Source code for langchain.memory.summary

from __future__ import annotations

from typing import Any, Dict, List, Type

from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage, SystemMessage, get_buffer_string
from langchain_core.prompts import BasePromptTemplate
from langchain_core.pydantic_v1 import BaseModel, root_validator

from langchain.chains.llm import LLMChain
from langchain.memory.chat_memory import BaseChatMemory
from langchain.memory.prompt import SUMMARY_PROMPT

[docs]class SummarizerMixin(BaseModel): """Mixin for summarizer.""" human_prefix: str = "Human" ai_prefix: str = "AI" llm: BaseLanguageModel prompt: BasePromptTemplate = SUMMARY_PROMPT summary_message_cls: Type[BaseMessage] = SystemMessage
[docs] 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)
[docs]class ConversationSummaryMemory(BaseChatMemory, SummarizerMixin): """Conversation summarizer to chat memory.""" buffer: str = "" memory_key: str = "history" #: :meta private:
[docs] @classmethod def from_messages( cls, llm: BaseLanguageModel, chat_memory: BaseChatMessageHistory, *, summarize_step: int = 2, **kwargs: Any, ) -> ConversationSummaryMemory: obj = cls(llm=llm, chat_memory=chat_memory, **kwargs) for i in range(0, len(obj.chat_memory.messages), summarize_step): obj.buffer = obj.predict_new_summary( obj.chat_memory.messages[i : i + summarize_step], obj.buffer ) return obj
@property def memory_variables(self) -> List[str]: """Will always return list of memory variables. :meta private: """ return [self.memory_key]
[docs] 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
[docs] 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 )
[docs] def clear(self) -> None: """Clear memory contents.""" super().clear() self.buffer = ""