix,
+            ai_prefix=self.ai_prefix,
+        )
+
+    @property
+    def buffer_as_messages(self) -> List[BaseMessage]:
+        """Exposes the buffer as a list of messages in case return_messages is False."""
+        return self.chat_memory.messages[-self.k * 2 :] if self.k > 0 else []
 
     @property
     def memory_variables(self) -> List[str]:
@@ -26,14 +41,6 @@ class ConversationBufferWindowMemory(BaseChatMemory):
         """
         return [self.memory_key]
 
-    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
         """Return history buffer."""
-
-        buffer: Any = self.buffer[-self.k * 2 :] if self.k > 0 else []
-        if not self.return_messages:
-            buffer = get_buffer_string(
-                buffer,
-                human_prefix=self.human_prefix,
-                ai_prefix=self.ai_prefix,
-            )
-        return {self.memory_key: buffer}
+        return {self.memory_key: self.buffer}