|
|
from typing import Any, Dict, List, Union |
|
|
|
|
|
from langchain_core._api import deprecated |
|
|
from langchain_core.messages import BaseMessage, get_buffer_string |
|
|
from langchain_core.utils import pre_init |
|
|
|
|
|
from langchain.memory.chat_memory import BaseChatMemory |
|
|
from langchain.memory.summary import SummarizerMixin |
|
|
|
|
|
|
|
|
@deprecated( |
|
|
since="0.3.1", |
|
|
removal="1.0.0", |
|
|
message=( |
|
|
"Please see the migration guide at: " |
|
|
"https://python.langchain.com/docs/versions/migrating_memory/" |
|
|
), |
|
|
) |
|
|
class ConversationSummaryBufferMemory(BaseChatMemory, SummarizerMixin): |
|
|
"""Buffer with summarizer for storing conversation memory. |
|
|
|
|
|
Provides a running summary of the conversation together with the most recent |
|
|
messages in the conversation under the constraint that the total number of |
|
|
tokens in the conversation does not exceed a certain limit. |
|
|
""" |
|
|
|
|
|
max_token_limit: int = 2000 |
|
|
moving_summary_buffer: str = "" |
|
|
memory_key: str = "history" |
|
|
|
|
|
@property |
|
|
def buffer(self) -> Union[str, List[BaseMessage]]: |
|
|
"""String buffer of memory.""" |
|
|
return self.load_memory_variables({})[self.memory_key] |
|
|
|
|
|
async def abuffer(self) -> Union[str, List[BaseMessage]]: |
|
|
"""Async memory buffer.""" |
|
|
memory_variables = await self.aload_memory_variables({}) |
|
|
return memory_variables[self.memory_key] |
|
|
|
|
|
@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, Any]: |
|
|
"""Return history buffer.""" |
|
|
buffer = self.chat_memory.messages |
|
|
if self.moving_summary_buffer != "": |
|
|
first_messages: List[BaseMessage] = [ |
|
|
self.summary_message_cls(content=self.moving_summary_buffer) |
|
|
] |
|
|
buffer = first_messages + buffer |
|
|
if self.return_messages: |
|
|
final_buffer: Any = buffer |
|
|
else: |
|
|
final_buffer = get_buffer_string( |
|
|
buffer, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix |
|
|
) |
|
|
return {self.memory_key: final_buffer} |
|
|
|
|
|
async def aload_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]: |
|
|
"""Asynchronously return key-value pairs given the text input to the chain.""" |
|
|
buffer = await self.chat_memory.aget_messages() |
|
|
if self.moving_summary_buffer != "": |
|
|
first_messages: List[BaseMessage] = [ |
|
|
self.summary_message_cls(content=self.moving_summary_buffer) |
|
|
] |
|
|
buffer = first_messages + buffer |
|
|
if self.return_messages: |
|
|
final_buffer: Any = buffer |
|
|
else: |
|
|
final_buffer = get_buffer_string( |
|
|
buffer, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix |
|
|
) |
|
|
return {self.memory_key: final_buffer} |
|
|
|
|
|
@pre_init |
|
|
def validate_prompt_input_variables(cls, values: Dict) -> Dict: |
|
|
"""Validate that prompt input variables are consistent.""" |
|
|
prompt_variables = values["prompt"].input_variables |
|
|
expected_keys = {"summary", "new_lines"} |
|
|
if expected_keys != set(prompt_variables): |
|
|
raise ValueError( |
|
|
"Got unexpected prompt input variables. The prompt expects " |
|
|
f"{prompt_variables}, but it should have {expected_keys}." |
|
|
) |
|
|
return values |
|
|
|
|
|
def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: |
|
|
"""Save context from this conversation to buffer.""" |
|
|
super().save_context(inputs, outputs) |
|
|
self.prune() |
|
|
|
|
|
async def asave_context( |
|
|
self, inputs: Dict[str, Any], outputs: Dict[str, str] |
|
|
) -> None: |
|
|
"""Asynchronously save context from this conversation to buffer.""" |
|
|
await super().asave_context(inputs, outputs) |
|
|
await self.aprune() |
|
|
|
|
|
def prune(self) -> None: |
|
|
"""Prune buffer if it exceeds max token limit""" |
|
|
buffer = self.chat_memory.messages |
|
|
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer) |
|
|
if curr_buffer_length > self.max_token_limit: |
|
|
pruned_memory = [] |
|
|
while curr_buffer_length > self.max_token_limit: |
|
|
pruned_memory.append(buffer.pop(0)) |
|
|
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer) |
|
|
self.moving_summary_buffer = self.predict_new_summary( |
|
|
pruned_memory, self.moving_summary_buffer |
|
|
) |
|
|
|
|
|
async def aprune(self) -> None: |
|
|
"""Asynchronously prune buffer if it exceeds max token limit""" |
|
|
buffer = self.chat_memory.messages |
|
|
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer) |
|
|
if curr_buffer_length > self.max_token_limit: |
|
|
pruned_memory = [] |
|
|
while curr_buffer_length > self.max_token_limit: |
|
|
pruned_memory.append(buffer.pop(0)) |
|
|
curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer) |
|
|
self.moving_summary_buffer = await self.apredict_new_summary( |
|
|
pruned_memory, self.moving_summary_buffer |
|
|
) |
|
|
|
|
|
def clear(self) -> None: |
|
|
"""Clear memory contents.""" |
|
|
super().clear() |
|
|
self.moving_summary_buffer = "" |
|
|
|
|
|
async def aclear(self) -> None: |
|
|
"""Asynchronously clear memory contents.""" |
|
|
await super().aclear() |
|
|
self.moving_summary_buffer = "" |
|
|
|
