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55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
from typing import Any, Dict, List
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from pydantic import BaseModel
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from langchain.memory.chat_memory import BaseChatMemory
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from langchain.schema import BaseLanguageModel, BaseMessage, get_buffer_string
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class ConversationTokenBufferMemory(BaseChatMemory, BaseModel):
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"""Buffer for storing conversation memory."""
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human_prefix: str = "Human"
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ai_prefix: str = "AI"
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llm: BaseLanguageModel
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memory_key: str = "history"
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max_token_limit: int = 2000
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@property
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def buffer(self) -> List[BaseMessage]:
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"""String buffer of memory."""
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return self.chat_memory.messages
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@property
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def memory_variables(self) -> List[str]:
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"""Will always return list of memory variables.
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:meta private:
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"""
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return [self.memory_key]
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def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
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"""Return history buffer."""
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buffer: Any = self.buffer
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if self.return_messages:
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final_buffer: Any = buffer
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else:
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final_buffer = get_buffer_string(
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buffer,
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human_prefix=self.human_prefix,
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ai_prefix=self.ai_prefix,
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)
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return {self.memory_key: final_buffer}
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def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
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"""Save context from this conversation to buffer. Pruned."""
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super().save_context(inputs, outputs)
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# Prune buffer if it exceeds max token limit
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buffer = self.chat_memory.messages
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curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
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if curr_buffer_length > self.max_token_limit:
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pruned_memory = []
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while curr_buffer_length > self.max_token_limit:
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pruned_memory.append(buffer.pop(0))
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curr_buffer_length = self.llm.get_num_tokens_from_messages(buffer)
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