Tommaso Teofili
Principal Software Engineer I
Tommaso is a principal software engineer at Elastic working on vector search and generative AI. He is passionate about search and AI and enjoys doing research. In his spare time he likes running and going out in the nature with his family and friends.
17% faster search, zero config: auto-calibrating vector quantization in Elasticsearch
Automatic calibration at merge time picks vector quantization parameters for each segment by predicting recall from a small sample. Here's how we built it into Elasticsearch's merge path.
How Elasticsearch auto-tunes vector quantization to hit your recall target
Learn the geometric model that lets Elasticsearch predict recall with R² > 0.98 accuracy and auto-select vector quantization parameters from a small data sample.
Adaptive early termination for HNSW in Elasticsearch
Introducing a new adaptive early termination strategy for HNSW in Elasticsearch.
Early termination in HNSW for faster approximate KNN search
Learn how HNSW can be made faster for KNN search, using smart early termination strategies.