Caching computed embeddings can significantly improve application performance by avoiding redundant computation when using Sentence Transformers on repeated sentences. When a sentence is processed through a transformer model, it undergoes tokenization and inference, which can be computationally expensive—especially for large models or frequent requests. By storing the embeddings of previously processed sentences, subsequent requests for the same text can skip the model inference step entirely. This reduces latency, lowers CPU/GPU usage, and minimizes costs in cloud-based scenarios where compute resources are billed by usage. For example, a chatbot handling common user queries could cache embeddings for frequently asked questions instead of reprocessing them each time.
A practical implementation might involve using a key-value store (like Redis or a simple Python dictionary) where the input text is the key and the embedding vector is the value. For instance, in a document search system, preprocessing all documents once and caching their embeddings eliminates the need to recompute them during every search query. Similarly, applications like recommendation engines or duplicate detection systems that process the same sentences across multiple user sessions would see reduced response times. Caching also helps in batch processing: if a dataset contains duplicates, processing them once and reusing the cached results can cut total execution time by avoiding redundant model calls.
However, caching requires careful design. Storage costs must be considered, as embeddings are high-dimensional vectors (e.g., 768 or 1024 floats per sentence). Techniques like serialization (using pickle or numpy arrays) and compression can help manage disk/memory usage. Additionally, applications must normalize input text (e.g., trimming whitespace, standardizing casing) to ensure identical sentences match cache keys exactly. If the model or its configuration changes, the cache must be invalidated or versioned to prevent serving outdated embeddings. For stateless systems, distributed caches ensure consistency across instances. Overall, caching embeddings strikes a balance between compute efficiency and storage trade-offs, making it especially valuable for high-throughput or repetitive workloads.