dj
ecad9ccd82
主要更新： - 新增 transform 意图：支持 Word/Excel/Markdown 格式互转 - 新增 edit 意图：使用 LLM 润色编辑文档内容 - 智能指令接口增加异步执行模式（async_execute 参数） - 修复 Word 模板导出文档损坏问题（改用临时文件方式） - 优化 intent_parser 增加 transform/edit 关键词识别 新增文件： - app/api/endpoints/instruction.py: 智能指令 API 端点 - app/services/multi_doc_reasoning_service.py: 多文档推理服务 其他优化： - RAG 服务混合搜索（BM25 + 向量）融合 - 模板填充服务表头匹配增强 - Word AI 解析服务返回结构完善 - 前端 InstructionChat 组件对接真实 API
447 lines
15 KiB
Python
447 lines
15 KiB
Python
"""
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多文档关联推理服务
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跨文档信息关联和推理
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"""
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import logging
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import re
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from typing import Any, Dict, List, Optional, Set, Tuple
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from collections import defaultdict
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from app.services.llm_service import llm_service
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from app.services.rag_service import rag_service
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logger = logging.getLogger(__name__)
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class MultiDocReasoningService:
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"""
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多文档关联推理服务
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功能：
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1. 实体跨文档追踪 - 追踪同一实体在不同文档中的描述
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2. 关系抽取与推理 - 抽取实体间关系并进行推理
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3. 信息补全 - 根据多个文档的信息互补填充缺失数据
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4. 冲突检测 - 检测不同文档间的信息冲突
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"""
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def __init__(self):
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self.llm = llm_service
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async def analyze_cross_documents(
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self,
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documents: List[Dict[str, Any]],
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query: Optional[str] = None,
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entity_types: Optional[List[str]] = None
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) -> Dict[str, Any]:
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"""
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跨文档分析
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Args:
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documents: 文档列表
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query: 查询意图（可选）
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entity_types: 要追踪的实体类型列表，如 ["机构", "人物", "地点", "数量"]
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Returns:
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跨文档分析结果
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"""
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if not documents:
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return {"success": False, "error": "没有可用的文档"}
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entity_types = entity_types or ["机构", "数量", "时间", "地点"]
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try:
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# 1. 提取各文档中的实体
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entities_per_doc = await self._extract_entities_from_docs(documents, entity_types)
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# 2. 跨文档实体对齐
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aligned_entities = self._align_entities_across_docs(entities_per_doc)
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# 3. 关系抽取
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relations = await self._extract_relations(documents)
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# 4. 构建知识图谱
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knowledge_graph = self._build_knowledge_graph(aligned_entities, relations)
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# 5. 信息补全
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completed_info = await self._complete_missing_info(knowledge_graph, documents)
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# 6. 冲突检测
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conflicts = self._detect_conflicts(aligned_entities)
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return {
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"success": True,
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"entities": aligned_entities,
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"relations": relations,
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"knowledge_graph": knowledge_graph,
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"completed_info": completed_info,
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"conflicts": conflicts,
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"summary": self._generate_summary(aligned_entities, conflicts)
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}
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except Exception as e:
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logger.error(f"跨文档分析失败: {e}")
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return {"success": False, "error": str(e)}
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async def _extract_entities_from_docs(
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self,
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documents: List[Dict[str, Any]],
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entity_types: List[str]
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) -> List[Dict[str, Any]]:
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"""从各文档中提取实体"""
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entities_per_doc = []
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for idx, doc in enumerate(documents):
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doc_id = doc.get("_id", f"doc_{idx}")
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content = doc.get("content", "")[:8000] # 限制长度
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# 使用 LLM 提取实体
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prompt = f"""从以下文档中提取指定的实体类型信息。
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实体类型: {', '.join(entity_types)}
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文档内容:
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{content}
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请按以下 JSON 格式输出（只需输出 JSON）：
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{{
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"entities": [
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{{"type": "机构", "name": "实体名称", "value": "相关数值（如有）", "context": "上下文描述"}},
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...
