Semantic search vs Vector search: Key differences, uses, & more

Elasticsearch vs Typesense (vs Meilisearch): Which search engine actually fits your needs in 2025?

We’ve been handed an exciting dream—and it hasn't quite lived up to expectations. 

With the rapid growth of AI, big names like Google promised a revolution in how we discover information. 

Search was meant to become faster, smarter, and more tailored to us. 

Yet, here we are, still wading through unrelated suggestions and one-size-fits-all recommendations. 

Personalized search? Perhaps more like personalized guesswork. 

So, what went wrong? Let’s explore it together. 

## What most "personalized" search experiences actually do

Every rose has its thorn. And so do personalized search engines. 

What’s sold under the sweet sauce of “customization” is often just a simple data retrieval. 

And even then, chances are you won’t get what you’re looking for. 

Why does this happen?

Here are a few reasons:

### Reason #1: Words set in stone

Personalized search engines often follow a hard-coded word order, preconditioning the final results. 

The [Prozis e-commerce company](https://www.prozis.com/) search engine relies on this rule to search for products. 

For instance, if we search for “nut butter for keto diet,” the results are ordered as “hazelnut butter,” “pistachio butter,” and “almond butter.” 

![Prozis e-commerce company search engine.png](https://unable-actionable-car.media.strapiapp.com/Prozis_e_commerce_company_search_engine_b24a69f8dd.png)

Image Source: [Prozis](https://www.prozis.com/)

But here’s the problem.

Hazelnut and pistachio butters, listed as first and second, are not keto-friendly, while almond butter, which actually is ketogenic, comes in third. 

What you see here is relevance ranking. 

The order of words predetermines and prioritizes the search results that closely match the query. 

Yet, something crucial is missing in this case: user intent. 

The next point on our list is about the same issue. 

### Reason #2: All eggs in one basket 

Oftentimes, it’s not the word order that creates stumbling blocks in personalized search. 

It’s an oversimplified user segmentation. 

Personalized search engines often group users into broad categories (e.g., “tech shoppers”, “outdoors enthusiasts”). 

And let’s be honest, it’s easier that way. 

However, the outputs we get are inaccurate and irrelevant recommendations. 

After all, there are nuances in everything. Let’s say two people are outdoor enthusiasts; one loves hiking, but the other hates getting on the trail and prefers luxurious glamping. 

Both belong to the same group. But are they really the same?

Streaming services are guilty of this, and [Netflix](https://www.netflix.com/) is a good example. 

Upon registration, you must choose three movies/TV shows you like. 

![Netflix collecting user preferences.png](https://unable-actionable-car.media.strapiapp.com/Netflix_collecting_user_preferences_3e0362f5b3.png)

Image Source: [Netflix](https://www.netflix.com/)

If you go for a selection of medical shows and series about law and law enforcement, guess what you’ll get. 

![Netflix top pics for you.png](https://unable-actionable-car.media.strapiapp.com/Netflix_top_pics_for_you_ec86072c2b.png)

Image Source: [Netflix](https://www.netflix.com/)

But do these three picks truly reflect *all *of your viewing preferences?

Hardly. 

Yes, you might like some shows about law enforcement, but you might also hate watching *Dexter*—a crime show—because it’s too gruesome. 

That’s not all, though. 

If you look closer at the shows suggested in the image above, you’ll see that some of them have been around for a while (like *House, Prison Break, *or* Suits)*, and you may have seen them already.  

Thus, the Netflix algorithm is also guilty of the filter-bubble effect, meaning you won’t be exposed to anything except what you already engage with.

### Reason #3: Money talks

Personalization implies prioritization. 

In our case, the prioritization of the user’s search intent and their interests. 

Most search engines claim they honor this priority. 

But not before they honor their obligations to the advertisers. 

Many online marketplaces follow this logic, and [AliExpress](https://www.aliexpress.us) is a good example. 

When you search for a product, the platform gives you various options, but not until it shows you two rows worth of paid ads. 

![AliExpress product search.png](https://unable-actionable-car.media.strapiapp.com/Ali_Express_product_search_fef4e07fee.png)

Image Source: [AliExpress](https://www.aliexpress.us)

To be fair, marketplaces like AliExpress need to work with advertisers to sustain their business. 

However, overrunning the search results with ads contradicts AliExpress’s claim about curating “a selection of products tailored specifically to you,” as they claim at the bottom of their homepage. 

You can’t expect a user to be satisfied with product recommendations after scrolling through several rows of ads. 

That’s just not what personalization is about. 

## What is true personalization?

The answer seems simple: personalization is about deeply understanding the user's needs. 

The actual matter is, of course, more complex. 

The biggest obstacle to personalized search is analyzing massive amounts of user data to deliver the most relevant search results. 

Brands and marketplaces heavily rely on AI for this task, but it’s not always the answer. 

After all, AliExpress [uses AI to personalize search results](https://www.alibabacloud.com/blog/alibaba-cloud-ai-search-solution-explained-intelligent-search-driven-by-large-language-models-helping-enterprises-in-digital_601971), but its system is far from perfect. 

AI can be a solid foundation for personalizing search results, but this foundation only maintains its integrity if it stands on the following five pillars. 

### Pillar #1: Intent-based personalization

Personalization happens when the user and the service provider have a complete rapport. 

In our case, the key to building this rapport (since a search engine is a machine) lies in Natural Language Processing, or NLP. 

NLP algorithms use [semantic network models](https://www.meilisearch.com/blog/semantic-search) to understand user intent, which makes personalization possible.

How does it work?

Essentially, semantic search targets the context behind words and analyzes structured data to understand the semantic meaning of the query. 

Amazon’s A9 search engine is probably the best example of this model in action.

Let’s take the key phrase *book emotional intelligence toddler. *

The keyword here is *toddler*, meaning we are not searching for an emotional intelligence guide for adults. 

The search engine picks up on that and shares exactly what we need: several book options for kids to read. 

![Amazon book search.png](https://unable-actionable-car.media.strapiapp.com/Amazon_book_search_d9ec09968e.png)

Image Source: [Amazon](https://www.amazon.com/)

We also tested our previous peanut butter example, and Amazon’s search engine gave only keto-friendly products as top results. 

![Amazon peanut butter search.png](https://unable-actionable-car.media.strapiapp.com/Amazon_peanut_butter_search_9969618bac.png)

Image Source: [Amazon](https://www.amazon.com/)

But do you still get [personalized recommendations](https://www.meilisearch.com/blog/personalization-recommendations) if there’s a typo in your query?

Let’s talk about it next. 

### Pillar #2: Typo tolerance

It’s hardly a pleasant experience to have to retype your query simply because you made a typo and got the wrong results. 

Search engines use the [latent semantic indexing method](https://www.meilisearch.com/blog/latent-semantic-indexing) of information retrieval to minimize such occurrences. 

LSI is robust against typos because it relies on semantic meaning to understand hidden concepts. 

And that’s precisely what you need, especially if you have a wide-range product offering. 

[Minipouce](https://www.minipouce.fr/) is a good example. 

Below, we intentionally made a typo in the word *ring *but still got relevant results. 

![Minipounce typo in search.png](https://unable-actionable-car.media.strapiapp.com/Minipounce_typo_in_search_9add534c79.png)

Image Source: [Minipouce](https://www.minipouce.fr)

Many products in the company’s catalog have similar names, which only increases the chance of making a typo. So Minipouce [used Meilisearch’s open-source version](https://www.meilisearch.com/blog/minipouce-top-grade-customer-experience) to optimize the product search for relevant results every time. 

### Pillar #3: Real-time adaptive learning

The results for a query should change dynamically during the interaction between the user and the search engine rather than simply relying on static filters. 

Why?

Interaction of any kind is never static.

A user’s preferences and behavior may change while searching for the right product or information, and true personalization must take this into account. 

That’s why it’s essential for a search engine to employ real-time adaptive learning. See how it works in a Meilisearch-powered search engine below.

![dynamic search with AI.gif](https://unable-actionable-car.media.strapiapp.com/dynamic_search_with_AI_b553f2e7b8.gif)

How does it work?

It’s a complex process, but we can break it down to three main steps:

* **Collecting and analyzing user data.** A search engine first collects information on user searches and related interactions to lay the ground for future personalization. 
* **Adjusting the algorithm.** The collected data and its analysis impact the ranking, related searches, and other search algorithm elements to provide the results based on particular intent. 
* **Adapting the results.** As a user further interacts with a search engine, it adapts its behavior to generate and prioritize relevant results to suit the user’s changing preferences. 

Of course, user behavior is not the only factor impacting the relevance of search results. 

The content a search engine is working with also matters. 

Consequently, so does the information retrieval method it uses. 

Let’s talk about it next. 

### Pillar #4: Correct information retrieval method

Information retrieval (IR) refers to the process of finding and retrieving relevant data. 

This process consists of three steps, which you can see below. 

* **Step #1:** Analyzing unstructured data, such as web pages and text documents. 
* **Step #2:** Indexing the data to make it structured, then processing the query to match the index. 
* **Step #3:** Ranking the query results based on relevance. 

![Process behind information retrieval methods.png](https://unable-actionable-car.media.strapiapp.com/Process_behind_information_retrieval_methods_3bdeb3ac9b.png)

You are already familiar with some IR methods, such as LSI mentioned above. 

And, as you may have noticed, they interpret the content a bit differently. 

[Semantic search](https://www.meilisearch.com/blog/choosing-the-best-model-for-semantic-search) focuses on a query’s intent and contextual meaning, which makes it particularly great for web search. 

LSI is a variation of semantic search that finds hidden concepts in words and documents. Thus, it’s ideal for tasks such as document clustering.  

