update

scripts/05_init_db.sh

@@ -38,42 +38,80 @@ info "Step 2/3：初始化 Milvus Collections..."
 if docker compose ps milvus | grep -q "healthy"; then
     docker compose run --rm --no-deps compliance-backend \
         python3 -c "
-import asyncio
 from pymilvus import connections, Collection, CollectionSchema, FieldSchema, DataType, utility
 
 connections.connect(host='milvus', port='19530')
 print('Milvus 连接成功')
 
-def create_collection(name, description):
+def create_regulation_chunks():
+    '''
+    regulation_chunks：法规专用 collection，带 sparse 向量和条款号字段。
+    开发阶段每次运行均强制重建（保持 schema 最新）。
+    '''
+    name = 'regulation_chunks'
+    if utility.has_collection(name):
+        utility.drop_collection(name)
+        print(f'  已删除旧 collection {name}（schema 升级）')
+
+    fields = [
+        FieldSchema(name='pk',              dtype=DataType.VARCHAR, is_primary=True, max_length=128),
+        FieldSchema(name='file_id',         dtype=DataType.VARCHAR, max_length=128),
+        FieldSchema(name='workspace_id',    dtype=DataType.VARCHAR, max_length=128),
+        FieldSchema(name='chunk_idx',       dtype=DataType.INT64),
+        FieldSchema(name='content',         dtype=DataType.VARCHAR, max_length=65535),
+        FieldSchema(name='dense_vec',       dtype=DataType.FLOAT_VECTOR, dim=1024),
+        FieldSchema(name='sparse_vec',      dtype=DataType.SPARSE_FLOAT_VECTOR),
+        FieldSchema(name='clause_no',       dtype=DataType.VARCHAR, max_length=64,  default_value=''),
+        FieldSchema(name='article_no',      dtype=DataType.VARCHAR, max_length=128, default_value=''),
+        FieldSchema(name='regulation_name', dtype=DataType.VARCHAR, max_length=512, default_value=''),
+        FieldSchema(name='metadata',        dtype=DataType.JSON),
+    ]
+    schema = CollectionSchema(fields, description='法规条款向量库（含稀疏向量和条款号）')
+    col = Collection(name, schema)
+
+    col.create_index('dense_vec', {
+        'metric_type': 'COSINE',
+        'index_type':  'HNSW',
+        'params': {'M': 16, 'efConstruction': 200}
+    })
+    col.create_index('sparse_vec', {
+        'metric_type': 'IP',
+        'index_type':  'SPARSE_INVERTED_INDEX',
+        'params': {'drop_ratio_build': 0.2}
+    })
+    col.load()
+    print(f'  Collection {name} 创建完成（dense+sparse 双索引）')
+
+
+def create_simple_collection(name, description):
+    '''doc_chunks / case_library：通用 collection，仅 dense 向量。'''
     if utility.has_collection(name):
         print(f'  Collection {name} 已存在，跳过')
         return
 
     fields = [
-        FieldSchema(name='id',        dtype=DataType.VARCHAR, is_primary=True, max_length=128),
+        FieldSchema(name='pk',           dtype=DataType.VARCHAR, is_primary=True, max_length=128),
         FieldSchema(name='file_id',      dtype=DataType.VARCHAR, max_length=128),
         FieldSchema(name='workspace_id', dtype=DataType.VARCHAR, max_length=128),
         FieldSchema(name='chunk_idx',    dtype=DataType.INT64),
         FieldSchema(name='content',      dtype=DataType.VARCHAR, max_length=65535),
-        FieldSchema(name='dense_vec', dtype=DataType.FLOAT_VECTOR, dim=1024),  # BGE-M3 dense
+        FieldSchema(name='dense_vec',    dtype=DataType.FLOAT_VECTOR, dim=1024),
         FieldSchema(name='metadata',     dtype=DataType.JSON),
     ]
     schema = CollectionSchema(fields, description=description)
     col = Collection(name, schema)
-
+    col.create_index('dense_vec', {
-    # 创建向量索引（HNSW，适合调研阶段）
-    index_params = {
         'metric_type': 'COSINE',
         'index_type':  'HNSW',
         'params': {'M': 16, 'efConstruction': 200}
-    }
+    })
-    col.create_index('dense_vec', index_params)
     col.load()
     print(f'  Collection {name} 创建完成')
 
