When [Bookshop.org](http://Bookshop.org)'s small engineering team struggled to push past a 14% search conversion rate, they faced a familiar dilemma. Despite having Elasticsearch—a powerful but complex search solution—their eight-person team was spending more time maintaining search infrastructure than improving their core platform. They needed something different: a solution that could deliver better results with less overhead.

Their challenge reflects a broader reality in modern software development. Every click, every search query, and every page load directly impact business metrics, yet the tools meant to optimize these experiences often become bottlenecks themselves. For engineering teams, especially smaller ones, the complexity of search infrastructure can become a _hidden tax_ on innovation and growth.

This is where Meilisearch comes in. Built to work seamlessly out of the box, it's the solution that lets developers focus on building rather than maintaining. Today, we're taking that mission further with [the launch of Meilisearch AI](https://www.meilisearch.com/launch-week)—our next step in making it radically easier for developers to build world-class search experiences.

In this article, we'll explore how modern search has evolved beyond simple queries, examine the technical challenges facing development teams, and introduce Meilisearch's innovative solutions—including our new AI capabilities. You'll learn how our approach to developer experience is transforming search implementation, and discover our roadmap for making advanced search features accessible to teams of all sizes.

## Redefining modern search

Modern search experiences extend far beyond the traditional search bar, encompassing two key facets: search and discovery. 

Search can take the appearance of inputs, menus, filters, or the trending UX of conversational search (i.e. chatbots). Discovery, on the other hand, plays a key role in navigating content and suggesting recommendations.

Across industries, we see search and discovery most prevalent as follows:

- **Traditional full-text search & semantic search:** standard in all industries ranging from SaaS to ecommerce, marketplaces, and content platforms
- **Conversational search:** prominent in RAG (Retrieval-Augmented Generation) and AI-enhanced search interfaces like Perplexity
- **Discovery for navigation:** common in social media and content platforms where a feed is the primary navigation
- **Recommendations:** tie in more critically with business value in ecommerce and content platforms

For early-stage startups and SMBs, implementing these search capabilities presents significant technical challenges. The complexity multiplies when adding AI integration and personalization layers. Addressing these pain points diverts the workforce from building core product value. Meilisearch is here to relieve you from this burden, allowing you to focus on what really matters. 

We want Meilisearch built-ins to handle modern use cases like [document embedding](https://www.meilisearch.com/docs/learn/ai_powered_search/document_template_best_practices). Complex setups—such as managing [heavyweight solutions like Elasticsearch](https://www.meilisearch.com/blog/meilisearch-vs-elasticsearch), [tweaking a Postgres instance](https://www.meilisearch.com/blog/postgres-full-text-search-limitations), or handling hybrid search—are additional integration burdens. This ignores the security consideration related to exposing front-end accessible APIs or the analytics necessary to collect business KPIs—things that don’t come packaged with custom-made solutions.

![bookshop_meilisearch.gif](https://unable-actionable-car.media.strapiapp.com/bookshop_meilisearch_9498082583.gif)

These DX considerations impact business metrics down the road as engineering bottlenecks lead to longer time-to-market, hindering growth for companies that want to move fast. In ecommerce, for example, search accuracy directly translates to conversion rate, as shown by how we helped [Bookshop increase search-based purchases by 43%](https://www.meilisearch.com/blog/bookshop-increases-search-based-purchases). On SaaS and content platforms, discovery enables engagement and adoption growth, both of which are key to keeping churn in check. 

## Leveraging DX to move mountains

Meilisearch is a no-configuration, plug-and-play solution that you can install and forget. At the heart of any search tool—and often the biggest challenge for developers—is ensuring relevant results. **CascadeRank** is our answer to [relevancy fine-tuning](https://www.meilisearch.com/docs/learn/relevancy/relevancy). Our algorithm uses human-readable ranking rules that enable configuration and easy customization. With Meilisearch, simplicity is key, allowing you to set up and focus on your core tasks. 

Though customization is easy, Meilisearch comes with strong defaults to handle common requirements out of the box—things like being typo-tolerant and prioritizing word proximity. Indeed, we’ve seen [Minipouce get better relevancy with Meilisearch’s default configuration than their previous Typesense setup](https://www.meilisearch.com/blog/minipouce-top-grade-customer-experience), leading to a 5x search volume increase. 

