| from typing import Any, Dict, List |
|
|
| from langchain_core._api import deprecated |
| from langchain_core.language_models import BaseLanguageModel |
| from langchain_core.messages import BaseMessage, get_buffer_string |
|
|
| from langchain.memory.chat_memory import BaseChatMemory |
|
|
|
|
| @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 ConversationTokenBufferMemory(BaseChatMemory): |
| """Conversation chat memory with token limit. |
| |
| Keeps only 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. |
| """ |
|
|
| human_prefix: str = "Human" |
| ai_prefix: str = "AI" |
| llm: BaseLanguageModel |
| memory_key: str = "history" |
| max_token_limit: int = 2000 |
|
|
| @property |
| def buffer(self) -> Any: |
| """String buffer of memory.""" |
| 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 False.""" |
| 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[BaseMessage]: |
| """Exposes the buffer as a list of messages in case return_messages is True.""" |
| return self.chat_memory.messages |
|
|
| @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.""" |
| 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. Pruned.""" |
| super().save_context(inputs, outputs) |
| |
| 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) |
|
|