-create_collection('regulation_chunks', '法规条款向量库')
+
-create_collection('doc_chunks',        '企业文档向量库')
+create_regulation_chunks()
-create_collection('case_library',      '行业案例库')
+create_simple_collection('doc_chunks',   '企业文档向量库')
+create_simple_collection('case_library', '行业案例库')
 
 print('Milvus 初始化完成')
 " 2>&1

services/compliance-backend/app/core/llm.py

@@ -30,10 +30,13 @@ RAG_SYSTEM_PROMPT = """你是一位专业的汽车行业合规专家，具备深
 
 回答规则：
 1. 仅基于提供的参考文献回答，不添加不在文献中的信息
-2. 每个关键陈述必须标注来源（格式：[来源：文件名，第X页]）
+2. 每个关键陈述必须标注来源，优先使用条款号格式：【法规名称·第X条】
+   若无条款号则使用：【法规名称·页码X】
 3. 如果参考文献不足以回答问题，明确说明
 4. 使用专业但清晰的语言，适合工程师和法务人员阅读
-5. 对于数值要求（如绝缘电阻值、时间限制等），精确引用原文"""
+5. 对于数值要求（如绝缘电阻值、时间限制等），精确引用原文
+
+引用格式示例：根据《道路运输车辆动态监督管理办法》【第十五条】规定，道路运输经营者应当……"""
 
 
 COMPLIANCE_CHECK_PROMPT = """你是一位专业的汽车合规审查专家。

services/compliance-backend/app/services/rag.py

@@ -32,7 +32,10 @@ async def hybrid_search(
         param={"metric_type": "COSINE", "params": {"ef": 100}},
         limit=top_k,
         expr=expr,
-        output_fields=["content", "metadata", "file_id", "chunk_idx"],
+        output_fields=[
+            "content", "metadata", "file_id", "chunk_idx",
+            "clause_no", "article_no", "regulation_name",
+        ],
     )
 
     chunks = []
@@ -44,6 +47,9 @@ async def hybrid_search(
                 "score":           float(hit.score),
                 "file_id":         hit.entity.get("file_id", ""),
                 "chunk_idx":       hit.entity.get("chunk_idx", 0),
+                "clause_no":       hit.entity.get("clause_no", ""),
+                "article_no":      hit.entity.get("article_no", ""),
+                "regulation_name": hit.entity.get("regulation_name", ""),
                 "metadata":        hit.entity.get("metadata", {}),
             })
     return chunks
@@ -59,12 +65,13 @@ async def generate_answer(query: str, chunks: list[dict]) -> dict:
     if not chunks:
         return {"answer": "未找到相关法规内容，请上传相关法规文档后重试。", "sources": []}
 
-    # 构建 RAG 上下文
+    # 构建 RAG 上下文（带法规名称和条款号，方便 LLM 生成引文锚定）
     context_parts = []
     for i, chunk in enumerate(chunks, 1):
-        meta = chunk.get("metadata", {})
+        reg_name  = chunk.get("regulation_name", "") or chunk.get("metadata", {}).get("filename", "未知文件")
-        source_info = f"[来源 {i}：{meta.get('filename', '未知文件')}，第 {meta.get('page', '?')} 页]"
+        clause_no = chunk.get("clause_no", "")
-        context_parts.append(f"{source_info}\n{chunk['content']}")
+        source_tag = f"[{reg_name}·{clause_no}]" if clause_no else f"[{reg_name}]"
+        context_parts.append(f"{source_tag}\n{chunk['content']}")
 
     context = "\n\n---\n\n".join(context_parts)
     user_prompt = f"参考文献：\n\n{context}\n\n问题：{query}\n\n请基于以上参考文献回答，并标注来源。"
@@ -85,6 +92,9 @@ async def generate_answer(query: str, chunks: list[dict]) -> dict:
             "file_id":         c.get("file_id", ""),
             "chunk_idx":       c.get("chunk_idx", 0),
             "score":           c.get("score", 0),
+            "clause_no":       c.get("clause_no", ""),
+            "article_no":      c.get("article_no", ""),
+            "regulation_name": c.get("regulation_name", ""),
             "metadata":        c.get("metadata", {}),
         }
         for c in chunks

