"""
Semantic Caching Layer - Intelligent caching using embedding similarity
"""

import hashlib
import json
import logging
import time
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple

logger = logging.getLogger(__name__)


@dataclass
class CacheEntry:
    """A single cache entry"""
    key: str
    query: str
    embedding: List[float]
    result: Any
    metadata: Dict[str, Any]
    created_at: float
    ttl: int
    
    @property
    def is_expired(self) -> bool:
        return time.time() - self.created_at > self.ttl
    
    @property
    def age_seconds(self) -> float:
        return time.time() - self.created_at


@dataclass
class CacheHit:
    """Result of a cache hit"""
    result: Any
    similarity: float
    age_seconds: float
    savings_tokens: int
    savings_cost: float


class SemanticCache:
    """
    In-memory semantic cache using embedding similarity.
    Caches: Tool execution results, LLM completions, web search results, code execution outputs
    """
    
    def __init__(
        self,
        similarity_threshold: float = 0.92,
        default_ttl: int = 604800,  # 7 days
        max_size: int = 10000
    ):
        self.similarity_threshold = similarity_threshold
        self.default_ttl = default_ttl
        self.max_size = max_size
        self._cache: Dict[str, CacheEntry] = {}
        self._hits = 0
        self._misses = 0
        self._tokens_saved = 0
        self._cost_saved = 0.0
    
    def _compute_embedding(self, text: str) -> List[float]:
        """
        Compute a simple embedding for the text.
        In production, use OpenAI or sentence-transformers.
        For now, use a simple hash-based approach.
        """
        # Simple character n-gram based embedding
        text = text.lower().strip()
        ngrams = []
        for i in range(len(text) - 2):
            ngrams.append(text[i:i+3])
        
        # Create a 128-dimensional embedding
        embedding = [0.0] * 128
        for ngram in ngrams:
            idx = hash(ngram) % 128
            embedding[idx] += 1.0
        
        # Normalize
        norm = sum(x**2 for x in embedding) ** 0.5
        if norm > 0:
            embedding = [x / norm for x in embedding]
        
        return embedding
    
    def _cosine_similarity(self, a: List[float], b: List[float]) -> float:
        """Compute cosine similarity between two vectors"""
        dot = sum(x * y for x, y in zip(a, b))
        norm_a = sum(x**2 for x in a) ** 0.5
        norm_b = sum(x**2 for x in b) ** 0.5
        
        if norm_a == 0 or norm_b == 0:
            return 0.0
        
        return dot / (norm_a * norm_b)
    
    def _generate_key(self, query: str) -> str:
        """Generate a cache key from query"""
        return hashlib.md5(query.encode()).hexdigest()
    
    async def check(self, query: str) -> Optional[CacheHit]:
        """Check if similar query exists in cache"""
        query_embedding = self._compute_embedding(query)
        
        best_match = None
        best_similarity = 0.0
        
        # Find best matching entry
        for entry in self._cache.values():
            if entry.is_expired:
                continue
            
            similarity = self._cosine_similarity(query_embedding, entry.embedding)
            
            if similarity > self.similarity_threshold and similarity > best_similarity:
                best_similarity = similarity
                best_match = entry
        
        if best_match:
            self._hits += 1
            tokens_saved = best_match.metadata.get("prompt_tokens", 0)
            cost_saved = best_match.metadata.get("estimated_cost", 0.0)
            self._tokens_saved += tokens_saved
            self._cost_saved += cost_saved
            
            logger.info(f"Cache hit: similarity={best_similarity:.3f}, key={best_match.key[:8]}")
            
            return CacheHit(
                result=best_match.result,
                similarity=best_similarity,
                age_seconds=best_match.age_seconds,
                savings_tokens=tokens_saved,
                savings_cost=cost_saved
            )
        
        self._misses += 1
        return None
    
    async def store(
        self,
        query: str,
        result: Any,
        metadata: Dict[str, Any] = None,
        ttl: int = None
    ) -> str:
        """Store result with embedding for future semantic matching"""
        
        # Clean up expired entries if cache is full
        if len(self._cache) >= self.max_size:
            self._cleanup_expired()
        
        # If still full, remove oldest entries
        if len(self._cache) >= self.max_size:
            oldest_key = min(
                self._cache.keys(),
                key=lambda k: self._cache[k].created_at
            )
            del self._cache[oldest_key]
        
        embedding = self._compute_embedding(query)
        key = self._generate_key(query)
        
        entry = CacheEntry(
            key=key,
            query=query,
            embedding=embedding,
            result=result,
            metadata=metadata or {},
            created_at=time.time(),
            ttl=ttl or self.default_ttl
        )
        
        self._cache[key] = entry
        logger.info(f"Cache stored: key={key[:8]}, entries={len(self._cache)}")
        
        return key
    
    def _cleanup_expired(self):
        """Remove expired entries"""
        expired_keys = [
            k for k, v in self._cache.items()
            if v.is_expired
        ]
        for key in expired_keys:
            del self._cache[key]
        
        if expired_keys:
            logger.info(f"Cleaned up {len(expired_keys)} expired cache entries")
    
    def get_stats(self) -> Dict[str, Any]:
        """Get cache statistics"""
        total = self._hits + self._misses
        hit_rate = self._hits / total if total > 0 else 0.0
        
        return {
            "hits": self._hits,
            "misses": self._misses,
            "hit_rate": hit_rate,
            "entries": len(self._cache),
            "tokens_saved": self._tokens_saved,
            "cost_saved": self._cost_saved
        }
    
    def clear(self):
        """Clear all cache entries"""
        self._cache.clear()
        self._hits = 0
        self._misses = 0
        self._tokens_saved = 0
        self._cost_saved = 0.0
        logger.info("Cache cleared")


# Global cache instance
semantic_cache = SemanticCache()