198 lines
8.2 KiB
Python
198 lines
8.2 KiB
Python
import random
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from typing import List, Optional
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from src.common.logger import get_logger
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from src.config.config import model_config
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from src.chat.utils.prompt_builder import Prompt
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from src.llm_models.payload_content.message import RoleType, Message
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from src.llm_models.utils_model import LLMRequest
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logger = get_logger("dream_generator")
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# 初始化 utils 模型用于生成梦境总结
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_dream_summary_model: Optional[LLMRequest] = None
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# 梦境风格列表（21种）
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DREAM_STYLES = [
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"保持诗意和想象力，自由编写",
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"诗意朦胧，如薄雾笼罩的清晨",
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"奇幻冒险，充满未知与探索",
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"温暖怀旧，带着时光的痕迹",
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"神秘悬疑，暗藏深意",
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"浪漫唯美，如诗如画",
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"科幻未来，科技与想象交织",
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"自然清新，如山林间的微风",
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"深沉哲思，引人深思",
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"轻松幽默，充满趣味",
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"悲伤忧郁，带着淡淡哀愁",
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"激昂热烈，充满活力",
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"宁静平和，如湖面般平静",
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"荒诞离奇，打破常规",
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"细腻温柔，如春风拂面",
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"壮阔宏大，气势磅礴",
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"简约纯粹，返璞归真",
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"复杂多变，层次丰富",
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"梦幻迷离，虚实难辨",
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"现实写意，贴近生活",
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"抽象概念，超越具象",
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]
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def get_random_dream_styles(count: int = 2) -> List[str]:
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"""从梦境风格列表中随机选择指定数量的风格"""
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return random.sample(DREAM_STYLES, min(count, len(DREAM_STYLES)))
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def get_dream_summary_model() -> LLMRequest:
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"""获取用于生成梦境总结的 utils 模型实例"""
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global _dream_summary_model
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if _dream_summary_model is None:
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_dream_summary_model = LLMRequest(
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model_set=model_config.model_task_config.utils,
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request_type="dream.summary",
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)
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return _dream_summary_model
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def init_dream_summary_prompt() -> None:
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"""初始化梦境总结的提示词"""
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Prompt(
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"""
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你刚刚完成了一次对聊天记录的记忆整理工作。以下是整理过程的摘要：
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整理过程：
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{conversation_text}
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请将这次整理涉及的相关信息改写为一个富有诗意和想象力的"梦境"，请你仅使用具体的记忆的内容，而不是整理过程编写。
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要求：
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1. 使用第一人称视角
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2. 叙述直白，不要复杂修辞，口语化
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3. 长度控制在200-800字
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4. 用中文输出
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梦境风格：
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{dream_styles}
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请直接输出梦境内容，不要添加其他说明：
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""",
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name="dream_summary_prompt",
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)
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async def generate_dream_summary(
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chat_id: str,
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conversation_messages: List[Message],
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total_iterations: int,
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time_cost: float,
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) -> None:
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"""生成梦境总结并输出到日志"""
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try:
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import json
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from src.chat.utils.prompt_builder import global_prompt_manager
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# 第一步：建立工具调用结果映射 (call_id -> result)
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tool_results_map: dict[str, str] = {}
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for msg in conversation_messages:
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if msg.role == RoleType.Tool and msg.tool_call_id:
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content = ""
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if msg.content:
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if isinstance(msg.content, list) and msg.content:
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content = msg.content[0].text if hasattr(msg.content[0], "text") else str(msg.content[0])
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else:
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content = str(msg.content)
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tool_results_map[msg.tool_call_id] = content
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# 第二步：详细记录所有工具调用操作和结果到日志
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tool_call_count = 0
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logger.info(f"[dream][工具调用详情] 开始记录 chat_id={chat_id} 的所有工具调用操作：")
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for msg in conversation_messages:
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if msg.role == RoleType.Assistant and msg.tool_calls:
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tool_call_count += 1
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# 提取思考内容
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thought_content = ""
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if msg.content:
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if isinstance(msg.content, list) and msg.content:
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thought_content = msg.content[0].text if hasattr(msg.content[0], "text") else str(msg.content[0])
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else:
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thought_content = str(msg.content)
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logger.info(f"[dream][工具调用详情] === 第 {tool_call_count} 组工具调用 ===")
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if thought_content:
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logger.info(f"[dream][工具调用详情] 思考内容：{thought_content[:500]}{'...' if len(thought_content) > 500 else ''}")
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# 记录每个工具调用的详细信息
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for idx, tool_call in enumerate(msg.tool_calls, 1):
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tool_name = tool_call.func_name
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tool_args = tool_call.args or {}
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tool_call_id = tool_call.call_id
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tool_result = tool_results_map.get(tool_call_id, "未找到执行结果")
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# 格式化参数
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try:
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args_str = json.dumps(tool_args, ensure_ascii=False, indent=2) if tool_args else "无参数"
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except Exception:
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args_str = str(tool_args)
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logger.info(f"[dream][工具调用详情] --- 工具 {idx}: {tool_name} ---")
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logger.info(f"[dream][工具调用详情] 调用参数：\n{args_str}")
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logger.info(f"[dream][工具调用详情] 执行结果：\n{tool_result}")
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logger.info(f"[dream][工具调用详情] {'-' * 60}")
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logger.info(f"[dream][工具调用详情] 共记录了 {tool_call_count} 组工具调用操作")
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# 第三步：构建对话历史摘要（用于生成梦境）
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conversation_summary = []
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for msg in conversation_messages:
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role = msg.role.value if hasattr(msg.role, "value") else str(msg.role)
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content = ""
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if msg.content:
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content = msg.content[0].text if isinstance(msg.content, list) and msg.content else str(msg.content)
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if role == "user" and "轮次信息" in content:
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# 跳过轮次信息消息
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continue
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if role == "assistant":
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# 只保留思考内容，简化工具调用信息
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if content:
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# 截取前500字符，避免过长
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content_preview = content[:500] + ("..." if len(content) > 500 else "")
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conversation_summary.append(f"[{role}] {content_preview}")
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elif role == "tool":
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# 工具结果，只保留关键信息
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if content:
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# 截取前300字符
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content_preview = content[:300] + ("..." if len(content) > 300 else "")
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conversation_summary.append(f"[工具执行] {content_preview}")
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conversation_text = "\n".join(conversation_summary[-20:]) # 只保留最后20条消息
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# 随机选择2个梦境风格
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selected_styles = get_random_dream_styles(2)
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dream_styles_text = "\n".join([f"{i+1}. {style}" for i, style in enumerate(selected_styles)])
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# 使用 Prompt 管理器格式化梦境生成 prompt
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dream_prompt = await global_prompt_manager.format_prompt(
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"dream_summary_prompt",
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chat_id=chat_id,
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total_iterations=total_iterations,
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time_cost=time_cost,
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conversation_text=conversation_text,
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dream_styles=dream_styles_text,
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)
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# 调用 utils 模型生成梦境
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summary_model = get_dream_summary_model()
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dream_content, (reasoning, model_name, _) = await summary_model.generate_response_async(
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dream_prompt,
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max_tokens=512,
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temperature=0.8,
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)
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if dream_content:
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logger.info(f"[dream][梦境总结] 对 chat_id={chat_id} 的整理过程梦境：\n{dream_content}")
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else:
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logger.warning("[dream][梦境总结] 未能生成梦境总结")
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except Exception as e:
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logger.error(f"[dream][梦境总结] 生成梦境总结失败: {e}", exc_info=True)
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init_dream_summary_prompt() |