Can embeddings be updated in real time?

Yes, embeddings can be updated in real time, but the approach depends on the system design and the tools being used. Embeddings are numerical representations of data (like text, images, or user behavior) generated by machine learning models. Traditional methods, such as training a model like Word2Vec or BERT from scratch, require offline batch processing and are not real-time. However, techniques like incremental updates, online learning, or dynamic embedding storage can enable real-time adjustments. For example, some models allow fine-tuning on new data without full retraining, while others rely on external systems to manage embeddings dynamically.

One practical way to update embeddings in real time is by using online learning models or vector databases. Online learning algorithms, such as those in fastText, can incrementally adjust embeddings as new data arrives. For instance, a recommendation system might track user interactions and update user/item embeddings immediately after each click. Vector databases like FAISS, Milvus, or Pinecone also support real-time updates by allowing new embeddings to be added or existing ones modified without rebuilding the entire index. For example, in a chat application, new user messages can be converted to embeddings on the fly and inserted into a database for instant similarity searches. These tools decouple the embedding generation process (which might still require a pre-trained model) from the storage and retrieval layer, enabling real-time flexibility.

However, real-time updates come with trade-offs. Embedding models that require heavy computation (e.g., transformer-based models) may not support instant parameter updates, so developers often combine static models with dynamic metadata. For example, a news aggregator might use a fixed NLP model to generate article embeddings but adjust recommendation scores in real time based on trending topics. Additionally, frequent updates can lead to inconsistencies if not managed carefully—such as stale cached embeddings or conflicting writes. Scalability is another concern: real-time systems need efficient pipelines to handle high-throughput updates. While possible, real-time embedding updates require careful architecture choices to balance speed, accuracy, and resource usage.