Can embeddings be precomputed?

Yes, embeddings can be precomputed. Embeddings are numerical representations of data (like text, images, or audio) generated by machine learning models, often used for tasks like similarity search or clustering. Precomputing embeddings means generating and storing these vectors in advance, rather than calculating them on the fly during runtime. This approach is common in systems where speed, scalability, or resource efficiency are critical. For example, a search engine might precompute embeddings for millions of documents to enable fast similarity comparisons when processing user queries. By doing this upfront, the system avoids the computational cost of generating embeddings during each request, reducing latency and server load.

Precomputed embeddings are particularly useful in applications with static or infrequently updated data. For instance, a recommendation system for an e-commerce platform could precompute product embeddings based on their descriptions and attributes. When a user interacts with the site, the system retrieves precomputed vectors for products and compares them to the user’s current session embedding (computed in real-time) to suggest relevant items. Tools like TensorFlow, PyTorch, or Hugging Face’s Transformers library are often used to generate embeddings, which are then stored in databases optimized for vector search, such as FAISS, Annoy, or Pinecone. Precomputation also simplifies deployment, as it decouples the embedding generation process (which may require GPUs) from lightweight application servers that handle user requests.

However, precomputation has trade-offs. If the underlying data changes frequently (e.g., social media posts or real-time user-generated content), embeddings can become outdated, requiring periodic recomputation. This adds complexity to data pipelines, as systems must track updates and trigger embedding regeneration. Storage costs also increase with scale—storing vectors for billions of items requires efficient compression or dimensionality reduction. Additionally, model updates (e.g., switching to a newer embedding model) may necessitate recomputing all embeddings. To mitigate these issues, developers often use hybrid approaches: precomputing embeddings for stable data while generating them dynamically for volatile or small datasets. This balance ensures efficiency without sacrificing accuracy in evolving systems.