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]
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}}
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只提取在文档中明确提到的实体，不要推测。"""
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messages = [
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{"role": "system", "content": "你是一个实体提取专家。请严格按JSON格式输出。"},
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{"role": "user", "content": prompt}
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]
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try:
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response = await self.llm.chat(messages=messages, temperature=0.1, max_tokens=3000)
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content_response = self.llm.extract_message_content(response)
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# 解析 JSON
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import json
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import re
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cleaned = content_response.strip()
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json_match = re.search(r'\{[\s\S]*\}', cleaned)
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if json_match:
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result = json.loads(json_match.group())
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entities = result.get("entities", [])
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entities_per_doc.append({
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"doc_id": doc_id,
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"doc_name": doc.get("metadata", {}).get("original_filename", f"文档{idx+1}"),
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"entities": entities
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})
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logger.info(f"文档 {doc_id} 提取到 {len(entities)} 个实体")
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except Exception as e:
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logger.warning(f"文档 {doc_id} 实体提取失败: {e}")
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return entities_per_doc
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def _align_entities_across_docs(
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self,
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entities_per_doc: List[Dict[str, Any]]
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) -> Dict[str, List[Dict[str, Any]]]:
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"""
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跨文档实体对齐
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将同一实体在不同文档中的描述进行关联
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"""
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aligned: Dict[str, List[Dict[str, Any]]] = defaultdict(list)
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for doc_data in entities_per_doc:
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doc_id = doc_data["doc_id"]
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doc_name = doc_data["doc_name"]
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for entity in doc_data.get("entities", []):
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entity_name = entity.get("name", "")
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if not entity_name:
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continue
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# 标准化实体名（去除空格和括号内容）
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normalized = self._normalize_entity_name(entity_name)
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aligned[normalized].append({
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"original_name": entity_name,
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"type": entity.get("type", "未知"),
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"value": entity.get("value", ""),
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"context": entity.get("context", ""),
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"source_doc": doc_name,
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"source_doc_id": doc_id
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})
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# 合并相同实体
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result = {}
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for normalized, appearances in aligned.items():
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if len(appearances) > 1:
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result[normalized] = appearances
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logger.info(f"实体对齐: {normalized} 在 {len(appearances)} 个文档中出现")
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return result
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def _normalize_entity_name(self, name: str) -> str:
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"""标准化实体名称"""
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# 去除空格
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name = name.strip()
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# 去除括号内容
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name = re.sub(r'[（(].*?[）)]', '', name)
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# 去除"第X名"等
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name = re.sub(r'^第\d+[名位个]', '', name)
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return name.strip()
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async def _extract_relations(
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self,
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documents: List[Dict[str, Any]]
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) -> List[Dict[str, str]]:
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"""从文档中抽取关系"""
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relations = []
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# 合并所有文档内容
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combined_content = "\n\n".join([
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f"【{doc.get('metadata', {}).get('original_filename', f'文档{i}')}】\n{doc.get('content', '')[:3000]}"
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for i, doc in enumerate(documents)
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])
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prompt = f"""从以下文档内容中抽取实体之间的关系。
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文档内容:
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{combined_content[:8000]}
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请识别以下类型的关系：
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- 包含关系 (A包含B)
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- 隶属关系 (A隶属于B)
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- 合作关系 (A与B合作)
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- 对比关系 (A vs B)
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- 时序关系 (A先于B发生)
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请按以下 JSON 格式输出（只需输出 JSON）：
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{{
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"relations": [
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{{"entity1": "实体1", "entity2": "实体2", "relation": "关系类型", "description": "关系描述"}},
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...
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]
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}}
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如果没有找到明确的关系，返回空数组。"""
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messages = [
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{"role": "system", "content": "你是一个关系抽取专家。请严格按JSON格式输出。"},
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{"role": "user", "content": prompt}
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]
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try:
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response = await self.llm.chat(messages=messages, temperature=0.1, max_tokens=3000)
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content_response = self.llm.extract_message_content(response)
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import json
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import re
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cleaned = content_response.strip()
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json_match = re.search(r'\{{[\s\S]*\}}', cleaned)
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if json_match:
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result = json.loads(json_match.group())
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relations = result.get("relations", [])
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logger.info(f"抽取到 {len(relations)} 个关系")
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except Exception as e:
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logger.warning(f"关系抽取失败: {e}")
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return relations
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def _build_knowledge_graph(
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self,
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aligned_entities: Dict[str, List[Dict[str, Any]]],
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relations: List[Dict[str, str]]
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) -> Dict[str, Any]:
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"""构建知识图谱"""
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nodes = []
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edges = []
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node_ids = set()
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# 添加实体节点
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for entity_name, appearances in aligned_entities.items():
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if len(appearances) < 1:
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continue
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first_appearance = appearances[0]
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node_id = f"entity_{len(nodes)}"
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# 收集该实体在所有文档中的值
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values = [a.get("value", "") for a in appearances if a.get("value")]
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primary_value = values[0] if values else ""
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nodes.append({
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"id": node_id,
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"name": entity_name,
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"type": first_appearance.get("type", "未知"),
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"value": primary_value,
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"occurrence_count": len(appearances),