services/compliance-backend/app/services/regulation_parser.py

@@ -0,0 +1,205 @@
+import re
+from dataclasses import dataclass
+
+# ── 正则模式 ────────────────────────────────────────────
+_CHAPTER = re.compile(
+    r'(?m)^(?:#{1,3}\s*)?(第[一二三四五六七八九十百零〇\d]+章\s*[^\n]{0,40})'
+)
+_CLAUSE = re.compile(
+    r'(?m)^(第[一二三四五六七八九十百零〇\d]+条)\s+(.{1,200})'
+)
+_DOC_NUM = re.compile(
+    r'[（(]\d{4}[）)][^\s（(）)]{0,10}第?\d+号'
+)
+_EFF_DATE = re.compile(
+    r'自(\d{4}年\d{1,2}月\d{1,2}日)起(?:施行|实施|执行)'
+)
+_ISSUER = re.compile(
+    r'^([一-鿿]{2,10}(?:部|局|委|厅|院|会|办)[一-鿿]{0,8}?)\s*$',
+    re.M,
+)
+_REG_TYPE_MAP = [
+    ("法律",   ["人民代表大会", "主席令", "全国人大常委会"]),
+    ("行政法规", ["国务院令", "国务院颁布"]),
+    ("部门规章", ["部令", "局令", "委令", "管理办法", "管理规定"]),
+    ("地方法规", ["省人民代表大会", "市人民代表大会", "自治区"]),
+    ("国家标准", ["GB ", "GB/T", "GBT", "国家标准"]),
+    ("行业标准", ["行业标准", "QC/T", "ISO ", "SAE "]),
+    ("规范指南", ["规范", "指南", "导则", "技术要求"]),
+]
+
+
+@dataclass
+class RegulationMeta:
+    regulation_name: str = ""
+    issuing_authority: str = ""
+    doc_number: str = ""
+    effective_date: str = ""
+    regulation_type: str = ""
+
+
+def extract_regulation_meta(markdown: str) -> RegulationMeta:
+    """
+    从解析后的 Markdown 中提取法规元数据。
+    仅扫描前 3000 字符以提升效率。
+    """
+    meta = RegulationMeta()
+    head = markdown[:3000]
+
+    # 法规名称：取第一个 # 标题行
+    title_m = re.search(r'^#{1,3}\s*(.+)$', head, re.M)
+    if title_m:
+        meta.regulation_name = title_m.group(1).strip().strip('《》')
+
+    # 文号（括号内含年份+数字，如"（2023）第36号"）
+    num_m = _DOC_NUM.search(head)
+    if num_m:
+        meta.doc_number = num_m.group(0)
+
+    # 施行日期
+    date_m = _EFF_DATE.search(head)
+    if date_m:
+        meta.effective_date = date_m.group(1)
+
+    # 发布机关（前 500 字内独占一行的机关名称）
+    issuer_m = _ISSUER.search(markdown[:500])
+    if issuer_m:
+        meta.issuing_authority = issuer_m.group(1)
+
+    # 法规类型（关键词顺序匹配，取第一个命中）
+    for reg_type, keywords in _REG_TYPE_MAP:
+        if any(kw in head for kw in keywords):
+            meta.regulation_type = reg_type
+            break
+
+    return meta
+
+
+# ── 分块 ────────────────────────────────────────────────
+
+def legal_chunk(
+    markdown: str,
+    meta: RegulationMeta,
+    chunk_size: int = 512,
+    overlap: int = 64,
+) -> list[dict]:
+    """
+    法规专用分块：优先按条款（第X条）边界分割，保留条款号和章节信息。
+    超长条款按段落/句子二次分块。
+
+    返回 list[dict]，每个元素包含：
+        idx              int    chunk 序号（从 0 开始）
+        content          str    分块文本
+        clause_no        str    条款号，如"第十二条"（无则空字符串）
+        article_no       str    所在章，如"第三章 生产管理"（无则空字符串）
+        regulation_name  str    法规名称（来自 meta）
+    """
+    chars_per_chunk = chunk_size * 2      # 中文约 2 字符/token
+    chars_overlap   = overlap * 2
+
+    segments = _split_by_structure(markdown)
+
+    chunks: list[dict] = []
+    current_chapter = ""
+    idx = 0
+
+    for seg in segments:
+        if seg["type"] == "chapter":
+            current_chapter = seg["heading"]
+            continue
+
