675 lines
29 KiB
Python
675 lines
29 KiB
Python
"""
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知识库文件 API 路由模块
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处理知识库文件的上传、列表、详情、删除和搜索功能。
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"""
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import os
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import time
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from pathlib import Path
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from urllib.parse import urlparse
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import asyncpg
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from fastapi import APIRouter, Depends, HTTPException, status, Query, UploadFile, File, BackgroundTasks
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from pydantic import BaseModel, Field
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from core.config import settings
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from core.dependencies import get_db, get_current_user
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from core.database import get_db_pool
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from core.exceptions import NotFoundError, BadRequestError
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from models.user import User
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from models.knowledge_base_file import FileUploadResponse, FileListResponse
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from services.knowledge_base_service import KnowledgeBaseService
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from services.knowledge_base_file_service import KnowledgeBaseFileService
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from services.vector_service import get_vector_service
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from services.oss_service import get_oss_service
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from utils.helpers import BaseResponse
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from logger.logging import get_logger
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logger = get_logger(__name__)
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# 创建知识库文件路由
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kb_file_router = APIRouter(prefix="/api/knowledge-base", tags=["知识库文件"])
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# 文件上传目录
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UPLOAD_DIR = "./uploads"
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Path(UPLOAD_DIR).mkdir(parents=True, exist_ok=True)
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# 支持的文件类型
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SUPPORTED_EXTENSIONS = {'.pdf', '.docx', '.xlsx', '.xls', '.csv', '.txt', '.png', '.jpg', '.jpeg', '.bmp'}
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FILE_TYPE_MAP = {
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'.pdf': 'pdf',
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'.docx': 'docx',
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'.xlsx': 'xlsx',
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'.xls': 'xls',
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'.csv': 'csv',
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'.txt': 'txt',
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'.png': 'png',
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'.jpg': 'jpg',
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'.jpeg': 'jpeg',
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'.bmp': 'bmp'
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}
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class UrlUploadRequest(BaseModel):
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"""URL 上传请求模型"""
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url: str = Field(..., description="网页 URL", min_length=1)
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async def _check_kb_access(conn: asyncpg.Connection, kb_id: int, user: User):
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"""检查知识库访问权限（企业版可见性）"""
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kb = await KnowledgeBaseService.get_knowledge_base_by_id(conn, kb_id, user)
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if not kb:
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raise NotFoundError("知识库")
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return kb
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async def process_file_background(
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file_id: int,
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file_path: str,
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knowledge_base_id: int,
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file_type: str = "pdf"
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):
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"""
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后台任务：处理文件向量化
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Args:
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file_id: 文件 ID
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file_path: 文件路径（可能是 OSS URL 或本地路径）
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knowledge_base_id: 知识库 ID
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file_type: 文件类型
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"""
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pool = await get_db_pool()
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async with pool.acquire() as conn:
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local_file_path = None
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try:
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logger.info(f"开始后台处理文件 ID: {file_id}, 路径: {file_path}, 类型: {file_type}")
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oss_service = get_oss_service()
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if oss_service.enabled and file_path.startswith(('http://', 'https://')):
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logger.info(f"检测到 OSS URL，开始下载文件: {file_path}")
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oss_object_name = oss_service.extract_object_name_from_url(file_path, knowledge_base_id)
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if not oss_object_name:
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logger.error(f"无法从 OSS URL 提取对象名称: {file_path}")
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await KnowledgeBaseFileService.update_file_status(conn, file_id, "failed", 0)
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return
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local_file_path = oss_service.download_file(oss_object_name)
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if not local_file_path:
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logger.error("从 OSS 下载文件失败")
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await KnowledgeBaseFileService.update_file_status(conn, file_id, "failed", 0)
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return
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logger.info(f"文件下载成功: {local_file_path}")
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actual_file_path = local_file_path
