init. project

rag-web-ui/backend/app/tools/__init__.py

@@ -0,0 +1,4 @@
+from app.tools.base import ToolDefinition
+from app.tools.registry import ToolRegistry
+
+__all__ = ["ToolDefinition", "ToolRegistry"]

rag-web-ui/backend/app/tools/base.py

@@ -0,0 +1,11 @@
+from dataclasses import dataclass
+from typing import Any, Dict
+
+
+@dataclass(frozen=True)
+class ToolDefinition:
+    name: str
+    version: str
+    description: str
+    input_schema: Dict[str, Any]
+    output_schema: Dict[str, Any]

rag-web-ui/backend/app/tools/registry.py

@@ -0,0 +1,19 @@
+from typing import Dict, List
+
+from app.tools.base import ToolDefinition
+
+
+class ToolRegistry:
+    _tools: Dict[str, ToolDefinition] = {}
+
+    @classmethod
+    def register(cls, definition: ToolDefinition) -> None:
+        cls._tools[definition.name] = definition
+
+    @classmethod
+    def get(cls, name: str) -> ToolDefinition:
+        return cls._tools[name]
+
+    @classmethod
+    def list(cls) -> List[ToolDefinition]:
+        return list(cls._tools.values())

rag-web-ui/backend/app/tools/srs_reqs_qwen/__init__.py

@@ -0,0 +1,3 @@
+from app.tools.srs_reqs_qwen.tool import SRSTool, get_srs_tool
+
+__all__ = ["SRSTool", "get_srs_tool"]

rag-web-ui/backend/app/tools/srs_reqs_qwen/default_config.yaml

@@ -0,0 +1,102 @@
+# 配置文件 - SRS 需求文档解析工具 (LLM增强版)
+# Configuration file for SRS Requirement Document Parser (LLM Enhanced Version)
+
+# LLM配置 - 阿里云千问
+llm:
+  # 是否启用LLM（设为false则使用纯规则提取）
+  enabled: true
+  # LLM提供商：qwen（阿里云千问）
+  provider: "qwen"
+  # 模型名称
+  model: "qwen3-max"
+  # API密钥统一由 rag-web-ui 的环境变量提供
+  api_key: ""
+  # 可选参数
+  temperature: 0.3
+  max_tokens: 1024
+
+# 文档解析配置
+document:
+  supported_formats:
+    - ".pdf"
+    - ".docx"
+  # 标题识别的样式列表
+  heading_styles:
+    - "Heading 1"
+    - "Heading 2"
+    - "Heading 3"
+    - "Heading 4"
+    - "Heading 5"
+  # 需要过滤的非需求章节（GJB438B标准）
+  non_requirement_sections:
+    - "标识"
+    - "系统概述"
+    - "文档概述"
+    - "引用文档"
+    - "合格性规定"
+    - "需求可追踪性"
+    - "注释"
+    - "附录"
+
+# 需求提取配置
+extraction:
+  # 需求类型关键字（用于自动判断需求类型）
+  requirement_types:
+    功能需求:
+      prefix: "FR"
+      keywords: ["功能", "feature", "requirement", "CSCI组成", "控制", "处理", "监测", "显示"]
+      priority: 1
+    接口需求:
+      prefix: "IR"
+      keywords: ["接口", "interface", "api", "外部接口", "内部接口", "CAN", "以太网", "通信"]
+      priority: 2
+    性能需求:
+      prefix: "PR"
+      keywords: ["性能", "performance", "速度", "响应时间", "吞吐量"]
+      priority: 3
+    安全需求:
+      prefix: "SR"
+      keywords: ["安全", "security", "安全性", "报警"]
+      priority: 4
+    可靠性需求:
+      prefix: "RR"
+      keywords: ["可靠", "reliability", "容错", "恢复", "冗余"]
+      priority: 5
+    其他需求:
+      prefix: "OR"
+      keywords: ["约束", "资源", "适应性", "保密", "环境", "计算机", "质量", "设计", "人员", "培训", "保障", "验收", "交付"]
+      priority: 6
+  splitter:
+    enabled: true
+    max_sentence_len: 120
+    min_clause_len: 12
+  semantic_guard:
+    enabled: true
+    preserve_condition_action_chain: true
+    preserve_alarm_chain: true
+  table_strategy:
+    llm_semantic_enabled: true
+    sequence_table_merge: "single_requirement"
+    merge_time_series_rows_min: 3
+  rewrite_policy:
+    llm_light_rewrite_enabled: true
+    preserve_ratio_min: 0.65
+    max_length_growth_ratio: 1.25
+  renumber_policy:
+    enabled: true
+    mode: "section_continuous"
+
+# 输出配置
+output:
+  format: "json"
+  indent: 2
+  # 是否美化输出（格式化）
+  pretty_print: true
+  # 是否包含元数据
+  include_metadata: true
+
+# 日志配置
+logging:
+  level: "INFO"  # DEBUG, INFO, WARNING, ERROR
+  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+  file: "srs_parser.log"

rag-web-ui/backend/app/tools/srs_reqs_qwen/src/__init__.py

@@ -0,0 +1,26 @@
+# src/__init__.py
+"""
+SRS 需求文档解析工具包
+"""
+
+__version__ = "1.0.0"
+__author__ = "SRS Parser Team"
+
+from .document_parser import DocumentParser
+from .llm_interface import LLMInterface, QwenLLM
+from .requirement_extractor import RequirementExtractor
+from .json_generator import JSONGenerator
+from .settings import AppSettings
+from .requirement_splitter import RequirementSplitter
+from .requirement_id_generator import RequirementIDGenerator
+
+__all__ = [
+    'DocumentParser',
+    'LLMInterface',
+    'QwenLLM',
+    'RequirementExtractor',
+    'JSONGenerator',
+    'AppSettings',
+    'RequirementSplitter',
+    'RequirementIDGenerator',
+]

