refactor: 架构优化 - 移除重复代码并统一工具类模式
重大架构改进： 1. 移除重复的TocGeneratorAgent实现（585行），保留模块化的TocAgent 2. 修复LLMService模块级强制初始化问题，采用标准单例模式 3. 统一所有工具类的使用模式，提高架构一致性 具体更改： - 删除 src/bidmaster/agents/toc_generator.py（完全重复实现） - 移除 llm.py 中的模块级实例化（llm_service = LLMService()） - 更新 tools/__init__.py 导出LLMService类而非实例 - 更新使用方改为LLMService()调用（generate_sub_chapters.py, review_structure.py） - 清理agents/__init__.py和analysis.py中的旧版本引用 收益： - 减少约600行重复代码 - 解决模块导入时的配置加载错误 - 实现惰性加载，配置只在使用时加载 - 提高代码可维护性和架构一致性 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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@ -1,14 +1,12 @@
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"""Agent层 - LangGraph编排"""
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from .analysis import AnalysisAgent
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from .toc_generator import TocGeneratorAgent
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from .builders import TocAgentBuilder
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from .builders.toc_builder import TocAgent
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from .interaction import InteractionHandler, InteractionMode
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__all__ = [
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"AnalysisAgent",
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"TocGeneratorAgent",
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"TocAgentBuilder",
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"TocAgent",
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"InteractionHandler",
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@ -254,8 +254,8 @@ def parse_content_node(state: AnalysisAgentState) -> AnalysisAgentState:
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def generate_toc_with_agent_node(state: AnalysisAgentState) -> AnalysisAgentState:
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"""节点5：使用TocGeneratorAgent生成目录结构"""
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logger.info("开始使用TocGeneratorAgent生成目录...")
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"""节点5：使用TocAgent生成目录结构"""
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logger.info("开始使用TocAgent生成目录...")
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try:
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technical_criteria = state["technical_criteria"]
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@ -306,7 +306,7 @@ def generate_toc_with_agent_node(state: AnalysisAgentState) -> AnalysisAgentStat
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# 更新状态
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state["preliminary_chapters"] = chapters
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state["technical_criteria"] = technical_criteria
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state["structure_review"] = {} # TocGeneratorAgent已包含审查
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state["structure_review"] = {} # TocAgent已包含审查
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state["current_step"] = "generate_toc"
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state["progress"] = 0.85
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@ -336,7 +336,7 @@ def finalize_structure_node(state: AnalysisAgentState) -> AnalysisAgentState:
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project_name=f"标书项目-{Path(state['source_file']).stem}",
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scoring_criteria=technical_criteria,
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deviation_items=state["deviation_items"],
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chapters=preliminary_chapters, # 使用TocGeneratorAgent生成的章节
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chapters=preliminary_chapters, # 使用TocAgent生成的章节
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scoring_file=state["source_file"]
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)
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@ -1,585 +0,0 @@
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"""目录生成Agent - 专门负责生成标书目录结构
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基于LangGraph实现的目录生成Agent，负责：
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1. 根据评分项类别生成一级章节
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2. AI智能生成二三级子标题
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3. 目录结构合理性审查与优化
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"""
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import asyncio
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import json
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import logging
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from typing import List, Dict, Any, TypedDict, Optional
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from langgraph.graph import StateGraph, END
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from pydantic import BaseModel, Field
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from ..tools.parser import ScoringCriteria, DocumentChapter
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from ..tools.llm import llm_service
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from ..config import get_settings
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logger = logging.getLogger(__name__)
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# 统一的类别名称映射
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CATEGORY_NAMES = {
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"compliance": "合规响应",
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"technical_solution": "技术方案",
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"equipment_spec": "设备规格",
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"quality_safety": "质量安全",
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"after_sales": "售后服务",
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"implementation": "实施方案"
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}
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class TocGeneratorState(TypedDict):
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"""目录生成Agent的状态定义"""
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# 输入参数
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technical_criteria: List[ScoringCriteria]
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generation_mode: str # "ai" 或 "template"
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template_file: Optional[str]
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# 执行状态
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current_step: str
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should_continue: bool
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# 中间数据
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category_groups: Dict[str, List[ScoringCriteria]] # 按类别分组的评分项
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preliminary_chapters: List[DocumentChapter] # 初步生成的章节
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structure_review: Dict[str, Any] # AI审查结果
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# 最终输出
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final_chapters: List[DocumentChapter]
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# 错误处理
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error: str
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warnings: List[str]
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class TocGeneratorResult(BaseModel):
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"""目录生成结果"""
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success: bool = Field(description="是否执行成功")
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chapters: List[DocumentChapter] = Field(default_factory=list, description="生成的章节结构")
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error_message: Optional[str] = Field(default=None, description="错误信息")
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warnings: List[str] = Field(default_factory=list, description="警告信息")
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# ========== LangGraph节点函数 ==========
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def group_criteria_node(state: TocGeneratorState) -> TocGeneratorState:
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"""节点1：按类别对评分项分组"""
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logger.info("开始对评分项按类别分组...")
