knowledge/service/trunks_service.py

from sqlalchemy import func
from sqlalchemy.orm import Session
from db.session import get_db
from typing import List, Optional
from model.trunks_model import Trunks
from db.session import SessionLocal
import logging
from utils.vectorizer import Vectorizer

logger = logging.getLogger(__name__)

class TrunksService:
    def __init__(self):
        self.db = next(get_db())


    def create_trunk(self, trunk_data: dict) -> Trunks:
        # 自动生成向量和全文检索字段
        content = trunk_data.get('content')
        if 'embedding' in trunk_data and len(trunk_data['embedding']) != 1024:
            raise ValueError("向量维度必须为1024")
        trunk_data['embedding'] = Vectorizer.get_embedding(content)
        if 'type' not in trunk_data:
            trunk_data['type'] = 'default'
        if 'title' not in trunk_data:
            from pathlib import Path
            trunk_data['title'] = Path(trunk_data['file_path']).stem
        print("embedding length:", len(trunk_data['embedding']))
        logger.debug(f"生成的embedding长度: {len(trunk_data['embedding'])}, 内容摘要: {content[:20]}")
        # trunk_data['content_tsvector'] = func.to_tsvector('chinese', content)
        
        
        db = SessionLocal()
        try:
            trunk = Trunks(**trunk_data)
            db.add(trunk)
            db.commit()
            db.refresh(trunk)
            return trunk
        except Exception as e:
            db.rollback()
            logger.error(f"创建trunk失败: {str(e)}")
            raise
        finally:
            db.close()

    def get_trunk_by_id(self, trunk_id: int) -> Optional[dict]:
        db = SessionLocal()
        try:
            trunk = db.query(Trunks).filter(Trunks.id == trunk_id).first()
            if trunk:
                return {
                    'id': trunk.id,
                    'file_path': trunk.file_path,
                    'content': trunk.content,
                    'embedding': trunk.embedding.tolist(),
                    'type': trunk.type,
                    'title':trunk.title
                }
            return None
        finally:
            db.close()

    def search_by_vector(self, text: str, limit: int = 10, metadata_condition: Optional[dict] = None, type: Optional[str] = None, conversation_id: Optional[str] = None) -> List[dict]:
       
        embedding = Vectorizer.get_embedding(text)
        db = SessionLocal()
        try:
            query = db.query(
                Trunks.id,
                Trunks.file_path,
                Trunks.content,
                Trunks.embedding.l2_distance(embedding).label('distance'),
                Trunks.title,
                Trunks.embedding,
                Trunks.page_no,
                Trunks.referrence
            )
            if metadata_condition:
                query = query.filter_by(**metadata_condition)
            if type:
                query = query.filter(Trunks.type == type)
            results = query.order_by('distance').limit(limit).all()
            result_list = [{
                'id': r.id,
                'file_path': r.file_path,
                'content': r.content,
                #保留小数点后三位   
                'distance': round(r.distance, 3),
                'title': r.title,
                'embedding': r.embedding.tolist(),
                'page_no': r.page_no,
                'referrence': r.referrence
            } for r in results]

            if conversation_id:
                self.set_cache(conversation_id, result_list)

            return result_list
        finally:
            db.close()

    def fulltext_search(self, query: str) -> List[Trunks]:
        db = SessionLocal()
        try:
            return db.query(Trunks).filter(
                Trunks.content_tsvector.match(query)
            ).all()
        finally:
            db.close()

    def update_trunk(self, trunk_id: int, update_data: dict) -> Optional[Trunks]:
        if 'content' in update_data:
            content = update_data['content']
            update_data['embedding'] = Vectorizer.get_embedding(content)
            if 'type' not in update_data:
                update_data['type'] = 'default'
            logger.debug(f"更新生成的embedding长度: {len(update_data['embedding'])}, 内容摘要: {content[:20]}")
            # update_data['content_tsvector'] = func.to_tsvector('chinese', content)
        
        db = SessionLocal()
        try:
            trunk = db.query(Trunks).filter(Trunks.id == trunk_id).first()
            if trunk:
                for key, value in update_data.items():
                    setattr(trunk, key, value)
                db.commit()
                db.refresh(trunk)
            return trunk
        except Exception as e:
            db.rollback()
            logger.error(f"更新trunk失败: {str(e)}")
            raise
        finally:
            db.close()

    def delete_trunk(self, trunk_id: int) -> bool:
        db = SessionLocal()
        try:
            trunk = db.query(Trunks).filter(Trunks.id == trunk_id).first()
            if trunk:
                db.delete(trunk)
                db.commit()
                return True
            return False
        except Exception as e:
            db.rollback()
            logger.error(f"删除trunk失败: {str(e)}")
            raise
        finally:
            db.close()

    _cache = {}

    def get_cache(self, conversation_id: str) -> List[dict]:
        """
        根据conversation_id获取缓存结果
        :param conversation_id: 会话ID
        :return: 结果列表
        """
        return self._cache.get(conversation_id, [])

    def set_cache(self, conversation_id: str, result: List[dict]) -> None:
        """
        设置缓存结果
        :param conversation_id: 会话ID
        :param result: 要缓存的结果
        """
        self._cache[conversation_id] = result

    def get_cached_result(self, conversation_id: str) -> List[dict]:
        """
        根据conversation_id获取缓存结果
        :param conversation_id: 会话ID
        :return: 结果列表
        """
        return self.get_cache(conversation_id)
        
        

    def paginated_search(self, search_params: dict) -> dict:
        """
        分页查询方法
        :param search_params: 包含keyword, pageNo, limit的字典
        :return: 包含结果列表和分页信息的字典
        """
        keyword = search_params.get('keyword', '')
        page_no = search_params.get('pageNo', 1)
        limit = search_params.get('limit', 10)
        
        if page_no < 1:
            page_no = 1
        if limit < 1:
            limit = 10
            
        embedding = Vectorizer.get_embedding(keyword)
        offset = (page_no - 1) * limit
        
        db = SessionLocal()
        try:
            # 获取总条数
            total_count = db.query(func.count(Trunks.id)).filter(Trunks.type == search_params.get('type')).scalar()
            
            # 执行向量搜索
            results = db.query(
                Trunks.id,
                Trunks.file_path,
                Trunks.content,
                Trunks.embedding.l2_distance(embedding).label('distance'),
                Trunks.title
            ).filter(Trunks.type == search_params.get('type')).order_by('distance').offset(offset).limit(limit).all()
            
            return {
                'data': [{
                    'id': r.id,
                    'file_path': r.file_path,
                    'content': r.content,
                    #保留小数点后三位   
                    'distance': round(r.distance, 3),
                    'title': r.title
                } for r in results],
                'pagination': {
                    'total': total_count,
                    'pageNo': page_no,
                    'limit': limit,
                    'totalPages': (total_count + limit - 1) // limit
                }
            }
        finally:
            db.close()