rag-web-ui/backend/app/tools/srs_reqs_qwen/src/document_parser.py

@@ -0,0 +1,709 @@
+# -*- coding: utf-8 -*-
+"""
+文档解析模块 - LLM增强版
+支持PDF和Docx格式，针对GJB438B标准SRS文档优化
+"""
+
+import os
+import re
+import logging
+import importlib
+from abc import ABC, abstractmethod
+from typing import List, Dict, Tuple, Optional, Any
+from pathlib import Path
+
+try:
+    from docx import Document
+    HAS_DOCX = True
+except ImportError:
+    HAS_DOCX = False
+
+try:
+    import PyPDF2
+    HAS_PDF = True
+except ImportError:
+    HAS_PDF = False
+
+HAS_PDF_TABLE = importlib.util.find_spec("pdfplumber") is not None
+
+logger = logging.getLogger(__name__)
+
+
+class Section:
+    """表示文档中的一个章节"""
+    
+    def __init__(self, level: int, title: str, number: str = None, content: str = "", uid: str = ""):
+        self.level = level
+        self.title = title
+        self.number = number
+        self.content = content
+        self.uid = uid
+        self.parent = None
+        self.children = []
+        self.tables = []
+        self.blocks = []
+    
+    def add_child(self, child: 'Section') -> None:
+        self.children.append(child)
+        child.parent = self
+    
+    def add_content(self, text: str) -> None:
+        text = (text or "").strip()
+        if not text:
+            return
+        if self.content:
+            self.content += "\n" + text
+        else:
+            self.content = text
+        self.blocks.append({"type": "text", "text": text})
+    
+    def add_table(self, table_data: List[List[str]]) -> None:
+        if not table_data:
+            return
+        self.tables.append(table_data)
+        table_index = len(self.tables) - 1
+        self.blocks.append({"type": "table", "table_index": table_index, "table": table_data})
+    
+    def generate_auto_number(self, parent_number: str = "", sibling_index: int = 1) -> None:
+        """
+        自动生成章节编号（当章节没有编号时）
+        
+        Args:
+            parent_number: 父章节编号
+            sibling_index: 在同级章节中的序号（从1开始）
+        """
+        if not self.number:
+            if parent_number:
+                self.number = f"{parent_number}.{sibling_index}"
+            else:
+                self.number = str(sibling_index)
+    
+    def __repr__(self) -> str:
+        return f"Section(level={self.level}, number='{self.number}', title='{self.title}')"
+
+
+class DocumentParser(ABC):
+    """文档解析器基类"""
+    
+    def __init__(self, file_path: str):
+        self.file_path = file_path
+        self.sections: List[Section] = []
+        self.document_title = ""
+        self.raw_text = ""
+        self.llm = None
+        self._uid_counter = 0
+    
+    def set_llm(self, llm) -> None:
+        """设置LLM实例"""
+        self.llm = llm
+    
+    @abstractmethod
+    def parse(self) -> List[Section]:
+        pass
+    
+    def get_document_title(self) -> str:
+        return self.document_title
+
+    def _next_uid(self) -> str:
+        self._uid_counter += 1
+        return f"sec-{self._uid_counter}"
+    
+    def _auto_number_sections(self, sections: List[Section], parent_number: str = "") -> None:
+        """
+        为没有编号的章节自动生成编号
+        
+        规则：使用Word样式确定级别，跳过前置章节（目录、概述等），
+        从第一个正文章节（如"外部接口"）开始编号为1
+        
+        Args:
+            sections: 章节列表
+            parent_number: 父章节编号
+        """
+        # 仅在顶级章节重编号
+        if not parent_number:
+            # 前置章节关键词（需要跳过的）
+            skip_keywords = ['目录', '封面', '扉页', '未命名', '年', '月']
+            # 正文章节关键词（遇到这些说明正文开始）
+            content_keywords = ['外部接口', '接口', '软件需求', '需求', '功能', '性能', '设计', '概述', '标识', '引言']
+            
+            start_index = 0
+            for idx, section in enumerate(sections):
+                # 优先检查是否是正文章节
+                is_content = any(kw in section.title for kw in content_keywords)
+                if is_content and section.level == 1:
+                    start_index = idx
+                    break
+            
+            # 重新编号所有章节
+            counter = 1
+            for i, section in enumerate(sections):
+                if i < start_index:
+                    # 前置章节不编号
+                    section.number = ""
+                else:
+                    # 正文章节：顶级章节从1开始编号
+                    if section.level == 1:
+                        section.number = str(counter)
+                        counter += 1
+                
+                # 递归处理子章节
+                if section.children:
+                    self._auto_number_sections(section.children, section.number)
+        else:
+            # 子章节编号
+            for i, section in enumerate(sections, 1):
+                if not section.number or self._is_chinese_number(section.number):
+                    section.generate_auto_number(parent_number, i)
+                if section.children:
+                    self._auto_number_sections(section.children, section.number)
+    
+    def _is_chinese_number(self, text: str) -> bool:
+        """检查是否是中文数字编号"""
+        chinese_numbers = '一二三四五六七八九十百千万'
+        return text and all(c in chinese_numbers for c in text)
+
+
+class DocxParser(DocumentParser):
+    """DOCX格式文档解析器"""
+    
+    def __init__(self, file_path: str):
+        if not HAS_DOCX:
+            raise ImportError("python-docx库未安装，请运行: pip install python-docx")
+        super().__init__(file_path)
+        self.document = None
+    
+    def parse(self) -> List[Section]:
+        try:
+            self.document = Document(self.file_path)
+            self.document_title = self.document.core_properties.title or "SRS Document"
+            
+            section_stack = {}
+            
+            for block in self._iter_block_items(self.document):
+                from docx.text.paragraph import Paragraph
+                from docx.table import Table
+                if isinstance(block, Paragraph):
+                    text = block.text.strip()
+                    if not text:
+                        continue
+                    
+                    heading_info = self._parse_heading(block, text)
+                    if heading_info:
+                        number, title, level = heading_info
+                        section = Section(level=level, title=title, number=number, uid=self._next_uid())
+                        
+                        if level == 1 or not section_stack:
+                            self.sections.append(section)
+                            section_stack = {1: section}
+                        else:
+                            parent_level = level - 1
+                            while parent_level >= 1 and parent_level not in section_stack:
+                                parent_level -= 1
+                            
+                            if parent_level >= 1 and parent_level in section_stack:
+                                section_stack[parent_level].add_child(section)
+                            elif self.sections:
+                                self.sections[-1].add_child(section)
+                        
+                        section_stack[level] = section
+                        for l in list(section_stack.keys()):
+                            if l > level:
+                                del section_stack[l]
+                    else:
+                        # 添加内容到当前章节
+                        if section_stack:
+                            max_level = max(section_stack.keys())
+                            section_stack[max_level].add_content(text)
+                        else:
+                            # 没有标题时，创建默认章节
+                            default_section = Section(level=1, title="未命名章节", number="", uid=self._next_uid())
+                            default_section.add_content(text)
+                            self.sections.append(default_section)
+                            section_stack = {1: default_section}
+                elif isinstance(block, Table):
+                    # 表格处理
+                    table_data = self._extract_table_data(block)
+                    if table_data:
+                        if section_stack:
+                            max_level = max(section_stack.keys())
+                            section_stack[max_level].add_table(table_data)
+                        else:
+                            default_section = Section(level=1, title="未命名章节", number="", uid=self._next_uid())
+                            default_section.add_table(table_data)
+                            self.sections.append(default_section)
+                            section_stack = {1: default_section}
+            
+            # 为没有编号的章节自动生成编号
+            self._auto_number_sections(self.sections)
+            
+            logger.info(f"完成Docx解析，提取{len(self.sections)}个顶级章节")
+            return self.sections
+            
+        except Exception as e:
+            logger.error(f"解析Docx文档失败: {e}")
+            raise
+    
+    def _is_valid_heading(self, text: str) -> bool:
+        """检查是否是有效的标题"""
+        if len(text) > 120 or '...' in text:
+            return False
+        # 标题应包含中文或字母
+        if not re.search(r'[\u4e00-\u9fa5A-Za-z]', text):
+            return False
+        # 过滤目录项（标题后跟页码，如"概述     2"或"概述 . . . . 2"）
+        if re.search(r'\s{2,}\d+$', text):  # 多个空格后跟数字结尾
+            return False
+        if re.search(r'[\.。\s]+\d+$', text):  # 点号或空格后跟数字结尾
+            return False
+        return True
+
+    def _parse_heading(self, paragraph, text: str) -> Optional[Tuple[str, str, int]]:
+        """解析标题，返回(编号, 标题, 级别)"""
+        style_name = paragraph.style.name if paragraph.style else ""
+        is_heading_style = style_name.lower().startswith('heading') if style_name else False
+        
+        # 数字编号标题
+        match = re.match(r'^(\d+(?:\.\d+)*)\s*[\.、]?\s*(.+)$', text)
+        if match and self._is_valid_heading(match.group(2)):
+            number = match.group(1)
+            title = match.group(2).strip()
+            level = len(number.split('.'))
+            return number, title, level
+        
+        # 中文编号标题
+        match = re.match(r'^([一二三四五六七八九十]+)[、\.]+\s*(.+)$', text)
+        if match and self._is_valid_heading(match.group(2)):
+            number = match.group(1)
+            title = match.group(2).strip()
+            level = 1
+            return number, title, level
+        
+        # 样式标题
+        if is_heading_style and self._is_valid_heading(text):
+            level = 1
+            level_match = re.search(r'(\d+)', style_name)
+            if level_match:
+                level = int(level_match.group(1))
+            return "", text, level
+        
+        return None
+
+    def _iter_block_items(self, parent):
+        """按文档顺序迭代段落和表格"""
+        from docx.text.paragraph import Paragraph
+        from docx.table import Table
+        from docx.oxml.text.paragraph import CT_P
+        from docx.oxml.table import CT_Tbl
+        
+        for child in parent.element.body.iterchildren():
+            if isinstance(child, CT_P):
+                yield Paragraph(child, parent)
+            elif isinstance(child, CT_Tbl):
+                yield Table(child, parent)
+
+    def _extract_table_data(self, table) -> List[List[str]]:
+        """提取表格数据"""
+        table_data = []
+        for row in table.rows:
+            row_data = []
+            for cell in row.cells:
+                text = cell.text.replace('\n', ' ').strip()
+                text = re.sub(r'\s+', ' ', text)
+                row_data.append(text)
+            if any(cell for cell in row_data):
+                table_data.append(row_data)
+        return table_data
+
+
+class PDFParser(DocumentParser):
+    """PDF格式文档解析器 - LLM增强版"""
+    
+    # GJB438B标准SRS文档的有效章节标题关键词
+    VALID_TITLE_KEYWORDS = [
+        '范围', '标识', '概述', '引用', '文档',
+        '需求', '功能', '接口', '性能', '安全', '保密',
+        '环境', '资源', '质量', '设计', '约束',
+        '人员', '培训', '保障', '验收', '交付', '包装',
+        '优先', '关键', '合格', '追踪', '注释',
+        'CSCI', '计算机', '软件', '硬件', '通信', '通讯',
+        '数据', '适应', '可靠', '内部', '外部',
+        '描述', '要求', '规定', '说明', '定义',
+        '电场', '防护', '装置', '控制', '监控', '显控'
+    ]
+    
+    # 明显无效的章节标题模式（噪声）
+    INVALID_TITLE_PATTERNS = [
+        '本文档可作为', '参比电位', '补偿电流', '以太网',
+        '电源', '软件接', '功能\\', '性能 \\', '输入/输出 \\',
+        '数据处理要求 \\', '固件 \\', '质量控制要求',
+        '信安科技', '浙江', '公司'
+    ]
+    
+    def __init__(self, file_path: str):
+        if not HAS_PDF:
+            raise ImportError("PyPDF2库未安装，请运行: pip install PyPDF2")
+        super().__init__(file_path)
+        self.document_title = "SRS Document"
+        self._page_texts: List[str] = []