Scalability is a concern for small teams and enterprises alike. Performance is the name of the game for user-facing search—both for search latency and indexing time. As demonstrated by [Podscan.fm](http://Podscan.fm) [searching across 600GB of data](https://x.com/arvidkahl/status/1807889668748275802), search remains fast against large datasets. Our [next-generation indexer boasts 4x faster updates](https://www.meilisearch.com/blog/introducing-indexer-2024), making Meilisearch suitable at scale for write-intensive workflows, too.

[![arvid-kahl-tweet.png](https://unable-actionable-car.media.strapiapp.com/arvid_kahl_tweet_0c41da4824.png)
](https://x.com/arvidkahl/status/1807889668748275802)

When it comes to security, no compromise is possible. So Meilisearch provides fundamental security features to handle simple and complex use cases. Meilisearch facilitates [API key management](https://www.meilisearch.com/docs/learn/security/basic_security) to facilitate multi-environments. The built-in [multi-tenancy features](https://www.meilisearch.com/docs/learn/security/multitenancy_tenant_tokens) also enable more advanced security patterns, allowing developers to customize their access rules.

Using our expertise in search requirements across various customers, we built [Meilisearch Cloud](https://www.meilisearch.com/cloud). It’s the quickest way to get started. In addition to freeing teams from managing infrastructure, it integrates analytics and monitoring, making it [the preferred production platform for companies like HitPay](https://www.meilisearch.com/blog/hitpay-streamlines-search). Open-source Meilisearch can be self-hosted via Docker and a plethora of hosting platforms. 

This feature set positions Meilisearch well as a simple solution to get the best of modern search experience. This includes benefiting from an easy LLM integration to leverage AI-powered solutions.

## Future-proofing your search strategy

With native [hybrid and vector search](https://www.meilisearch.com/docs/learn/ai_powered_search/getting_started_with_ai_search) integration, Meilisearch makes it simple to implement AI-powered search capabilities. But we’re not stopping there—our 2025 roadmap is packed with features enabling businesses to uncover more value from AI while sustaining performance and scalability.

### Improved conversions with personalization

[Our research shows that personalization significantly improves conversion rates](https://www.meilisearch.com/blog/personalization-recommendations) across diverse business types. We aim to release a personalization framework enabling the collection of end-user context on each request. This comprehensive context management system allows developers to leverage user preferences, recent interactions, and behavioral patterns to further customize the relevancy of results on a per-user basis. 

### Retrieval augmented generation without overhead 

Although Meilisearch already enables [building retrieval-augmented generation (RAG)](https://www.meilisearch.com/blog/how-to-build-rag), we want to integrate built-in RAG capabilities in the engine, seamlessly combining full-text search, vector search, and LLM interaction. Then, we want to tackle inherent problems in state-of-the-art RAG applications by tackling the orchestration of filtering, ranking, and results merging in RAG scenarios. Of course, we want this to come with strong defaults, making it easy for developers to get started.

### Advanced ranking integrations

Our goal is to provide a native way for users to leverage AI reranking models to improve the relevancy of results. This provides a more flexible approach to configuring result relevancy by allowing real-time results reranking based on user context and behavior. We also want to enable organizations to integrate with their own custom ranking models to allow for further dynamic adjustment of results based on hand-picked factors.

### Reliability as you scale

We also plan to improve Meilisearch’s infrastructure to handle additional use cases better. In particular, we want to continue improving the distributed system capabilities for better scaling and high availability by building upon our sharding experiments with [federated remote search](https://www.meilisearch.com/docs/learn/multi_search/implement_sharding). Another layer of performance is integrating more sophisticated caching mechanisms to improve out-of-the-box performance. Monitoring and observability are also continuously improved based on user feedback.

## Meilisearch AI, refreshingly simple search DX

The release of Meilisearch AI marks a new age for Meilisearch users, fully integrating with the modern landscape of AI-powered applications. But it’s also a continuous fulfillment of our core value—making good search UX easy to build. We believe focusing on developer experience enables engineers to achieve faster time-to-value and maintain focus on their core business.

Meilisearch ships frequently to keep packaging built-in features, enabling fast-shipping companies to do what they do best. For more information on the Meilisearch AI release, consult our [launch week page](https://www.meilisearch.com/launch-week). 

Start with a [14-day free trial of Meilisearch Cloud](https://cloud.meilisearch.com/) or [request a demo](https://dub.sh/meili-sales) to discuss your needs with our search experts.

