Meilisearch latest news and company updates

Building a RAG system with Meilisearch: a comprehensive guide
Discover best practices for building a RAG system, with tips on optimizing documents, integrating AI, and why effective retrieval is key to success.

Beyond the hype: practical AI search strategies that deliver ROI
Unlock the power of AI-powered search for your SaaS business. Learn key features, budgeting tips, and implementation strategies to boost user engagement

Software engineering predictive search: a complete guide
Learn how to implement predictive search in your software applications. Discover key concepts, optimization techniques, and real-world examples.

Searching across multiple languages
Discover how easy it can be to implement advanced multilingual search and give your users the seamless, relevant results they deserve—regardless of language.

How to add AI-powered search to a React app
Build a React movie search and recommendation app with Meilisearch's AI-powered search.

Choosing the best model for semantic search
A comparison of model performance, cost, and relevancy in regard to building semantic search.

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

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.

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.

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.