Search is one of the most important features in modern applications. Users expect instant answers, useful suggestions, and results that match their intent even when they make spelling mistakes. Most traditional search systems struggle to deliver this experience without complex and heavy infrastructure. Meilisearch is an open-source search engine that changes this by offering a fast and developer-friendly engine that is easy to set up and extend. Meilisearch When you combine Meilisearch with AI models for natural language understanding and semantic relevance, you can build a powerful and intuitive search experience that feels modern and intelligent. semantic relevance This article explains how Meilisearch works, how to set it up, and how to integrate AI models to deliver better relevance and better ranking. You will also see how hybrid search and semantic embeddings work, and how to deploy Melisearch to the cloud using Sevalla. What we will Cover Understanding Meilisearch
Setting Up Meilisearch
Using Hybrid Search and AI Together
Using AI To Rewrite Queries
Deploying Meilisearch to the Cloud using Sevalla
Conclusion Understanding Meilisearch Setting Up Meilisearch Using Hybrid Search and AI Together Using AI To Rewrite Queries Deploying Meilisearch to the Cloud using Sevalla Conclusion Understanding Meilisearch Meilisearch is a lightning-fast search engine that fits easily into any application. It is built in Rust and designed to deliver results in less than fifty milliseconds. It supports semantic search, hybrid search, typo tolerance, sorting, geosearch, and extensive language support. Melisearch also provides a clean RESTful API and a wide range of SDKs that make integration easy with JavaScript, Python, Go, PHP, Ruby, Rust and many other languages. You can try Meilisearch by exploring the official demos. These demos show how Meilisearch is not limited to one specific use case but can fit many types of workflows. official demos Meilisearch supports two editions. The Community Edition is fully open source under the MIT license and can be used freely even for commercial products. The Enterprise Edition introduces features like sharding and is governed by a commercial license. You can deploy Meilisearch yourself or choose Meilisearch Cloud, which handles hosting, updates, monitoring and analytics without requiring server maintenance. sharding Setting Up Meilisearch Starting Meilisearch is simple. You can download the binary and run it directly or you can start it through Docker. Running through Docker is the fastest way to test it on your machine. docker run -it --rm -p 7700:7700 getmeili/meilisearch:latest docker run -it --rm -p 7700:7700 getmeili/meilisearch:latest Once the server is running, you can communicate with it using HTTP. The simplest use case is indexing documents into an index and querying them. Here is an example using Python. import requests

docs = [
    {"id": 1, "title": "AI in Finance", "text": "How AI is changing banks"},
    {"id": 2, "title": "AI in Law", "text": "How AI helps legal teams"},
]
requests.put(
    "http://localhost:7700/indexes/articles/documents",
    json=docs
) import requests

docs = [
    {"id": 1, "title": "AI in Finance", "text": "How AI is changing banks"},
    {"id": 2, "title": "AI in Law", "text": "How AI helps legal teams"},
]
requests.put(
    "http://localhost:7700/indexes/articles/documents",
    json=docs
) Searching is just as easy. import requests

query = "ai banks"
res = requests.post(
    "http://localhost:7700/indexes/articles/search",
    json={"q": query}
)
print(res.json()) import requests

query = "ai banks"
res = requests.post(
    "http://localhost:7700/indexes/articles/search",
    json={"q": query}
)
print(res.json()) The engine returns results in milliseconds. Typo tolerance, word proximity and relevance ranking work out of the box. Meilisearch automatically handles synonyms if you configure them and allows custom sorting rules for attributes like price or date. It also supports faceting, filters and geosearch, which makes it suitable for e-commerce, travel apps, real estate listings and data-heavy dashboards. Using Hybrid Search and AI Together Hybrid search combines full-text search with semantic vector search. Meilisearch supports semantic search through vector fields and enables the combination of both approaches. This helps you serve users who type vague queries or natural language questions. The AI model provides embeddings that capture meaning, while Meilisearch handles the fast retrieval and ranking. embeddings To add semantic search, you first generate embeddings for your documents using an AI model. Here is a simple example using OpenAI embeddings. from openai import OpenAI
import requests

client = OpenAI()
def embed(text):
    out = client.embeddings.create(
        model="text-embedding-3-small",
        input=text
    )
    return out.data[0].embedding
doc = {
    "id": 3,
    "title": "AI in Insurance",
    "text": "How AI powers underwriting",
    "vector": embed("How AI powers underwriting")
}
requests.put(
    "http://localhost:7700/indexes/articles/documents",
    json=[doc]
) from openai import OpenAI
import requests

