Imagine this: You're a SaaS product manager who just left a board meeting where the CEO announced, "We need to invest in AI search!" The budget is set, the engineering team is eager, and time is running out. But as you explore **AI-powered search**, the reality strikes hard. The field is filled with buzzwords, big promises, and high costs. You start to question, "Will this really deliver the ROI we need?"

The problem is an unrealistic outlook, wrong metrics, and the "more is better" mentality regarding features. SaaS companies spend millions on AI search, only to deal with rising costs, unhappy users, and doubts about their approach.

But there's good news. With a **pragmatic, business-focused approach**, you can use AI search to achieve real results. This can be done without overspending or losing focus.

This article will cover crucial strategies for **implementing AI-powered search** effectively. Topics include budgeting, feature selection, measuring success, and future-proofing your SaaS. Prepare to move beyond the hype and truly harness AI search.

## Core features of new generation AI-powered search

### Demystifying AI search technologies

AI-powered search represents a significant leap forward from traditional keyword matching. It employs sophisticated algorithms that understand context and user intent.

At its core, this technology combines machine learning and natural language processing to interpret queries as humans naturally express them. For instance, when Bookshop.org implemented AI search, their system understood complex queries about books. It accounted for variations in titles, author names, and even thematic searches that traditional systems would struggle with.

![Differnent Layers Of Ai Powered Search](https://unable-actionable-car.media.strapiapp.com/ai_powered_search_834ac28328.avif)

### Storage layer

#### Vector database

[Vector databases](https://www.meilisearch.com/blog/what-is-a-vector-database) form the foundation of modern AI search systems. They store complex data representations that capture semantic relationships between items. Unlike traditional databases, these specialized systems can efficiently process high-dimensional data. **This enables lightning-fast similarity searches across millions of items.**

#### Multiple DBs for full-text search

The architecture of modern search systems often employs multiple specialized databases features working together. These include prefix databases for instant search suggestions, inverted indexes for rapid full-text search, and specialized stores for handling typos and variations.

### Capabilities layer

#### Full-text search

[Full-text search](https://www.meilisearch.com/blog/how-full-text-search-engines-work) remains a cornerstone of modern search systems. It provides precise lexical matching with advanced features like typo tolerance and prefix searching. This capability is especially valuable in e-commerce scenarios. Users often search for specific product codes or exact phrases.

#### Vector search

[Vector search](https://www.meilisearch.com/blog/introducing-vector-search) enables semantic understanding by converting search queries and content into mathematical representations that capture meaning, not just keywords. **This technology powers the ability to find relevant results even when queries don't exactly match the target content.** It was transformative for Bookshop.org's vast catalog of six million books.

#### Hybrid search

[Hybrid search](https://www.meilisearch.com/blog/introducing-hybrid-search) combines the precision of full-text search with the semantic understanding of vector search. It offers the best of both worlds. This approach allows systems to handle both precise queries and more exploratory searches effectively. It delivers relevant results regardless of how users express their needs.

### Application layer

The application layer transforms raw search capabilities into practical features that drive business value. It includes personalization systems that adapt results based on user behavior, [semantic search that understands natural language queries](https://www.meilisearch.com/blog/choosing-the-best-model-for-semantic-search), and recommendation engines that suggest related items.

Hugging Face leverages this layer to help users discover relevant AI models based on specific use cases and preferences. Similarly, HitPay uses it to enable merchants to prioritize specific products in search results based on business rules.

<Callout intent="note">
	
	**Want to dive deeper?** Check out our [detailed comparison of full-text vs vector search](https://www.meilisearch.com/blog/full-text-search-vs-vector-search) to understand their unique strengths and use cases.
	
	
</Callout>

## Use cases that demonstrate tangible benefits

AI-powered search is transforming how users interact with digital platforms beyond the traditional search bar.

[Bookshop.org's implementation](https://www.meilisearch.com/blog/bookshop-increases-search-based-purchases) demonstrates a compelling impact: **their conversion rate jumped from 14% to 20% after switching to AI-powered search, representing a 43% increase in search-to-purchase conversion**. This improvement came from the system's ability to understand complex book queries and handle their massive six-million-item catalog effectively.

[For Hugging Face](https://www.meilisearch.com/blog/hugging-face-democratizes-ai), **AI search solved the challenge of making 220,000 AI models, 38,000 datasets, and 60,000 demos discoverable to their technical audience**. Their implementation goes beyond simple keyword matching, allowing developers to find relevant models based on use cases, performance characteristics, and technical requirements. The system's ability to understand technical context and relationships between different AI models has made it significantly easier for users to find the right tools for their specific needs.

[HitPay's experience](https://blog.meilisearch.com/hitpay-streamlines-search/) highlights how AI search can bridge the gap between online and offline retail operations. Their point-of-sale system uses AI search to help sales assistants quickly locate specific products across multiple locations, while simultaneously powering their e-commerce storefronts. The system's ability to sync product catalogs across channels and prioritize frequently accessed items has resulted in a 50% increase in search API response speed. **This directly improves the customer experience both in-store and online.**

## Budgeting for AI-powered search

### Determining AI budget allocation

<Callout intent="note">
	
	Enterprises are rapidly adopting generative AI, [dedicating upwards of 10% of their IT budgets by 2025](https://techstrong.ai/articles/generative-ai-consuming-larger-percentage-of-it-budgets/). You can allocate between 5-15% of IT budgets to AI initiatives. the ideal investment varies based on company size, sector, and goals.
	
	
</Callout>

When budgeting for AI-powered search integration, consider these factors:

- The scale of your existing search infrastructure
- The complexity of your data and search requirements
- The size of your user base and expected growth
- Your team's existing AI and machine learning expertise
- The level of customization you require

### Breakdown of costs: R&D, implementation, ongoing optimization

#### The true cost of AI search implementation

Implementing AI-powered search requires careful consideration of both direct and indirect costs. The initial investment covers infrastructure setup, data preparation, and integration costs.

For example, Bookshop.org's transition from Elasticsearch to a more sophisticated AI search solution involved not only technology costs but also resources to properly index and configure their six-million-item catalog. Companies must also factor in ongoing costs for maintenance, updates, and infrastructure scaling as search volume grows.

<Callout intent="note">
	
	[Check out our analysis of Meilisearch vs. Elasticsearch](https://www.meilisearch.com/blog/meilisearch-vs-elasticsearch), for a detailed comparison of the two search solutions.
	
	
</Callout>

**HitPay demonstrates this approach – they began with an open-source solution to validate their use case before transitioning to a managed cloud service.** This allowed them to optimize costs while ensuring reliability for their business-critical search functions.

#### Avoiding budget black holes

The most significant hidden costs often stem from unexpected infrastructure scaling needs and ongoing optimization requirements. Companies frequently underestimate the resources needed to maintain search relevance and manage growing data volumes.

![ai powered search](https://unable-actionable-car.media.strapiapp.com/ai_powered_search_4def6c80b2.avif)

#### Infrastructure costs and scalability considerations

Infrastructure is critical for AI-powered search implementation.** Key considerations include:

- **Computing power:** AI models, especially during training, require substantial computational resources. This may necessitate investment in high-performance hardware or cloud computing services.
- **Storage:** AI systems need to process and store large amounts of data. Ensure your storage solutions can handle this increased load.
- **Network capacity:** If your AI search system processes queries in real-time, upgrade your network infrastructure to handle increased traffic.
- **Scalability:** As your user base grows, your AI search system needs to scale. Cloud-based solutions offer flexibility for scaling but may have higher long-term costs compared to on-premises solutions.

### ROI calculation framework for AI search investments

Measuring return on investment requires tracking both direct and indirect impacts of AI search implementation. Key metrics should include:

- Conversion rate improvements (like Bookshop.org's 43% increase)
- Operational efficiency gains (such as HitPay's 50% faster search response times)
- Customer satisfaction metrics
- Reduced support costs from improved self-service capabilities

For SaaS businesses, significant returns often come from improved user engagement and reduced churn. When calculating ROI, companies should consider both immediate performance improvements and long-term benefits, like increased customer lifetime value and reduced operational costs.

## KPIs for measuring success

### Beyond vanity metrics

Basic metrics like search volume and click-through rates provide some insight, but often mask deeper performance issues. The most telling indicators come from user behavior patterns and business outcomes.

For example, Bookshop.org's success wasn't measured by the number of searches performed, but by the 20% conversion rate from search to purchase. **This metric is directly tied to revenue generation.**

### Decoding search performance that actually matters

Search performance metrics should reflect real user success rates rather than just technical measurements.

Hitpay's 50% improvement in search API response speed impacts in-store customer service and online shopping experiences.

