初始化提交

backend/app/modules/knocking/opener.py

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+"""叩心枢开场消息生成器。
+
+设计 docs/domain/core-domains.md §6。
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import UTC, datetime
+
+from app.ai_provider.manager import ProviderManagerFactory
+from app.core.config import settings
+from app.core.logging import logger
+from app.modules.knocking.conditions import ConditionResult
+from app.modules.personality.emotion import EmotionState, to_prompt_text
+
+# ---------------------------------------------------------------------------
+# 回退模板
+# ---------------------------------------------------------------------------
+
+_FALLBACK_TEMPLATES: dict[str, str] = {
+    "idle": "{{name}}默默地看了你一眼。",
+    "time_of_day": "{{name}}正望着窗外出神。",
+    "anniversary": "今日似乎是个特别的日子。",
+    "reunion": "{{name}}注意到你回来了。",
+    "emotion": "{{name}}轻轻叹了口气。",
+    "curiosity": "{{name}}似乎有什么话想说。",
+    "memory_recall": "{{name}}若有所思地望向远方。",
+    "big_cycle_completed": "{{name}}似乎有所感悟。",
+    "evolution_applied": "{{name}}看上去有些不一样了。",
+    "relationship_milestone": "{{name}}望着你，嘴角泛起一丝笑意。",
+    "default": "{{name}}若有所思。",
+}
+
+_FALLBACK = "{{name}}正望着窗外出神。"
+
+
+@dataclass
+class ChatMessageBrief:
+    """简略对话记录（只携带必要字段，避免依赖 ORM）。"""
+
+    role: str = ""
+    content: str = ""
+
+
+@dataclass
+class OpenerResult:
+    """开场消息生成结果。"""
+
+    text: str
+    used_fallback: bool = False
+
+
+async def generate_opener(
+    character_id: str,
+    character_name: str,
+    personality_summary: str | None,
+    emotion: EmotionState | None,
+    condition_result: ConditionResult,
+    *,
+    chat_history: list[ChatMessageBrief] | None = None,
+    memory_context: str | None = None,
+    now: datetime | None = None,
+) -> OpenerResult:
+    """根据条件触发结果生成一句开场白。
+
+    依次尝试：
+    1. 调用神识（LLM）生成带人格注入的开场白
+    2. 失败时使用模板化回退
+
+    若提供 ``chat_history``，会将其拼入 prompt 使开场更贴合最近对话脉络。
+    若提供 ``memory_context``，会将其作为角色的长期记忆注入 prompt。
+    """
+    now = now or datetime.now(UTC)
+    timestamp = now.strftime("%Y-%m-%d %H:%M")
+
+    # 构造 prompt
+    emotion_text = to_prompt_text(emotion) if emotion else "平静"
+    summary = personality_summary or f"你是一个名叫{character_name}的角色。{_DEFAULT_PERSONA}"
+    trigger_reason = condition_result.trigger_reason or ""
+
+    context_parts = _build_context_parts(condition_result)
+
+    system_prompt = _build_system_prompt(summary, emotion_text, timestamp)
+    user_prompt = _build_user_prompt(
+        character_name, trigger_reason, context_parts, chat_history, memory_context
+    )
+
+    try:
+        manager = ProviderManagerFactory.create()
+        result = await manager.chat(
+            manager.to_message_list([system_prompt], [("user", user_prompt)]),
+            model_hint=settings.big_cycle_model,
+        )
+        text = result.content.strip()
+        if not text:
+            raise ValueError("模型返回空开场文本")
+        return OpenerResult(text=text, used_fallback=False)
+    except Exception as exc:
+        logger.warning(
+            "knocking_pivot_opener_fallback",
+            character_id=character_id,
+            condition=condition_result.condition_name,
+            error=str(exc),
+        )
+        template = _FALLBACK_TEMPLATES.get(condition_result.condition_name, _FALLBACK)
+        fallback = template.replace("{{name}}", character_name)
+        return OpenerResult(text=fallback, used_fallback=True)
+
+
+def _build_context_parts(condition_result: ConditionResult) -> list[str]:
+    """组装条件相关的上下文片段。"""
+    parts: list[str] = []
+    if condition_result.context.get("anniversary_kind"):
+        kind_label = condition_result.context.get("anniversary_label", "")
+        parts.append(f"今天是一个特别的日子：{kind_label}。")
+    if condition_result.context.get("curiosity_question"):
+        parts.append(
+            f"你一直想问用户一个问题：「{condition_result.context['curiosity_question']}」"
+        )
+    if condition_result.context.get("time_of_day") == "late_night":
+        parts.append("夜深了，万籁俱寂。")
+    if condition_result.context.get("memory_title"):
+        title = condition_result.context["memory_title"]
+        parts.append(f"你最近想起了一件事——「{title}」。")
+    if condition_result.context.get("big_cycle_summary"):
+        parts.append("你刚刚审视过自己的内心，有了一些新的思考。")
+    if condition_result.context.get("evolution_title"):
+        title = condition_result.context["evolution_title"]
+        parts.append(f"你感觉自己有了一些新的变化：{title}。")
+    if condition_result.context.get("milestone_type") == "messages":
+        count = condition_result.context["milestone_value"]
+        parts.append(f"你和用户的对话已经累积了 {count} 条。")
+    if condition_result.context.get("milestone_type") == "days":
+        days = condition_result.context["milestone_value"]
+        parts.append(f"从你们第一次对话至今，已经过去了 {days} 天。")
+    return parts
+
+
+def _format_chat_history(history: list[ChatMessageBrief]) -> str:
+    """将最近对话历史格式化为文本。"""
+    lines = ["\n[最近对话记录]"]
+    for msg in history:
+        speaker = "你" if msg.role == "assistant" else "用户"
+        lines.append(f"{speaker}：{msg.content[:200]}")
+    return "\n".join(lines)
+
+
+def _build_system_prompt(personality_summary: str, emotion_text: str, timestamp: str) -> str:
+    return (
+        "你是一位角色扮演助手。以下是你的人物设定：\n"
+        f"{personality_summary}\n\n"
+        "[当前状态]\n"
+        f"- 当前时间: {timestamp}\n"
+        f"- 情绪状态: {emotion_text}\n"
+    )
+
+
+def _build_user_prompt(
+    character_name: str,
+    trigger_reason: str,
+    context_parts: list[str],
+    chat_history: list[ChatMessageBrief] | None = None,
+    memory_context: str | None = None,
+) -> str:
+    lines = ["[任务]"]
+    lines.append(f"根据以上设定，生成一句自然、贴合{character_name}性格和当前情绪的开场白。")
+    lines.append("要求：")
+    lines.append("1. 一句话，不超过 80 字")
+    lines.append("2. 符合角色的表达风格和说话方式、语气")
+    lines.append("3. 反映当前情绪状态")
+    lines.append("4. 不要问「你还在吗？」之类的问题")
+    lines.append("5. 直接输出开场白，不要任何前缀或引号")
+    if trigger_reason:
+        lines.append(f"\n触发原因：{trigger_reason}")
+    if context_parts:
+        lines.append(f"\n{' '.join(context_parts)}")
+    if memory_context:
+        lines.append(f"\n{memory_context}")
+    if chat_history:
+        lines.append(_format_chat_history(chat_history))
+        lines.append("\n注意：延续上次对话的语境自然地开启新话题，不要重复上一句的内容。")
+    return "\n".join(lines)
+
+
+_DEFAULT_PERSONA = (
+    "你对用户保持着温和的关注，"
+    "平日里话不多，但每次开口都带着真诚。"
+    "你善于观察，能从细微之处感知对方的情绪。"
+)