client = OpenAI()
def embed(text):
    out = client.embeddings.create(
        model="text-embedding-3-small",
        input=text
    )
    return out.data[0].embedding
doc = {
    "id": 3,
    "title": "AI in Insurance",
    "text": "How AI powers underwriting",
    "vector": embed("How AI powers underwriting")
}
requests.put(
    "http://localhost:7700/indexes/articles/documents",
    json=[doc]
) When the user searches, you embed the query and compute similarity. You can mix this with keyword search results returned from Meilisearch. The combined ranking creates a better experience than either approach alone. Using AI To Rewrite Queries Users often type incomplete or unstructured queries. They might type “How does AI help banks?” instead of keywords. You can use an AI model to rewrite this into something more search-friendly. The rewritten query produces better results in Meilisearch while still respecting the user’s original intent. from openai import OpenAI
import requests

client = OpenAI()
def rewrite_query(user_query):
    prompt = f"Rewrite this for search: {user_query}"
    out = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    )
    return out.choices[0].message.content
user_query = "how does ai help banks"
rewritten = rewrite_query(user_query)
res = requests.post(
    "http://localhost:7700/indexes/articles/search",
    json={"q": rewritten}
)
print(res.json()) from openai import OpenAI
import requests

client = OpenAI()
def rewrite_query(user_query):
    prompt = f"Rewrite this for search: {user_query}"
    out = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": prompt}]
    )
    return out.choices[0].message.content
user_query = "how does ai help banks"
rewritten = rewrite_query(user_query)
res = requests.post(
    "http://localhost:7700/indexes/articles/search",
    json={"q": rewritten}
)
print(res.json()) This pattern improves accuracy for question-based searches across blogs, documentation platforms and knowledge bases. You can also use the model to normalise queries, remove ambiguous text, expand synonyms and fix spelling mistakes before they reach Meilisearch. Deploying Meilisearch to the Cloud using Sevalla You can deploy Meilisearch anywhere. It runs on a small server, a local machine or inside containers. Self-hosting gives you full control and is usually preferred by technical teams who want to keep sensitive data in-house. You can choose any cloud provider, like AWS, DigitalOcean, or others to set up Melisearch. But I will be using Sevalla. Sevalla is a PaaS provider designed for developers and dev teams shipping features and updates constantly in the most efficient way. It offers application hosting, database, object storage, and static site hosting for your projects. Sevalla I am using Sevalla for two reasons: Every platform will charge you for creating a cloud resource. Sevalla comes with a $50 credit for us to use, so we won’t incur any costs for this example.
Sevalla has a template for Melisearch, so it simplifies the manual installation and setup for each resource you will need for installation. Every platform will charge you for creating a cloud resource. Sevalla comes with a $50 credit for us to use, so we won’t incur any costs for this example. Sevalla has a template for Melisearch, so it simplifies the manual installation and setup for each resource you will need for installation. template for Melisearch Log in to Sevalla and click on Templates. You can see Melisearch as one of the templates. Click Deploy. Log in You will see the resources being provisioned for the application. Once the deployment is complete, click on “Visit app”. You now have a production-grade Melisearch server running on the cloud. You can use this to set up indexes for your database and use the JavaScript or other SDKs to interact with Melisearch. Here is the full list of apis provided by Melisearch. full list of apis Limitations of Melisearch Meilisearch is designed for speed and simplicity, but these strengths also create some limitations. It is not built for heavy enterprise-scale clustering unless you use the paid Enterprise Edition, and the Community Edition lacks native sharding or high-availability setups. Large indexes with millions of documents can consume significant memory because of Meilisearch’s in-memory architecture, which may require careful tuning or higher system resources. Meilisearch’s ranking rules are powerful but not as configurable as engines like Elasticsearch, which support more complex scoring logic and advanced query DSLs. Vector search support exists, but it is still evolving and may not match specialised vector databases for tasks like large-scale semantic retrieval or multi-embedding workflows. Meilisearch is excellent for most app-level search needs, but teams building massive, distributed, analytics-heavy search systems may find these constraints important to consider. Conclusion Meilisearch gives you a fast and elegant base for search. AI models add understanding and personalisation. When these two work together, you get a search experience that feels instant, adaptive and intelligent. You can start small with keyword search and then add query rewriting, embeddings, hybrid search and reranking. You can also use suggestions, synonyms and filters to improve the user journey. With its simple API, wide language support and strong ecosystem, Meilisearch makes it easy to build a search that feels right at home in any modern app. Hope you enjoyed this article. Find me on Linkedin or visit my website. Hope you enjoyed this article. Find me on Linkedin Linkedin or visit my website. visit my website

How to Build an AI-Driven Search Experience using Meilisearch and Sevalla

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