knowledge/router/text_search.py

from fastapi import APIRouter, HTTPException, Depends
from pydantic import BaseModel, Field, validator
from typing import List, Optional
from service.trunks_service import TrunksService
from utils.sentence_util import SentenceUtil

from utils.vector_distance import VectorDistance
from model.response import StandardResponse
from utils.vectorizer import Vectorizer
# from utils.find_text_in_pdf import find_text_in_pdf
import os
DISTANCE_THRESHOLD = 0.73
import logging
import time
from db.session import get_db
from sqlalchemy.orm import Session
from service.kg_node_service import KGNodeService
from service.kg_prop_service import KGPropService
from service.kg_edge_service import KGEdgeService

from cachetools import TTLCache

# 使用TextSimilarityFinder进行文本相似度匹配
from utils.text_similarity import TextSimilarityFinder

logger = logging.getLogger(__name__)
router = APIRouter(tags=["Text Search"])

# 创建全局缓存实例
cache = TTLCache(maxsize=1000, ttl=3600)

class TextSearchRequest(BaseModel):
    text: str
    conversation_id: Optional[str] = None
    need_convert: Optional[bool] = False

class TextCompareRequest(BaseModel):
    sentence: str
    text: str

class TextMatchRequest(BaseModel):
    text: str = Field(..., min_length=1, max_length=10000, description="需要搜索的文本内容")

    @validator('text')
    def validate_text(cls, v):
        # 保留所有可打印字符、换行符和中文字符
        v = ''.join(char for char in v if char.isprintable() or char in '\n\r')
        
        # 转义JSON特殊字符
        # 先处理反斜杠，避免后续转义时出现问题
        v = v.replace('\\', '\\\\')
        # 处理引号和其他特殊字符
        v = v.replace('"', '\\"')
        v = v.replace('/', '\\/')
        # 处理控制字符
        v = v.replace('\n', '\\n')
        v = v.replace('\r', '\\r')
        v = v.replace('\t', '\\t')
        v = v.replace('\b', '\\b')
        v = v.replace('\f', '\\f')
        # 处理Unicode转义
        # v = v.replace('\u', '\\u')
        
        return v

class TextCompareMultiRequest(BaseModel):
    origin: str
    similar: str

class NodePropsSearchRequest(BaseModel):
    node_id: int
    props_ids: List[int]
    symptoms: Optional[List[str]] = None

@router.post("/kgrt_api/text/clear_cache", response_model=StandardResponse)
async def clear_cache():
    try:
        # 清除全局缓存
        cache.clear()
        return StandardResponse(success=True, data={"message": "缓存已清除"})
    except Exception as e:
        logger.error(f"清除缓存失败: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/kgrt_api/text/search", response_model=StandardResponse)
@router.post("/knowledge/text/search", response_model=StandardResponse)
async def search_text(request: TextSearchRequest):
    try:
        #判断request.text是否为json格式，如果是，使用JsonToText的convert方法转换为text
        if request.text.startswith('{') and request.text.endswith('}'):
            from utils.json_to_text import JsonToTextConverter
            converter = JsonToTextConverter()
            request.text = converter.convert(request.text)

        # 使用TextSplitter拆分文本
        sentences = SentenceUtil.split_text(request.text)
        if not sentences:
            return StandardResponse(success=True, data={"answer": "", "references": []})
        
        # 初始化服务和结果列表
        trunks_service = TrunksService()
        result_sentences = []
        all_references = []
        reference_index = 1
        
        # 根据conversation_id获取缓存结果
        cached_results = trunks_service.get_cached_result(request.conversation_id) if request.conversation_id else []
        
        for sentence in sentences:
            # if request.need_convert:
            sentence = sentence.replace("\n", "<br>")
            if len(sentence) < 10:
                result_sentences.append(sentence)
                continue
            if cached_results:
                # 如果有缓存结果，计算向量距离
                min_distance = float('inf')
                best_result = None
                sentence_vector = Vectorizer.get_embedding(sentence)
                
                for cached_result in cached_results:
                    content_vector = cached_result['embedding']
                    distance = VectorDistance.calculate_distance(sentence_vector, content_vector)
                    if distance < min_distance:
                        min_distance = distance
                        best_result = {**cached_result, 'distance': distance}
                        
                
                if best_result and best_result['distance'] < DISTANCE_THRESHOLD:
                    search_results = [best_result]
                else:
                    search_results = []
            else:
                # 如果没有缓存结果，进行向量搜索
                search_results = trunks_service.search_by_vector(
                    text=sentence,
                    limit=1,
                    type='trunk'
                )
            
