Spaces:
Runtime error
Runtime error
from typing import Any, Dict, List | |
from pydantic import BaseModel | |
from langchain.memory.chat_memory import BaseChatMemory | |
from langchain.schema import BaseLanguageModel, BaseMessage, get_buffer_string | |
class ConversationTokenBufferMemory(BaseChatMemory, BaseModel): | |
"""Buffer for storing conversation memory.""" | |
human_prefix: str = "Human" | |
ai_prefix: str = "AI" | |
llm: BaseLanguageModel | |
memory_key: str = "history" | |
max_token_limit: int = 2000 | |
def buffer(self) -> List[BaseMessage]: | |
"""String buffer of memory.""" | |
return self.chat_memory.messages | |
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: Any = self.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} | |
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) | |
# 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) | |