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"sources": [a.get("source_doc", "") for a in appearances]
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})
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node_ids.add(entity_name)
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# 添加关系边
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for relation in relations:
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entity1 = self._normalize_entity_name(relation.get("entity1", ""))
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entity2 = self._normalize_entity_name(relation.get("entity2", ""))
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if entity1 in node_ids and entity2 in node_ids:
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edges.append({
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"source": entity1,
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"target": entity2,
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"relation": relation.get("relation", "相关"),
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"description": relation.get("description", "")
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})
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return {
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"nodes": nodes,
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"edges": edges,
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"stats": {
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"entity_count": len(nodes),
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"relation_count": len(edges)
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}
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}
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async def _complete_missing_info(
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self,
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knowledge_graph: Dict[str, Any],
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documents: List[Dict[str, Any]]
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) -> List[Dict[str, Any]]:
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"""根据多个文档补全信息"""
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completed = []
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for node in knowledge_graph.get("nodes", []):
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if not node.get("value") and node.get("occurrence_count", 0) > 1:
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# 实体在多个文档中出现但没有数值，尝试从 RAG 检索补充
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query = f"{node['name']} 数值 数据"
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results = rag_service.retrieve(query, top_k=3, min_score=0.3)
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if results:
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completed.append({
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"entity": node["name"],
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"type": node.get("type", "未知"),
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"source": "rag_inference",
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"context": results[0].get("content", "")[:200],
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"confidence": results[0].get("score", 0)
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})
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return completed
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def _detect_conflicts(
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self,
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aligned_entities: Dict[str, List[Dict[str, Any]]]
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) -> List[Dict[str, Any]]:
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"""检测不同文档间的信息冲突"""
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conflicts = []
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for entity_name, appearances in aligned_entities.items():
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if len(appearances) < 2:
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continue
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# 检查数值冲突
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values = {}
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for appearance in appearances:
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val = appearance.get("value", "")
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if val:
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source = appearance.get("source_doc", "未知来源")
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values[source] = val
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if len(values) > 1:
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unique_values = set(values.values())
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if len(unique_values) > 1:
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conflicts.append({
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"entity": entity_name,
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"type": "value_conflict",
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"details": values,
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"description": f"实体 '{entity_name}' 在不同文档中有不同数值: {values}"
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})
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return conflicts
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def _generate_summary(
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self,
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aligned_entities: Dict[str, List[Dict[str, Any]]],
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conflicts: List[Dict[str, Any]]
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) -> str:
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"""生成摘要"""
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summary_parts = []
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total_entities = sum(len(appearances) for appearances in aligned_entities.values())
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multi_doc_entities = sum(1 for appearances in aligned_entities.values() if len(appearances) > 1)
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summary_parts.append(f"跨文档分析完成：发现 {total_entities} 个实体")
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summary_parts.append(f"其中 {multi_doc_entities} 个实体在多个文档中被提及")
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if conflicts:
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summary_parts.append(f"检测到 {len(conflicts)} 个潜在冲突")
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return "; ".join(summary_parts)
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async def answer_cross_doc_question(
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self,
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question: str,
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documents: List[Dict[str, Any]]
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) -> Dict[str, Any]:
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"""
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跨文档问答
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Args:
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question: 问题
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documents: 文档列表
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Returns:
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答案结果
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"""
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# 先进行跨文档分析
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analysis_result = await self.analyze_cross_documents(documents, query=question)
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# 构建上下文
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context_parts = []
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# 添加实体信息
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for entity_name, appearances in analysis_result.get("entities", {}).items():
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contexts = [f"{a.get('source_doc')}: {a.get('context', '')}" for a in appearances[:2]]
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if contexts:
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context_parts.append(f"【{entity_name}】{' | '.join(contexts)}")
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# 添加关系信息
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for relation in analysis_result.get("relations", [])[:5]:
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context_parts.append(f"【关系】{relation.get('entity1')} {relation.get('relation')} {relation.get('entity2')}: {relation.get('description', '')}")
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context_text = "\n\n".join(context_parts) if context_parts else "未找到相关实体和关系"
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# 使用 LLM 生成答案
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prompt = f"""基于以下跨文档分析结果，回答用户问题。
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问题: {question}
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分析结果:
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{context_text}
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请直接回答问题，如果分析结果中没有相关信息，请说明"根据提供的文档无法回答该问题"。"""
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messages = [
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{"role": "system", "content": "你是一个基于文档的问答助手。请根据提供的信息回答问题。"},
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{"role": "user", "content": prompt}
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]
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try:
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response = await self.llm.chat(messages=messages, temperature=0.2, max_tokens=2000)
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answer = self.llm.extract_message_content(response)
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return {
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"success": True,
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"question": question,
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"answer": answer,
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"supporting_entities": list(analysis_result.get("entities", {}).keys())[:10],
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"relations_count": len(analysis_result.get("relations", []))
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}
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except Exception as e:
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logger.error(f"跨文档问答失败: {e}")
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return {"success": False, "error": str(e)}
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# 全局单例
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multi_doc_reasoning_service = MultiDocReasoningService()
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