| from typing import List, Literal, Optional | |
| from pydantic import BaseModel, Field | |
| class DocumentMetadata(BaseModel): | |
| file_name: str | |
| page_count: int | |
| character_count: int | |
| extraction_method: str | |
| parsing_notes: str | |
| class DocumentChunk(BaseModel): | |
| chunk_id: int = Field(ge=1) | |
| page_number: int = Field(ge=1) | |
| text: str | |
| class DocumentFinding(BaseModel): | |
| """A structured finding that remains traceable to the source document.""" | |
| finding_id: str | |
| category: Literal["key_fact", "entity", "date", "risk", "action_item"] | |
| text: str | |
| page_number: int = Field(ge=1) | |
| chunk_id: Optional[int] = Field(default=None, ge=1) | |
| class DocumentSummary(BaseModel): | |
| executive_summary: str | |
| key_facts: List[str] = Field(default_factory=list) | |
| key_entities: List[str] = Field(default_factory=list) | |
| important_dates: List[str] = Field(default_factory=list) | |
| risks_or_issues: List[str] = Field(default_factory=list) | |
| action_items: List[str] = Field(default_factory=list) | |
| findings: List[DocumentFinding] = Field(default_factory=list) | |
| class TableExtraction(BaseModel): | |
| table_id: int | |
| page_number: Optional[int] = None | |
| rows: List[dict] = Field(default_factory=list) | |
| class DocumentIntelligenceReport(BaseModel): | |
| metadata: DocumentMetadata | |
| summary: DocumentSummary | |
| chunks: List[DocumentChunk] | |
| tables: List[TableExtraction] = Field(default_factory=list) | |
| class DocumentProcessingEvent(BaseModel): | |
| """Observable event emitted by the real deterministic document pipeline.""" | |
| event: str | |
| message: str | |
| page_count: Optional[int] = None | |
| chunk_count: Optional[int] = None | |
| finding_count: Optional[int] = None | |
| report: Optional[DocumentIntelligenceReport] = None | |