4645e67611
- Add current_version_embedding_status/embedded_count/term_count to GlossaryResponse
- Batch-fetch current versions in list endpoint (single extra query, not N queries)
- Add get_versions_by_ids() helper to glossary_service
- Fix GlossaryList.tsx: embeddingBadge('') → embeddingBadge(g) with real status + pct
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
142 lines
4 KiB
Python
142 lines
4 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from enum import StrEnum
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from pydantic import BaseModel, Field
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class GlossarySource(StrEnum):
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XLSX_UPLOAD = "xlsx_upload"
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FRAZE_API = "fraze_api" # reserved for future FRAZE integration
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class GlossaryStatus(StrEnum):
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ACTIVE = "active"
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ARCHIVED = "archived"
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class EmbeddingStatus(StrEnum):
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PENDING = "pending"
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IN_PROGRESS = "in_progress"
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DONE = "done"
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FAILED = "failed"
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class Glossary(BaseModel):
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id: str | None = Field(None, alias="_id")
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client_id: str
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name: str
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description: str | None = None
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source_locale: str # BCP-47 source column, e.g. "en-GB"
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source: GlossarySource = GlossarySource.XLSX_UPLOAD
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status: GlossaryStatus = GlossaryStatus.ACTIVE
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current_version_id: str | None = None
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created_at: datetime = Field(default_factory=datetime.utcnow)
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created_by: str # user_id
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model_config = {"populate_by_name": True, "arbitrary_types_allowed": True}
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class GlossaryVersion(BaseModel):
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id: str | None = Field(None, alias="_id")
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glossary_id: str
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version_number: int
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source_xlsx_gcs_path: str | None = None # GCS path to original file
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term_count: int = 0
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embedded_count: int = 0
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embedding_status: EmbeddingStatus = EmbeddingStatus.PENDING
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created_at: datetime = Field(default_factory=datetime.utcnow)
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created_by: str
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change_note: str | None = None
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model_config = {"populate_by_name": True}
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class GlossaryTerm(BaseModel):
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"""One source term with its per-locale translations."""
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id: str | None = Field(None, alias="_id")
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glossary_id: str
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version_id: str
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cid: str | None = None # 3M Content ID from xlsx
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tid: str | None = None # 3M Term ID from xlsx
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source_term: str # canonical source text (whitespace-normalised)
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source_term_lower: str # lowercase for case-insensitive index
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translations: dict[str, str] = {} # {locale_code: translated_text}
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embedding: list[float] | None = None # 768-dim Gemini embedding
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model_config = {"populate_by_name": True}
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# ── Schema models (API request/response) ──────────────────────────────────────
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class GlossaryCreate(BaseModel):
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name: str
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description: str | None = None
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source_locale: str
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change_note: str | None = None
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class GlossaryVersionCreate(BaseModel):
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source_locale: str
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change_note: str | None = None
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class GlossaryResponse(BaseModel):
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id: str
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client_id: str
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name: str
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description: str | None = None
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source_locale: str
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source: GlossarySource
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status: GlossaryStatus
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current_version_id: str | None = None
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current_version_embedding_status: EmbeddingStatus | None = None
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current_version_embedded_count: int | None = None
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current_version_term_count: int | None = None
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created_at: datetime
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created_by: str
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class GlossaryVersionResponse(BaseModel):
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id: str
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glossary_id: str
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version_number: int
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term_count: int
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embedded_count: int
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embedding_status: EmbeddingStatus
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created_at: datetime
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created_by: str
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change_note: str | None = None
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class GlossaryDetailResponse(GlossaryResponse):
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versions: list[GlossaryVersionResponse] = []
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class GlossaryTermPreview(BaseModel):
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"""Subset of GlossaryTerm for UI previews."""
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source_term: str
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translations: dict[str, str]
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class MatchedTerm(BaseModel):
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"""A term matched against VTT source text, with the target-locale translation."""
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source_term: str
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target_translation: str
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match_kind: str # "exact" | "vector"
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score: float # 1.0 for exact, cosine similarity for vector
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def glossary_from_doc(doc: dict) -> Glossary:
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doc = dict(doc)
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if "_id" in doc:
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doc["_id"] = str(doc["_id"])
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return Glossary.model_validate(doc)
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def glossary_version_from_doc(doc: dict) -> GlossaryVersion:
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doc = dict(doc)
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if "_id" in doc:
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doc["_id"] = str(doc["_id"])
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return GlossaryVersion.model_validate(doc)
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