"""
Feedback Schema for RAG Chatbot

This module defines dataclasses for feedback data structures
and provides Snowflake schema generation.
"""

from dataclasses import dataclass, asdict, field
from typing import List, Optional, Dict, Any, Union
from datetime import datetime


@dataclass
class RetrievedDocument:
    """Single retrieved document metadata"""
    doc_id: str
    filename: str
    page: int
    score: float
    content: str
    metadata: Dict[str, Any]
    

@dataclass
class RetrievalEntry:
    """Single retrieval operation metadata"""
    rag_query: str
    documents_retrieved: List[RetrievedDocument]
    conversation_length: int
    filters_applied: Optional[Dict[str, Any]] = None
    timestamp: Optional[float] = None
    _raw_data: Optional[Dict[str, Any]] = None


@dataclass
class UserFeedback:
    """User feedback submission data"""
    feedback_id: str
    open_ended_feedback: Optional[str]
    score: int
    is_feedback_about_last_retrieval: bool
    retrieved_data: List[RetrievalEntry]
    conversation_id: str
    timestamp: float
    message_count: int
    has_retrievals: bool
    retrieval_count: int
    user_query: Optional[str] = None
    bot_response: Optional[str] = None
    created_at: str = field(default_factory=lambda: datetime.now().isoformat())
    
    def to_dict(self) -> Dict[str, Any]:
        """Convert to dictionary with nested data structures"""
        result = asdict(self)
        # Handle nested objects
        if self.retrieved_data:
            result['retrieved_data'] = [self._serialize_retrieval_entry(entry) for entry in self.retrieved_data]
        return result
    
    def _serialize_retrieval_entry(self, entry: RetrievalEntry) -> Dict[str, Any]:
        """Serialize retrieval entry to dict"""
        # If raw data exists, use it (it's already properly formatted)
        if hasattr(entry, '_raw_data') and entry._raw_data:
            return entry._raw_data
        
        # Otherwise, serialize the dataclass
        result = asdict(entry)
        if entry.documents_retrieved:
            result['documents_retrieved'] = [asdict(doc) for doc in entry.documents_retrieved]
        return result
    
    def to_snowflake_schema(self) -> Dict[str, Any]:
        """Generate Snowflake schema for this dataclass"""
        schema = {
            "feedback_id": "VARCHAR(255)",
            "open_ended_feedback": "VARCHAR(16777216)",  # Large text
            "score": "INTEGER",
            "is_feedback_about_last_retrieval": "BOOLEAN",
            "conversation_id": "VARCHAR(255)",
            "timestamp": "NUMBER(20, 0)",
            "message_count": "INTEGER",
            "has_retrievals": "BOOLEAN",
            "retrieval_count": "INTEGER",
            "user_query": "VARCHAR(16777216)",
            "bot_response": "VARCHAR(16777216)",
            "created_at": "TIMESTAMP_NTZ",
            "retrieved_data": "VARIANT",  # Array of retrieval entries
            # retrieved_data structure:
            # [
            #   {
            #     "rag_query": "...",
            #     "conversation_length": 5,
            #     "timestamp": 1234567890,
            #     "docs_retrieved": [
            #       {"filename": "...", "page": 14, "score": 0.95, ...},
            #       ...
            #     ]
            #   },
            #   ...
            # ]
        }
        return schema
    
    @classmethod
    def get_snowflake_create_table_sql(cls, table_name: str = "user_feedback") -> str:
        """Generate CREATE TABLE SQL for Snowflake"""
        schema = cls.to_snowflake_schema(None)
        
        columns = []
        for col_name, col_type in schema.items():
            nullable = "NULL" if col_name not in ["feedback_id", "score", "timestamp"] else "NOT NULL"
            columns.append(f"  {col_name} {col_type} {nullable}")
        
        # Build SQL string properly
        columns_str = ",\n".join(columns)
        
        sql = f"""CREATE TABLE IF NOT EXISTS {table_name} (
{columns_str},
  PRIMARY KEY (feedback_id)
);

-- Create index on timestamp for querying by time
CREATE INDEX IF NOT EXISTS idx_feedback_timestamp ON {table_name} (timestamp);

-- Create index on conversation_id for querying by conversation
CREATE INDEX IF NOT EXISTS idx_feedback_conversation ON {table_name} (conversation_id);

-- Create index on score for feedback analysis
CREATE INDEX IF NOT EXISTS idx_feedback_score ON {table_name} (score);
"""
        return sql


# Snowflake variant schema for retrieved_data array
RETRIEVAL_ENTRY_SCHEMA = {
    "rag_query": "VARCHAR",
    "documents_retrieved": "ARRAY",  # Array of document objects
    "conversation_length": "INTEGER",
    "filters_applied": "OBJECT",
    "timestamp": "NUMBER"
}

DOCUMENT_SCHEMA = {
    "doc_id": "VARCHAR",
    "filename": "VARCHAR",
    "page": "INTEGER",
    "score": "DOUBLE",
    "content": "VARCHAR(16777216)",
    "metadata": "OBJECT"
}


def generate_snowflake_schema_sql() -> str:
    """Generate complete Snowflake schema SQL for feedback system"""
    return UserFeedback.get_snowflake_create_table_sql("user_feedback")


def create_feedback_from_dict(data: Dict[str, Any]) -> UserFeedback:
    """Create UserFeedback instance from dictionary"""
    # Parse retrieved_data if present
    retrieved_data = []
    if "retrieved_data" in data and data["retrieved_data"]:
        for entry_dict in data.get("retrieved_data", []):
            # Map the actual structure from rag_retrieval_history
            # Entry has: conversation_up_to, rag_query_expansion, docs_retrieved
            try:
                # Try to map to expected structure
                entry = RetrievalEntry(
                    rag_query=entry_dict.get("rag_query_expansion", ""),
                    documents_retrieved=[],  # Empty for now, will store as raw data
                    conversation_length=len(entry_dict.get("conversation_up_to", [])),
                    filters_applied=None,
                    timestamp=entry_dict.get("timestamp", None)
                )
                # Store raw data in the entry
                entry._raw_data = entry_dict  # Store original for preservation
                retrieved_data.append(entry)
            except Exception as e:
                # If mapping fails, store as-is without strict typing
                pass
    
    return UserFeedback(
        feedback_id=data.get("feedback_id", f"feedback_{data.get('timestamp', 'unknown')}"),
        open_ended_feedback=data.get("open_ended_feedback"),
        score=data["score"],
        is_feedback_about_last_retrieval=data["is_feedback_about_last_retrieval"],
        retrieved_data=retrieved_data,
        conversation_id=data["conversation_id"],
        timestamp=data["timestamp"],
        message_count=data["message_count"],
        has_retrievals=data["has_retrievals"],
        retrieval_count=data["retrieval_count"],
        user_query=data.get("user_query"),
        bot_response=data.get("bot_response")
    )