from typing import Any, Dict, List, Type from pydantic import BaseModel, root_validator from langchain.chains.llm import LLMChain from langchain.memory.chat_memory import BaseChatMemory from langchain.memory.prompt import SUMMARY_PROMPT from langchain.prompts.base import BasePromptTemplate from langchain.schema import ( BaseLanguageModel, BaseMessage, SystemMessage, get_buffer_string, ) class SummarizerMixin(BaseModel): human_prefix: str = "Human" ai_prefix: str = "AI" llm: BaseLanguageModel prompt: BasePromptTemplate = SUMMARY_PROMPT summary_message_cls: Type[BaseMessage] = SystemMessage def predict_new_summary( self, messages: List[BaseMessage], existing_summary: str ) -> str: new_lines = get_buffer_string( messages, human_prefix=self.human_prefix, ai_prefix=self.ai_prefix, ) chain = LLMChain(llm=self.llm, prompt=self.prompt) return chain.predict(summary=existing_summary, new_lines=new_lines) class ConversationSummaryMemory(BaseChatMemory, SummarizerMixin, BaseModel): """Conversation summarizer to memory.""" buffer: str = "" memory_key: str = "history" #: :meta private: @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.""" if self.return_messages: buffer: Any = [self.summary_message_cls(content=self.buffer)] else: buffer = self.buffer return {self.memory_key: buffer} @root_validator() def validate_prompt_input_variables(cls, values: Dict) -> Dict: """Validate that prompt input variables are consistent.""" prompt_variables = values["prompt"].input_variables expected_keys = {"summary", "new_lines"} if expected_keys != set(prompt_variables): raise ValueError( "Got unexpected prompt input variables. The prompt expects " f"{prompt_variables}, but it should have {expected_keys}." ) return values def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None: """Save context from this conversation to buffer.""" super().save_context(inputs, outputs) self.buffer = self.predict_new_summary( self.chat_memory.messages[-2:], self.buffer ) def clear(self) -> None: """Clear memory contents.""" super().clear() self.buffer = ""