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Semantic Cache

通过 Node 脚本把问题用 OpenAI 嵌入向量化，在 Redis 中按余弦相似度查找相似问题的缓存回答；命中直接返回，未命中则调用 LLM 并写入缓存。提供 store、lookup、stats、clear、query 命令，阈值与 TTL 可用环境变量配置。

rylinjames
rylinjames
热度 61.35
最近更新 2026-05-11 17:36:35
分类：开发依赖：代码执行 / 第三方 API / 内部调用大模型应用场景：API 集成 / 自动化脚本 / 工作流

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add semantic-cache
Metadata
name
semantic-cache
description
Semantic cache for LLM API calls using Redis. Caches responses by meaning, not exact match. Activate when the user wants to cache AI responses, reduce API costs, speed up repeated queries, or add semantic caching to any workflow. Use this skill to check cache before making expensive LLM calls and store results for future similar queries.
metadata
{"openclaw":{"requires":{"env":["REDIS_URL","OPENAI_API_KEY"],"bins":["node"]},"primaryEnv":"REDIS_URL","emoji":"⚡","homepage":"https://github.com/openclaw/clawhub","install":[{"kind":"node","package":"redis","bins":[]},{"kind":"node","package":"openai","bins":[]}]}}

Semantic Cache

Cache LLM responses by meaning using Redis vector search. Similar questions return cached answers instantly instead of making expensive API calls.

How It Works

  1. User asks a question or makes an LLM request
  2. The question is embedded into a vector using OpenAI text-embedding-3-small
  3. Redis vector search finds semantically similar cached queries (cosine similarity > 0.80)
  4. Cache hit: Return the cached response instantly (~100ms)
  5. Cache miss: Pass through to the LLM, cache the response for future similar queries

Commands

Cache a query and response

node scripts/cache.js store "What is the capital of France?" "The capital of France is Paris."

Check cache for a similar query

node scripts/cache.js lookup "What's France's capital city?"

Cache stats

node scripts/cache.js stats

Clear all cached entries

node scripts/cache.js clear

Interactive mode — wraps any LLM call with caching

node scripts/cache.js query "Your question here"

This checks cache first. On miss, calls OpenAI, caches the result, and returns it.

When to Use This Skill

  • Before making any LLM API call, check if a semantically similar query was already answered
  • When building agents that answer repetitive questions (support bots, FAQ systems)
  • When you want to reduce OpenAI/Anthropic API costs by 40-80%
  • When you need faster response times for common queries

Configuration

Set these environment variables:

  • REDIS_URL — Redis connection string with vector search support (Redis Cloud or Redis Stack)
  • OPENAI_API_KEY — For generating embeddings
  • SEMANTIC_CACHE_THRESHOLD — Similarity threshold 0-1 (default: 0.80, higher = stricter matching)
  • SEMANTIC_CACHE_TTL — Cache TTL in seconds (default: 86400 = 24 hours)

Example Workflow

User: "How do I reset my password?"
  -> Embed query -> Search Redis -> MISS
  -> Call LLM -> Get response -> Cache it -> Return response

User: "I forgot my password, how do I change it?"
  -> Embed query -> Search Redis -> HIT (92.7% similar)
  -> Return cached response in 8ms (saved ~2 seconds + API cost)

Performance

  • Cache lookup: ~5-15ms (vs 1-5 seconds for LLM call)
  • Embedding generation: ~50-100ms
  • Storage per entry: ~6KB (1536-dim vector + metadata)
  • Supports millions of cached entries