+    
+    def parse(self) -> List[Section]:
+        """解析PDF文档"""
+        try:
+            # 1. 提取所有文本
+            self.raw_text = self._extract_all_text()
+            
+            # 2. 清洗文本
+            cleaned_text = self._clean_text(self.raw_text)
+            
+            # 3. 识别章节结构
+            self.sections = self._parse_sections(cleaned_text)
+            
+            # 4. 使用LLM验证和清理章节（如果可用）
+            if self.llm:
+                self.sections = self._llm_validate_sections(self.sections)
+
+            # 章节识别失败时，创建兜底章节避免后续表格数据丢失。
+            if not self.sections:
+                fallback = Section(level=1, title="未命名章节", number="1", uid=self._next_uid())
+                if cleaned_text:
+                    fallback.add_content(cleaned_text)
+                self.sections = [fallback]
+
+            # 5. 提取并挂接PDF表格到章节（若依赖可用）
+            pdf_tables = self._extract_pdf_tables()
+            if pdf_tables:
+                self._attach_pdf_tables_to_sections(pdf_tables)
+            
+            # 6. 为没有编号的章节自动生成编号
+            self._auto_number_sections(self.sections)
+            
+            logger.info(f"完成PDF解析，提取{len(self.sections)}个顶级章节")
+            return self.sections
+            
+        except Exception as e:
+            logger.error(f"解析PDF文档失败: {e}")
+            raise
+    
+    def _extract_all_text(self) -> str:
+        """从PDF提取所有文本"""
+        all_text = []
+        with open(self.file_path, 'rb') as f:
+            pdf_reader = PyPDF2.PdfReader(f)
+            for page in pdf_reader.pages:
+                text = page.extract_text()
+                if text:
+                    all_text.append(text)
+        self._page_texts = all_text
+        return '\n'.join(all_text)
+
+    def _extract_pdf_tables(self) -> List[Dict[str, Any]]:
+        """提取PDF中的表格数据。"""
+        if not HAS_PDF_TABLE:
+            logger.warning("未安装pdfplumber，跳过PDF表格提取。可执行: pip install pdfplumber")
+            return []
+
+        tables: List[Dict[str, Any]] = []
+        try:
+            pdfplumber = importlib.import_module("pdfplumber")
+            with pdfplumber.open(self.file_path) as pdf:
+                for page_idx, page in enumerate(pdf.pages):
+                    page_text = ""
+                    if page_idx < len(self._page_texts):
+                        page_text = self._page_texts[page_idx]
+
+                    extracted_tables = page.extract_tables() or []
+                    for table_idx, table in enumerate(extracted_tables):
+                        cleaned_table: List[List[str]] = []
+                        for row in table or []:
+                            cells = [re.sub(r'\s+', ' ', str(cell or '')).strip() for cell in row]
+                            if any(cells):
+                                cleaned_table.append(cells)
+
+                        if cleaned_table:
+                            tables.append(
+                                {
+                                    "page_idx": page_idx,
+                                    "table_idx": table_idx,
+                                    "page_text": page_text,
+                                    "data": cleaned_table,
+                                }
+                            )
+        except Exception as e:
+            logger.warning(f"PDF表格提取失败，继续纯文本流程: {e}")
+            return []
+
+        logger.info(f"PDF表格提取完成，共{len(tables)}个表格")
+        return tables
+
+    def _attach_pdf_tables_to_sections(self, tables: List[Dict[str, Any]]) -> None:
+        """将提取出的PDF表格挂接到最匹配的章节。"""
+        flat_sections = self._flatten_sections(self.sections)
+        if not flat_sections:
+            return
+
+        last_section: Optional[Section] = None
+        for table in tables:
+            matched = self._match_table_section(table.get("page_text", ""), flat_sections)
+            target = matched or last_section or flat_sections[0]
+            target.add_table(table["data"])
+            last_section = target
+
+    def _flatten_sections(self, sections: List[Section]) -> List[Section]:
+        """按文档顺序拉平章节树。"""
+        result: List[Section] = []
+        for section in sections:
+            result.append(section)
+            if section.children:
+                result.extend(self._flatten_sections(section.children))
+        return result
+
+    def _match_table_section(self, page_text: str, sections: List[Section]) -> Optional[Section]:
+        """基于页文本匹配表格归属章节。"""
+        normalized_page = re.sub(r"\s+", "", (page_text or "")).lower()
+        if not normalized_page:
+            return None
+
+        matched: Optional[Section] = None
+        matched_score = -1
+        for section in sections:
+            title = (section.title or "").strip()
+            if not title:
+                continue
+
+            number = (section.number or "").strip()
+            candidates = [title]
+            if number:
+                candidates.append(f"{number}{title}")
+                candidates.append(f"{number} {title}")
+
+            for candidate in candidates:
+                normalized_candidate = re.sub(r"\s+", "", candidate).lower()
+                if normalized_candidate and normalized_candidate in normalized_page:
+                    score = len(normalized_candidate)
+                    if score > matched_score:
+                        matched = section
+                        matched_score = score
+
+        return matched
+    
+    def _clean_text(self, text: str) -> str:
+        """清洗PDF提取的文本"""
+        lines = text.split('\n')
+        cleaned_lines = []
+        
+        for line in lines:
+            line = line.strip()
+            if not line:
+                continue
+            # 跳过页码（通常是1-3位数字单独一行）
+            if re.match(r'^\d{1,3}$', line):
+                continue
+            # 跳过目录行
+            if line.count('.') > 10 and '...' in line:
+                continue
+            
+            cleaned_lines.append(line)
+        
+        return '\n'.join(cleaned_lines)
+    
+    def _parse_sections(self, text: str) -> List[Section]:
+        """解析章节结构"""
+        sections = []
+        section_stack = {}
+        lines = text.split('\n')
+        current_section = None
+        content_buffer = []
+        found_sections = set()
+        
+        for line in lines:
+            line = line.strip()
+            if not line:
+                continue
+            
+            # 尝试匹配章节标题
+            section_info = self._match_section_header(line, found_sections)
+            
+            if section_info:
+                number, title = section_info
+                level = len(number.split('.'))
+                
+                # 保存之前章节的内容
+                if current_section and content_buffer:
+                    current_section.add_content('\n'.join(content_buffer))
+                    content_buffer = []
+                
+                # 创建新章节
+                section = Section(level=level, title=title, number=number, uid=self._next_uid())
+                found_sections.add(number)
+                
+                # 建立层次结构
+                if level == 1:
+                    sections.append(section)
+                    section_stack = {1: section}
+                else:
+                    parent_level = level - 1
+                    while parent_level >= 1 and parent_level not in section_stack:
+                        parent_level -= 1
+                    
+                    if parent_level >= 1 and parent_level in section_stack:
+                        section_stack[parent_level].add_child(section)
+                    elif sections:
+                        sections[-1].add_child(section)
+                    else:
+                        sections.append(section)
+                        section_stack = {1: section}
+                
+                section_stack[level] = section
+                for l in list(section_stack.keys()):
+                    if l > level:
+                        del section_stack[l]
+                
+                current_section = section
+            else:
+                # 收集内容
+                if line and not self._is_noise(line):
+                    content_buffer.append(line)
+        
+        # 保存最后一个章节的内容
+        if current_section and content_buffer:
+            current_section.add_content('\n'.join(content_buffer))
+        
+        return sections
+    
+    def _match_section_header(self, line: str, found_sections: set) -> Optional[Tuple[str, str]]:
+        """
+        匹配章节标题
+        
+        Returns:
+            (章节编号, 章节标题) 或 None
+        """
+        # 模式: "3.1功能需求" 或 "3.1 功能需求"
+        match = re.match(r'^(\d+(?:\.\d+)*)\s*(.+)$', line)
+        if not match:
+            return None
+        
+        number = match.group(1)
+        title = match.group(2).strip()
+        
+        # 排除目录行
+        if '...' in title or title.count('.') > 5:
+            return None
+        
+        # 验证章节编号
+        parts = number.split('.')
+        first_part = int(parts[0])
+        
+        # 放宽一级章节编号范围（非严格GJB结构）
+        if first_part < 1 or first_part > 30:
+            return None
+        
+        # 检查子部分是否合理
+        for part in parts[1:]:
+            if int(part) > 20:
+                return None
+        
+        # 避免重复
+        if number in found_sections:
+            return None
+        
+        # 标题长度检查
+        if len(title) > 60 or len(title) < 2:
+            return None
+        
+        # 放宽标题字符要求（兼容部分PDF字体导致中文抽取异常的情况）
+        if not re.search(r'[\u4e00-\u9fa5A-Za-z]', title):
+            return None
+        
+        # 检查是否包含无效模式
+        for invalid_pattern in self.INVALID_TITLE_PATTERNS:
+            if invalid_pattern in title:
+                return None
+        
+        # 标题不能以数字开头
+        if title[0].isdigit():
+            return None
+        
+        # 数字比例检查
+        digit_ratio = sum(c.isdigit() for c in title) / max(len(title), 1)
+        if digit_ratio > 0.3:
+            return None
+        
+        # 检查标题是否包含反斜杠（通常是表格噪声）
+        if '\\' in title and '需求' not in title:
+            return None
+        
+        return (number, title)
+    
+    def _is_noise(self, line: str) -> bool:
+        """检查是否是噪声行"""
+        # 纯数字行
+        if re.match(r'^[\d\s,.]+$', line):
+            return True
+        # 非常短的行
+        if len(line) < 3:
+            return True
+        # 罗马数字
+        if re.match(r'^[ivxIVX]+$', line):
+            return True
+        return False
+    
+    def _llm_validate_sections(self, sections: List[Section]) -> List[Section]:
+        """使用LLM验证章节是否有效"""
+        if not self.llm:
+            return sections
+        
+        validated_sections = []
+        
+        for section in sections:
+            # 验证顶级章节
+            if self._is_valid_section_with_llm(section):
+                # 递归验证子章节
+                section.children = self._validate_children(section.children)
+                validated_sections.append(section)
+        
+        return validated_sections
+    
+    def _validate_children(self, children: List[Section]) -> List[Section]:
+        """递归验证子章节"""
+        validated = []
+        for child in children:
+            if self._is_valid_section_with_llm(child):
+                child.children = self._validate_children(child.children)
+                validated.append(child)
+        return validated
+    
+    def _is_valid_section_with_llm(self, section: Section) -> bool:
+        """使用LLM判断章节是否有效"""
+        # 先用规则快速过滤明显无效的章节
+        invalid_titles = [
+            '本文档可作为', '故障', '实时', '输入/输出',
+            '固件', '功能\\', '\\4.', '\\3.'
+        ]
+        for invalid in invalid_titles:
+            if invalid in section.title:
+                logger.debug(f"过滤无效章节: {section.number} {section.title}")
+                return False
+        
+        # 对于需求相关章节（第3章），额外验证
+        if section.number and section.number.startswith('3'):
+            # 检查标题是否看起来像是有效的需求章节标题
+            # 有效的标题应该是完整的中文短语
+            if '\\' in section.title or '/' in section.title:
+                if not any(kw in section.title for kw in ['输入', '输出', '接口']):
+                    return False
+        
+        return True
+
+
+def create_parser(file_path: str) -> DocumentParser:
+    """
+    工厂函数：根据文件扩展名创建相应的解析器
+    """
+    ext = Path(file_path).suffix.lower()
+    
+    if ext == '.docx':
+        return DocxParser(file_path)
+    elif ext == '.pdf':
+        return PDFParser(file_path)
+    else:
+        raise ValueError(f"不支持的文件格式: {ext}")

