feat(rag): 数据驱动的检索优化与表格/图片处理增强
- 移除硬编码关键词列表，改为数据驱动的图片意图检测 - 新增 _section_similarity() 层级章节相似度函数，替代正则匹配 - 新增 _rescue_table_chunks() 表格救援机制，基于 _in_budget_context 标记 - 修复 _build_context_with_budget 表格组超预算不 break 的问题 - 新增 _filter_images_by_answer() 后置图片过滤 - 表格切片 rerank 分数保护策略（同 section 表格不被误删） - 跨页表格合并逻辑改进（通用标题检测 + 页码不可用降级策略） - 意图分析增强追问补全 + 缓存类型隔离 - 语义缓存 key 加入 collections 防止跨上下文误命中 - 使用 retrieval_query 替代原始 message 进行检索 - engine.py: CloudReranker 模型更新 + 移除 top_n + table 邻居扩展 - LLM prompt 增加表格处理规则和章节路径提示 🤖 Generated with [Qoder][https://qoder.com]
This commit is contained in:
lacerate551
@@ -188,6 +188,84 @@ def _get_full_table_from_docstore(chunk_id: str) -> Optional[str]:
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return None
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def _strip_semantic_prefix(doc: str, chunk_type: str) -> str:
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"""
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去除切片 doc 中的冗余语义前缀，保留关键标识信息
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表格切片的 doc 由 _build_semantic_content_for_table 生成，格式为：
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主题：section_path（保留，用于关联章节）
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字段：A, B, C（去除，冗余）
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描述：该表包含N行数据（去除，冗余）
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示例：字段=值（去除，冗余）
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表格内容：
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| A | B | C |
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|---|---|---|
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| 1 | 2 | 3 |
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优化策略：
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- 保留"主题："行（表格所属章节标识）
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- 去除"字段："/"描述："/"示例："行（冗余信息）
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- 添加"【表格】"标记，让 LLM 明确识别表格类型
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- 保留 Markdown 表格内容（| 开头）和 HTML 表格内容（<table/<tr 等）
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Args:
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doc: 原始 doc 内容
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chunk_type: 切片类型
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Returns:
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精简后的 doc 内容
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"""
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if chunk_type != 'table' or not doc:
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return doc
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import re
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_html_table_re = re.compile(r'^\s*<\s*(table|tr|td|th|tbody|thead|caption)', re.IGNORECASE)
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lines = doc.split('\n')
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result_lines = []
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in_table_section = False
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theme_line = None
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for line in lines:
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# 保留"主题："行（表格所属章节标识）
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if line.startswith('主题：'):
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theme_line = line
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continue
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# 跳过冗余的语义增强行
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if line.startswith('字段：') or line.startswith('描述：') or line.startswith('示例：'):
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continue
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# 遇到"表格内容："标记，进入表格区域
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if line.strip() == '表格内容：':
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in_table_section = True
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continue
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# 表格区域的内容直接保留
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if in_table_section:
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result_lines.append(line)
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elif line.startswith('|'):
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# 没有"表格内容："标记时，直接遇到 Markdown 表格行
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in_table_section = True
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result_lines.append(line)
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elif _html_table_re.match(line):
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# 没有"表格内容："标记时，直接遇到 HTML 表格标签
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in_table_section = True
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result_lines.append(line)
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# 构建结果：主题行 + 【表格】标记 + 表格内容
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if result_lines:
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output_parts = []
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if theme_line:
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output_parts.append(theme_line)
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output_parts.append('【表格】')
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output_parts.extend(result_lines)
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return '\n'.join(output_parts)
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return doc
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def _order_text_contexts_for_prompt(contexts: List[Dict], query: str, max_chunks: int,
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min_score: float = 0.0) -> List[Dict]:
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"""
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@@ -248,8 +326,31 @@ def _order_text_contexts_for_prompt(contexts: List[Dict], query: str, max_chunks
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})
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# Phase 1：按 Rerank 分数过滤低分切片
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# 保护策略：同一 section 内如有切片通过阈值，则同 section 的 table 切片也保留
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# 原因：表格切片的 rerank 分数往往偏低（尤其是元问题如"有表格吗？"），
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# 但它们与同 section 的 text 切片属于同一语义单元，不应割裂
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# 安全下限：被保护的 table 切片自身 score 不得低于 min_score * 0.3，
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# 防止 section 粒度较粗时完全不相关的表格被无条件保护
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if min_score > 0:
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text_contexts = [c for c in text_contexts if c.get('score', 0) >= min_score]
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_table_floor = min_score * 0.3 # table 保护最低分数下限
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# 先找出所有通过阈值的 section
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passing_sections = set()
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for c in text_contexts:
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if c.get('score', 0) >= min_score:
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meta = c.get('meta', {})
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section_key = (meta.get('source', ''), meta.get('section', '') or meta.get('section_path', ''))
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if section_key[1]: # 有 section 信息的才保护
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passing_sections.add(section_key)
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text_contexts = [
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c for c in text_contexts
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if c.get('score', 0) >= min_score
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or (
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c.get('meta', {}).get('chunk_type') == 'table'
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and c.get('score', 0) >= _table_floor