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try:
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technical_criteria = state["technical_criteria"]
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if not technical_criteria:
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raise ValueError("缺少技术评分项")
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# 按类别分组
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category_groups = {
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"technical_solution": [],
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"equipment_spec": [],
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"implementation": [],
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"quality_safety": [],
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"after_sales": [],
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"compliance": []
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}
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for criteria in technical_criteria:
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category_key = criteria.category.value
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if category_key in category_groups:
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category_groups[category_key].append(criteria)
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else:
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category_groups["technical_solution"].append(criteria)
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# 只保留有评分项的类别
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category_groups = {k: v for k, v in category_groups.items() if v}
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logger.info(f"分组完成: {len(category_groups)}个类别")
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state["category_groups"] = category_groups
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state["current_step"] = "group_criteria"
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except Exception as e:
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logger.error(f"评分项分组失败: {e}")
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state["error"] = str(e)
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state["should_continue"] = False
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return state
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def generate_first_level_node(state: TocGeneratorState) -> TocGeneratorState:
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"""节点2：生成一级章节"""
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logger.info("开始生成一级章节...")
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try:
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category_groups = state["category_groups"]
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technical_criteria = state["technical_criteria"]
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chapters = []
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chapter_index = 1
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# 按原始顺序确定章节顺序
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def get_category_first_index(category):
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for i, criteria in enumerate(technical_criteria):
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if criteria.category.value == category:
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return i
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return 999
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sorted_categories = sorted(category_groups.keys(), key=get_category_first_index)
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# 为每个类别创建一级章节
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for category in sorted_categories:
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criteria_list = category_groups[category]
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if not criteria_list:
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continue
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total_score = sum(c.max_score for c in criteria_list)
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chapter_id = f"chapter_{chapter_index:02d}_{category}"
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category_name = CATEGORY_NAMES.get(category, category)
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chapter_title = category_name
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if total_score > 0:
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chapter_title += f" ({total_score}分)"
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chapter = DocumentChapter(
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id=chapter_id,
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title=chapter_title,
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level=1,
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score=total_score,
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template_placeholder=f"{{{{{chapter_id}_content}}}}"
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)
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chapters.append(chapter)
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chapter_index += 1
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logger.info(f"生成{len(chapters)}个一级章节")
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state["preliminary_chapters"] = chapters
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state["current_step"] = "generate_first_level"
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except Exception as e:
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logger.error(f"一级章节生成失败: {e}")
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state["error"] = str(e)
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state["should_continue"] = False
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return state
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def generate_sub_chapters_node(state: TocGeneratorState) -> TocGeneratorState:
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"""节点3：生成二三级子标题"""
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logger.info("开始生成子标题...")
