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semantic_llm_cache/config.py

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+"""Configuration management for prompt-cache."""
+
+from dataclasses import dataclass
+from typing import Any, Callable, Optional
+
+
+@dataclass
+class CacheConfig:
+    """Configuration for cache behavior."""
+
+    similarity_threshold: float = 1.0  # 1.0 = exact match, lower = semantic
+    ttl: Optional[int] = 3600  # Time to live in seconds, None = forever
+    namespace: str = "default"  # Isolate different use cases
+    enabled: bool = True  # Enable/disable caching
+    key_func: Optional[Callable[[Any], str]] = None  # Custom cache key function
+
+    # Cost estimation for statistics (USD per 1K tokens)
+    input_cost_per_1k: float = 0.001  # Default ~$1/1M for cheaper models
+    output_cost_per_1k: float = 0.002  # Default ~$2/1M for cheaper models
+
+    # Performance settings
+    max_cache_size: Optional[int] = None  # LRU eviction when set
+    embedding_model: str = "all-MiniLM-L6-v2"  # Default sentence-transformer model
+
+    def __post_init__(self) -> None:
+        """Validate configuration."""
+        if not 0.0 <= self.similarity_threshold <= 1.0:
+            raise ValueError("similarity_threshold must be between 0.0 and 1.0")
+        if self.ttl is not None and self.ttl <= 0:
+            raise ValueError("ttl must be positive or None")
+        if self.max_cache_size is not None and self.max_cache_size <= 0:
+            raise ValueError("max_cache_size must be positive or None")
+
+
+@dataclass
+class CacheEntry:
+    """A cached response with metadata."""
+
+    prompt: str
+    response: Any
+    embedding: Optional[list[float]] = None  # Normalized embedding vector
+    created_at: float = 0.0  # Unix timestamp
+    ttl: Optional[int] = None  # Time to live in seconds
+    namespace: str = "default"
+    hit_count: int = 0
+
+    # Approximate token counts for cost estimation
+    input_tokens: int = 0
+    output_tokens: int = 0
+
+    def is_expired(self, current_time: float) -> bool:
+        """Check if entry has expired based on TTL."""
+        if self.ttl is None:
+            return False
+        return (current_time - self.created_at) > self.ttl
+
+    def estimate_cost(self, input_cost: float, output_cost: float) -> float:
+        """Estimate cost savings in USD."""
+        input_savings = (self.input_tokens / 1000) * input_cost
+        output_savings = (self.output_tokens / 1000) * output_cost
+        return input_savings + output_savings