lix-open-cache 2.1.6

Multi-layer Redis caching + Huffman disk archival for conversational AI — session memory, semantic deduplication, and compressed storage

lix-open-cache

Standalone multi-layer caching and session management for conversational AI. Works with just Redis — no server needed.

pip install lix-open-cache

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", "22C 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 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

Dependencies

Only 3: redis, numpy, loguru

Research Paper

A Three-Layer Caching Architecture for Low-Latency LLM Web Search on Commodity CPU Hardware Ayushman Bhattacharya, 2026 Read the paper

License

MIT