from fastapi import APIRouter, HTTPException
from pydantic import BaseModel
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 = 0.65
import logging

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

@router.post("/search", response_model=StandardResponse)
async def search_text(request: TextSearchRequest):
    try:
        # 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
                )
            
            # 处理搜索结果
            for result in search_results:
                distance = 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"] == 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": result["id"],
                        "content": result["content"],
                        "file_path": result.get("file_path", ""),
                        "title": result.get("title", ""),
                        "distance": distance,
                        "referrence": 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))

text_search_router = router