feat: 实现智能指令的格式转换和文档编辑功能
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
This commit is contained in:
446
backend/app/services/multi_doc_reasoning_service.py
Normal file
446
backend/app/services/multi_doc_reasoning_service.py
Normal file
@@ -0,0 +1,446 @@
|
||||
"""
|
||||
多文档关联推理服务
|
||||
|
||||
跨文档信息关联和推理
|
||||
"""
|
||||
import logging
|
||||
import re
|
||||
from typing import Any, Dict, List, Optional, Set, Tuple
|
||||
from collections import defaultdict
|
||||
|
||||
from app.services.llm_service import llm_service
|
||||
from app.services.rag_service import rag_service
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class MultiDocReasoningService:
|
||||
"""
|
||||
多文档关联推理服务
|
||||
|
||||
功能：
|
||||
1. 实体跨文档追踪 - 追踪同一实体在不同文档中的描述
|
||||
2. 关系抽取与推理 - 抽取实体间关系并进行推理
|
||||
3. 信息补全 - 根据多个文档的信息互补填充缺失数据
|
||||
4. 冲突检测 - 检测不同文档间的信息冲突
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.llm = llm_service
|
||||
|
||||
async def analyze_cross_documents(
|
||||
self,
|
||||
documents: List[Dict[str, Any]],
|
||||
query: Optional[str] = None,
|
||||
entity_types: Optional[List[str]] = None
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
跨文档分析
|
||||
|
||||
Args:
|
||||
documents: 文档列表
|
||||
query: 查询意图（可选）
|
||||
entity_types: 要追踪的实体类型列表，如 ["机构", "人物", "地点", "数量"]
|
||||
|
||||
Returns:
|
||||
跨文档分析结果
|
||||
"""
|
||||
if not documents:
|
||||
return {"success": False, "error": "没有可用的文档"}
|
||||
|
||||
entity_types = entity_types or ["机构", "数量", "时间", "地点"]
|
||||
|
||||
try:
|
||||
# 1. 提取各文档中的实体
|
||||
entities_per_doc = await self._extract_entities_from_docs(documents, entity_types)
|
||||
|
||||
# 2. 跨文档实体对齐
|
||||
aligned_entities = self._align_entities_across_docs(entities_per_doc)
|
||||
|
||||
# 3. 关系抽取
|
||||
relations = await self._extract_relations(documents)
|
||||
|
||||
# 4. 构建知识图谱
|
||||
knowledge_graph = self._build_knowledge_graph(aligned_entities, relations)
|
||||
|
||||
# 5. 信息补全
|
||||
completed_info = await self._complete_missing_info(knowledge_graph, documents)
|
||||
|
||||
# 6. 冲突检测
|
||||
conflicts = self._detect_conflicts(aligned_entities)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"entities": aligned_entities,
|
||||
"relations": relations,
|
||||
"knowledge_graph": knowledge_graph,
|
||||
"completed_info": completed_info,
|
||||
"conflicts": conflicts,
|
||||
"summary": self._generate_summary(aligned_entities, conflicts)
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"跨文档分析失败: {e}")
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
async def _extract_entities_from_docs(
|
||||
self,
|
||||
documents: List[Dict[str, Any]],
|
||||
entity_types: List[str]
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""从各文档中提取实体"""
|
||||
entities_per_doc = []
|
||||
|
||||
for idx, doc in enumerate(documents):
|
||||
doc_id = doc.get("_id", f"doc_{idx}")
|
||||
content = doc.get("content", "")[:8000] # 限制长度
|
||||
|
||||
# 使用 LLM 提取实体
|
||||
prompt = f"""从以下文档中提取指定的实体类型信息。
|
||||
|
||||
实体类型: {', '.join(entity_types)}
|
||||
|
||||
文档内容:
|
||||
{content}
|
||||
|
||||
请按以下 JSON 格式输出（只需输出 JSON）：
|
||||
{{
|
||||
"entities": [
|
||||
{{"type": "机构", "name": "实体名称", "value": "相关数值（如有）", "context": "上下文描述"}},
|
||||
...
