This tutorial will guide you through setting up a simple text search application. | docs.vespa.ai
At this point, we assume you have read our Text Search Tutorial and accomplished the following steps.| docs.vespa.ai
Introducing miniCOIL, a lightweight sparse neural retriever capable of generalization.| qdrant.tech
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 more effective search experience.| Vespa Blog
Improvements made to triple the query performance of lexical search in Vespa.| Vespa Blog
Disclaimers before we start For those who don’t want to read/don’t care that much, here are the results. I hope after seeing them you are compelled to read. TL;DR: I wrote a super fast phrase search algorithm using AVX-512 and achieved wins up to 1600x the performance of Meilisearch. The source code can be found here, and here is the crate. The contents of this blog post are inspired by the wonderful idea of Doug Turnbull from the series of blog posts about Roaringish. Here we will take t...| Gabriel’s Blog
ベクトルデータベース ベクトルデータベースとは、データ (文書、音声、画像など) をベクトルとして保存・検索するデータベースです。 ベクトル埋め込み ベクトル埋め込みとは、データ (文書、音声、画像など) の意味や関係性をベクトルに変換する...| ほげほげテクノロジー - IT 技術学習サイト
This document describes how to tune certain features of an application for high query serving performance,| docs.vespa.ai
[Updated on 2020-11-12: add an example on closed-book factual QA using OpenAI API (beta). A model that can answer any question with regard to factual knowledge can lead to many useful and practical applications, such as working as a chatbot or an AI assistant🤖. In this post, we will review several common approaches for building such an open-domain question answering system. Disclaimers given so many papers in the wild:| lilianweng.github.io