The most telling metrics often combine multiple data points to create a complete picture of search effectiveness. Consider tracking:

- Search-to-conversion ratio: Like Bookshop.org's jump from 14% to 20% in search-driven purchases
- Query refinement rate: How often users need to modify their search terms to find what they need
- Time-to-content: How quickly users find relevant information after initiating a search
- Zero-result rates: The percentage of searches that yield no results, indicating gaps in content or search capability

These metrics should be analyzed in context with your specific business objectives. For a knowledge base, success might mean reduced time-to-answer. For an e-commerce platform, it could mean higher average order values from search-driven purchases.

### Warning signs: KPIs that signal AI search failure

Several indicators can reveal problems with your AI search implementation. Watch for:

- High abandonment rates after search attempts
- Increased support tickets related to finding information
- Growing use of alternative navigation methods
- Declining engagement with search-discovered content

When Hugging Face noticed users bypassing their search functionality in favor of direct repository browsing, it signaled a need to improve their search relevance and feature set. **Monitoring these warning signs helps identify** issues before they impact user satisfaction and business outcomes.

## Selecting the right AI search features for your SaaS business

### What use cases they enable

AI search features should align with specific business objectives and user needs.

Bookshop.org's implementation demonstrates this principle perfectly. Their focus on semantic understanding and typo tolerance addressed the challenge of helping users find books across a vast catalog, even with imperfect queries. **This targeted approach led to their impressive 43% increase in conversion rates.**

### AI search feature mapping

Semantic search capabilities are valuable for content-rich platforms. Hugging Face's implementation helps users navigate their extensive repository of AI models. Users can find relevant models even when search terms don't exactly match model names or descriptions, significantly improving discovery rates.

Similarity search is particularly useful in e-commerce contexts. HitPay uses this feature to help merchants connect customers with related products. This goes beyond category matching to understand product relationships based on multiple attributes, enhancing cross-selling opportunities.

Personalization features are crucial for platforms with diverse user bases. For instance, HitPay's implementation allows merchants to pin frequently accessed products, automatically adapting search results based on business-specific patterns and preferences.

### When more features mean less value

Not every AI search feature delivers equal value for every business case. Hugging Face's experience shows the importance of prioritizing features that align with core user needs. **They focused on making their vast technical documentation and model repository discoverable rather than implementing every available search feature.**

The key to feature selection lies in understanding user behavior patterns and business objectives. For example, while multi-modal search might seem cutting-edge, Bookshop.org achieved significant results by focusing on fundamental features like semantic understanding and typo tolerance. These features directly addressed their users' primary needs.

<Callout intent="tip">
	
	Ready to supercharge your search experience? [Explore Meilisearch Cloud](https://meilisearch.com/cloud) and deliver lightning-fast search results that will keep your users engaged and boost your conversion rates.
	
	
</Callout>

## Best practices for implementing AI-powered search

### The implementation gauntlet

Successful AI search implementation requires a methodical approach that prioritizes user needs over technical complexity. HitPay's implementation journey is instructive.

They started with a simple internal dashboard search before expanding to customer-facing features. This gradual rollout allowed them to refine their approach with minimal risk. **They achieved a 50% improvement in search API response speed while maintaining system stability.**

When Bookshop.org transitioned to their new search system, they started with a test environment. This allowed them to validate their approach before full deployment. This methodical strategy enabled them to **maintain search performance during their busy holiday season** while implementing significant improvements.

### Critical decision points in AI search deployment

Key decisions during implementation should focus on:

- **Initial feature set selection based on immediate user needs**
- Data indexing strategy and update frequency
- Performance monitoring and optimization approaches
- User feedback collection and integration methods

Bookshop.org's team made their ranking rules accessible to non-developers, allowing for quick iterations based on business needs **without requiring engineering intervention**. This approach enabled them to maintain search relevance during peak periods while continuously improving the user experience.

## Future proofing your SaaS with AI-powered search

### Cutting through the AI hype cycle

The future of AI-powered search extends beyond current capabilities. Successful implementation requires distinguishing between genuine innovations and marketing hype.

Hugging Face's approach to testing vector database integration for semantic search demonstrates how companies can practically evaluate emerging technologies.

**Organizations can evaluate search solutions based on actual business needs. This approach helps avoid chasing trendy technologies that fail to deliver meaningful results.**

<Callout intent="tip">
	
	Stay informed about the latest in search technology, product updates, and industry insights. [Subscribe to our newsletter](https://share-eu1.hsforms.com/1LN5N0x_GQgq7ss7tXmSykwfg3aq) and be the first to know about Meilisearch Cloud's exciting developments.
	
	
</Callout>

### Strategic long-term AI search planning

Long-term success requires building flexibility into search implementations. HitPay's evolution from basic search functionality to a sophisticated multi-channel solution shows how search capabilities can grow alongside business needs. Their experience highlights the importance of choosing solutions that can scale and adapt as requirements change, rather than being locked into rigid implementations that limit future options.

The most sustainable approaches focus on creating adaptable search infrastructures that can incorporate new capabilities as they mature. Bookshop.org's implementation demonstrates this principle. **Their search architecture allows them to continuously refine ranking rules and add new features without disrupting core functionality.** This flexibility enabled them to achieve immediate benefits while maintaining the ability to adopt new AI capabilities as they become available.

As AI technologies evolve, AI-powered search has become a critical differentiator for SaaS businesses. Success stories of companies like Bookshop.org, HitPay, and Hugging Face show that thoughtful implementation of AI search capabilities can deliver concrete business results.

These include increased conversion rates and improved user engagement. The key lies in selecting and implementing solutions that align with specific business needs while maintaining the flexibility to adapt as technology and user expectations evolve.

<Callout intent="tip">
	
	Need a custom search solution tailored to your unique requirements? [Schedule a Consultation](https://meet.meilisearch.com/meetings/cloud/presentation?uuid=d7d3d29c-635f-4544-877d-86ecbd29e7b8) with our expert team and transform your search strategy.
	
	
</Callout>

Beyond the hype: practical AI search strategies that deliver ROI


Have you ever noticed how Google suggests exactly what you're looking for before you finish typing? That's predictive search in action. It revolutionizes user interaction with software applications. In this guide, we'll explore implementing predictive search in your applications.

## Introduction to predictive search

### Definition and importance of predictive search

**Predictive search is an intelligent system that anticipates user intent and suggests relevant results in real-time as users type**. Unlike traditional search methods that wait for a complete query, predictive search processes each keystroke to provide immediate, relevant suggestions.

The importance of predictive search in software engineering is significant. It bridges user intent and content discovery, reducing the time and effort users spend searching. Modern applications leverage predictive search to enhance productivity and streamline workflows.

Software engineering teams implementing predictive search can expect improved user engagement metrics. For instance, e-commerce platforms often see a 25-30% increase in conversion rates with predictive search features. Developer tools report significant reductions in time spent searching for code snippets or documentation.

### Predictive search enhancing autocomplete

Traditional autocomplete completes words based on character matching. Predictive search, however, analyzes patterns in user behavior, search history, and contextual data to offer intelligent suggestions.

The personalization aspect of predictive search sets it apart from basic autocomplete. **Predictive search creates a more tailored experience by considering factors like previous searches, user location, and trending queries**.

For example, when a developer searches for "deploy" in a cloud platform's console, the system prioritizes suggestions based on their commonly used services or recent deployments.

<Callout intent="note">
	
	For example, in an e-commerce platform, **autocomplete might suggest "laptop bag"** when a user types "lap", predictive search might also suggest "computer accessories," "mobile workstation," or specific laptop models based on browsing history and market trends.
	
	
</Callout>

## Core concepts and components

### Predictive models in software engineering

Predictive models form the backbone of modern search capabilities. **They utilize complex algorithms to analyze user input patterns and generate relevant suggestions**.

Businesses can leverage large datasets, with the specific type of data varying depending on the industry and business objectives. It is possible to use various data sources like browsing history, user interactions, and market trends to improve predictive search strategies.

![Examples Of Data Predictive Search](https://unable-actionable-car.media.strapiapp.com/predictive_search_b4778b9493.avif)

These models process queries through multiple layers of analysis, including string matching, semantic understanding, and historical pattern recognition.

Predictive models often rely on prefix trees (or radix trees) for efficient string matching and retrieval. This structure enables rapid processing of partial queries, allowing the system to generate suggestions with minimal latency. The models continuously learn from user interactions, refining their prediction accuracy over time.

### Search engines and search queries

Search engines in predictive systems employ sophisticated indexing mechanisms to aid rapid query processing. Key components include query processors, suggestion generators, and ranking algorithms.

