classCondenseResult(BaseModel):analysis:str=Field(        ...,description="""A summary of the conversation so far, capturing technical details, code patterns, and architectural decisions.""",    )context:str=Field(        ...,description="""The context to continue the conversation with. If applicable based on the current task, this should include:1. Primary Request and Intent: Capture all of the user's explicit requests and intents in detail2. Key Technical Concepts: List all important technical concepts, technologies, and frameworks discussed.3. Files and Code Sections: Enumerate specific files and code sections examined, modified, or created. Pay special attention to the most recent messages and include full code snippets where applicable and include a summary of why this file read or edit is important.4. Problem Solving: Document problems solved and any ongoing troubleshooting efforts.5. Pending Tasks: Outline any pending tasks that you have explicitly been asked to work on.6. Current Work: Describe in detail precisely what was being worked on immediately before this summary request, paying special attention to the most recent messages from both user and assistant. Include file names and code snippets where applicable.7. Optional Next Step: List the next step that you will take that is related to the most recent work you were doing. IMPORTANT: ensure that this step is DIRECTLY in line with the user's explicit requests, and the task you were working on immediately before this summary request. If your last task was concluded, then only list next steps if they are explicitly in line with the users request. Do not start on tangential requests without confirming with the user first.8. If there is a next step, include direct quotes from the most recent conversation showing exactly what task you were working on and where you left off. This should be verbatim to ensure there's no drift in task interpretation.""",    )classCompactor:def__init__(self,message_history:list[ModelMessage]|None,model_config:ModelConfig):self.message_history=deepcopy(message_history)ifmessage_historyelse []self.model_config=model_configself.settings=Settings()self.agent_name="compactor"self.usage=PaintressUsage()self.prompt_render=PromptRender()self.compactor_model_config=get_model_config(self.settings.compact_model)self.system_prompt=self.prompt_render.render_system_prompt(self.compactor_model_config.system_prompt_template)self.agent:Agent[None,CondenseResult]=Agent(model=self.compactor_model_config.llm_model_name,model_settings=self.compactor_model_config.llm_model_settings,system_prompt=self.system_prompt,output_type=ToolOutput(type_=CondenseResult,name="condense",description="""Your task is to create a detailed summary of the conversation so far, paying close attention to the user's explicit requests and your previous actions. This summary should be thorough in capturing technical details, code patterns, and architectural decisions that would be essential for continuing with the conversation and supporting any continuing tasks.The user will be presented with a preview of your generated summary and can choose to use it to compact their context window or keep chatting in the current conversation.Users may refer to this tool as 'smol' or 'compact' as well. You should consider these to be equivalent to 'condense' when used in a similar context.                """,max_retries=5,            ),retries=3,        )def_split_history(self,n:int)->tuple[list[ModelMessage],list[ModelMessage]]:"""        Returns a tuple of (history, keep_messages)        """ifnotn:# No keep messagesreturnself.message_history, []user_prompt_indices= []fori,msginenumerate(self.message_history):ifnotisinstance(msg,ModelRequest):continueifany(isinstance(p,UserPromptPart)forpinmsg.parts)andnotany(isinstance(p,ToolReturnPart)forpinmsg.parts            ):user_prompt_indices.append(i)iflen(user_prompt_indices)<n:# No enough history to keeplogger.warning(f"History too short to keep{n} messages, will try to keep nothing.")returnself.message_history, []return (self.message_history[:user_prompt_indices[-n]],self.message_history[user_prompt_indices[-n] :],        )defneed_compact(self)->bool:current_token_comsumption=get_current_token_comsumption(self.message_history)token_threshold=self.model_config.auto_compact_threshold*self.model_config.context_window_sizewill_overflow= (current_token_comsumptionor0)+self.model_config.llm_model_settings.get("max_tokens",0        )>=self.model_config.context_window_sizelogger.info(f"Current token consumption:{current_token_comsumption} vs{token_threshold}, will overflow:{will_overflow}"        )returncurrent_token_comsumptionisNoneorcurrent_token_comsumption>=token_thresholdorwill_overflow@retry(stop=stop_after_attempt(2),wait=wait_fixed(1),    )asyncdefcompact(self,ctx:AgentContext,force_compact:bool=False,compact_strategy:CompactStrategy=None    )->list[ModelMessage]:ifnot (force_compactorself.need_compact()):returnself.message_historylogger.info("Splitting history for compaction...")compact_strategy=compact_strategyorself.model_config.compact_strategymatchcompact_strategy:caseCompactStrategy.none:history_messages,keep_messages=self._split_history(0)caseCompactStrategy.last_two:history_messages,keep_messages=self._split_history(2)case _:raiseNotImplementedError(f"Compact strategy{self.model_config.compact_strategy} not implemented")ifnothistory_messages:logger.info("No history to compact, returning keep messages.")returnkeep_messageslogger.info("Compacting history...")result=awaitself.agent.run("The user has accepted the condensed conversation summary you generated. Use `condense` to generate a summary and context of the conversation so far. ""This summary covers important details of the historical conversation with the user which has been truncated. ""It's crucial that you respond by ONLY asking the user what you should work on next. ""You should NOT take any initiative or make any assumptions about continuing with work. ""Keep this response CONCISE and wrap your analysis in <analysis> and <context> tags to organize your thoughts and ensure you've covered all necessary points. ",message_history=fix_system_prompt(history_messages,self.system_prompt),model_settings=self.compactor_model_config.with_context(ctx).llm_model_settings,        )ctx.usage.set_agent_usage(self.agent_name,self.compactor_model_config.llm_model_id,result.usage())summary_prompt=f"""Condensed conversation summary(not in the history):<condense><analysis>{result.output.analysis}</analysis><context>{result.output.context}</context></condense>"""logger.info(f"{summary_prompt}")return [ModelRequest(parts=[SystemPromptPart(content=self.prompt_render.render_system_prompt(self.model_config.system_prompt_template)                    ),UserPromptPart(content="Please summary the conversation"),                ]            ),ModelResponse(parts=[TextPart(content=summary_prompt)],            ),*keep_messages,        ]