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try:
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preliminary_chapters = state["preliminary_chapters"]
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technical_criteria = state["technical_criteria"]
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generation_mode = state.get("generation_mode", "ai")
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template_file = state.get("template_file")
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enhanced_chapters = []
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for chapter in preliminary_chapters:
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# 找到该章节对应的评分项
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# 从章节ID提取类别
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if "_" in chapter.id:
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parts = chapter.id.split("_")
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if len(parts) >= 3:
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category = "_".join(parts[2:])
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# 找到该类别的所有评分项
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corresponding_criteria = [
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c for c in technical_criteria
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if c.category.value == category
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]
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if corresponding_criteria:
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if generation_mode == "ai":
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sub_chapters = _generate_ai_sub_chapters(corresponding_criteria, chapter)
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else:
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sub_chapters = _generate_template_sub_chapters(
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corresponding_criteria[0], chapter, template_file
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)
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chapter.children = sub_chapters
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logger.info(f"章节 {chapter.title} 生成了 {len(sub_chapters)} 个子标题")
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enhanced_chapters.append(chapter)
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state["preliminary_chapters"] = enhanced_chapters
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state["current_step"] = "generate_sub_chapters"
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except Exception as e:
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logger.error(f"子标题生成失败: {e}")
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state["error"] = str(e)
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state["should_continue"] = False
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return state
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def review_structure_node(state: TocGeneratorState) -> TocGeneratorState:
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"""节点4：AI审查目录结构"""
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logger.info("开始AI审查目录结构...")
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try:
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technical_criteria = state["technical_criteria"]
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preliminary_chapters = state["preliminary_chapters"]
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# 构建审查提示词
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criteria_summary = _format_criteria_for_review(technical_criteria)
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chapters_summary = _format_chapters_for_review(preliminary_chapters)
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review_prompt = f"""
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请审查这个标书目录结构的合理性和完整性。
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【技术评分项分布】:
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{criteria_summary}
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【当前生成的章节结构】:
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{chapters_summary}
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【审查要求】:
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1. 是否缺少重要的标准章节？
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2. 章节顺序是否合理？
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3. 每个评分项是否都有对应章节？
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返回JSON格式：
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{{
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"overall_assessment": "总体评价",
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"suggestions": [
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{{"type": "add/modify/reorder", "description": "建议内容", "priority": "high/medium/low"}}
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],
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"optimization_score": 85
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}}
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只返回JSON："""
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# 使用统一的LLM服务
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response = llm_service.call(review_prompt)
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if response:
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try:
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clean_response = response.strip()
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if clean_response.startswith("```json"):
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clean_response = clean_response[7:]
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if clean_response.endswith("```"):
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clean_response = clean_response[:-3]
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review_result = json.loads(clean_response.strip())
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except json.JSONDecodeError:
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review_result = {"overall_assessment": "解析失败", "suggestions": []}
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else:
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review_result = {"overall_assessment": "AI审查失败", "suggestions": []}
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state["structure_review"] = review_result
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state["current_step"] = "review_structure"
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# 根据审查结果添加警告
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if review_result.get("suggestions"):
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state["warnings"].append(f"AI审查: {len(review_result['suggestions'])}条优化建议")
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except Exception as e:
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logger.error(f"AI审查失败: {e}")
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state["warnings"].append(f"AI审查跳过: {str(e)}")
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state["structure_review"] = {}
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return state
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def finalize_chapters_node(state: TocGeneratorState) -> TocGeneratorState:
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"""节点5：最终确定章节结构"""
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logger.info("最终确定章节结构...")
