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](https://www.meilisearch.com/docs/learn/what_is_meilisearch/overview?utm_campaign=vector-search&utm_source=blog&utm_medium=launch#features)_ 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](https://www.meilisearch.com/docs/learn/experimental/vector_search?utm_campaign=vector-search&utm_source=blog&utm_medium=launch),_ 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.

Democratizing search
--------------------

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](/blog/hugging-face-democratizes-ai/ 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.”_

Building the future of search with vector search
------------------------------------------------

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

Search evolution culminates in a hybrid search
----------------------------------------------

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
---------------

<Callout>
  For more guidance on how to get started, explore our [step-by-step tutorial on building semantic search with LangChain](/blog/langchain-semantic-search-tutorial/?utm_campaign=vector-search&utm_source=blog&utm_medium=launch)
</Callout>
```json
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,
)

```

If you want to use Meilisearch vector search capabilities without LangChain, choose your favorite language and check out the [relevant SDK documentation here](https://www.meilisearch.com/docs/learn/what_is_meilisearch/sdks?utm_campaign=vector-search&utm_source=blog&utm_medium=launch).

Want the quality of open-source without the headache of managing your servers and service? [Sign up for a free trial](https://meilisearch.com/cloud?utm_campaign=vector-search&utm_source=blog&utm_medium=launch) with Meilisearch Cloud today.

Introducing Meilisearch's vector search and vector database to navigate the future of search

Vector search enables efficient retrieval of objects sharing similar characteristics. This AI-powered search technique uses embedding vectors. These vectors are mathematical representations of objects generated by machine learning models (like LLMs). Starting with the 1.3 release, Meilisearch supports storing and searching vectors.

<Callout>
  Meilisearch v1.3 is out! Read the [release notes](/blog/v1-3-release/?utm_campaign=vector-search&utm_source=blog&utm_medium=announcement)
</Callout>

Powering the Future of Search
-----------------------------

Vector search might be the dawn of a new era for search. Its use cases are numerous. In [ecommerce](https://ecommerce.meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=announcement), it enables offering recommendations for similar products. It also allows building multi-modal search, like image or audio search. Building upon conversational AI technologies enables the creation of Q&A applications. Combining vector search with user-provided information like geolocation and search history can power even more contextual search experiences.

Vector search is also the foundation for [semantic search](https://en.wikipedia.org/wiki/Semantic_search), which aims to understand the query’s meaning. Conversely, traditional lexical search only matches keywords. With semantic search, a `warm clothes` query could give results like `gloves`, `coat`, and more results related to winter clothing.

Vector search unlocks a world of new capabilities for search. Take a look at what some users have already implemented:

*   [similar videos recommendations](https://github.com/adrienpoly/rubyvideo/pull/19) on a site listing Ruby conferences
*   a [documentation chatbot](https://blazy-chat.vercel.app/) proof of concept using [GPT3.5](https://platform.openai.com/docs/models/gpt-3-5) and [LangChain](https://github.com/hwchase17/langchain)

<Callout>
  Meilisearch is available as a [LangChain vector store](https://python.langchain.com/docs/integrations/vectorstores/meilisearch).
</Callout>

Getting Started with Meilisearch Vector Store
---------------------------------------------

  
Starting with v1.3, you can use Meilisearch as a vector store. Meilisearch allows you to store vector embeddings alongside your documents conveniently. You will need to create the vector embeddings using your third-party tool of choice ([Hugging Face](https://huggingface.co/), [OpenAI](https://openai.com/)).

First, spin up a Meilisearch instance. You can [install Meilisearch locally](https://www.meilisearch.com/docs/learn/getting_started/installation?utm_campaign=vector-search&utm_source=blog&utm_medium=announcement#local-installation) or create a [Meilisearch Cloud](https://www.meilisearch.com/cloud?utm_campaign=vector-search&utm_source=blog&utm_medium=announcement) account.