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and (c.get('meta', {}).get('source', ''), c.get('meta', {}).get('section', '') or c.get('meta', {}).get('section_path', '')) in passing_sections
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)
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]
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chart_contexts = [c for c in chart_contexts if c.get('score', 0) >= min_score]
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# 合并：文本切片优先，图表切片补充
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@@ -258,7 +359,11 @@ def _order_text_contexts_for_prompt(contexts: List[Dict], query: str, max_chunks
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combined_contexts = text_contexts + chart_contexts[:max_chart_contexts]
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if not _is_enum_query(query):
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return combined_contexts[:max_chunks]
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# 表格不受 max_chunks 限制（CrossEncoder 对表格评分偏低，
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# 但表格是结构化关键内容，不应因分数低而被截断）
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table_ctx = [c for c in combined_contexts if c.get('meta', {}).get('chunk_type') == 'table']
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non_table_ctx = [c for c in combined_contexts if c.get('meta', {}).get('chunk_type') != 'table']
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return non_table_ctx[:max_chunks] + table_ctx
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def sort_key(ctx):
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meta = ctx.get('meta', {})
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@@ -312,6 +417,153 @@ def _order_text_contexts_for_prompt(contexts: List[Dict], query: str, max_chunks
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return ordered
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def _process_table_doc(doc: str, meta: Dict) -> str:
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"""
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处理单个切片的 doc：精简表格语义前缀 + 注入嵌入图片 URL
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集中处理两处共用逻辑（正常路径和预算截断路径），避免重复代码。
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Args:
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doc: 切片原始 doc
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meta: 切片 metadata
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Returns:
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处理后的 doc
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"""
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doc = _strip_semantic_prefix(doc, meta.get('chunk_type', ''))
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if meta.get('chunk_type') == 'table' and meta.get('images_json'):
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try:
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img_list = json.loads(meta['images_json'])
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if img_list:
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img_urls = [
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f"/images/{img.get('id', '')}"
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for img in img_list
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if isinstance(img, dict) and img.get('id')
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]
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if img_urls:
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doc += "\n\n[该表格包含以下图片，可在回答中引用]: " + ", ".join(img_urls)
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except (json.JSONDecodeError, TypeError):
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pass
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return doc
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def _section_similarity(section_a: str, section_b: str) -> float:
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"""
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计算两个章节路径的层级相似度（数据驱动，无需硬编码格式假设）。
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策略：
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1. 如果两个路径都有数值编号（如 "2.3"），优先用精确数值匹配
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2. 否则按 " > " 层级拆分，计算 Jaccard 相似度
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3. 无数值编号时优雅降级，适用于 "第七章 附则" 或无结构文档
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Returns: 0.0 ~ 1.0
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"""
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import re as _re
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if not section_a or not section_b:
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return 0.0
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# 快速路径：完全相同
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if section_a == section_b:
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return 1.0
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# 优先精确匹配：数值编号（如 "2.3"）
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num_a = _re.search(r'(\d+\.\d+)', section_a)
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num_b = _re.search(r'(\d+\.\d+)', section_b)
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if num_a and num_b:
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return 1.0 if num_a.group(1) == num_b.group(1) else 0.0
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# 层级文本匹配：拆分路径为各级标题，计算 Jaccard 系数
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def _split_levels(path):
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parts = [p.strip() for p in path.split('>') if p.strip()]
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# 去掉常见序号前缀（如 "1.1 "、"第1章 "），保留语义部分
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cleaned = set()
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for p in parts:
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cleaned.add(_re.sub(r'^(?:\d+[\.\d]*\s*|第\s*\d+\s*章\s*|[一二三四五六七八九十]+、\s*)', '', p).strip())
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return cleaned
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levels_a = _split_levels(section_a)
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levels_b = _split_levels(section_b)
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if not levels_a or not levels_b:
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return 0.0
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overlap = len(levels_a & levels_b)
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union = len(levels_a | levels_b)
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return overlap / union if union > 0 else 0.0
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def _rescue_table_chunks(contexts: List[Dict], context_text: str,
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retrieval_query: str, max_rescue_chars: int = 3000) -> str:
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"""
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表格救援：当查询涉及表格但上下文预算截断了表格内容时，将最相关的表格补回。
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根因：CrossEncoder 对 Markdown 表格格式的评分普遍偏低，
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导致 _build_context_with_budget 按分数排序时表格被排到最后并被截断。
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此函数作为安全网，确保与查询最相关的表格始终出现在上下文中。
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Args:
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contexts: 经过 _order_text_contexts_for_prompt 处理的全部文本切片
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context_text: _build_context_with_budget 的输出
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retrieval_query: 改写后的检索查询
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max_rescue_chars: 救援表格的最大字符数
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Returns:
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可能追加了表格内容的上下文文本
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"""
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# 1. 数据驱动检测：检索结果中是否有表格类型切片（无需硬编码关键词）