rag-web-ui/backend/app/tools/srs_reqs_qwen/src/json_generator.py

@@ -0,0 +1,198 @@
+# -*- coding: utf-8 -*-
+"""
+JSON生成器模块 - LLM增强版
+将提取的需求和章节结构转换为结构化JSON输出
+"""
+
+import json
+import logging
+from datetime import datetime
+from typing import List, Dict, Any, Optional
+from .document_parser import Section
+from .requirement_extractor import Requirement
+from .settings import AppSettings
+
+logger = logging.getLogger(__name__)
+
+
+class JSONGenerator:
+    """JSON输出生成器"""
+    
+    def __init__(self, config: Dict = None):
+        self.config = config or {}
+        self.settings = AppSettings(self.config)
+    
+    def generate(self, sections: List[Section], requirements: List[Requirement],
+                 document_title: str = "SRS Document") -> Dict[str, Any]:
+        """
+        生成JSON输出
+        
+        Args:
+            sections: 章节列表
+            requirements: 需求列表
+            document_title: 文档标题
+            
+        Returns:
+            结构化JSON字典
+        """
+        # 按章节组织需求
+        reqs_by_section = self._group_requirements_by_section(requirements)
+        
+        # 统计需求类型
+        type_stats = self._calculate_type_statistics(requirements)
+        
+        # 构建输出结构
+        output = {
+            "文档元数据": {
+                "标题": document_title,
+                "生成时间": datetime.now().isoformat(),
+                "总需求数": len(requirements),
+                "需求类型统计": type_stats
+            },
+            "需求内容": self._build_requirement_content(sections, reqs_by_section)
+        }
+        
+        logger.info(f"生成JSON输出，共{len(requirements)}个需求")
+        return output
+    
+    def _group_requirements_by_section(self, requirements: List[Requirement]) -> Dict[str, List[Requirement]]:
+        """按章节编号分组需求"""
+        grouped = {}
+        for req in requirements:
+            section_key = req.section_uid or req.section_number or 'unknown'
+            if section_key not in grouped:
+                grouped[section_key] = []
+            grouped[section_key].append(req)
+        return grouped
+    
+    def _calculate_type_statistics(self, requirements: List[Requirement]) -> Dict[str, int]:
+        """计算需求类型统计"""
+        stats = {}
+        for req in requirements:
+            type_chinese = self.settings.type_chinese.get(req.type, '其他需求')
+            if type_chinese not in stats:
+                stats[type_chinese] = 0
+            stats[type_chinese] += 1
+        return stats
+    
+    def _should_include_section(self, section: Section) -> bool:
+        """判断章节是否应该包含在输出中"""
+        return not self.settings.is_non_requirement_section(section.title)
+    
+    def _build_requirement_content(self, sections: List[Section],
+                                   reqs_by_section: Dict[str, List[Requirement]]) -> Dict[str, Any]:
+        """构建需求内容的层次结构"""
+        content = {}
+        
+        for section in sections:
+            # 只处理需求相关章节
+            if not self._should_include_section(section):
+                # 但仍需检查子章节
+                for child in section.children:
+                    child_content = self._build_section_content_recursive(child, reqs_by_section)
+                    if child_content:
+                        key = f"{child.number} {child.title}" if child.number else child.title
+                        content[key] = child_content
+                continue
+            
+            section_content = self._build_section_content_recursive(section, reqs_by_section)
+            if section_content:
+                key = f"{section.number} {section.title}" if section.number else section.title
+                content[key] = section_content
+        
+        return content
+    
+    def _build_section_content_recursive(self, section: Section,
+                                         reqs_by_section: Dict[str, List[Requirement]]) -> Optional[Dict[str, Any]]:
+        """递归构建章节内容"""
+        # 检查是否应该包含此章节
+        if not self._should_include_section(section):
+            return None
+        
+        # 章节基本信息
+        result = {
+            "章节信息": {
+                "章节编号": section.number or "",
+                "章节标题": section.title,
+                "章节级别": section.level
+            }
+        }
+        
+        # 检查是否有子章节
+        has_valid_children = False
+        subsections = {}
+        
+        for child in section.children:
+            child_content = self._build_section_content_recursive(child, reqs_by_section)
+            if child_content:
+                has_valid_children = True
+                key = f"{child.number} {child.title}" if child.number else child.title
+                subsections[key] = child_content
+        
+        # 添加当前章节需求
+        reqs = reqs_by_section.get(section.uid or section.number or 'unknown', [])
+        reqs = sorted(reqs, key=lambda r: getattr(r, 'source_order', 0))
+        if reqs:
+            result["需求列表"] = []
+            for req in reqs:
+                # 需求类型放在最前面
+                type_chinese = self.settings.type_chinese.get(req.type, '功能需求')
+                req_dict = {
+                    "需求类型": type_chinese,
+                    "需求编号": req.id,
+                    "需求描述": req.description
+                }
+                # 接口需求增加额外字段
+                if req.type == 'interface':
+                    req_dict["接口名称"] = req.interface_name
+                    req_dict["接口类型"] = req.interface_type
+                    req_dict["来源"] = req.source
+                    req_dict["目的地"] = req.destination
+                result["需求列表"].append(req_dict)
+        
+        # 如果有子章节，添加子章节
+        if has_valid_children:
+            result["子章节"] = subsections
+        
+        # 如果章节既没有需求也没有子章节，返回None
+        if "需求列表" not in result and "子章节" not in result:
+            return None
+        
+        return result
+    
+    def save_to_file(self, output: Dict[str, Any], file_path: str) -> None:
+        """
+        将输出保存到文件
+        
+        Args:
+            output: 输出字典
+            file_path: 输出文件路径
+        """
+        try:
+            output_cfg = self.config.get("output", {})
+            indent = output_cfg.get("indent", 2)
+            pretty = output_cfg.get("pretty_print", True)
+            with open(file_path, 'w', encoding='utf-8') as f:
+                json.dump(output, f, ensure_ascii=False, indent=indent if pretty else None)
+            logger.info(f"成功保存JSON到: {file_path}")
+        except Exception as e:
+            logger.error(f"保存JSON文件失败: {e}")
+            raise
+    
+    def generate_and_save(self, sections: List[Section], requirements: List[Requirement],
+                         document_title: str, file_path: str) -> Dict[str, Any]:
+        """
+        生成并保存JSON
+        
+        Args:
+            sections: 章节列表
+            requirements: 需求列表
+            document_title: 文档标题
+            file_path: 输出文件路径
+            
+        Returns:
+            生成的输出字典
+        """
+        output = self.generate(sections, requirements, document_title)
+        self.save_to_file(output, file_path)
+        return output