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else:
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actual_file_path = file_path
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# 处理文档（传入 file_id 和 OSS URL）
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vector_service = get_vector_service()
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result = await vector_service.process_document(
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actual_file_path,
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knowledge_base_id,
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file_type,
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file_id=file_id,
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source_url=file_path # 🔑 传递原始 OSS URL
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)
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# 检查处理结果
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if not result.success:
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logger.warning(f"文件处理失败 ID: {file_id}, 原因: {result.error_message}")
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await KnowledgeBaseFileService.update_file_status(conn, file_id, "failed", 0)
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return
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# 生成文件摘要
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summary_text = None
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try:
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from services.summary_service import SummaryService
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from langchain_core.documents import Document
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# 判断是否为图片类型
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image_types = {'png', 'jpg', 'jpeg', 'bmp'}
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is_image = file_type.lower() in image_types
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if is_image:
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# 🎨 使用视觉模型处理图片
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from services.vision_service import VisionService
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logger.info(f"🎨 使用视觉模型为知识库图片 {file_id} 生成描述")
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# 生成带签名的临时访问 URL（用于私有 OSS）
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vision_image_url = file_path
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if file_path.startswith(('http://', 'https://')):
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# 是 OSS URL，生成签名 URL 供视觉模型访问
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try:
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oss_object_name = oss_service.extract_object_name_from_url(file_path, knowledge_base_id)
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if oss_object_name:
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# 生成有效期 1 小时的签名 URL
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signed_url = oss_service.get_signed_url(oss_object_name, expires=3600)
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if signed_url:
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vision_image_url = signed_url
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logger.info(f"🔐 已生成签名 URL 供视觉模型访问（有效期1小时）")
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else:
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logger.warning(f"生成签名 URL 失败，尝试使用原始 URL")
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else:
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logger.warning(f"无法从 OSS URL 提取对象名称，使用原始 URL")
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except Exception as e:
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logger.warning(f"生成签名 URL 时出错，使用原始 URL: {e}")
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# 调用视觉模型
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vision_prompt = "详细描述图片的内容，包括主要元素、颜色、布局、文字信息等。回答需要详细且准确。"
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vision_description = await VisionService.get_image_description(
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image_url=vision_image_url,
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prompt=vision_prompt
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)
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if vision_description:
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logger.info(f"✅ 视觉模型返回结果:")
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logger.info(f"{'='*60}")
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logger.info(f"图片URL: {file_path}")
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logger.info(f"描述内容: {vision_description}")
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logger.info(f"描述长度: {len(vision_description)} 字符")
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logger.info(f"{'='*60}")
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# 组合 OCR 文字和视觉描述
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ocr_text = "\n\n".join([content for _, content, _, _ in result.chunks])
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combined_content = f"【图片内容描述】\n{vision_description}\n\n【图片文字识别（OCR）】\n{ocr_text}" if ocr_text.strip() else f"【图片内容描述】\n{vision_description}"
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logger.info(f"📝 组合内容长度: {len(combined_content)} 字符")
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logger.info(f" - 视觉描述: {len(vision_description)} 字符")
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logger.info(f" - OCR文字: {len(ocr_text)} 字符")
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# 将组合内容转换为 Document 对象
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docs = [Document(page_content=combined_content)]
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# 生成摘要
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summary_text = await SummaryService.generate_file_summary(docs, max_docs=1)
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else:
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logger.warning(f"⚠️ 视觉模型未返回描述，降级使用 OCR 文字生成摘要")
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# 降级使用 OCR 文字
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file_content = "\n\n".join([content for _, content, _, _ in result.chunks])
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docs = [Document(page_content=file_content)]
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summary_text = await SummaryService.generate_file_summary(docs, max_docs=1)
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else:
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# 非图片文件，使用原有逻辑
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# 拼接所有 chunks 的内容用于生成摘要
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file_content = "\n\n".join([content for _, content, _, _ in result.chunks])
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# 限制内容长度，避免超出 LLM 限制
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max_content_length = 10000 # 约 3000-4000 tokens
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if len(file_content) > max_content_length:
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file_content = file_content[:max_content_length] + "..."
