lix-open-cache v2.1.6

lix-open-cache v2.1.6

Standalone multi-layer Redis caching + Huffman disk archival for conversational AI.

Install

pip install lix-open-cache==2.1.6

Quick Start

from lix_open_cache import CacheConfig, CacheCoordinator

config = CacheConfig(redis_host="localhost", redis_port=6379)
cache = CacheCoordinator(session_id="user-abc", config=config)

# Store & retrieve conversation context
cache.add_message_to_context("user", "What's the weather in Tokyo?")
cache.add_message_to_context("assistant", "22°C and sunny.")
history = cache.get_context_messages()

# Semantic cache — skip LLM on similar queries
import numpy as np
embedding = np.random.rand(384).astype(np.float32)
cached = cache.get_semantic_response("https://weather.com", embedding)

Three Cache Layers

Layer	Purpose	Backend	TTL
Session Context Window	Rolling 20-message window + disk overflow	Redis DB 2 + .huff files	24h
Semantic Query Cache	Deduplicate similar queries (cosine ≥ 0.90)	Redis DB 0	5 min
URL Embedding Cache	Cache embedding vectors per URL	Redis DB 1	24h

Key Features

• Two-tier hybrid storage — Redis hot window + Huffman-compressed disk cold archive
• LRU eviction daemon — auto-migrates idle sessions to disk, re-hydrates on return
• smart_context() — recent messages + semantically relevant history from disk
• Pure Python Huffman codec — ~54% compression, zero native dependencies
• CacheConfig dataclass — all tunables in one place, 12-factor env var support
• Connection pooling — shared Redis pools keyed by (host, port, db)

Dependencies

Only 3: redis, numpy, loguru

Links

• PyPI
• Docs
• Research Paper
• Live Demo