"""
CAG Service (Cache-Augmented Generation)
Semantic caching layer for RAG system using Qdrant

This module implements intelligent caching to reduce latency and LLM costs
by serving semantically similar queries from cache.
"""

from typing import Optional, Dict, Any, Tuple
from datetime import datetime, timedelta
import numpy as np
from qdrant_client import QdrantClient
from qdrant_client.models import (
    Distance, VectorParams, PointStruct,
    SearchParams, Filter, FieldCondition, MatchValue, Range
)
import uuid
import os


class CAGService:
    """
    Cache-Augmented Generation Service
    
    Features:
    - Semantic similarity-based cache lookup (cosine similarity)
    - TTL (Time-To-Live) for automatic cache expiration
    - Configurable similarity threshold
    """
    
    def __init__(
        self,
        embedding_service,
        qdrant_url: Optional[str] = None,
        qdrant_api_key: Optional[str] = None,
        cache_collection: str = "semantic_cache",
        vector_size: int = 1024,
        similarity_threshold: float = 0.9,
        ttl_hours: int = 24
    ):
        """
        Initialize CAG Service
        
        Args:
            embedding_service: Embedding service for query encoding
            qdrant_url: Qdrant Cloud URL
            qdrant_api_key: Qdrant API key
            cache_collection: Collection name for cache
            vector_size: Embedding dimension
            similarity_threshold: Min similarity for cache hit (0-1)
            ttl_hours: Cache entry lifetime in hours
        """
        self.embedding_service = embedding_service
        self.cache_collection = cache_collection
        self.similarity_threshold = similarity_threshold
        self.ttl_hours = ttl_hours
        
        # Initialize Qdrant client
        url = qdrant_url or os.getenv("QDRANT_URL")
        api_key = qdrant_api_key or os.getenv("QDRANT_API_KEY")
        
        if not url or not api_key:
            raise ValueError("QDRANT_URL and QDRANT_API_KEY required for CAG")
        
        self.client = QdrantClient(url=url, api_key=api_key)
        self.vector_size = vector_size
        
        # Ensure cache collection exists
        self._ensure_cache_collection()
        
        print(f"✓ CAG Service initialized (cache: {cache_collection}, threshold: {similarity_threshold})")
    
    def _ensure_cache_collection(self):
        """Create cache collection if it doesn't exist"""
        collections = self.client.get_collections().collections
        exists = any(c.name == self.cache_collection for c in collections)
        
        if not exists:
            print(f"Creating semantic cache collection: {self.cache_collection}")
            self.client.create_collection(
                collection_name=self.cache_collection,
                vectors_config=VectorParams(
                    size=self.vector_size,
                    distance=Distance.COSINE
                )
            )
            print("✓ Semantic cache collection created")
    
    def check_cache(
        self,
        query: str
    ) -> Optional[Dict[str, Any]]:
        """
        Check if query has a cached response
        
        Args:
            query: User query string
            
        Returns:
            Cached data if found (with response, context, metadata), None otherwise
        """
        # Generate query embedding
        query_embedding = self.embedding_service.encode_text(query)
        
        if len(query_embedding.shape) > 1:
            query_embedding = query_embedding.flatten()
        
        # Search for similar queries in cache
        search_result = self.client.query_points(
        collection_name=self.cache_collection,
        query=query_embedding.tolist(),
        limit=1,
        score_threshold=self.similarity_threshold,
        with_payload=True
    ).points
        
        if not search_result:
            return None
        
        hit = search_result[0]
        
        # Check TTL
        cached_at = datetime.fromisoformat(hit.payload.get("cached_at"))
        expires_at = cached_at + timedelta(hours=self.ttl_hours)
        
        if datetime.utcnow() > expires_at:
            # Cache expired, delete it
            self.client.delete(
                collection_name=self.cache_collection,
                points_selector=[hit.id]
            )
            return None
        
        # Cache hit!
        return {
            "response": hit.payload.get("response"),
            "context_used": hit.payload.get("context_used", []),
            "rag_stats": hit.payload.get("rag_stats"),
            "cached_query": hit.payload.get("original_query"),
            "similarity_score": float(hit.score),
            "cached_at": cached_at.isoformat(),
            "cache_hit": True
        }
    
    def save_to_cache(
        self,
        query: str,
        response: str,
        context_used: list,
        rag_stats: Optional[Dict] = None
    ) -> str:
        """
        Save query-response pair to cache
        
        Args:
            query: Original user query
            response: Generated response
            context_used: Retrieved context documents
            rag_stats: RAG pipeline statistics
            
        Returns:
            Cache entry ID
        """
        # Generate query embedding
        query_embedding = self.embedding_service.encode_text(query)
        
        if len(query_embedding.shape) > 1:
            query_embedding = query_embedding.flatten()
        
        # Create cache entry
        cache_id = str(uuid.uuid4())
        
        point = PointStruct(
            id=cache_id,
            vector=query_embedding.tolist(),
            payload={
                "original_query": query,
                "response": response,
                "context_used": context_used,
                "rag_stats": rag_stats or {},
                "cached_at": datetime.utcnow().isoformat(),
                "cache_type": "semantic"
            }
        )
        
        # Save to Qdrant
        self.client.upsert(
            collection_name=self.cache_collection,
            points=[point]
        )
        
        return cache_id
    
    def clear_cache(self) -> bool:
        """
        Clear all cache entries
        
        Returns:
            Success status
        """
        try:
            # Delete and recreate collection
            self.client.delete_collection(collection_name=self.cache_collection)
            self._ensure_cache_collection()
            print("✓ Semantic cache cleared")
            return True
        except Exception as e:
            print(f"Error clearing cache: {e}")
            return False
    
    def get_cache_stats(self) -> Dict[str, Any]:
        """
        Get cache statistics
        
        Returns:
            Cache statistics (size, hit rate, etc.)
        """
        try:
            info = self.client.get_collection(collection_name=self.cache_collection)
            return {
                "total_entries": info.points_count,
                "vectors_count": info.vectors_count,
                "status": info.status,
                "ttl_hours": self.ttl_hours,
                "similarity_threshold": self.similarity_threshold
            }
        except Exception as e:
            print(f"Error getting cache stats: {e}")
            return {}