rag-web-ui/backend/app/tools/srs_reqs_qwen/src/llm_interface.py

@@ -0,0 +1,197 @@
+# src/llm_interface.py
+"""
+LLM接口模块 - 支持多个LLM提供商
+"""
+
+import logging
+import json
+from abc import ABC, abstractmethod
+from typing import Dict, List, Optional, Any
+
+from .utils import get_env_or_config
+
+logger = logging.getLogger(__name__)
+
+
+class LLMInterface(ABC):
+    """LLM接口基类"""
+    
+    def __init__(self, api_key: str = None, model: str = None, **kwargs):
+        """
+        初始化LLM接口
+        
+        Args:
+            api_key: API密钥
+            model: 模型名称
+            **kwargs: 其他参数（如temperature, max_tokens等）
+        """
+        self.api_key = api_key
+        self.model = model
+        self.extra_params = kwargs
+    
+    @abstractmethod
+    def call(self, prompt: str) -> str:
+        """
+        调用LLM API
+        
+        Args:
+            prompt: 提示词
+            
+        Returns:
+            LLM的响应文本
+        """
+        pass
+    
+    @abstractmethod
+    def call_json(self, prompt: str) -> Dict[str, Any]:
+        """
+        调用LLM API并获取JSON格式的响应
+        
+        Args:
+            prompt: 提示词
+            
+        Returns:
+            解析后的JSON字典
+        """
+        pass
+    
+    def validate_config(self) -> bool:
+        """验证配置是否完整"""
+        return bool(self.api_key and self.model)
+
+
+class QwenLLM(LLMInterface):
+    """阿里云千问LLM实现"""
+    
+    def __init__(self, api_key: str = None, model: str = "qwen-plus",
+                 api_endpoint: str = None, **kwargs):
+        """
+        初始化千问LLM
+        
+        Args:
+            api_key: 阿里云API密钥
+            model: 模型名称（如qwen-plus, qwen-turbo）
+            api_endpoint: API端点地址
+            **kwargs: 其他参数
+        """
+        super().__init__(api_key, model, **kwargs)
+        self.api_endpoint = api_endpoint or "https://dashscope.aliyuncs.com/compatible-mode/v1"
+        self._check_dashscope_import()
+    
+    def _check_dashscope_import(self) -> None:
+        """检查dashscope库是否已安装"""
+        try:
+            import dashscope
+            self.dashscope = dashscope
+        except ImportError:
+            logger.error("dashscope库未安装，请运行: pip install dashscope")
+            raise
+    
+    def call(self, prompt: str) -> str:
+        """
+        调用千问LLM
+        
+        Args:
+            prompt: 提示词
+            
+        Returns:
+            LLM的响应文本
+        """
+        if not self.validate_config():
+            raise ValueError("LLM配置不完整（api_key或model未设置）")
+        
+        try:
+            from dashscope import Generation
+            
+            # 设置API密钥
+            self.dashscope.api_key = self.api_key
+            
+            # 构建请求参数 - dashscope 1.7.0 格式
+            response = Generation.call(
+                model=self.model,
+                messages=[
+                    {'role': 'user', 'content': prompt}
+                ],
+                result_format='message'  # 使用message格式
+            )
+            
+            # 调试输出
+            logger.debug(f"API响应类型: {type(response)}")
+            logger.debug(f"API响应内容: {response}")
+            
+            # 处理响应
+            if isinstance(response, dict):
+                # dict格式响应
+                status_code = response.get('status_code', 200)
+                if status_code == 200:
+                    output = response.get('output', {})
+                    if 'choices' in output:
+                        return output['choices'][0]['message']['content']
+                    elif 'text' in output:
+                        return output['text']
+                    else:
+                        # 尝试直接获取text
+                        return output.get('text', str(output))
+                else:
+                    error_msg = response.get('message', response.get('code', 'Unknown error'))
+                    logger.error(f"千问API返回错误: {error_msg}")
+                    raise Exception(f"API调用失败: {error_msg}")
+            else:
+                # 对象格式响应
+                if hasattr(response, 'status_code') and response.status_code == 200:
+                    output = response.output
+                    if hasattr(output, 'choices'):
+                        return output.choices[0].message.content
+                    elif hasattr(output, 'text'):
+                        return output.text
+                    else:
+                        return str(output)
+                elif hasattr(response, 'status_code'):
+                    error_msg = getattr(response, 'message', str(response))
+                    raise Exception(f"API调用失败: {error_msg}")
+                else:
+                    return str(response)
+                
+        except Exception as e:
+            logger.error(f"调用千问LLM失败: {e}")
+            raise
+    
+    def call_json(self, prompt: str) -> Dict[str, Any]:
+        """
+        调用千问LLM并获取JSON格式响应
+        
+        Args:
+            prompt: 提示词
+            
+        Returns:
+            解析后的JSON字典
+        """
+        # 添加JSON格式要求到提示词
+        json_prompt = prompt + "\n\n请确保响应是有效的JSON格式。"
+        
+        response = self.call(json_prompt)
+        
+        try:
+            # 尝试解析JSON
+            # 首先尝试直接解析
+            return json.loads(response)
+        except json.JSONDecodeError:
+            # 尝试提取JSON代码块
+            try:
+                import re
+                # 查找JSON代码块
+                json_match = re.search(r'```json\s*(.*?)\s*```', response, re.DOTALL)
+                if json_match:
+                    return json.loads(json_match.group(1))
+                
+                # 尝试查找任何JSON对象
+                json_match = re.search(r'\{.*\}', response, re.DOTALL)
+                if json_match:
+                    return json.loads(json_match.group(0))
+                    
+            except Exception as e:
+                logger.warning(f"无法从响应中提取JSON: {e}")
+            
+            # 如果都失败，返回错误信息
+            logger.error(f"无法解析LLM响应为JSON: {response}")
+            return {"error": "Failed to parse response as JSON", "raw_response": response}

