LongjoeAgent/api/models.py

from pydantic import BaseModel
from typing import Optional, List, Dict, Any


class AsyncChatResponse(BaseModel):
    """异步聊天响应"""

    success: bool
    task_id: str
    message: str
    polling_endpoint: str


class ChatResultResponse(BaseModel):
    """聊天结果响应"""

    success: bool
    task_id: str
    status: str  # pending, processing, completed, failed
    response: Optional[Dict[str, Any]] = None
    error: Optional[str] = None
    created_at: Optional[str] = None
    updated_at: Optional[str] = None


class ChatRequest(BaseModel):
    message: str
    thread_id: str = "default"
    username: str = "default"
    backend_url: str = ""
    token: str = ""
    include_thoughts: bool = False
    include_tool_calls: bool = False


class MessageModel(BaseModel):
    type: str
    content: str
    tool_calls: Optional[List[Dict]] = None
    tool_call_id: Optional[str] = None
    name: Optional[str] = None


class ChatResponse(BaseModel):
    final_answer: str
    all_ai_messages: List[MessageModel]
    all_messages: List[MessageModel]
    tool_calls: List[Dict]
    thread_id: str
    user_identifier: str
    backend_config: Dict
    success: bool
    error: Optional[str] = None


class OCRRequest(BaseModel):
    """图片处理请求"""

    image: str
    type: str
    file_type: int = 1


class MessageCreateBill(BaseModel):
    """创建单据请求"""

    message: str
    document_type: str = None


class ImageVectorItem(BaseModel):
    """图片向量项"""

    image_id: str  # 图片ID
    vector: List[float]  # 图片特征向量
    image_name: Optional[str] = None  # 图片名称，可选
    image_path: Optional[str] = None  # 图片路径，可选


class ImageVectorRequest(BaseModel):
    """计算图片特征向量请求"""

    image: str  # Base64编码的图片数据
    image_id: Optional[str] = None  # 图片ID，可选


class ImageVectorResponse(BaseModel):
    """计算图片特征向量响应"""

    success: bool
    image_id: Optional[str] = None
    vector: Optional[List[float]] = None
    error: Optional[str] = None


class BuildIndexRequest(BaseModel):
    """构建索引请求"""

    image_vectors: List[ImageVectorItem]  # 图片向量列表


class BuildIndexResponse(BaseModel):
    """构建索引响应"""

    success: bool
    indexed_count: int  # 索引的图片数量
    error: Optional[str] = None


class SearchResultItem(BaseModel):
    """搜索结果项"""

    image_id: str  # 图片ID
    similarity: float  # 相似度
    image_name: Optional[str] = None  # 图片名称
    image_path: Optional[str] = None  # 图片路径


class SearchRequest(BaseModel):
    """搜索请求（支持以图搜图和以文搜图）"""

    image: Optional[str] = None  # Base64编码的图片数据，可选
    text: Optional[str] = None  # 文字描述，可选
    top_k: int = 10  # 返回结果数量


class SearchResponse(BaseModel):
    """搜索响应"""

    success: bool
    results: List[SearchResultItem]  # 搜索结果列表
    total_count: int  # 总结果数量
    processing_time: float  # 处理时间（秒）
    error: Optional[str] = None


class SearchHistoryRequest(BaseModel):
    """记录搜索历史请求"""

    empid: int  # 员工ID
    search_type: int  # 搜索类型：1-以图搜图，2-以文搜图，3-图文混合搜索
    search_content: str  # 搜索内容
    result_count: int  # 搜索结果数量
    result_content: str  # 搜索结果内容（JSON格式）


class SearchHistoryResponse(BaseModel):
    """记录搜索历史响应"""

    success: bool
    history_id: Optional[int] = None
    error: Optional[str] = None