2dee32e5ad
重构DefaultReplyer对话提示构建逻辑，统一使用SmartPrompt抽象组件替代原冗长的模板选择与参数拼装流程。将原160+行的分支式模板处理逻辑压缩为统一的SmartPrompt.build_prompt()调用，实现提示构建策略的可插拔与可维护性提升。 - 新增 src.chat.utils.smart_prompt 模块（SmartPrompt类及参数封装） - 移除旧的normal/s4u分支硬编码，由SmartPrompt内部按需适配 - 事件管理器仅补充HandlerResult显式导入，无功能变动
452 lines
15 KiB
Python
452 lines
15 KiB
Python
"""
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智能Prompt系统 - 基于现有模板系统的增强构建器
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"""
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import asyncio
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import time
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from datetime import datetime
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from dataclasses import dataclass, field
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from typing import Dict, Any, Optional, List, Literal
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from contextlib import asynccontextmanager
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from src.chat.utils.prompt_builder import global_prompt_manager, Prompt
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@dataclass
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class SmartPromptParameters:
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"""智能提示词参数系统"""
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# === 核心对话参数 ===
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reply_to: str = ""
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extra_info: str = ""
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available_actions: Dict[str, Any] = field(default_factory=dict)
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# === 功能开关 ===
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enable_tool: bool = True
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enable_memory: bool = True
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enable_expression: bool = True
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enable_relation: bool = True
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enable_cross_context: bool = True
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enable_knowledge: bool = True
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# === 行为配置 ===
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prompt_mode: Literal["s4u", "normal", "minimal"] = "s4u"
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context_level: Literal["full", "core", "minimal"] = "full"
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response_style: Optional[str] = None
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tone_override: Optional[str] = None
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# === 智能过滤 ===
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max_context_messages: int = 50
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memory_depth: int = 3
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expression_count: int = 5
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knowledge_depth: int = 3
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# === 性能控制 ===
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max_tokens: int = 2048
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timeout_seconds: float = 30.0
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enable_cache: bool = True
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cache_ttl: int = 300
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# === 调试选项 ===
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debug_mode: bool = False
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include_timing: bool = False
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trace_id: Optional[str] = None
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def validate(self) -> List[str]:
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"""参数验证"""
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errors = []
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if not isinstance(self.reply_to, str):
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errors.append("reply_to必须是字符串类型")
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if self.timeout_seconds <= 0:
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errors.append("timeout_seconds必须大于0")
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if self.max_tokens <= 0:
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errors.append("max_tokens必须大于0")
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return errors
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@dataclass
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class ChatContext:
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"""聊天上下文信息"""
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chat_id: str = ""
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platform: str = ""
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is_group: bool = False
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user_id: str = ""
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user_nickname: str = ""
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group_id: Optional[str] = None
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timestamp: datetime = field(default_factory=datetime.now)
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class ContextData:
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"""构建上下文数据容器"""
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def __init__(self):
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self.data: Dict[str, Any] = {}
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self.timing: Dict[str, float] = {}
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self.errors: List[str] = []
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def set(self, key: str, value: Any, timing: float = 0.0):
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"""设置数据"""
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self.data[key] = value
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if timing > 0:
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self.timing[key] = timing
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def get(self, key: str, default: Any = None) -> Any:
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"""获取数据"""
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return self.data.get(key, default)
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def merge(self, other_data: Dict[str, Any]):
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"""合并数据"""
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self.data.update(other_data)
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def auto_compensate(self):
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"""自动补偿缺失数据"""
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defaults = {
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"expression_habits_block": "",
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"memory_block": "",
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"relation_info_block": "",
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"tool_info_block": "",
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"knowledge_prompt": "",
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"cross_context_block": "",
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"time_block": f"当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
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"mood_state": "平静",
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"identity": "你是一个智能助手",
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}
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for key, default_value in defaults.items():
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if key not in self.data:
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self.data[key] = default_value
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def to_dict(self) -> Dict[str, Any]:
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"""转换为字典"""
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return self.data.copy()
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class SmartPromptCache:
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"""智能缓存系统"""
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def __init__(self):
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self._cache: Dict[str, tuple[str, float, int]] = {}
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def _generate_key(self, params: SmartPromptParameters, context: ChatContext) -> str:
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"""生成缓存键"""
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key_parts = [
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params.reply_to,
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context.chat_id,
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str(params.enable_tool),
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str(params.enable_memory),
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params.prompt_mode,
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]
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return "|".join(key_parts)
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def get(self, params: SmartPromptParameters, context: ChatContext) -> Optional[str]:
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"""获取缓存"""
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if not params.enable_cache:
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return None
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key = self._generate_key(params, context)
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if key in self._cache:
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text, timestamp, ttl = self._cache[key]
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if time.time() - timestamp < ttl:
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return text
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else:
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del self._cache[key]
