8275f3a71b
Full backend service with: - FastAPI REST API with CRUD, search, reprocess endpoints - PostgreSQL + pgvector for items and semantic search - Redis + RQ for background job processing - Meilisearch for fast keyword/filter search - Browserless/Chrome for JS rendering and screenshots - OpenAI structured output for AI classification - Local file storage with S3-ready abstraction - Gateway auth via X-Gateway-User-Id header - Own docker-compose stack (6 containers) Classification: fixed folders (Home/Family/Work/Travel/Knowledge/Faith/Projects) and fixed tags (28 predefined). AI assigns exactly 1 folder, 2-3 tags, title, summary, and confidence score per item. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
110 lines
2.4 KiB
Python
110 lines
2.4 KiB
Python
"""Pydantic schemas for API request/response."""
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from __future__ import annotations
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from datetime import datetime
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from typing import Optional
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from pydantic import BaseModel, Field
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# ── Request schemas ──
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class ItemCreate(BaseModel):
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type: str = "link"
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url: Optional[str] = None
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raw_content: Optional[str] = None
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title: Optional[str] = None
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folder: Optional[str] = None
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tags: Optional[list[str]] = None
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class ItemUpdate(BaseModel):
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title: Optional[str] = None
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folder: Optional[str] = None
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tags: Optional[list[str]] = None
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raw_content: Optional[str] = None
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class SearchQuery(BaseModel):
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q: str
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folder: Optional[str] = None
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tags: Optional[list[str]] = None
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type: Optional[str] = None
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limit: int = Field(default=20, le=100)
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offset: int = 0
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class SemanticSearchQuery(BaseModel):
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q: str
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folder: Optional[str] = None
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type: Optional[str] = None
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limit: int = Field(default=20, le=100)
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class HybridSearchQuery(BaseModel):
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q: str
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folder: Optional[str] = None
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tags: Optional[list[str]] = None
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type: Optional[str] = None
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limit: int = Field(default=20, le=100)
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# ── Response schemas ──
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class AssetOut(BaseModel):
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id: str
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asset_type: str
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filename: str
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content_type: Optional[str] = None
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size_bytes: Optional[int] = None
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created_at: datetime
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model_config = {"from_attributes": True}
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class ItemOut(BaseModel):
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id: str
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type: str
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title: Optional[str] = None
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url: Optional[str] = None
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folder: Optional[str] = None
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tags: Optional[list[str]] = None
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summary: Optional[str] = None
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confidence: Optional[float] = None
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processing_status: str
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processing_error: Optional[str] = None
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metadata_json: Optional[dict] = None
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created_at: datetime
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updated_at: datetime
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assets: list[AssetOut] = []
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model_config = {"from_attributes": True}
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class ItemList(BaseModel):
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items: list[ItemOut]
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total: int
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class SearchResult(BaseModel):
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items: list[ItemOut]
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total: int
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query: str
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class ConfigOut(BaseModel):
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folders: list[str]
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tags: list[str]
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# ── OpenAI classification schema ──
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class ClassificationResult(BaseModel):
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"""What the AI returns for each item."""
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folder: str
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tags: list[str] = Field(min_length=2, max_length=3)
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title: str
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summary: str
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confidence: float = Field(ge=0.0, le=1.0)
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