These elements work together to produce and prioritize results based on various relevance factors.

![Layers Of Logic Search Engine](https://unable-actionable-car.media.strapiapp.com/predictive_search_2c856b34f6.avif)

Modern search queries go through steps including tokenization, spell checking, and context analysis. **This ensures that even imperfect queries can yield useful suggestions**. Balancing immediate results and accuracy often involves using caching mechanisms to optimize frequently requested queries.

### Machine learning and AI power

Artificial intelligence significantly enhances predictive search through advanced natural language processing and machine learning algorithms.

These technologies help systems understand user intent beyond literal string matching, incorporating semantic understanding and contextual relevance.

AI improves search accuracy by:

- Capturing user intent with natural language understanding
- Adapting ranking algorithms to changing user behavior patterns
- Identifying relationships between different terms through semantic analysis
- Tailoring suggestions based on user profiles and historical interactions

Combining machine learning with traditional search algorithms creates a hybrid approach that delivers both speed and accuracy.

## Capabilities required for predictive search

A robust predictive search implementation requires several key capabilities to deliver a seamless user experience. Fast indexing and efficient query processing are foundational, but there's more to consider.

### Meilisearch's approach to enabling predictive search

Meilisearch combines traditional full-text search with AI-powered capabilities. Its architecture enables fast, relevant results and maintains simplicity in implementation.

**The integration process focuses on three key areas**: data indexing, search configuration, and result optimization. Here's an example of configuring search settings:

```typescript
// config/search.ts
const searchConfig = {
  settings: {
    rankingRules: [
      'words',
      'typo',
      'proximity',
      'attribute',
      'sort',
      'exactness'
    ],
    searchableAttributes: [
      'title',
      'description',
      'category'
    ],
    displayedAttributes: [
      'title',
      'description',
      'category',
      'thumbnail'
    ],
    // Enable AI-powered search with OpenAI
    embedder: {
      type: 'openai',
      documentTemplate: "Product: {{title}}nDescription: {{description}}"
    }
  }
};

await searchClient.index('products').updateSettings(searchConfig);
```

**This configuration enables both traditional search capabilities and AI-powered features**, creating a hybrid search experience that delivers both speed and relevance. The system handles typos, synonyms, and contextual understanding while maintaining sub-50ms response times.

<Callout intent="tip">
	
	Ready to supercharge your search experience? [Explore Meilisearch Cloud](https://meilisearch.com/cloud) and deliver lightning-fast search results that will keep your users engaged and boost your conversion rates.
	
	
</Callout>

## Applications and use cases

Modern software applications leverage predictive search in numerous ways, from e-commerce platforms to content management systems. Understanding these practical applications helps developers implement the right search strategies for their specific use cases.

### App search

**App search is a common implementation of predictive search in software applications**. Mobile apps benefit particularly due to limited screen space and typing constraints.

For instance, food delivery apps use predictive search to help users find restaurants or dishes quickly, considering factors like previous orders and current location.

### Site and media search

**Site and media search capabilities have evolved to handle complex content structures**. Content management systems and documentation platforms use predictive search to help users navigate large information hierarchies.

For example, technical documentation sites can suggest relevant API endpoints or code examples as developers type their queries.

<Callout intent="note">
	
	Check out how Meilisearch powers the discovery of 300,000+ AI models on [Hugging Face](https://www.meilisearch.com/blog/hugging-face-democratizes-ai), demonstrating the power of predictive search in organizing and accessing vast AI resources.
	
	
</Callout>

### Ecommerce search

E-commerce platforms showcase the potential of predictive search through sophisticated product discovery. Online stores suggest not only product names but also categories, brands, and specific features. **This capability extends to handling complex queries like "red summer dress under $50" accurately**.

<Callout intent="note">
	
	For another compelling example of predictive search in action, explore [Meilisearch's case study with Bookshop.org](https://www.meilisearch.com/blog/bookshop-increases-search-based-purchases), where they achieved a remarkable 43% increase in search-based purchases by enhancing their book discovery platform.
	
	
</Callout>

### Enterprise search

[Enterprise search](meilisearch.com/blog/enterprise-search) presents unique challenges because it handles diverse data sources while maintaining security constraints. Predictive search in enterprise environments must consider user permissions, department-specific terminology, and internal document hierarchies.

For instance, a company's internal tools might suggest different results for the same query based on the user's role and access level.

## Techniques and methodologies

### Genetic programming and test models

Genetic programming enhances predictive search capabilities by **evolving and refining search algorithms**. These techniques mimic natural selection to optimize search patterns and improve relevancy.

The system dynamically generates search algorithm variations. It then evaluates these algorithms by analyzing user interactions. Based on performance metrics, the system selects the most effective algorithm combinations to enhance future search experiences.

Test models developed through genetic programming adapt to changing search patterns. For instance, e-commerce search systems can automatically adjust prediction weights based on seasonal trends, user behavior shifts, and new product introductions. **The evolutionary nature of these algorithms ensures relevant search suggestions** as user preferences and patterns evolve.

### Base software and search base

Establishing a robust search base requires careful consideration of data structure and indexing strategies.

<Callout intent="note">
	
	For deeper insights into optimizing search indexing, check our comprehensive guide on [best practices for faster indexing](https://meilisearch.com/blog/best-practices-for-faster-indexing).
	
	
</Callout>

Typically, it consists of an inverted index system enabling quick lookup of terms while maintaining relationships between search elements. **This base structure must support both exact matches and fuzzy searching capabilities**.

Integration of predictive search within software frameworks demands:

- Efficient data storage and retrieval mechanisms to handle large volumes of search queries
- Flexible indexing strategies accommodating both structured and unstructured data
- Scalable architecture maintaining performance under increasing load
- Real-time update capabilities to reflect changes in underlying data

The search base must also handle edge cases and special scenarios. For example, with multilingual search capabilities, the base system must account for different character sets, writing systems, and language-specific patterns. **This ensures reliable and effective predictive search functionality across diverse use cases** and user requirements.

## Capabilities

Low latency is crucial for predictive search systems because users expect instant feedback while typing. Modern search implementations must maintain response times under 100ms to feel instantaneous. This speed requirement becomes even more critical when handling complex queries across large datasets.

Several optimization techniques help achieve these performance goals. **Intelligent caching strategies store frequently accessed results and partial queries**, significantly reducing database load and response times for common searches. Here's how response times typically break down:

| Operation | Target Latency | Optimization Method |
| --- | --- | --- |
| Query Processing | &lt; 20ms | In-memory processing |
| Index Lookup | &lt; 30ms | Cached indices |
| Result Ranking | &lt; 30ms | Pre-computed scores |
| Network Transfer | &lt; 20ms | Edge caching |

Ensuring reliability involves implementing robust error handling and failover mechanisms. **Search systems must gracefully handle scenarios like network issues, invalid queries, or backend service disruptions**. Implementing circuit breakers and fallback options helps maintain service availability.

Performance monitoring is vital for maintaining optimal search functionality. Modern search implementations use real-time metrics tracking to identify bottlenecks and optimization opportunities.

Key performance benchmarks for predictive search systems include:

- **Query response time:** 95th percentile under 200ms
- **Suggestion relevancy:** Minimum 80% accuracy rate for top 5 suggestions
- **System throughput:** Ability to handle 1000+ queries per second per node
- **Cache hit ratio:** At least 85% cache effectiveness

<Callout intent="tip">
	
	Meilisearch Cloud includes [built-in monitoring tools](https://www.meilisearch.com/docs/learn/analytics/monitoring) that enable real-time tracking of search performance metrics.
	
	
</Callout>

The architecture should scale horizontally to handle increasing load while maintaining performance. This typically involves:

1. Distributed index sharding for better resource utilization
2. Load balancing across multiple search nodes
3. Asynchronous index updates to prevent blocking operations
4. Smart query routing based on user location and server load

Reliability also extends to data consistency and accuracy. Search systems must handle index updates and real-time data changes without compromising search quality or availability. **This often requires sophisticated versioning and synchronization mechanisms** to ensure users always see the most relevant and up-to-date results.

## Future directions and innovations

As predictive search technology continues to evolve, several emerging trends and innovations are reshaping the future of search systems.

### Integration with advanced technologies

The future of predictive search is being shaped by **advancements in artificial intelligence and machine learning technologies**. Large language models (LLMs) are transforming how search systems understand and process user queries. These models can interpret natural language with high accuracy, enabling more conversational and context-aware search experiences.

Integration with advanced technologies is already showing promising results. Some systems are experimenting with multimodal search capabilities, processing text, images, and voice inputs simultaneously. This enables more intuitive search interfaces that match how humans naturally communicate.

