410 lines
17 KiB
Python
410 lines
17 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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内容整合API模型定义
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"""
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from typing import List, Dict, Any, Optional, Union
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from datetime import datetime
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from pydantic import BaseModel, Field
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class XHSSearchRequest(BaseModel):
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"""小红书搜索请求模型"""
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keyword: str = Field(..., min_length=1, max_length=100, description="搜索关键词")
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max_notes: int = Field(default=20, ge=1, le=100, description="最大笔记数量")
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sort_type: int = Field(default=0, ge=0, le=4, description="排序类型 (0:综合排序, 1:最新, 2:最多点赞, 3:最多评论, 4:最多收藏)")
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note_type: int = Field(default=0, ge=0, le=2, description="笔记类型 (0:不限, 1:视频笔记, 2:普通笔记)")
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download_media: bool = Field(default=True, description="是否下载媒体文件")
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class Config:
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schema_extra = {
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"example": {
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"keyword": "上海迪士尼攻略",
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"max_notes": 20,
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"sort_type": 0,
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"note_type": 0,
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"download_media": True
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}
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}
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class IntegrationRequest(BaseModel):
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"""内容整合请求模型"""
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keyword: str = Field(..., min_length=1, max_length=100, description="搜索关键词")
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document_paths: List[str] = Field(..., description="文档路径列表")
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max_notes: int = Field(default=20, ge=1, le=100, description="最大笔记数量")
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sort_type: int = Field(default=0, ge=0, le=4, description="排序类型")
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note_type: int = Field(default=0, ge=0, le=2, description="笔记类型")
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download_media: bool = Field(default=True, description="是否下载媒体文件")
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output_format: str = Field(default='summary', description="输出格式")
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include_llm_processing: bool = Field(default=True, description="是否包含LLM处理")
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class Config:
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schema_extra = {
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"example": {
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"keyword": "上海迪士尼攻略",
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"document_paths": [
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"/path/to/document1.pdf",
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"/path/to/document2.docx"
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],
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"max_notes": 20,
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"sort_type": 0,
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"note_type": 0,
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"download_media": True,
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"output_format": "summary",
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"include_llm_processing": True
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}
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}
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class BatchIntegrationRequest(BaseModel):
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"""批量整合请求模型"""
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tasks: List[IntegrationRequest] = Field(..., description="整合任务列表")
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class Config:
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schema_extra = {
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"example": {
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"tasks": [
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{
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"keyword": "上海迪士尼攻略",
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"document_paths": ["/path/to/document1.pdf"],
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"max_notes": 20
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},
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{
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"keyword": "北京环球影城攻略",
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"document_paths": ["/path/to/document2.pdf"],
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"max_notes": 15
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}
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]
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}
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}
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class CookieManagementRequest(BaseModel):
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"""Cookie管理请求模型"""
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name: str = Field(..., min_length=1, max_length=50, description="Cookie名称")
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cookie_string: str = Field(..., min_length=10, description="Cookie字符串")
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user_info: Optional[Dict[str, Any]] = Field(default=None, description="用户信息")
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class Config:
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schema_extra = {
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"example": {
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"name": "user1",
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"cookie_string": "your_cookie_string_here",
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"user_info": {
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"username": "用户1",
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"description": "测试用户"
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}
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}
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}
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class ExportRequest(BaseModel):
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"""导出请求模型"""
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task_id: str = Field(..., description="任务ID")
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output_format: str = Field(default='summary', description="输出格式")
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filename: Optional[str] = Field(default=None, description="文件名")
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class Config:
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schema_extra = {
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"example": {
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"task_id": "task_123456",
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"output_format": "summary",
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"filename": "result.txt"
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}
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}
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class XHSNoteResponse(BaseModel):
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"""小红书笔记响应模型"""
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note_id: str = Field(..., description="笔记ID")
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title: str = Field(..., description="笔记标题")
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content: str = Field(..., description="笔记内容")
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author: str = Field(..., description="作者昵称")
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author_id: str = Field(..., description="作者ID")
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tags: List[str] = Field(default_factory=list, description="标签列表")
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images: List[str] = Field(default_factory=list, description="图片列表")
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videos: List[str] = Field(default_factory=list, description="视频列表")
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likes: int = Field(default=0, description="点赞数")
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comments: int = Field(default=0, description="评论数")
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shares: int = Field(default=0, description="分享数")
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created_time: str = Field(..., description="创建时间")
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note_url: str = Field(..., description="笔记URL")
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class XHSSearchResponse(BaseModel):
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"""小红书搜索响应模型"""
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keyword: str = Field(..., description="搜索关键词")
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notes: List[XHSNoteResponse] = Field(default_factory=list, description="笔记列表")
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total_count: int = Field(..., description="总数量")
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success: bool = Field(..., description="是否成功")
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error_message: str = Field(default="", description="错误信息")
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processing_time: float = Field(default=0.0, description="处理时间")
