RAG - Semantic Caching

A developer explains how semantic caching can optimize RAG pipelines by storing previous search results in in-memory databases like Redis or Valkey. The technique reduces redundant calls to vector databases and LLMs by retrieving cached answers for semantically similar queries, though careful cache invalidation strategies are needed to ensure data freshness.

When a user submits a query, the query is converted into an embedding and searched against the vector database to retrieve the relevant documents. But what happens if the user asks the same or a very similar query again? This is where semantic caching comes into the picture. Instead of searching the vector database again, the system stores the previous search result in a cache. A cache is a temporary storage where frequently accessed or recently queried results are stored. When the user asks the same or a semantically similar query again, the system can retrieve the result directly from the cache instead of querying the vector database again. We can use Redis or Valkey for semantic caching. These are in-memory databases , which means they store data in RAM instead of disk. Since data is stored in memory, retrieval is much faster compared to traditional databases. Typically, we store: Suppose a user asks: "What is today's gold price?" The query and its corresponding answer are stored in Redis. Later, another user asks: "Gold price today?" Although both queries have the same meaning, Redis cannot directly retrieve the previous answer because it expects the key to match exactly. This is one of the limitations of using Redis as a simple key-value store. One approach is: KEYS .This allows semantically similar queries to reuse cached results even when the text is different. Semantic caching can be implemented in two ways: One of the most important aspects of semantic caching is cache invalidation , which determines how long cached data should remain valid before it is automatically removed or refreshed. For example, suppose a user asks: "What is today's gold price?" The answer should only be valid for a limited period. If the application returns yesterday's gold price, the information becomes incorrect. There is no single solution for cache invalidation. The appropriate strategy depends on the application and the type of data being cached. Different scenarios need to be considered before deciding when cached data should expire. In-memory databases are well suited for: By understanding the meaning of the query, we can define guardrails to determine which queries should be cached and when the cache should be invalidated. The main objective is to optimize the RAG pipeline by reducing unnecessary calls to both the vector database and the LLM. Although it is not possible to eliminate duplicate requests completely, semantic caching can significantly reduce them. We should not store every query in an in-memory database. Only queries that are valuable for caching should be stored because RAM has limited storage capacity . Therefore, an effective caching strategy should carefully decide which queries are worth storing and for how long.