+        text   = seg["content"]
+        clause = seg.get("clause_no", "")
+
+        if not text.strip():
+            continue
+
+        if len(text) <= chars_per_chunk:
+            chunks.append(_make_chunk(idx, text, clause, current_chapter, meta))
+            idx += 1
+        else:
+            # 条款文本过长：二次字符分块
+            sub_texts = _char_split(text, chars_per_chunk, chars_overlap)
+            for sub_i, sub_text in enumerate(sub_texts):
+                sub_clause = f"{clause}（{sub_i + 1}）" if clause else ""
+                chunks.append(_make_chunk(idx, sub_text, sub_clause, current_chapter, meta))
+                idx += 1
+
+    # 如果结构化分块失败（无任何条款），降级为通用字符分块
+    if not chunks:
+        for text in _char_split(markdown, chars_per_chunk, chars_overlap):
+            chunks.append(_make_chunk(idx, text, "", "", meta))
+            idx += 1
+
+    return chunks
+
+
+def _make_chunk(idx, content, clause_no, article_no, meta: RegulationMeta) -> dict:
+    return {
+        "idx":             idx,
+        "content":         content.strip(),
+        "clause_no":       clause_no,
+        "article_no":      article_no,
+        "regulation_name": meta.regulation_name,
+    }
+
+
+def _split_by_structure(markdown: str) -> list[dict]:
+    """将 Markdown 按章标题和条款号拆分成有序段落列表。"""
+    segments: list[dict] = []
+    lines = markdown.splitlines(keepends=True)
+    buffer: list[str] = []
+    current_clause = ""
+
+    def flush_buffer():
+        content = "".join(buffer).strip()
+        if content:
+            segments.append({
+                "type":      "clause",
+                "clause_no": current_clause,
+                "content":   content,
+            })
+
+    for line in lines:
+        stripped = line.strip()
+
+        # 检测章标题
+        chapter_m = _CHAPTER.match(stripped)
+        if chapter_m:
+            flush_buffer()
+            buffer.clear()
+            current_clause = ""  # 章标题后重置条款号
+            segments.append({"type": "chapter", "heading": chapter_m.group(1).strip()})
+            continue
+
+        # 检测条款起始（第X条 ...）
+        clause_m = _CLAUSE.match(stripped)
+        if clause_m:
+            flush_buffer()
+            buffer.clear()
+            current_clause = clause_m.group(1)   # 如"第十二条"
+            buffer.append(line)
+            continue
+
+        buffer.append(line)
+
+    flush_buffer()
+    return segments
+
+
+def _char_split(text: str, size: int, overlap: int) -> list[str]:
+    """按字符数分块，在段落/句子边界处截断。"""
+    results: list[str] = []
+    start = 0
+    while start < len(text):
+        end = min(start + size, len(text))
+        # 尝试在语义边界回退截断点
+        if end < len(text):
+            for sep in ("\n\n", "\n", "。", "；", ";", ".", " "):
+                pos = text.rfind(sep, start, end)
+                if pos > start:
+                    end = pos + len(sep)
+                    break
+        results.append(text[start:end])
+        start = max(start + 1, end - overlap)
+    return results