### Trends in predictive search

LLMs are revolutionizing predictive search capabilities by fundamentally transforming how we interact with search engines. These sophisticated systems can now comprehend complex queries and generate natural, contextually appropriate suggestions that feel remarkably human-like.

Modern search systems now interpret both technical and informal language, moving beyond pattern matching to understand the true meaning behind user queries.

This advancement has enabled several crucial improvements in search functionality:

- More precise interpretation of user queries
- Sophisticated handling of ambiguous searches
- Enhanced recognition of specialized terminology
- Intelligent, context-aware result ranking

**These innovations are already being implemented in various forms**. Some search engines use hybrid approaches that combine traditional search algorithms with [AI-powered semantic understanding](meilisearch.com/blog/ai-powered-search). This allows them to maintain the speed and reliability of conventional search while leveraging advanced AI capabilities.

## Beyond horizons: the transformative potential of predictive search

Predictive search has evolved from a convenient feature to an essential component of modern software applications.

Successful implementation requires careful consideration of architecture, performance optimization, and user experience design.

The integration of advanced AI technologies and mobile-first approaches will continue to push the boundaries of what's possible in predictive search. This makes it an exciting area for continued innovation and development.

<Callout intent="tip">
	
	Unlock advanced search potential with customizable relevancy, typo tolerance, and more. [Enhance Your Search Strategy](https://meilisearch.com/features) with Meilisearch Cloud's powerful search capabilities.
	
	
</Callout>

Software engineering predictive search: a complete guide

_This blog post was originally published on kero's [personal blog](https://blog.kerollmops.com/meilisearch-indexes-embeddings-7x-faster-with-binary-quantization)._

<Callout intent="note">
This is one blog post in a series:
- [Spotify-Inspired: Elevating Meilisearch with Hybrid Search and Rust](https://blog.kerollmops.com/spotify-inspired-elevating-meilisearch-with-hybrid-search-and-rust)
- [Multithreading and Memory-Mapping: Refining ANN Performance with Arroy](https://blog.kerollmops.com/multithreading-and-memory-mapping-refining-ann-performance-with-arroy-in-rust)
- [Meilisearch Expands Search Power with Arroy's Filtered Disk ANN](https://blog.kerollmops.com/meilisearch-expands-search-power-with-arroy-s-filtered-disk-ann)
- [How Meilisearch Updates a Millions Vector Embeddings Database in Under a Minute](https://blog.kerollmops.com/how-meilisearch-updates-a-millions-vector-embeddings-database-in-under-a-minute)
- Meilisearch Indexes Embeddings 7x Faster with Binary Quantization.
</Callout>

For the past year at Meilisearch, [we’ve been working hard on hybrid search](https://github.com/meilisearch/meilisearch/issues/3838), which mixes keyword search with semantic search using [Arroy: our vector store, based on a Spotify technology](https://blog.kerollmops.com/spotify-inspired-elevating-meilisearch-with-hybrid-search-and-rust). A vector store is a data structure that efficiently stores embeddings (vectors) for fast and relevant retrieval, called Approximate Nearest Neighbors (ANN) search.

When our customers started to use arroy heavily, some of them started reaching the machine limits. For example, for a customer with a 768 dimensions model, we found out that we couldn’t index more than 15M of embeddings on a machine with 64GiB of RAM.

### Binary Quantization to the Rescue

The concept behind this elaborate term is that we are going to *quantize* the embeddings. Quantizing a number involves spreading it into a defined number of values; for *binary* quantization, two values can be represented on a single bit, e.g., 0.2561 becomes 1, and -0.568 becomes -1.

This implies that we could convert a 32-bit float number into a 1-bit number, which would divide the disk and RAM usage by 32. That would mean we could index up to 480M embeddings with 64GiB of RAM, instead of being limited to 15M embeddings.

An issue we faced is that bits cannot represent -1 and 1 but only 0 and 1. We had to use some tricks to make it work and fake 0s into -1s in our computations. For example, one of the functions that we had to optimize a lot was the function in charge of converting a binary quantized vector to 32-bit float vectors.

We do not publicly expose raw quantized embeddings and utilize this function to provide real 32-bit float embeddings to the users. However, the primary reason for converting 1-bit quantized embeddings to 32-bit floats is to ensure high precision when [generating hyperplanes to group embeddings together](https://blog.kerollmops.com/spotify-inspired-elevating-meilisearch-with-hybrid-search-and-rust#spotify-s-hyperplane-trees-for-efficient-anns) and maintaining relevant ANNs.

![Profiling backtrace showing slow 1-bit to 32-bit float conversion](https://unable-actionable-car.media.strapiapp.com/68747470733a2f2f692e696d6775722e636f6d2f497165713649562e706e67_596adc5fd5.png)

_Converting a quantized embedding back to 32-bit floating points takes up to 24.21s on a 1m53s run._

Below is the SIMD neon-specific version of this function. It processes raw bytes corresponding to a 1-bit quantized embedding, converting them into a properly aligned 32-bit float embedding. Using masks, the function separates the low and high bits, processing them in batches of four in 128-bit registers, which are then transformed into four 32-bit floats. These values are collectively written to the output vector.

```rust
unsafe fn to_vec_neon(bytes: &[u8]) -> Vec<f32> {
    let mut output: Vec<f32> = vec![0.0; bytes.len() / u32::BITS];
    let output_ptr = output.as_mut_ptr();
    let low_mask = [0b_0000_0001, 0b_0000_0010, 0b_0000_0100, 0b_0000_1000];
    let high_mask = [0b_0001_0000, 0b_0010_0000, 0b_0100_0000, 0b_1000_0000];
    let ones = unsafe { vld1q_dup_f32(&1.0) };
    let minus = unsafe { vld1q_dup_f32(&-1.0) };

    for (current_byte, base) in bytes.iter().enumerate() {
        unsafe {
            let base = *base as u32;
            let base = vld1q_dup_u32(&base);
            for (i, mask) in [low_mask, high_mask].iter().enumerate() {
                let mask = vld1q_u32(mask.as_ptr());
                let mask = vandq_u32(base, mask);
                // 0xffffffff if equal to zero and 0x00000000 otherwise
                let mask = vceqzq_u32(mask);
                let lane = vbslq_f32(mask, minus, ones);
                let offset = output_ptr.add(current_byte * 8 + i * 4);
                vst1q_f32(offset, lane);
            }
        }
    }

    output
}
```

_<a href="https://x.com/lemire/status/1835700368648323261">Daniel Lemire doesn't seem to have found a better algorithm</a>_ 🤭

### The Results

We chatted about the theoretical benefits of this approach earlier, but how does it work out in real life? Here are different tables summarizing the results for you.

<details>
  <summary>Open to see the original benchmarks</summary>
  <img src="https://unable-actionable-car.media.strapiapp.com/68747470733a2f2f692e696d6775722e636f6d2f67667765626b592e706e67_a96a995c7c.png"  alt="Original arroy benchmark results"/>
</details>

#### Embeddings with 1024 Dimensions

When examining small embeddings, we can observe a significant impact on relevancy due to binary quantization. The primary advantages of this approach lie in faster insertion times and reduced storage requirements. It is not advisable to enable this feature for small embeddings, as they already occupy minimal disk space. Storing over four million 1024-dimensional embeddings requires just 8 GiB of disk space.

| Version                 | Recall @1 | Recall @20 | Recall @100 | Indexing time | Search time | On disk size |
|-------------------------|-----------|------------|-------------|---------------|-------------|--------------|
| Cosine                  | **1.00**  | **0.72**   | **0.84**    | 491s          | **23s**     | 8 GiB        |
| Binary Quantized Cosine | 0.87      | 0.50       | 0.52        | **147s**      | 27s         | **850 MiB**  |

#### Embeddings with 1536 Dimensions

Binary quantization proves advantageous for managing larger embeddings. It significantly reduces search times, utilizing just 15% of the original disk space. Indexing about 500,000 1536-dimensional embeddings is achieved in a quarter of the time. Despite this, there remains a noticeable impact on relevancy...

| Version                 | Recall @1 | Recall @20 | Recall @100 | Indexing time | Search time | On disk size |
|-------------------------|-----------|------------|-------------|---------------|-------------|--------------|
| Cosine                  | **1.00**  | **0.82**   | **0.90**    | 1219s         | 50s         | 13 GiB       |
| Binary Quantized Cosine | **1.00**  | 0.70       | 0.70        | **235s**      | **47s**     | **2 GiB**    |

#### Embeddings with 3072 Dimensions

The impact of binary quantization on large embeddings is evident. While relevance may be slightly affected, the reduction in disk usage, search time, and indexing time is significant. Being able to store the same number of embeddings with just 15% of the space and index 6 times faster is a major achievement! It takes half as much disk space to store a million 3072-dimensional binary quantized embeddings than four million 1024-dimensional embeddings.

| Version                 | Recall @1 | Recall @20 | Recall @100 | Indexing time | Search time | On disk size |
|-------------------------|-----------|------------|-------------|---------------|-------------|--------------|
| Cosine                  | **1.00**  | **0.86**   | **0.92**    | 4000s         | 150s        | 26 GiB       |
| Binary Quantized Cosine | **1.00**  | 0.77       | 0.77        | **560s**      | **100s**    | **4 GiB**    |

### Conclusion

The impact of binary quantization on recall was expected to decrease, given the reduced information in the algorithm. However, for datasets with over 1500 dimensions, the impact on recall is relatively minor compared to the significant benefits in disk space and indexing speed.