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has_table_in_contexts = any(
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ctx.get('meta', {}).get('chunk_type') == 'table' for ctx in contexts
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)
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if not has_table_in_contexts:
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return context_text
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# 2. 检测现有上下文是否已包含表格数据（Markdown 表格至少 2 列）
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if '|' in context_text and context_text.count('|') > 4:
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return context_text
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# 3. 从 contexts 中找被截断的表格切片
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# 使用 _in_budget_context 标记精确判断（由 _build_context_with_budget 设置）
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table_candidates = []
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for ctx in contexts:
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if ctx.get('meta', {}).get('chunk_type') != 'table':
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continue
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# 精确判断：如果 _build_context_with_budget 已标记该 chunk 为已纳入，跳过
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if ctx.get('_in_budget_context'):
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continue
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table_candidates.append(ctx)
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if not table_candidates:
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return context_text
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# 4. 按分数降序取最佳表格，追加到上下文
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table_candidates.sort(key=lambda c: c.get('score', 0), reverse=True)
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rescue_parts = []
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rescue_chars = 0
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for ctx in table_candidates[:3]: # 最多救援 3 个表格
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meta = ctx.get('meta', {})
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doc = _process_table_doc(ctx.get('doc', ''), meta)
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section = meta.get('section', '') or meta.get('section_path', '')
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part_text = ""
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if section:
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part_text += f"━ {section} ━\n"
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part_text += doc
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if rescue_chars + len(part_text) > max_rescue_chars:
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break
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rescue_parts.append(part_text)
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rescue_chars += len(part_text)
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if rescue_parts:
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separator = "\n\n--- 以下为与查询最相关的表格（CrossEncoder 评分偏低，自动补入）---\n\n"
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context_text += separator + "\n\n".join(rescue_parts)
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return context_text
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def _build_context_with_budget(contexts: List[Dict], max_chars: int, soft_limit: int) -> str:
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"""
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Phase 2：按字符预算构建上下文文本。
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@@ -361,22 +613,46 @@ def _build_context_with_budget(contexts: List[Dict], max_chars: int, soft_limit:
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total_chars = 0
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for key in group_order:
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group = groups[key]
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group_text = "\n\n".join(ctx.get('doc', '') for ctx in group)
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source, section = key
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# 超过软限制后，只接受高分组
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if total_chars > soft_limit and group_max_score(key) < 0.1:
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# 判断是否包含表格切片（表格是结构化关键内容，不受预算截断）
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has_table = any(c.get('meta', {}).get('chunk_type') == 'table' for c in group)
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# 构建组文本，表格切片附加图片 URL 供 LLM 引用
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doc_parts = []
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for ctx in group:
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doc = _process_table_doc(ctx.get('doc', ''), ctx.get('meta', {}))
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doc_parts.append(doc)
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group_text = "\n\n".join(doc_parts)
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# 在组首插入章节路径标题行，帮助 LLM 区分不同章节
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section_header = ''
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if section:
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section_header = f"━ {section} ━"
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group_text = section_header + "\n" + group_text
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# 超过软限制后，只接受高分组（但表格组始终保留，不因分数低被跳过）
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if total_chars > soft_limit and group_max_score(key) < 0.1 and not has_table:
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continue
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if total_chars + len(group_text) > max_chars:
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# 尝试逐条加入该组，直到预算满
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# 先加入章节标题（如果有）
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if section_header and total_chars + len(section_header) + 2 <= max_chars:
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parts.append(section_header)
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total_chars += len(section_header) + 2
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for ctx in group:
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doc = ctx.get('doc', '')
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doc = _process_table_doc(ctx.get('doc', ''), ctx.get('meta', {}))
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if total_chars + len(doc) + 2 > max_chars: # +2 for "\n\n"
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break
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ctx['_in_budget_context'] = True # 标记已纳入上下文
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parts.append(doc)
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total_chars += len(doc) + 2
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# 超预算后一律 break，被截断的表格由 _rescue_table_chunks 补回
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break
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for ctx in group:
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ctx['_in_budget_context'] = True # 标记已纳入上下文
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parts.append(group_text)
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total_chars += len(group_text) + 2 # +2 for "\n\n"
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@@ -477,6 +753,17 @@ def _attach_citations(answer: str, contexts: List[Dict]) -> Dict[str, Any]:
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if len(candidates) > 1 and (candidates[0][1] - candidates[1][1]) < 0.1:
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selected_ids.append(candidates[1][0])
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# 按 _raw_chunk_id 去重：不同 composite_key 可能指向同一个底层 chunk
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# 避免同一 chunk 产生重复引用标记（如 [3][3]）
