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Tasks 5-7: eval harness, FastAPI backend, Paper & Ink UI- src/eval/ β€” precision/recall/F1 harness with type-aware comparators,micro/macro F1, CSV + markdown reports, --model benchmark flag- src/api/ β€” FastAPI backend with /extract, /schemas, /health,request-ID middleware, typed error envelope, injectable extractor- ui/ β€” Vite + React + TS + Tailwind + Motion + React Three FiberPaper & Ink editorial UI with 3D paper hero, dark/light mode,confidence inkwell, wax-stamp metrics, kinetic typography- 95 passing tests (up from 54); UI is a separate npm workspace
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"""API-facing error envelope and typed exceptions.
Every 4xx / 5xx response from this service returns the same JSON shape:
{
"error": {
"code": "unsupported_doc_type",
"message": "Human-readable explanation.",
"request_id": "abc-123",
"details": {...optional...}
}
}
Consistent shape makes clients (Streamlit, curl, external integrations) easier
to write than raw FastAPI HTTPException details.
"""
from __future__ import annotations
from typing import Any
from pydantic import BaseModel, Field
class ErrorDetail(BaseModel):
code: str
message: str
request_id: str | None = None
details: dict[str, Any] = Field(default_factory=dict)
class ErrorEnvelope(BaseModel):
error: ErrorDetail
class APIError(Exception):
"""Base class β€” subclass rather than raising HTTPException directly."""
status_code: int = 500
code: str = "internal_error"
def __init__(self, message: str, *, details: dict[str, Any] | None = None):
super().__init__(message)
self.message = message
self.details = details or {}
class UnsupportedDocType(APIError):
status_code = 400
code = "unsupported_doc_type"
class UnsupportedFileType(APIError):
status_code = 415
code = "unsupported_media_type"
class FileTooLarge(APIError):
status_code = 413
code = "file_too_large"
class EmptyDocument(APIError):
status_code = 422
code = "empty_document"
class ExtractionFailed(APIError):
status_code = 502
code = "extraction_failed"