"""
Ontology Service - Extracts domain-specific knowledge from files.

Finds files processed within lookback window and applies matching
OntologyConfig rules to extract structured knowledge using custom agents.
"""

from typing import Any, Optional


async def extract_ontologies(
    user_id: str,
    lookback_hours: int = 24,
    limit: Optional[int] = None,
) -> dict[str, Any]:
    """
    Extract domain-specific knowledge from files using custom agents.

    Finds files processed within lookback window and applies matching
    OntologyConfig rules to extract structured knowledge.

    Process:
    1. Query REM for files processed by this user (lookback window)
    2. For each file, find matching OntologyConfig rules
    3. Load agent schemas from database
    4. Execute agents on file content
    5. Generate embeddings for extracted data
    6. Store Ontology entities

    Args:
        user_id: User to process
        lookback_hours: Hours to look back (default: 24)
        limit: Max files to process

    Returns:
        Statistics about ontology extraction
    """
    # TODO: Implement using REM query API + OntologyExtractorService
    # Query files with timestamp filter and processing_status='completed'
    # Load matching OntologyConfigs from database
    # Use OntologyExtractorService to extract ontologies
    # Generate embeddings for embedding_text field

    # Stub implementation
    return {
        "user_id": user_id,
        "lookback_hours": lookback_hours,
        "files_queried": 0,
        "configs_matched": 0,
        "ontologies_created": 0,
        "embeddings_generated": 0,
        "agent_calls_made": 0,
        "status": "stub_not_implemented",
    }
