The 10 best AI enterprise search tools and platforms [2025]

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://meilisearch.org.cn/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://meilisearch.org.cn/docs/learn/ai_powered_search/document_template_best_practices). Complex setups—such as managing [heavyweight solutions like Elasticsearch](https://meilisearch.org.cn/blog/meilisearch-vs-elasticsearch), [tweaking a Postgres instance](https://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/docs/learn/security/basic_security) to facilitate multi-environments. The built-in [multi-tenancy features](https://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/blog/ai-powered-search) is crucial for applications requiring faster, broader, and even less structured [data retrieval](https://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn), 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://hugging-face.cn/sentence-transformers/all-MiniLM-L6-v2) | 384 | 512 |
| [OSS bge-small-en-v1.5](https://hugging-face.cn/BAAI/bge-small-en-v1.5) | 1024 | 512 |
| [OSS bge-large-en-v1.5](https://hugging-face.cn/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://hugging-face.cn/BAAI/bge-small-en-v1.5) | ±800ms |
| [Cloudflare bge-large-en-v1.5](https://hugging-face.cn/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://hugging-face.cn/models?search=gte-small) | ±20ms |
| [Local all-MiniLM-L6-v2](https://hugging-face.cn/sentence-transformers/all-MiniLM-L6-v2) | ±10ms |
| [Local bge-small-en-v1.5](https://hugging-face.cn/BAAI/bge-small-en-v1.5) | ±20ms |
| [Local bge-large-en-v1.5](https://hugging-face.cn/BAAI/bge-large-en-v1.5) | ±60ms |
| [Mistral](https://docs.mistral.ai/capabilities/embeddings/) | ±200ms |
| [Jina colbert](https://hugging-face.cn/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://meilisearch.org.cn/docs/learn/experimental/vector_search#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://hugging-face.cn/BAAI/bge-small-en-v1.5) | 152s |
| [Cloudflare bge-large-en-v1.5](https://hugging-face.cn/BAAI/bge-large-en-v1.5) | 159s |
| [Jina Colbert V2](https://hugging-face.cn/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://hugging-face.cn/sentence-transformers/all-MiniLM-L6-v2) | 880s |
| [Local bge-small-en-v1.5](https://hugging-face.cn/BAAI/bge-small-en-v1.5)| 3379s |
| [Local bge-large-en-v1.5](https://hugging-face.cn/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://meilisearch.org.cn/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://hugging-face.cn/sentence-transformers/all-MiniLM-L6-v2)|384|512|±10ms|880s|Free|
|[OSS bge-small-en-v1.5](https://hugging-face.cn/BAAI/bge-small-en-v1.5)|1024|512|±20ms|3379s|Free|
|[OSS bge-large-en-v1.5](https://hugging-face.cn/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

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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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.org.cn/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://meilisearch.org.cn/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.org.cn/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.org.cn/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://meilisearch.org.cn/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://meilisearch.org.cn/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.org.cn/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://meilisearch.org.cn/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.

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