Meilisearch latest news and company updates

Why traditional hybrid search falls short (and how we fixed it)
Meilisearch's innovative scoring system revolutionizes hybrid search by properly combining full-text and semantic search, delivering more relevant results than traditional rank fusion methods.

Introducing Meilisearch's next-generation indexer: 4x faster updates, 30% less storage
Indexer edition 2024 revolutionizes search performance with parallel processing, optimized RAM usage, and enhanced observability. See what's new in our latest release.

Meilisearch indexes embeddings 7x faster with binary quantization
By implementing binary quantization with the vector store Arroy, significant reductions in disk space usage and indexing time for large embeddings have been achieved while maintaining search relevance and efficiency.

Meilisearch is too slow
In this blog post, we explore the enhancements needed for Meilisearch's document indexer. We'll discuss the current indexing engine, its drawbacks, and new techniques to optimize performance.

How Meilisearch updates a database with millions of vector embeddings in under a minute
How we implemented incremental indexing in our vector store.

Meilisearch expands search power with Arroy's Filtered Disk ANN
How we implemented Meilisearch filtering capabilities with Arroy's Filtered Disk ANN

Multithreading and Memory-Mapping: Refining ANN performance with Arroy
Overcoming the challenges to enhance ANN performance with Rust.

Spotify-Inspired: Elevating Meilisearch with Hybrid Search and Rust
How we created Arroy, a Rust library building upon the foundations of Spotify's Annoy.

From ranking to scoring
Our journey to add relevancy scores to search results in Meilisearch.

Squeezing millions of documents in 128 TB of virtual memory
How dynamic management of virtual memory enabled us to remove limitations in Meilisearch indexing policy.