To implement caching for semantic search, you need to store and reuse computed results from previous queries to reduce processing time and system load. The core idea is to cache embeddings (vector representations of text) and their associated search results. When a new query arrives, generate its embedding, check the cache for similar embeddings, and return stored results if a match exists. This approach avoids redundant computation of embeddings and repeated searches against your database or index.
Start by setting up a vector cache using a tool like Redis, FAISS, or a dedicated vector database. For example, when a user searches for “best budget laptops,” generate the query’s embedding using a model like Sentence-BERT. Compare this embedding against cached embeddings using cosine similarity or another distance metric. If a similar cached embedding exists (e.g., with a similarity score above a threshold like 0.85), return the precomputed results. If not, process the query normally and add the new embedding and results to the cache. Tools like Redis allow you to set expiration times (TTL) to automatically remove stale entries, while FAISS optimizes fast similarity searches across large datasets.
In practice, you’ll need to balance cache size, similarity thresholds, and update frequency. For instance, an e-commerce platform might cache queries like “wireless headphones under $100” and map them to product IDs. If the product catalog changes, you’ll need a strategy to invalidate outdated entries—either by clearing the cache periodically, using event-driven updates (e.g., flushing entries when prices change), or limiting the cache duration. Tools like Hugging Face’s transformers library for embedding generation and redis-py for cache management can simplify implementation. Testing different similarity thresholds (e.g., 0.8 vs. 0.9) and monitoring cache hit rates will help optimize performance for your specific use case.