rag-web-ui/backend/app/tools/srs_reqs_qwen/src/requirement_id_generator.py

@@ -0,0 +1,74 @@
+# -*- coding: utf-8 -*-
+"""
+需求编号生成与提取工具。
+"""
+
+import re
+from typing import Optional, Tuple, Dict
+
+
+class RequirementIDGenerator:
+    def __init__(self, type_prefix: Dict[str, str]):
+        self.type_prefix = type_prefix
+
+    def normalize(self, req_id: str) -> str:
+        if not req_id:
+            return ""
+        return str(req_id).strip()
+
+    def extract_from_text(self, text: str) -> Tuple[Optional[str], str]:
+        if not text:
+            return None, text
+
+        pattern1 = r"^\s*([A-Za-z]{2,10}[-_]\d+(?:[-.\d]+)*)\s*[:：\)\]】]?\s*(.+)$"
+        match = re.match(pattern1, text)
+        if match:
+            return match.group(1).strip(), match.group(2).strip()
+
+        pattern2 = r"^\s*([A-Za-z]\d+)\s*[:：\)\]】]?\s*(.+)$"
+        match = re.match(pattern2, text)
+        if match:
+            return match.group(1).strip(), match.group(2).strip()
+
+        pattern3 = r"^\s*([a-z0-9]{1,2}[\)）])\s*(.+)$"
+        match = re.match(pattern3, text)
+        if match:
+            code = match.group(1).strip().rstrip(")）")
+            return code, match.group(2).strip()
+
+        return None, text
+
+    def generate(
+        self,
+        req_type: str,
+        section_number: str,
+        index: int,
+        doc_req_id: str = "",
+        parent_req_id: str = "",
+        split_index: int = 1,
+        split_total: int = 1,
+    ) -> str:
+        base_id = self._generate_base(req_type, section_number, index, doc_req_id, parent_req_id)
+        if split_total > 1:
+            return f"{base_id}-S{split_index}"
+        return base_id
+
+    def _generate_base(
+        self,
+        req_type: str,
+        section_number: str,
+        index: int,
+        doc_req_id: str,
+        parent_req_id: str,
+    ) -> str:
+        if doc_req_id:
+            complete_id_pattern = r"^[A-Za-z0-9]{2,10}[-_].+$"
+            if re.match(complete_id_pattern, doc_req_id):
+                return doc_req_id.replace("_", "-")
+
+        if doc_req_id and parent_req_id:
+            return f"{parent_req_id}-{doc_req_id}"
+
+        prefix = self.type_prefix.get(req_type, "FR")
+        section_part = section_number if section_number else "NA"
+        return f"{prefix}-{section_part}-{index}"

rag-web-ui/backend/app/tools/srs_reqs_qwen/src/requirement_splitter.py

@@ -0,0 +1,188 @@
+# -*- coding: utf-8 -*-
+"""
+需求长句拆分器。
+将复合长句拆分为可验证的原子需求片段。
+"""
+
+import re
+from typing import List
+
+
+class RequirementSplitter:
+    ACTION_HINTS = [
+        "产生",
+        "发送",
+        "设置",
+        "进入",
+        "退出",
+        "关闭",
+        "开启",
+        "监测",
+        "判断",
+        "记录",
+        "上传",
+        "重启",
+        "恢复",
+        "关断",
+        "断电",
+        "加电",
+        "执行",
+        "进行",
+    ]
+
+    CONNECTOR_HINTS = ["并", "并且", "同时", "然后", "且", "以及", "及"]
+    CONDITIONAL_HINTS = ["如果", "当", "若", "在", "其中", "此时", "满足"]
+    CONTEXT_PRONOUN_HINTS = ["该", "其", "上述", "此", "这些", "那些"]
+
+    def __init__(self, max_sentence_len: int = 120, min_clause_len: int = 12):
+        self.max_sentence_len = max_sentence_len
+        self.min_clause_len = min_clause_len
+
+    def split(self, text: str) -> List[str]:
+        cleaned = self._clean(text)
+        if not cleaned:
+            return []
+
+        if self._contains_strong_semantic_chain(cleaned):
+            return [cleaned]
+
+        # 先按强分隔符切分为主片段。
+        base_parts = self._split_by_strong_punctuation(cleaned)
+
+        result: List[str] = []
+        for part in base_parts:
+            if len(part) <= self.max_sentence_len:
+                result.append(part)
+                continue
+
+            # 对超长片段进一步基于逗号和连接词拆分。
+            refined = self._split_long_clause(part)
+            result.extend(refined)
+
+        result = self._merge_semantic_chain(result)
+        result = self._merge_too_short(result)
+        return self._deduplicate(result)
+
+    def _contains_strong_semantic_chain(self, text: str) -> bool:
+        # 条件-动作链完整时，避免强拆。
+        has_conditional = any(h in text for h in ["如果", "若", "当"])
+        has_result = "则" in text or "时" in text
+        action_count = sum(1 for h in self.ACTION_HINTS if h in text)
+        if has_conditional and has_result and action_count >= 2:
+            return True
+        return False
+
+    def _clean(self, text: str) -> str:
+        text = re.sub(r"\s+", " ", text or "")
+        return text.strip(" ;；。")
+
+    def _split_by_strong_punctuation(self, text: str) -> List[str]:
+        chunks = re.split(r"[；;。]", text)
+        return [c.strip(" ，,") for c in chunks if c and c.strip(" ，,")]
+
+    def _split_long_clause(self, clause: str) -> List[str]:
+        if self._contains_strong_semantic_chain(clause):
+            return [clause]
+
+        raw_parts = [x.strip() for x in re.split(r"[，,]", clause) if x.strip()]
+        if len(raw_parts) <= 1:
+            return [clause]
+
+        assembled: List[str] = []
+        current = raw_parts[0]
+
+        for fragment in raw_parts[1:]:
+            if self._should_split(current, fragment):
+                assembled.append(current.strip())
+                current = fragment
+            else:
+                current = f"{current}，{fragment}"
+
+        if current.strip():
+            assembled.append(current.strip())
+
+        return assembled
+
+    def _should_split(self, current: str, fragment: str) -> bool:
+        if len(current) < self.min_clause_len:
+            return False
+
+        # 指代承接片段通常是语义延续，不应切断。
+        if any(fragment.startswith(h) for h in self.CONTEXT_PRONOUN_HINTS):
+            return False
+
+        # 条件链中带“则/并/同时”的后继片段，优先保持在同一需求中。
+        if self._contains_strong_semantic_chain(current + "，" + fragment):
+            return False
+
+        frag_starts_with_condition = any(fragment.startswith(h) for h in self.CONDITIONAL_HINTS)
+        if frag_starts_with_condition:
+            return False
+
+        has_connector = any(fragment.startswith(h) for h in self.CONNECTOR_HINTS)
+        has_action = any(h in fragment for h in self.ACTION_HINTS)
+        current_has_action = any(h in current for h in self.ACTION_HINTS)
+
+        # 连接词 + 动作词，且当前片段已经包含动作，优先拆分。
+        if has_connector and has_action and current_has_action:
+            return True
+
+        # 无连接词但出现新的动作片段且整体过长，也拆分。
+        if has_action and current_has_action and len(current) >= self.max_sentence_len // 2:
+            return True
+
+        return False
+
+    def _merge_semantic_chain(self, parts: List[str]) -> List[str]:
+        if not parts:
+            return []
+
+        merged: List[str] = [parts[0]]
+        for part in parts[1:]:
+            prev = merged[-1]
+            if self._should_merge(prev, part):
+                merged[-1] = f"{prev}；{part}"
+            else:
+                merged.append(part)
+        return merged
+
+    def _should_merge(self, prev: str, current: str) -> bool:
+        # 指代开头：如“该报警信号...”。
+        if any(current.startswith(h) for h in self.CONTEXT_PRONOUN_HINTS):
+            return True
+
+        # 报警触发后的持续条件与动作属于同一链。
+        if ("报警" in prev and "持续" in current) or ("产生" in prev and "报警" in prev and "持续" in current):
+            return True
+
+        # 状态迁移 + 后续控制动作保持合并。
+        if ("进入" in prev or "设置" in prev or "发送" in prev) and ("则" in current or "连续" in current):
+            return True
+
+        # 条件链分裂片段重新合并。
+        if self._contains_strong_semantic_chain(prev + "，" + current):
+            return True
+
+        return False
+
+    def _merge_too_short(self, parts: List[str]) -> List[str]:
+        if not parts:
+            return []
+
+        merged: List[str] = []
+        for part in parts:
+            if merged and len(part) < self.min_clause_len:
+                merged[-1] = f"{merged[-1]}，{part}"
+            else:
+                merged.append(part)
+        return merged
+
+    def _deduplicate(self, parts: List[str]) -> List[str]:
+        seen = set()
+        result = []
+        for part in parts:
+            key = re.sub(r"\s+", "", part)
+            if key and key not in seen:
+                seen.add(key)
+                result.append(part)
+        return result

