buffer memory old version (#1581)

bring back an older version of memory since people seem to be using it
more widely

langchain/chains/conversation/memory.py

@@ -1,6 +1,9 @@
 """Memory modules for conversation prompts."""
 
-from langchain.memory.buffer import ConversationBufferMemory
+from langchain.memory.buffer import (
+    ConversationBufferMemory,
+    ConversationStringBufferMemory,
+)
 from langchain.memory.buffer_window import ConversationBufferWindowMemory
 from langchain.memory.combined import CombinedMemory
 from langchain.memory.entity import ConversationEntityMemory
@@ -18,4 +21,5 @@ __all__ = [
     "ConversationEntityMemory",
     "ConversationBufferMemory",
     "CombinedMemory",
+    "ConversationStringBufferMemory",
 ]

langchain/memory/__init__.py

@@ -1,4 +1,7 @@
-from langchain.memory.buffer import ConversationBufferMemory
+from langchain.memory.buffer import (
+    ConversationBufferMemory,
+    ConversationStringBufferMemory,
+)
 from langchain.memory.buffer_window import ConversationBufferWindowMemory
 from langchain.memory.chat_memory import ChatMessageHistory
 from langchain.memory.combined import CombinedMemory
@@ -18,4 +21,5 @@ __all__ = [
     "ConversationEntityMemory",
     "ConversationSummaryMemory",
     "ChatMessageHistory",
+    "ConversationStringBufferMemory",
 ]

langchain/memory/buffer.py

@@ -1,9 +1,9 @@
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel
+from pydantic import BaseModel, root_validator
 
-from langchain.memory.chat_memory import BaseChatMemory
+from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
-from langchain.memory.utils import get_buffer_string
+from langchain.memory.utils import get_buffer_string, get_prompt_input_key
 
 
 class ConversationBufferMemory(BaseChatMemory, BaseModel):
@@ -36,3 +36,55 @@ class ConversationBufferMemory(BaseChatMemory, BaseModel):
     def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
         """Return history buffer."""
         return {self.memory_key: self.buffer}
+
+
+class ConversationStringBufferMemory(BaseMemory, BaseModel):
+    """Buffer for storing conversation memory."""
+
+    human_prefix: str = "Human"
+    ai_prefix: str = "AI"
+    """Prefix to use for AI generated responses."""
+    buffer: str = ""
+    output_key: Optional[str] = None
+    input_key: Optional[str] = None
+    memory_key: str = "history"  #: :meta private:
+
+    @root_validator()
+    def validate_chains(cls, values: Dict) -> Dict:
+        """Validate that return messages is not True."""
+        if values.get("return_messages", False):
+            raise ValueError(
+                "return_messages must be False for ConversationStringBufferMemory"
+            )
+        return values
+
+    @property
+    def memory_variables(self) -> List[str]:
+        """Will always return list of memory variables.
+        :meta private:
+        """
+        return [self.memory_key]
+
+    def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:
+        """Return history buffer."""
+        return {self.memory_key: self.buffer}
+
+    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
+        """Save context from this conversation to buffer."""
+        if self.input_key is None:
+            prompt_input_key = get_prompt_input_key(inputs, self.memory_variables)
+        else:
+            prompt_input_key = self.input_key
+        if self.output_key is None:
+            if len(outputs) != 1:
+                raise ValueError(f"One output key expected, got {outputs.keys()}")
+            output_key = list(outputs.keys())[0]
+        else:
+            output_key = self.output_key
+        human = f"{self.human_prefix}: " + inputs[prompt_input_key]
+        ai = f"{self.ai_prefix}: " + outputs[output_key]
+        self.buffer += "\n" + "\n".join([human, ai])
+
+    def clear(self) -> None:
+        """Clear memory contents."""
+        self.buffer = ""