Building the future of search with Meilisearch AI

What are vector embeddings? A complete guide [2025]

Semantic search transforms how we find information online by focusing on understanding the meaning behind the words and phrases in user searches rather than relying solely on word-for-word matching.

Using machine learning (ML) and natural language processing (NLP), it decodes intent, context, and relationships between words, thus allowing for more conversational queries. This makes it a vital tool for businesses needing fast, accurate, and relevant results from large or complex data sets.

Embedding models are ML techniques that render words and phrases into complex numerical representations, which are then classified based on their context and relationships. However, not all models are the same. Accuracy, speed, indexing efficiency, and pricing can vary significantly between them.

Choosing the right semantic search model is crucial for delivering precise, efficient, and scalable search experiences for specific tasks.

Understanding the trade-offs—whether for multilingual support, search latency, or computational efficiency—will help you implement a high-performing semantic search system for your brand.

## What is semantic search?

Semantic search is an advanced AI-powered approach to information retrieval that focuses on NLP, ML, and knowledge representation to deduce the intent and contextual meaning of user queries.

Traditional keyword-based search engines match exact strings of words, but that’s not always enough. Semantic search goes further by incorporating **named entity recognition**, **relationships between words**, and **contextual disambiguation** to deliver more relevant results.

Semantic search engines can understand synonyms, paraphrased queries, and even infer implicit meanings exclusively because they use deep learning models such as transformers.

Given its ability to enhance the search criteria, [AI-powered search](https://www.meilisearch.com/blog/ai-powered-search) is crucial for applications requiring faster, broader, and even less structured [data retrieval](https://www.meilisearch.com/blog/how-to-cache-semantic-search).

## Why is semantic search important?
Semantic search is important because it improves search capability and relevance, thus helping users find information faster and with fewer irrelevant results. It also reduces uncertainty by interpreting natural language queries more clearly.

Like similarity search, it allows users to phrase their questions conversationally and intuitively rather than using specific—sometimes even long-tail—keywords. Think of it like this: if you can ask a question from your neighbor on a bus, then you can ask it with a quick semantic search.

Additionally, businesses benefit from semantic search by **enhancing customer experiences**, **improving product discovery**, and **optimizing knowledge management systems**.

For example, e-commerce stores like Bookshop.org can [boost search-based sales by up to 43%](https://www.meilisearch.com/blog/bookshop-increases-search-based-purchases) by implementing semantic search tools like Meilisearch.

A core component of semantic search is embedding models through which similarity computations are made possible.

## What are embedding models?

Embedding models are machine learning models that transform words, phrases, or documents into dense numerical representations called embeddings.

These vector representations encode word relationships, which allow search engines to compare semantic meaning and context rather than rely solely on exact similarity between words.

For instance, embeddings generated for words like “**phone**,” “**mobile**,” and “**cell**” will be more closely aligned in vector space than, say, “**cell**” and “**bell**,” even though the latter two are closer in letter-by-letter similarity.

This allows a search engine to retrieve relevant results even if the exact keyword is absent, underpinning various NLP and LLM (large language model) applications.

### What’s the difference between semantic and search embeddings?

Semantic and search embeddings serve distinct purposes in NLP applications, particularly in semantic search.

**Semantic embeddings** capture the meaning and relationships between words, which is helpful for tasks like document classification, recommendation systems, language translation, and sentiment analysis. They do this by reflecting the words’ conceptual similarities, even if they are not used in the same context.

On the other hand, **search embeddings** are specifically optimized for retrieval tasks, thus ensuring queries and indexed documents align effectively in vector space to maximize search relevance.

Unlike generic semantic embeddings, search embeddings often incorporate domain- and background-specific optimizations to fine-tune the effectiveness of the specific retrieval activity.

For example, a semantic embedding model might learn that "mac" is related to pasta, the makeup company, and the Apple laptop computer. However, a _search embedding model_ trained on computers and hardware would prioritize the third meaning when processing search queries in that domain.

Embedding models serve as the backbone of semantic search by enabling similarity-based retrieval mechanisms that align with user intent.

## What is the role of embedding models in semantic search?

Embedding models power semantic search by converting text into a structured format that can be efficiently indexed and retrieved.