There’s also [neural search](https://www.meilisearch.com/blog/neural-search) that employs deep neural networks (DNNs) to understand the context behind a search query. 

Neural search differs from the above methods in its complexity. It explores contextual relations on a more nuanced level, which makes it better for search engines involving conversational queries. 

There is also [hybrid search](https://www.meilisearch.com/blog/hybrid-search), a blend of all aforementioned models, which is often used in enhanced information retrieval systems. 

What about more real-life uses for each of these IR methods?

Here are some suggestions. 

![Use cases for information retrieval systems.png](https://unable-actionable-car.media.strapiapp.com/Use_cases_for_information_retrieval_systems_df58d9c62f.png)

These are, of course, just a few examples, as IRs can have way more applications. 

But there’s one thing that unites all of them. 

No matter what IR a search engine uses, it deals with personal user data. 

And that calls for privacy concerns. 

Let’s talk about that next. 


### Pillar #5: Respect for privacy

It’s no secret that AI-powered search engines rely on machine learning to provide the most accurate results. 

This, of course, implies that they analyze data from millions of searches and have to examine user data.

But how can you ensure privacy while maintaining the search engine’s ability to provide top-tier personalization?

The following strategies help get this job done. 

* **Data encryption:** protecting the data from third parties by encrypting it at rest and in transit. 
* **Access controls:** introducing multi-factor authentication and role-based permissions. 
* **Limited data sharing:** regulating data sharing with third parties. 
* **Federated learning:** training AI to learn from new system updates instead of raw user data.
* **Effective anonymization:** preventing user de-anonymization by hiding personally identifiable information. 

The key point, of course, is to ask for user permission to use any data they share and, if they change their mind later, to give them complete control over how their information is processed. 

## Build a strong foundation for personalized search

True personalization isn’t just about serving the results that *seem* tailored. 

Most so-called personalized searches fall short because they prioritize static logic, broad segmentation, and advertiser interests instead of user intent. 

But not all is lost. 

Grounding personalization in NLP, using correct information retrieval methods, and, most importantly, respecting privacy are the keys to delivering search experiences that truly matter.

Most personalized search experiences are a lie

What is vector search? Complete guide [2025]

Personalized search is the key to keeping short-attention-span folks interested in your product or service. Users expect results that feel made for them, and personalized search is the key to meeting that expectation — and turning search into a conversion engine.

Here’s what we’ll cover:

* What personalized search means and how it’s different from traditional search.
* How it works behind the scenes: from data collection to result ranking.
* What types of data power it and why they matter.
* Benefits and challenges to consider before implementing it.
* Use cases across industries like e-commerce, streaming, and SaaS.
* Examples in action that demonstrate how personalized search is already everywhere.
* How to implement it using Meilisearch’s high-performance, developer-friendly toolkit.


## What is personalized search?

As the name suggests, personalized search is when search results are tailored to the searchers based on the latter’s unique characteristics, behaviors, and preferences. 

It refers to the search method where your demographics and behavior — past searches, purchases, browsing activity, location, and interests — are used to present the most relevant search results for a search query you initiate.

Unlike traditional search, which delivers the same results to everyone using the same query, personalized search relies on user data to adapt what each person sees in the results. 

You’ve likely seen this in action on platforms like Netflix (suggested titles based on your viewing history), Spotify (custom playlists tailored to your listening history), or Amazon (product recommendations based on your purchasing history).

Today, delivering generic results isn’t just outdated — it’s a missed opportunity. Users expect relevance as the default. 

The better you get at providing it, the more likely they are to click, buy, and stay.

## How does personalized search work?

Personalized search combines behavioral insights and machine learning to make real-time changes to what users see.

![How personalized search works.png](https://unable-actionable-car.media.strapiapp.com/How_personalized_search_works_14ee43ed10.png)

Here’s a high-level look at how [AI-powered personalized search](https://www.meilisearch.com/blog/ai-powered-search ) works:


### 1. Data collection

The system gathers user-specific signals, such as search history, clicks, the pages users visit, location, and past purchases.


### 2. User modeling

These signals are then used to build a user profile. This user profile could be:

* Session-based (just the current visit)
* Persistent (across different sessions)
* Anonymous or authenticated


### 3. Query handling

Once the user submits a search query, the algorithm interprets it within the context of the user’s profile created in the previous step, adjusting the results based on perceived preferences.


### 4. Ranking & re-ranking

Finally, the system layers personalization on top of relevance scoring. Even if a product matches a keyword exactly, its position can shift depending on what is most relevant *for that user*.

Behind the scenes, features such as collaborative filtering (recommending items based on similar users’ preferences), content-based filtering (suggesting items similar to past content the user has interacted with), and hybrid models determine what rises to the top. Many systems also employ heuristics to facilitate fast decision-making and predict user needs.

Meilisearch supports these processes through its flexible API, which allows the integration of user-specific filters and [vector-based recommendations](https://www.meilisearch.com/blog/personalization-recommendations ) without compromising on speed, even at scale.


## What data is used in personalized search?

Personalized search can draw from a wide range of data signals. Some are implicit (inferred through behavior) while others are explicit (user-defined preferences).

Here are the common types of data used:

* Search history, like past searches and interactions
* Click, browse, and time-spent-on behavior
* Device type and OS
* Geolocation for localized results
* Time of day/week
* Account information (if logged in)
* Language and filter preferences
* Social signals (likes, shares, follows)
* Interaction with filters, facets, or categories

The richness of your personalization depends on the data you can ethically and transparently collect without violating user trust or data privacy regulations. This user data then informs algorithms to deliver the best results for the highest user satisfaction.


## What is the difference between personalized and traditional search?

The main difference between personalized and traditional search is how results are selected and ranked.

Traditional search relies heavily — or entirely — on matching query terms with indexed content using a static algorithm. Everyone sees the same result for the same input. It’s predictable and uniform, but it's also less relevant.

Personalized search, by contrast, incorporates user data and adjusts results based on the individual user. This provides unique results and improves relevance, but adds complexity, especially around data management, compliance, and UX consistency.

Here’s a quick side-by-side comparison.

![What is the difference between personalized and traditional search_.png](https://unable-actionable-car.media.strapiapp.com/What_is_the_difference_between_personalized_and_traditional_search_b9f818eebf.png)

Personalized search’s reliance on user data introduces variability and complexity. 

Even though it may require more setup, it also returns more value, especially in customer-facing products and services.


## What are the benefits of personalized search?

Personalized search delivers measurable benefits across UX, engagement, revenue, and brand performance.

Here are the key advantages:

* **Higher customer engagement:** Users spend more time interacting with your site or product because of the greater relevance of search results.
* **Boosted revenue:** Relevant suggestions improve click-through rates and sales for most brands.
* **Lower bounce rates:** Users find what they’re looking for faster, so they stay on your site longer.
* **Surfaced niche content:** Personalization helps uncover niche or under-ranked content that users might otherwise miss.
* **Improved retention and loyalty:** A personalized experience builds long-term trust and brand affinity.
* **Better monetization opportunities:** You can highlight relevant high-margin or preferred content/products more effectively.

These benefits drive tangible business outcomes, resulting in a revenue boost of [up to 40%](https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying) for companies employing personalization. 


## What are the challenges of personalized search?

Personalized search is not without its drawbacks across user data privacy, scalability, and complexity.

![Challenges of personalized search.png](https://unable-actionable-car.media.strapiapp.com/Challenges_of_personalized_search_1b30b4a33b.png)

Implementing personalized search involves hurdles such as:


### 1. Data privacy and compliance

Personalized search often relies on collecting user data, raising GDPR, CCPA, and similar concerns.

Use opt-in consent, clearly communicate the type of data collected, and anonymize or aggregate data wherever possible. Consent management should be baked into your UX from day one.


### 2. Cold start problem

New users or fresh content lack enough data to be included in personalization, taking search back to its traditional state.

You can overcome this by combining contextual signals (e.g., from geolocation or device) with collaborative or content-based filtering. You can also use fallback strategies like popular or trending content to give your personalized search algorithm a foundation.


### 3. Performance and scalability

Real-time re-ranking and personalization at scale are computationally intensive, which raises concerns about costs and scalability.

Use caching, vector databases, or edge computing to maintain speed without sacrificing relevance.


### 4. Over-personalization

Too much personalization can trap users in a ‘filter bubble,’ where they're only shown results they’re likely to agree with, limiting content diversity and discovery.

A good solution for this would be to blend personalized and generic results. For example, use sections like ‘For You’ and ‘Trending Now.’ You could also allow manual user adjustment for a more human-in-the-loop approach.


### 5. Integration complexity

Personalization involves combining multiple data sources, APIs, systems, and service layers, which can be pretty complex.

Choose modular customizable tools like Meilisearch that make it easier to implement personalization in stages for streamlined integration.

Balancing the benefits and drawbacks of personalized search will take you beyond basic search into new realms of possibility.


## What are the use cases for search personalization?

Personalized search has different use cases and applications across various industries. 

![What are the use cases for search personalization_.png](https://unable-actionable-car.media.strapiapp.com/What_are_the_use_cases_for_search_personalization_dbdca13b7e.png)

Some areas where personalized search shines are:


### E-commerce

In e-commerce, personalized search helps users discover what they're likely to purchase. By analyzing previous purchases, browsing history, and the most frequently accessed categories or brands, marketplace search results prioritize items that match users' purchasing habits, thereby predicting their choices.

You can also include price sensitivity and availability to create more relevant, context-based product recommendations.


### Media streaming

Media platforms employ personalization to display content that users are likely to enjoy, based on their viewing history, preferred genres, or browsing behavior. 

For example, the platform could suggest a new period-piece series after the user completes a historical show, or optimize search results to highlight content with familiar actors or directors.

