77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
"""
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MaiSaka - Reply 回复生成器
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根据想法和上下文生成口语化回复。
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"""
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from typing import Optional
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from .llm_service import MaiSakaLLMService
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def format_chat_history(messages: list) -> str:
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"""将聊天消息列表格式化为可读文本。"""
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parts: list[str] = []
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for msg in messages:
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role = msg.get("role", "?")
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content = msg.get("content", "") or ""
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if role == "system":
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parts.append(f"[系统] {content[:500]}")
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elif role == "user":
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parts.append(f"[用户] {content[:500]}")
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elif role == "assistant":
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if content:
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parts.append(f"[助手思考] {content[:500]}")
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for tc in msg.get("tool_calls", []):
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func = tc.get("function", {})
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name = func.get("name", "?")
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args = func.get("arguments", "")
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if isinstance(args, str) and len(args) > 200:
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args = args[:200] + "..."
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parts.append(f"[助手调用 {name}] {args}")
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elif role == "tool":
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parts.append(f"[工具结果] {content[:300]}")
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return "\n".join(parts)
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class Replyer:
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"""
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回复生成器。
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根据给定的想法（reason）和对话上下文，生成符合人设的口语化回复。
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"""
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def __init__(self, llm_service: Optional[MaiSakaLLMService] = None):
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"""
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初始化回复器。
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Args:
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llm_service: LLM 服务实例，如果为 None 则需要在调用前设置
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"""
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self._llm_service = llm_service
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self._enabled = True
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def set_llm_service(self, llm_service: MaiSakaLLMService) -> None:
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"""设置 LLM 服务"""
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self._llm_service = llm_service
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def set_enabled(self, enabled: bool) -> None:
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"""启用/禁用回复功能"""
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self._enabled = enabled
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async def reply(self, reason: str, chat_history: list) -> str:
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"""
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根据想法和上下文生成回复。
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Args:
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reason: 想要回复的方式、想法、内容（不包含具体回复内容）
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chat_history: 对话历史上下文
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Returns:
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生成的回复内容，失败时返回默认回复
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"""
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if not self._enabled or not reason or self._llm_service is None:
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return "..."
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# 直接使用 LLM 服务的 generate_reply 方法
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# 该方法使用主项目的 replyer 模型配置
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return await self._llm_service.generate_reply(reason, chat_history)
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