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logger.info(f"正在为文件 {file_id} 生成摘要，内容长度: {len(file_content)} 字符")
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# 将文本内容转换为 Document 对象
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docs = [Document(page_content=file_content)]
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# 生成摘要
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summary_text = await SummaryService.generate_file_summary(docs, max_docs=1)
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if summary_text:
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logger.info(f"📝 文件 {file_id} 摘要生成成功:")
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logger.info(f"{'='*60}")
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logger.info(f"摘要内容: {summary_text}")
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logger.info(f"{'='*60}")
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else:
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logger.warning(f"文件 {file_id} 摘要生成失败，返回为空")
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except Exception as e:
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logger.error(f"生成文件摘要失败: {e}")
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import traceback
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logger.error(f"错误堆栈: {traceback.format_exc()}")
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# 摘要生成失败不影响主流程，继续处理
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# 保存成功处理的结果（包含 summary）
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await KnowledgeBaseFileService.save_chunks(
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conn, file_id, knowledge_base_id, result.chunks, summary=summary_text
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)
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# 🔑 关键：更新 ChromaDB 中的 summary metadata
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if summary_text:
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success = vector_service.update_kb_file_summary_in_vectors(
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knowledge_base_id=knowledge_base_id,
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file_id=file_id,
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summary=summary_text
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)
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if success:
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logger.info(f"✅ ChromaDB metadata 已同步 summary")
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else:
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logger.warning(f"⚠️ ChromaDB metadata 同步 summary 失败，但不影响主流程")
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await KnowledgeBaseFileService.update_file_status(conn, file_id, "completed", result.chunk_count)
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logger.info(f"文件处理完成 ID: {file_id}, 类型: {file_type}, 块数: {result.chunk_count}, 摘要: {'已生成' if summary_text else '未生成'}")
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except Exception as e:
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logger.error(f"后台处理文件异常 ID: {file_id}, 类型: {file_type}, 错误: {e}")
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await KnowledgeBaseFileService.update_file_status(conn, file_id, "failed", 0)
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finally:
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if local_file_path and os.path.exists(local_file_path):
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try:
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os.remove(local_file_path)
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logger.debug(f"已删除临时文件: {local_file_path}")
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except Exception as e:
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logger.warning(f"删除临时文件失败: {e}")
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async def process_url_background(file_id: int, url: str, knowledge_base_id: int):
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"""后台任务：处理 URL 向量化"""
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pool = await get_db_pool()
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async with pool.acquire() as conn:
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try:
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logger.info(f"开始后台处理 URL ID: {file_id}, URL: {url}")
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# 处理 URL
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vector_service = get_vector_service()
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result = await vector_service.process_url(url, knowledge_base_id)
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# 检查处理结果
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if not result.success:
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logger.warning(f"URL 处理失败 ID: {file_id}, 原因: {result.error_message}")
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await KnowledgeBaseFileService.update_file_status(conn, file_id, "failed", 0)
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return
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# 生成文件摘要
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summary_text = None
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try:
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from services.summary_service import SummaryService
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from langchain_core.documents import Document
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# 拼接所有 chunks 的内容用于生成摘要
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file_content = "\n\n".join([content for _, content, _, _ in result.chunks])
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# 限制内容长度
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max_content_length = 10000
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if len(file_content) > max_content_length:
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file_content = file_content[:max_content_length] + "..."
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logger.info(f"正在为 URL {file_id} 生成摘要，内容长度: {len(file_content)} 字符")
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docs = [Document(page_content=file_content)]
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summary_text = await SummaryService.generate_file_summary(docs, max_docs=1)
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if summary_text:
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logger.info(f"📝 URL {file_id} 摘要生成成功")
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else:
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logger.warning(f"URL {file_id} 摘要生成失败，返回为空")
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except Exception as e:
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logger.error(f"生成 URL 摘要失败: {e}")
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# 保存成功处理的结果（包含 summary）
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await KnowledgeBaseFileService.save_chunks(
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conn, file_id, knowledge_base_id, result.chunks, summary=summary_text
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)
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# 更新 ChromaDB metadata（URL 暂不支持 file_id，跳过）
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# if summary_text:
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# vector_service.update_kb_file_summary_in_vectors(...)
