All Stories

Improve throughput and concurrency with HTTP/2

The Vespa HTTP container now accepts HTTP/2 with TLS enabled. Learn how this improves HTTP throughput and efficiency, and how to get started using HTTP/2.

Pretrained Transformer Language Models for Search - part 4

This is the fourth blog post in a series of posts where we introduce using pretrained Transformer models for search and document ranking with Vespa.ai.

Build a basic text search application from python with Vespa: Part 2

Evaluate search engine experiments from python.

Pretrained Transformer Language Models for Search - part 3

This is the third blog post in a series of posts where we introduce using pretrained Transformer models for search and document ranking with Vespa.ai.

Pretrained Transformer Language Models for Search - part 2

This is the second blog post in a series of posts where we introduce using pretrained Transformer models for search and document ranking with Vespa.ai.

Vespa Product Updates, May 2021

Advances in features and performance include new int8 and bfloat16 tensor cell types, compact tensor feed format, Approximate Nearest Neighbor using Hamming distance, hash-based attribute dictionaries and case-sensitive attribute search...

Build a News recommendation app from python with Vespa: Part 3

Part 3 - Efficient use of click-through rate via parent-child relationship.

Pretrained Transformer Language Models for Search - part 1

This is the first blog post in a series of posts where we introduce using pretrained Transformer models for search and document ranking with Vespa.ai.

Build sentence/paragraph level QA application from python with Vespa

Retrieve paragraph and sentence level information with sparse and dense ranking features.

Vespa Product Updates, March 2021

Advances in Vespa features and performance last month include mass update/delete in /document/v1/, improved memory usage, OR-to-WeakAnd and better full node protection.

Build a News recommendation app from python with Vespa: Part 2

Part 2 - From news search to news recommendation with embeddings.