A semantic cache differs from traditional caching methods in that it stores the meaning of a query or requests instead of just the raw data. Doing so can reduce the number of queries a server needs to process by recalling previous queries and their results. Traditional caching methods store data based on physical characteristics, which may not account for its meaning.
Semantic caching stores data based on its meaning, which means that two queries with the same meaning will return the same result, even if the underlying data has changed. This can be useful for complex queries involving multiple tables or data sources. However, the most significant advantage of semantic caching is its ability to reduce server load. By caching LLM responses, for example, semantic caching can shorten data retrieval time, lower API call expenses, and improve scalability.
Customizing and monitoring the cache's performance can also make it more efficient. Since the cache stores previous queries and results, it can quickly serve up the results of a query without the need for processing. As a result, response times can be faster, and users can experience better application performance.
In summary, semantic caching is a powerful cache that can enhance the efficiency of servers and application user experiences. Storing query and request meaning can decrease the number of queries that need to be processed, allowing results to be served quickly and accurately.