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await KnowledgeBaseFileService.update_file_status(conn, file_id, "completed", result.chunk_count)
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logger.info(f"URL 处理完成 ID: {file_id}, 块数: {result.chunk_count}, 摘要: {'已生成' if summary_text else '未生成'}")
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except Exception as e:
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logger.error(f"后台处理 URL 异常 ID: {file_id}, 错误: {e}")
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await KnowledgeBaseFileService.update_file_status(conn, file_id, "failed", 0)
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@kb_file_router.post("/{kb_id}/upload", response_model=BaseResponse, summary="上传文件到知识库")
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async def upload_file(
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kb_id: int,
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background_tasks: BackgroundTasks,
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file: UploadFile = File(...),
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current_user: User = Depends(get_current_user),
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conn: asyncpg.Connection = Depends(get_db)
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):
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"""上传文件到知识库并进行向量化处理"""
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try:
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logger.info(f"📤 开始上传文件到知识库 {kb_id}: {file.filename}, 用户: {current_user.username}")
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await _check_kb_access(conn, kb_id, current_user)
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# 检查文件类型
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file_ext = Path(file.filename).suffix.lower()
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if file_ext not in SUPPORTED_EXTENSIONS:
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logger.warning(f"❌ 不支持的文件类型: {file_ext}, 文件: {file.filename}")
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raise BadRequestError(f"不支持的文件类型: {file_ext}，支持的类型: {', '.join(SUPPORTED_EXTENSIONS)}")
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file_type = FILE_TYPE_MAP[file_ext]
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logger.info(f"📋 文件类型识别: {file_ext} -> {file_type}")
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content = await file.read()
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file_size = len(content)
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file_size_mb = file_size / (1024 * 1024)
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# 检查文件大小（限制 15MB）
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MAX_FILE_SIZE = 15 * 1024 * 1024 # 15MB
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if file_size > MAX_FILE_SIZE:
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logger.warning(f"❌ 文件大小超限: {file_size_mb:.2f}MB (最大 15MB), 文件: {file.filename}")
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raise BadRequestError(f"文件大小超过限制，当前: {file_size_mb:.2f}MB，最大允许: 15MB")
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logger.info(f"✅ 文件大小验证通过: {file_size_mb:.2f}MB ({file_size} bytes)")
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# 生成唯一文件名
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timestamp = int(time.time() * 1000)
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unique_filename = f"{timestamp}_{file.filename}"
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oss_object_name = f"kb_{kb_id}/{unique_filename}"
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# 上传文件
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oss_service = get_oss_service()
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file_path = None
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file_url = None
