Build lightweight AI agent admin

2026-06-08 18:14:59 +08:00
commit e164840f43
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+"""
+知识库分段 Schema
+"""
+from typing import Optional, List, Dict, Any
+from datetime import datetime
+
+from pydantic import BaseModel, Field, ConfigDict
+
+from app.base_schema import CSTDatetime
+
+
+class SegmentResponse(BaseModel):
+    """分段输出"""
+    id: str
+    knowledge_base_id: str
+    document_id: str
+    document_name: str = ""
+    position: int = 0
+    content: str
+    answer: Optional[str] = None
+    token_count: int = 0
+    char_count: int = 0
+    word_count: int = 0
+    page_number: Optional[int] = None
+    keywords: Optional[List[str]] = None
+    metadata: Optional[Dict[str, Any]] = None
+    embedding_status: str = "pending"
+    enabled: bool = True
+    hit_count: int = 0
+    sys_create_datetime: Optional[CSTDatetime] = None
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class SegmentListResponse(BaseModel):
+    """分段列表输出"""
+    id: str
+    document_id: str
+    document_name: str = ""
+    position: int = 0
+    content: str
+    answer: Optional[str] = None
+    token_count: int = 0
+    char_count: int = 0
+    word_count: int = 0
+    page_number: Optional[int] = None
+    keywords: Optional[List[str]] = None
+    extra_metadata: Optional[Dict[str, Any]] = None
+    enabled: bool = True
+    hit_count: int = 0
+    embedding_status: str = "pending"
+    sys_create_datetime: Optional[CSTDatetime] = None
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class SegmentUpdateInput(BaseModel):
+    """更新分段"""
+    content: Optional[str] = Field(None, description="分段内容")
+    keywords: Optional[List[str]] = Field(None, description="关键词")
+    enabled: Optional[bool] = Field(None, description="是否启用")
+    extra_metadata: Optional[Dict[str, Any]] = Field(None, description="元数据")
+
+
+class SegmentCreateInput(BaseModel):
+    """手动创建分段"""
+    content: str = Field(..., min_length=1, description="分段内容")
+    answer: Optional[str] = Field(None, description="Q&A 模式的答案")
+    keywords: Optional[List[str]] = Field(None, description="关键词")
+
+
+class ChunkPreviewInput(BaseModel):
+    """分块预览输入"""
+    file_id: str = Field(..., description="文件ID")
+    chunk_strategy: str = Field(default="recursive", description="分块策略")
+    chunk_size: int = Field(default=500, ge=100, le=4000, description="分块大小")
+    chunk_overlap: int = Field(default=50, ge=0, le=500, description="分块重叠")
+    separator: Optional[str] = Field(None, description="自定义分隔符")
+    process_rules: Optional[Dict[str, Any]] = Field(None, description="预处理规则")
+
+
+class ChunkPreviewItem(BaseModel):
+    """分块预览结果项"""
+    position: int = 0
+    content: str = ""
+    char_count: int = 0
+    token_count: int = 0
+    word_count: int = 0
+    answer: Optional[str] = None
+    metadata: Optional[Dict[str, Any]] = None
+
+
+class ChunkPreviewResponse(BaseModel):
+    """分块预览响应"""
+    chunks: List[ChunkPreviewItem] = Field(default_factory=list)
+    total: int = 0
+    strategy: str = ""
+    chunk_size: int = 0
+    chunk_overlap: int = 0
+
+
+class RetrievalInput(BaseModel):
+    """检索输入"""
+    model_config = ConfigDict(protected_namespaces=())
+    
+    query: str = Field(..., min_length=1, description="查询文本")
+    knowledge_base_ids: List[str] = Field(..., min_length=1, description="知识库ID列表")
+    top_k: int = Field(default=5, ge=1, le=20, description="返回数量")
+    score_threshold: float = Field(default=0.5, ge=0, le=1, description="相似度阈值")
+    retrieval_mode: Optional[str] = Field(None, description="检索模式（不传则使用知识库配置）")
+    rerank_enabled: Optional[bool] = Field(None, description="是否启用重排序（不传则使用知识库配置）")
+    rerank_model_id: Optional[str] = Field(None, description="重排序模型ID（不传则使用知识库配置）")
+    metadata_filter: Optional[Dict[str, Any]] = Field(None, description="元数据过滤条件")
+
+
+class RetrievalResult(BaseModel):
+    """检索结果"""
+    segment_id: str
+    document_id: str
+    document_name: str = ""
+    knowledge_base_id: str
+    knowledge_base_name: str = ""
+    content: str
+    score: float = 0.0
+    token_count: int = 0
+    page_number: Optional[int] = None
+    metadata: Optional[Dict[str, Any]] = None
+    keywords: Optional[List[str]] = None
+    match_source: Optional[str] = Field(None, description="命中来源: vector/fulltext/annotation")
+    parent_content: Optional[str] = Field(None, description="父分段内容（Small-to-Big 模式）")
+
+
+class RetrievalResponse(BaseModel):
+    """检索响应"""
+    results: List[RetrievalResult] = Field(default_factory=list)
+    total: int = 0
+    query: str = ""
+    elapsed_time: int = 0
+    retrieval_mode: str = ""
+    rerank_applied: bool = False