214 lines
6.3 KiB
Python
214 lines
6.3 KiB
Python
"""
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聊天总结服务
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负责调用LLM对历史消息进行总结
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"""
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import json
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from typing import List, Dict, Any, Optional
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from datetime import datetime
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from models.internal import ChatMessage, SummaryConfig
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from core.config import settings
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class ChatSummaryService:
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"""聊天总结服务类"""
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@staticmethod
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async def summarize_messages(
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messages: List[ChatMessage],
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start_floor: int,
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end_floor: int,
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summary_config: SummaryConfig,
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api_config: Dict[str, str]
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) -> str:
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"""
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对指定范围的消息进行总结
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Args:
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messages: 完整的消息列表
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start_floor: 总结起始楼层（1-based）
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end_floor: 总结结束楼层（1-based）
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summary_config: 总结配置
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api_config: API配置 {api_url, api_key, model}
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Returns:
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总结文本
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"""
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# 1. 提取需要总结的消息
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messages_to_summarize = ChatSummaryService._extract_messages(
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messages, start_floor, end_floor, summary_config.includeUserInput
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)
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if not messages_to_summarize:
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return ""
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# 2. 构建总结提示词
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prompt = ChatSummaryService._build_summary_prompt(
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messages_to_summarize, summary_config
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)
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# 3. 调用LLM生成总结
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summary_text = await ChatSummaryService._call_llm_for_summary(
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prompt, api_config, summary_config.maxSummaryLength
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)
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return summary_text
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@staticmethod
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def _extract_messages(
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messages: List[ChatMessage],
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start_floor: int,
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end_floor: int,
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include_user_input: bool
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) -> List[ChatMessage]:
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"""
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提取需要总结的消息
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✅ 根据用户需求：后端不筛选，全部输入给LLM
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Args:
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messages: 完整消息列表
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start_floor: 起始楼层
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end_floor: 结束楼层
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include_user_input: 是否包含用户输入（此参数目前不使用，但保留以保持接口兼容）
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Returns:
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需要总结的消息列表（全部消息，不做筛选）
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"""
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# 转换为0-based索引
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start_idx = start_floor - 1
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end_idx = end_floor - 1
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# 提取范围内的所有消息（不筛选）
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return messages[start_idx:end_idx + 1]
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@staticmethod
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def _build_summary_prompt(
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messages: List[ChatMessage],
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summary_config: SummaryConfig
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) -> str:
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"""
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构建总结提示词
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Args:
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messages: 需要总结的消息列表
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summary_config: 总结配置
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Returns:
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完整的提示词
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"""
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# 使用用户自定义的总结提示词，或默认提示词
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base_prompt = summary_config.summaryPrompt or (
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"请总结以下对话内容，保留关键信息和上下文。"
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"用简洁的语言概括主要事件、人物状态和重要细节。"
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)
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# 构建对话内容
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conversation_text = "\n\n".join([
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f"{'用户' if msg.is_user else msg.name}: {msg.mes}"
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for msg in messages
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])
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# 组合完整提示词
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full_prompt = f"""{base_prompt}
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对话内容：
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{conversation_text}
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总结要求：
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1. 保持简洁明了
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2. 保留关键情节和设定
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3. 不超过{summary_config.maxSummaryLength}字
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4. 使用客观叙述语气
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总结："""
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return full_prompt
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@staticmethod
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async def _call_llm_for_summary(
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prompt: str,
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api_config: Dict[str, str],
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max_length: int
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) -> str:
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"""
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调用LLM生成总结
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Args:
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prompt: 总结提示词
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api_config: API配置
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max_length: 最大长度限制
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Returns:
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总结文本
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"""
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try:
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# 导入LLM客户端
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from utils.llm_client import llm_client
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# 构建消息
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messages = [
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{
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"role": "system",
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"content": "你是一个专业的对话总结助手，擅长提取关键信息并用简洁的语言概括。"
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},
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{
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"role": "user",
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"content": prompt
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}
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]
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# 调用LLM
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response = await llm_client.chat_completion(
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messages=messages,
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api_url=api_config.get("api_url", ""),
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api_key=api_config.get("api_key", ""),
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model=api_config.get("model", "gpt-3.5-turbo"),
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temperature=0.3, # 总结需要较低的随机性
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max_tokens=max_length,
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request_timeout=30
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)
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# 提取总结文本
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if isinstance(response, dict):
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summary = response.get("choices", [{}])[0].get("message", {}).get("content", "")
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else:
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summary = str(response)
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# 清理和截断
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summary = summary.strip()
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if len(summary) > max_length:
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summary = summary[:max_length] + "..."
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return summary
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except Exception as e:
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print(f"[ChatSummary] ❌ LLM总结失败: {e}")
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# 返回降级总结（基于规则的简单摘要）
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return ChatSummaryService._fallback_summary(prompt)
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@staticmethod
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def _fallback_summary(prompt: str) -> str:
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"""
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降级总结（当LLM调用失败时使用）
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Args:
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prompt: 原始提示词
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Returns:
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简单的降级总结
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"""
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# 提取对话中的关键信息
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lines = prompt.split("\n")
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user_lines = [l for l in lines if l.startswith("用户:")]
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ai_lines = [l for l in lines if l.startswith("AI:")]
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fallback = f"[自动总结] 对话包含 {len(user_lines)} 条用户消息和 {len(ai_lines)} 条AI回复。"
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return fallback
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# 全局实例
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chat_summary_service = ChatSummaryService()
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