In practice, we observed that space utilization decreased not by 32 times but by just 15% of the original space, a notable achievement. This is primarily due to arroy storing not only user vectors but also internal nodes for efficient searching. Split nodes (hyperplanes) are retained as full 32-bit floats to maintain precision.

- Indexing speed improves nearly 5 times with 1536 dimensions and about 7 times with 3072 dimensions.
- Disk size shrinks to around 6.5 times smaller.
- Search time shows slight improvements for embeddings starting at 1536 dimensions.

We recommend using Meilisearch/arroy's binary quantization feature for OpenAI users with large embeddings. In an upcoming article, we will compare Meilisearch/arroy with competitors, focusing on our Filtered DiskANN feature. Stay tuned!

Meilisearch indexes embeddings 7x faster with binary quantization

We've been busy working on things you've asked for, and the team is excited to share what's new in Meilisearch this quarter.

## A revamped search preview 🔍

The [search preview](https://www.meilisearch.com/docs/learn/getting_started/search_preview) has been one of our most-used features, but it didn't let you test everything Meilisearch can do. We've now rebuilt our search preview to make it more useful to assess test search result relevancy when you are tweaking an index's settings.

What's included:

- Show/hide the results' [ranking score](https://www.meilisearch.com/docs/learn/relevancy/ranking_score):

![ranking score.png](https://unable-actionable-car.media.strapiapp.com/image_38_9583f10fe8.png)

- Check and customize search options
- Configure filterable attributes as facets
- Test [AI-powered searches](https://www.meilisearch.com/docs/learn/ai_powered_search/getting_started_with_ai_search) if this functionality has been enabled for your project.

An updated preview allows you to test everything in real-time, making it easier to get your search working exactly how you want.

## New index settings in the UI ⚙️

We've fixed one of your biggest frustrations - you and your team no longer need to rely on JSON input to [change your index settings](https://www.meilisearch.com/docs/learn/configuration/configuring_index_settings). Instead of typing JSON code, you'll now see straightforward controls in the UI for adjusting settings like:

- Add and delete searchable attributes:
![searchable attributes/index settings.png](https://unable-actionable-car.media.strapiapp.com/image_37_5bea152778.png)

- Change the ranks of your searchable attributes
- Reset to default

Now anyone on your team can get access to user-friendly modification of the index settings without needing to understand JSON syntax.

## Speed up your search  with feature opt-out 🚀

We've got some updates for those of you working with massive datasets. To **reduce disk usage** and **speed up indexing by up to 15%**, you can now optimize Meilisearch's performance: 

- Choose to opt-out of prefix search and facet search features
- Reduce indexing time and save disk space

This has been one of our most requested features from users dealing with large-scale deployments, and we're excited to see how you'll use it.

## What else is cooking? 🎯
#### New Japan region is coming in Q4! 🇯🇵
Soon, you’ll be able to host your Meilisearch projects in Japan. This means much faster searches for users in Asia-Pacific - instead of connecting to Singapore, they'll connect directly to servers in Japan. The difference? A search that used to take 80+ milliseconds will happen almost instantly.

To use it, you’ll just need to pick Japan when you create a new project in the [cloud](https://cloud.meilisearch.com) dashboard. Keep an eye on our updates to know exactly when it’s released!


We are not stopping here. Our team is already working on our early 2025 releases. Here's a quick spoiler:

- AI-powered hybrid search general availability
- New Cloud dashboard features
- Even better performance optimizations

## Try it out!

Want to test these features? [Sign up for Meilisearch Cloud](https://cloud.meilisearch.com/) to experience these new features firsthand.

---

We built these changes based on your feedback, and we'd love to know if they help. Try them out and tell us what you think.

For  a more in-depth look at these updates, visit our [documentation](https://docs.meilisearch.com/).

Meilisearch Q4 2024 release wrap-up

##  🐿️ Meilisearch 1.11 release

![Release notes - Meilisearch 1.11](https://unable-actionable-car.media.strapiapp.com/meilisearch_1_11_59ea013828.jpg)

Meilisearch 1.11 introduces key **improvements** in **AI-powered search**, including **binary quantization**. This release also **enhances federated search** functionality based on user feedback and introduces a **new filter operator**.

[Discover updates](https://www.meilisearch.com/blog/meilisearch-1-11)

***


## 🌎 TutKit's journey with Meilisearch 

[![Meilisearch powers multilingual learning at scale for Tutkit](https://unable-actionable-car.media.strapiapp.com/Tut_Kit_516315e429.png)](https://www.meilisearch.com/blog/tutkit-multilingual-learning-at-scale)

Discover how Tutkit.com scaled its learning platform to handle search across **15,000+ resources in 26 languages**.

[Read case study](https://www.meilisearch.com/blog/tutkit-multilingual-learning-at-scale)

***

## 🎥 Webinar recording
[![October 9th.png](https://unable-actionable-car.media.strapiapp.com/October_9th_b09a084f13.png)](https://www.youtube.com/watch?v=CvutT64u0fE)


Missed our October event? No worries—we've got you covered with the full recording!

 

Discover Meilisearch's **roadmap** and latest **updates**, learn more about **federated search and its benefits** and see a live demo of the **new Cloud functionality**.

[Watch it now!](https://www.youtube.com/watch?v=CvutT64u0fE)

***

## 📰 In the news
### ⚛️  [Add AI-powered search to React](https://www.meilisearch.com/blog/add-ai-powered-search-to-react)
Build a React movie search and recommendation app with Meilisearch's AI-powered search.

### 🔍 [Federated search guide](https://www.meilisearch.com/docs/learn/multi_search/performing_federated_search)

Learn how to create separate indexes for different CRM data types and query them simultaneously for a unified results list.

### ✨ [Semantic search with Cloudflare embeddings](https://www.meilisearch.com/docs/guides/ai/cloudflare)

Learn how to set up Meilisearch with Cloudflare Worker AI embeddings to enable semantic search capabilities.

### 🛍️ [Shopify storefront guide for Next.js](https://www.meilisearch.com/blog/next-js-shopify-storefront-with-blazity)

Learn to build a Shopify storefront with Next.js and the [Blazity commerce starter](https://github.com/Blazity/enterprise-commerce), using Meilisearch hybrid search to serve all front-end data.

### 🔒 [Laravel multitenancy guide](https://www.meilisearch.com/docs/guides/security/laravel_multitenancy)

How to implement search in a multitenant Laravel application.

***

## 🤗 Made with Meilisearch

A community member built [Firebits.io](https://firebits.io) and shared it with the Discord community. Firebits is a modern online Second Brain that helps you organize and share your online learning resources. Its search is, of course, powered by Meilisearch 🎉 

![firebits-2.gif](https://unable-actionable-car.media.strapiapp.com/firebits_2_869f9caaa0.gif)

We’re always looking to promote new projects from the community. Post yours in the [#built-with-meilisearch](https://discord.meilisearch.com/) channel!

***

## 🦸‍♀️ We’re hiring!

See our open positions:

- [Chief of Staff](https://jobs.lever.co/meili/4abb7650-a5ca-4aac-9f35-e2fe5144b78d)

***

## 🤓 Did you know?

The fastest way to try Meilisearch is our [Cloud free trial](https://www.meilisearch.com/cloud).

[Get started!](https://www.meilisearch.com/cloud)

***

Stay in the loop of everything Meilisearch by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter?utm_campaign=newsletter&utm_source=blog&utm_medium=october-24-updates). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/).



Meilisearch October updates

[TutKit](https://www.tutkit.com/en) is a comprehensive multilingual learning and content platform that offers training materials, marketing resources, and creative assets to its subscribers worldwide. Built by German agency [4eck Media](https://4eck-media.de/), the platform stands as a testament to the power of effective search functionality in educational platforms. 