            # 处理搜索结果
            for search_result in search_results:
                distance = search_result.get("distance", DISTANCE_THRESHOLD)
                if distance >= DISTANCE_THRESHOLD:
                    result_sentences.append(sentence)
                    continue
                
                # 检查是否已存在相同引用
                existing_ref = next((ref for ref in all_references if ref["id"] == search_result["id"]), None)
                current_index = reference_index
                if existing_ref:
                    current_index = int(existing_ref["index"])
                else:
                    # 添加到引用列表
                    # 从referrence中提取文件名
                    file_name = ""
                    referrence = search_result.get("referrence", "")
                    if referrence and "/books/" in referrence:
                        file_name = referrence.split("/books/")[-1]
                        # 去除文件扩展名
                        file_name = os.path.splitext(file_name)[0]

                    reference = {
                        "index": str(reference_index),
                        "id": search_result["id"],
                        "content": search_result["content"],
                        "file_path": search_result.get("file_path", ""),
                        "title": search_result.get("title", ""),
                        "distance": distance,
                        "file_name": file_name,
                        "referrence": referrence
                    }
                    
                    
                    all_references.append(reference)
                    reference_index += 1
                
                # 添加引用标记
                if sentence.endswith('<br>'):
                    # 如果有多个<br>,在所有<br>前添加^[current_index]^
                    result_sentence = sentence.replace('<br>', f'^[{current_index}]^<br>')
                else:
                    # 直接在句子末尾添加^[current_index]^
                    result_sentence = f'{sentence}^[{current_index}]^'
                
                result_sentences.append(result_sentence)
     
        # 组装返回数据
        response_data = {
            "answer": result_sentences,
            "references": all_references
        }
        
        return StandardResponse(success=True, data=response_data)
        
    except Exception as e:
        logger.error(f"Text search failed: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/kgrt_api/text/match", response_model=StandardResponse)
@router.post("/knowledge/text/match", response_model=StandardResponse)
async def match_text(request: TextCompareRequest):
    try:
        sentences = SentenceUtil.split_text(request.text)
        sentence_vector = Vectorizer.get_embedding(request.sentence)
        min_distance = float('inf')
        best_sentence = ""
        result_sentences = []
        for temp in sentences:
            result_sentences.append(temp)
            if len(temp) < 10:
                continue
            temp_vector = Vectorizer.get_embedding(temp)
            distance = VectorDistance.calculate_distance(sentence_vector, temp_vector)
            if distance < min_distance and distance < DISTANCE_THRESHOLD:
                min_distance = distance
                best_sentence = temp

        for i in range(len(result_sentences)):
            result_sentences[i] = {"sentence": result_sentences[i], "matched": False}
            if result_sentences[i]["sentence"] == best_sentence:
                result_sentences[i]["matched"] = True    
                
        return StandardResponse(success=True, records=result_sentences)
    except Exception as e:
        logger.error(f"Text comparison failed: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/kgrt_api/text/mr_search", response_model=StandardResponse)
@router.post("/knowledge/text/mr_search", response_model=StandardResponse)
async def mr_search_text_content(request: TextMatchRequest):
    try:
        # 初始化服务
        trunks_service = TrunksService()
        
        # 获取文本向量并搜索相似内容
        search_results = trunks_service.search_by_vector(
            text=request.text,
            limit=10,
            type="mr"
        )

        # 处理搜索结果
        records = []
        for result in search_results:
            distance = result.get("distance", DISTANCE_THRESHOLD)
            if distance >= DISTANCE_THRESHOLD:
                continue

            # 添加到引用列表
            record = {
                "content": result["content"],
                "file_path": result.get("file_path", ""),
                "title": result.get("title", ""),
                "distance": distance,
            }
            records.append(record)

        # 组装返回数据
        response_data = {
            "records": records
        }
        
        return StandardResponse(success=True, data=response_data)
        
    except Exception as e:
        logger.error(f"Mr search failed: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

@router.post("/kgrt_api/text/mr_match", response_model=StandardResponse)
@router.post("/knowledge/text/mr_match", response_model=StandardResponse)
async def compare_text(request: TextCompareMultiRequest):
    start_time = time.time()
    try:
        # 拆分两段文本
        origin_sentences = SentenceUtil.split_text(request.origin)
        similar_sentences = SentenceUtil.split_text(request.similar)
        end_time = time.time()
        logger.info(f"mr_match接口处理文本耗时: {(end_time - start_time) * 1000:.2f}ms")
        