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return None
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def set(self, params: SmartPromptParameters, context: ChatContext, text: str):
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"""设置缓存"""
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if not params.enable_cache:
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return
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key = self._generate_key(params, context)
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self._cache[key] = (text, time.time(), params.cache_ttl)
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def clear(self):
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"""清空缓存"""
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self._cache.clear()
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class SmartPromptBuilder:
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"""智能提示词构建器"""
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def __init__(self):
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self.cache = SmartPromptCache()
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async def build_context_data(
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self,
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context: ChatContext,
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params: SmartPromptParameters
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) -> ContextData:
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"""并行构建上下文数据"""
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# 检查缓存
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cached_result = self.cache.get(params, context)
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if cached_result:
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context_data = ContextData()
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context_data.data["_cached_text"] = cached_result
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return context_data
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# 创建构建任务
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tasks = []
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context_data = ContextData()
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# 根据参数启用不同的构建任务
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if params.enable_expression:
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tasks.append(self._build_expression_habits(context, params))
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if params.enable_memory:
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tasks.append(self._build_memory_block(context, params))
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if params.enable_relation:
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tasks.append(self._build_relation_info(context, params))
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if params.enable_tool:
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tasks.append(self._build_tool_info(context, params))
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if params.enable_knowledge:
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tasks.append(self._build_knowledge_info(context, params))
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if params.enable_cross_context:
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tasks.append(self._build_cross_context(context, params))
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# 并行执行所有任务
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start_time = time.time()
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try:
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results = await asyncio.wait_for(
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asyncio.gather(*tasks, return_exceptions=True),
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timeout=params.timeout_seconds
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)
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# 处理结果
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for i, result in enumerate(results):
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if isinstance(result, Exception):
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context_data.errors.append(f"任务{i}失败: {str(result)}")
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else:
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context_data.merge(result)
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except asyncio.TimeoutError:
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context_data.errors.append(f"构建超时 ({params.timeout_seconds}s)")
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# 自动补偿缺失数据
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context_data.auto_compensate()
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# 添加时间信息
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if params.include_timing:
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context_data.set("build_time", time.time() - start_time)
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return context_data
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async def _build_expression_habits(self, context: ChatContext, params: SmartPromptParameters) -> Dict[str, Any]:
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"""构建表达习惯 - 集成现有DefaultReplyer的表达方式"""
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# 这里需要更复杂的集成，暂时返回空
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return {
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"expression_habits_block": ""
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}
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async def _build_memory_block(self, context: ChatContext, params: SmartPromptParameters) -> Dict[str, Any]:
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"""构建记忆块 - 集成现有DefaultReplyer的记忆构建"""
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# 这里需要集成真正的记忆构建逻辑
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return {
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"memory_block": ""
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}
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async def _build_relation_info(self, context: ChatContext, params: SmartPromptParameters) -> Dict[str, Any]:
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"""构建关系信息 - 集成现有DefaultReplyer的关系构建"""
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# 这里需要集成真正的关系构建逻辑
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return {
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"relation_info_block": ""
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}
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async def _build_tool_info(self, context: ChatContext, params: SmartPromptParameters) -> Dict[str, Any]:
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"""构建工具信息 - 集成现有DefaultReplyer的工具构建"""
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# 这里需要集成真正的工具构建逻辑
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return {
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"tool_info_block": ""
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}
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async def _build_knowledge_info(self, context: ChatContext, params: SmartPromptParameters) -> Dict[str, Any]:
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"""构建知识信息 - 集成现有DefaultReplyer的知识构建"""
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# 这里需要集成真正的知识构建逻辑
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return {
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"knowledge_prompt": ""
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}
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async def _build_cross_context(self, context: ChatContext, params: SmartPromptParameters) -> Dict[str, Any]:
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"""构建跨群上下文 - 集成现有DefaultReplyer的跨群构建"""
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# 这里需要集成真正的跨群构建逻辑
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return {
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"cross_context_block": ""
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}
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class SmartPrompt:
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"""智能提示词核心类 - 完全基于现有模板系统"""
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def __init__(
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self,
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template_name: str = "default",
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parameters: Optional[SmartPromptParameters] = None,
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context: Optional[ChatContext] = None,
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):
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self.template_name = template_name
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self.parameters = parameters or SmartPromptParameters()
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self.context = context or ChatContext()
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self.builder = SmartPromptBuilder()
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self._cached_text: Optional[str] = None
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self._cache_time: float = 0
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async def to_text(self) -> str:
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"""异步渲染为文本 - 完全使用现有模板系统"""
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return await self.build_prompt()
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def to_text_sync(self) -> str:
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"""同步渲染为文本"""
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return asyncio.run(self.build_prompt())
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async def build_prompt(self) -> str:
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"""构建Prompt - 替代to_text方法以兼容调用方式"""
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# 参数验证