**TutKit self-hosts Meilisearch to power high-performance search functionality across their Laravel-based learning platform, managing 15,000+ multilingual content pieces with millisecond response times.**

![TutKit.gif](https://unable-actionable-car.media.strapiapp.com/Tut_Kit_f4b038e4a6.gif)


>"With Meilisearch, we have chosen the right company for the perfect search." - Matthias Petri, founder of TutKit.com and managing director of [4eck Media](https://4eck-media.de/).

The challenge: scaling search for multilingual content
---------

TutKit.com's initial search functionality, implemented by an external agency using MySQL, suffered from severe performance issues due to inefficient programming. With the platform hosting tens of thousands of content pieces, users endured unacceptable loading times of up to 10 seconds for search results—a critical flaw for a content platform. The system also lacked proper multilingual support, making it impossible to effectively filter and display results across different languages.

Frustrated with these limitations, the TutKit team decided to take matters into their own hands. They began a comprehensive refactoring project, bringing the development in-house with a clear goal: to create an ultra-fast search system that could handle multiple languages.


The solution: implementing Meilisearch
---------
The search for a solution that would provide complete control over search results led Tutkit to Meilisearch. While the platform's modern UX and compelling website initially caught their attention, the final decision was driven by several additional factors:
- **Lightning-fast search performance**: while the platform's modern UX and compelling website initially caught their attention, it was Meilisearch's exceptional speed that stood out—presenting a dramatic improvement over their existing system
- **Robust multilingual support.** Meilisearch powers a fast search performance across 26  languages on TutKit platform, coupled with straightforward management capabilities.
- **Regular platform updates and improvements.** Meilisearch’s regular monthly updates continue to bring valuable improvements, enhancing the experience for both the internal TutKit team and the website visitors.

Technical implementation
---------
The TutKit development team executed a comprehensive implementation strategy:

 **1. System setup:** the implementation of Meilisearch began with a straightforward server installation from the official website. To ensure reliable operation, the team configured a system to manage the Meilisearch instance, including automatic startup and failure recovery. This configuration guarantees continuous service availability, even after server restarts.
 
**2. Security:** the instance was implemented through a dual-key system: 
- a master key for comprehensive backend access, which allows full access to the Meilisearch instance (including creation and index management), is used exclusively in the backend. 
- A custom API key with limited permissions for front-end operations. 
This separation creates a robust security layer, protecting backend operations while maintaining efficient front-end interactions. The Laravel application connects to Meilisearch using the API key, effectively minimizing the risk of unauthorized access and potential data breaches.

**3. Optimized data flow:** built on the Meilisearch PHP library, TutKit's custom Laravel integration ensures only necessary changes reach the search index. This selective approach minimizes data processing overhead while maintaining complete control over the implementation, resulting in streamlined index updates and improved search performance.

**4. Smart batch processing:** A custom Laravel console command handles large-scale updates and initial indexing processes. By targeting only relevant records and executing selectively for major changes, this tool significantly reduces server load and maintains system reliability through efficient resource utilization.

**5. Real-time synchronization:** The system automatically detects and processes changes in Laravel models, updating Meilisearch indices instantly without full reindexing. This targeted approach keeps search data synchronized with minimal resource usage, ensuring fast, responsive search functionality for users.

Impact and results
---------
The implementation of Meilisearch has delivered significant benefits for TutKit:
**Performance improvements:** thanks to Meilisearch's continuously improving indexing speed, TutKit can process new data into their search index with remarkable efficiency. The platform's lightning-fast search capabilities significantly reduce server load by eliminating the need for resource-intensive MySQL queries. 

**Real-time filtering capabilities** further enhance the system's responsiveness, allowing users to instantly refine and view their search results.

**Cost benefits:** adoption of Meilisearch as an open-source solution has delivered cost advantages for TutKit. Beyond eliminating ongoing licensing fees typically associated with paid providers, the platform's intuitive nature allowed TutKit's in-house development team to handle implementation and maintenance independently, avoiding external developer costs.

**User experience impact:** improved search functionality has directly enhanced the visitor experience on TutKit's platform. Fast, accurate search results have eliminated user frustration caused by long wait times and irrelevant results. This enhanced user experience has proven valuable for business outcomes, as satisfied users are more likely to convert into paying customers.

**Scalability success:** despite managing hundreds of thousands of content pieces across multiple languages, the platform maintains its exceptional performance. Meilisearch consistently delivers precise, relevant results while handling TutKit's growing content library and language variants. This robust scalability gives the team confidence in their ability to expand the platform's capabilities over the long term.

Looking forward
---------

TutKit continues to scale its platform, adding more content and language variants with confidence in Meilisearch's ability to handle growing demands. The team looks forward to the further development of [AI-powered features](https://www.meilisearch.com/solutions/hybrid-search) that could further enhance search intelligence and simplify the user interface by reducing the need for extensive filtering options.

Matthias Petri, founder of TutKit.com and managing director of [4eck Media](https://4eck-media.de/) said: "A good search is like a stage for large content platforms, where exactly what the user wants to see is always played. Without Meilisearch, our platform would be just one of many - with mediocre search results and without the clear added value that we would like to offer our customers."

For TutKit, Meilisearch has proven to be more than just a search solution – it's become a cornerstone of their platform's success, enabling them to deliver on their promise of effective learning and productive application for users across the globe.


TutKit's journey with Meilisearch: powering multilingual learning at scale.

We're excited to introduce Meilisearch v1.11. In this article, we'll cover the most significant changes. For a full list of updates, visit the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.11.0) on GitHub.

<Callout>
  Meilisearch 1.11 is available on [Meilisearch Cloud](https://cloud.meilisearch.com/?utm_campaign=release-v1-11&utm_source=blog), too—upgrade now!
</Callout>

## Experimental: vector database indexing performance improvements

Meilisearch v1.11 introduces a [new embedder option](https://www.meilisearch.com/docs/reference/api/settings?utm_campaign=release-v1-11&utm_source=blog#binaryquantized): `binaryQuantized`.

Binary quantization compresses vectors by encoding each dimension with 1-bit values, significantly boosting performance, especially in datasets with over 1M documents and models with more than 1024 dimensions.

Smaller embeddings (under 1024 dimensions) tend to give worse results. With fewer dimensions, the binary vector doesn't retain enough information for good relevancy.


<Callout>
Enabling this feature permanently updates all vectors to contain only 1s and -1s, which can greatly affect search relevancy. This change is irreversible.
</Callout>

Before enabling this feature, we recommend testing it in a smaller or duplicate index in a development environment to evaluate its impact on your use case.

## Experimental: AI-powered search improvements

### Breaking changes

This release introduces several breaking changes for feature stabilization. 

####  `embedder` parameter becomes mandatory

In previous versions, the default embedder was automatically used for search and similar requests when no embedder was specified. Starting with v1.11, it’s now mandatory to specify the `embedder` parameter when performing:
- Semantic or hybrid search via [`GET`](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#search-in-an-index-with-get) and [`POST`](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#search-in-an-index-with-post) where the embedder must be included as part of the `hybrid` object.
- Similarity search via [`GET`](https://www.meilisearch.com/docs/reference/api/similar?utm_campaign=release-v1-11&utm_source=blog#get-similar-documents-with-get) and [`POST`](https://www.meilisearch.com/docs/reference/api/similar?utm_campaign=release-v1-11&utm_source=blog#get-similar-documents-with-post), where the `embedder` is required as a top-level parameter.


```javascript
// Semantic and hybrid search
client.index('INDEX_NAME').search(
  query, {
    hybrid: {
      embedder: 'EMBEDDER_NAME' // mandatory as of v1.11
   }
  }
)

// Similarity search
client.index('INDEX_NAME').searchSimilarDocuments({ 
  id: 'TARGET_DOCUMENT_ID',
  embedder: 'EMBEDDER_NAME' // mandatory as of v1.11
})
```

Additionally, even for full-vector searches (when using `vector` without `q`), you must include the `hybrid` object with the `embedder`.

```javascript
client.index('INDEX_NAME').search({
  vector: [0, 1, 2],
  hybrid: {
      embedder: 'EMBEDDER_NAME'// mandatory as of v1.11
   }
})
```

#### Non-zero `semanticRatio` now ignored when passing `vector` without `q`

Before Meilisearch v1.11, when performing a search with the [`vector`](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#vector-experimental) and [`hybrid`](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#hybrid-search-experimental) parameters but without [`q`](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#query-q), a [placeholder keyword search](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#placeholder-search) occurred unless `hybrid.semanticRatio` was set to `1.0`. In v1.11, a semantic search is always performed unless `hybrid.semanticRatio` is set to `0.0`.