Instead of relying on exact keyword matches, these models compare **query** embeddings with **indexed** document embeddings. This enables nearest-neighbor searches and significantly improves retrieval accuracy over traditional algorithms that depend on keyword matching.

These models often use transformer-based architectures such as BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), and their derivative neural networks to capture context-aware representations.

That’s how they can handle nuanced queries at scale. For instance, even though “cat” and “bat” sound (and look) similar, only one of them may be used in context as a conventional house pet.

Several fine-tuned and pre-trained models continue to emerge as industry standards for semantic search. Each improves precision, relevance, efficiency, and/or scalability in its own way.

## What are the most commonly used models for semantic search?

Different embedding models vary in terms of vector dimensionality, context length, and certain other performance characteristics.

In the context of LLMs, dimensionality points to the number of components in a vector, with each corresponding to an attribute of the encoded variable. Additionally, context length relates to the amount of text—usually measured in tokens—a model can “remember” and refer to at any given time.

To assess these variations, a series of benchmark tests were conducted using [Meilisearch](https://www.meilisearch.com), evaluating each model's effectiveness in real-world search scenarios that you will encounter.

These tests measured factors such as **retrieval accuracy**, **indexing speed**, and **query latency**, which is how they evaluated each model’s performance under specific search conditions.

| **Model/Service** | **Dimensions** | **Context Length** |
| --- | --- | --- |
| [Cohere embed-english-v3.0](https://docs.cohere.com/docs/embeddings) | 1024 | 512 |
| [Cohere embed-english-light-v3.0](https://docs.cohere.com/docs/embeddings) | 384 | 512 |
| [Cohere embed-multilingual-v3.0](https://docs.cohere.com/docs/embeddings) | 1024 | 512 |
| [Cohere embed-multilingual-light-v3.0](https://docs.cohere.com/docs/embeddings) | 384 | 512 |
| [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 |
| [OpenAI text-embedding-3-large](https://platform.openai.com/docs/guides/embeddings) | 3072 | 8192 |
| [Mistral](https://docs.mistral.ai/capabilities/embeddings/) | 1024 | 8192 |
| [VoyageAI voyage-2](https://www.voyageai.com/) | 1024 | 4000 |
| [VoyageAI voyage-large-2](https://www.voyageai.com/) | 1536 | 16000 |
| [VoyageAI voyage-multilingual-2](https://www.voyageai.com/) | 1024 | 32000 |
| [Jina Colbert v2](https://jina.ai/news/jina-colbert-v2-multilingual-late-interaction-retriever-for-embedding-and-reranking/) | 128, 96, or 64 | 8192 |
| [OSS all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 384 | 512 |
| [OSS bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 1024 | 512 |
| [OSS bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1536 | 512 |

Your choice of a model for semantic search will depend on a variety of factors, including, but not limited to, its **accuracy**, **computational efficiency**, and **cost**.

## What factors should be considered when choosing the best model for semantic search?

### 1. Results relevancy
Relevance is the cornerstone of effective semantic search, especially in achieving optimal user experience. The right model should balance precision, recall, and speed to ensure users receive highly relevant results without excessive noise.

This balance becomes especially important when comparing hybrid approaches like vector and full-text search. When selecting an embedding model, think about the following:

- multilingual support;
- handling multi-modal data;
- domain-specific performance.

In this context, bigger might not always mean better. While larger models often provide better accuracy, smaller models can offer competitive results at lower computational costs.

Moreover, effectively structuring data, such as with optimized document templates in Meilisearch, can improve search quality.

### 2. Search performance
Search latency is a critical factor in user experience. Search-as-you-type has become the standard for customer-facing applications as fast, responsive search results improve user engagement and retention. 

Local embedding models are ideal to achieve lightning-fast performance, as they eliminate the need for round trips to external services and reduce latency. If you must rely on remote models, hosting your search service close to the embedding service can minimize delays and improve user experience.

