In the previous update,
we mentioned Vector Embeddings, Vespa Cloud Model Hub, Paged Attributes, ARM64 Support, and Result Highlighting.
Today, we’re excited to share the following updates:
Improved performance when using ANN and pre-filter
Since Vespa 8.78.45, multithreaded pre-filtering before running the
approximate nearest neighbor query operator is supported by using
in the rank-profile.
Multithreading can cut latencies for applications using pre-filtering,
where the filtering amounts to a significant part of the query latency.
Better hit estimates from parent document attributes
Applications can use parent/child to normalize data –
keeping fields common for many documents in a parent schema.
This simplifies updating such fields and makes the update use fewer resources with many children.
When using parent fields in matching,
one can use fast-search
for better performance by using a dictionary.
Since Vespa 8.84.14, a parent field with fast-search set will have a better hit estimate using the dictionary data.
The estimate is then used when creating the query plan to limit the candidate result set quicker,
resulting in lower query latency.
New XGBoost and LightGBM model training notebooks
Vespa supports gradient boosting decision tree (GBDT) models trained with
XGBoost and LightGBM.
To get you started, we have released two new sample notebooks for easy training of XGBoost and LightGBM models in
Vespa sample apps notebooks.
Linked from these is an exciting blog post series on using these models in Product Search applications.
Vespa Cloud on GCP
Vespa Cloud has been available in AWS zones since its start in 2019.
Now, we are happy to announce Vespa Cloud availability in Google Cloud Platform (GCP) zones!
To add a GCP zone to your application,
<region>gcp-us-central1-f</region> to deployment.xml.
See the announcement for more details.