This approach reduces choice fatigue, improves user retention, and helps surface lesser-known but contextually relevant content that might otherwise be buried in extensive catalogs.


### Travel booking

Travel search personalization tailors results by analyzing details from previous searches, favorite spots, airlines, hotel categories, or even loyalty programs. 

For example, if you’re a frequent flyer, you might find business class options at the top of your search results. A returning user could be shown offers for destinations similar to those they’ve visited.


### SaaS platforms

Personalized search in SaaS applications can prioritize the most relevant documentation, feature, or dashboard based on a user's role, past use, or recent activity. 

For instance, developers might first receive content regarding API references, while analytics tools may be the most relevant content for marketers.

Such role-based tailoring speeds up onboarding, reduces support overhead, and enables users to derive value from the product sooner, thereby improving employee engagement and retention.


### Online education

Learning management systems and online learning platforms use personalized search to suggest courses, modules, or learning pathways to students based on their interests, skill levels, and learning history. Whether resuming a suspended course or exploring a new subject, students can use personalization to discover relevant resources, certifications, or subsequent modules.


## What is an example of personalized search?

Let’s bring personalized search use cases to life with some real-world examples.


### 1. ‘Pizza near me’

Let’s say you are searching for good pizza places in your location using the keyword ‘pizza near me.’

Without personalization, a system, such as a food delivery app or Google, will show all registered and available pizza places on the app. 

Limited personalization may result in filtering based on a 5- or 10-mile radius around your location, such as Google’s ‘See results closer to you.’

![Results closer to you Google feature.png](https://unable-actionable-car.media.strapiapp.com/Results_closer_to_you_Google_feature_e9aed062ff.png)

Source: [Google](https://www.google.com/?gl=us)

With actual real-time personalization, the app will show and rank pizzerias you’ve ordered from before, that are open now, and fit your exact delivery radius. 


### 2. Online shopping

Online marketplaces like Amazon and Trendyol aim to offer customers the widest selection of lifestyle products. First visitors will likely receive ‘random’ suggestions, perhaps only personalized to their device type or geolocation.

After a few rounds of online ‘window shopping’ or order placing, the results become more targeted, prioritizing your favorite categories, brands, preferred price range, or products you viewed last time.

![Marketplace product recommendations.png](https://unable-actionable-car.media.strapiapp.com/Marketplace_product_recommendations_b03c36171e.png)

Source: [Trendyol](https://www.trendyol.com/)

Personalization reduces friction, makes discovery intuitive, and accelerates action in every case. 

But how do you get all this for your system? 

Meilisearch makes it easy!


## How to implement search personalization

Now that we’ve covered the ‘why’ and ‘what,’ let’s look at the ‘how.’

With Meilisearch, real-time search, relevance tuning, and fast indexing enable personalized search through features like filters, [vector search](https://www.meilisearch.com/blog/what-is-vector-search), and multi-tenancy.

Here’s how to approach it:


### With localized content

Personalized search with localized content uses geosearch or filters to prioritize results based on user location, language, or locale. 

Meilisearch’s [sorting features](https://www.meilisearch.com/docs/learn/filtering_and_sorting/sort_search_results) and [geospatial search capabilities](https://www.meilisearch.com/docs/learn/filtering_and_sorting/geosearch) enable this.

For example, to sort correctly indexed restaurants based on their proximity to the Eiffel Tower, the following Python script for the `_geoPoint` sorting function may come in handy:


```python
client.index('restaurants').search('', {
  'sort': ['_geoPoint(48.8561446,2.2978204):asc', 'rating:desc']
})
```


For documents to be correctly sorted based on their geographic location, ensure they contain a valid `_geo` field.


### With recent searches

Perhaps the most widely used personalization criterion is the user’s past searches. Using the user’s recent activity in the search system, the application can suggest and provide the most relevant results. 


## Bringing it all together: personalized search for real impact

Personalized search is no longer a ‘nice-to-have’; it’s a competitive necessity.

It transforms users' interactions with and feelings about your platform by delivering natural, effortless, and uniquely relevant search results. 

The result? Higher engagement, better retention, and stronger conversion metrics.


Personalized search: Deliver ultra-relevant results that convert

Firebase is a BaaS (Backend as a Service) platform. One of its backend services is the Cloud Firestore, which allows you to perform full-text search using a third-party extension.

Firebase users can query document collections in Cloud Firestore with a set of functions available in SDKs for several programming languages, including Swift, Kotlin, Python, Java, PHP, Ruby, and more.

Cloud Firestore does not provide native indexing, so users must use third-party services for document indexing and performing AI-powered searches.

This tutorial will guide you through the steps to index documents from Cloud Firestore using the [Meilisearch extension](https://extensions.dev/extensions/meilisearch/firestore-meilisearch).

## 1. Deploy Meilisearch

You first need to have Meilisearch running on a cloud service. You can either self-host it or use [Meilisearch Cloud](https://www.meilisearch.com/cloud) for an easier setup.

![image5.png](https://unable-actionable-car.media.strapiapp.com/image5_15f2d9cbbd.png)

This step is required because Meilisearch will provide the indexation of Firebase documents and search capabilities. Meilisearch utilizes machine learning (ML) models to convert documents (JSON files) into vector embeddings, which are stored in a vector space for fast and real-time database retrieval.

To install Meilisearch locally, you can run the following cURL command:


```sh
# Install Meilisearch
curl -L https://install.meilisearch.com | sh
```


Then you need to run Meilisearch:


```sh
# Launch Meilisearch
./meilisearch --master-key="aSampleMasterKey"
```


Let’s now see how to create a Firebase project connected to Meilisearch using the [Meilisearch extension](https://extensions.dev/extensions/meilisearch/firestore-meilisearch).

## 2. Create a Firebase project

Note: You can skip this step if you already have a Firebase Project with a Firebase database containing your documents.

Once you have Meilisearch up and running, you can create a Firebase project, configure it, and add a Firestore database.

There are several ways to configure a project on Firebase. You can do it directly on the [Firebase Console](https://console.firebase.google.com/) or use the [API](https://firebase.google.com/docs/projects/api/workflow_set-up-and-manage-project).

![image23.png](https://unable-actionable-car.media.strapiapp.com/image23_2100dd9bbe.png)

On the console, your project’s page looks like this:

![image24.png](https://unable-actionable-car.media.strapiapp.com/image24_3e3f17f124.png)

On the left side, click ‘Build’ and choose ‘Firestore Database.’

![image3.png](https://unable-actionable-car.media.strapiapp.com/image3_1976cbbf78.png)

From there, you can start adding documents to your new database.

![image6.png](https://unable-actionable-car.media.strapiapp.com/image6_5297a42b83.png)

Now let’s see how to connect [firestore-meilisearch](https://github.com/meilisearch/firestore-meilisearch) to Cloud Firestore.

## 3. Install Meilisearch’s Firebase extension

If you look at the extensions in your Firestore Cloud page, you won’t find Meilisearch as a default. Therefore, you need to install it from the [Firebase Extensions Hub](https://extensions.dev/extensions/meilisearch/firestore-meilisearch).

![image14.png](https://unable-actionable-car.media.strapiapp.com/image14_579572e53f.png)

You need to activate the [Blaze pricing plan to add this and other extensions](https://firebase.google.com/pricing?authuser=0). You will be charged a small amount (typically around $0.01/month) for the Firebase resources required by this extension, even if it is not used.

![image17.png](https://unable-actionable-car.media.strapiapp.com/image17_27fac8457a.png)

If you don’t have a billing account, you can quickly create one in the [Google Cloud Console](https://cloud.google.com/billing/docs/how-to/manage-billing-account). Once done, you can activate the extension and set up a budget amount.

![image20.png](https://unable-actionable-car.media.strapiapp.com/image20_33bc9c5dec.png)

The billing account is successfully connected to the Blaze pricing plan with these steps.

![image4.png](https://unable-actionable-car.media.strapiapp.com/image4_70d7ece7bc.png)

The next step to activate the [firestore-meilisearch](https://github.com/meilisearch/firestore-meilisearch) extension is to review the APIs in your Firebase project. If you don’t have the required ones enabled, you’ll be asked to activate them.

![image11.png](https://unable-actionable-car.media.strapiapp.com/image11_60c2a351ea.png)

After enabling the APIs, you’ll be granted a service account. Finally, you’ll be asked to configure the extension, which includes setting a location, a collection path, the fields to index, and the Meilisearch index name, host, and key (optionally). 

In the following example, we created a collection for movies:

![image1.png](https://unable-actionable-car.media.strapiapp.com/image1_2882d97b25.png)

You’re now ready to install the extension, which takes between three and five minutes.

![image21.png](https://unable-actionable-car.media.strapiapp.com/image21_f8058db5af.png)

Once installed, you can test Meilisearch, but first, you need to check if you have a collection in your Firebase project with the same name as the one configured in the extension.

![image2.png](https://unable-actionable-car.media.strapiapp.com/image2_c13c720cbe.png)

If not, you can visit the [project’s dashboard](https://console.firebase.google.com/) and add your collection accordingly.

![image19.png](https://unable-actionable-car.media.strapiapp.com/image19_2925f03110.png)

This is the data we are using in our example:

title: The Hobbit: An Unexpected Journey

poster &lt;[https://image.tmdb.org/t/p/w500/yHA9Fc37VmpUA5UncTxxo3rTGVA.jpg](https://image.tmdb.org/t/p/w500/yHA9Fc37VmpUA5UncTxxo3rTGVA.jpg)>

overview: Bilbo Baggins, a hobbit enjoying his quiet life, is swept into an epic quest by Gandalf the Grey and thirteen dwarves who seek to reclaim their mountain home from Smaug, the dragon.

release_date: 1353888000

Once you have added this movie, you should be able to see it when accessing Meilisearch’s search preview:

![image7.png](https://unable-actionable-car.media.strapiapp.com/image7_db9ecdd0ae.png)

Well done! You have successfully installed the Meilisearch Firestore extension!