        # 初始化结果列表
        origin_results = []
        
        # 过滤短句并预计算向量
        valid_origin_sentences = [(sent, len(sent) >= 10) for sent in origin_sentences]
        valid_similar_sentences = [(sent, len(sent) >= 10) for sent in similar_sentences]
        
        # 初始化similar_results，所有matched设为False
        similar_results = [{"sentence": sent, "matched": False} for sent, _ in valid_similar_sentences]
        
        # 批量获取向量
        origin_vectors = {}
        similar_vectors = {}
        origin_batch = [sent for sent, is_valid in valid_origin_sentences if is_valid]
        similar_batch = [sent for sent, is_valid in valid_similar_sentences if is_valid]
        
        if origin_batch:
            origin_embeddings = [Vectorizer.get_embedding(sent) for sent in origin_batch]
            origin_vectors = dict(zip(origin_batch, origin_embeddings))
        
        if similar_batch:
            similar_embeddings = [Vectorizer.get_embedding(sent) for sent in similar_batch]
            similar_vectors = dict(zip(similar_batch, similar_embeddings))

        end_time = time.time()
        logger.info(f"mr_match接口处理向量耗时: {(end_time - start_time) * 1000:.2f}ms") 
        # 处理origin文本
        for origin_sent, is_valid in valid_origin_sentences:
            if not is_valid:
                origin_results.append({"sentence": origin_sent, "matched": False})
                continue
            
            origin_vector = origin_vectors[origin_sent]
            matched = False
            
            # 优化的相似度计算
            for i, similar_result in enumerate(similar_results):
                if similar_result["matched"]:
                    continue
                    
                similar_sent = similar_result["sentence"]
                if len(similar_sent) < 10:
                    continue
                    
                similar_vector = similar_vectors.get(similar_sent)
                if not similar_vector:
                    continue
                    
                distance = VectorDistance.calculate_distance(origin_vector, similar_vector)
                if distance < DISTANCE_THRESHOLD:
                    matched = True
                    similar_results[i]["matched"] = True
                    break
            
            origin_results.append({"sentence": origin_sent, "matched": matched})
        
        response_data = {
            "origin": origin_results,
            "similar": similar_results
        }
        
        end_time = time.time()
        logger.info(f"mr_match接口耗时: {(end_time - start_time) * 1000:.2f}ms")
        return StandardResponse(success=True, data=response_data)
    except Exception as e:
        end_time = time.time()
        logger.error(f"Text comparison failed: {str(e)}")
        logger.info(f"mr_match接口耗时: {(end_time - start_time) * 1000:.2f}ms")
        raise HTTPException(status_code=500, detail=str(e))

def _check_cache(node_id: int) -> Optional[dict]:
    """检查并返回缓存结果"""
    cache_key = f"xunzheng_{node_id}"
    cached_result = cache.get(cache_key)
    if cached_result:
        logger.info(f"从缓存获取结果，node_id: {node_id}")
        return cached_result
    return None

def _get_node_info(node_service: KGNodeService, node_id: int) -> dict:
    """获取并验证节点信息"""
    node = node_service.get_node(node_id)
    if not node:
        raise ValueError(f"节点不存在: {node_id}")
    return {
        "id": node_id,
        "name": node.get('name', ''),
        "category": node.get('category', ''),
        "props": [],
        "files": [],
        "distance": 0
    }

def _process_search_result(search_result: dict, reference_index: int) -> tuple[dict, str]:
    """处理搜索结果，返回引用信息和文件名"""
    file_name = ""
    referrence = search_result.get("referrence", "")
    if referrence and "/books/" in referrence:
        file_name = referrence.split("/books/")[-1]
        file_name = os.path.splitext(file_name)[0]

    reference = {
        "index": str(reference_index),
        "id": search_result["id"],
        "content": search_result["content"],
        "file_path": search_result.get("file_path", ""),
        "title": search_result.get("title", ""),
        "distance": search_result.get("distance", DISTANCE_THRESHOLD),
        "page_no": search_result.get("page_no", ""),
        "file_name": file_name,
        "referrence": referrence
    }
    return reference, file_name

def _get_file_type(file_name: str) -> str:
    """根据文件名确定文件类型"""
    file_name_lower = file_name.lower()
    if file_name_lower.endswith(".pdf"):
        return "pdf"
    elif file_name_lower.endswith((".doc", ".docx")):
        return "doc"
    elif file_name_lower.endswith((".xls", ".xlsx")):
        return "excel"
    elif file_name_lower.endswith((".ppt", ".pptx")):
        return "ppt"
    return "other"