The table below showcases latency benchmarks for various local embedding models and embedding APIs. All requests originate from a Meilisearch instance hosted in the AWS London data center.

| **Model/Service** | **Latency** |
| --- | --- | 
| [Cloudflare bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | ±800ms |
| [Cloudflare bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | ±500ms |
| [Cohere embed-english-v3.0](https://docs.cohere.com/docs/embeddings) | ±170ms |
| [Cohere embed-english-light-v3.0](https://docs.cohere.com/docs/embeddings) | ±160ms |
| [Local gte-small](https://huggingface.co/models?search=gte-small) | ±20ms |
| [Local all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | ±10ms |
| [Local bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | ±20ms |
| [Local bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | ±60ms |
| [Mistral](https://docs.mistral.ai/capabilities/embeddings/) | ±200ms |
| [Jina colbert](https://huggingface.co/jinaai/jina-colbert-v2) | ±400ms |
| [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings) | ±460ms |
| [OpenAI text-embedding-3-large](https://platform.openai.com/docs/guides/embeddings) | ±750ms |
| [VoyageAI voyage-2](https://www.voyageai.com/) | ±350ms |
| [VoyageAI voyage-large-2](https://www.voyageai.com/) | ±400ms |

Benchmarks conducted in Meilisearch highlight the stark differences in latency across various models, with local models achieving response times as low as 10ms while some cloud-based services reach 800ms.

### 3. Indexing performance

Efficient indexing is another critical factor for the scalability of your search solution. As expected, the time required to process and store embeddings varies widely among models. Notable metrics influencing processing time are API rate limits, batch processing capabilities, and model dimensions.

Local models without GPUs may experience slower indexing due to limited processing power, while third-party services vary in speed based on their infrastructure and agreements.

As mentioned earlier, minimizing data travel time between your application and the model can reduce latency and optimize indexing. Evaluating these factors ensures your chosen model and service can effectively meet your needs.

The benchmark below compares the indexing performance of a collection of 10k e-commerce documents (with [automatic embedding generation](https://www.meilisearch.com/docs/learn/experimental/vector_search?utm_campaign=vector-search&utm_source=blog&utm_medium=models-comparison#generate-auto-embeddings)).

| **Model/Service** | **Indexation time** | 
| --- | --- | 
| [Cohere embed-english-v3.0](https://docs.cohere.com/docs/embeddings) | 43s |
| [Cohere embed-english-light-v3.0](https://docs.cohere.com/docs/embeddings) | 16s |
| [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings) | 95s |
| [OpenAI text-embedding-3-large](https://platform.openai.com/docs/guides/embeddings) | 151s |
| [Cloudflare bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 152s |
| [Cloudflare bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 159s |
| [Jina Colbert V2](https://huggingface.co/jinaai/jina-colbert-v2) | 375s |
| [VoyageAI voyage-large-2](https://www.voyageai.com/) | 409s |
| [Mistral](https://docs.mistral.ai/capabilities/embeddings/) | 409s |
| [Local all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 880s |
| [Local bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)| 3379s |
| [Local bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)| 9132s |

Meilisearch benchmarks indicate that indexing times range from under a minute for optimized cloud-based solutions to several hours for certain local models without GPU acceleration.

These should be important points to ponder when you weigh the frequency and volume of data updates in your applications. This is because they directly impact how quickly your system can process frequent or large data updates, which is crucial for maintaining the performance and responsiveness of your search solution.

### 4. Pricing
Embedding model costs vary depending on the provider and usage model. While local models are free to run, they require computational resources, potentially necessitating GPU investment.

On the other hand, cloud-based services charge per million tokens (or thousand neurons for Cloudflare), with costs ranging from $0.02 to $0.18 per million tokens.

| **Provider** | **Price** |
| --- | --- | 
| Cohere | $0.10 per million tokens for Embed 3 |
| OpenAI | $0.02 per million tokens for text-embedding-3-small |
| | $0.10 per million tokens for text-embedding-ada-002 |
| | $0.13 per million tokens for text-embedding-3-large | 
| Cloudflare | $0.011 per 1,000 Neurons |
| Jina | $0.18 per million tokens |
| Mistral | $0.10 per million tokens |
| VoyageAI |$0.02 per million tokens for voyage-3-lite |
| | $0.06 per million tokens for voyage-3|
| | $0.12 per million tokens for voyage-multimodal-3 |
| | $0.18 per million tokens for voyage-code-3| 
| | $0.18 per million tokens for voyage-3-large |
| Local model | Free |

As such, analyze cost-effectiveness against search demand and performance requirements. It's often a good idea to start with a well-known model that's easy to set up and has robust community support. You can migrate to a cloud provider like AWS for improved performance when necessary. 

Alternatively, you can choose an open-source model to self-host, giving you more flexibility. Just be aware that optimizing local models for high volume may require scaling your infrastructure.