|
||||
]
|
||||
}}
|
||||
|
||||
只提取在文档中明确提到的实体，不要推测。"""
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": "你是一个实体提取专家。请严格按JSON格式输出。"},
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
|
||||
try:
|
||||
response = await self.llm.chat(messages=messages, temperature=0.1, max_tokens=3000)
|
||||
content_response = self.llm.extract_message_content(response)
|
||||
|
||||
# 解析 JSON
|
||||
import json
|
||||
import re
|
||||
cleaned = content_response.strip()
|
||||
json_match = re.search(r'\{[\s\S]*\}', cleaned)
|
||||
if json_match:
|
||||
result = json.loads(json_match.group())
|
||||
entities = result.get("entities", [])
|
||||
entities_per_doc.append({
|
||||
"doc_id": doc_id,
|
||||
"doc_name": doc.get("metadata", {}).get("original_filename", f"文档{idx+1}"),
|
||||
"entities": entities
|
||||
})
|
||||
logger.info(f"文档 {doc_id} 提取到 {len(entities)} 个实体")
|
||||
except Exception as e:
|
||||
logger.warning(f"文档 {doc_id} 实体提取失败: {e}")
|
||||
|
||||
return entities_per_doc
|
||||
|
||||
def _align_entities_across_docs(
|
||||
self,
|
||||
entities_per_doc: List[Dict[str, Any]]
|
||||
) -> Dict[str, List[Dict[str, Any]]]:
|
||||
"""
|
||||
跨文档实体对齐
|
||||
|
||||
将同一实体在不同文档中的描述进行关联
|
||||
"""
|
||||
aligned: Dict[str, List[Dict[str, Any]]] = defaultdict(list)
|
||||
|
||||
for doc_data in entities_per_doc:
|
||||
doc_id = doc_data["doc_id"]
|
||||
doc_name = doc_data["doc_name"]
|
||||
|
||||
for entity in doc_data.get("entities", []):
|
||||
entity_name = entity.get("name", "")
|
||||
if not entity_name:
|
||||
continue
|
||||
|
||||
# 标准化实体名（去除空格和括号内容）
|
||||
normalized = self._normalize_entity_name(entity_name)
|
||||
|
||||
aligned[normalized].append({
|
||||
"original_name": entity_name,
|
||||
"type": entity.get("type", "未知"),
|
||||
"value": entity.get("value", ""),
|
||||
"context": entity.get("context", ""),
|
||||
"source_doc": doc_name,
|
||||
"source_doc_id": doc_id
|
||||
})
|
||||
|
||||
# 合并相同实体
|
||||
result = {}
|
||||
for normalized, appearances in aligned.items():
|
||||
if len(appearances) > 1:
|
||||
result[normalized] = appearances
|
||||
logger.info(f"实体对齐: {normalized} 在 {len(appearances)} 个文档中出现")
|
||||
|
||||
return result
|
||||
|
||||
def _normalize_entity_name(self, name: str) -> str:
|
||||
"""标准化实体名称"""
|
||||
# 去除空格
|
||||
name = name.strip()
|
||||
# 去除括号内容
|
||||
name = re.sub(r'[（(].*?[）)]', '', name)
|
||||
# 去除"第X名"等
|
||||
name = re.sub(r'^第\d+[名位个]', '', name)
|
||||
return name.strip()
|
||||
|
||||
async def _extract_relations(
|
||||
self,
|
||||
documents: List[Dict[str, Any]]
|
||||
) -> List[Dict[str, str]]:
|
||||
"""从文档中抽取关系"""
|
||||
relations = []
|
||||
|
||||
# 合并所有文档内容
|
||||
combined_content = "\n\n".join([
|
||||
f"【{doc.get('metadata', {}).get('original_filename', f'文档{i}')}】\n{doc.get('content', '')[:3000]}"
|
||||
for i, doc in enumerate(documents)
|
||||
])
|
||||
|
||||
prompt = f"""从以下文档内容中抽取实体之间的关系。
|
||||
|
||||
文档内容:
|
||||
{combined_content[:8000]}
|
||||
|
||||
请识别以下类型的关系：
|
||||
- 包含关系 (A包含B)
|
||||
- 隶属关系 (A隶属于B)
|
||||
- 合作关系 (A与B合作)
|
||||
- 对比关系 (A vs B)
|
||||
- 时序关系 (A先于B发生)
|
||||
|
||||
请按以下 JSON 格式输出（只需输出 JSON）：
|
||||
{{
|
||||
"relations": [
|
||||
{{"entity1": "实体1", "entity2": "实体2", "relation": "关系类型", "description": "关系描述"}},
|
||||
...