Then, enable the _vector store_ experimental feature:

```Bash
curl \
  -X PATCH 'http://localhost:7700/experimental-features/' \
  -H 'Content-Type: application/json'  \
  --data-binary '{
    "vectorStore": true
  }'
```
<Callout>
  This guide uses \`curl\` to make HTTP requests to communicate with Meilisearch. In practice, we recommend using our [SDKs](https://github.com/meilisearch/integration-guides) instead.
</Callout>

Meilisearch now accepts a `_vector` field in your documents. Use it to store the vector embeddings corresponding to your document in this field.

```Bash
curl -X POST -H 'content-type: application/json' \
     'localhost:7700/indexes/songs/documents' \
     --data-binary '[
         { "id": 0, "_vectors": [0, 0.8, -0.2], "title": "Across The Universe" },
         { "id": 1, "_vectors": [1, -0.2, 0], "title": "All Things Must Pass" },
         { "id": 2, "_vectors": [0.5, 3, 1], "title": "And Your Bird Can Sing" }
     ]'
```

After storing your vectorized documents in Meilisearch, you can query them using the `search` or `multi-search` route. To do this, you need to compute the vector of your query (using a third-party tool) and send it in the `vector` field.

```Bash
curl -X POST -H 'content-type: application/json' \
   'localhost:7700/indexes/songs/search' \
   --data-binary '{ "vector": [0, 1, 2] }'
```

When using vector search, returned documents include a `semanticScore` field:

```Bash
{
  "hits": [
    { "id": 0, "_vectors": [0, 0.8, -0.2], "title": "Across The Universe", "_semanticScore": 0.6754 },
    { "id": 1, "_vectors": [1, -0.2, 0], "title": "All Things Must Pass", "_semanticScore": 0.7546 },
    { "id": 2, "_vectors": [0.5, 3, 1], "title": "And Your Bird Can Sing", "_semanticScore": 0.78 }
  ],
  "query": "",
  "vector": [0, 1, 2],
  "processingTimeMs": 0,
  "limit": 20,
  "offset": 0,
  "estimatedTotalHits": 2
}

```
<Callout>
  This API is experimental. You can help us improve it by sharing your feedback in [this Github discussion](https://github.com/meilisearch/product/discussions/677?utm_campaign=vector-search&utm_source=blog&utm_medium=textlink).
</Callout>

A First Step Toward Semantic Search
-----------------------------------

Vector search is our first step towards semantic search. But our long-term goal is to provide hybrid search—combining the benefits of full-text and semantic search to provide the most relevant search experience. Clément Renault, founder and CTO of Meilisearch, shared his thoughts on Github about [exploring semantic search](https://github.com/meilisearch/meilisearch/issues/3838?utm_campaign=vector-search&utm_source=blog&utm_medium=textlink) — read for a founder’s perspective. We can’t wait to share more with you!

Drop your email below to learn more about our progress with AI-powered search. We’ll keep you posted on all updates regarding vector search & semantic search.

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We’re excited to walk our first steps toward semantic search. We can’t wait to hear your thoughts on integrating Meilisearch as a vector store. You can give your feedback in [this Github discussion](https://github.com/meilisearch/product/discussions/677?utm_campaign=vector-search&utm_source=blog&utm_medium=textlink).

You can stay in the loop by subscribing to our [newsletter](https://meilisearch.us2.list-manage.com/subscribe?u=27870f7b71c908a8b359599fb&id=79582d828e&utm_campaign=vector-search&utm_source=blog&utm_medium=textlink). To learn more about Meilisearch's future and help shape it, look at our [roadmap](https://roadmap.meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=textlink) and participate in our [Product Discussions](https://github.com/meilisearch/product/discussions/?utm_campaign=vector-search&utm_source=blog&utm_medium=textlink).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=announcement).

I’ll see you there.

Meilisearch latest news and company updates

Introducing Meilisearch's vector search and vector database to navigate the future of search

Vector storage is coming to Meilisearch to empower search through AI