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try:
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preliminary_chapters = state["preliminary_chapters"]
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structure_review = state.get("structure_review", {})
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final_chapters = preliminary_chapters # 直接使用，不做防护编程
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# 应用高优先级建议
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suggestions = structure_review.get("suggestions", [])
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for suggestion in suggestions:
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if suggestion.get("priority") == "high":
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# 这里可以根据建议类型进行调整
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logger.info(f"应用高优先级建议: {suggestion.get('description')}")
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# 确保评标索引表存在（作为第一章）
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has_index = any("评标索引" in ch.title for ch in final_chapters)
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if not has_index:
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index_chapter = DocumentChapter(
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id="evaluation_index",
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title="评标索引表（技术评分完全对应）", # 不包含编号
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level=1,
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template_placeholder="{{evaluation_index_content}}"
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)
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final_chapters.insert(0, index_chapter)
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state["final_chapters"] = final_chapters
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state["current_step"] = "finalize"
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state["should_continue"] = False
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logger.info(f"最终生成{len(final_chapters)}个章节")
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except Exception as e:
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logger.error(f"章节最终确定失败: {e}")
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state["error"] = str(e)
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state["should_continue"] = False
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return state
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# ========== 辅助函数 ==========
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def _generate_ai_sub_chapters(criteria_list: List[ScoringCriteria],
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parent_chapter: DocumentChapter) -> List[DocumentChapter]:
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"""AI生成子标题"""
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try:
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criteria_info = []
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for criteria in criteria_list:
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criteria_info.append(f"- {criteria.item_name} ({criteria.max_score}分)")
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prompt = f"""
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为以下大类别生成专业的标书子标题：
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【大类别】: {parent_chapter.title}
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【评分项】:
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{chr(10).join(criteria_info)}
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生成要求：
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1. 为每个评分项生成对应的子标题名称（不要包含编号）
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2. 重要评分项可添加三级子标题（不要包含编号）
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3. 只返回标题文本，编号由Word自动管理
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返回JSON格式：
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{{
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"sub_chapters": [
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{{"title": "技术架构设计", "level": 2, "score": 5, "children": []}}
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]
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}}
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只返回JSON："""
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# 使用统一的LLM服务
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response = llm_service.call(prompt)
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if not response:
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raise ValueError("AI生成子标题失败: API无响应")
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try:
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clean_response = response.strip()