def _process_sentence_search(node_name: str, prop_title: str, sentences: list, trunks_service: TrunksService) -> tuple[list, list]:
    """处理句子搜索，返回结果句子和引用列表"""
    result_sentences = []
    all_references = []
    reference_index = 1
    i = 0
    
    while i < len(sentences):
        sentence = sentences[i]

        search_text = f"{node_name}:{prop_title}:{sentence}"
        if len(sentence) < 10 and i + 1 < len(sentences):
            next_sentence = sentences[i + 1]
            result_sentences.append({"sentence": sentence, "flag": ""})
            search_text = f"{node_name}:{prop_title}:{sentence} {next_sentence}"
            i += 1
        elif len(sentence) < 10:
            result_sentences.append({"sentence": sentence, "flag": ""})
            i += 1
            continue
        
        i += 1

        # 使用向量搜索获取相似内容
        search_results = trunks_service.search_by_vector(
            text=search_text,
            limit=500,
            type='trunk'
        )
        # 查询1000条切片数据
        # db_trunks = db.query(Trunks).filter(Trunks.type == 'trunk').limit(1000).all()

        # 准备语料库数据
        trunk_texts = []
        trunk_ids = []
        # 创建一个字典来存储trunk的详细信息
        trunk_details = {}

        for trunk in search_results:
            trunk_texts.append(trunk.get('content'))
            trunk_ids.append(trunk.get('id'))
            # 缓存trunk的详细信息
            trunk_details[trunk.get('id')] = {
                'id': trunk.get('id'),
                'content': trunk.get('content'),
                'file_path': trunk.get('file_path'),
                'title': trunk.get('title'),
                'referrence': trunk.get('referrence'),
                'page_no': trunk.get('page_no')
            }

        # 初始化TextSimilarityFinder并加载语料库
        similarity_finder = TextSimilarityFinder(method='tfidf', use_jieba=True)
        similarity_finder.load_corpus(trunk_texts, trunk_ids)

        # 使用TextSimilarityFinder进行相似度匹配
        similar_results = similarity_finder.find_most_similar(search_text, top_n=1)
        
        if not similar_results:  # 设置相似度阈值
            result_sentences.append({"sentence": sentence, "flag": ""})
            continue
        
        # 获取最相似的文本对应的trunk_id
        trunk_id = similar_results[0]['path']

        # 从缓存中获取trunk详细信息
        trunk_info = trunk_details.get(trunk_id)

        if trunk_info:
            search_result = {
                **trunk_info,
                'distance': similar_results[0]['similarity']  # 转换相似度为距离
            }
            # 检查相似度是否达到阈值
            if search_result['distance'] >= DISTANCE_THRESHOLD:
                result_sentences.append({"sentence": sentence, "flag": ""})
                continue
                # 检查是否已存在相同引用
            existing_ref = next((ref for ref in all_references if ref["id"] == search_result["id"]), None)
            current_index = int(existing_ref["index"]) if existing_ref else reference_index

            if not existing_ref:
                reference, _ = _process_search_result(search_result, reference_index)
                all_references.append(reference)
                reference_index += 1

            result_sentences.append({"sentence": sentence, "flag": str(current_index)})
    
    return result_sentences, all_references

@router.post("/kgrt_api/text/eb_search", response_model=StandardResponse)
@router.post("/knowledge/text/eb_search", response_model=StandardResponse)
async def node_props_search(request: NodePropsSearchRequest, db: Session = Depends(get_db)):
    try:
        start_time = time.time()
        
        # 检查缓存
        cached_result = _check_cache(request.node_id)
        if cached_result:
            return StandardResponse(success=True, data=cached_result)

        # 初始化服务
        trunks_service = TrunksService()
        node_service = KGNodeService(db)
        prop_service = KGPropService(db)
        edge_service = KGEdgeService(db)

        # 获取节点信息
        result = _get_node_info(node_service, request.node_id)
        node_name = result["name"]

        # 处理症状列表
        symptom_list = []
        if request.symptoms:
            for symptom in request.symptoms:
                try:
                    # 添加原始症状
                    symptom_list.append(symptom)
                    # 获取症状节点
                    symptom_node = node_service.get_node_by_name_category(symptom, '症状')
                    # 获取症状相关同义词
                    edges = edge_service.get_edges_by_nodes(src_id=symptom_node['id'], category='症状同义词')
                    if edges:
                        # 添加同义词
                        for edge in edges:
                            if edge['dest_node'] and edge['dest_node'].get('name'):
                                symptom_list.append(edge['dest_node']['name'])
                except ValueError:
                    # 如果找不到节点，只添加原始症状
                    symptom_list.append(symptom)
            