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seen_raw_ids = set()
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deduped_ids = []
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for cid in selected_ids:
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raw_id = ctx_by_chunk[cid].get('_raw_chunk_id', cid)
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if raw_id not in seen_raw_ids:
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seen_raw_ids.add(raw_id)
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deduped_ids.append(cid)
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selected_ids = deduped_ids
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if selected_ids:
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for cid in selected_ids:
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if cid not in cited_set:
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@@ -492,17 +779,20 @@ def _attach_citations(answer: str, contexts: List[Dict]) -> Dict[str, Any]:
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result_parts.append(para + sep)
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|
||||
# 构建引用列表（按出现顺序），使用原始 chunk_id 构建 citation
|
||||
# 按 _raw_chunk_id 去重，避免同一 chunk 产生重复引用条目
|
||||
citations = []
|
||||
seen_citation_raw_ids = set()
|
||||
for composite_key in cited_chunks_ordered:
|
||||
ctx = ctx_by_chunk.get(composite_key)
|
||||
if ctx:
|
||||
raw_id = ctx.get('_raw_chunk_id') or composite_key
|
||||
if raw_id in seen_citation_raw_ids:
|
||||
continue # 同一 chunk 已在引用列表中，跳过
|
||||
seen_citation_raw_ids.add(raw_id)
|
||||
meta = ctx.get('meta', {})
|
||||
full_content = ctx.get('doc', '')
|
||||
citation = _build_citation(meta, full_content)
|
||||
# 确保 citation 中的 chunk_id 使用原始值（不含 collection 前缀）
|
||||
raw_id = ctx.get('_raw_chunk_id')
|
||||
if raw_id:
|
||||
citation['chunk_id'] = raw_id
|
||||
citation['chunk_id'] = raw_id
|
||||
citations.append(citation)
|
||||
|
||||
return {
|
||||
@@ -735,8 +1025,8 @@ def score_image_relevance(query: str, meta: Dict, doc: str = '') -> float:
|
||||
|
||||
# 3. 整体文本相似度（字符级别）
|
||||
if search_text:
|
||||
# 检查查询的核心词是否在描述中
|
||||
query_core = re.sub(r'[图表图片如图所示]', '', query) # 去掉泛词
|
||||
# 复用已过滤停用词的 query_keywords，避免字符级误删（如"表现"→"现"）
|
||||
query_core = "".join(query_keywords)
|
||||
if query_core:
|
||||
overlap = len(set(query_core) & set(search_text))
|
||||
score += min(overlap * 0.2, 3.0)
|
||||
@@ -763,6 +1053,77 @@ def score_image_relevance(query: str, meta: Dict, doc: str = '') -> float:
|
||||
return score
|
||||
|
||||
|
||||
def _filter_images_by_answer(selected_images: List[Dict], answer: str) -> List[Dict]:
|
||||
"""
|
||||
后置图片过滤：根据 LLM 生成的回答内容反向筛选图片。
|
||||
|
||||
只有图片描述与回答内容有足够关键词重叠时才保留，
|
||||
确保展示的图片与回答内容一致，避免不相关图片干扰用户。
|
||||
|
||||
过滤规则:
|
||||
1. 从回答中提取 2 字及以上的中文关键词（jieba 分词）
|
||||
2. 对每张图片，检查其描述（full_description / description）中匹配了多少关键词
|
||||
3. 匹配数 >= 阈值则保留，否则丢弃
|
||||
4. 如果过滤后图片为 0，保留分数最高的 1 张（兜底）
|
||||
5. 用户明确指定图号（如图 2.1）时不过滤
|
||||
|
||||
Args:
|
||||
selected_images: select_images 返回的候选图片列表
|
||||
answer: LLM 生成的回答文本
|
||||
|
||||
Returns:
|
||||
过滤后的图片列表
|
||||
"""
|
||||
if not selected_images or len(selected_images) <= 1:
|
||||
return selected_images
|
||||
|
||||
# 如果回答中提到了具体图号，说明 LLM 认为这些图是相关的，不过滤
|
||||
import re
|
||||
if re.search(r'图\s*\d+\.?\d*', answer):
|
||||
return selected_images
|
||||
|
||||
# 从回答中提取关键词
|
||||
try:
|
||||
import jieba
|
||||
answer_keywords = set(
|
||||
w for w in jieba.lcut(answer)
|
||||
if len(w) >= 2 and re.search(r'[\u4e00-\u9fff]', w)
|
||||
)
|
||||
except ImportError:
|
||||
# jieba 不可用时回退到 bigram
|
||||
chars = re.findall(r'[\u4e00-\u9fff]', answer)
|
||||
answer_keywords = set(chars[i] + chars[i + 1] for i in range(len(chars) - 1))
|
||||
|
||||
if not answer_keywords:
|
||||
return selected_images
|
||||
|
||||
# 动态阈值：回答关键词越多，阈值越高（至少匹配 15% 或 2 个关键词，取较小值）
|
||||
threshold = max(2, min(3, round(len(answer_keywords) * 0.15)))
|
||||
|
||||
filtered = []
|
||||
for img in selected_images:
|
||||
desc = img.get('full_description', '') or img.get('description', '') or ''
|
||||
if not desc:
|
||||
# 没有描述的图片，保留（无法判断）
|
||||
filtered.append(img)
|
||||
continue
|
||||
|
||||
# 计算描述与回答的关键词重叠数
|
||||
overlap = sum(1 for kw in answer_keywords if kw in desc)
|
||||
if overlap >= threshold:
|
||||
filtered.append(img)
|
||||
|
||||
# 兜底：如果过滤后为空，保留分数最高的 1 张
|
||||
if not filtered and selected_images:
|
||||
filtered = [max(selected_images, key=lambda x: x.get('score', 0))]
|
||||
|
||||
if len(filtered) < len(selected_images):
|
||||
logger.info(f"[图片后置过滤] {len(selected_images)} → {len(filtered)} 张 "
|
||||
f"(回答关键词 {len(answer_keywords)} 个, 阈值 {threshold})")
|
||||
|
||||
return filtered
|
||||
|
||||
|
||||
def select_images(contexts: List[Dict], query: str) -> List[Dict]:
|
||||
"""
|
||||
选择要展示的图片（打分排序 + 预算控制）
|
||||
@@ -792,26 +1153,39 @@ def select_images(contexts: List[Dict], query: str) -> List[Dict]:
|
||||
"""
|
||||
import re
|
||||
|
||||
# 动态预算：列举型查询允许更多图片
|
||||
list_keywords = ["哪些", "有什么", "包含", "列出", "所有", "全部"]
|
||||
is_list_query = any(kw in query for kw in list_keywords)
|
||||
# 动态预算：数据驱动，不依赖硬编码关键词列表
|
||||
# 核心策略：宽松预选 + 后置过滤（_filter_images_by_answer）精准裁剪
|
||||
|
||||
# 检测查询中的图片编号（如 "图2.1"）
|
||||
# 精确查图：用户指定了具体图号（如 "图2.3"）—— 结构化模式匹配，非硬编码
|
||||
figure_pattern = r'图\s*(\d+\.?\d*)'
|
||||
figure_matches = re.findall(figure_pattern, query)
|
||||
has_figure_query = bool(figure_matches)
|
||||
|
||||
# 新增：从检索文本中提取图表引用（见表2.2、见图2.5 等）
|
||||
# 同时记录引用所在的文件来源
|
||||
# 数据驱动的图片意图检测：检查检索结果中是否包含图片/图表类型切片
|
||||
# 原理：如果向量检索返回了 image/chart 类型 chunk 或含 images_json 的 table chunk，
|
||||
# 说明知识库中存在与查询语义相关的图片内容，应给予展示机会
|
||||
has_image_data = False
|
||||
_image_chunk_count = 0
|
||||
_table_image_count = 0
|
||||
for ctx in contexts:
|
||||
meta = ctx.get('meta', {})
|
||||
ct = meta.get('chunk_type', '')
|
||||
if ct in ('image', 'chart'):
|
||||
_image_chunk_count += 1
|
||||
has_image_data = True
|
||||
if ct == 'table' and meta.get('images_json'):
|
||||
_table_image_count += 1
|
||||
has_image_data = True
|
||||
|
||||
# 从检索文本中提取图表引用（见表2.2、见图2.5 等）
|
||||
# 重要：只从语义相关的 top 5 文本块提取，避免不相关引用干扰
|
||||
referenced_figures = {} # {图号: set(文件来源)}
|
||||
referenced_tables = {} # {表号: set(文件来源)}
|
||||
|
||||
for ctx in contexts[:5]: # 只从前5个最相关的文本块提取引用
|
||||
for ctx in contexts[:5]:
|
||||
doc_text = ctx.get('doc', '')
|
||||
source = ctx.get('meta', {}).get('source', '')
|
||||
|
||||
# 提取 "见图X.X"、"如图X.X" 或单独的 "图X.X"
|
||||
fig_refs = re.findall(r'(?:[见如])?图\s*(\d+\.?\d*)', doc_text)
|
||||
for fig_num in fig_refs:
|
||||
if fig_num not in referenced_figures:
|
||||
@@ -819,7 +1193,6 @@ def select_images(contexts: List[Dict], query: str) -> List[Dict]:
|
||||
if source:
|
||||
referenced_figures[fig_num].add(source)
|
||||
|
||||
# 提取 "见表X.X"、"如表X.X" 或单独的 "表X.X"
|
||||
table_refs = re.findall(r'(?:[见如])?表\s*(\d+\.?\d*)', doc_text)
|
||||
for table_num in table_refs:
|
||||
if table_num not in referenced_tables:
|
||||
@@ -831,63 +1204,84 @@ def select_images(contexts: List[Dict], query: str) -> List[Dict]:
|
||||
|
||||
# 获取检索结果中涉及的主要文件来源
|
||||
primary_sources = set()
|
||||
for ctx in contexts[:5]: # 只看前5个最相关的
|
||||
for ctx in contexts[:5]:
|
||||
source = ctx.get('meta', {}).get('source', '')
|
||||
if source:
|
||||
primary_sources.add(source)
|
||||
|
||||
# 图片意图检测：区分精确查图和泛指
|
||||
strong_image_keywords = ["示意图", "流程图", "结构图", "过程线", "曲线图", "分布图", "图示", "看图", "显示图"]
|
||||
weak_image_keywords = ["图片", "图表", "如图", "图", "统计"]
|
||||
|
||||
has_strong_image_intent = any(kw in query for kw in strong_image_keywords)
|
||||
has_weak_image_intent = any(kw in query for kw in weak_image_keywords)
|
||||
|
||||
# 精确查图：用户指定了具体图号（如 "图2.3"），只返回最匹配的 1-2 张
|
||||
# 动态预算：根据检索结果数据驱动设置
|
||||
# 后置过滤 _filter_images_by_answer 会根据回答内容精准裁剪
|
||||
if has_figure_query:
|
||||
# 精确查图：用户指定了具体图号
|
||||
MAX_IMAGES = 2
|
||||
MIN_SCORE = 5.0 # 精确匹配应该高分
|
||||
# 强图片意图：明确要看某种图
|
||||
elif has_strong_image_intent:
|
||||
MAX_IMAGES = 3
|
||||
MIN_SCORE = 5.0 # 提高阈值，避免不相关图片通过
|
||||
# 列举型查询
|
||||
elif is_list_query:
|
||||