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if clean_response.startswith("```json"):
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clean_response = clean_response[7:]
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if clean_response.endswith("```"):
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clean_response = clean_response[:-3]
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result_data = json.loads(clean_response.strip())
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sub_chapters_data = result_data.get("sub_chapters", [])
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sub_chapters = []
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for i, sub_data in enumerate(sub_chapters_data, 1):
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# 直接使用标题文本，不添加编号
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title = sub_data.get("title", f"子标题{i}")
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sub_chapter = DocumentChapter(
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id=f"{parent_chapter.id}_sub_{i:02d}",
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title=title, # 不添加编号
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level=sub_data.get("level", 2),
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score=sub_data.get("score", 0),
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template_placeholder=f"{{{{{parent_chapter.id}_sub_{i:02d}_content}}}}"
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)
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# 处理三级标题
|
||||
for j, child_data in enumerate(sub_data.get("children", []), 1):
|
||||
child_title = child_data.get("title", f"三级标题{j}")
|
||||
|
||||
child_chapter = DocumentChapter(
|
||||
id=f"{parent_chapter.id}_sub_{i:02d}_{j:02d}",
|
||||
title=child_title, # 不添加编号
|
||||
level=child_data.get("level", 3),
|
||||
template_placeholder=f"{{{{{parent_chapter.id}_sub_{i:02d}_{j:02d}_content}}}}"
|
||||
)
|
||||
sub_chapter.children.append(child_chapter)
|
||||
|
||||
sub_chapters.append(sub_chapter)
|
||||
|
||||
return sub_chapters
|
||||
|
||||
except (json.JSONDecodeError, KeyError) as e:
|
||||
logger.error(f"解析AI响应失败: {e}")
|
||||
raise ValueError(f"解析AI响应失败: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"AI生成子标题失败: {e}")
|
||||
raise # 立即失败，不掩盖错误
|
||||
|
||||
|
||||
def _generate_template_sub_chapters(criteria: ScoringCriteria,
|
||||
parent_chapter: DocumentChapter,
|
||||
template_file: Optional[str]) -> List[DocumentChapter]:
|
||||
"""基于模板生成子标题"""
|
||||
# 提供默认结构（不包含编号）
|
||||
default_sub_chapters = [
|
||||
DocumentChapter(
|
||||
id=f"{parent_chapter.id}_def_01",
|
||||
title="方案概述", # 不包含编号
|
||||
level=2,
|
||||
template_placeholder=f"{{{{{parent_chapter.id}_def_01_content}}}}"
|
||||
),
|
||||
DocumentChapter(
|
||||
id=f"{parent_chapter.id}_def_02",
|
||||
title="具体实施", # 不包含编号
|
||||
level=2,
|
||||
template_placeholder=f"{{{{{parent_chapter.id}_def_02_content}}}}"
|
||||
),
|
||||
DocumentChapter(
|
||||
id=f"{parent_chapter.id}_def_03",
|
||||
title="保障措施", # 不包含编号
|
||||
level=2,
|
||||
template_placeholder=f"{{{{{parent_chapter.id}_def_03_content}}}}"
|
||||
)
|
||||
]
|
||||
|
||||
return default_sub_chapters
|
||||
|
||||
|
||||
def _format_criteria_for_review(technical_criteria: List[ScoringCriteria]) -> str:
|
||||
"""格式化评分项用于审查"""
|
||||
lines = []
|
||||
|
||||
category_groups = {}
|
||||
for criteria in technical_criteria:
|
||||
category = criteria.category.value
|
||||
if category not in category_groups:
|
||||
category_groups[category] = []
|
||||
category_groups[category].append(criteria)
|
||||
|
||||
for category, items in category_groups.items():
|
||||
category_name = CATEGORY_NAMES.get(category, category)
|
||||
lines.append(f"【{category_name}】({len(items)}项):")
|
||||
for item in items:
|
||||
lines.append(f" - {item.item_name} ({item.max_score}分)")
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _format_chapters_for_review(chapters: List[DocumentChapter]) -> str:
|
||||
"""格式化章节用于审查"""
|
||||
lines = []
|
||||
for chapter in chapters:
|
||||
lines.append(chapter.title)
|
||||
for sub in chapter.children:
|
||||
lines.append(f" {sub.title}")
|
||||
for child in sub.children:
|
||||
lines.append(f" {child.title}")
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
# ========== 条件判断 ==========
|
||||
|
||||
def should_continue(state: TocGeneratorState) -> str:
|
||||
"""判断是否继续"""
|
||||
if not state.get("should_continue", True) or state.get("error"):
|
||||
return "end"
|
||||
return "continue"
|
||||
|
||||
|
||||
class TocGeneratorAgent:
|
||||
"""目录生成Agent"""
|
||||
|
||||
def __init__(self):
|
||||
self.settings = get_settings()
|
||||
