LLM (Large Language Model) caching is a technique for improving the performance and efficiency of LLMs. There are two main approaches to LLM caching: standard caching and semantic caching.
Standard Caching:
This is similar to how traditional web caching works. It stores the exact queries and their corresponding LLM responses.
When a new query comes in, the system first checks the cache.
If the exact query match is found in the cache, the stored response is retrieved and delivered directly, saving time and resources by avoiding a new request to the LLM itself.
This approach is fast and simple to implement.
However, it has limitations:
It only works for exact query matches. Even a slight variation in the wording of the query will result in a cache miss and require processing by the LLM.
The cache can become large and unwieldy as it stores a vast number of specific queries and responses.
Semantic Caching:
This approach focuses on the meaning of the query rather than the exact wording.
It utilizes techniques like natural language processing (NLP) to understand the intent and meaning behind a user's query.
The LLM responses are also encoded using techniques like word embeddings to capture their semantic meaning.
When a new query comes in, the system compares its meaning (embedding) to the stored responses in the cache.
If a cached response is found to be semantically similar to the new query, it is retrieved and delivered.
This approach offers several advantages:
It can handle variations in query wording as long as the meaning remains similar.
It can be more efficient in terms of storage space as it focuses on semantic representations rather than storing exact queries and responses.
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Gemini
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