### Additions and improvements

#### Default OpenAI model update

The default [OpenAI model](https://www.meilisearch.com/docs/reference/api/settings?utm_campaign=release-v1-11&utm_source=blog#model) has been updated from `text-embedding-ada-002` to the new `text-embedding-3-small`. This new model is both more cost-effective and more accurate than the legacy `ada` model, offering better performance at a lower cost.

#### New embedder parameter

Meilisearch 1.11's new `documentTemplateMaxBytes` [parameter](https://www.meilisearch.com/docs/reference/api/settings?utm_campaign=release-v1-11&utm_source=blog#documenttemplatemaxbytes) allows truncating the document's template text when it exceeds the specified byte limit. It is set, by default, to 400 bytes. 

This change is backward compatible. Existing embedders will retain the 400-byte default, with no need to recompute embeddings during the upgrade. The new limit will only apply when a document is modified and the template is re-rendered.

#### Improved document template

Fields in `documentTemplate` now include the `field.is_searchable` property. The default [document template](https://www.meilisearch.com/docs/reference/api/settings?utm_campaign=release-v1-11&utm_source=blog#documenttemplate) will filter out both empty fields and fields not in the searchable attributes list. The new default template only applies to embedders created starting with v1.11.


```liquid
// v1.11 default document template
{% for field in fields %}
  {% if field.is_searchable and not field.value == nil %}
    {{ field.name }}: {{ field.value }}
  {% endif %}
{% endfor %}

// v1.10 default document template
{% for field in fields %}
  {{ field.name }}: {{ field.value }}
{% endfor %}
```

## Federated search improvements: facet distribution and stats

Meilisearch v1.11 introduces two new options for federated searches: `facetsByIndex` and `mergeFacets`. These options allow you to request facet [distribution](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#facetdistribution) and [stats](https://www.meilisearch.com/docs/reference/api/search?utm_campaign=release-v1-11&utm_source=blog#facetstats) for federated search results.

### Facet Information by index

To retrieve facet distribution and stats for each individual index, use the `facetsByIndex` [option](https://www.meilisearch.com/docs/reference/api/multi_search?utm_campaign=release-v1-11&utm_source=blog#facetsbyindex). 


```json
{
  "federation": {
    "facetsByIndex": {
      "movies": ["title", "id"],
      "comics": ["title"]
    }
  },
  "queries": [
    { "q": "Batman", "indexUid": "movies" },
    { "q": "Batman", "indexUid": "comics" }
  ]
}
```

The response will include facet distribution and stats for each index:


```json
{
  "facetsByIndex": {
    "movies": {
      // Facet distribution for queries on the `movies` index
      "distribution": {
        "title": {
          "Batman returns": 1
        },
        "id": {
          "42": 1
        }
      },
      // Facet stats for queries on the `movies` index
      "stats": {
        "id": {
          "min": 42,
          "max": 42
        }
      }
    },
    "comics": {
      // Facet distribution for queries on the `comics` index
      "distribution": {
        "title": {
          "Batman: the killing joke": 1
        }
      },
      // Facet stats for queries on the `comics` index (empty because only numeric values are considered)
      "stats": {}
    }
  }
}
```

### Merged facet information

To get facet distribution and stats from all indexes in a single result, use both [`facetsByIndex`](https://www.meilisearch.com/docs/reference/api/multi_search?utm_campaign=release-v1-11&utm_source=blog#facetsbyindex) and [`mergeFacets`](https://www.meilisearch.com/docs/reference/api/multi_search?utm_campaign=release-v1-11&utm_source=blog#mergefacets):


```json
POST /multi-search
{
  "federation": {
    "facetsByIndex": {
      "movies": ["title", "id"],
      "comics": ["title"]
    },
    "mergeFacets": { "maxValuesPerFacet": 10 }
  },
  "queries": [
    { "q": "Batman", "indexUid": "movies" },
    { "q": "Batman", "indexUid": "comics" }
  ]
}
```

The response will include merged facet data:


```json
{
  "facetDistribution": {
    "title": {
      "Batman returns": 1,
      "Batman: the killing joke": 1
    },
    "id": {
      "42": 1
    }
  },
  "facetStats": {
    "id": {
      "min": 42,
      "max": 42
    }
  }
}
```

<Callout>
Learn more about [federated search and its use cases](https://www.meilisearch.com/blog/what-is-federated-search?utm_campaign=release-v1-11&utm_source=blog).
</Callout>

## Experimental: new `STARTS_WITH` filter operator

The new `STARTS_WITH` [filter operator](https://www.meilisearch.com/docs/learn/filtering_and_sorting/filter_expression_reference?utm_campaign=release-v1-11&utm_source=blog#starts-with) matches any field values that start with the specified string pattern:


```json
// returns all heroes whose name starts with 'super'
"filter": "hero STARTS_WITH super"
```

This is an experimental feature. To enable it, use the [experimental features](https://www.meilisearch.com/docs/reference/api/experimental_features?utm_campaign=release-v1-11&utm_source=blog) endpoint and set the `containsFilter` option to `true`. Note that this will also activate the `CONTAINS` filter operator.

<Callout>
Share your feedback via this [GitHub discussion](https://github.com/orgs/meilisearch/discussions/763)
</Callout>

## Contributors shout-out

We want to give a massive thank you to the contributors who made this release possible: [@iornstein](https://github.com/iornstein) for their help with [Meilisearch](https://github.com/meilisearch/meilisearch), and [@luflow](https://github.com/luflow), [@tkhshtsh0917](https://github.com/tkhshtsh0917) for their work on [charabia](https://github.com/meilisearch/charabia).

We also want to extend special thanks to our SDK maintainers working hard to make Meilisearch available across many [languages and frameworks](https://www.meilisearch.com/docs/learn/what_is_meilisearch/sdks?utm_campaign=release-v1-11&utm_source=blog).

* * *

And that’s a wrap for v1.11! These release notes only highlights the most significant updates. For an exhaustive listing, read the [changelog](https://github.com/meilisearch/meilisearch/releases/tag/v1.11.0) on Github.

Upgrade with ease using Meilisearch Cloud.

[Open Meilisearch Cloud](https://cloud.meilisearch.com/login?utm_campaign=release-v1-11&utm_source=blog)

* * *

Stay in the loop of everything Meilisearch by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter?utm_campaign=release-v1-11&utm_source=blog). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/?utm_campaign=release-v1-11&utm_source=blog) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/?utm_campaign=release-v1-11&utm_source=blog).

Meilisearch 1.11

_Bildhistoria chose Meilisearch to power the search through its quickly growing database of historical photographs and affiliated data, driving the search from multiple sources of metadata and mapsearch._

> _“Bildhistoria's large volumes of historical photos, persons, places and organizations require a fast and easy to maintain search engine, like Meilisearch. Meilisearch is used on so many places around Bildhistoria, enabling our users to explore photos and learn more about ancestors and local history. This makes Meilisearch instrumental in the success of our community.”_ - Per Rohdin, board member of the community, and project manager at Bildhistoria.

[**Bildhistoria**](https://bildhistoria.se/)**, a Swedish non-profit, operates the country's largest collection of copyright-free historical photographs. It focuses on collecting and preserving historical photographs from museums and private collections. While the project has not yet reached full general availability, Bildhistoria already boasts an impressive archive of around 90,000 images dating from the 1950s and earlier.**

The project aims to create a dynamic and collaborative platform where the public can interact with history, and where professional and amateur genealogists alike can contribute by enriching the photographs with additional data.

**Challenge: Bildhistoria tackles data fragmentation and community engagement**
-------------------------------------------------------------------------------

From the start, Bildhistoria faced three main challenges in bringing their vision to life:

**Challenge: searching across multiple types of data:** Bildhistoria's main challenge with search is effectively searching across the different data types. The platform features not only photographs but also information about people’s names, organizations, and locations associated with each image. The team needed a search solution that could seamlessly navigate through these different data types. To address this, they have implemented Meilisearch’s "[multi-index search](https://www.meilisearch.com/docs/reference/api/multi_search)" that returns results from all the different indexes, allowing users to find relevant information regardless of whether they are searching for a person, place, or organization.

**Challenge: data scattered across multiple sources:** Historical photographs were scattered across various sources, including museums, archives, and private collections. There was no centralized platform in Sweden to consolidate these copyright-free images, making it difficult for researchers and the public to access this wealth of visual history.