### 5. Other optimization techniques

To maximize search performance, [hybrid search approaches](https://www.meilisearch.com/blog/full-text-search-vs-vector-search) combining full-text and vector search can yield the best results. To refine semantic search performance, consider the following optimizations:

- Experiment with model presets, as some models allow tuning for query vs. document embedding, which can improve relevancy.
- Assess specialized models, especially those employing retrieval-augmented generation, as domain-specific models may offer superior results for particular use cases.
- Explore models that provide reranking functions that further enhance search precision.
- Test higher-tier accounts as premium tiers may provide faster processing and reduced rate limiting.
- Optimize data transfer with quantization options to reduce API response sizes and improve efficiency.

Carefully evaluating these factors will help you select the best semantic search model to meet your needs.

## The bottom line
Now that we’ve taken this journey together, let’s round up what we’ve discovered.

| **Model/Service** | **Dimensions** | **Context Length** | **Latency** | **Indexation time** | **Pricing (per million tokens)** |
| --- | --- | --- | --- | --- | --- | 
| [Cohere embed-english-v3.0](https://docs.cohere.com/docs/embeddings) | 1024 | 512 | ±170ms | 43s | $0.10|
|[Cohere embed-english-light-v3.0](https://docs.cohere.com/docs/embeddings)|384|512|±160ms|16s|$0.10|
| [OpenAI text-embedding-3-small](https://platform.openai.com/docs/guides/embeddings) |1536| 8192| ±460ms| 95s|$0.02|
|[OpenAI text-embedding-3-large](https://platform.openai.com/docs/guides/embeddings)| 3072|8192|±750ms|151s|$0.13|
|[Mistral](https://docs.mistral.ai/capabilities/embeddings/)|1024|8192|±200ms|409s|$0.10|
|[VoyageAI voyage-2](https://www.voyageai.com/)|1024|4000|±350ms|330s|$0.10|
| [VoyageAI voyage-large-2](https://www.voyageai.com/)|1536|16000|±400ms|409s|$0.12|
|[Jina Colbert v2](https://jina.ai/news/jina-colbert-v2-multilingual-late-interaction-retriever-for-embedding-and-reranking/)|128, 96, or 64|8192|±400ms|375s|$0.18|
|[OSS all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)|384|512|±10ms|880s|Free|
|[OSS bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)|1024|512|±20ms|3379s|Free|
|[OSS bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5)|1536|512|±60ms|9132s|Free|

Choosing the best model for semantic search depends on what (and how) you aim to achieve with your specific use case, budget, and performance requirements. For most cases, cloud-based solutions such as those provided by Cohere or OpenAI may be optimal.

As the needs of your organization grow, it might be worth the cost and effort to upgrade to a local or self-hosted solution. Understanding your own needs is paramount to making a well-informed decision.

If you’re unsure which model is best for you or if you’re looking for a tailored solution, contact a search expert at Meilisearch.

## Frequently asked questions (FAQs)

### How does semantic search differ from keyword-based search?
Semantic search focuses on understanding meaning, while keyword-based search relies on exact word matches.
### What are the key features of a good semantic search model?
A good model should offer high accuracy, low latency, efficient indexing, and cost-effectiveness.
### How do transformer-based models improve semantic search?
Transformers process text in context, capturing contextual relationships between words to enhance search relevance while reducing downtime.
### How do vector databases enhance semantic search?
Vector databases work to efficiently store embeddings, enabling fast and scalable search operations.
### What are some open-source models for semantic search?
Popular open-source semantic search models include all-MiniLM-L6-v2 and Universal Sentence Encoder.




How to choose the best model for semantic search

What is a vector database? What you need to know [2025]

What is latent semantic indexing (LSI) and how does it work?

You know what's amazing about search? It's at the heart of how people interact with your application. But let's be honest: building and managing search infrastructure can feel like you're spending more time wrestling with configuration than creating value for your users. That's why we're excited to share something that changes this completely: the Meilisearch MCP server.

## Why MCP? Building bridges between search and AI

Before we dive in, let's talk about what MCP (Model Context Protocol) is and why we're excited about it. [MCP](https://www.anthropic.com/news/model-context-protocol) is an emerging open protocol that standardizes how applications provide context to Large Language Models (LLMs). Think of it as a universal translator between your tools and AI models.