            # 按照字符长度进行倒序排序
            symptom_list.sort(key=len, reverse=True)

        # 遍历props_ids查询属性信息
        for prop_id in request.props_ids:
            prop = prop_service.get_props_by_id(prop_id)
            if not prop:
                logger.warning(f"属性不存在: {prop_id}")
                continue

            prop_title = prop.get('prop_title', '')
            prop_value = prop.get('prop_value', '')

            # 创建属性结果对象
            prop_result = {
                "id": prop_id,
                "category": prop.get('category', 0),
                "prop_name": prop.get('prop_name', ''),
                "prop_value": prop_value,
                "prop_title": prop_title,
                "type": prop.get('type', 1)
            }
            result["props"].append(prop_result)

            # 如果prop_value为'无'，则跳过搜索
            if prop_value == '无':
                prop_result["answer"] = [{
                    "sentence": prop_value,
                    "flag": ""
                }]
                continue

            # 先用完整的prop_value进行搜索
            search_text = f"{node_name}:{prop_title}:{prop_value}"
            # 使用向量搜索获取相似内容
            search_results = trunks_service.search_by_vector(
                text=search_text,
                limit=500,
                type='trunk'
            )
            
            # 准备语料库数据
            trunk_texts = []
            trunk_ids = []
            
            # 创建一个字典来存储trunk的详细信息
            trunk_details = {}
            
            for trunk in search_results:
                trunk_texts.append(trunk.get('content'))
                trunk_ids.append(trunk.get('id'))
                # 缓存trunk的详细信息
                trunk_details[trunk.get('id')] = {
                    'id': trunk.get('id'),
                    'content': trunk.get('content'),
                    'file_path': trunk.get('file_path'),
                    'title': trunk.get('title'),
                    'referrence': trunk.get('referrence'),
                    'page_no': trunk.get('page_no')
                }
            
            # 初始化TextSimilarityFinder并加载语料库
            similarity_finder = TextSimilarityFinder(method='tfidf', use_jieba=True)
            similarity_finder.load_corpus(trunk_texts, trunk_ids)

            similar_results = similarity_finder.find_most_similar(search_text, top_n=1)
            
            # 处理搜索结果
            if similar_results:  # 设置相似度阈值
                # 获取最相似的文本对应的trunk_id
                trunk_id = similar_results[0]['path']
                
                # 从缓存中获取trunk详细信息
                trunk_info = trunk_details.get(trunk_id)
                
                if trunk_info:
                    search_result = {
                        **trunk_info,
                        'distance': similar_results[0]['similarity']  # 转换相似度为距离
                    }
                    
                    reference, _ = _process_search_result(search_result, 1)
                    prop_result["references"] = [reference]
                    # 处理症状标记
                    marked_sentence = prop_value
                    if prop_title == '临床表现' and symptom_list:
                        # 创建一个标记位置的列表，记录每个位置是否已被标记
                        marked_positions = [False] * len(marked_sentence)
                        
                        # 创建一个列表来存储已处理的症状
                        processed_symptoms = []
                        
                        for symptom in symptom_list:
                            # 检查是否已处理过该症状或其子集
                            if any(symptom in processed_sym or processed_sym in symptom for processed_sym in processed_symptoms):
                                continue
                                
                            # 查找所有匹配位置
                            start_pos = 0
                            while True:
                                pos = marked_sentence.find(symptom, start_pos)
                                if pos == -1:
                                    break
                                    
                                # 检查这个位置是否已被标记
                                if not any(marked_positions[pos:pos + len(symptom)]):
                                    # 标记这个范围的所有位置
                                    for i in range(pos, pos + len(symptom)):
                                        marked_positions[i] = True
                                    # 替换文本
                                    marked_sentence = marked_sentence[:pos] + f'<i style="color:red;">{symptom}</i>' + marked_sentence[pos + len(symptom):]
                                    # 将成功标记的症状添加到已处理列表中
                                    if symptom not in processed_symptoms:
                                        processed_symptoms.append(symptom)
                                    # 更新标记位置数组以适应新插入的标签
                                    new_positions = [False] * (len('<i style="color:red;">') + len('</i>'))
                                    marked_positions = marked_positions[:pos] + new_positions + marked_positions[pos:]
                                