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logger.info(f"☁️ 开始上传文件，OSS 状态: {'已启用' if oss_service.enabled else '未启用'}")
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if oss_service.enabled:
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logger.info(f"☁️ 上传文件到 OSS: {oss_object_name}")
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file_url = oss_service.upload_file_from_bytes(content, oss_object_name, file.filename)
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if file_url:
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file_path = file_url
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logger.info(f"✅ 文件已上传到 OSS: {file_url}")
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# 🔑 图片审核：在创建文件记录前进行审核
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if file_type in ['png', 'jpg', 'jpeg', 'bmp']:
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from core.dependencies import get_moderation_service
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from core.config import settings
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from core.exceptions import ModerationError
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from models.moderation import ModerationDecision
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moderation_service = await get_moderation_service()
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if moderation_service and settings.moderation_enabled:
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try:
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logger.info(f"🔍 开始图片审核: {file.filename}")
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# 使用 OSS URL 进行审核
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result = await moderation_service.moderate_image(
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image_source=file_url,
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source_type="url",
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request_id=f"kb_file_{timestamp}"
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)
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# 检查审核结果
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if result.decision == ModerationDecision.BLOCK:
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# 删除已上传的 OSS 文件
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oss_service.delete_file(oss_object_name)
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logger.warning(
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f"❌ 图片审核不通过: {file.filename}, "
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f"原因: {result.message}, "
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f"标签: {[label.label for label in result.labels]}"
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)
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raise BadRequestError(
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result.message or "图片包含不当内容，无法上传"
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)
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logger.info(
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f"✅ 图片审核通过: {file.filename}, "
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f"决策: {result.decision.value}"
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)
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except ModerationError as e:
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# 审核服务错误，删除 OSS 文件并返回错误
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oss_service.delete_file(oss_object_name)
|
||
logger.error(f"❌ 图片审核服务错误: {e}")
|
||
raise BadRequestError("图片审核服务暂时不可用，请稍后重试")
|
||
else:
|
||
logger.warning("⚠️ OSS 上传失败，回退到本地存储")
|
||
|
||
if not file_path:
|
||
kb_dir = Path(UPLOAD_DIR) / f"kb_{kb_id}"
|
||
kb_dir.mkdir(parents=True, exist_ok=True)
|
||
local_path = kb_dir / unique_filename
|
||
with open(local_path, "wb") as f:
|
||
f.write(content)
|
||
file_path = str(local_path)
|
||
logger.info(f"💾 文件已保存到本地: {file_path}")
|