![](https://unable-actionable-car.media.strapiapp.com/AD_4n_Xf_R_Ormjkncn9_Uj8i_Un6vaqs_Wi_I_Uln1hutwuz0h_KI_wm_GIA_P_Wry5j_Rj5_R_Altplg_VV_5_Jy_ANKPL_2_Hayt3_Jv_Ku_A_Gw9ou_P8l0va_9f26862acc.jpg)

**Challenge: Community accessibility and engagement:** Unlike traditional digital archives, Bildhistoria is an interactive platform where users can contribute additional historical facts and enrich the metadata associated with the photographs. The community of genealogists is large and diverse, spanning professional organizations to amateur genealogists and the general public. Bildhistoria’s team knew from the beginning that the only way to make the project work was to make it easily accessible and easy to use for everyone.

**Solution: Bildhistoria chose Meilisearch for simplified operations, team-wide accessibility, and rapid data synchronization**
-------------------------------------------------------------------------------------------------------------------------------

After careful consideration, Bildhistoria chose Meilisearch as their search solution for several key reasons:

**Less operational overhead:** Meilisearch proved much easier to set up and connect to existing indexes compared to alternatives like Elasticsearch. This was crucial for a non-profit organization with limited resources.

**Accessibility for all team members:** Meilisearch's intuitive admin interface allowed even non-technical staff to understand and configure search settings. This democratization of the search configuration process was particularly valuable for fine-tuning Bildhistoria’s search experience.

**Fast synchronization:** Bildhistoria required efficient update mechanisms to keep Meilisearch indexes in sync with their database changes. Meilisearch's ability to handle rapid updates and re-indexing was crucial for maintaining an up-to-date and responsive search experience.

**Implementation**
------------------

Bildhistoria's implementation of Meilisearch involved several key steps:

1.  The team initially deployed the open-source version on Azure but faced challenges with the hosting environment.
2.  Bildhistoria team then switched to Meilisearch Cloud, which provided better management and support, allowing them to focus on their core mission rather than infrastructure concerns.
3.  Meilisearch was integrated throughout the platform, powering the main search functionality and an innovative map search feature.
4.  Finally, Bildhistoria invested significant effort in creating an efficient data synchronization pipeline between its database and Meilisearch to handle the constant flow of new information and user contributions.

**Results**
-----------

The implementation of Meilisearch has yielded impressive results for Bildhistoria:

**Multi-index search:** Users can now perform searches that capture results across various types of data—photographs, persons, organizations, and places. This holistic approach has dramatically improved the user experience, allowing for a more comprehensive exploration of historical connections.

**Improved user experience:** The search functionality provides alternative search suggestions for similar queries and provides rich context for each image.

**Interactive map search:** Meilisearch’s [Geosearch](https://www.meilisearch.com/docs/learn/filtering_and_sorting/geosearch) allows users to explore photographs associated with specific locations in Sweden. As users interact with the map, new searches are performed in real-time, updating the displayed photographs.

![](https://unable-actionable-car.media.strapiapp.com/Bildhistoria_Mapseaechdemogif_ezgif_com_video_to_gif_converter_a70a28b422.gif)

Overall, the team noticed a significant improvement in engagement and user experience since the implementation of Meilisearch.

**Conclusion: Meilisearch is a key part of Bildhistoria's vision for the future**
---------------------------------------------------------------------------------

Looking to the future, Bildhistoria has ambitious plans to further leverage Meilisearch to expand its platform's capabilities to become Sweden's leading platform for historical photographs. While initially focused on the Swedish market, Bildhistoria is open to European collaborations to scale the concept internationally. The next steps for improving the user experience on the platform would include:

*   Integrate AI-driven facial recognition across photographs to link images of the same individuals across different time periods and locations.
*   Add a _houses and buildings_ index, enabling users to trace the lifecycle of specific structures through photographs.
*   Implement features encouraging community collaboration, such as allowing premium users to follow and receive notifications about new edits and contributions.
*   Scale operations to manage a vast archive of 1-3 million photographs by collaborating with more museums and institutions.

As the project progresses and reaches full production mode, Bildhistoria continues to assemble the right technical team to establish a sustainable and long-term setup for the community, laying the groundwork for its future growth and impact.

Meilisearch powers the discovery of history for Bildhistoria

📅 Save the date: October 9th
-----------------------------

[![What's new in Meilisearch: roadmap, v1.10, federated search, and Cloud news!](https://unable-actionable-car.media.strapiapp.com/October_9th_2_f41b190f00.png)](https://us06web.zoom.us/meeting/register/tZUpcu2gqT4pHtcB0Wh8P-aIUd3e4LF-T-UX#/registration)

Join us on October 9th for a **Meilisearch webinar** featuring:

*   A **live demo** of Meilisearch Cloud's latest capabilities
*   A sneak peek at our **product roadmap** and vision
*   Insights on **federated search**
*   **Q&A session** with our experts

We look forward to seeing you there!

<Callout>
  Wednesday, October 9th  
4PM CEST | 3PM BST | 10AM EDT
</Callout>

[Register now](https://us06web.zoom.us/meeting/register/tZUpcu2gqT4pHtcB0Wh8P-aIUd3e4LF-T-UX#/registration)

👀 Meilisearch 1.11 pre-release
-------------------------------

[![Release candidate - Meilisearch 1.11](https://unable-actionable-car.media.strapiapp.com/146_1_12d5d22c9f.jpg)](https://github.com/meilisearch/meilisearch/releases/tag/v1.11.0-rc.0)

Meilisearch 1.11 RC: Major improvements to **hybrid search performance** and usability, plus enhanced **federated search**

❗️Not production-ready. Your feedback is crucial!

[Preview updates](https://github.com/meilisearch/meilisearch/releases/tag/v1.11.0-rc.0)

🤔 Choosing the best model for semantic search
----------------------------------------------

[![](https://unable-actionable-car.media.strapiapp.com/best_sematic_model_8490bd88d2.png)](/blog/choosing-the-best-model-for-semantic-search/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

A **comparison** of model performance, cost, and relevancy.

[Read guide](/blog/choosing-the-best-model-for-semantic-search/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

🎉 Join Meilisearch's referral program
--------------------------------------

[![New referral program](https://unable-actionable-car.media.strapiapp.com/Referral_program_804eb40f12.png)](https://help.meilisearch.com/en/article/join-meilisearch-referral-program-and-earn-rewards-t3m5rl/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

Earn **rewards** while helping grow the community! 🤝 It's a **limited-time** program—don't miss it!

[Learn more](https://help.meilisearch.com/en/article/join-meilisearch-referral-program-and-earn-rewards-t3m5rl/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

📰 In the news
--------------

💡 [What is federated search?](/blog/what-is-federated-search/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

Learn how it surpasses multi-index search, and elevates both user experience and developer efficiency.

🎮 [Meilisearch playground](https://playground.meilisearch.com/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

Explore and compare Meilisearch with other search engines. Test multiple search modes and AI models and evaluate their performance!

📣 [RFC: Similar Documents API](https://github.com/meilisearch/meilisearch/issues/4736)

Join the discussion to help us shape the future of the similar  documents API in Meilisearch.

📣 [RFC: Improving the search preview](https://github.com/orgs/meilisearch/discussions/766)

We want your feedback: how can we make it easier to test your search settings?

✨ [Semantic search with Mistral embeddings](https://www.meilisearch.com/docs/guides/ai/mistral?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

Learn how to set up Meilisearch with Mistral embeddings to enhance your search experience and retrieve more relevant results.

🤗 Made with Meilisearch
------------------------

Discover how [Bookshop.org](https://bookshop.org/) increased search-bases purchases by 43% after switching to Meilisearch. 

[![Bookshop.org success story, +43% search-based purchases with Meilisearch](https://unable-actionable-car.media.strapiapp.com/59_fc6540f28f.png)](/blog/bookshop-increases-search-based-purchases/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

[Read case study](/blog/bookshop-increases-search-based-purchases/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)

_We’re always looking to promote new projects from the community. Post yours in the_ [_#built-with-meilisearch_](https://discord.gg/uB94t8PGV4) _channel!_

🦸 We're hiring!
----------------

See our open positions:

*   [Senior Full-Stack Software Engineer](https://www.meilisearch.com/careers?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)
*   [Founding Sales & Business Developer](https://www.meilisearch.com/careers?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates)  
    

* * *

Stay in the loop of everything Meilisearch by subscribing to our [monthly newsletter](https://dub.sh/meili-newsletter?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates). To learn more about Meilisearch's future and help shape it, take a look at our [roadmap](https://roadmap.meilisearch.com/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates) and participate in our [Product Discussions](https://github.com/orgs/meilisearch/discussions?ref=blog.meilisearch.com).

For anything else, join our developers community on [Discord](https://discord.meilisearch.com/?utm_campaign=newsletter&utm_source=blog&utm_medium=september-24-updates).

Meilisearch latest news and company updates