Even though MCP isn't yet fully standardized, we believe it represents the future of how developers will interact with their tools. Here's why:
- It creates a consistent way for AI models to interact with different tools and services
- It puts security and user control at the forefront
- It enables natural language interactions while maintaining precise control
- It's being built in the open, with input from the developer community

## From complex commands to natural conversations

Remember the last time you needed to update your search settings? It probably involved looking up API documentation, crafting the perfect JSON payload, and triple-checking your syntax. We've all been there. But what if you could just tell your search engine what you want, in plain English?

That's exactly what we've built. By implementing MCP, we've enabled natural conversations with Meilisearch through AI assistants like Claude. Here's what this means in practice using [our movies database](https://where2watch.meilisearch.com/) as an example:

### Before:
```bash
curl \
  -X POST 'http://localhost:7700/indexes/movies/settings' \
  -H 'Content-Type: application/json' \
  -H 'Authorization: Bearer MASTER_KEY' \
  --data-binary '{
    "rankingRules": [
      "words",
      "typo",
      "proximity",
      "attribute",
      "sort",
      "exactness"
    ]
  }'
```

### After:
> "Update the ranking rules for the movies index to prioritize word matches and handle typos."

That's it. When paired with advanced LLMs like Claude Sonnet, the AI understands your intent and handles all the technical details behind the scenes. No more context-switching between documentation tabs.

## What can you do with it today?

Thanks to the capabilities of modern LLMs, our MCP integration already enables natural language control over your Meilisearch instance. Here's what you can do right now:

1. **Index management made simple**
```"Create a new index for our product catalog and optimize it for e-commerce search"```
The AI will guide you through setting up your index with appropriate configurations for e-commerce.

2. **Search optimization**
```"Our users are having trouble finding products when they make typos. How can we fix this?"```
Get instant guidance on configuring typo tolerance and seeing real-time results.

3. **Performance insights**
```"How is our search performance looking? Any areas we should optimize?"```
Understand your search analytics and get actionable recommendations.

## How does it work?

Our current MCP implementation exposes Meilisearch's capabilities through a set of carefully designed tools that AI assistants can use to manage your search infrastructure. When paired with advanced LLMs like [Claude Sonnet](https://www.anthropic.com/news/claude-3-5-sonnet), these tools enable natural conversations about complex search operations.

The best part? It works seamlessly with Claude Desktop and other MCP-compatible clients, giving you:
- Natural language control over your search infrastructure
- Instant access to best practices and optimization tips
- Real-time insights and analytics

## Getting started in 2 minutes

1. Install [Claude Desktop](https://claude.ai/download) (it's free!)
2. Add our MCP server to your configuration:

```json
{
  "mcpServers": {
    "meilisearch": {
      "command": "uvx",
      "args": ["-n", "meilisearch-mcp"]
    }
  }
}
```
3. Start managing your search like you're chatting with a colleague!

## Building the future of search

This is just the beginning of our mission to make search infrastructure more accessible. Here's what we're working on next:

### Smart prompts
We're developing pre-built templates for common scenarios like:
- Optimizing index settings for specific use cases (e-commerce, documentation, etc.)
- Debugging search behavior and performance issues
- Understanding and improving search analytics
- Implementing industry-specific best practices

### Enhanced capabilities
- More sophisticated analytics and monitoring tools
- Expanded AI assistant integrations
- Advanced collaborative features for teams
- Deeper integration with Meilisearch Cloud

## Open source and open for contributions

True to our values, the Meilisearch MCP server is completely open source. We believe in building in public and learning from our community. Check out our implementation, contribute, or adapt it for your needs:

[GitHub repository](https://github.com/meilisearch/meilisearch-mcp)

## Join us in simplifying search

- ⭐ Star [our repository](https://github.com/meilisearch/meilisearch)
- 💬 Join our [Discord community](https://discord.meilisearch.com/) & share your use cases (or do it on social media and tag us)
- 🛠️ Contribute to development on [GitHub](https://github.com/meilisearch/meilisearch-mcp)

We're excited to see how you'll use this to build better search experiences. Try it out and let us know what you think!

How we made Meilisearch talk to AI: introducing our MCP server

Building a RAG system with Meilisearch: a comprehensive guide

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

In today's interconnected world, many applications need to handle content in multiple languages. Meilisearch offers powerful capabilities for multilingual search, combining intelligent language handling with specialized models for optimal relevance. This guide will walk you through setting up multilingual search with Meilisearch, covering both full-text and semantic search approaches.

Why multilingual search matters
===============================

Before we dive into the technical details, let's consider why multilingual search is crucial:

1.  Global reach: Expand your audience by catering to users in their native languages.
2.  Improved user experience: Provide relevant results regardless of the language used in queries or content.
3.  Increased engagement: Users are more likely to interact with content they can easily find and understand.

Now, let's explore how Meilisearch makes multilingual search a breeze.

Full-text search: intelligent language handling
-----------------------------------------------

At its core, Meilisearch excels at handling diverse languages out of the box. Our [advanced tokenizer](https://www.meilisearch.com/docs/learn/indexing/tokenization?utm_campaign=vector-search&utm_source=blog&utm_medium=multilingual_search) is designed to work with:

*   Languages that use whitespace to separate words (like English, Spanish, and French)
*   Languages with different writing systems (such as Chinese, Japanese, and Thai)

### Automatic language detection

When you add documents to your Meilisearch index, the engine automatically detects the language of each field. This means you don't need to specify languages manually in most cases - Meilisearch will handle it for you.

For example, if your documents contain a mix of English and Spanish content:

