Adolfo Delorenzo
44fa7e6845
- runner.py: multi-turn tool-call loop (LLM -> tool -> observe -> respond) - runner.py: max 5 iterations guard against runaway tool chains - runner.py: confirmation gate — returns confirmation msg, stops loop - runner.py: audit logging for every LLM call via audit_logger - tasks.py: AuditLogger initialized at task start with session factory - tasks.py: tool registry built from agent.tool_assignments - tasks.py: pending tool confirmation flow via Redis (10 min TTL) - tasks.py: memory persistence skipped for confirmation request responses - llm-pool/router.py: LLMResponse model with content + tool_calls fields - llm-pool/router.py: tools parameter forwarded to litellm.acompletion() - llm-pool/main.py: CompleteRequest accepts optional tools list - llm-pool/main.py: CompleteResponse includes tool_calls field - Migration renamed to 004 (003 was already taken by escalation migration) - [Rule 1 - Bug] Renamed 003_phase2_audit_kb.py -> 004 to fix duplicate revision ID
239 lines
7.8 KiB
Python
239 lines
7.8 KiB
Python
"""Phase 2: audit_events table (immutable) and kb_documents/kb_chunks tables
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Revision ID: 004
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Revises: 003
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Create Date: 2026-03-23
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This migration adds:
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1. audit_events — append-only audit trail for all agent actions
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- REVOKE UPDATE, DELETE from konstruct_app (immutability enforced at DB level)
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- GRANT SELECT, INSERT only
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- RLS for tenant isolation
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- Composite index on (tenant_id, created_at DESC) for efficient queries
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2. kb_documents and kb_chunks — knowledge base storage
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- kb_chunks has a vector(384) embedding column
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- HNSW index for approximate nearest neighbor cosine search
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- Full CRUD grants for kb tables (mutable)
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- RLS on both tables
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Key design decision: audit_events immutability is enforced at the DB level via
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REVOKE. Even if application code attempts an UPDATE or DELETE, PostgreSQL will
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reject it with a permission error. This provides a hard compliance guarantee
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that the audit trail cannot be tampered with via the application role.
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"""
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from __future__ import annotations
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from typing import Sequence, Union
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import sqlalchemy as sa
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from alembic import op
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from sqlalchemy.dialects.postgresql import JSONB, UUID
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# revision identifiers, used by Alembic.
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revision: str = "004"
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down_revision: Union[str, None] = "003"
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branch_labels: Union[str, Sequence[str], None] = None
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depends_on: Union[str, Sequence[str], None] = None
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def upgrade() -> None:
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# =========================================================================
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# 1. audit_events — immutable audit trail
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# =========================================================================
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op.create_table(
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"audit_events",
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sa.Column(
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"id",
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UUID(as_uuid=True),
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primary_key=True,
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server_default=sa.text("gen_random_uuid()"),
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),
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sa.Column(
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"tenant_id",
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UUID(as_uuid=True),
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nullable=False,
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),
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sa.Column(
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"agent_id",
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UUID(as_uuid=True),
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nullable=True,
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),
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sa.Column(
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"user_id",
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sa.Text,
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nullable=True,
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),
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sa.Column(
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"action_type",
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sa.Text,
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nullable=False,
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comment="llm_call | tool_invocation | escalation",
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),
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sa.Column(
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"input_summary",
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sa.Text,
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nullable=True,
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),
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sa.Column(
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"output_summary",
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sa.Text,
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nullable=True,
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),
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sa.Column(
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"latency_ms",
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sa.Integer,
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nullable=True,
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),
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sa.Column(
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"metadata",
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JSONB,
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nullable=False,
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server_default=sa.text("'{}'::jsonb"),
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),
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sa.Column(
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"created_at",
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sa.DateTime(timezone=True),
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nullable=False,
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server_default=sa.text("NOW()"),
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),
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)
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# Index for efficient per-tenant queries ordered by time (most recent first)
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op.create_index(
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"ix_audit_events_tenant_created",
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"audit_events",
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["tenant_id", "created_at"],
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postgresql_ops={"created_at": "DESC"},
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)
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# Apply Row Level Security
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op.execute("ALTER TABLE audit_events ENABLE ROW LEVEL SECURITY")
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op.execute("ALTER TABLE audit_events FORCE ROW LEVEL SECURITY")
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op.execute("""
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CREATE POLICY tenant_isolation ON audit_events
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USING (tenant_id = current_setting('app.current_tenant', TRUE)::uuid)
