Metadata-Version: 2.4
Name: cachemind
Version: 0.1.2
Summary: Semantic caching framework for LLMs
Author: Ayush De
License: MIT
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: faiss-cpu
Dynamic: license-file

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)  

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

License: MIT  
