Which database does this skill use for caching?

This skill is specifically optimized for Redis, utilizing the RedisVL (Redis Vector Library) for efficient vector similarity search and metadata filtering.

Can I use this with any LLM provider?

Yes, the semantic cache layer acts as a provider-agnostic middleware that sits between your application logic and any LLM API, such as Anthropic Claude or OpenAI.

How do I determine the right similarity threshold?

The skill recommends starting at 0.92 for a balance of accuracy and hit rate. You can increase it toward 0.98 for strict accuracy or lower it toward 0.85 if your application allows for more generalized responses.

What is semantic caching compared to traditional caching?

Traditional caching requires an exact string match to return a result. Semantic caching uses vector embeddings to find similar meanings, allowing the system to reuse responses even if the wording of the query is slightly different.

Semantic Caching for LLMs

Name: Semantic Caching for LLMs
Author: yonatangross

byyonatangross

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Data Science & ML

Optimizes LLM performance and reduces API costs by implementing Redis-powered semantic similarity caching.

About

The Semantic Caching skill enables Claude to implement sophisticated caching strategies for LLM-powered applications using Redis and vector embeddings. It provides a production-ready framework for multi-level cache hierarchies, allowing systems to retrieve contextually similar previous responses instead of making costly new API calls. By leveraging semantic similarity thresholds rather than simple string matching, this skill helps developers significantly reduce token consumption, decrease response latency, and improve the scalability of AI services.

Key Features

Automatic TTL management and cache promotion logic
RedisVL integration for high-performance vector similarity search
Multi-level cache hierarchy (Exact, Semantic, Prompt, LLM)
Configurable similarity thresholds for balancing accuracy and hit rates
Metadata filtering for agent-specific and multi-tenant caching
29 GitHub stars

Use Cases

Improving response times for common user queries through vector lookups
Reducing API costs in high-traffic production LLM applications
Optimizing multi-agent workflows with shared semantic memory layers

About

Key Features

Automatic TTL management and cache promotion logic
RedisVL integration for high-performance vector similarity search
Multi-level cache hierarchy (Exact, Semantic, Prompt, LLM)
Configurable similarity thresholds for balancing accuracy and hit rates
Metadata filtering for agent-specific and multi-tenant caching
29 GitHub stars

Use Cases

Improving response times for common user queries through vector lookups
Reducing API costs in high-traffic production LLM applications
Optimizing multi-agent workflows with shared semantic memory layers