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errors = self.parameters.validate()
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if errors:
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raise ValueError(f"参数验证失败: {', '.join(errors)}")
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# 检查缓存
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if self._cached_text and self.parameters.enable_cache:
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if time.time() - self._cache_time < self.parameters.cache_ttl:
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return self._cached_text
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# 构建上下文数据
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context_data = await self.builder.build_context_data(self.context, self.parameters)
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# 检查是否有缓存的文本
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if "_cached_text" in context_data.data:
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return context_data.data["_cached_text"]
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# 获取模板 - 完全使用现有系统
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template = await self._get_template()
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# 渲染最终文本 - 完全使用现有系统
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text = await self._render_template(template, context_data)
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# 缓存结果
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if self.parameters.enable_cache:
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self._cached_text = text
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self._cache_time = time.time()
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self.builder.cache.set(self.parameters, self.context, text)
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return text
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async def _get_template(self) -> Prompt:
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"""获取模板 - 完全使用现有系统"""
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try:
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return await global_prompt_manager.get_prompt_async(self.template_name)
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except KeyError:
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# 使用默认模板
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return Prompt("你是一个智能助手。用户说：{reply_target_block}", name="default")
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async def _render_template(self, template: Prompt, context_data: ContextData) -> str:
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"""渲染模板 - 完全使用现有系统"""
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# 准备渲染参数
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render_params = {
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**context_data.to_dict(),
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"reply_target_block": self._build_reply_target_block(),
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"extra_info_block": self.parameters.extra_info,
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"action_descriptions": self._build_action_descriptions(),
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}
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# 根据模式选择不同的渲染策略
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if self.parameters.prompt_mode == "minimal":
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# 最小化模式，只包含核心信息
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minimal_params = {
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"reply_target_block": render_params["reply_target_block"],
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"identity": render_params.get("identity", ""),
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"time_block": render_params.get("time_block", ""),
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}
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# 使用现有模板的format方法
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return template.format(**minimal_params)
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else:
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# 完整模式 - 使用现有系统的格式化方法
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return template.format(**render_params)
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def _build_reply_target_block(self) -> str:
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"""构建回复目标块"""
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if not self.parameters.reply_to:
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return "现在，请进行回复。"
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sender, content = self._parse_reply_to(self.parameters.reply_to)
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if sender and content:
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return f"现在{sender}说：{content}。请对此进行回复。"
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else:
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return f"现在有消息：{self.parameters.reply_to}。请对此进行回复。"
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def _build_action_descriptions(self) -> str:
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"""构建动作描述"""
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if not self.parameters.available_actions:
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return ""
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descriptions = []
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for action_name, action_info in self.parameters.available_actions.items():
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if isinstance(action_info, dict) and "description" in action_info:
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descriptions.append(f"- {action_name}: {action_info['description']}")
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else:
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descriptions.append(f"- {action_name}")
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if descriptions:
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return "你有以下动作能力：\n" + "\n".join(descriptions) + "\n"
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return ""
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def _parse_reply_to(self, reply_to: str) -> tuple[str, str]:
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"""解析回复目标"""
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if ":" in reply_to or "：" in reply_to:
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import re
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parts = re.split(r"[:：]", reply_to, maxsplit=1)
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if len(parts) == 2:
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return parts[0].strip(), parts[1].strip()
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return "", reply_to.strip()
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def __str__(self) -> str:
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"""字符串表示"""
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return f"SmartPrompt(template={self.template_name}, mode={self.parameters.prompt_mode})"
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def __repr__(self) -> str:
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"""详细表示"""
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return f"SmartPrompt(template='{self.template_name}', parameters={self.parameters}, context={self.context})"
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# 工厂函数
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def create_smart_prompt(
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template_name: str = "default",
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reply_to: str = "",
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extra_info: str = "",
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enable_tool: bool = True,
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prompt_mode: str = "s4u",
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chat_id: str = "",
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**kwargs
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) -> SmartPrompt:
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"""快速创建智能Prompt实例的工厂函数"""
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parameters = SmartPromptParameters(
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reply_to=reply_to,
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extra_info=extra_info,
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enable_tool=enable_tool,
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prompt_mode=prompt_mode,
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**kwargs
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)
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context = ChatContext(chat_id=chat_id)
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return SmartPrompt(
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template_name=template_name,
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parameters=parameters,
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context=context
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)
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# 便捷装饰器
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def prompt_template(name: str):
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"""模板注册装饰器 - 与现有系统保持一致"""
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def decorator(func):
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def wrapper(*args, **kwargs):
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template_content = func(*args, **kwargs)
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Prompt(template_content, name=name)
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return template_content
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return wrapper
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return decorator |