|
||||
]
|
||||
}}
|
||||
|
||||
如果没有找到明确的关系，返回空数组。"""
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": "你是一个关系抽取专家。请严格按JSON格式输出。"},
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
|
||||
try:
|
||||
response = await self.llm.chat(messages=messages, temperature=0.1, max_tokens=3000)
|
||||
content_response = self.llm.extract_message_content(response)
|
||||
|
||||
import json
|
||||
import re
|
||||
cleaned = content_response.strip()
|
||||
json_match = re.search(r'\{{[\s\S]*\}}', cleaned)
|
||||
if json_match:
|
||||
result = json.loads(json_match.group())
|
||||
relations = result.get("relations", [])
|
||||
logger.info(f"抽取到 {len(relations)} 个关系")
|
||||
except Exception as e:
|
||||
logger.warning(f"关系抽取失败: {e}")
|
||||
|
||||
return relations
|
||||
|
||||
def _build_knowledge_graph(
|
||||
self,
|
||||
aligned_entities: Dict[str, List[Dict[str, Any]]],
|
||||
relations: List[Dict[str, str]]
|
||||
) -> Dict[str, Any]:
|
||||
"""构建知识图谱"""
|
||||
nodes = []
|
||||
edges = []
|
||||
node_ids = set()
|
||||
|
||||
# 添加实体节点
|
||||
for entity_name, appearances in aligned_entities.items():
|
||||
if len(appearances) < 1:
|
||||
continue
|
||||
|
||||
first_appearance = appearances[0]
|
||||
node_id = f"entity_{len(nodes)}"
|
||||
|
||||
# 收集该实体在所有文档中的值
|
||||
values = [a.get("value", "") for a in appearances if a.get("value")]
|
||||
primary_value = values[0] if values else ""
|
||||
|
||||
nodes.append({
|
||||
"id": node_id,
|
||||
"name": entity_name,
|
||||
"type": first_appearance.get("type", "未知"),
|
||||
"value": primary_value,
|
||||
"occurrence_count": len(appearances),
|
||||
"sources": [a.get("source_doc", "") for a in appearances]
|
||||
})
|
||||
node_ids.add(entity_name)
|
||||
|
||||
# 添加关系边
|
||||
for relation in relations:
|
||||
entity1 = self._normalize_entity_name(relation.get("entity1", ""))
|
||||
entity2 = self._normalize_entity_name(relation.get("entity2", ""))
|
||||
|
||||
if entity1 in node_ids and entity2 in node_ids:
|
||||
edges.append({
|
||||
"source": entity1,
|
||||
"target": entity2,
|
||||
"relation": relation.get("relation", "相关"),
|
||||
"description": relation.get("description", "")
|
||||
})
|
||||
|
||||
return {
|
||||
"nodes": nodes,
|
||||
"edges": edges,
|
||||
"stats": {
|
||||
"entity_count": len(nodes),
|
||||
"relation_count": len(edges)
|
||||
}
|
||||
}
|
||||
|
||||
async def _complete_missing_info(
|
||||
self,
|
||||
knowledge_graph: Dict[str, Any],
|
||||
documents: List[Dict[str, Any]]
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""根据多个文档补全信息"""
|
||||
completed = []
|
||||
|
||||
for node in knowledge_graph.get("nodes", []):
|
||||
if not node.get("value") and node.get("occurrence_count", 0) > 1:
|
||||
# 实体在多个文档中出现但没有数值，尝试从 RAG 检索补充
|
||||
query = f"{node['name']} 数值 数据"
|
||||
results = rag_service.retrieve(query, top_k=3, min_score=0.3)
|
||||
|
||||
if results:
|
||||
completed.append({
|
||||
"entity": node["name"],
|
||||
"type": node.get("type", "未知"),
|
||||
"source": "rag_inference",
|
||||
"context": results[0].get("content", "")[:200],
|
||||
"confidence": results[0].get("score", 0)
|
||||
})
|
||||
|
||||
return completed
|
||||
|
||||
def _detect_conflicts(
|
||||
self,
|
||||
aligned_entities: Dict[str, List[Dict[str, Any]]]
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""检测不同文档间的信息冲突"""
|
||||
conflicts = []
|
||||
|
||||
for entity_name, appearances in aligned_entities.items():