||
|
||
# 创建文件记录
|
||
logger.info(f"📝 创建文件记录: {file.filename}")
|
||
file_record = await KnowledgeBaseFileService.create_file_record(
|
||
conn, kb_id, current_user.id, file.filename, file_path, file_size, file_type
|
||
)
|
||
logger.info(f"✅ 文件记录已创建: ID={file_record.id}, 状态={file_record.status}")
|
||
|
||
# 添加后台任务
|
||
logger.info(f"🚀 添加后台向量化任务: file_id={file_record.id}, type={file_type}")
|
||
background_tasks.add_task(process_file_background, file_record.id, file_path, kb_id, file_type)
|
||
|
||
return BaseResponse(
|
||
code=200,
|
||
msg="文件上传成功，正在处理中",
|
||
data=FileUploadResponse(
|
||
id=file_record.id,
|
||
file_name=file_record.file_name,
|
||
file_size=file_record.file_size,
|
||
status=file_record.status,
|
||
chunk_count=file_record.chunk_count,
|
||
created_at=file_record.created_at,
|
||
file_url=file_url or file_path
|
||
).dict()
|
||
)
|
||
|
||
except BadRequestError:
|
||
raise
|
||
except ValueError as e:
|
||
# 文件名重复等业务错误
|
||
logger.warning(f"文件上传验证失败: {e}")
|
||
raise BadRequestError(str(e))
|
||
except Exception as e:
|
||
logger.error(f"上传文件失败: {e}")
|
||
raise BadRequestError(f"上传文件失败: {str(e)}")
|
||
|
||
|
||
@kb_file_router.post("/{kb_id}/upload-url", response_model=BaseResponse, summary="上传 URL 到知识库")
|
||
async def upload_url(
|
||
kb_id: int,
|
||
background_tasks: BackgroundTasks,
|
||
request: UrlUploadRequest,
|
||
current_user: User = Depends(get_current_user),
|
||
conn: asyncpg.Connection = Depends(get_db)
|
||
):
|
||
"""上传 URL 到知识库并进行向量化处理"""
|
||
await _check_kb_access(conn, kb_id, current_user)
|
||
|
||
url = request.url.strip()
|
||
if not url.startswith(('http://', 'https://')):
|
||
raise BadRequestError("URL 格式不正确，必须以 http:// 或 https:// 开头")
|
||
|
||
# 生成文件名
|
||
parsed_url = urlparse(url)
|
||
file_name = f"{parsed_url.netloc}{parsed_url.path}".replace('/', '_')[:200]
|
||
if not file_name:
|
||
file_name = "webpage"
|
||
file_name = f"{file_name}.url"
|
||
|
||
# 创建文件记录
|
||
file_record = await KnowledgeBaseFileService.create_file_record(
|
||
conn, kb_id, current_user.id, file_name, url, 0, "url"
|
||
)
|
||
|
||
logger.info(f"URL 已记录: {url}, 文件 ID: {file_record.id}")
|
||
background_tasks.add_task(process_url_background, file_record.id, url, kb_id)
|
||
|
||
return BaseResponse(
|
||
code=200,
|
||
msg="URL 上传成功，正在处理中",
|
||
data=FileUploadResponse(
|
||
id=file_record.id,
|
||
file_name=file_record.file_name,
|
||
file_size=file_record.file_size,
|
||
status=file_record.status,
|
||
chunk_count=file_record.chunk_count,
|
||
created_at=file_record.created_at
|
||
).dict()
|
||
)
|
||
|
||
|
||
@kb_file_router.get("/{kb_id}/files", response_model=BaseResponse, summary="获取知识库文件列表")
|
||
async def get_knowledge_base_files(
|
||
kb_id: int,
|
||
page: int = Query(1, ge=1, description="页码"),
|
||
page_size: int = Query(20, ge=1, le=100, description="每页数量"),
|
||
current_user: User = Depends(get_current_user),
|
||
conn: asyncpg.Connection = Depends(get_db)
|
||
):
|
||
"""获取知识库的文件列表"""
|
||
await _check_kb_access(conn, kb_id, current_user)
|
||
|
||
files, total = await KnowledgeBaseFileService.get_files_by_kb(
|
||
conn, kb_id, current_user.id, page, page_size
|
||
)
|
||
|
||
items = [
|
||
FileUploadResponse(
|
||
id=f.id,
|
||
file_name=f.file_name,
|
||
file_size=f.file_size,
|
||
status=f.status,
|
||
chunk_count=f.chunk_count,
|
||
created_at=f.created_at,
|
||
file_url=f.file_path
|
||
).dict()
|
||
for f in files
|
||
]
|
||
|
||
return BaseResponse(
|
||
code=200,
|
||
msg="获取文件列表成功",
|
||
data=FileListResponse(total=total, items=items).dict()
|
||
)
|
||
|
||
|
||
@kb_file_router.get("/{kb_id}/files/{file_id}", response_model=BaseResponse, summary="获取文件详情")
|
||
async def get_file_detail(
|
||
kb_id: int,
|
||
file_id: int,
|
||
current_user: User = Depends(get_current_user),
|
||
conn: asyncpg.Connection = Depends(get_db)
|
||
):
|
||
"""获取文件详情"""
|
||
await _check_kb_access(conn, kb_id, current_user)
|
||
|
||
