| 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 = "" |
|
|
