187 lines
6.3 KiB
Python
187 lines
6.3 KiB
Python
"""回复生成结果相关数据模型。
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该模块用于描述新版回复链中的三个层次：
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1. LLM 原始完成结果。
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2. 生成过程中的耗时与调试信息。
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3. 回复链最终返回给上层的结构化结果。
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"""
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from dataclasses import dataclass, field
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from typing import TYPE_CHECKING, Any, Dict, List, Optional
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from . import BaseDataModel
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if TYPE_CHECKING:
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from src.common.data_models.message_component_data_model import MessageSequence
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from src.llm_models.payload_content.tool_option import ToolCall
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@dataclass
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class LLMCompletionResult(BaseDataModel):
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"""一次 LLM 调用的原始完成结果。
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该模型只描述模型调用本身的输入与输出，不承载回复切分、
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消息序列拼装或表达方式选择等后处理结果。
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"""
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request_prompt: str = field(
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default_factory=str,
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metadata={"description": "实际发送给模型的 Prompt 文本。"},
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)
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response_text: str = field(
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default_factory=str,
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metadata={"description": "模型返回的主文本内容。"},
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)
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reasoning_text: str = field(
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default_factory=str,
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metadata={"description": "模型返回的推理内容。"},
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)
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model_name: str = field(
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default_factory=str,
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metadata={"description": "本次请求实际使用的模型名称。"},
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)
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tool_calls: List["ToolCall"] = field(
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default_factory=list,
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metadata={"description": "模型返回的工具调用列表。"},
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)
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prompt_tokens: int = field(
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default=0,
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metadata={"description": "本次请求的输入 Token 数。"},
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)
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completion_tokens: int = field(
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default=0,
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metadata={"description": "本次请求的输出 Token 数。"},
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)
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total_tokens: int = field(
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default=0,
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metadata={"description": "本次请求的总 Token 数。"},
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)
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@dataclass
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class GenerationMetrics(BaseDataModel):
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"""一次生成流程的耗时与调试指标。"""
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prompt_ms: Optional[float] = field(
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default=None,
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metadata={"description": "Prompt 构建耗时，单位为毫秒。"},
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)
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llm_ms: Optional[float] = field(
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default=None,
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metadata={"description": "LLM 调用耗时，单位为毫秒。"},
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)
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overall_ms: Optional[float] = field(
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default=None,
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metadata={"description": "整个生成流程总耗时，单位为毫秒。"},
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)
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stage_logs: List[str] = field(
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default_factory=list,
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metadata={"description": "各阶段的简短耗时日志列表。"},
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)
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extra: Dict[str, Any] = field(
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default_factory=dict,
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metadata={"description": "额外指标字典，用于承载动态监控信息。"},
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)
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@dataclass
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class ReplyGenerationResult(BaseDataModel):
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"""回复链的最终结构化结果。"""
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success: bool = field(
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default=False,
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metadata={"description": "本次回复生成是否成功。"},
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)
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completion: LLMCompletionResult = field(
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default_factory=LLMCompletionResult,
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metadata={"description": "一次 LLM 调用的原始完成结果。"},
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)
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metrics: GenerationMetrics = field(
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default_factory=GenerationMetrics,
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metadata={"description": "本次生成的耗时与调试指标。"},
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)
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selected_expression_ids: List[int] = field(
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default_factory=list,
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metadata={"description": "本次选中的表达方式 ID 列表。"},
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)
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text_fragments: List[str] = field(
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default_factory=list,
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metadata={"description": "对模型输出进行切分、规范化后的文本片段列表。"},
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)
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message_sequence: Optional["MessageSequence"] = field(
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default=None,
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metadata={"description": "最终可直接发送的消息序列。"},
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)
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error_message: str = field(
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default_factory=str,
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metadata={"description": "失败时的错误描述；成功时通常为空字符串。"},
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)
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monitor_detail: Optional[Dict[str, Any]] = field(
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default=None,
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metadata={"description": "供监控层直接消费的通用 tool 展示详情。"},
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)
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def build_reply_monitor_detail(result: ReplyGenerationResult) -> Dict[str, Any]:
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"""构建 reply 工具统一监控详情结构。"""
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detail: Dict[str, Any] = {}
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prompt_text = result.completion.request_prompt.strip()
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reasoning_text = result.completion.reasoning_text.strip()
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output_text = result.completion.response_text.strip()
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if prompt_text:
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detail["prompt_text"] = prompt_text
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if reasoning_text:
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detail["reasoning_text"] = reasoning_text
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if output_text:
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detail["output_text"] = output_text
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metrics: Dict[str, Any] = {}
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if result.completion.model_name.strip():
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metrics["model_name"] = result.completion.model_name.strip()
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if result.completion.prompt_tokens > 0:
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metrics["prompt_tokens"] = result.completion.prompt_tokens
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if result.completion.completion_tokens > 0:
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metrics["completion_tokens"] = result.completion.completion_tokens
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if result.completion.total_tokens > 0:
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metrics["total_tokens"] = result.completion.total_tokens
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if result.metrics.prompt_ms is not None:
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metrics["prompt_ms"] = result.metrics.prompt_ms
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if result.metrics.llm_ms is not None:
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metrics["llm_ms"] = result.metrics.llm_ms
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if result.metrics.overall_ms is not None:
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metrics["overall_ms"] = result.metrics.overall_ms
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if metrics:
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detail["metrics"] = metrics
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extra_sections: List[Dict[str, str]] = []
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if result.selected_expression_ids:
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extra_sections.append({
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"title": "已选表达方式",
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"content": ", ".join(str(item) for item in result.selected_expression_ids),
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})
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if result.metrics.stage_logs:
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extra_sections.append({
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"title": "阶段日志",
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"content": "\n".join(result.metrics.stage_logs),
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})
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if result.error_message.strip():
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extra_sections.append({
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"title": "错误信息",
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"content": result.error_message.strip(),
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})
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if extra_sections:
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detail["extra_sections"] = extra_sections
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return detail
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__all__ = [
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"GenerationMetrics",
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"LLMCompletionResult",
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"ReplyGenerationResult",
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"build_reply_monitor_detail",
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]
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