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class DocumentContentResponse(BaseModel):
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"""文档内容响应模型"""
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file_path: str = Field(..., description="文件路径")
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content: str = Field(..., description="文档内容")
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file_type: str = Field(..., description="文件类型")
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metadata: Dict[str, Any] = Field(default_factory=dict, description="元数据")
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class IntegratedContentResponse(BaseModel):
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"""整合内容响应模型"""
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documents: List[DocumentContentResponse] = Field(default_factory=list, description="文档列表")
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integrated_text: str = Field(..., description="整合后的文本")
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summary: str = Field(..., description="摘要")
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key_topics: List[str] = Field(default_factory=list, description="关键主题")
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total_length: int = Field(..., description="总长度")
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class IntegrationResponse(BaseModel):
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"""整合响应模型"""
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task_id: str = Field(..., description="任务ID")
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keyword: str = Field(..., description="关键词")
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success: bool = Field(..., description="是否成功")
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xhs_content: Optional[XHSSearchResponse] = Field(default=None, description="小红书内容")
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document_content: Optional[IntegratedContentResponse] = Field(default=None, description="文档内容")
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integrated_summary: str = Field(default="", description="整合摘要")
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processing_time: float = Field(default=0.0, description="处理时间")
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created_time: datetime = Field(default_factory=datetime.now, description="创建时间")
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error_message: str = Field(default="", description="错误信息")
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class Config:
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json_encoders = {
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datetime: lambda v: v.isoformat()
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}
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class CookieStatsResponse(BaseModel):
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"""Cookie统计响应模型"""
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total_cookies: int = Field(..., description="总Cookie数量")
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valid_cookies: int = Field(..., description="有效Cookie数量")
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invalid_cookies: int = Field(..., description="无效Cookie数量")
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current_cookie: Optional[str] = Field(default=None, description="当前使用的Cookie")
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cookie_details: List[Dict[str, Any]] = Field(default_factory=list, description="Cookie详情")
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class ServiceStatusResponse(BaseModel):
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"""服务状态响应模型"""
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status: str = Field(..., description="服务状态")
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startup_time: datetime = Field(..., description="启动时间")
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output_path: str = Field(..., description="输出路径")
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xhs_service_available: bool = Field(..., description="小红书服务是否可用")
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document_service_available: bool = Field(..., description="文档服务是否可用")
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integration_stats: Dict[str, Any] = Field(default_factory=dict, description="整合统计")
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class Config:
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json_encoders = {
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datetime: lambda v: v.isoformat()
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}
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class HealthCheckResponse(BaseModel):
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"""健康检查响应模型"""
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status: str = Field(..., description="健康状态")
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timestamp: datetime = Field(default_factory=datetime.now, description="检查时间")
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services: Dict[str, bool] = Field(default_factory=dict, description="服务状态")
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class Config:
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json_encoders = {
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datetime: lambda v: v.isoformat()
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}
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class TaskSummaryResponse(BaseModel):
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"""任务摘要响应模型"""
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task_id: str = Field(..., description="任务ID")
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keyword: str = Field(..., description="关键词")
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success: bool = Field(..., description="是否成功")
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processing_time: float = Field(..., description="处理时间")
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created_time: datetime = Field(..., description="创建时间")
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notes_count: int = Field(default=0, description="笔记数量")
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documents_count: int = Field(default=0, description="文档数量")
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content_preview: str = Field(default="", description="内容预览")
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class Config:
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json_encoders = {
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datetime: lambda v: v.isoformat()
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}
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class ValidationResponse(BaseModel):
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"""验证响应模型"""
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validation_results: Dict[str, bool] = Field(..., description="验证结果")
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valid_count: int = Field(..., description="有效文档数量")
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invalid_count: int = Field(..., description="无效文档数量")
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invalid_documents: List[str] = Field(default_factory=list, description="无效文档列表")
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class ApiResponse(BaseModel):
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"""通用API响应模型"""
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success: bool = Field(..., description="是否成功")
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message: str = Field(default="", description="响应消息")
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data: Optional[Any] = Field(default=None, description="响应数据")
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timestamp: datetime = Field(default_factory=datetime.now, description="时间戳")
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class Config:
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json_encoders = {
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datetime: lambda v: v.isoformat()
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}
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class ErrorResponse(BaseModel):
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"""错误响应模型"""
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success: bool = Field(default=False, description="是否成功")
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error_code: str = Field(..., description="错误码")
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error_message: str = Field(..., description="错误信息")
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timestamp: datetime = Field(default_factory=datetime.now, description="时间戳")
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class Config:
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json_encoders = {
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datetime: lambda v: v.isoformat()
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}
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class CookieConfig(BaseModel):
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"""Cookie配置模型"""
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cookie_string: str = Field(..., description="完整的Cookie字符串")
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name: Optional[str] = Field(None, description="Cookie名称/标识")
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user_info: Optional[Dict[str, Any]] = Field(None, description="用户信息")
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priority: int = Field(default=1, ge=1, le=10, description="优先级 (1-10, 数字越大优先级越高)")
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class Config:
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schema_extra = {
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"example": {
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"cookie_string": "a1=your_a1_value; web_session=your_session_value; webId=your_webid",
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"name": "user1",
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"user_info": {"username": "用户1", "description": "主要账号"},