MIN_SCORE = 5.0
|
||||
elif has_image_data:
|
||||
# 检索结果中有图片数据 → 宽松预算，给后置过滤留足候选空间
|
||||
MAX_IMAGES = 5
|
||||
MIN_SCORE = 3.0
|
||||
# 有图表引用：检索文本中提到了图表
|
||||
MIN_SCORE = 2.0
|
||||
elif has_referenced_figures:
|
||||
# 检索文本中引用了图表编号
|
||||
MAX_IMAGES = 3
|
||||
MIN_SCORE = 2.0 # 降低阈值，让引用的图表能通过
|
||||
# 弱图片意图：只是提到"图"字，可能是泛指（如 "发电量图"）
|
||||
elif has_weak_image_intent:
|
||||
MAX_IMAGES = 1 # 只返回最相关的一张
|
||||
MIN_SCORE = 2.0 # 降低阈值，让语义相关的图片能通过
|
||||
# 普通查询
|
||||
MIN_SCORE = 2.0
|
||||
else:
|
||||
# 无图片数据 → 保守默认值
|
||||
MAX_IMAGES = 2
|
||||
MIN_SCORE = 3.0
|
||||
|
||||
# 获取检索结果中涉及的主要章节（只看前 3 个最相关的文本块）
|
||||
primary_sections = set()
|
||||
# 动态调整：当表格嵌入大量图片时，提升上限以展示完整内容
|
||||
if has_image_data and _table_image_count > 0:
|
||||
total_table_images = 0
|
||||
for ctx in contexts:
|
||||
meta = ctx.get('meta', {})
|
||||
if meta.get('chunk_type') == 'table' and meta.get('images_json'):
|
||||
try:
|
||||
total_table_images += len(json.loads(meta['images_json']))
|
||||
except (json.JSONDecodeError, TypeError):
|
||||
pass
|
||||
if total_table_images > MAX_IMAGES:
|
||||
MAX_IMAGES = min(total_table_images, 15) # 上限 15，防止图片过多
|
||||
|
||||
# 获取检索结果中涉及的主要章节路径（只看前 3 个最相关的文本块）
|
||||
primary_section_paths = set()
|
||||
for ctx in contexts[:3]:
|
||||
section = ctx.get('meta', {}).get('section', '') or ctx.get('meta', {}).get('section_path', '')
|
||||
if section:
|
||||
# 提取章节编号
|
||||
# 优先匹配 X.X 格式（如 "2.3发电"），再匹配 第X章 格式
|
||||
section_num = re.search(r'(\d+\.\d+)', section)
|
||||
if not section_num:
|
||||
# 尝试匹配 "第X章" 格式
|
||||
chapter_match = re.search(r'第\s*(\d+)\s*章', section)
|
||||
if chapter_match:
|
||||
section_num = chapter_match
|
||||
if section_num:
|
||||
primary_sections.add(section_num.group(1))
|
||||
primary_section_paths.add(section)
|
||||
|
||||
# ========== P1.5 预计算：表格主题相关性评分 ==========
|
||||
# 从查询中提取关键词片段（使用 jieba 分词 + 2字及以上的词，自动过滤停用词）
|
||||
try:
|
||||
import jieba
|
||||
_query_kw_segments = [w for w in jieba.lcut(query)
|
||||
if len(w) >= 2 and re.search(r'[\u4e00-\u9fff]', w)]
|
||||
except ImportError:
|
||||
# jieba 不可用时回退到 bigram
|
||||
_chars = re.findall(r'[\u4e00-\u9fff]', query)
|
||||
_query_kw_segments = [_chars[i] + _chars[i+1] for i in range(len(_chars) - 1)]
|
||||
|
||||
# 对所有含 images_json 的表格切片计算主题匹配分
|
||||
_table_topic_scores = {}
|
||||
for _tc in contexts:
|
||||
_tm = _tc.get('meta', {})
|
||||
if _tm.get('chunk_type') == 'table' and _tm.get('images_json'):
|
||||
_t_section = (_tm.get('section', '') or _tm.get('section_path', ''))
|
||||
_t_title = _tm.get('title', '') or ''
|
||||
_t_combined = _t_title + _t_section
|
||||
_score = sum(1 for kw in _query_kw_segments if kw in _t_combined)
|
||||
_table_topic_scores[id(_tc)] = _score
|
||||
|
||||
# 自适应阈值：要求至少匹配 70% 的最佳表格得分（至少 2 分）
|
||||
# 例如查询 "设施设备的参考样式" → 4 个关键词 → 最佳匹配 4 → 阈值 max(2, 2) = 2
|
||||
# "形象识别标识" 只匹配 "参考"+"样式" = 2 → 但阈值=3(70%of4)时被过滤
|
||||
_best_table_score = max(_table_topic_scores.values()) if _table_topic_scores else 0
|
||||
_table_topic_threshold = max(2, round(_best_table_score * 0.7)) if _best_table_score >= 2 else 0
|
||||
|
||||
scored_images = []
|
||||
for ctx in contexts:
|
||||
meta = ctx.get('meta', {})
|
||||
chunk_type = meta.get('chunk_type', 'text')
|
||||
s = None # P1 评分，用于 P1.5 继承
|
||||
doc = ctx.get('doc', '') # 默认文档内容
|
||||
|
||||
# 处理图片类型和有关联图片的表格类型
|
||||
if meta.get('image_path') and chunk_type in ('image', 'chart', 'table'):
|
||||
@@ -917,16 +1311,19 @@ def select_images(contexts: List[Dict], query: str) -> List[Dict]:
|
||||
|
||||
# 图片章节
|
||||
img_section = meta.get('section', '') or meta.get('section_path', '')
|
||||
# 只匹配 X.X 格式的章节号，避免匹配年份
|
||||
img_section_num = re.search(r'(\d+\.\d+)', img_section)
|
||||
img_section_id = img_section_num.group(1) if img_section_num else None
|
||||
|
||||
# ========== 核心修复：图片必须与主要文本切片章节关联 ==========
|
||||
# 如果有主要章节，且图片章节不在其中，大幅降低分数
|
||||
# ========== 章节关联检测：基于层级相似度，无需硬编码格式假设 ==========
|
||||
# 计算图片章节与主要检索结果的最高相似度
|
||||
section_penalty = 0.0
|
||||
if primary_sections and img_section_id and img_section_id not in primary_sections:
|
||||
max_section_sim = 0.0
|
||||
if primary_section_paths:
|
||||
for ps in primary_section_paths:
|
||||
sim = _section_similarity(img_section, ps)
|
||||
max_section_sim = max(max_section_sim, sim)
|
||||
# 当相似度低于阈值且有足够的章节信息时，判定为不相关
|
||||
if primary_section_paths and img_section and max_section_sim < 0.3:
|
||||
# 图片章节与主要检索结果不匹配，惩罚
|
||||
section_penalty = -5.0 # 大幅降低分数
|
||||
section_penalty = -5.0
|
||||
# 除非图片被文本切片明确引用
|
||||
is_referenced = False
|
||||
for fig_num in referenced_figures:
|
||||
@@ -949,10 +1346,10 @@ def select_images(contexts: List[Dict], query: str) -> List[Dict]:
|
||||
for fig_num, sources in referenced_figures.items():
|
||||
# 只检查 doc 字段，不检查 meta（避免 section 中的误匹配）
|
||||
if f"图{fig_num}" in doc or f"图 {fig_num}" in doc:
|
||||
# 检查图片章节是否与主要章节匹配
|
||||
section_match = img_section_id and img_section_id in primary_sections
|
||||
# 使用层级相似度判断章节关联性
|
||||
section_match = max_section_sim >= 0.3
|
||||
|
||||
# P2：只有章节匹配才加分，移除"s >= 5.0"漏洞
|
||||
# 章节匹配时才加分
|
||||
if section_match:
|
||||
# 图号匹配加分
|
||||
s += 8.0
|
||||
@@ -967,10 +1364,10 @@ def select_images(contexts: List[Dict], query: str) -> List[Dict]:
|
||||
for table_num, sources in referenced_tables.items():
|
||||
# 只检查 doc 字段
|
||||
if f"表{table_num}" in doc or f"表 {table_num}" in doc:
|
||||
# 检查图片章节是否与主要章节匹配
|
||||
section_match = img_section_id and img_section_id in primary_sections
|
||||
# 使用层级相似度判断章节关联性
|
||||
section_match = max_section_sim >= 0.3
|
||||
|
||||
# P2：只有章节匹配才加分，移除"s >= 5.0"漏洞
|
||||
# 章节匹配时才加分
|
||||
if section_match:
|
||||
# 表号匹配加分
|
||||
s += 8.0
|
||||
@@ -998,6 +1395,47 @@ def select_images(contexts: List[Dict], query: str) -> List[Dict]:
|
||||
# ========== P1.5：处理表格切片的 images_json（跨页表格多图）==========
|
||||
# 当表格切片有 images_json 字段时，添加所有关联图片
|
||||
if chunk_type == 'table' and meta.get('images_json'):
|
||||
# 如果 P1 未执行（表格无 image_path），需要独立计算分数
|
||||
if s is None:
|
||||
doc = ctx.get('image_description', '') or meta.get('vlm_desc', '') or ctx.get('doc', '')
|
||||
s = score_image_relevance(query, meta, doc)
|
||||
|
||||
# 章节相关性过滤：使用层级相似度，无需硬编码章节格式假设
|
||||
table_section = meta.get('section', '') or meta.get('section_path', '')
|
||||
|
||||
section_relevant = True
|
||||
if primary_section_paths and table_section:
|
||||
# 计算表格章节与所有主要章节的最高相似度
|
||||
max_sim = max(
|
||||
(_section_similarity(table_section, ps) for ps in primary_section_paths),
|
||||
default=0.0
|
||||
)
|
||||
if max_sim < 0.3:
|
||||
section_relevant = False
|
||||
elif primary_section_paths and not table_section:
|
||||
# 表格无章节信息时优雅降级：不做章节过滤，仅依赖主题分数
|
||||
pass
|
||||
|
||||
if not section_relevant:
|
||||
# 例外：如果表格标题/内容被查询直接提及，仍视为相关
|
||||
table_title = meta.get('title', '') or ''
|
||||
table_doc = ctx.get('doc', '') or ''
|
||||
if table_title and table_title in query:
|
||||
section_relevant = True
|
||||
elif table_doc and any(kw in table_doc for kw in query.split() if len(kw) >= 2):
|
||||
section_relevant = True
|
||||
|
||||
if not section_relevant:
|
||||
logger.debug(f"P1.5 跳过无关表格图片: path={table_section}, title={meta.get('title', '')}")
|
||||
continue # 跳过此表格的所有嵌入图片
|
||||
|
||||
# 标题/主题相关性过滤：基于预计算的关键词匹配评分
|
||||
# 如果最佳表格得分 >= 2，则过滤掉得分为 0 的表格
|
||||
_this_topic_score = _table_topic_scores.get(id(ctx), 0)
|
||||
if _this_topic_score < _table_topic_threshold:
|
||||
logger.debug(f"P1.5 跳过主题不匹配表格: topic_score={_this_topic_score}, threshold={_table_topic_threshold}, title={meta.get('title', '')}")
|
||||
continue
|
||||
|
||||
try:
|
||||
images_list = json.loads(meta['images_json'])
|
||||
for img_info in images_list:
|
||||
@@ -1354,6 +1792,18 @@ def rag():
|
||||
except Exception as e:
|
||||
logger.debug(f"创建会话失败: {e}")
|
||||
|
||||
# ==================== collections 历史推断（开发环境） ====================
|
||||
# 当 collections 未显式指定（或仅为默认 public_kb）且有会话历史时，
|
||||
# 从历史消息中推断上次使用的 KB，自动恢复以避免用户忘切 KB
|
||||
if (not collections or collections == ['public_kb']) and history:
|
||||
for _msg in reversed(history):
|
||||
if _msg.get("role") == "assistant":
|
||||
_meta = _msg.get("metadata", {})
