mirror of
https://github.com/basicmachines-co/basic-memory
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161 lines
6.0 KiB
Python
161 lines
6.0 KiB
Python
"""Knowledge graph models."""
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from datetime import datetime
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from typing import Optional
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from sqlalchemy import Integer, String, Text, ForeignKey, UniqueConstraint, text, DateTime, Index
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from basic_memory.models.base import Base
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from basic_memory.models.documents import Document
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from enum import Enum
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class Entity(Base):
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"""
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Core entity in the knowledge graph.
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Entities represent semantic nodes maintained by the AI layer. Each entity:
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- Has a unique numeric ID (database-generated)
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- Maps to a document file on disk (optional)
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- Maintains a checksum for change detection
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- Tracks both source document and semantic properties
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"""
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__tablename__ = "entity"
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__table_args__ = (
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UniqueConstraint("entity_type", "name", name="uix_entity_type_name"),
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Index("ix_entity_type", "entity_type"),
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Index("ix_entity_doc_id", "doc_id"),
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Index("ix_entity_created_at", "created_at"), # For timeline queries
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Index("ix_entity_updated_at", "updated_at") # For timeline queries
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)
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# Core identity
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id: Mapped[int] = mapped_column(Integer, primary_key=True)
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name: Mapped[str] = mapped_column(String)
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entity_type: Mapped[str] = mapped_column(String)
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# Normalized path for URIs
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# (entity_type, path_id) are unique
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path_id: Mapped[str] = mapped_column(String, index=True)
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# Actual filesystem relative path
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file_path: Mapped[str] = mapped_column(String, unique=True, index=True)
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# Content and validation
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description: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
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checksum: Mapped[Optional[str]] = mapped_column(String, nullable=True)
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# Metadata and tracking
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created_at: Mapped[datetime] = mapped_column(DateTime, server_default=text("CURRENT_TIMESTAMP"))
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updated_at: Mapped[datetime] = mapped_column(
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DateTime, server_default=text("CURRENT_TIMESTAMP"), onupdate=text("CURRENT_TIMESTAMP")
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)
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# Relations
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doc_id: Mapped[Optional[int]] = mapped_column(
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Integer, ForeignKey("document.id", ondelete="SET NULL"), nullable=True
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)
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# Relationships
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observations = relationship(
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"Observation", back_populates="entity", cascade="all, delete-orphan"
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)
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outgoing_relations = relationship(
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"Relation",
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back_populates="from_entity",
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foreign_keys="[Relation.from_id]",
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cascade="all, delete-orphan",
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)
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incoming_relations = relationship(
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"Relation",
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back_populates="to_entity",
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foreign_keys="[Relation.to_id]",
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cascade="all, delete-orphan",
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)
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document: Mapped[Optional[Document]] = relationship(Document, back_populates="entities")
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@property
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def relations(self):
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return self.incoming_relations + self.outgoing_relations
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def __repr__(self) -> str:
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return f"Entity(id={self.id}, name='{self.name}', type='{self.entity_type}')"
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class ObservationCategory(str, Enum):
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TECH = "tech"
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DESIGN = "design"
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FEATURE = "feature"
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NOTE = "note"
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ISSUE = "issue"
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TODO = "todo"
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class Observation(Base):
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"""
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An observation about an entity.
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Observations are atomic facts or notes about an entity.
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"""
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__tablename__ = "observation"
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__table_args__ = (
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Index("ix_observation_entity_id", "entity_id"), # Add FK index
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Index("ix_observation_category", "category"), # Add category index
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Index("ix_observation_created_at", "created_at"), # For timeline queries
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Index("ix_observation_updated_at", "updated_at"), # For timeline queries
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)
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id: Mapped[int] = mapped_column(Integer, primary_key=True)
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entity_id: Mapped[int] = mapped_column(Integer, ForeignKey("entity.id", ondelete="CASCADE"))
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content: Mapped[str] = mapped_column(Text)
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category: Mapped[str] = mapped_column(
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String,
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nullable=False,
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default=ObservationCategory.NOTE.value,
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server_default=ObservationCategory.NOTE.value
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)
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context: Mapped[str] = mapped_column(Text, nullable=True)
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created_at: Mapped[datetime] = mapped_column(DateTime, server_default=text("CURRENT_TIMESTAMP"))
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updated_at: Mapped[datetime] = mapped_column(
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DateTime, server_default=text("CURRENT_TIMESTAMP"), onupdate=text("CURRENT_TIMESTAMP")
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)
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# Relationships
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entity = relationship("Entity", back_populates="observations")
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def __repr__(self) -> str:
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return f"Observation(id={self.id}, entity_id={self.entity_id}, content='{self.content}')"
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class Relation(Base):
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"""
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A directed relation between two entities.
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"""
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__tablename__ = "relation"
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__table_args__ = (
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UniqueConstraint("from_id", "to_id", "relation_type", name="uix_relation"),
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Index("ix_relation_type", "relation_type"),
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Index("ix_relation_from_id", "from_id"), # Add FK indexes
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Index("ix_relation_to_id", "to_id"),
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Index("ix_relation_created_at", "created_at"), # For timeline queries
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Index("ix_relation_updated_at", "updated_at"), # For timeline queries
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)
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id: Mapped[int] = mapped_column(Integer, primary_key=True)
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from_id: Mapped[int] = mapped_column(Integer, ForeignKey("entity.id", ondelete="CASCADE"))
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to_id: Mapped[int] = mapped_column(Integer, ForeignKey("entity.id", ondelete="CASCADE"))
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relation_type: Mapped[str] = mapped_column(String)
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context: Mapped[str] = mapped_column(Text, nullable=True)
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created_at: Mapped[datetime] = mapped_column(DateTime, server_default=text("CURRENT_TIMESTAMP"))
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updated_at: Mapped[datetime] = mapped_column(
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DateTime, server_default=text("CURRENT_TIMESTAMP"), onupdate=text("CURRENT_TIMESTAMP")
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)
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# Relationships
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from_entity = relationship("Entity", foreign_keys=[from_id], back_populates="outgoing_relations")
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to_entity = relationship("Entity", foreign_keys=[to_id], back_populates="incoming_relations")
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def __repr__(self) -> str:
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return f"Relation(id={self.id}, from_id={self.from_id}, to_id={self.to_id}, type='{self.relation_type}')"
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