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Quick Start with Logstash: from data to Vespa schema

Fastest way to get your data into Vespa. Logstash generates the schema. Then deploys the application package to Vespa. Next Logstash run does the actual writes.

Introducing Document Enrichment with Large Language Models in Vespa

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 search relevance and create a...

Vespa Newsletter, April 2025

Advances in Vespa features and performance include Lexical Search Query Performance, Pyvespa Relevance Evaluator, Global-phase rank-score-drop-limit, and Compact tensor representation.

Introducing Vespa Voice — Your Signal for What’s Next in AI-Driven Search Infrastructure

Introducing Vespa Voice: a podcast on AI infrastructure, hybrid search, and RAG.

Tripling the query performance of lexical search

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Tripling the query performance of lexical search

Improvements made to triple the query performance of lexical search in Vespa.

Modernize your retrieval pipeline with ModernBERT and Vespa

Learn how the ModernBERT backbone model paves the way for more efficient and effective retrieval pipelines, and how to use ModernBERT in Vespa.

Advanced Video Retrieval at Scale: A Quick Start Using Vespa and TwelveLabs

A guide on implementing advanced video retrieval at scale using Vespa and TwelveLabs' multi-modal embedding models.

AI in Insurance with Vespa.ai

The evolution of language models combined with state-of-the-art information retrieval is reshaping the insurance landscape.

Vespa Newsletter, January 2025

Advances in Vespa features and performance include Pyvespa Querybuilder, Vespa input/output plugins for Logstash, ModernBERT models, and Vespa CLI multi-get.

Vespa with Logstash Recipes

Tutorials on feeding data to Vespa from CSV files, PostgreSQL, Kafka, Elasticsearch and another Vespa.

Architecture Inversion: Scale By Moving Computation Not Data

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Architecture Inversion: Scale By Moving Computation Not Data

Have you ever wondered how the world’s largest internet and social media companies can deliver algorithmic content to so many users so fast?

Shrinking Embeddings for Speed and Accuracy in AI Models

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Shrinking Embeddings for Speed and Accuracy in AI Models

How MRL and BQL Make AI-Powered Representations Efficient