|
||||
if isinstance(_meta, dict) and _meta.get("collections"):
|
||||
collections = _meta["collections"]
|
||||
logger.info(f"[KB推断] 从历史推断 collections: {collections}")
|
||||
break
|
||||
|
||||
# 提前获取 session_repo 引用，避免在生成器内部访问 current_app
|
||||
# （生成器执行时应用上下文可能已结束）
|
||||
session_repo_ref = None
|
||||
@@ -1453,6 +1903,9 @@ def rag():
|
||||
except Exception as e:
|
||||
logger.warning(f"意图分析失败: {e}，继续执行检索流程")
|
||||
|
||||
# 构建检索查询：使用改写后的完整问题（解决追问偏离问题）
|
||||
retrieval_query = intent.rewritten_query if (intent and intent.rewritten_query) else message
|
||||
|
||||
# 1.5 语义缓存检查（跳过检索+生成全流程）
|
||||
_semantic_cache_emb = None
|
||||
try:
|
||||
@@ -1461,9 +1914,13 @@ def rag():
|
||||
_sc = get_semantic_cache()
|
||||
_eng = _get_eng()
|
||||
if _sc and _eng and hasattr(_eng, 'embedding_model'):
|
||||
_semantic_cache_emb = _eng.embedding_model.encode(message)
|
||||
# 缓存 key 使用 retrieval_query + collections，避免不同上下文的追问命中错误缓存
|
||||
_cache_collections = ','.join(sorted(collections)) if collections else ''
|
||||
_cache_key_text = f"{retrieval_query}|{_cache_collections}"
|
||||
_semantic_cache_emb = _eng.embedding_model.encode(_cache_key_text)
|
||||
cached = _sc.get(_semantic_cache_emb)
|
||||
if cached is not None:
|
||||
# 防御性校验：必须是 RAG 回答缓存（非 intent_analyzer 缓存），且回答非空
|
||||
if cached is not None and cached.get("cache_type") == "rag_answer" and cached.get("answer"):
|
||||
logger.info(f"[语义缓存] 命中: {message[:50]}...")
|
||||
cached_answer = cached.get("answer", "")
|
||||
# 流式返回缓存的答案
|
||||
@@ -1504,7 +1961,7 @@ def rag():
|
||||
sub_queries = intent.sub_queries
|
||||
|
||||
search_result = search_hybrid(
|
||||
message,
|
||||
retrieval_query,
|
||||
top_k=RAG_SEARCH_TOP_K,
|
||||
candidates=RAG_SEARCH_CANDIDATES,
|
||||
allowed_collections=collections,
|
||||
@@ -1525,18 +1982,19 @@ def rag():
|
||||
metas = search_result.get('metadatas', [[]])[0]
|
||||
scores = search_result.get('scores', [[]])[0]
|
||||
|
||||
# 图片相关性提升：检测图片编号或强意图
|
||||
# 图片相关性提升：数据驱动检测（无硬编码关键词）
|
||||
import re
|
||||
figure_pattern = r'图\s*(\d+\.?\d*)'
|
||||
figure_matches = re.findall(figure_pattern, message)
|
||||
figure_matches = re.findall(figure_pattern, retrieval_query)
|
||||
has_figure_query = bool(figure_matches)
|
||||
|
||||
# Bug 4 修复：缩小 strong_image_keywords，移除歧义词
|
||||
# "过程线" 是水文术语不是图片意图，"图片"/"图表"/"如图" 太泛
|
||||
strong_image_keywords = ["示意图", "流程图", "结构图", "曲线图", "分布图", "图示", "看图", "显示图", "给我看"]
|
||||
has_image_intent = has_figure_query or any(kw in message for kw in strong_image_keywords)
|
||||
# 数据驱动：检查检索结果中是否有图片/图表类型切片
|
||||
has_image_data = any(
|
||||
m.get('chunk_type') in ('image', 'chart') for m in metas
|
||||
)
|
||||
has_image_intent = has_figure_query or has_image_data
|
||||
|
||||
# Bug 2 修复：不重排 contexts，只在 meta 里打标记给后续的 select_images 用
|
||||
# 给图片/图表切片打 boost 标记，供后续 select_images 使用
|
||||
if has_image_intent:
|
||||
for i, (doc, meta, score) in enumerate(zip(docs, metas, scores)):
|
||||
if meta.get('chunk_type') in ('image', 'chart'):
|
||||
@@ -1658,18 +2116,12 @@ def rag():
|
||||
missing_figures = referenced_figures - existing_figure_images
|
||||
missing_tables = referenced_tables - existing_table_images
|
||||
|
||||
# 计算主要章节（用于补充检索过滤）
|
||||
primary_sections_for_supplement = set()
|
||||
# 计算主要章节路径（用于补充检索过滤）
|
||||
primary_section_paths_for_supp = set()
|
||||
for ctx in text_contexts[:3]:
|
||||
section = ctx.get('meta', {}).get('section', '') or ctx.get('meta', {}).get('section_path', '')
|
||||
if section:
|
||||
section_num = re.search(r'(\d+\.\d+)', section)
|
||||
if not section_num:
|
||||
chapter_match = re.search(r'第\s*(\d+)\s*章', section)
|
||||
if chapter_match:
|
||||
section_num = chapter_match
|
||||
if section_num:
|
||||
primary_sections_for_supplement.add(section_num.group(1))
|
||||
primary_section_paths_for_supp.add(section)
|
||||
|
||||
if missing_figures or missing_tables:
|
||||
# 补充检索
|
||||
@@ -1726,14 +2178,18 @@ def rag():
|
||||
|
||||
if is_match:
|
||||
# 额外检查：图片章节是否与主要章节匹配
|
||||
# 避免补充检索到不相关的图片
|
||||
# 使用层级相似度判断，无需硬编码格式假设
|
||||
supp_section = supp_meta.get('section', '') or supp_meta.get('section_path', '')
|
||||
supp_section_num = re.search(r'(\d+\.\d+)', supp_section)
|
||||
supp_section_id = supp_section_num.group(1) if supp_section_num else None
|
||||
|
||||
# 如果图片章节不在主要章节中，跳过
|
||||
if primary_sections_for_supplement and supp_section_id and supp_section_id not in primary_sections_for_supplement:
|
||||
# 不是主要章节的图片，跳过
|
||||
if primary_section_paths_for_supp and supp_section:
|
||||
supp_max_sim = max(
|
||||
(_section_similarity(supp_section, ps) for ps in primary_section_paths_for_supp),
|
||||
default=0.0
|
||||
)
|
||||
if supp_max_sim < 0.3:
|
||||
continue
|
||||
elif primary_section_paths_for_supp and not supp_section:
|
||||
# 补充检索的图片无章节信息时优雅降级：跳过
|
||||
continue
|
||||
|
||||
# Bug 6a 修复：补充检索的图片也要做 full_description 替换
|
||||
@@ -1772,12 +2228,12 @@ def rag():
|
||||
import asyncio
|
||||
from knowledge.lazy_enhance import enhance_retrieved_chunks
|
||||
kb_name = collections[0] if collections else 'public_kb'
|
||||
asyncio.run(enhance_retrieved_chunks(contexts, message, kb_name))
|
||||
asyncio.run(enhance_retrieved_chunks(contexts, retrieval_query, kb_name))
|
||||
except Exception as e:
|
||||
logger.warning(f"懒加载增强失败: {e}")
|
||||
|
||||
# 3. 选择要展示的图片（Phase 5）
|
||||
selected_images = select_images(contexts, message)
|
||||
selected_images = select_images(contexts, retrieval_query)
|
||||
|
||||
# 调试事件：图片选择详情
|
||||
if IS_DEV:
|
||||
@@ -1785,16 +2241,32 @@ def rag():
|
||||
|
||||
# 4. 构建 prompt（Phase 6：LLM 图片感知）
|
||||
# Bug 1 修复：文本切片用于 top 5 名额竞争，图片描述不参与竞争
|
||||
text_contexts = _order_text_contexts_for_prompt(contexts, message, MAX_CONTEXT_CHUNKS,
|
||||
text_contexts = _order_text_contexts_for_prompt(contexts, retrieval_query, MAX_CONTEXT_CHUNKS,
|
||||
min_score=RERANK_CONTEXT_MIN_SCORE)
|
||||
# Phase 2：按字符预算构建上下文
|
||||
_is_comparison = intent and intent.intent == "comparison"
|
||||
if _is_enum_query(message) or _is_comparison:
|
||||
if _is_enum_query(retrieval_query) or _is_comparison:
|
||||
# 列举类 / 对比类查询：保持原始顺序，不做预算截断
|
||||
context_text = "\n\n".join([ctx.get('doc', '') for ctx in text_contexts])
|
||||
# 仍然注入 section_path 标题，帮助 LLM 区分不同章节
|
||||
enum_parts = []
|
||||
prev_section = None
|
||||
for ctx in text_contexts:
|
||||
meta = ctx.get('meta', {})
|
||||
section = meta.get('section', '') or meta.get('section_path', '')
|
||||
if section and section != prev_section:
|
||||
enum_parts.append(f"━ {section} ━")
|
||||
prev_section = section
|
||||
# 精简表格切片：去除冗余的语义增强前缀
|
||||
doc = _strip_semantic_prefix(ctx.get('doc', ''), meta.get('chunk_type', ''))
|
||||
enum_parts.append(doc)
|
||||
context_text = "\n\n".join(enum_parts)
|
||||
else:
|
||||
context_text = _build_context_with_budget(text_contexts, CONTEXT_MAX_CHARS, CONTEXT_SOFT_LIMIT)
|
||||
|
||||
# 表格救援：CrossEncoder 对表格评分偏低，导致表格被预算截断
|
||||
# 当查询涉及表格但上下文中没有表格数据时，从被截断的切片中补回
|
||||
context_text = _rescue_table_chunks(text_contexts, context_text, retrieval_query)
|
||||
|
||||
# Phase 4：计算置信度分数（top-3 平均 Rerank 分数）
|
||||
_top_scores = [ctx.get('score', 0) for ctx in text_contexts[:3]]
|
||||
_confidence_score = round(sum(_top_scores) / len(_top_scores), 4) if _top_scores else 0.0
|
||||
@@ -1802,6 +2274,17 @@ def rag():
|
||||
# Bug 6b 优化：直接使用 selected_images 中的 full_description
|
||||
# 这样 LLM 既能看到文本切片，也能知道图片内容
|
||||
if selected_images:
|
||||
# 区分表格嵌入图片和独立图片
|
||||
has_table_embedded_images = any(
|
||||
img.get('type') == 'table_image' for img in selected_images
|
||||
)
|
||||
# 检查上下文中是否有表格含嵌入图片
|
||||
has_table_with_images = any(
|
||||
ctx.get('meta', {}).get('chunk_type') == 'table'
|
||||
and ctx.get('meta', {}).get('images_json')
|
||||
for ctx in text_contexts
|
||||
)
|
||||
|
||||
image_descriptions = []
|
||||
for i, img in enumerate(selected_images, 1):
|
||||
# 直接使用 select_images 时带上的 full_description
|
||||
@@ -1814,6 +2297,16 @@ def rag():
|
||||
image_descriptions.append(f"【图片{i}】{full_desc}{source_info}")
|
||||
if image_descriptions:
|
||||
context_text += "\n\n【相关图片信息】\n" + "\n\n".join(image_descriptions)
