Company news Introducing hybrid search: combining full-text and semantic search for optimal balance Meilisearch's AI journey began last summer with vector search and storage. Today, we unveil hybrid search with autogenerated embedders, advancing our AI capabilities.
Company news Meilisearch February Updates 🥳 Join our launch event Join us on March 7th to discuss our recent releases and get a sneak peak of what’s coming on Meilisearch Cloud. Spoilers—hybrid search, new
State of search What are vector embeddings? In machine learning and AI, vector embeddings are a way to represent complex data, such as words, sentences, or even images as points in a vector space, using vectors of real numbers.
State of search What is a vector database? Vector databases are specialized systems to store, manage, and query data in the form of vector embeddings. They are optimized for similarity search, which involves finding the most similar items to a given query vector.
Release Cloud monitoring metrics have arrived! Get an overview of your Meilisearch project's health and swiftly tackle any issue.
Company news Meilisearch January Updates Your monthly dose of Meilisearch updates. Januar 2024 edition.
State of search Meilisearch vs Typesense Comparing the key features between open-source search engines Meilisearch and Typesense.
Release Meilisearch v1.6 Meilisearch 1.6 brings hybrid search and significant advancements in indexing speed.
Company news Meilisearch December Updates Your monthly dose of Meilisearch updates. December 2023 edition.
Engineering Meilisearch expands search power with Arroy's Filtered Disk ANN How we implemented Meilisearch filtering capabilities with Arroy's Filtered Disk ANN