When you add, update, and delete documents from your Firestore collection, they will be mirrored in Meilisearch.


### Managing your extension

To access the overview panel of the extension, first click on the ‘Extensions’ tab on Firebase’s sidebar menu, then find the Meilisearch extension and click on ‘Manage.’ Finally, click on ‘Extension configuration’ and you can now change the collection, host, index, and so on.

![image13.png](https://unable-actionable-car.media.strapiapp.com/image13_7ac6468575.png)

Accessing your logs allows you to monitor your cloud functions and obtain different levels of information. You may access an extension’s logs via the ‘Extensions’ tab. Alternatively, you can also visit the ‘Functions’ tab and select ‘Logs,’ as indicated in the image below.

![image10.png](https://unable-actionable-car.media.strapiapp.com/image10_a35bee898c.png)

## 4. Set up a search interface

In the previous chapter, we successfully installed and configured the Meilisearch extension on Firestore. Now it’s time to query data using a simple search engine application. Here we will see how to create a search bar for your project using this [GitHub repository](https://github.com/meilisearch/meilisearch-js-plugins/tree/main/packages/instant-meilisearch).


### Populating your Firestore collection with data

The first step is to add more documents to our Firestore database. In our case, we will use this [collection of movies](https://www.meilisearch.com/movies.json). Refer to the Firebase documentation for more information on adding and managing [data](https://github.com/meilisearch/firestore-meilisearch).

Once you have finished adding content to your Firestore database, visit your Meilisearch search preview to check that Meilisearch has successfully indexed your documents.

![image12.png](https://unable-actionable-car.media.strapiapp.com/image12_92dfdaf356.png)

### Search in your document

Now let's create a great search experience. There are many ways to do this, but we’ll keep it simple this time and use [instant-meilisearch](https://github.com/meilisearch/meilisearch-js-plugins/tree/main/packages/instant-meilisearch), our plugin for the instant-search JavaScript library.

Create an index.html file in your preferred development environment. This HTML page should contain a search bar and a box to contain the returned search results:


```html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Meilisearch demo</title>
    <link rel="stylesheet" href="<https://cdn.jsdelivr.net/npm/instantsearch.css/themes/algolia-min.css>" />
</head>
<body>
    <div>
        <div id="searchbox"></div>
        <div id="hits"></div>
      </div>
  
      <script src="<https://cdn.jsdelivr.net/npm/@meilisearch/instant-meilisearch/dist/instant-meilisearch.umd.min.js>"></script>
      <script src="<https://cdn.jsdelivr.net/npm/instantsearch.js@4>"></script>
      <script>
          const searchClient = instantMeiliSearch(
              'your Meilisearch url',
              'your Meilisearch API key'
            )
            const search = instantsearch({
              indexName: 'movies',
              searchClient
            })

            search.addWidgets([
              instantsearch.widgets.searchBox({
                container: '#searchbox',
              }),
              instantsearch.widgets.hits({
                container: '#hits',
                templates: {
                  item: `
                    <div style="text-align:center;">
                      <div class="hit-name">
                        {{#helpers.highlight}}{ "attribute": "title" }{{/helpers.highlight}}
                      </div>
                      </br>
                      <div>
                        <img src="{{poster}}" width="200px" />
                      </div>
                    </div>
                  `,
                },
              }),
            ])

            search.start()
      </script>
</body>
</html>

```


Remember to replace the example’s configuration with your own dataset. This might include the indexName if you named your index something other than ‘movies,’ your instance’s URL, and your API authentication token.

If everything went well, you should be able to access your index.html file and start searching right away:

![image15.png](https://unable-actionable-car.media.strapiapp.com/image15_80c0be763b.png)

Congrats! You have added full-text search capability to your Cloud Firestore. Your Firebase collection and your Meilisearch index will automatically stay in sync as long as the firestore-meilisearch extension is installed.


## How is Firebase search used?

Firebase offers full-text search capabilities on indexed fields through third-party solutions. Common use cases include:


* **User profiles**: Filtering users by name or email.
* **Product catalogs**: Searching items by category or price.
* **Blog posts**: Finding articles by title or tags.
* **Geolocation**: Querying places using geo data.


### Does Firebase have vector search?

No. Firebase lacks a built-in [vector search](https://www.meilisearch.com/blog/introducing-vector-search) for semantic or AI-powered queries. However, the Meilisearch [extension](https://extensions.dev/extensions/meilisearch/firestore-meilisearch) enables vector search by indexing Firestore documents, allowing similarity-based searches.


### Does Firebase support full-text search?

No. Firebase only supports exact string matching. For features like typo-tolerance, synonyms, or ranked results, the Meilisearch [extension](https://extensions.dev/extensions/meilisearch/firestore-meilisearch) indexes Firestore data to deliver fast, flexible full-text search.


## Enable reliable search in your Firebase app with Meilisearch

Adding search to your Firebase app doesn’t have to be a complex process. In fact, you can give users fast, accurate results with the proper setup. 

The key is to start simple, scale smart, and use Meilisearch.

How to implement search in Firebase

Imagine you're building a blog in Rails, and your users keep misspelling post titles in the search bar, only to find no results. Frustrating, right? 

What if your Rails app or website had a search feature that’s lightning-fast *and *smart enough to handle typos?

Meilisearch is your answer. It's a lightweight, typo-tolerant, and lightning-fast search engine that offers a seamless developer experience and out-of-the-box relevancy tuning.

You can make a search engine with Ruby on Rails in Meilisearch by:

1. Setting up Meilisearch and installing dependencies.
2. Configuring Meilisearch in the Rails app.
3. Defining a model and enabling Meilisearch indexing.
4. Populating and indexing data.
5. Implementing a backend and frontend.
6. Customizing settings.

A Ruby on Rails–powered search engine can be used for building e-commerce platforms, blogs, or searchable knowledge bases. Regardless of your use case, a search engine built with Ruby on Rails in Meilisearch comes with a slick search interface that’s both powerful and user-friendly.

## 1. Set up Meilisearch and install dependencies

First, we need to get Meilisearch up and running and integrate it with our Rails app. This will lay the foundation for your search engine.

Meilisearch is open source and free if you choose to [host it locally](https://www.meilisearch.com/docs/learn/self_hosted/getting_started_with_self_hosted_meilisearch). Or, you can choose Meilisearch Cloud for a fully managed hosting experience, with no need to handle infrastructure or updates yourself. 

For this tutorial, we’ll use a local instance, which is perfect for testing. You’ll need Ruby 3.0+ and Rails 6.1+ to ensure compatibility. Older versions may still allow it to function, but they are not officially supported.

The `meilisearch-rails` gem simplifies the integration, handling indexing and syncing with your database. Let’s install Meilisearch and add the gem to your project.

To install Meilisearch locally, follow the instructions in the official [Meilisearch documentation on GitHub](https://github.com/meilisearch/meilisearch-rails). You can download the binary and run it with a simple command. Once running, Meilisearch is accessible at [http://127.0.0.1:3000](http://127.0.0.1:3000).

Open your `Gemfile `and, with `gem` in the command line, add:


```ruby
gem install meilisearch-rails

source 'https://rubygems.org'

gem 'meilisearch-rails'
```


Run `bundle install` to install the gem. If you hit any issues, double-check your Ruby and Rails versions, as the gem requires recent versions for full compatibility.

With Meilisearch and the gem installed, we’re ready to configure our Rails app to connect to it.


## 2. Configure Meilisearch in your Rails app

Now, let’s configure our Rails app to connect to Meilisearch. Think of this step as setting the coordinates that guide your app to the search engine.

Create a new configuration file to specify the `MEILISEARCH_HOST `and `MEILISEARCH_API_KEY`. This ensures smooth and secure communication between your app and Meilisearch. 

In development, you can skip the API key, but in production, it’s essential for maximum security. Proper configuration, such as what we’re doing now, prevents connection errors and ensures smooth indexing and searching.

Create a new file at `config/initializers/meilisearch.rb`:


```ruby
Meilisearch::Rails.configuration = {
  meilisearch_url: ENV.fetch('MEILISEARCH_HOST', 'http://localhost:7700'),
  meilisearch_api_key: ENV.fetch('MEILISEARCH_API_KEY', 'YourMeilisearchAPIKey')
}
```


If you’re using Meilisearch Cloud, replace the URL with your cloud instance’s address, which is available after signing up for Meilisearch Cloud. For local development, the default URL works fine. If you encounter connection issues, ensure Meilisearch is running and the URL is correct.

Now that our app knows where to find Meilisearch, let’s set up a model to index data.


## 3. Define a model and enable Meilisearch indexing

This step is where the magic starts: we’ll make our data searchable by indexing it with Meilisearch. Indexing means cataloging your information and data so Meilisearch can quickly find matches during user searches.

For this example, we’ll use the hypothetical Rails model `Article` and include the `MeiliSearch::Rails` module to enable automatic indexing. You’ll decide which attributes to index, like `title `and `body`, and configure how they’re used in searches. Meilisearch syncs with your database via `ActiveRecord `callbacks, so you don’t have to worry about updating it manually.