file = await KnowledgeBaseFileService.get_file_by_id(conn, file_id, current_user.id)
|
||
if not file or file.knowledge_base_id != kb_id:
|
||
raise NotFoundError("文件")
|
||
|
||
return BaseResponse(
|
||
code=200,
|
||
msg="获取文件详情成功",
|
||
data=FileUploadResponse(
|
||
id=file.id,
|
||
file_name=file.file_name,
|
||
file_size=file.file_size,
|
||
status=file.status,
|
||
chunk_count=file.chunk_count,
|
||
created_at=file.created_at,
|
||
file_url=file.file_path
|
||
).dict()
|
||
)
|
||
|
||
|
||
@kb_file_router.get("/{kb_id}/files/{file_id}/status", response_model=BaseResponse, summary="查询文件处理状态")
|
||
async def get_file_processing_status(
|
||
kb_id: int,
|
||
file_id: int,
|
||
current_user: User = Depends(get_current_user),
|
||
conn: asyncpg.Connection = Depends(get_db)
|
||
):
|
||
"""
|
||
查询知识库文件的处理状态（用于前端轮询）
|
||
|
||
Returns:
|
||
- status: processing（处理中）/ completed（已完成）/ failed（失败）
|
||
- chunk_count: 已处理的文档块数量
|
||
- file_name: 文件名
|
||
- file_type: 文件类型
|
||
- created_at: 创建时间
|
||
- updated_at: 更新时间
|
||
"""
|
||
await _check_kb_access(conn, kb_id, current_user)
|
||
|
||
file = await KnowledgeBaseFileService.get_file_by_id(conn, file_id, current_user.id)
|
||
if not file or file.knowledge_base_id != kb_id:
|
||
raise NotFoundError("文件")
|
||
|
||
return BaseResponse(
|
||
code=200,
|
||
msg="获取文件状态成功",
|
||
data={
|
||
"id": file.id,
|
||
"file_name": file.file_name,
|
||
"file_type": file.file_type,
|
||
"status": file.status,
|
||
"chunk_count": file.chunk_count,
|
||
"created_at": file.created_at.isoformat() if file.created_at else None,
|
||
"updated_at": file.updated_at.isoformat() if file.updated_at else None,
|
||
}
|
||
)
|
||
|
||
|
||
@kb_file_router.delete("/{kb_id}/files/{file_id}", response_model=BaseResponse, summary="删除文件")
|
||
async def delete_file(
|
||
kb_id: int,
|
||
file_id: int,
|
||
current_user: User = Depends(get_current_user),
|
||
conn: asyncpg.Connection = Depends(get_db)
|
||
):
|
||
"""删除知识库中的文件"""
|
||
await _check_kb_access(conn, kb_id, current_user)
|
||
|
||
file = await KnowledgeBaseFileService.get_file_by_id(conn, file_id, current_user.id)
|
||
if not file or file.knowledge_base_id != kb_id:
|
||
raise NotFoundError("文件")
|
||
|
||
# 删除文件记录
|
||
success, vector_ids = await KnowledgeBaseFileService.delete_file(conn, file_id, current_user.id)
|
||
if not success:
|
||
raise NotFoundError("文件")
|
||
|
||
# 删除向量
|
||
if vector_ids:
|
||
try:
|
||
vector_service = get_vector_service()
|
||
vector_service.delete_vectors_by_ids(kb_id, vector_ids)
|
||
logger.info(f"已删除 {len(vector_ids)} 个向量")
|
||
except Exception as e:
|
||
logger.warning(f"删除向量库中的向量失败: {e}")
|
||
|
||
# 删除物理文件
|
||
try:
|
||
oss_service = get_oss_service()
|
||
if oss_service.enabled and file.file_path.startswith(('http://', 'https://')):
|
||
oss_object_name = oss_service.extract_object_name_from_url(file.file_path, kb_id)
|
||
if oss_object_name:
|
||
oss_service.delete_file(oss_object_name)
|
||
logger.info(f"已删除 OSS 文件: {oss_object_name}")
|
||
elif os.path.exists(file.file_path):
|
||
os.remove(file.file_path)
|
||
logger.info(f"已删除本地文件: {file.file_path}")
|
||
except Exception as e:
|
||
logger.warning(f"删除物理文件失败: {e}")
|
||
|
||
return BaseResponse(code=200, msg="删除文件成功", data={"id": file_id})
|
||
|
||
|
||
@kb_file_router.post("/{kb_id}/search", response_model=BaseResponse, summary="在知识库中搜索")
|
||
async def search_in_knowledge_base(
|
||
kb_id: int,
|
||
query: str = Query(..., description="搜索查询"),
|
||
k: int = Query(5, ge=1, le=20, description="返回结果数量"),
|
||
current_user: User = Depends(get_current_user),
|
||
conn: asyncpg.Connection = Depends(get_db)
|
||
):
|
||
"""在知识库中进行语义搜索"""
|
||
await _check_kb_access(conn, kb_id, current_user)
|
||
|
||
vector_service = get_vector_service()
|
||
results = vector_service.search_similar(kb_id, query, k)
|
||
|
||
return BaseResponse(
|
||
code=200,
|
||
msg="搜索成功",
|
||
data={"query": query, "results": results, "count": len(results)}
|
||
)
|