To implement efficient caching for multimodal search, focus on caching intermediate results and final outputs while accounting for the unique challenges of handling multiple data types (text, images, audio, etc.). Start by identifying which parts of the search pipeline are computationally expensive and repeatable. For example, feature extraction (like generating image embeddings with a CNN or text embeddings with a transformer) and fusion steps (combining modalities) are often good candidates for caching. Use a layered approach: cache raw data embeddings, fused representations, and even common query-result pairs. Tools like Redis or Memcached work well for fast lookups, while serialization formats like Protocol Buffers help store complex data efficiently.
Design cache keys carefully to balance uniqueness and collision risk. For instance, generate keys by hashing a combination of normalized input data (e.g., resized image pixels, lowercased text) and model version metadata. If a user searches for “red sneakers” with an image of shoes, preprocess the image (resize, normalize) and text (remove stopwords), then create a hash of both. This ensures identical or near-identical queries hit the cache. For fused modalities, include a hash of each modality’s processed input and the fusion method (e.g., concatenation weights). To handle partial hits—like a cached image embedding but new text—cache intermediate results separately. For example, store image embeddings keyed by image hash and text embeddings by text hash, then check if a fused query can reuse either.
Monitor and adjust cache policies based on usage patterns. Implement Least Recently Used (LRU) eviction for general cases, but consider hybrid strategies—like keeping high-priority embeddings (e.g., frequently searched product images) in a dedicated cache tier. Use TTL (time-to-live) for time-sensitive data, such as trending search terms. For scalability, partition the cache by modality (image_cache, text_cache) or shard by user ID if searches are user-specific. Tools like Redis Cluster automate sharding, while custom logic can route image-related keys to servers with more RAM. Periodically analyze cache hit rates and latency metrics to spot bottlenecks—if text searches have a 90% hit rate but image searches only 30%, allocate more resources to image embedding storage or optimize the image preprocessing pipeline.