First, generate a model if you don’t have one:


```ruby
rails generate model Article title:string body:text category:string
rails db:migrate
```


Then, open `app/models/article.rb` and add:


```ruby
class Article < ApplicationRecord
  include MeiliSearch::Rails

  meilisearch do
    attribute :title, :body, :category
    searchable_attributes [:title, :body]
    filterable_attributes [:category]
    sortable_attributes [:created_at]
  end
end
```


Here’s what’s happening:

* `attribute` lists the fields requiring indexing.
* `searchable_attributes` are fields users can search.
* `filterable_attributes` allow filtering of the results, e.g., by category.
* `sortable_attributes `enable sorting, e.g., by creation date.

Here, choosing the right attributes is crucial. For example, indexing `title` and `body` makes sense for a blog, but you might want to add `tags` or `author` depending on your use case. Choose only the most relevant fields to index for better performance and faster search results.

With our model ready, let’s add some data to the index.


## 4. Populate and index sample data

To test our search engine, we need data to search through. For this purpose, we will populate our database with sample articles.

Using a seed file, we’ll create sample records that Meilisearch will automatically index. This will let you see how Meilisearch handles real data and prepare you for testing search queries. Meilisearch’s efficiency really shines here, as it can index thousands of records at lightning speeds.

To start, create or update `db/seeds.rb` with the articles you want searched:


```ruby
# Clear existing articles — use only in development or testing!
Article.destroy_all
articles = [
  { title: "Ruby on Rails Basics", body: "Learn the fundamentals of Rails.", category: "Beginner" },
  { title: "Advanced Rails Techniques", body: "Dive into complex Rails features.", category: "Advanced" },
  { title: "Rails Performance Tips", body: "Optimize your Rails app.", category: "Performance" },
  { title: "Building APIs with Rails", body: "Create robust APIs in Rails.", category: "API" },
  { title: "Rails Security Best Practices", body: "Secure your Rails application.", category: "Security" }
]
articles.each { |article| Article.create!(article) }
puts "Articles seeded!"
```


Then, run:


```ruby
rails db:seed
```


Meilisearch will index these records automatically using the aforementioned `ActiveRecord `callbacks. To verify, you can use Meilisearch’s API or dashboard to check the `articles `index. If (correct) data isn’t appearing, ensure your Meilisearch instance is running and the configuration is correct.

Now, things get even more interesting — it’s time to implement the search functionality.


<Callout intent="tip">
Ready to try it out? See Meilisearch’s search capability in full force in a [free trial](https://www.meilisearch.com/cloud)!
</Callout>


## 5. Implement backend search functionality

First, we’ll create a controller to handle search queries and display accurate results.

This step sets up a `SearchController `to process user input and fetch results from Meilisearch. The `meilisearch-rails` gem makes this easy by providing a `search `method that returns `ActiveRecord `objects, so you can treat results like regular model instances. Additionally, Meilisearch’s typo tolerance ensures users get relevant results even with misspellings.

Create `app/controllers/search_controller.rb`:


```ruby
class SearchController < ApplicationController
  def index
    @query = params[:q]
    @results = Article.search(@query) if @query.present?
  end
end
```


Update your routes in `config/routes.rb`:


```ruby
Rails.application.routes.draw do
  get 'search', to: 'search#index'
end
```


Create a view at `app/views/search/index.html.erb`:


```erb
<h1>Search Articles</h1>
<%= form_with url: search_path, method: :get, local: true do %>
  <%= text_field_tag :q, params[:q], placeholder: "Search articles..." %>
  <%= submit_tag "Search" %>
<% end %>

<% if @results %>
  <h2>Results for "<%= @query %>"</h2>
  <ul>
    <% @results.each do |result| %>
      <li><strong><%= result.title %></strong>: <%= result.body %></li>
    <% end %>
  </ul>
<% end %>
```


Visit [http://localhost:3000/search](http://localhost:3000/search) and try queries like ‘Ralis’ (a typo for ‘Rails’). Meilisearch’s typo tolerance will still return relevant results, as expected. If results aren’t appearing at all, or they seem to be incorrect, check your model’s indexing settings or Meilisearch logs.

Now, let’s take it up a notch with a frontend search interface through our [JavaScript API Client](https://github.com/meilisearch/meilisearch-js/) or [Instant Meilisearch plugin](https://github.com/meilisearch/instant-meilisearch).


## 6. Add a frontend search with instant Meilisearch

For a search-as-you-type experience, we’ll integrate Meilisearch’s simple frontend interface using `instant-meilisearch`.

This step enables dynamic, instant search functionality in your app, giving users an interactive experience as they type. Meilisearch’s impressive response speed — with response times ≤50ms — ensures results appear almost instantly. We’ll use a simple JavaScript setup, but you can extend it with [frameworks like React](https://www.meilisearch.com/with/react) for a polished UI.

Install the library via `npm `(or `yarn`). Note: this assumes your Rails app is already set up with a JavaScript bundler such as Webpacker or Importmaps.


```
npm install @meilisearch/instant-meilisearch
```


Create `app/javascript/search.js`:


```js
import { instantMeiliSearch } from '@meilisearch/instant-meilisearch';

const searchClient = instantMeiliSearch(
  '',// URL of Meilisearch instance
  '' // Meilisearch API key
);

const search = async (query) => {
  const response = await searchClient.search([{
    indexName: 'articles',
    query
  }]);
  const resultsDiv = document.getElementById('results');
  resultsDiv.innerHTML = response.hits.map(hit => `<div>${hit.title}: ${hit.body}</div>`).join('');
};

document.getElementById('search-input').addEventListener('input', (e) => {
  search(e.target.value);
});
```


Update `app/views/search/index.html.erb`:


```erb
<h1>Search Articles</h1>
<input type="text" id="search-input" placeholder="Search articles...">
<div id="results"></div>
```


As before, test the search at [http://localhost:3000/search](http://localhost:3000/search). Typing ‘Rail’ should show results instantly.

Next, let’s explore our customization options.


## 7. Customize Meilisearch settings

To make your search engine even better, you can tweak Meilisearch’s settings to fit your product’s needs.

Meilisearch allows you to customize ranking rules, synonyms, and stop words to improve search relevance. For example, you might want ‘Rails’ and ‘Ruby on Rails’ to be treated as synonyms, or prioritize results based on recency.

For instance, here is how to adjust ranking rules to prioritize certain fields:


```ruby
Article.meilisearch_index.update_settings({
  rankingRules: [
    "words",
    "typo",
    "proximity",
    "attribute",
    "sort",
    "exactness",
    "created_at:desc"
  ]
})
```


These settings ensure newer articles rank higher. Feel free to play around and test different configurations to see what works best for your users.

Finally, let’s ensure your search index stays in sync with your database for the most accurate results.


## 8. Handle updates and deletions

The `meilisearch-rails` gem automatically handles indexing when you create or update records. However, it’s still worth understanding how to manage updates and deletions yourself, especially if you’re building more complex web applications. This will ensure your search results always reflect the latest data and avoid stale or missing entries.

To manually delete a record from your index:


```ruby
article = Article.find(1)
article.remove_from_index!
Article.destroy! # Removes from Meilisearch index
```


If you need to reindex all records, for example, after changing any indexed attributes, run:


```
Article.reindex!
```


To clear your index entirely, use:


```
Article.clear_index!
```


Be cautious with reindexing large datasets, however, as it can be resource-intensive. In production environments, you can schedule such tasks during low-traffic periods.


## Can I make a search engine with Ruby on Rails for free?

Yes, you can build a search engine with Ruby on Rails and Meilisearch at no cost for development and small-scale production.

Meilisearch’s open-source version is free to self-host on your own servers or local machine, and the `meilisearch-rails` gem has no licensing fees. For development, this is ideal, as you only need a server to run Meilisearch. 

For production, however, hosting costs depend on your infrastructure. Small apps can run on affordable servers, while the costs may skyrocket for larger projects.

Meilisearch Cloud offers a 14-day free trial without requiring a credit card, making it perfect for testing. Post-trial, pricing scales with usage (documents and search requests), starting with the Build plan for small businesses at $30/month.

Let’s compare Meilisearch to other open-source search engines.

## What are the best open-source search engines for Ruby on Rails?

Open-source search engines provide powerful, license-free solutions for adding search to Rails apps. Here are the top options:

* **Meilisearch**: Known for its speed (under 50ms) and simplicity, Meilisearch integrates seamlessly with Rails via the `meilisearch-rails` gem. It’s ideal for small to medium apps, offering [typo-tolerance](https://www.meilisearch.com/blog/typo-tolerance) and search-as-you-type features. Its lightweight nature makes it easy to set up and maintain yourself, while also providing a hosted option for a maintenance-free experience.
* **Elasticsearch**: A robust engine, Elasticsearch excels in handling large datasets and complex queries. Gems like Searchkick simplify integration. It’s perfect for enterprise apps but requires more resources and setup than Meilisearch.
* **Solr**: Solr offers advanced functionalities like full-text search and faceting. The Sunspot gem provides Rails integration. It’s highly configurable, which can get complex for smaller projects.
* **Typesense**: A lightweight alternative for creating search engines for Ruby on Rails, Typesense offers fast, typo-tolerant search. While it lacks a dedicated Rails gem, its API is easy to use for simple setups.

Now, let’s look at other languages for building search engines.


## What programming languages besides Ruby are used to build search engines?

Aside from Ruby, several programming languages are well-suited for building search engines, each with its unique strengths. Some common languages you can use to build search engines are:


* **JavaScript**: Using Node.js, you can build server-side search with libraries like Lunr or integrations with Meilisearch. JavaScript’s asynchronous nature is great for real-time search. Learn how to [build a search engine in JavaScript](https://www.meilisearch.com/blog/javascript-search-engine).
* **PHP**:Frameworks like Laravel use Scout to integrate with Meilisearch, making PHP a solid choice for web apps that include search engines. Learn how to [build a search engine in PHP](https://www.meilisearch.com/blog/php-search-engine).
* **Golang**: Golang’s performance and concurrency make it ideal for high-speed search engines. Libraries like Bleve support full-text search, further increasing its applicability. Learn how to [build a search engine with Golang](https://www.meilisearch.com/blog/golang-search-engine).