rag-web-ui/backend/app/tools/srs_reqs_qwen/src/settings.py

@@ -0,0 +1,162 @@
+# -*- coding: utf-8 -*-
+"""
+统一配置与映射模块。
+将需求类型、章节过滤、输出映射和拆分参数收敛到单一入口。
+"""
+
+from dataclasses import dataclass
+from typing import Dict, List, Any
+
+
+@dataclass
+class RequirementTypeRule:
+    key: str
+    chinese_name: str
+    prefix: str
+    keywords: List[str]
+    priority: int
+
+
+class AppSettings:
+    """从 config 读取并提供统一访问接口。"""
+
+    TYPE_NAME_MAP = {
+        "功能需求": "functional",
+        "接口需求": "interface",
+        "性能需求": "performance",
+        "安全需求": "security",
+        "可靠性需求": "reliability",
+        "其他需求": "other",
+    }
+
+    DEFAULT_NON_REQUIREMENT_SECTIONS = [
+        "标识",
+        "系统概述",
+        "文档概述",
+        "引用文档",
+        "合格性规定",
+        "需求可追踪性",
+        "注释",
+        "附录",
+        "范围",
+        "概述",
+    ]
+
+    DEFAULT_TYPE_CHINESE = {
+        "functional": "功能需求",
+        "interface": "接口需求",
+        "performance": "其他需求",
+        "security": "其他需求",
+        "reliability": "其他需求",
+        "other": "其他需求",
+    }
+
+    DEFAULT_PREFIX = {
+        "functional": "FR",
+        "interface": "IR",
+        "performance": "PR",
+        "security": "SR",
+        "reliability": "RR",
+        "other": "OR",
+    }
+
+    def __init__(self, config: Dict[str, Any] = None):
+        self.config = config or {}
+
+        document_cfg = self.config.get("document", {})
+        self.non_requirement_sections = document_cfg.get(
+            "non_requirement_sections", self.DEFAULT_NON_REQUIREMENT_SECTIONS
+        )
+
+        extraction_cfg = self.config.get("extraction", {})
+        req_types_cfg = extraction_cfg.get("requirement_types", {})
+
+        self.requirement_rules = self._build_rules(req_types_cfg)
+        self.type_prefix = self._build_type_prefix(req_types_cfg)
+        self.type_chinese = self._build_type_chinese(req_types_cfg)
+
+        splitter_cfg = extraction_cfg.get("splitter", {})
+        self.splitter_max_sentence_len = int(splitter_cfg.get("max_sentence_len", 120))
+        self.splitter_min_clause_len = int(splitter_cfg.get("min_clause_len", 12))
+        self.splitter_enabled = bool(splitter_cfg.get("enabled", True))
+
+        semantic_cfg = extraction_cfg.get("semantic_guard", {})
+        self.semantic_guard_enabled = bool(semantic_cfg.get("enabled", True))
+        self.preserve_condition_action_chain = bool(
+            semantic_cfg.get("preserve_condition_action_chain", True)
+        )
+        self.preserve_alarm_chain = bool(semantic_cfg.get("preserve_alarm_chain", True))
+
+        table_cfg = extraction_cfg.get("table_strategy", {})
+        self.table_llm_semantic_enabled = bool(table_cfg.get("llm_semantic_enabled", True))
+        self.sequence_table_merge = table_cfg.get("sequence_table_merge", "single_requirement")
+        self.merge_time_series_rows_min = int(table_cfg.get("merge_time_series_rows_min", 3))
+
+        rewrite_cfg = extraction_cfg.get("rewrite_policy", {})
+        self.llm_light_rewrite_enabled = bool(rewrite_cfg.get("llm_light_rewrite_enabled", True))
+        self.preserve_ratio_min = float(rewrite_cfg.get("preserve_ratio_min", 0.65))
+        self.max_length_growth_ratio = float(rewrite_cfg.get("max_length_growth_ratio", 1.25))
+
+        renumber_cfg = extraction_cfg.get("renumber_policy", {})
+        self.renumber_enabled = bool(renumber_cfg.get("enabled", True))
+        self.renumber_mode = renumber_cfg.get("mode", "section_continuous")
+
+    def _build_rules(self, req_types_cfg: Dict[str, Dict[str, Any]]) -> List[RequirementTypeRule]:
+        rules: List[RequirementTypeRule] = []
+        if not req_types_cfg:
+            # 用默认两类保证兼容旧行为
+            return [
+                RequirementTypeRule(
+                    key="interface",
+                    chinese_name="接口需求",
+                    prefix="IR",
+                    keywords=["接口", "interface", "api", "串口", "通信", "CAN", "以太网"],
+                    priority=1,
+                ),
+                RequirementTypeRule(
+                    key="functional",
+                    chinese_name="功能需求",
+                    prefix="FR",
+                    keywords=["功能", "控制", "处理", "监测", "显示"],
+                    priority=2,
+                ),
+            ]
+
+        for zh_name, item in req_types_cfg.items():
+            key = self.TYPE_NAME_MAP.get(zh_name, "other")
+            rules.append(
+                RequirementTypeRule(
+                    key=key,
+                    chinese_name=zh_name,
+                    prefix=item.get("prefix", self.DEFAULT_PREFIX.get(key, "FR")),
+                    keywords=item.get("keywords", []),
+                    priority=int(item.get("priority", 99)),
+                )
+            )
+
+        return sorted(rules, key=lambda x: x.priority)
+
+    def _build_type_prefix(self, req_types_cfg: Dict[str, Dict[str, Any]]) -> Dict[str, str]:
+        mapping = dict(self.DEFAULT_PREFIX)
+        for zh_name, key in self.TYPE_NAME_MAP.items():
+            if zh_name in req_types_cfg:
+                mapping[key] = req_types_cfg[zh_name].get("prefix", mapping[key])
+        return mapping
+
+    def _build_type_chinese(self, req_types_cfg: Dict[str, Dict[str, Any]]) -> Dict[str, str]:
+        mapping = dict(self.DEFAULT_TYPE_CHINESE)
+        for zh_name, key in self.TYPE_NAME_MAP.items():
+            if zh_name in req_types_cfg:
+                mapping[key] = zh_name
+        return mapping
+
+    def is_non_requirement_section(self, title: str) -> bool:
+        return any(keyword in title for keyword in self.non_requirement_sections)
+
+    def detect_requirement_type(self, title: str, content: str) -> str:
+        combined_text = f"{title} {(content or '')[:500]}".lower()
+        for rule in self.requirement_rules:
+            for keyword in rule.keywords:
+                if keyword.lower() in combined_text:
+                    return rule.key
+        return "functional"

