"""Initial migration: conversations, messages, knowledge_chunks.

Revision ID: 001
Revises:
Create Date: 2026-04-28 00:00:00.000000

"""
from typing import Sequence, Union

from alembic import op
import sqlalchemy as sa
from sqlalchemy.dialects import postgresql

# revision identifiers, used by Alembic.
revision: str = "001"
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:
    # Enable pgvector extension
    op.execute("CREATE EXTENSION IF NOT EXISTS vector")

    # Conversations table
    op.create_table(
        "conversations",
        sa.Column("id", sa.String(36), primary_key=True),
        sa.Column("phone_number", sa.String(20), nullable=False, index=True),
        sa.Column("patient_id", sa.String(100), nullable=True, index=True),
        sa.Column("patient_name", sa.String(255), nullable=True),
        sa.Column(
            "status",
            sa.Enum("active", "paused", "resolved", "escalated", "appointment_confirmed", name="conversationstatus"),
            nullable=False,
            server_default="active",
        ),
        sa.Column("context", postgresql.JSONB(astext_type=sa.Text()), server_default="{}"),
        sa.Column("last_message_at", sa.DateTime(timezone=True), nullable=True),
        sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.func.now()),
        sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.func.now(), onupdate=sa.func.now()),
    )

    # Messages table
    op.create_table(
        "messages",
        sa.Column("id", sa.String(36), primary_key=True),
        sa.Column("conversation_id", sa.String(36), nullable=False, index=True),
        sa.Column("direction", sa.String(10), nullable=False),
        sa.Column("role", sa.String(20), nullable=False),
        sa.Column("message_type", sa.String(50), server_default="text"),
        sa.Column("content", sa.Text(), nullable=False),
        sa.Column("whatsapp_message_id", sa.String(100), nullable=True),
        sa.Column("tool_calls", postgresql.JSONB(astext_type=sa.Text()), nullable=True),
        sa.Column("tool_results", postgresql.JSONB(astext_type=sa.Text()), nullable=True),
        sa.Column("tokens_used", sa.Integer(), server_default="0"),
        sa.Column("metadata", postgresql.JSONB(astext_type=sa.Text()), server_default="{}"),
        sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.func.now()),
    )

    # Knowledge chunks table (for RAG)
    op.create_table(
        "knowledge_chunks",
        sa.Column("id", sa.String(36), primary_key=True, server_default=sa.text("gen_random_uuid()::text")),
        sa.Column("content", sa.Text(), nullable=False),
        sa.Column("metadata", postgresql.JSONB(astext_type=sa.Text()), server_default="{}"),
        sa.Column("category", sa.String(50), server_default="general"),
        sa.Column("source", sa.String(255), server_default=""),
        sa.Column("embedding", sa.String(), nullable=True),  # Stored as string; pgvector uses special type
        sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.func.now()),
        sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.func.now()),
    )

    # Create pgvector column properly using raw SQL
    op.execute("""
        ALTER TABLE knowledge_chunks
        ALTER COLUMN embedding TYPE vector(1536)
        USING embedding::vector(1536)
    """)

    # Indexes
    op.create_index("idx_knowledge_category", "knowledge_chunks", ["category"])
    op.create_index("idx_knowledge_source", "knowledge_chunks", ["source"])
    op.execute("""
        CREATE INDEX idx_knowledge_embedding
        ON knowledge_chunks
        USING ivfflat (embedding vector_cosine_ops)
        WITH (lists = 100)
    """)


def downgrade() -> None:
    op.drop_index("idx_knowledge_embedding", table_name="knowledge_chunks")
    op.drop_index("idx_knowledge_source", table_name="knowledge_chunks")
    op.drop_index("idx_knowledge_category", table_name="knowledge_chunks")
    op.drop_table("knowledge_chunks")
    op.drop_table("messages")
    op.drop_table("conversations")
    op.execute("DROP TYPE IF EXISTS conversationstatus")
    op.execute("DROP EXTENSION IF EXISTS vector")