## Creating a search engine in Ruby on Rails made simple

Building a search engine with Ruby on Rails and Meilisearch is easier than you’d think. Set up Meilisearch, index data, add a backend and frontend, and customize the settings to your heart’s content. Meilisearch’s speed, typo-tolerance, and seamless Rails integration make it a top choice for developers looking for fast search engine configuration.

Full tutorial on how to design a search engine with Ruby on Rails

If you want to build a fast and reliable search engine, the simplest answer is Golang and Meilisearch Go SDK. Combine these, and not only can you index your content as it should be, but you can also search queries within milliseconds and keep everything lightweight as ever.

In this guide, you’ll learn how to:

* Set up Go and connect it to Meilisearch
* Index structured data using JSON
* Build a clean API for real-time search

We’re going to share a method that is simple, fast, and works at scale flawlessly. This guide will help you avoid all the heavy frameworks and large teams that only slow you down. So, if you’re ready to build a lightweight search in Golang, let’s begin.

## 1. Set up your Golang development environment

First, you must prepare your Go environment and install the Meilisearch Go SDK. This is the primary prerequisite for building your search engine. It’s crucial because it enables your system to actually run Go code and communicate with your Meilisearch instance. 

Start by initializing your Go module:


```
go mod init search-engine
```


Then, install the Meilisearch SDK:


```
go get github.com/meilisearch/meilisearch-go
```


Again, we must confirm that Go is installed and Meilisearch runs locally or in the cloud. Then can use this setup to [index data](https://www.meilisearch.com/blog/introducing-indexer-2024) and handle queries for API creation. 

Once your environment is ready, you can connect to Meilisearch and start building.


## 2. Connect to a search engine database

Before all the indexing and searching, your Golang project must connect to a Meilisearch instance.

Meilisearch is an open-source, lightning-fast search engine that supports real-time search. It is also capable of [typo tolerance](https://www.meilisearch.com/blog/typo-tolerance) and full-text indexing, making your search even more potent. It’s time to connect the Meilisearch Go SDK to your database and perform search operations. 

Here’s how to initialize the client in Go:


```go
package main

import (
  "github.com/meilisearch/meilisearch-go"
)

func main() {
  client := meilisearch.NewClient(meilisearch.ClientConfig{
    Host: "http://127.0.0.1:7700",
    APIKey: "masterKey",
  })

  // Use `client` to index documents and run search queries
}
```


Remember, if you’re using the hosted version, you must replace the host URL with the respective Meilisearch Cloud address. 

Once connected, your Go application can add documents, create indexes, and run lightning-fast search queries with Meilisearch’s JSON-based API.


## 3. Create your document indexing logic

Is your Go app connected to Meilisearch already? Next up is indexing your data. Since Meilisearch works using JSON docs, you must define your content in structs before pushing it to the search engine. This enables [full-text search](https://www.meilisearch.com/blog/how-full-text-search-engines-work), filters, and fast retrieval.

Here’s how to index documents in Go:


```go
type Movie struct {
  ID     int    `json:"id"`
  Title  string `json:"title"`
  Genre  string `json:"genre"`
}

movies := []Movie{
  {ID: 1, Title: "Inception", Genre: "Sci-Fi"},
  {ID: 2, Title: "The Matrix", Genre: "Action"},
}

res, err := client.Index("movies").AddDocuments(movies)
if err != nil {
  log.Fatal(err)
}
```


Each document becomes searchable immediately after it is indexed. You can create multiple indexes like “products,” “posts,” or “users,” depending on your project. Document indexing is the backbone of your [Golang search engine](https://www.meilisearch.com/blog/golang-search-engine#{step-name}). This is the reason why Meilisearch can parse and retrieve results in milliseconds.


## 4. Build the search API and frontend

The connection is set, and the documents have been indexed. It is time to build the search functionality into your Golang application. The aim is to create an API endpoint that takes the search query to Meilisearch and returns the desired results. This is where features such as full-text search, typo tolerance, and filtering truly matter.

Here’s a simple search handler in Go:

```go
http.HandleFunc("/search", func(w http.ResponseWriter, r *http.Request) {
  query := r.URL.Query().Get("q")
  searchRes, err := client.Index("movies").Search(query, &meilisearch.SearchRequest{})
  if err != nil {
    http.Error(w, err.Error(), http.StatusInternalServerError)
    return
  }
  json.NewEncoder(w).Encode(searchRes.Hits)
})
```


Another good news is that this endpoint can be integrated with various frontend frameworks, such as React, Vue, plain HTML, and JavaScript. The accuracy and speed of all search results depend on the Meilisearch Go SDK.


## 5. Test and refine your Golang search engine

With your Golang search engine running, it's time to test real queries and improve your results, using all the capabilities we have covered so far. 

The next step is to search queries using partial matches, typos, and filters. Track how Meilisearch ranks and returns results. Measure speed. Review how your backend handles load. If using the Meilisearch Go SDK, confirm that your API responds in milliseconds and parses JSON documents accurately.

Want deeper insights? If you're using [Meilisearch Cloud](https://www.meilisearch.com/cloud), check the built-in analytics:

* Track top search terms
* Monitor failed searches
* Analyze performance by endpoint

Refine your transformer logic, tweak `rankingRules`, adjust typo tolerance, or set filters like `filterableAttributes`.


## What are some examples of Golang search engine uses?

If your goal is to build a fast search engine that’s equally responsive and lightweight, Golang is ideal. It provides a bonus set of features, including typo tolerance, ranking rules, and filterable results, when combined with the Meilisearch Go SDK.

Let’s look at some of the most frequent and efficient [use cases for search engines](https://www.meilisearch.com/blog/golang-search-engine#examples) in Go.

* **App search:** This is one of the most prominent uses of a Go search engine. Startups using lightweight applications with custom algorithms and text analysis often opt for this. Plus, they get a responsive UI and can use tokenization to handle dynamic query input in literal milliseconds.
* **Site search for blogs and documentation:** You can use Golang with Meilisearch to build fast, Boolean-enabled search features that support conjunction and disjunction queries. It can crawl and index Markdown or JSON content from static site generators like Gatsby or Hugo.
* **eCommerce product search:** When customers visit your e-commerce store, they don’t have the time for time-consuming or inaccurate search results. You will lose that customer instantly. \
To avoid such situations, developers use Golang with Meilisearch to create smart search functionality that retains customers through filters such as price, tags, category, and in-stock items.
* **Internal tool search:** It is also used in internal systems for teams. For instance, a team handling tools without a dedicated search system will frustrate members because they cannot find the tool they seek. \
On the other hand, if you use Go and Meilisearch, you can build an instant search system with quick and accurate response times. It utilizes the principles of tokenization and exact matching to return search results from your dashboards, CRMs, and inventory tools.
* **Knowledge base and help desk search:** When someone needs help, they don’t want to dig through articles. Meilisearch resolves this by giving them instant search results through Boolean logic and tokenization. Add Golang on top of this, and you can tie all of this together rather nicely. 
* **Regulated industry search (legal, healthcare, finance):** Private records are private for a reason. They are only available to a certain number of people. And these people expect to find these private records instantly. For instance, large datasets like legal contracts or medical files must be indexed properly so the search engine can bring them up quickly. 

Search engines built with Golang and Meilisearch are better than those without them, especially regarding performance, relevance, and customization. 


## Which languages besides Go are used to build a search engine?

We’ve learned how great Go is, and how it gets even better when paired with Meilisearch. Well, it’s not your only option. Various other programming languages can also help you develop real-time search features that might get the job done. Let’s look at some below:


* You can [build a search engine in JavaScript](https://www.meilisearch.com/blog/javascript-search-engine) if you're working with interactive frontend frameworks like React or Vue. JavaScript is perfect for browser-based apps that need fast, client-side search.
* Developers who want to work with data-heavy applications often [build search engines using Python](https://www.meilisearch.com/blog/python-search-egine). It’s a flexible backend language that works well for indexing large datasets and performing complex queries.
* For server-rendered applications and CMS platforms, you can [build a search engine with PHP](https://www.meilisearch.com/blog/php-search-engine). It’s ideal for adding search to WordPress, Laravel, or custom e-commerce backends.

Whichever you choose, Meilisearch offers SDKs and documentation to help you integrate search fast.


## Can I build a search engine in Golang for free?

You can build a full-featured search engine in Golang for free using open-source tools like [Meilisearch](https://github.com/meilisearch/meilisearch-go). Golang and Meilisearch are free to use and offer powerful capabilities out of the box—no licensing fees or paid tiers to get started.

You can run Meilisearch locally or host it on your own infrastructure without paying anything. Even if you scale later, it remains cost-effective with minimal setup and maintenance. The Meilisearch Go SDK handles everything from indexing to real-time queries with clean code and full documentation.

If you want a fast, flexible, and affordable search engine, Golang and Meilisearch are a great pair.


## Building a Search Engine in Golang Made Simple

Building a search engine in Golang doesn’t need to be complicated. With the Meilisearch Go SDK, you can create a fast, lightweight, and scalable search solution using just a few lines of code.

Golang’s performance makes it ideal for backend services, while Meilisearch supports full-text search, typo tolerance, and efficient indexing with clean query string handling. You can define various query types depending on your content and use case.

Whether you're indexing JSON documents or managing custom data structures, the setup is simple and fast. This makes it great for app search, e-commerce, internal tools, or site search. If you’ve worked with BleveSearch or understand Rust syntax, you'll find the transition smooth and the developer experience clean.

