Introducing hybrid search: learn more and sign up for a waitlist!

Create a superior search experience

AI-driven hybrid search combines best-in-class full-text search and semantic search capabilities

Back to the Future

Feel-good Sci-Fi classics

Back to the Future II

1989

Back to the Future

1985

Back to the Future III

1990

Take full control of your search setup for optimal results

Adjust your semantic ratio to achieve the ideal balance between AI-powered semantic search and full-text search capabilities.

  • Full-text search ➜ illustration

    Full-text search ➜

    Best when you know precisely what to look for

  • Semantic search  ➜ illustration

    Semantic search ➜

    Ideal when searching for concepts instead of focusing solely on words

  • ✨ Hybrid search illustration

    ✨ Hybrid search

    Combine the best of both worlds

The easiest path to creating a top-notch search experience

    Generate AI embedders

    Generate vector embeddings using a third party, or submit your locally generated embeddings. - Open AI embedders - Provide own embeddings - Open source embedders (*coming soon)

    Prompting made better

    Add a prompt template of key information to help ingest prompts and help the model interpret relevant metadata from the get-go.

    NEW

    Configure your semantic ratio

    Choose where you want your results to fall on the spectrum from pure keyword-based to fully semantic search.

    Search enters your AI application stack

    Meilisearch comes with tailored SDKs for your favorite framework or language.

    from langchain.vectorstores import Meilisearch
    from langchain.embeddings.openai import OpenAIEmbeddings
    from langchain.document_loaders import JSONLoader


    
    # Load documents

    loader = JSONLoader(
    
    file_path="./movies.json",
    
    jq_schema=".[] | {id: .id, title: .title, overview: .overview}",
         text_content=False,
    )
    documents = loader.load()


    
    # Index documents
    embeddings = OpenAIEmbeddings()
    vector_store = Meilisearch.from_documents(documents=documents, embedding=embeddings)
    
    # Search
    query = "superhero fighting villains in a city corrupted by crime"
    results = vector_store.similarity_search(
    
    query=query,
    
    k=3,
    )

    18K USERS WORLDWIDE ARE USING MEILISEARCH TO POWER THEIR SEARCH EXPERIENCE.

    Why our customers choose Meilisearch Cloud

    Logo
    Bookshop customer spotlight

    Bookshop.org increased search-based purchases by 43%

    • A streamlined process that keeps the decision-makers involved
    • Quick iterative product cycle
    • Hosting and infrastructure maintenance in safe hands
    Logo
    HitPay customer spotlight

    HitPay streamlines customers’ search capabilities while staying laser-focused on its core offering.

    • Easy to integrate
    • Developer experience
    • Higher out-of-the-box relevancy
    Logo
    Qogita customer spotlight

    Qogita simplifies B2B trade with Meilisearch Cloud.

    • Cost-effectiveness
    • Developer experience
    • Agile product delivery

    Waitlist

    Eager to experience hybrid search?

    Sign up for a waitlist now!