|
||||
|
||||
# 根据图片类型给出不同的回答指令
|
||||
if has_table_embedded_images and has_table_with_images:
|
||||
context_text += (
|
||||
"\n\n【回答要求】参考资料中包含表格及其嵌入图片。"
|
||||
"请以**表格形式**呈现数据（保持原始表格结构），"
|
||||
"并在对应单元格中使用 `` 格式嵌入图片。"
|
||||
"不要将表格内容转为纯文本描述，不要把图片与表格分开展示。"
|
||||
)
|
||||
else:
|
||||
# 添加指令让 LLM 介绍图片
|
||||
context_text += "\n\n【回答要求】回答时请简要介绍每张图片的内容和用途。"
|
||||
|
||||
@@ -1847,7 +2340,7 @@ def rag():
|
||||
)
|
||||
enhanced_context = instruction_instruction + "\n\n" + enhanced_context
|
||||
|
||||
if _is_enum_query(message):
|
||||
if _is_enum_query(retrieval_query):
|
||||
enum_instruction = (
|
||||
"\n\n【回答要求】如果参考资料中包含编号列表、禁止情形、要求或条款，"
|
||||
"请按资料中的原始顺序完整列出；不要合并相邻条目，不要跳项，"
|
||||
@@ -1914,6 +2407,9 @@ def rag():
|
||||
# else: 没有匹配到，保留原选择（不再截断到1张）
|
||||
# else: LLM 没有提图号，保留原选择（不再截断到1张）
|
||||
|
||||
# 后置图片过滤：用回答内容反向筛选图片，确保图片与回答一致
|
||||
selected_images = _filter_images_by_answer(selected_images, full_answer_text)
|
||||
|
||||
rich_media = {'images': selected_images, 'tables': [], 'sections': []}
|
||||
|
||||
# 7. 去掉 LLM 添加的数字引用标记，避免与后端引用重复
|
||||
@@ -1941,6 +2437,8 @@ def rag():
|
||||
assistant_metadata['sources'] = sources
|
||||
if citation_result.get('citations'):
|
||||
assistant_metadata['citations'] = citation_result['citations']
|
||||
# 记录本次检索使用的向量库（用于后续追问时自动恢复 KB 选择）
|
||||
assistant_metadata['collections'] = collections
|
||||
session_repo_ref.add_message(session_id, 'assistant', filtered_answer, assistant_metadata)
|
||||
# 更新会话最后活跃时间
|
||||
if hasattr(session_repo_ref, 'update_last_active'):
|
||||
@@ -1990,9 +2488,12 @@ def rag():
|
||||
from core.engine import get_engine as _get_eng
|
||||
_eng = _get_eng()
|
||||
if _eng and hasattr(_eng, 'embedding_model'):
|
||||
_semantic_cache_emb = _eng.embedding_model.encode(message)
|
||||
_cache_collections = ','.join(sorted(collections)) if collections else ''
|
||||
_cache_key_text = f"{retrieval_query}|{_cache_collections}"
|
||||
_semantic_cache_emb = _eng.embedding_model.encode(_cache_key_text)
|
||||
if _semantic_cache_emb is not None:
|
||||
_sc.set(_semantic_cache_emb, {
|
||||
"cache_type": "rag_answer",
|
||||
"answer": filtered_answer,
|
||||
"sources": sources,
|
||||
"citations": citation_result.get("citations", []),
|
||||
|
||||
@@ -29,6 +29,7 @@ RAG 核心引擎
|
||||
|
||||
import os
|
||||
import gc
|
||||
import re
|
||||
import time
|
||||
import logging
|
||||
import threading
|
||||
@@ -112,7 +113,7 @@ except ImportError:
|
||||
RERANK_DEVICE = "auto"
|
||||
RERANK_USE_ONNX = False
|
||||
RERANK_BACKEND = "local"
|
||||
RERANK_CLOUD_MODEL = "qwen3-rerank"
|
||||
RERANK_CLOUD_MODEL = "xop3qwen8breranker"
|
||||
RERANK_CLOUD_API_KEY = ""
|
||||
RERANK_CLOUD_BASE_URL = "https://dashscope.aliyuncs.com/compatible-api/v1/reranks"
|
||||
RERANK_CLOUD_TIMEOUT = 15
|
||||
@@ -272,8 +273,7 @@ class CloudReranker:
|
||||
body = {
|
||||
"model": self.model,
|
||||
"query": query,
|
||||
"documents": documents,
|
||||
"top_n": len(documents)
|
||||
"documents": documents
|
||||
}
|
||||
|
||||
session = self._get_session()
|
||||
@@ -1231,6 +1231,7 @@ class RAGEngine:
|
||||
logger.warning(f"扩展连续切片失败: {e}")
|
||||
return {'ids': [], 'documents': [], 'metadatas': []}
|
||||
|
||||
# 扩展同 section 的 text 邻居
|
||||
where_filter = {"$and": [{"source": source}, {"chunk_type": "text"}]}
|
||||
if section:
|
||||
where_filter["$and"].append({"section": section})
|
||||
@@ -1241,6 +1242,20 @@ class RAGEngine:
|
||||
if not neighbors.get('ids') or len(neighbors.get('ids', [])) <= 1:
|
||||
neighbors = _get_neighbors({"$and": [{"source": source}, {"chunk_type": "text"}]})
|
||||
|
||||
# 同时扩展同 section 的 table 邻居（table 切片的 rerank 分数往往偏低，
|
||||
# 但与同 section 的 text 切片属于同一语义单元，不应割裂）
|
||||
table_where = {"$and": [{"source": source}, {"chunk_type": "table"}]}
|
||||
if section:
|
||||
table_where["$and"].append({"section": section})
|
||||
table_neighbors = _get_neighbors(table_where)
|
||||
|
||||
# 当 section 为空时，table 查询只有 source 条件，可能拉入大量无关表格，
|
||||
# 缩小 chunk_index 窗口至 ±1 以降低噪音；有 section 时使用正常窗口
|
||||
if section:
|
||||
_t_before, _t_after = CONTEXT_EXPANSION_BEFORE, CONTEXT_EXPANSION_AFTER
|
||||
else:
|
||||
_t_before, _t_after = 1, 1
|
||||
|
||||
neighbor_rows = []
|
||||
for n_id, n_doc, n_meta in zip(
|
||||
neighbors.get('ids', []),
|
||||
@@ -1253,6 +1268,18 @@ class RAGEngine:
|
||||
if seed_index - CONTEXT_EXPANSION_BEFORE <= n_index <= seed_index + CONTEXT_EXPANSION_AFTER:
|
||||
neighbor_rows.append((n_index, n_id, n_doc, n_meta))
|
||||
|
||||
# 同 section 的 table 邻居也加入扩展范围
|
||||
for n_id, n_doc, n_meta in zip(
|
||||
table_neighbors.get('ids', []),
|
||||
table_neighbors.get('documents', []),
|
||||
table_neighbors.get('metadatas', [])
|
||||
):
|
||||
n_index = self._to_int(n_meta.get('chunk_index'))
|
||||
if n_index is None:
|
||||
continue
|
||||
if seed_index - _t_before <= n_index <= seed_index + _t_after:
|
||||
neighbor_rows.append((n_index, n_id, n_doc, n_meta))
|
||||
|
||||
seed_neighbors_added = 0
|
||||
for n_index, n_id, n_doc, n_meta in sorted(neighbor_rows, key=lambda row: row[0]):
|
||||
if len(items) >= max_chunks:
|
||||
@@ -2041,21 +2068,33 @@ class RAGEngine:
|
||||
"content": (
|
||||
"你是一个严谨的知识库问答助手。"
|
||||
"你必须且只能根据用户提供的【参考资料】回答问题。"
|
||||
"参考资料中每段内容前标有章节路径（━格式），请注意区分不同章节的内容，"
|
||||
"特别当不同章节标题相似或包含相同关键词时，务必根据章节路径准确定位，不要混淆。"
|
||||
"如果参考资料中有答案，必须引用对应内容回答，并在回答末尾标注引用编号（如[1]、[2]）。"
|
||||
"如果参考资料中确实没有相关信息，简短说明即可，不要编造或补充资料外的内容。"
|
||||
"禁止使用参考资料以外的知识进行补充或推测。"
|
||||
"【重要-表格处理规则】当用户询问表格、要求展示表格内容时，你必须将参考资料中的 Markdown 表格原样输出（保留 | 分隔符和表格结构），"
|
||||
"不要仅用文字描述表格存在或仅列出章节名称。如果参考资料中多个章节都有表格，"
|
||||
"优先展示与用户问题最相关的表格完整内容。"
|
||||
)
|
||||
})
|
||||
|
||||
# 添加当前问题（带上下文）- 强化指令
|
||||
if context:
|
||||
# 检测用户问题是否涉及表格，加入针对性指令
|
||||
_table_hint = ""
|
||||
# 检测上下文中是否包含 Markdown 表格（数据驱动，无需硬编码关键词）
|
||||
_has_table_in_context = bool(re.search(r'\|.+\|', context)) if context else False
|
||||
if _has_table_in_context:
|
||||
_table_hint = "\n注意：参考资料中包含 Markdown 格式的表格数据，请务必将相关表格以原始 Markdown 表格格式完整展示在回答中，不要仅用文字描述。"
|
||||
|
||||
user_message = f"""【参考资料】
|
||||
{context}
|
||||
|
||||
【用户问题】
|
||||
{query}
|
||||
|
||||
请仔细阅读以上全部参考资料后回答。如果参考资料中包含相关内容，必须引用回答并标注编号。如果资料中没有相关信息，请明确说明。"""
|
||||
请仔细阅读以上全部参考资料后回答。注意参考资料中标有章节路径，请根据章节路径准确定位相关内容。如果参考资料中包含相关内容，必须引用回答并标注编号。如果资料中没有相关信息，请明确说明。{_table_hint}"""
|
||||
else:
|
||||
user_message = query
|
||||
|
||||
|
||||
@@ -83,9 +83,16 @@ class IntentAnalyzer:
|
||||
根据对话历史和当前用户消息，输出一个 JSON 对象，包含以下字段：
|
||||
|
||||
1. **rewritten_query**: 改写后的完整问题
|
||||
- 如果问题包含指代（如"这两张图片"、"继续说"），将其改写为完整、独立的问题
|
||||
- 例如："分析一下这两张图片" → "分析一下对话历史中提到的图片"
|
||||
- 如果问题本身已经完整，直接返回原文
|
||||
- **指代消解**：如果问题包含指代（如"这两张图片"、"继续说"），将其改写为完整、独立的问题
|
||||
- 例如："分析一下这两张图片" → "分析一下对话历史中提到的图片"
|
||||
- **追问补全**：如果问题是省略式追问（省略了上一轮讨论的主题实体），必须补全为完整问题
|
||||
- 判断方法：当前问题缺少主语/宾语，且对话历史中可以推断出省略的实体
|
||||
- 补全方法：从上一轮用户问题中提取主题实体，与追问组合成完整问题
|
||||
- 例如：
|
||||
- 上一轮问"吸烟点C1类是什么区？"，追问"有完整表格吗？" → "吸烟点C1类有完整表格吗？"
|
||||
- 上一轮问"三峡工程的投资情况"，追问"建设地点在哪？" → "三峡工程的建设地点在哪？"
|
||||
- 上一轮问"货源投放有哪些原则？"，追问"具体内容是什么？" → "货源投放原则的具体内容是什么？"
|
||||
- 如果问题本身已经完整且独立，直接返回原文
|
||||
|
||||
2. **use_context**: 布尔值
|
||||
- true: 问题依赖历史对话中的信息，答案已经在历史回答中
|
||||
@@ -105,7 +112,9 @@ class IntentAnalyzer:
|
||||
- 推理类（intent="reasoning"）：生成最多2个子查询
|
||||
* 原问题的检索查询
|
||||
* 一个补充角度的检索查询（如原因、背景、影响等），帮助获取更全面的上下文
|
||||
- 其他类（factual/instruction/other）：严格只生成1个子查询（原问题）
|
||||
- 其他类（factual/instruction/other）：严格只生成1个子查询
|
||||
* 子查询应基于 rewritten_query（改写后的完整问题），而非用户原始输入
|
||||
* 例如：追问"有完整表格吗？"改写为"吸烟点C1类有完整表格吗？"后，子查询应为"吸烟点C1类的完整表格内容"
|
||||
- 不要为同一实体生成语义重叠的查询
|
||||
- 子查询应保持原问题的关键词，长度20-60字符为宜
|
||||
|
||||
@@ -307,7 +316,8 @@ class IntentAnalyzer:
|
||||
|
||||
if cache_emb is not None:
|
||||
cached = cache.get(cache_emb)
|
||||
if cached:
|
||||
# 确保缓存条目是意图分析结果（非 RAG 回答缓存）
|
||||
if cached and cached.get("cache_type") != "rag_answer":
|
||||
logger.info(f"意图分析缓存命中: {cached.get('reason', '')[:50]}")
|
||||
return IntentAnalysis.from_dict(cached)
|
||||
else:
|
||||
@@ -377,9 +387,11 @@ class IntentAnalyzer:
|
||||
intent=intent_type
|
||||
)
|
||||
|
||||
# 存入语义缓存
|
||||
# 存入语义缓存（标记类型，避免与 RAG 回答缓存混淆）
|
||||
if cache and cache_emb is not None:
