"""文档和总结相关的数据模型"""

from enum import StrEnum

from pydantic import BaseModel, Field


class SummaryLevel(StrEnum):
    """总结层级"""

    L3 = "L3"  # 完整三级：总结 → 大纲 → 内容大纲
    L2_HALF = "L2.5"  # 2.5 级回退：总结 → 内容大纲（跳过大纲）


class DocumentSummary(BaseModel):
    """文档三级总结结果"""

    l1_summary: str = Field(description="L1 总结：一句话高度概括")
    l2_outline: str | None = Field(default=None, description="L2 大纲：主要章节和关键主题")
    l3_content_outline: str = Field(description="L3/L2.5 内容大纲：详细内容摘要")
    level: SummaryLevel = Field(description="实际使用的总结层级")


class DocumentInput(BaseModel):
    """文档入库输入"""

    text: str = Field(description="文档纯文本内容")
    title: str = Field(default="", description="文档标题")
    source: str = Field(default="", description="来源标识（文件路径/URL等）")
    metadata: dict[str, str] = Field(default_factory=dict, description="附加元数据")


class ChunkModel(BaseModel):
    """文档分块结果"""

    doc_id: str = Field(description="所属文档 ID")
    chunk_index: int = Field(description="chunk 在文档内的序号")
    text: str = Field(description="chunk 文本内容")
    section_path: str = Field(default="", description="chunk 所在的章节路径")


class IngestionResult(BaseModel):
    """文档入库结果"""

    document_id: str = Field(description="写入后的文档 ID")
    summary: DocumentSummary = Field(description="三级总结结果")
    category: str = Field(description="分类标签（主类目）")
    collection: str = Field(description="写入的 Qdrant 集合名")
    chunks_count: int = Field(default=0, description="写入的 chunk 数量")
    tags: list[str] = Field(default_factory=list, description="附加分类标签")
    category_confidence: float = Field(default=0.0, description="主类目分类置信度")