|
||||
if len(appearances) < 2:
|
||||
continue
|
||||
|
||||
# 检查数值冲突
|
||||
values = {}
|
||||
for appearance in appearances:
|
||||
val = appearance.get("value", "")
|
||||
if val:
|
||||
source = appearance.get("source_doc", "未知来源")
|
||||
values[source] = val
|
||||
|
||||
if len(values) > 1:
|
||||
unique_values = set(values.values())
|
||||
if len(unique_values) > 1:
|
||||
conflicts.append({
|
||||
"entity": entity_name,
|
||||
"type": "value_conflict",
|
||||
"details": values,
|
||||
"description": f"实体 '{entity_name}' 在不同文档中有不同数值: {values}"
|
||||
})
|
||||
|
||||
return conflicts
|
||||
|
||||
def _generate_summary(
|
||||
self,
|
||||
aligned_entities: Dict[str, List[Dict[str, Any]]],
|
||||
conflicts: List[Dict[str, Any]]
|
||||
) -> str:
|
||||
"""生成摘要"""
|
||||
summary_parts = []
|
||||
|
||||
total_entities = sum(len(appearances) for appearances in aligned_entities.values())
|
||||
multi_doc_entities = sum(1 for appearances in aligned_entities.values() if len(appearances) > 1)
|
||||
|
||||
summary_parts.append(f"跨文档分析完成：发现 {total_entities} 个实体")
|
||||
summary_parts.append(f"其中 {multi_doc_entities} 个实体在多个文档中被提及")
|
||||
|
||||
if conflicts:
|
||||
summary_parts.append(f"检测到 {len(conflicts)} 个潜在冲突")
|
||||
|
||||
return "; ".join(summary_parts)
|
||||
|
||||
async def answer_cross_doc_question(
|
||||
self,
|
||||
question: str,
|
||||
documents: List[Dict[str, Any]]
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
跨文档问答
|
||||
|
||||
Args:
|
||||
question: 问题
|
||||
documents: 文档列表
|
||||
|
||||
Returns:
|
||||
答案结果
|
||||
"""
|
||||
# 先进行跨文档分析
|
||||
analysis_result = await self.analyze_cross_documents(documents, query=question)
|
||||
|
||||
# 构建上下文
|
||||
context_parts = []
|
||||
|
||||
# 添加实体信息
|
||||
for entity_name, appearances in analysis_result.get("entities", {}).items():
|
||||
contexts = [f"{a.get('source_doc')}: {a.get('context', '')}" for a in appearances[:2]]
|
||||
if contexts:
|
||||
context_parts.append(f"【{entity_name}】{' | '.join(contexts)}")
|
||||
|
||||
# 添加关系信息
|
||||
for relation in analysis_result.get("relations", [])[:5]:
|
||||
context_parts.append(f"【关系】{relation.get('entity1')} {relation.get('relation')} {relation.get('entity2')}: {relation.get('description', '')}")
|
||||
|
||||
context_text = "\n\n".join(context_parts) if context_parts else "未找到相关实体和关系"
|
||||
|
||||
# 使用 LLM 生成答案
|
||||
prompt = f"""基于以下跨文档分析结果，回答用户问题。
|
||||
|
||||
问题: {question}
|
||||
|
||||
分析结果:
|
||||
{context_text}
|
||||
|
||||
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"。"""
|
||||
|
||||
messages = [
|
||||
{"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。"},
|
||||
{"role": "user", "content": prompt}
|
||||
]
|
||||
|
||||
try:
|
||||
response = await self.llm.chat(messages=messages, temperature=0.2, max_tokens=2000)
|
||||
answer = self.llm.extract_message_content(response)
|
||||
|
||||
return {
|
||||
"success": True,
|
||||
"question": question,
|
||||
"answer": answer,
|
||||
"supporting_entities": list(analysis_result.get("entities", {}).keys())[:10],
|
||||
"relations_count": len(analysis_result.get("relations", []))
|
||||
}
|
||||
except Exception as e:
|
||||
logger.error(f"跨文档问答失败: {e}")
|
||||
return {"success": False, "error": str(e)}
|
||||
|
||||
|
||||
# 全局单例
|
||||
multi_doc_reasoning_service = MultiDocReasoningService()
|
||||
Reference in New Issue
Block a user