```json
[
  {
    "id": 1,
    "title": "The quick brown fox",
    "description": "El zorro marrón rápido salta sobre el perro perezoso"
  },
  {
    "id": 2,
    "title": "La luna y las estrellas",
    "description": "A beautiful night sky filled with twinkling stars"
  }
]
```

Meilisearch will automatically detect and properly handle the different languages in each field, ensuring accurate search results across your multilingual content.

### Specifying languages for specific fields

While automatic detection works well in most cases, you might want more control over language handling for certain fields. Meilisearch allows you to [specify languages](https://www.meilisearch.com/docs/reference/api/settings#localized-attributes?utm_campaign=vector-search&utm_source=blog&utm_medium=multilingual_search) for particular attributes using the `localizedAttributes` setting.

Here's how you can set it up:

```json
{
  "localizedAttributes": [
    {
      "locales": ["eng"],
      "attributePatterns": ["title_en", "*_english"]
    },
    {
      "locales": ["spa"],
      "attributePatterns": ["description_es", "*_spanish"]
    }
  ]
}
```

This configuration tells Meilisearch to treat fields ending with `_en` or `_english` as English, and fields ending with `_es` or `_spanish` as Spanish. This approach is particularly useful when you have a consistent naming convention for your multilingual fields or when you want to ensure specific treatment for certain attributes.

Semantic search: Enhancing multilingual relevance
=================================================

While full-text search works well for many multilingual scenarios, semantic search can provide even better results, especially for complex queries or when dealing with nuanced meanings across languages.

Meilisearch supports various embedding models for semantic search, offering powerful auto-embedding capabilities. This means you can easily set up semantic search without having to manage the embedding process yourself. Meilisearch handles the generation and storage of embeddings automatically, seamlessly integrating them into your search experience.

For multilingual applications, we recommend using specialized multilingual models rather than relying on general-purpose models. These models are designed to understand and represent meaning across multiple languages, leading to more relevant search results in multilingual contexts.

One excellent option for multilingual semantic search is the [Cohere Multilingual embedding model](https://docs.cohere.com/docs/multilingual-language-models). It offers a great balance of speed and relevance across a wide range of languages. Cohere's model is particularly adept at understanding context and nuance in different languages, making it an ideal choice for diverse, multilingual content. Follow [the guide](https://www.meilisearch.com/docs/guides/ai/cohere?utm_campaign=vector-search&utm_source=blog&utm_medium=multilingual_search) if you want to do it now.

To explore different models and their performance, you can use the [Meilisearch playground](https://playground.meilisearch.com). This interactive tool allows you to test various embedding models and see how they perform with your specific multilingual queries and content.

Conclusion
----------

We encourage you to experiment with different settings and models to find the optimal configuration for your specific use case. And remember, the Meilisearch community is always here to help - join us on [Discord](https://discord.meilisearch.com/) or check out our documentation for more detailed information and support.

Ready to get started with multilingual search? Try Meilisearch today [with a 14 day trial](https://cloud.meilisearch.com/?utm_campaign=vector-search&utm_source=blog&utm_medium=multilingual_search)!

Meilisearch latest news and company updates