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.text_splitter import TextSplitter
from utils.vector_distance import VectorDistance
from model.response import StandardResponse
from utils.vectorizer import Vectorizer
DISTANCE_THRESHOLD = 0.8
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

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

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]
    conversation_id: Optional[str] = None

@router.post("/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 = TextSplitter.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
                    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,
                        "referrence": search_result.get("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("/match", response_model=StandardResponse)
async def match_text(request: TextCompareRequest):
    try:
        sentences = TextSplitter.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("/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("/mr_match", response_model=StandardResponse)
async def compare_text(request: TextCompareMultiRequest):
    start_time = time.time()
    try:
        # 拆分两段文本
        origin_sentences = TextSplitter.split_text(request.origin)
        similar_sentences = TextSplitter.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))

@router.post("/eb_search", response_model=StandardResponse)
async def node_props_search(request: NodePropsSearchRequest, db: Session = Depends(get_db)):
    try:
        start_time = time.time()
        # 初始化服务
        trunks_service = TrunksService()
        node_service = KGNodeService(db)
        prop_service = KGPropService(db)

        # 根据node_id查询节点信息
        node = node_service.get_node(request.node_id)
        if not node:
            raise ValueError(f"节点不存在: {request.node_id}")

        node_name = node.get('name', '')

        # 初始化结果
        result = {
            "id": request.node_id,
            "name": node_name,
            "category": node.get('category', ''),
            "props": [],
            "distance": 0
        }

        # 缓存结果
        cached_results = trunks_service.get_cached_result(request.conversation_id) if request.conversation_id else []

        # 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', '')

            # 拆分属性值为句子
            sentences = TextSplitter.split_text(prop_value)
            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)
            }

            # 添加到结果中
            result["props"].append(prop_result)

            # 处理属性值中的句子
            result_sentences = []
            all_references = []
            reference_index = 1

            # 对每个句子进行向量搜索
            for sentence in sentences:
                original_sentence = sentence
                sentence = sentence.replace("\n", "<br>")
                if len(sentence) < 10:
                    result_sentences.append(sentence)
                    continue

                # 构建搜索文本
                search_text = f"{node_name}:{prop_title}:{sentence}"

                # 检查缓存
                if cached_results:
                    min_distance = float('inf')
                    best_result = None
                    sentence_vector = Vectorizer.get_embedding(search_text)

                    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:
                    # 进行向量搜索
                    search_results = trunks_service.search_by_vector(
                        text=search_text,
                        limit=1,
                        type='trunk',
                        conversation_id=request.conversation_id
                    )

                # 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
                        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,
                            "referrence": search_result.get("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)

            # 更新属性值，添加引用信息
            if all_references:
                prop_result["references"] = all_references

            # 将处理后的句子添加到结果中
            if result_sentences:
                prop_result["answer"] = result_sentences

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