|
||||
cache.set(cache_emb, analysis.to_dict())
|
||||
cache_data = analysis.to_dict()
|
||||
cache_data["cache_type"] = "intent_analysis"
|
||||
cache.set(cache_emb, cache_data)
|
||||
|
||||
# 存入精确匹配缓存
|
||||
if len(self._exact_cache) < self._exact_cache_max:
|
||||
@@ -437,6 +449,7 @@ class IntentAnalyzer:
|
||||
if not history:
|
||||
return "（无历史对话）"
|
||||
|
||||
import re
|
||||
parts = []
|
||||
|
||||
# 提取最近 3 轮对话
|
||||
@@ -445,6 +458,7 @@ class IntentAnalyzer:
|
||||
for msg in recent_history:
|
||||
role = "用户" if msg.get("role") == "user" else "助手"
|
||||
content = msg.get("content", "")
|
||||
original_content = content # 保留原始内容用于结构化提取
|
||||
|
||||
# 截断过长的内容
|
||||
if len(content) > 500:
|
||||
@@ -452,9 +466,10 @@ class IntentAnalyzer:
|
||||
|
||||
parts.append(f"【{role}】{content}")
|
||||
|
||||
# 提取图片信息
|
||||
# 提取结构化信息（从 assistant 消息中提取章节、表格、来源等）
|
||||
metadata = msg.get("metadata", {})
|
||||
if isinstance(metadata, dict):
|
||||
# 已有的图片提取
|
||||
images = metadata.get("images", [])
|
||||
if images:
|
||||
for img in images[:3]:
|
||||
@@ -463,6 +478,43 @@ class IntentAnalyzer:
|
||||
img_type = img.get("type", "图片")
|
||||
parts.append(f" └─ {img_type}: {desc}")
|
||||
|
||||
# 来源文件提取
|
||||
sources = metadata.get("sources", [])
|
||||
if sources:
|
||||
source_names = []
|
||||
for s in sources[:3]:
|
||||
if isinstance(s, dict):
|
||||
name = s.get("source", "") or s.get("name", "")
|
||||
if name:
|
||||
source_names.append(name)
|
||||
elif isinstance(s, str):
|
||||
source_names.append(s)
|
||||
if source_names:
|
||||
parts.append(f" └─ 来源文件: {', '.join(source_names)}")
|
||||
|
||||
# collections（检索知识库）提取
|
||||
colls = metadata.get("collections", [])
|
||||
if colls:
|
||||
parts.append(f" └─ 检索知识库: {', '.join(colls)}")
|
||||
|
||||
# 从 assistant 原始内容中提取章节路径和表格结构
|
||||
if role == "助手" and original_content:
|
||||
# 提取章节路径：━ xxx ━ 格式
|
||||
sections = re.findall(r'━\s*(.+?)\s*━', original_content)
|
||||
if sections:
|
||||
unique_sections = list(dict.fromkeys(sections)) # 去重保序
|
||||
parts.append(f" └─ 涉及章节: {'; '.join(unique_sections[:3])}")
|
||||
|
||||
# 提取表格列名：| A | B | C | 格式的表头行
|
||||
table_headers = re.findall(r'^\|\s*(.+?)\s*\|', original_content, re.MULTILINE)
|
||||
if table_headers:
|
||||
# 取第一个表格的列名
|
||||
first_header = table_headers[0]
|
||||
cols = [c.strip() for c in first_header.split('|') if c.strip()]
|
||||
# 排除分隔符行（--- 格式）
|
||||
if cols and not all(re.match(r'^[-:]+$', c) for c in cols):
|
||||
parts.append(f" └─ 含表格，列名: {', '.join(cols[:6])}")
|
||||
|
||||
# 添加图片上下文
|
||||
if context_images:
|
||||
parts.append("\n【上下文中的图片】")
|
||||
|
||||
@@ -393,6 +393,11 @@ class KnowledgeBaseManager(
|
||||
if len(chunks) < 2:
|
||||
return chunks
|
||||
|
||||
# 检测页码是否可靠：若所有 chunk 的 page_start 相同（如 Word 文档 page_idx 全为 0），
|
||||
# 则页码信息不可用，需要启用降级合并规则
|
||||
page_values = set(getattr(c, 'page_start', 0) for c in chunks)
|
||||
pages_unavailable = len(page_values) <= 1
|
||||
|
||||
merged_chunks = []
|
||||
i = 0
|
||||
merge_count = 0
|
||||
@@ -405,6 +410,7 @@ class KnowledgeBaseManager(
|
||||
# 查找下一个表格（跳过中间的"续表"文本）
|
||||
next_table_idx = None
|
||||
next_chunk = None
|
||||
intermediate_texts = [] # 收集中间文本用于降级判断
|
||||
|
||||
for j in range(i + 1, min(i + 4, len(chunks))): # 最多向前看3个切片
|
||||
candidate = chunks[j]
|
||||
@@ -419,7 +425,11 @@ class KnowledgeBaseManager(
|
||||
elif candidate_type == 'text' and ('续表' in candidate_title or '续表' in candidate_content):
|
||||
# 遇到"续表"文本，继续查找下一个表格
|
||||
continue
|
||||
elif candidate_type not in ('text',):
|
||||
elif candidate_type == 'text':
|
||||
# 非"续表"文本，收集后停止查找
|
||||
intermediate_texts.append(candidate)
|
||||
break
|
||||
else:
|
||||
# 遇到非文本类型，停止查找
|
||||
break
|
||||
|
||||
@@ -439,24 +449,59 @@ class KnowledgeBaseManager(
|
||||
# 获取内容（用于检测"续表"）
|
||||
next_content = getattr(next_chunk, 'content', '')
|
||||
|
||||
# 通用/无意义标题集合，这些标题不能用于"标题相似"判定
|
||||
_GENERIC_TITLES = {'表格', 'table', '表格', ''}
|
||||
|
||||
# 判断是否为跨页表格
|
||||
is_cross_page = False
|
||||
|
||||
# 规则1: 页码连续（如果页码有效）
|
||||
page_valid = curr_page_end > 0 and next_page_start > 0
|
||||
if page_valid and curr_page_end + 1 == next_page_start:
|
||||
is_cross_page = True
|
||||
# 页码连续时，还需标题匹配或为通用标题才合并
|
||||
# 避免把不同页面上不相关的表格错误合并
|
||||
if curr_title == next_title or curr_title in _GENERIC_TITLES and next_title in _GENERIC_TITLES:
|
||||
is_cross_page = True
|
||||
elif curr_title and next_title:
|
||||
clean_next_r1 = next_title.replace('续表', '').strip()
|
||||
if curr_title in clean_next_r1 or clean_next_r1 in curr_title:
|
||||
is_cross_page = True
|
||||
|
||||
# 规则2: 第二个表格标题或内容包含"续表"
|
||||
elif '续表' in next_title or '续表' in next_content:
|
||||
is_cross_page = True
|
||||
|
||||
# 规则3: 标题相似（去掉"续表"后比较）
|
||||
elif curr_title and next_title:
|
||||
# 排除通用标题（如"表格"），防止把所有标题为"表格"的相邻表格都误合并
|
||||
elif (curr_title and next_title
|
||||
and curr_title not in _GENERIC_TITLES
|
||||
and next_title not in _GENERIC_TITLES):
|
||||
clean_next = next_title.replace('续表', '').strip()
|
||||
if curr_title in clean_next or clean_next in curr_title:
|
||||
if clean_next and (curr_title in clean_next or clean_next in curr_title):
|
||||
is_cross_page = True
|
||||
|
||||
# 规则4（降级）: 页码不可用（如 Word 文档 page_idx 全为 0）
|
||||
# 仅当页码信息缺失时才启用此规则，避免 PDF 正常页码时被误合并
|
||||
if (not is_cross_page
|
||||
and pages_unavailable
|
||||
and curr_title in _GENERIC_TITLES
|
||||
and next_title in _GENERIC_TITLES):
|
||||
# 检查中间文本是否暗示跨页延续（空、短文本、续表标记等）
|
||||
has_separating_content = False
|
||||
for text_chunk in intermediate_texts:
|
||||
tc = (getattr(text_chunk, 'content', '') or '').strip()
|
||||
tt = (getattr(text_chunk, 'title', '') or '').strip()
|
||||
if not tc:
|
||||
continue # 空文本不算分隔
|
||||
if '续表' in tc or '续表' in tt:
|
||||
continue # 续表标记，说明是跨页
|
||||
# 有实质性中间内容（如分类标题"A3类：xxx"），不合并
|
||||
has_separating_content = True
|
||||
break
|
||||
if not has_separating_content:
|
||||
is_cross_page = True
|
||||
logger.debug(f"降级合并(页码不可用): '{curr_title}' + '{next_title}'")
|
||||
|
||||
if is_cross_page:
|
||||
# 执行合并
|
||||
merge_count += 1
|
||||
@@ -469,14 +514,27 @@ class KnowledgeBaseManager(
|
||||
# 合并两个表格的 HTML
|
||||
current.table_html = curr_html + '\n' + next_html
|
||||
|
||||
# 合并 image_path 到 images
|
||||
# 合并 image_path 和嵌入图片到 images
|
||||
curr_img = getattr(current, 'image_path', None)
|
||||
next_img = getattr(next_chunk, 'image_path', None)
|
||||
merged_images = []
|
||||
if curr_img:
|
||||
curr_images = getattr(current, 'images', None) or []
|
||||
next_images = getattr(next_chunk, 'images', None) or []
|
||||
|
||||
# 合并两个表格的所有图片（image_path + 嵌入图片）
|
||||
merged_images = list(curr_images) # 保留当前表格的嵌入图片
|
||||
# 添加 image_path 图片（如果不在列表中）
|
||||
existing_ids = {img.get('id', '') for img in merged_images if isinstance(img, dict)}
|
||||
if curr_img and curr_img not in existing_ids:
|
||||
merged_images.append({'id': curr_img, 'page': curr_page_end})
|
||||
if next_img:
|
||||
existing_ids.add(curr_img)
|
||||
for img in next_images: # 添加下一个表格的嵌入图片
|
||||
img_id = img.get('id', '') if isinstance(img, dict) else ''
|
||||
if img_id and img_id not in existing_ids:
|
||||
merged_images.append(img)
|
||||
existing_ids.add(img_id)
|
||||
if next_img and next_img not in existing_ids:
|
||||
merged_images.append({'id': next_img, 'page': next_page_start})
|
||||
|
||||
if merged_images:
|
||||
current.images = merged_images
|
||||
# 保留第一个图片作为主 image_path
|
||||
@@ -525,7 +583,7 @@ class KnowledgeBaseManager(
|
||||
try:
|
||||
from config import get_llm_client, DASHSCOPE_MODEL
|
||||
client = get_llm_client()
|
||||
summary = call_llm(client, prompt, DASHSCOPE_MODEL, max_tokens=100)
|
||||
summary = call_llm(client, prompt, DASHSCOPE_MODEL, max_tokens=512)
|
||||
return summary.strip() if summary else ""
|
||||
except Exception as e:
|
||||
logger.warning(f"生成表格摘要失败: {e}")
|
||||
@@ -584,7 +642,7 @@ class KnowledgeBaseManager(
|
||||
]
|
||||
}
|
||||
],
|
||||
max_tokens=200
|
||||
max_tokens=512
|
||||
)
|
||||
|
||||
description = response.choices[0].message.content
|
||||
|
||||
Reference in New Issue
Block a user