Cache LLM Responses for Speed and Savings
Semantic Kernel caching reduces LLM response latency and API costs by storing and reusing semantically similar query results.
Why it matters
Reduce latency and API costs by implementing semantic caching for your Large Language Model (LLM) responses. This asset stores and retrieves previous LLM outputs, avoiding redundant calls and improving application performance.
Outcomes
What it gets done
Implement semantic caching for LLM interactions.
Reduce API call volume and associated costs.
Improve response times by retrieving cached results.
Integrate caching logic into existing LLM applications.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/sk-concept-caching | bash Overview
Semantic Kernel - Caching
This skill implements semantic caching for LLM responses within Semantic Kernel, storing previous answers and retrieving them when semantically similar queries are detected. It reduces both API costs and response latency by eliminating redundant calls to language models when queries are sufficiently similar to previously answered ones. Use this when your application handles repetitive or similar queries where users ask the same questions in different ways, such as in customer support chatbots or FAQ systems. It is most valuable in high-traffic scenarios where reducing API costs and improving response speed provide measurable operational benefits.
What it does
Semantic Kernel caching stores LLM responses and retrieves them when semantically similar queries are detected, eliminating redundant API calls. This skill reduces both response latency and API costs by recognizing when a new query is sufficiently similar to a previously answered one, returning the cached result instead of making a new LLM request.
When to use - and when NOT to
Use semantic caching when your application handles repetitive or similar queries where users ask the same questions in different words, such as customer support chatbots, FAQ systems, or documentation assistants where query patterns repeat frequently. It is particularly valuable in high-traffic scenarios where API cost reduction and faster response times directly impact operational efficiency.
Do not use semantic caching when you require real-time data or when responses must reflect the absolute latest information, as cached results may be stale. Avoid it for highly personalized queries where context variations matter significantly, as semantic similarity matching may incorrectly return cached responses that lack necessary nuance.
Inputs and outputs
You provide queries or prompts to the LLM through Semantic Kernel's caching layer. The system evaluates whether a semantically similar query has been processed before. If a match is found above the similarity threshold, you receive the cached response immediately. If no match exists, the query proceeds to the LLM, and both the query and response are stored for future cache hits.
Who it's for
This skill serves developers building LLM-powered applications who need to optimize for cost and performance, particularly those managing high query volumes in production environments. It benefits engineering teams working on conversational AI, knowledge bases, or any application where users frequently ask variations of the same questions. For teams prioritizing response speed and budget management over absolute real-time accuracy, semantic caching provides measurable operational improvements.
Source README
Semantic caching of LLM responses to reduce latency and API costs
Examples (1 files):
semantic_caching.py
FAQ
Common questions
Discussion
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