services/compliance-backend/app/worker.py

@@ -46,8 +46,9 @@ async def _process_file(file_id: str, task_id: str, workspace_id: str):
     from sqlalchemy import select
     from .core.deps import AsyncSessionLocal, get_milvus_collection
     from .models.db import File, Task
-    from .services.parse import parse_document, chunk_text
+    from .services.parse import parse_document
     from .services.embed import embed_texts
+    from .services.regulation_parser import extract_regulation_meta, legal_chunk
 
     async with AsyncSessionLocal() as db:
         # 查找文件记录
@@ -68,7 +69,7 @@ async def _process_file(file_id: str, task_id: str, workspace_id: str):
                 task.progress = 10
             await db.commit()
 
-            # Step 1：解析文档
+            # Step 1：解析文档（调用 mcp-server）
             file_content = Path(file_record.storage_path).read_bytes()
             parse_result = await parse_document(file_content, file_record.original_name)
             markdown = parse_result.get("markdown", "")
@@ -76,16 +77,27 @@ async def _process_file(file_id: str, task_id: str, workspace_id: str):
             if not markdown.strip():
                 raise ValueError("文档解析结果为空")
 
+            # Step 2：提取法规元数据（发布机关/文号/施行日期/法规类型）
+            reg_meta = extract_regulation_meta(markdown)
             file_record.status = "parsed"
+            file_record.metadata = {
+                "regulation_name":   reg_meta.regulation_name,
+                "issuing_authority": reg_meta.issuing_authority,
+                "doc_number":        reg_meta.doc_number,
+                "effective_date":    reg_meta.effective_date,
+                "regulation_type":   reg_meta.regulation_type,
+                "parser":            parse_result.get("parser", ""),
+                "page_count":        parse_result.get("page_count", 0),
+            }
             if task:
                 task.progress = 40
             await db.commit()
 
-            # Step 2：分块
+            # Step 3：法规专用分块（按章/条边界分割，保留条款号）
-            chunks = chunk_text(markdown, chunk_size=512, overlap=64)
+            chunks = legal_chunk(markdown, reg_meta, chunk_size=512, overlap=64)
-            logger.info(f"文件 {file_id} 分割为 {len(chunks)} 块")
+            logger.info(f"文件 {file_id} 分割为 {len(chunks)} 块，法规：{reg_meta.regulation_name!r}")
 
-            # Step 3：向量化（分批处理）
+            # Step 4：向量化并写入 Milvus（分批处理）
             batch_size = 16
             col = get_milvus_collection("regulation_chunks")
 
@@ -93,16 +105,26 @@ async def _process_file(file_id: str, task_id: str, workspace_id: str):
                 batch = chunks[i:i + batch_size]
                 texts = [c["content"] for c in batch]
                 embed_result = await embed_texts(texts, batch_size=batch_size)
-                dense_vecs = embed_result["dense"]
+
+                dense_vecs  = embed_result["dense"]    # list[list[float]], 1024维
+                sparse_vecs = embed_result.get("sparse", [{}] * len(batch))  # list[dict[str,float]]
 
                 entities = [
-                    [f"{file_id}_{c['idx']}" for c in batch],
+                    [f"{file_id}_{c['idx']}" for c in batch],           # pk
-                    [file_id] * len(batch),
+                    [file_id]     * len(batch),                           # file_id
-                    [workspace_id] * len(batch),
+                    [workspace_id] * len(batch),                          # workspace_id
-                    [c["idx"] for c in batch],
+                    [c["idx"]    for c in batch],                         # chunk_idx
-                    [c["content"] for c in batch],
+                    [c["content"] for c in batch],                        # content
-                    dense_vecs,
+                    dense_vecs,                                           # dense_vec
-                    [{"filename": file_record.original_name, "page": c.get("page", 0)} for c in batch],
+                    sparse_vecs,                                          # sparse_vec
+                    [c["clause_no"]       for c in batch],               # clause_no
+                    [c["article_no"]      for c in batch],               # article_no
+                    [c["regulation_name"] for c in batch],               # regulation_name
+                    [{                                                    # metadata
+                        "filename":   file_record.original_name,
+                        "page":       c.get("page", 0),
+                        "doc_number": reg_meta.doc_number,
+                    } for c in batch],
                 ]
                 col.insert(entities)
 
@@ -119,7 +141,7 @@ async def _process_file(file_id: str, task_id: str, workspace_id: str):
                 task.progress = 100
                 task.completed_at = datetime.now(timezone.utc)
             await db.commit()
-            logger.info(f"文件 {file_id} 处理完成")
+            logger.info(f"文件 {file_id} 处理完成，共 {len(chunks)} 个向量块")
 
         except Exception as e:
             logger.error(f"文件 {file_id} 处理失败：{e}")