Cybersecurity-Projects/PROJECTS/advanced/ai-threat-detection/backend/alembic/versions/65c8ac60f6f6_initial_schema.py

144 lines
4.7 KiB
Python

"""
©AngelaMos | 2026
initial schema
Revision ID: 65c8ac60f6f6
Revises:
Create Date: 2026-02-11 17:43:24.263837
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
import sqlmodel
revision: str = "65c8ac60f6f6"
down_revision: Union[str, None] = None
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
op.create_table(
"threat_events",
sa.Column("id", sa.Uuid(), nullable=False),
sa.Column(
"created_at",
sa.DateTime(timezone=True),
server_default=sa.text("CURRENT_TIMESTAMP"),
nullable=False,
),
sa.Column(
"source_ip", sqlmodel.sql.sqltypes.AutoString(length=45), nullable=False
),
sa.Column(
"request_method",
sqlmodel.sql.sqltypes.AutoString(length=10),
nullable=False,
),
sa.Column(
"request_path", sqlmodel.sql.sqltypes.AutoString(), nullable=False
),
sa.Column("status_code", sa.SmallInteger(), nullable=False),
sa.Column("response_size", sa.Integer(), nullable=False),
sa.Column(
"user_agent", sqlmodel.sql.sqltypes.AutoString(), nullable=False
),
sa.Column("threat_score", sa.Float(), nullable=False),
sa.Column(
"severity", sqlmodel.sql.sqltypes.AutoString(length=6), nullable=False
),
sa.Column("component_scores", sa.JSON(), nullable=False),
sa.Column(
"geo_country",
sqlmodel.sql.sqltypes.AutoString(length=2),
nullable=True,
),
sa.Column(
"geo_city",
sqlmodel.sql.sqltypes.AutoString(length=255),
nullable=True,
),
sa.Column("geo_lat", sa.Float(), nullable=True),
sa.Column("geo_lon", sa.Float(), nullable=True),
sa.Column("feature_vector", sa.JSON(), nullable=False),
sa.Column("matched_rules", sa.JSON(), nullable=True),
sa.Column(
"model_version",
sqlmodel.sql.sqltypes.AutoString(length=64),
nullable=True,
),
sa.Column("reviewed", sa.Boolean(), nullable=False),
sa.Column(
"review_label",
sqlmodel.sql.sqltypes.AutoString(length=20),
nullable=True,
),
sa.PrimaryKeyConstraint("id"),
)
op.create_index("idx_threat_events_created_at", "threat_events", ["created_at"])
op.create_index("idx_threat_events_source_ip", "threat_events", ["source_ip"])
op.create_index("idx_threat_events_severity", "threat_events", ["severity"])
op.create_index("idx_threat_events_score", "threat_events", ["threat_score"])
op.create_index(
"idx_threat_events_reviewed",
"threat_events",
["reviewed"],
postgresql_where=sa.text("reviewed = FALSE"),
)
op.create_table(
"model_metadata",
sa.Column("id", sa.Uuid(), nullable=False),
sa.Column(
"created_at",
sa.DateTime(timezone=True),
server_default=sa.text("CURRENT_TIMESTAMP"),
nullable=False,
),
sa.Column(
"model_type",
sqlmodel.sql.sqltypes.AutoString(length=30),
nullable=False,
),
sa.Column(
"version", sqlmodel.sql.sqltypes.AutoString(length=64), nullable=False
),
sa.Column("training_samples", sa.Integer(), nullable=False),
sa.Column("metrics", sa.JSON(), nullable=False),
sa.Column(
"artifact_path", sqlmodel.sql.sqltypes.AutoString(), nullable=False
),
sa.Column("is_active", sa.Boolean(), nullable=False),
sa.Column(
"mlflow_run_id",
sqlmodel.sql.sqltypes.AutoString(length=64),
nullable=True,
),
sa.Column("threshold", sa.Float(), nullable=True),
sa.Column("notes", sqlmodel.sql.sqltypes.AutoString(), nullable=True),
sa.PrimaryKeyConstraint("id"),
)
op.create_index(
"idx_model_metadata_active",
"model_metadata",
["model_type"],
unique=True,
postgresql_where=sa.text("is_active = TRUE"),
)
def downgrade() -> None:
op.drop_index("idx_model_metadata_active", table_name="model_metadata")
op.drop_table("model_metadata")
op.drop_index("idx_threat_events_reviewed", table_name="threat_events")
op.drop_index("idx_threat_events_score", table_name="threat_events")
op.drop_index("idx_threat_events_severity", table_name="threat_events")
op.drop_index("idx_threat_events_source_ip", table_name="threat_events")
op.drop_index("idx_threat_events_created_at", table_name="threat_events")
op.drop_table("threat_events")