At Meilisearch, we have long maintained our commitment to simplifying and streamlining the search process by prioritizing intuitive, user-friendly, and human-like search experiences.

In today's search technology landscape, keyword search continues to play a pivotal role, swiftly locating content via specific keywords. Leveraging advanced algorithms and robust indexing, it ensures users find relevant information across vast data volumes.

However, traditional keyword approaches have inherent limitations in understanding user intent and at times can be overly predictable and simplistic. This is why we are introducing Meilisearch's vector search, which aims to challenge and enhance the familiar keyword search paradigm.

Vector search for AI-enhanced future

The market-wide introduction of vector search marks the arrival of a new era for search technology, with countless versatile applications. In e-commerce, it offers product recommendations based on similarities and multi-modal search capabilities such as image, audio, and video search. The fusion of vector search with conversational AI technologies opens doors for intuitive Q&A applications. Adding user data like geolocation and search history further expands its potential.

New search technology enhances relevancy alongside traditional keywords with semantic understanding going beyond simple keyword matching. It is capable of comprehending intent, returning results like [gloves] from a [warm clothes] query. These applications rely on vector embeddings—a type of data representation with semantic information that enables AI to gain understanding and maintain long-term memory. Stored in vector databases, embeddings represent different data dimensions, essential for understanding patterns and relationships. AI-powered vector database then leverages this information when matching search queries to answers.

In a data-rich world, effective content discovery is not a luxury - it’s crucial. Meilisearch's vision balances ambition with accessibility, aiming to make cutting-edge search technology inclusive. Key Meilisearch partners, such as Hugging Face, are already actively testing Meilisearch’s new vector database and storage capabilities.

Quentin de Quelen, Meilisearch’s CEO and Co-founder, said: “Meilisearch's hybrid search will democratize advanced search technology, focusing on simplicity and open-source customizability.”

This release brings semantic and hybrid search capabilities to enhance applications for image, video, and audio search, product recommendations for e-commerce, precise personalization, and intuitive Q&A interactions.

Here are a few examples of potential applications made possible with the introduction of vector search:

  • Semantic understanding: Enable your search engine to act as a human-like assistant, delivering natural, context-aware results in a more conversational manner
  • Multiformat capabilities: Cover images, audio, and video, ensuring versatility.
  • Personalization: Matches preferences for tailored results, with a deeper understanding of user behavior and preferences,
  • Precise Recommendations: Enhance the discovery experience by suggesting similar items that align with users' genuine interests.
  • Improved accuracy: Improve relevancy and search accuracy through a deep understanding of each search query's meaning

Meilisearch's journey leads to hybrid search, which combines traditional full-text efficiency with the innovation of semantic search and the adaptability of vector database. The hybrid approach enables search solutions to cater to a broad spectrum of use cases driven by customer-generated data sources. By integrating models from AI solution providers like OpenAI, Cohere AI, or Hugging Face, Meilisearch empowers users to calibrate vector embeddings and infuse their business logic into the search engine.

Getting started

For more guidance on how to get started, explore our step-by-step tutorial on building semantic search with LangChain.
from langchain.vectorstores import Meilisearch
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import JSONLoader

# Load documents
loader = JSONLoader(
    jq_schema=".[] | {id: .id, title: .title, overview: .overview}",
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(

If you want to use Meilisearch vector search capabilities without LangChain, choose your favorite language and check out the relevant SDK documentation here.

Want the quality of open-source without the headache of managing your servers and service? Sign up for a free trial with Meilisearch Cloud today.