kplam
51dc8dc4f6
- FastAPI + Qdrant + Redis + Ollama 技术栈 - L1→L2→L3→chunk 四层分层检索（dense + sparse RRF 融合） - 文档三级总结与 2.5 级回退 - query 解析路由与分类 - /admin 管理页面
52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
"""文档和总结相关的数据模型"""
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from enum import StrEnum
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from pydantic import BaseModel, Field
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class SummaryLevel(StrEnum):
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"""总结层级"""
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L3 = "L3" # 完整三级：总结 → 大纲 → 内容大纲
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L2_HALF = "L2.5" # 2.5 级回退：总结 → 内容大纲（跳过大纲）
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class DocumentSummary(BaseModel):
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"""文档三级总结结果"""
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l1_summary: str = Field(description="L1 总结：一句话高度概括")
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l2_outline: str | None = Field(default=None, description="L2 大纲：主要章节和关键主题")
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l3_content_outline: str = Field(description="L3/L2.5 内容大纲：详细内容摘要")
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level: SummaryLevel = Field(description="实际使用的总结层级")
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class DocumentInput(BaseModel):
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"""文档入库输入"""
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text: str = Field(description="文档纯文本内容")
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title: str = Field(default="", description="文档标题")
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source: str = Field(default="", description="来源标识（文件路径/URL等）")
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metadata: dict[str, str] = Field(default_factory=dict, description="附加元数据")
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class ChunkModel(BaseModel):
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"""文档分块结果"""
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doc_id: str = Field(description="所属文档 ID")
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chunk_index: int = Field(description="chunk 在文档内的序号")
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text: str = Field(description="chunk 文本内容")
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section_path: str = Field(default="", description="chunk 所在的章节路径")
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class IngestionResult(BaseModel):
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"""文档入库结果"""
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document_id: str = Field(description="写入后的文档 ID")
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summary: DocumentSummary = Field(description="三级总结结果")
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category: str = Field(description="分类标签（主类目）")
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collection: str = Field(description="写入的 Qdrant 集合名")
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chunks_count: int = Field(default=0, description="写入的 chunk 数量")
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tags: list[str] = Field(default_factory=list, description="附加分类标签")
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category_confidence: float = Field(default=0.0, description="主类目分类置信度")
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