                                start_pos = pos + len('<i style="color:red;">') + len(symptom) + len('</i>')
                    prop_result["answer"] = [{
                        "sentence": marked_sentence,
                        "flag": "1"
                    }]
                else:
                    # 如果整体搜索没有找到匹配结果，则进行句子拆分搜索
                    sentences = SentenceUtil.split_text(prop_value)
            else:
                # 如果整体搜索没有找到匹配结果，则进行句子拆分搜索
                sentences = SentenceUtil.split_text(prop_value)
                result_sentences, references = _process_sentence_search(
                    node_name, prop_title, sentences, trunks_service
                )
                # 处理症状标记
                if prop_title == '临床表现' and symptom_list and result_sentences:
                    for sentence in result_sentences:
                        marked_sentence = sentence["sentence"]
                        # 创建一个标记位置的列表，记录每个位置是否已被标记
                        marked_positions = [False] * len(marked_sentence)

                        # 创建一个列表来存储已处理的症状
                        processed_symptoms = []
                        
                        for symptom in symptom_list:
                            # 检查是否已处理过该症状或其子集
                            if any(symptom in processed_sym or processed_sym in symptom for processed_sym in processed_symptoms):
                                continue
                                
                            # 查找所有匹配位置
                            start_pos = 0
                            while True:
                                pos = marked_sentence.find(symptom, start_pos)
                                if pos == -1:
                                    break

                                # 检查这个位置是否已被标记
                                if not any(marked_positions[pos:pos + len(symptom)]):
                                    # 标记这个范围的所有位置
                                    for i in range(pos, pos + len(symptom)):
                                        marked_positions[i] = True
                                    # 替换文本
                                    marked_sentence = marked_sentence[:pos] + f'<i style="color:red;">{symptom}</i>' + marked_sentence[pos + len(symptom):]
                                    # 将成功标记的症状添加到已处理列表中
                                    if symptom not in processed_symptoms:
                                        processed_symptoms.append(symptom)
                                    # 更新标记位置数组以适应新插入的标签
                                    new_positions = [False] * (len('<i style="color:red;">') + len('</i>'))
                                    marked_positions = marked_positions[:pos] + new_positions + marked_positions[pos:]

                                start_pos = pos + len('<i style="color:red;">') + len(symptom) + len('</i>')

                        sentence["sentence"] = marked_sentence
                if references:
                    prop_result["references"] = references
                if result_sentences:
                    prop_result["answer"] = result_sentences

        # 处理文件信息
        all_files = set()
        file_index_map = {}
        file_index = 1

        # 收集文件信息
        for prop_result in result["props"]:
            if "references" not in prop_result:
                continue
            for ref in prop_result["references"]:
                referrence = ref.get("referrence", "")
                if not (referrence and "/books/" in referrence):
                    continue
                file_name = referrence.split("/books/")[-1]
                if not file_name:
                    continue
                file_type = _get_file_type(file_name)
                if file_name not in file_index_map:
                    file_index_map[file_name] = file_index
                    file_index += 1
                all_files.add((file_name, file_type))

        # 更新引用索引
        for prop_result in result["props"]:
            if "references" not in prop_result:
                continue
            for ref in prop_result["references"]:
                referrence = ref.get("referrence", "")
                if referrence and "/books/" in referrence:
                    file_name = referrence.split("/books/")[-1]
                    if file_name in file_index_map:
                        ref["index"] = f"{file_index_map[file_name]}-{ref['index']}"

            # 更新answer中的flag
            if "answer" in prop_result:
                for sentence in prop_result["answer"]:
                    if sentence["flag"]:
                        for ref in prop_result["references"]:
                            if ref["index"].endswith(f"-{sentence['flag']}"):
                                sentence["flag"] = ref["index"]
                                break

        # 添加文件信息到结果
        result["files"] = sorted([{
            "file_name": file_name,
            "file_type": file_type,
            "index": str(file_index_map[file_name])
        } for file_name, file_type in all_files], key=lambda x: int(x["index"]))

        end_time = time.time()
        logger.info(f"node_props_search接口耗时: {(end_time - start_time) * 1000:.2f}ms")

        # 缓存结果
        cache_key = f"xunzheng_{request.node_id}"
        cache[cache_key] = result

        return StandardResponse(success=True, data=result)
    except Exception as e:
        logger.error(f"Node props search failed: {str(e)}")
        raise HTTPException(status_code=500, detail=str(e))

text_search_router = router