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"priority": 5
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}
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}
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class BatchSearchRequest(BaseModel):
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"""批量搜索请求模型"""
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# Cookie配置 - 必填
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cookies: Union[
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List[CookieConfig], # 方式1: 详细的Cookie配置列表
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Dict[str, str], # 方式2: 简单的键值对
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str # 方式3: 单一Cookie字符串
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] = Field(..., description="Cookie配置信息，支持多种格式")
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keywords: List[str] = Field(..., description="批量搜索关键词列表")
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max_notes_per_keyword: int = Field(default=20, ge=1, le=100, description="每个关键词的最大笔记数量")
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sort_type: int = Field(default=0, ge=0, le=4, description="排序类型 (0:综合排序, 1:最新, 2:最多点赞, 3:最多评论, 4:最多收藏)")
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note_type: int = Field(default=0, ge=0, le=2, description="笔记类型 (0:不限, 1:视频笔记, 2:普通笔记)")
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save_images: bool = Field(default=True, description="是否保存图片")
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image_storage_path: str = Field(default="data/images", description="图像存储路径")
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get_detailed_content: bool = Field(default=True, description="是否获取详细内容")
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output_path: str = Field(default="data/output", description="输出路径")
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cookie_rotation: bool = Field(default=True, description="是否启用Cookie轮换")
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max_retries: int = Field(default=3, ge=1, le=10, description="每个关键词的最大重试次数")
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class Config:
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schema_extra = {
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"example": {
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"cookies": [
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{
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"cookie_string": "a1=main_user_a1; web_session=main_session; webId=main_webid",
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"name": "main_user",
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"priority": 5,
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"user_info": {"username": "主要用户"}
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},
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{
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"cookie_string": "a1=backup_user_a1; web_session=backup_session; webId=backup_webid",
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"name": "backup_user",
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"priority": 3
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}
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],
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"keywords": ["北京旅游", "上海美食", "广州攻略"],
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"max_notes_per_keyword": 20,
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"cookie_rotation": True,
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"max_retries": 3,
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"save_images": True,
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"get_detailed_content": True
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}
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}
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class NoteInfo(BaseModel):
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"""笔记信息模型"""
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note_id: str = Field(..., description="笔记ID")
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title: str = Field(..., description="笔记标题")
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author: str = Field(..., description="作者昵称")
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content: Optional[str] = Field(None, description="笔记内容")
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like_count: int = Field(default=0, description="点赞数")
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comment_count: int = Field(default=0, description="评论数")
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share_count: int = Field(default=0, description="分享数")
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collect_count: int = Field(default=0, description="收藏数")
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note_url: str = Field(..., description="笔记链接")
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images: List[str] = Field(default_factory=list, description="图片URL列表")
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saved_image_paths: List[str] = Field(default_factory=list, description="保存的图片本地路径")
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publish_time: Optional[str] = Field(None, description="发布时间")
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location: Optional[str] = Field(None, description="位置信息")
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tags: List[str] = Field(default_factory=list, description="标签列表")
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class KeywordSearchResult(BaseModel):
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"""单个关键词搜索结果"""
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keyword: str = Field(..., description="搜索关键词")
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total_notes: int = Field(..., description="搜索到的笔记总数")
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notes: List[NoteInfo] = Field(..., description="笔记列表")
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search_time: str = Field(..., description="搜索时间")
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success: bool = Field(..., description="搜索是否成功")
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error_message: Optional[str] = Field(None, description="错误信息")
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class BatchSearchResponse(BaseModel):
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"""批量搜索响应模型"""
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total_keywords: int = Field(..., description="总关键词数量")
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successful_searches: int = Field(..., description="成功搜索数量")
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failed_searches: int = Field(..., description="失败搜索数量")
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total_notes: int = Field(..., description="总笔记数量")
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results: List[KeywordSearchResult] = Field(..., description="搜索结果列表")
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image_storage_path: str = Field(..., description="图像存储路径")
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output_path: str = Field(..., description="输出路径")
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processing_time: float = Field(..., description="处理时间(秒)")
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summary: Dict[str, Any] = Field(..., description="结果摘要")
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class Config:
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schema_extra = {
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"example": {
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"total_keywords": 3,
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"successful_searches": 2,
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"failed_searches": 1,
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"total_notes": 35,
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"results": [
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{
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"keyword": "北京旅游",
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"total_notes": 20,
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"notes": [
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{
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"note_id": "64a1b2c3d4e5f6g7",
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"title": "北京三日游攻略",
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"author": "旅游达人",
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"content": "详细的北京旅游攻略...",
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"like_count": 150,
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"comment_count": 30,
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"note_url": "https://www.xiaohongshu.com/discovery/item/64a1b2c3d4e5f6g7",
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"images": ["https://sns-img-hw.xhscdn.com/example1.jpg"],
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"saved_image_paths": ["data/images/beijing_travel_1.jpg"]
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}
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],
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"search_time": "2024-01-01 12:00:00",
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"success": True
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}
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],
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"image_storage_path": "data/images",
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"output_path": "data/output",
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"processing_time": 45.5,
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"summary": {
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"top_authors": ["旅游达人", "美食博主"],
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"total_interactions": 5000,
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"saved_images": 25
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}
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}
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} |