Stable Initial Release

SemantiCache v0.1.1 Release Notes

Super excited to announce the initial release of SemantiCache, a semantic caching library designed to optimize query-response handling in LLM applications.

Key Features

• Vector-based Caching: Leverage FAISS and HuggingFace embeddings for efficient similarity search.
• Automatic Cache Management: Supports TTL and size-based trimming to maintain optimal cache performance.
• Leaderboard Tracking: Easily monitor the most frequently accessed queries.
• Persistent Storage: Maintain cache state across sessions with robust file-based persistence.

Installation

Install via pip:

pip install semanticache

View on PyPI

Quick Start

from semanticache import SemantiCache

# Initialize the cache
cache = SemantiCache(
    trim_by_size=True,
    cache_path="./sem_cache",
    config_path="./sem_config",
    cache_size=100,
    ttl=3600,
    threshold=0.1,
    leaderboard_top_n=5,
    log_level="INFO"
)

# Store a query-response pair
cache.set("What is the capital of France?", "Paris")

# Retrieve a cached response
response = cache.get("What is the capital of France?")
print(response)  # Output: Paris

Documentation & Contributions

For detailed documentation, refer to the SemantiCache Docs.

Contributions and suggestions for improvements (like additional tests and support for alternate vector engines) are welcome.

Acknowledgments & License

Built on top of FAISS, HuggingFace, and LangChain Community. SemantiCache is licensed under the GNU General Public License v3.

Enjoy a smarter caching experience for your LLM apps with SemantiCache!