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>
81 lines
3.2 KiB
Python
81 lines
3.2 KiB
Python
"""SQLAlchemy models for the brain service."""
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import uuid
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from datetime import datetime
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from pgvector.sqlalchemy import Vector
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from sqlalchemy import (
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Column, String, Text, Integer, Float, DateTime, ForeignKey, Index, text
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)
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from sqlalchemy.dialects.postgresql import JSONB, UUID, ARRAY
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from sqlalchemy.orm import relationship
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from app.config import OPENAI_EMBED_DIM
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from app.database import Base
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def new_id():
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return str(uuid.uuid4())
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class Item(Base):
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__tablename__ = "items"
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id = Column(UUID(as_uuid=False), primary_key=True, default=new_id)
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user_id = Column(String(64), nullable=False, index=True)
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type = Column(String(32), nullable=False, default="link") # link|note|pdf|image|document|file
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title = Column(Text, nullable=True)
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url = Column(Text, nullable=True)
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raw_content = Column(Text, nullable=True) # original user input (note body, etc.)
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extracted_text = Column(Text, nullable=True) # full extracted text from page/doc
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folder = Column(String(64), nullable=True)
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tags = Column(ARRAY(String), nullable=True, default=list)
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summary = Column(Text, nullable=True)
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confidence = Column(Float, nullable=True)
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metadata_json = Column(JSONB, nullable=True, default=dict)
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processing_status = Column(String(32), nullable=False, default="pending") # pending|processing|ready|failed
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processing_error = Column(Text, nullable=True)
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# Embedding (pgvector)
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embedding = Column(Vector(OPENAI_EMBED_DIM), nullable=True)
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created_at = Column(DateTime, default=datetime.utcnow, nullable=False)
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updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow, nullable=False)
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# Relationships
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assets = relationship("ItemAsset", back_populates="item", cascade="all, delete-orphan")
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__table_args__ = (
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Index("ix_items_user_status", "user_id", "processing_status"),
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Index("ix_items_user_folder", "user_id", "folder"),
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Index("ix_items_created", "created_at"),
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)
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class ItemAsset(Base):
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__tablename__ = "item_assets"
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id = Column(UUID(as_uuid=False), primary_key=True, default=new_id)
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item_id = Column(UUID(as_uuid=False), ForeignKey("items.id", ondelete="CASCADE"), nullable=False, index=True)
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asset_type = Column(String(32), nullable=False) # screenshot|archived_html|original_upload|extracted_file
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filename = Column(String(512), nullable=False)
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content_type = Column(String(128), nullable=True)
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size_bytes = Column(Integer, nullable=True)
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storage_path = Column(String(1024), nullable=False) # relative path in storage
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created_at = Column(DateTime, default=datetime.utcnow, nullable=False)
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# Relationships
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item = relationship("Item", back_populates="assets")
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class AppLink(Base):
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"""Placeholder for future cross-app linking (e.g. link a saved item to a trip or task)."""
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__tablename__ = "app_links"
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id = Column(UUID(as_uuid=False), primary_key=True, default=new_id)
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item_id = Column(UUID(as_uuid=False), ForeignKey("items.id", ondelete="CASCADE"), nullable=False, index=True)
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app = Column(String(64), nullable=False) # trips|tasks|fitness|inventory
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app_entity_id = Column(String(128), nullable=False)
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created_at = Column(DateTime, default=datetime.utcnow, nullable=False)
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