self.graph = self._build_graph()
|
||||
|
||||
def _build_graph(self) -> StateGraph:
|
||||
"""构建工作流"""
|
||||
workflow = StateGraph(TocGeneratorState)
|
||||
|
||||
# 添加节点
|
||||
workflow.add_node("group_criteria", group_criteria_node)
|
||||
workflow.add_node("generate_first_level", generate_first_level_node)
|
||||
workflow.add_node("generate_sub_chapters", generate_sub_chapters_node)
|
||||
workflow.add_node("review_structure", review_structure_node)
|
||||
workflow.add_node("finalize_chapters", finalize_chapters_node)
|
||||
|
||||
# 设置入口
|
||||
workflow.set_entry_point("group_criteria")
|
||||
|
||||
# 添加边
|
||||
workflow.add_conditional_edges(
|
||||
"group_criteria",
|
||||
should_continue,
|
||||
{"continue": "generate_first_level", "end": END}
|
||||
)
|
||||
|
||||
workflow.add_conditional_edges(
|
||||
"generate_first_level",
|
||||
should_continue,
|
||||
{"continue": "generate_sub_chapters", "end": END}
|
||||
)
|
||||
|
||||
workflow.add_conditional_edges(
|
||||
"generate_sub_chapters",
|
||||
should_continue,
|
||||
{"continue": "review_structure", "end": END}
|
||||
)
|
||||
|
||||
workflow.add_conditional_edges(
|
||||
"review_structure",
|
||||
should_continue,
|
||||
{"continue": "finalize_chapters", "end": END}
|
||||
)
|
||||
|
||||
workflow.add_edge("finalize_chapters", END)
|
||||
|
||||
return workflow.compile()
|
||||
|
||||
async def generate(self,
|
||||
technical_criteria: List[ScoringCriteria],
|
||||
generation_mode: str = "ai",
|
||||
template_file: Optional[str] = None) -> TocGeneratorResult:
|
||||
"""生成目录结构"""
|
||||
logger.info("开始执行TocGeneratorAgent")
|
||||
|
||||
# 初始化状态
|
||||
initial_state = TocGeneratorState(
|
||||
technical_criteria=technical_criteria,
|
||||
generation_mode=generation_mode,
|
||||
template_file=template_file,
|
||||
current_step="",
|
||||
should_continue=True,
|
||||
category_groups={},
|
||||
preliminary_chapters=[],
|
||||
structure_review={},
|
||||
final_chapters=[],
|
||||
error="",
|
||||
warnings=[]
|
||||
)
|
||||
|
||||
try:
|
||||
# 执行工作流
|
||||
final_state = await self.graph.ainvoke(initial_state)
|
||||
|
||||
if final_state.get("error"):
|
||||
return TocGeneratorResult(
|
||||
success=False,
|
||||
error_message=final_state["error"],
|
||||
warnings=final_state.get("warnings", [])
|
||||
)
|
||||
|
||||
return TocGeneratorResult(
|
||||
success=True,
|
||||
chapters=final_state["final_chapters"],
|
||||
warnings=final_state.get("warnings", [])
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"TocGeneratorAgent执行异常: {e}")
|
||||
return TocGeneratorResult(
|
||||
success=False,
|
||||
error_message=str(e)
|
||||
)
|
||||
|
||||
def generate_sync(self,
|
||||
technical_criteria: List[ScoringCriteria],
|
||||
generation_mode: str = "ai",
|
||||
template_file: Optional[str] = None) -> TocGeneratorResult:
|
||||
"""同步接口"""
|
||||
return asyncio.run(self.generate(technical_criteria, generation_mode, template_file))
|
||||
@ -9,7 +9,7 @@ from typing import Dict, List, Any, Optional
|
||||
|
||||
from ..base import BaseNode, NodeContext
|
||||
from ...tools.parser import ScoringCriteria, DocumentChapter
|
||||
from ...tools.llm import llm_service
|
||||
from ...tools.llm import LLMService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -168,7 +168,7 @@ class GenerateSubChaptersNode(BaseNode):
|
||||
只返回JSON："""
|
||||
|
||||
# 使用统一的LLM服务
|
||||
response = llm_service.call(prompt)
|
||||
response = LLMService().call(prompt)
|
||||
if not response:
|
||||
raise ValueError("AI生成子标题失败: API无响应")
|
||||
|
||||
|
||||
@ -9,7 +9,7 @@ from typing import Dict, List, Any
|
||||
|
||||
from ..base import BaseNode, NodeContext
|
||||
from ...tools.parser import ScoringCriteria, DocumentChapter
|
||||
from ...tools.llm import llm_service
|
||||
from ...tools.llm import LLMService
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -114,7 +114,7 @@ class ReviewStructureNode(BaseNode):
|
||||
|
||||
try:
|
||||
# 使用统一的LLM服务
|
||||
response = llm_service.call(review_prompt)
|
||||
response = LLMService().call(review_prompt)
|
||||
|
||||
if response:
|
||||
return self._parse_review_response(response)
|
||||
|
||||
@ -1,13 +1,13 @@
|
||||
"""工具层 - 原子化工具集"""
|
||||
|
||||
from .llm import llm_service
|
||||
from .llm import LLMService
|
||||
from .parser import BidParser
|
||||
from .rag import RAGTool
|
||||
from .table import TableGenerator
|
||||
from .word import WordProcessor
|
||||
|
||||
__all__ = [
|
||||
"llm_service",
|
||||
"LLMService",
|
||||
"BidParser",
|
||||
"RAGTool",
|
||||
"TableGenerator",
|
||||
|
||||
@ -65,5 +65,4 @@ class LLMService:
|
||||
raise # 立即失败
|
||||
|
||||
|
||||
# 全局单例
|
||||
llm_service = LLMService()
|
||||
# 移除模块级实例化，使用标准单例模式：LLMService()
|
||||
Loading…
Reference in New Issue
Block a user