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101 lines
3.2 KiB
Python
101 lines
3.2 KiB
Python
from __future__ import annotations
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from typing import Any, Dict, List, Type
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from pydantic import BaseModel, root_validator
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from langchain.base_language import BaseLanguageModel
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from langchain.chains.llm import LLMChain
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from langchain.memory.chat_memory import BaseChatMemory
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from langchain.memory.prompt import SUMMARY_PROMPT
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from langchain.prompts.base import BasePromptTemplate
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from langchain.schema import (
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BaseChatMessageHistory,
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BaseMessage,
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SystemMessage,
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get_buffer_string,
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)
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class SummarizerMixin(BaseModel):
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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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prompt: BasePromptTemplate = SUMMARY_PROMPT
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summary_message_cls: Type[BaseMessage] = SystemMessage
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def predict_new_summary(
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self, messages: List[BaseMessage], existing_summary: str
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) -> str:
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new_lines = get_buffer_string(
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messages,
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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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chain = LLMChain(llm=self.llm, prompt=self.prompt)
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return chain.predict(summary=existing_summary, new_lines=new_lines)
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class ConversationSummaryMemory(BaseChatMemory, SummarizerMixin):
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"""Conversation summarizer to memory."""
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buffer: str = ""
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memory_key: str = "history" #: :meta private:
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@classmethod
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def from_messages(
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cls,
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llm: BaseLanguageModel,
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chat_memory: BaseChatMessageHistory,
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*,
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summarize_step: int = 2,
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**kwargs: Any,
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) -> ConversationSummaryMemory:
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obj = cls(llm=llm, chat_memory=chat_memory, **kwargs)
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for i in range(0, len(obj.chat_memory.messages), summarize_step):
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obj.buffer = obj.predict_new_summary(
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obj.chat_memory.messages[i : i + summarize_step], obj.buffer
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)
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return obj
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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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if self.return_messages:
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buffer: Any = [self.summary_message_cls(content=self.buffer)]
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else:
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buffer = self.buffer
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return {self.memory_key: buffer}
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@root_validator()
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def validate_prompt_input_variables(cls, values: Dict) -> Dict:
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"""Validate that prompt input variables are consistent."""
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prompt_variables = values["prompt"].input_variables
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expected_keys = {"summary", "new_lines"}
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if expected_keys != set(prompt_variables):
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raise ValueError(
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"Got unexpected prompt input variables. The prompt expects "
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f"{prompt_variables}, but it should have {expected_keys}."
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)
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return values
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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."""
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super().save_context(inputs, outputs)
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self.buffer = self.predict_new_summary(
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self.chat_memory.messages[-2:], self.buffer
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)
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def clear(self) -> None:
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"""Clear memory contents."""
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super().clear()
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self.buffer = ""
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