Document enrichment with LLMs can be used to transform raw text into structured form and expand it with additional contextual information. This helps to improve...
We are thrilled to announce significant updates to Vespa’s support for inference with text embedding models that maps texts into vector representations.
Announcing global-phase re-ranking support in Vespa, unlocking efficient re-ranking with precise cross-encoder models. Cross-encoder models minimize distraction in retrieval-augmented completions generated by Large Language Models....
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....
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