cachemind 0.1.0

Semantic caching framework for LLMs

CacheMind is a lightweight semantic caching layer for LLM applications.

It helps reduce redundant LLM calls to improve response time and save LLM token costs.

📦Installation pip install cachemind

⚡Quick Start from cachemind import CacheMind cache = CacheMind()

response = cache.query("What is artificial intelligence?") print(response)

🧩Customization CacheMind is designed to be flexible. You can plug in your own components for LLM, embedding, vector store, and policy.

Custom Embedding class MyEmbedding: def encode(self, text): return [0.1] * 384 cache = CacheMind(embedding=MyEmbedding())

📊 Metrics CacheMind also supports metrics tracking, allowing you to monitor cache performance and effectiveness over time.

stats = cache.metrics.get_stats() print(stats)

Example output: { "total_queries": 10, "cache_hits": 6, "cache_misses": 4, "hit_rate": 0.6, "tokens_saved": 120, "tokens_used": 80 }

License: MIT