Building a search engine in Golang: A comprehensive guide

If you’re looking for a fail-proof, fast and scalable search functionality to directly add to your static site, the answer is Gatsby search. With just a few clicks, Gatsby search enables any user to look for relevant content across a Gatsby site, so you don’t have to worry about traditional navigation. 

Gatsby users can implement search by using plugins such as `gatsby-plugin-meilisearch`, `gatsby-plugin-local-search`, or by connecting external search APIs. These tools use GraphQL queries to index data from Markdown, JSON, or HTML files and support client-side or pre-rendered search components.

This article is your guide on how to add a real-time, full-text search engine to your Gatsby site. We’ll also cover details about the plugins, configurations, and writing GraphQL queries. And on top of that, you’ll learn how to transform data and deploy a working search index. 

Let’s start by going through the steps to add search to any Gatsby project using Meilisearch. 

## 1. Install the Meilisearch plugin for your Gatsby site

The first step is to install an open-source plugin: <code>[gatsby-plugin-meilisearch](https://www.gatsbyjs.com/plugins/gatsby-plugin-meilisearch/)</code>. Why? 

This `gatsby-plugin` is the connection between your Gatsby site and Meilisearch instance using defined API credentials. The main benefit is that it allows your project to generate a live search index during build time. Additionally, it supports [full-text search](https://www.meilisearch.com/blog/how-full-text-search-engines-work), filters, and smooth client-side search operations. So, let’s begin.

Use npm:


```
npm install gatsby-plugin-meilisearch
```


After installation, go to your `gatsby-config.js file`. This is where you define site settings and Gatsby plugin options. Add the plugin to the plugins array with the correct host and apiKey for your Meilisearch instance.


```js
// gatsby-config.js

module.exports = {
  plugins: [
    {
      resolve: 'gatsby-plugin-meilisearch',
      options: {
        host: 'http://localhost:7700',
        apiKey: 'masterKey',
      },
    },
  ],
}
```


You can learn more about this plugin on [GitHub](https://github.com/meilisearch/gatsby-plugin-meilisearch).

This sets up the foundation for your search engine. Next, you’ll define the search index and configure the data you want to fetch.


## 2. Configure the search index and GraphQL query

Once you have installed the `gatsby-plugin-meilisearch`, you need to define the content you want to send to Meilisearch. This will happen inside `gatsby-config.js`, where you set up the indexes array. 

Each entry represents a search index. This is also where you specify the GraphQL query and index name aligned with how you transform the data. 

Here’s an example configuration:


```js
// gatsby-config.js

module.exports = {
  plugins: [
    {
      resolve: 'gatsby-plugin-meilisearch',
      options: {
        host: 'http://localhost:7700',
        apiKey: 'masterKey',
        indexes: [
          {
            indexUid: 'pages_index',
            query: `
              {
                allSitePage {
                  nodes {
                    path
                  }
                }
              }
            `,
            transformer: data =>
              data.allSitePage.nodes.map((node, index) => ({
                id: index,
                path: node.path,
              })),
          },
        ],
      },
    },
  ],
}
```


This `graphql` query fetches data from your Gatsby site pages. The transformer function prepares it for indexing by assigning each entry a unique ID. This guarantees Meilisearch can now store and return the relevant search results properly.


## 3. Build and index your Gatsby content

Now, it’s time to build and index the content. We’ve already configured the [search index](https://www.meilisearch.com/blog/introducing-indexer-2024) and GraphQL. When you run `gatsby build`, `gatsby-plugin-meilisearch` takes the data from your Gatsby site and pushes it to your Meilisearch instance. This step connects your local content with a real-time search index.

To run the build:


```
gatsby build
```


Once complete, you’ll see a message like:


```
success gatsby-plugin-meilisearch - Documents added to Meilisearch
```


This is confirmation that your data has been indexed. The Meilisearch dashboard or local instance will help you inspect the indexed content. 

If you forget this step, your search query will not return any results since there will be no content to search through.

You can also refer to the official [Gatsby plugin documentation](https://www.gatsbyjs.com/plugins/gatsby-plugin-meilisearch/) for additional options, such as `batchSize`, `skipIndexing`, or custom settings like `searchableAttributes`.

Now that your content is indexed, it's time to connect the search UI.


## 4. Add a search component to your Gatsby frontend

Building a user interface comes after your Gatsby site content is indexed. To simplify this, you can use a search component like <code>[docs-searchbar.js](https://www.meilisearch.com/blog/add-a-search-bar-to-your-gatsby-project-using-meilisearch)</code>. It works because it integrates with Meilisearch and supports the search functionality we’re building.

Start by installing the package:


```
yarn add docs-searchbar.js
```


Then, in your `src/components` folder, locate the appropriate `JS file` (like <code>[AppBar.js](http://AppBar.js)</code>). Now, import the search module and CSS:


```js
import 'docs-searchbar.js/dist/cdn/docs-searchbar.css'
```


In your React component, use a `useEffect `hook to initialize the search:


```js
useEffect(() => {
  const docsSearchBar = require('docs-searchbar.js').default
  docsSearchBar({
    hostUrl: 'http://localhost:7700',
    apiKey: 'masterKey',
    indexUid: 'your-index',
    inputSelector: '#search-bar-input',
    meilisearchOptions: { limit: 5 },
    enhancedSearchInput: true,
  })
}, [])
```


Finally, add the search input field in your JSX:


```js
<input type="search" id="search-bar-input" placeholder="Search..." className="searchbox" />
```


This setup connects your static frontend to a live search index. Users get results as they type, and search query parsing, autocomplete, and typo tolerance are fully supported.

Next up is customization. You will learn how to modify a search experience to better match your site’s design and structure.


## 5. Customize your search experience

To unlock customization for your Gatsby site, you need a working search component. Once it’s working, you can modify the search experience according to your site’s structure and design. 

To begin, you will modify the transformer function in `gatsby-config.js`. 

This function determines how your data appears in Meilisearch. You can enrich each search index item with metadata like `frontmatter.title`, `description`, `slug`, or any custom fields.


```js
transformer: (data) =>
  data.allMdx.edges.map(({ node }) => ({
    id: node.id,
    title: node.frontmatter.title,
    description: node.frontmatter.description,
    url: node.slug,
  }))
```


Prioritization is a significant part of customization. To prioritize which fields Meilisearch should match first, you will update `searchableAttributes `in the plugin settings:


```js
settings: {
  searchableAttributes: ['title', 'description', 'content']
}
```


Meilisearch brings several other perks to your customization process. For instance, you can easily adjust query behavior, such as typo tolerance and filtering. Plus, you can always tweak the CSS styles of your search input and add a `classname `to match your design system.

Building components manually through React components? You might refer to the [React Search Engine guide](https://www.meilisearch.com/blog/react-search-engine) for increased frontend control. Customization is all about enhancing the user experience, and this is no exception. 

## 6. Build and deploy your Gatsby site with search

With the search functionality in place and customization all done, it’s time for the final step: to build and deploy your Gatsby site. Let’s run the protection build command:


```
gatsby build
```


During build, `gatsby-plugin-meilisearch` runs your GraphQL query, applies the transformer, and pushes data to the search index. This process runs on every deployment if your Gatsby site is hosted on platforms like Netlify.

Once live, test your searchbox, confirm search input behavior, and validate query results. On Meilisearch Cloud, you also get access to built-in analytics.

Use the official [Gatsby plugin documentation](https://www.gatsbyjs.com/plugins/gatsby-plugin-meilisearch/) or this[ Gatsby search tutorial for full setup steps](https://www.meilisearch.com/blog/add-a-search-bar-to-your-gatsby-project-using-meilisearch).


## How is search used in Gatsby?

Search adds speed and usability to static content—Gatsby sites often power blogs, documentation, ecommerce, and marketing pages.

With `gatsby-plugin`, content in Markdown, JSON, or HTML becomes searchable. This improves navigation and makes your site search fast and efficient.

Let’s have a look at some of the key use cases:

* **Site search:** A quick way to help users locate what they are looking for on your Gatsby site.
* **Docs search:** Makes it easier to look up commands in developer documentation built with Markdown and frontmatter.
* **Blog search:** Filtering by tag, category, or title makes it way easier to find the content you care about.
* **e-Commerce search:** Helps shoppers look up products and see suggestions as they type.
* **Static knowledge bases:** It is responsible for fast client-side search across indexed content.

In short, search is responsible for the user experience and engagement across all these formats.


### Does Gatsby support server-side search?

No, it does not. Gatsby is a static site generator, so it builds all your content ahead of time and shows pre-made HTML pages. An outside tool like Meilisearch could be the answer if you want to add live search to your site. It works by handling queries and returning the results through the API. For example, one way to add the desired search on your site is by using the [Meilisearch React integration](https://www.meilisearch.com/blog/react-search-engine).


### Does Gatsby support local search?

Yes. Gatsby supports local search using plugins like `gatsby-plugin-local-search` and `lunr`. These tools index content at build time and provide lightweight, browser-based search without relying on external APIs. While this suits smaller projects, larger sites often use Meilisearch for faster indexing, typo tolerance, and advanced search functionality. For complex content-driven sites, Meilisearch is a more flexible option.


## Building powerful search in Gatsby sites

Search is a must-have on any content-rich Gatsby site. Fast search is invaluable for blogs, docs, and especially online stores. Tools like `gatsby-plugin-meilisearch` bring real-time, typo-tolerant search functionality to your React components.

Meilisearch connects through your `gatsby-config.js `using a GraphQL query, a transformer function, and a defined search index. It supports Markdown, JSON, and nested content structures. With minimal setup, your static Gatsby site can offer dynamic search query handling and a modern user experience.