rag-web-ui/backend/app/tools/srs_reqs_qwen/src/utils.py

@@ -0,0 +1,134 @@
+# src/utils.py
+"""
+工具函数模块 - 提供各种辅助功能
+"""
+
+import os
+import logging
+from pathlib import Path
+from typing import Dict, Any, List, Optional
+import yaml
+
+logger = logging.getLogger(__name__)
+
+
+def load_config(config_path: str = None) -> Dict[str, Any]:
+    """
+    加载配置文件
+    
+    Args:
+        config_path: 配置文件路径，如果为None则使用默认路径
+        
+    Returns:
+        配置字典
+    """
+    if config_path is None:
+        config_path = os.path.join(os.path.dirname(__file__), '..', 'config.yaml')
+    
+    if not os.path.exists(config_path):
+        logger.warning(f"配置文件不存在: {config_path}")
+        return {}
+    
+    try:
+        with open(config_path, 'r', encoding='utf-8') as f:
+            config = yaml.safe_load(f)
+        logger.info(f"成功加载配置文件: {config_path}")
+        return config or {}
+    except Exception as e:
+        logger.error(f"加载配置文件失败: {e}")
+        return {}
+
+
+def setup_logging(config: Dict[str, Any]) -> None:
+    """
+    配置日志系统
+    
+    Args:
+        config: 配置字典
+    """
+    logging_config = config.get('logging', {})
+    level = logging_config.get('level', 'INFO')
+    log_format = logging_config.get('format', '%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    log_file = logging_config.get('file', None)
+    
+    # 创建logger
+    logging.basicConfig(
+        level=getattr(logging, level),
+        format=log_format,
+        handlers=[
+            logging.StreamHandler(),
+            logging.FileHandler(log_file) if log_file else logging.NullHandler()
+        ]
+    )
+
+
+def validate_file_path(file_path: str, allowed_extensions: List[str] = None) -> bool:
+    """
+    验证文件路径的合法性
+    
+    Args:
+        file_path: 文件路径
+        allowed_extensions: 允许的文件扩展名列表（如['.pdf', '.docx']）
+        
+    Returns:
+        文件是否合法
+    """
+    if not os.path.exists(file_path):
+        logger.error(f"文件不存在: {file_path}")
+        return False
+    
+    if not os.path.isfile(file_path):
+        logger.error(f"路径不是文件: {file_path}")
+        return False
+    
+    if allowed_extensions:
+        ext = Path(file_path).suffix.lower()
+        if ext not in allowed_extensions:
+            logger.error(f"不支持的文件格式: {ext}")
+            return False
+    
+    return True
+
+
+def ensure_directory_exists(directory: str) -> bool:
+    """
+    确保目录存在，如果不存在则创建
+    
+    Args:
+        directory: 目录路径
+        
+    Returns:
+        目录是否存在或创建成功
+    """
+    try:
+        Path(directory).mkdir(parents=True, exist_ok=True)
+        return True
+    except Exception as e:
+        logger.error(f"创建目录失败: {e}")
+        return False
+
+
+def get_env_or_config(env_var: str, config_dict: Dict[str, Any], 
+                      default: Any = None) -> Any:
+    """
+    优先从环境变量读取，其次从配置字典读取
+    
+    Args:
+        env_var: 环境变量名
+        config_dict: 配置字典
+        default: 默认值
+        
+    Returns:
+        获取到的值
+    """
+    # 尝试从环境变量读取
+    env_value = os.environ.get(env_var)
+    if env_value:
+        return env_value
+    
+    # 尝试从配置字典读取
+    config_value = config_dict.get(env_var)
+    if config_value and not config_value.startswith('${'):
+        return config_value
+    
+    return default

rag-web-ui/backend/app/tools/srs_reqs_qwen/tool.py

@@ -0,0 +1,148 @@
+from __future__ import annotations
+
+import os
+from pathlib import Path
+from typing import Any, Dict, List
+
+import yaml
+
+from app.core.config import settings
+from app.tools.base import ToolDefinition
+from app.tools.registry import ToolRegistry
+from app.tools.srs_reqs_qwen.src.document_parser import create_parser
+from app.tools.srs_reqs_qwen.src.json_generator import JSONGenerator
+from app.tools.srs_reqs_qwen.src.llm_interface import QwenLLM
+from app.tools.srs_reqs_qwen.src.requirement_extractor import RequirementExtractor
+
+
+class SRSTool:
+    TOOL_NAME = "srs.requirement_extractor"
+
+    DEFINITION = ToolDefinition(
+        name=TOOL_NAME,
+        version="1.0.0",
+        description="Extract structured requirements from SRS documents.",
+        input_schema={
+            "type": "object",
+            "properties": {
+                "file_path": {"type": "string"},
+                "enable_llm": {"type": "boolean"},
+            },
+            "required": ["file_path"],
+        },
+        output_schema={
+            "type": "object",
+            "properties": {
+                "document_name": {"type": "string"},
+                "generated_at": {"type": "string"},
+                "requirements": {"type": "array"},
+                "statistics": {"type": "object"},
+                "raw_output": {"type": "object"},
+            },
+        },
+    )
+
+    PRIORITY_BY_TYPE = {
+        "functional": "中",
+        "interface": "高",
+        "performance": "中",
+        "security": "高",
+        "reliability": "中",
+        "other": "低",
+    }
+
+    def __init__(self) -> None:
+        ToolRegistry.register(self.DEFINITION)
+
+    def run(self, file_path: str, enable_llm: bool = True) -> Dict[str, Any]:
+        config = self._load_config()
+        llm = self._build_llm(config, enable_llm=enable_llm)
+
+        parser = create_parser(file_path)
+        if llm is not None:
+            parser.set_llm(llm)
+
+        sections = parser.parse()
+        document_title = parser.get_document_title() or Path(file_path).name
+
+        extractor = RequirementExtractor(config, llm=llm)
+        extracted = extractor.extract_from_sections(sections)
+        stats = extractor.get_statistics()
+
+        generator = JSONGenerator(config)
+        raw_output = generator.generate(sections, extracted, document_title)
+
+        requirements = self._normalize_requirements(extracted)
+
+        return {
+            "document_name": Path(file_path).name,
+            "document_title": document_title,
+            "generated_at": raw_output.get("文档元数据", {}).get("生成时间"),
+            "requirements": requirements,
+            "statistics": stats,
+            "raw_output": raw_output,
+        }
+
+    def _normalize_requirements(self, extracted: List[Any]) -> List[Dict[str, Any]]:
+        normalized: List[Dict[str, Any]] = []
+        for index, req in enumerate(extracted, start=1):
+            description = (req.description or "").strip()
+            title = description[:40] if description else f"需求项 {index}"
+            source_field = f"{req.section_number} {req.section_title}".strip() or "文档解析"
+            normalized.append(
+                {
+                    "id": req.id,
+                    "title": title,
+                    "description": description,
+                    "priority": self.PRIORITY_BY_TYPE.get(req.type, "中"),
+                    "acceptance_criteria": [description] if description else ["待补充验收标准"],
+                    "source_field": source_field,
+                    "section_number": req.section_number,
+                    "section_title": req.section_title,
+                    "requirement_type": req.type,
+                    "sort_order": index,
+                }
+            )
+        return normalized
+
+    def _load_config(self) -> Dict[str, Any]:
+        config_path = Path(__file__).with_name("default_config.yaml")
+        if config_path.exists():
+            with config_path.open("r", encoding="utf-8") as handle:
+                config = yaml.safe_load(handle) or {}
+        else:
+            config = {}
+
+        config.setdefault("llm", {})
+        config["llm"]["model"] = settings.DASH_SCOPE_CHAT_MODEL or settings.OPENAI_MODEL
+        config["llm"]["api_key"] = settings.DASH_SCOPE_API_KEY or os.getenv("DASHSCOPE_API_KEY", "")
+        config["llm"]["api_base"] = settings.DASH_SCOPE_API_BASE
+        config["llm"]["enabled"] = bool(config["llm"].get("api_key"))
+        return config
+
+    def _build_llm(self, config: Dict[str, Any], enable_llm: bool) -> QwenLLM | None:
+        if not enable_llm:
+            return None
+
+        llm_cfg = config.get("llm", {})
+        api_key = llm_cfg.get("api_key")
+        if not api_key:
+            return None
+
+        return QwenLLM(
+            api_key=api_key,
+            model=llm_cfg.get("model", "qwen3-max"),
+            api_endpoint=llm_cfg.get("api_base") or settings.DASH_SCOPE_API_BASE,
+            temperature=llm_cfg.get("temperature", 0.3),
+            max_tokens=llm_cfg.get("max_tokens", 1024),
+        )
+
+
+_SRS_TOOL_SINGLETON: SRSTool | None = None
+
+
+def get_srs_tool() -> SRSTool:
+    global _SRS_TOOL_SINGLETON
+    if _SRS_TOOL_SINGLETON is None:
+        _SRS_TOOL_SINGLETON = SRSTool()
+    return _SRS_TOOL_SINGLETON