"""
Core AI Foundry classes for building enterprise AI systems.
"""

import logging
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, field
from pathlib import Path

logger = logging.getLogger(__name__)

@dataclass
class ComplianceConfig:
    """Configuration for compliance and governance rules."""
    regulations: List[str] = field(default_factory=lambda: ["general"])
    audit_enabled: bool = True
    pii_redaction: bool = True
    audit_db_uri: Optional[str] = None
    retention_days: int = 365 * 7  # 7 years default for financial/medical

class ComplianceAwareRAG:
    """
    Main RAG builder with built-in compliance tracking.
    
    Example:
        >>> rag = ComplianceAwareRAG(
        ...     documents="./legal_docs",
        ...     compliance_rules=["gdpr", "sox"]
        ... )
        >>> rag.build()
    """
    
    def __init__(
        self,
        documents: str,
        compliance_rules: List[str] = None,
        audit_trail_db: Optional[str] = None,
        embedding_model: str = "text-embedding-3-small"
    ):
        self.documents_path = Path(documents)
        self.compliance_config = ComplianceConfig(
            regulations=compliance_rules or ["general"],
            audit_db_uri=audit_trail_db
        )
        self.embedding_model = embedding_model
        self._vector_store = None
        self._audit_logger = None
        
        logger.info(f"Initialized ComplianceAwareRAG for {documents}")
        logger.info(f"Compliance rules: {compliance_rules}")
    
    def build(self) -> "ComplianceAwareRAG":
        """Build the RAG pipeline with compliance hooks."""
        logger.info("Building RAG pipeline...")
        # Core pipeline steps
        self._validate_documents()
        self._setup_audit_logging()
        self._process_documents()
        self._create_vector_store()
        logger.info("RAG pipeline built successfully")
        return self
    
    def _validate_documents(self):
        """Validate document structure and permissions."""
        if not self.documents_path.exists():
            raise FileNotFoundError(f"Documents path not found: {self.documents_path}")
        logger.debug(f"Validated documents at {self.documents_path}")
    
    def _setup_audit_logging(self):
        """Initialize audit logging system."""
        if self.compliance_config.audit_enabled:
            from aifoundry.compliance import AuditLogger
            self._audit_logger = AuditLogger(
                storage=self.compliance_config.audit_db_uri,
                retention_days=self.compliance_config.retention_days
            )
            logger.debug("Audit logging enabled")
    
    def _process_documents(self):
        """Process documents with PII detection and chunking."""
        logger.info("Processing documents...")
        # Placeholder for actual document processing
        # This would integrate with aifoundry.connectors
        pass
    
    def _create_vector_store(self):
        """Create vector store with metadata for audit trails."""
        logger.info("Creating vector store...")
        # Placeholder for vector store creation
        # This would integrate with aifoundry.rag
        pass
    
    def query(self, query: str, user_id: Optional[str] = None) -> Dict[str, Any]:
        """
        Query the RAG system with automatic audit logging.
        
        Args:
            query: The user's query
            user_id: Optional user ID for audit trails
        
        Returns:
            Response dictionary with answer and metadata
        """
        logger.info(f"Processing query: {query[:50]}...")
        
        # If audit logging is enabled, log the query
        if self._audit_logger and user_id:
            self._audit_logger.log_query(
                query=query,
                user_id=user_id,
                component="rag_system"
            )
        
        # Placeholder for actual RAG query logic
        response = {
            "answer": "This is a placeholder response.",
            "sources": ["doc1.pdf", "doc2.pdf"],
            "confidence": 0.85,
            "compliance_checked": True
        }
        
        # Log the response if audit is enabled
        if self._audit_logger and user_id:
            self._audit_logger.log_response(
                query=query,
                response=response["answer"],
                user_id=user_id,
                metadata={
                    "sources": response["sources"],
                    "confidence": response["confidence"]
                }
            )
        
        return response

class EnterprisePipeline:
    """Orchestrates end-to-end AI pipeline with compliance checks."""
    
    def __init__(self, sources: List[str], compliance_level: str = "general"):
        self.sources = sources
        self.compliance_level = compliance_level
        self.processors = []
        logger.info(f"Initialized EnterprisePipeline with {len(sources)} sources")
    
    def run(self) -> Dict[str, Any]:
        """Execute the full pipeline."""
        logger.info("Starting enterprise pipeline execution")
        
        results = {
            "processed_documents": 0,
            "pii_found": 0,
            "errors": [],
            "output_path": None
        }
        
        # Process each source
        for source in self.sources:
            try:
                logger.debug(f"Processing source: {source}")
                # Process source (would integrate with connectors)
                results["processed_documents"] += 1
            except Exception as e:
                logger.error(f"Error processing {source}: {e}")
                results["errors"].append(str(e))
        
        logger.info(f"Pipeline completed. Processed: {results['processed_documents']} documents")
        return results