How to add search to your Gatsby site: Step-by-step guide

PHP combined with Meilisearch helps you create a search engine that’s accurate and straightforward to implement. Its capabilities include support for full-text search and typo tolerance, so you get lightning-quick responses every time. 

This guide is your go-to resource for setting up the engine, sending queries, and returning precise results using the official Meilisearch PHP SDK. Additionally, you will learn to avoid the complex logic of heavy configurations. 

Here’s what we will cover:

* How to set up Meilisearch in your PHP project
* How to index data and implement full-text search
* How to customize for UX through filters, sorting, and analytics 

This [step-by-step guide](https://www.meilisearch.com/blog/php-search-engine#set-up-meilisearch-in-your-php-project) is your answer whether you’re looking for a simple PHP script, web app, or even integration with Laravel. It outlines everything required to create a top-tier search experience that fits into your back end like a glove.


## 1. Set up Meilisearch in your PHP Project

The first step in setting up Meilisearch in your PHP project is to install the `Meilisearch PHP SDK`. This is also required to build a high-performance PHP search engine. Once you install the SDK, your PHP will get backend access to Meilisearch’s API.

Now, you can manage documents and run full-text searches easily. Additionally, it works smoothly with Laravel, SQL databases like MySQL, or a custom PHP script on any web app. Compatibility will never be the issue. 

Let’s get into the technicalities of this step.

Install the required packages using `Composer`. Here’s how to do it with Guzzle:


```
composer require meilisearch/meilisearch-php guzzlehttp/guzzle http-interop/http-factory-guzzle:^1.0
```


Then, create a connection using your Meilisearch host and API key:


```
require_once __DIR__ . '/vendor/autoload.php';

use Meilisearch\Client;

$client = new Client('http://127.0.0.1:7700', 'masterKey');
```


This connection we have formed is the core of your PHP search engine’s functionality. The features we highlighted, such as autocomplete, filters, [typo tolerance](https://www.meilisearch.com/blog/typo-tolerance), and fast full-text search, result from this connection. Now, the client will be initialized. Once it does, you can easily begin indexing content or organizing your data. 

Compatibility with Laravel, MySQL, and SQLite is what makes Meilisearch a go-to for content handling in modern PHP workflows. 

If you want to see what kinds of PHP projects can benefit from Meilisearch, check out [these real-world examples](https://www.meilisearch.com/blog/php-search-engine#examples).


## 2. Add Documents to Your PHP Search Engine

To add documents to your PHP search engine, verify that your PHP client is connected, as shown in the last step. These documents are often stored in JSON and serve as your primary data source for the search engine.

Each can include searchable fields like titles, categories, or metadata. Meilisearch’s magic handles this messy input, even with typos, and gives you the best results across web pages and backend APIs. 

Here’s how to add documents using a PHP script:


```php
$index = $client->index('movies');

$documents = [
  ['id' => 1, 'title' => 'Inception', 'genres' => ['Action', 'Sci-Fi']],
  ['id' => 2, 'title' => 'Interstellar', 'genres' => ['Adventure', 'Drama']]
];

$index->addDocuments($documents);
```


This step ensures that key features like text search, filtering, typo tolerance, and autocomplete are now functional. It works with any option, including Laravel, MySQL database, or a standalone PHP script. Remember, you won’t get accurate results from your PHP search engine if you don’t add the documents. 

You can visit our [multi-language guide](https://www.meilisearch.com/blog/php-search-engine#other-languages) to see how it compares with other languages.


## 3. Build the Search Function and Return Results

Now that your search engine has the documents that it needs to return accurate results, we will shift to writing the PHP function that performs the actual search.

How does it work? The function will take any user’s input and send a request to Meilisearch. From here, Meilisearch will return matching results that you are looking for. This feature is the heart of any PHP engine. 

Here’s an example of a basic search function using the Meilisearch PHP library:


```php
$results = $index->search('Interstellar')->getHits();

foreach ($results as $hit) {
  echo '<h2>' . $hit['title'] . '</h2>';
}
```


This is a prime example of how PHP and HTML work together to return the relevant results from the Meilisearch API. To format the output, we will use CSS. We will rely on caching and integrate it into a Laravel or MySQL-based web application for optimization. 

You can build a [PHP search engine for free](https://www.meilisearch.com/blog/php-search-engine#free), but some features, like advanced analytics, may require Meilisearch Cloud.


## 4. Add Filters and Facets for Smarter Search

Keyword matching is only the first element of building a real search engine in PHP. You need much more, including filters. Using Boolean conditions, Meilisearch lets your users narrow down their search based on filters, such as category, tag, or even custom fields. 

Before this filtering, you need to define which fields are filterable:


```php
$index->updateFilterableAttributes([
  'genres',
  'id'
]);
```


Now, you can run filtered searches using SQL-style logic:


```php
$index->search('drama', [
  'filter' => ['genres = Drama AND id > 1']
]);
```


This search functionality is important for any PHP script powering a blog, forum, or ecommerce site. It saves time and helps users find what they are looking for. To make your results even more dynamic, you can always count on formatting in HTML or improving markup with CSS. Don’t forget to connect the output to a MySQL database as well.

These filters are a straight-up bonus for your the SEO and usefulness of your PHP search engine.


## 5. Track User Behavior with Search Analytics

Analytics rules the world of data; our case is no different. Once the PHP engine is live, you must constantly measure results to improve it. 

Meilisearch Cloud has built-in analytics to track various metrics, including top queries, zero-search results, user search patterns, etc. But if you’re self-hosting, you must track these metrics using PHP, JSON logging, or browser-side JavaScript.  

These insights will help you improve the overall search experience through advanced data-driven changes and customization. For example, if users keep typing terms that don’t match your content, you might need to improve your SQL or update your search algorithm. You get instant improvements in SEO and user experience. 

Tracking doesn’t need to be basic. You can improve it by combining it with the data from your MySQL database. The plugins in Laravel apps gladly sync with your analytics. So, whether working with [full-text search](https://www.meilisearch.com/blog/how-full-text-search-engines-work) or [faceted filters](https://www.meilisearch.com/blog/nested-hierarchical-facets-guide), analytics helps you deliver more relevant results to users.


## What are some examples of PHP search engine uses?

A PHP search engine can be a basic text box, or more – the choice is yours. For instance, it can be built with tools like Meilisearch PHP, and it becomes an unstoppable machine that finds content across all types of applications. These could include small personal sites or large eCommerce brand platforms. 

Let’s have a look at where it’s most commonly used:


1. **App Search:** Developers often use PHP to supercharge the search functionality inside web applications. With Meilisearch, you can improve your search navigation in internal dashboards, customer portals, or even complex tools to generate more relevant results. 
2. **Site Search:** Do you have static content that needs to be scanned? A PHP script can power a custom site search that will do just that. From blogs to a complex documentation hub with SQL-backed content or even Markdown rendered with PHP, this custom site search will do it all.
3. **eCommerce Search:** PHP enables top-tier customization that helps users find items faster and more accurately through filters for categories, products, brands, pricing, etc.
4. **Forum Search:** Communities built in PHP (such as phpBB or custom Laravel forums) often struggle with slow or limited search functionality. A dedicated engine improves how users find threads or replies.
5. **Media Search:** You can build a PHP searchable video or image archive, tagging assets with keywords stored in a MySQL database and filtering them through a search API.
6. **Document Search:** Many backend systems use PHP for uploading and managing files. With the right indexing logic, users can search by file names, metadata, or even parsed content.

If you wish to see more examples, you can view them in our [PHP search engine examples](https://www.meilisearch.com/blog/php-search-engine#examples) section.


## Which languages besides PHP are used to build a search engine?

While building a PHP search engine is a popular approach, developers often choose other languages based on their project’s architecture, database integration, and overall search engine functionality.



* **JavaScript** is often used to build client-side search experiences in SPAs or modern web apps. With frameworks like React or Vue, you can integrate real-time search UIs powered by APIs or hosted engines.[ ](https://www.meilisearch.com/blog/javascript-search-engine)Learn how to build a [search engine in JavaScript](https://www.meilisearch.com/blog/javascript-search-engine).
* **Python** is a flexible scripting language ideal for crawling, indexing, and building logic-heavy search pipelines. It’s often used in AI-based semantic search or research-focused projects. Learn how to build a [search engine using Python](https://www.meilisearch.com/blog/python-search-egine).
* **Golang** offers concurrency and performance benefits that suit high-traffic search systems. It’s often used for scalable backend search services that demand speed.[ ](https://www.meilisearch.com/blog/golang-search-egine)Learn how to build a [search engine with Golang](https://www.meilisearch.com/blog/golang-search-egine).

Each of these programming languages is fully compatible with open-source tools like Meilisearch. They allow you to build scalable, high-performance search engines suited to different project needs.


## Can I build a search engine in PHP for free?

Yes, you can build a functional [PHP search engine without licensing costs](https://www.meilisearch.com/blog/php-search-engine#free). The Meilisearch PHP SDK is open-source and can be hosted on a Linux server or a local machine. It supports JSON, HTML, and XML, and works with Laravel, MySQL, or standalone PHP scripts.

However, production-grade systems may incur additional costs. These include third-party plugin dependencies, hosting expenses, API rate limits, and advanced search features such as analytics or autocomplete, which may require Meilisearch Cloud. This setup is effective for beginners and adaptable for long-term scalability.


## Built a high-functioning PHP search engine with Meilisearch

Meilisearch delivers essential search functionality for PHP applications. It represents features like full-text search, filters, typo tolerance, and autocomplete. It integrates easily with Laravel, MySQL, and JSON workflows. Naturally, this makes it ideal for e-commerce search boxes, forums, and custom crawlers.

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