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

Use the power of
AI to create more meaningful search experiences

Entering the new frontier of information retrieval with vector search.

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Fitting seamlessly in the current AI landscape

Integrated into the familiar set of tools.

Meilisearch connecting with OpenAI, Cohere, LangChain and Hugging Face

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

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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
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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
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Qogita customer spotlight

Qogita simplifies B2B trade with Meilisearch Cloud.

  • Cost-effectiveness
  • Developer experience
  • Agile product delivery
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