TL;DR 我们很高兴在 Manticore Search 中引入两个重要新功能：模糊搜索和查询建议（或“自动补全”）。这些功能提升了搜索能力，提供更友好的用户体验。您可以在我们的开源 GitHub Issue Search Demo 中看到它们的实际效果。 了解 GitHub 演示 中的工作原理 探索开源项目： GitHub 演示仓库 引言 您可能 已经阅读 过关于 GitHub Issue Search 演示以及我们 如何为其添加语义搜索 的内容。最近，我们为其添加了自动 ...
This article presents a prototype that enhances GitHub's search functionality using semantic search technology with Manticore Search's Vector Search. It highlights the limitations of traditional keyword searches and introduces vector search as a more effective method that understands context and meaning, rather than just matching keywords. The piece outlines ...
Replication: creating a cluster, connecting a node, changing table settings and applying them to old documents.
In this article, we will examine the purpose of Manticore Columnar storage, how it differs from the row-wise storage, and in which cases it makes sense to use it. We will also get acquainted with the basic structure of the storage format and the specifics of its integration into the query processing workflow of the search daemon.
Full-text search matches exact keywords and is fast and precise, especially for structured queries. It can also handle features like fuzzy matching, stemming, and prefix/infix searches. Vector search, also known as semantic search, uses machine learning to understand the meaning behind words, making it great for open-ended or natural language queries. While ...
Full-text search and semantic search are two powerful approaches in modern information retrieval. Full-text search excels in comprehensive content scanning and keyword matching, using techniques like inverted indexes and relevance scoring. Semantic search, leveraging natural language processing and machine learning, shines in understanding contextual meaning ...