From 64fd711cd5309a93d2a1c467f29f42ba89c491f9 Mon Sep 17 00:00:00 2001 From: minecraft1024a Date: Thu, 2 Oct 2025 17:07:11 +0800 Subject: [PATCH] =?UTF-8?q?feat(proactive=5Fthinker):=20=E5=A2=9E=E5=BC=BA?= =?UTF-8?q?=E4=B8=BB=E5=8A=A8=E6=80=9D=E8=80=83=E7=9A=84=E4=B8=8A=E4=B8=8B?= =?UTF-8?q?=E6=96=87=E5=B9=B6=E4=BC=98=E5=8C=96=E5=9B=9E=E5=A4=8D=E6=B5=81?= =?UTF-8?q?=E7=A8=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 在主动思考的决策和规划阶段引入了更丰富的上下文信息，包括最近的动作历史记录，以避免重复或不合时宜的互动。 主要更新： - 在决策和规划的上下文中加入了最近的动作历史，让模型了解之前的思考过程。 - 决策结果（无论是否回复）现在都会被记录到数据库中，以供未来参考。 - 使用 `generator_api` 对生成的回复进行分段处理，以发送更自然、更像人类的文本消息。 - 将决策理由传递给规划提示词，使生成的回复更有针对性。 --- .../proactive_thinker_executor.py | 49 ++++++++++++++++--- 1 file changed, 43 insertions(+), 6 deletions(-) diff --git a/src/plugins/built_in/proactive_thinker/proactive_thinker_executor.py b/src/plugins/built_in/proactive_thinker/proactive_thinker_executor.py index 297c005e2..4c61ce3e7 100644 --- a/src/plugins/built_in/proactive_thinker/proactive_thinker_executor.py +++ b/src/plugins/built_in/proactive_thinker/proactive_thinker_executor.py @@ -3,7 +3,7 @@ from typing import Optional, Dict, Any from datetime import datetime from src.common.logger import get_logger -from src.plugin_system.apis import chat_api, person_api, schedule_api, send_api, llm_api, message_api +from src.plugin_system.apis import chat_api, person_api, schedule_api, send_api, llm_api, message_api, generator_api, database_api from src.config.config import global_config, model_config from src.person_info.person_info import get_person_info_manager @@ -38,23 +38,44 @@ class ProactiveThinkerExecutor: # 2. 决策阶段 decision_result = await self._make_decision(context, start_mode) + + if not decision_result or not decision_result.get("should_reply"): reason = decision_result.get("reason", "未提供") if decision_result else "决策过程返回None" logger.info(f"决策结果为：不回复。原因: {reason}") + await database_api.store_action_info( + chat_stream=self._get_stream_from_id(stream_id), + action_name="proactive_decision", + action_prompt_display=f"主动思考决定不回复,原因: {reason}", + action_done = True, + action_data=decision_result + ) return # 3. 规划与执行阶段 topic = decision_result.get("topic", "打个招呼") + reason = decision_result.get("reason", "无") + await database_api.store_action_info( + chat_stream=self._get_stream_from_id(stream_id), + action_name="proactive_decision", + action_prompt_display=f"主动思考决定回复,原因: {reason},话题:{topic}", + action_done = True, + action_data=decision_result + ) logger.info(f"决策结果为：回复。话题: {topic}") - plan_prompt = self._build_plan_prompt(context, start_mode, topic) + plan_prompt = self._build_plan_prompt(context, start_mode, topic, reason) is_success, response, _, _ = await llm_api.generate_with_model(prompt=plan_prompt, model_config=model_config.model_task_config.utils) - if is_success: + if is_success and response: stream = self._get_stream_from_id(stream_id) if stream: - await send_api.text_to_stream(stream_id=stream.stream_id, text=response) + # 使用消息分割器处理并发送消息 + reply_set = generator_api.process_human_text(response, enable_splitter=True, enable_chinese_typo=False) + for reply_type, content in reply_set: + if reply_type == "text": + await send_api.text_to_stream(stream_id=stream.stream_id, text=content) else: logger.warning(f"无法发送消息，因为找不到 stream_id 为 {stream_id} 的聊天流") @@ -99,12 +120,22 @@ class ProactiveThinkerExecutor: # 获取最近聊天记录 recent_messages = await message_api.get_recent_messages(stream_id, limit=10) recent_chat_history = await message_api.build_readable_messages_to_str(recent_messages) if recent_messages else "无" + + # 获取最近的动作历史 + action_history = await database_api.db_query( + database_api.MODEL_MAPPING["ActionRecords"], + filters={"chat_id": stream_id, "action_name": "proactive_decision"}, + limit=3, + order_by=["-time"] + ) + action_history_context = "\n".join([f"- {a['action_data']}" for a in action_history]) if action_history else "无" return { "person_id": person_id, "user_info": user_info, "schedule_context": schedule_context, "recent_chat_history": recent_chat_history, + "action_history_context": action_history_context, "relationship": { "short_impression": short_impression, "impression": impression, @@ -185,7 +216,7 @@ class ProactiveThinkerExecutor: logger.error(f"决策LLM返回的JSON格式无效: {response}") return {"should_reply": False, "reason": "决策模型返回格式错误"} - def _build_plan_prompt(self, context: Dict[str, Any], start_mode: str, topic: str) -> str: + def _build_plan_prompt(self, context: Dict[str, Any], start_mode: str, topic: str, reason: str) -> str: """ 根据启动模式和决策话题，构建最终的规划提示词 """ @@ -204,7 +235,8 @@ class ProactiveThinkerExecutor: # 任务 你需要主动向一个新朋友 '{user_info.user_nickname}' 发起对话。这是你们的第一次交流，或者很久没聊了。 -# 情境分析 +# 决策上下文 +- **决策理由**: {reason} - **你和Ta的关系**: - 简短印象: {relationship['short_impression']} - 详细印象: {relationship['impression']} @@ -227,6 +259,9 @@ class ProactiveThinkerExecutor: # 任务 现在是 {context['current_time']}，你需要主动向你的朋友 '{user_info.user_nickname}' 发起对话。 +# 决策上下文 +- **决策理由**: {reason} + # 情境分析 1. **你的日程**: {context['schedule_context']} @@ -235,6 +270,8 @@ class ProactiveThinkerExecutor: - 好感度: {relationship['attitude']}/100 3. **最近的聊天摘要**: {context['recent_chat_history']} +4. **你最近的相关动作**: +{context['action_history_context']} # 对话指引 - 你决定和Ta聊聊关于“{topic}”的话题。