| | 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) |
| |
|
