Effective caching strategies for embedding generation typically involve a mix of input-based caching, model-aware versioning, and precomputation. Embeddings are deterministic for the same input and model version, making them ideal for caching. The goal is to reduce redundant computation while ensuring consistency when models or data change. Below are practical approaches tailored for developers.
First, input-based caching stores embeddings using a unique key derived from the input data. For text embeddings, this could be a hash (e.g., SHA-256) of the input string combined with the model version. Tools like Redis or Memcached are well-suited for this. For example, if your application processes frequent queries like "weather in New York," generating and caching the embedding once saves compute resources. However, hashing large inputs (e.g., high-resolution images) can be costly, so consider truncating or sampling data for the key. Always include the model version in the key to avoid serving outdated embeddings when models are updated.
Second, model-aware versioning and precomputation address scenarios where embeddings depend on specific model versions. When deploying a new embedding model, invalidate old entries by including the model version in cache keys (e.g., model-v3:input_hash). For applications with predictable or recurring inputs (e.g., product descriptions in an e-commerce search), precompute embeddings during off-peak hours and load them into a cache at startup. This reduces latency during peak traffic. For example, a recommendation system could precompute embeddings for all products and store them in a database, fetching them on demand. If inputs change infrequently (e.g., Wikipedia articles), schedule batch jobs to refresh the cache periodically.
Third, tiered caching and cache size management balance speed and resource usage. Store frequently accessed embeddings in-memory (e.g., Redis) for low latency, while less frequent ones can reside in a database or disk cache. Use LRU (Least Recently Used) eviction policies to manage memory limits. For applications involving similarity search (e.g., finding related documents), combine caching with vector databases like FAISS or Pinecone to cache precomputed nearest-neighbor results. For example, a support chatbot could cache embeddings of common user questions and their corresponding answers, reducing real-time computation. Monitor cache hit rates and adjust TTL (Time to Live) settings to handle cases where source data might change (e.g., user-edited content), though embeddings typically remain valid unless the model or input data is updated.