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""")
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# Grant SELECT + INSERT only — immutability enforced by revoking UPDATE/DELETE
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op.execute("GRANT SELECT, INSERT ON audit_events TO konstruct_app")
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op.execute("REVOKE UPDATE, DELETE ON audit_events FROM konstruct_app")
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# =========================================================================
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# 2. kb_documents — knowledge base document metadata
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# =========================================================================
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op.create_table(
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"kb_documents",
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sa.Column(
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"id",
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UUID(as_uuid=True),
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primary_key=True,
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server_default=sa.text("gen_random_uuid()"),
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),
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sa.Column(
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"tenant_id",
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UUID(as_uuid=True),
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nullable=False,
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),
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sa.Column(
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"agent_id",
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UUID(as_uuid=True),
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nullable=False,
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),
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sa.Column("filename", sa.Text, nullable=True),
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sa.Column("source_url", sa.Text, nullable=True),
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sa.Column("content_type", sa.Text, nullable=True),
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sa.Column(
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"created_at",
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sa.DateTime(timezone=True),
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nullable=False,
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server_default=sa.text("NOW()"),
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),
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)
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op.create_index("ix_kb_documents_tenant", "kb_documents", ["tenant_id"])
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op.create_index("ix_kb_documents_agent", "kb_documents", ["agent_id"])
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# Apply Row Level Security on kb_documents
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op.execute("ALTER TABLE kb_documents ENABLE ROW LEVEL SECURITY")
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op.execute("ALTER TABLE kb_documents FORCE ROW LEVEL SECURITY")
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op.execute("""
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CREATE POLICY tenant_isolation ON kb_documents
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USING (tenant_id = current_setting('app.current_tenant', TRUE)::uuid)
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""")
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op.execute("GRANT SELECT, INSERT, UPDATE, DELETE ON kb_documents TO konstruct_app")
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# =========================================================================
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# 3. kb_chunks — chunked text with vector embeddings
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# =========================================================================
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op.create_table(
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"kb_chunks",
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sa.Column(
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"id",
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UUID(as_uuid=True),
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primary_key=True,
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server_default=sa.text("gen_random_uuid()"),
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),
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sa.Column(
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"tenant_id",
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UUID(as_uuid=True),
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nullable=False,
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),
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sa.Column(
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"document_id",
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UUID(as_uuid=True),
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sa.ForeignKey("kb_documents.id", ondelete="CASCADE"),
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nullable=False,
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),
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sa.Column("content", sa.Text, nullable=False),
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sa.Column("chunk_index", sa.Integer, nullable=True),
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sa.Column(
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"created_at",
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sa.DateTime(timezone=True),
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nullable=False,
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server_default=sa.text("NOW()"),
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),
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# embedding column added via raw DDL below (pgvector type)
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)
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# Add embedding column as vector(384) — raw DDL required for pgvector type
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op.execute("ALTER TABLE kb_chunks ADD COLUMN embedding vector(384) NOT NULL DEFAULT array_fill(0, ARRAY[384])::vector")
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# Remove the default after adding — embeddings must be explicitly provided
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op.execute("ALTER TABLE kb_chunks ALTER COLUMN embedding DROP DEFAULT")
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op.create_index("ix_kb_chunks_tenant", "kb_chunks", ["tenant_id"])
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op.create_index("ix_kb_chunks_document", "kb_chunks", ["document_id"])
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# HNSW index for approximate nearest-neighbor cosine search
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op.execute("""
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CREATE INDEX ix_kb_chunks_hnsw
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ON kb_chunks
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USING hnsw (embedding vector_cosine_ops)
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WITH (m = 16, ef_construction = 64)
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""")
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# Apply Row Level Security on kb_chunks
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op.execute("ALTER TABLE kb_chunks ENABLE ROW LEVEL SECURITY")
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op.execute("ALTER TABLE kb_chunks FORCE ROW LEVEL SECURITY")
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op.execute("""
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CREATE POLICY tenant_isolation ON kb_chunks
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USING (tenant_id = current_setting('app.current_tenant', TRUE)::uuid)
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""")
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op.execute("GRANT SELECT, INSERT, UPDATE, DELETE ON kb_chunks TO konstruct_app")
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def downgrade() -> None:
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op.execute("REVOKE ALL ON kb_chunks FROM konstruct_app")
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op.drop_table("kb_chunks")
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op.execute("REVOKE ALL ON kb_documents FROM konstruct_app")
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op.drop_table("kb_documents")
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op.execute("REVOKE ALL ON audit_events FROM konstruct_app")
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op.drop_table("audit_events")
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