How does vector search compare to the thesaurus?

The thesaurus manages manual rules (A = B), while vector search uses AI to automatically deduce the semantic relationship between terms. AI offers significantly superior scale and precision for search thanks to vector data and numerical representations.

Can vector search handle multilingual queries?

Yes. The Artificial Intelligence models used for vector search are often trained on vast multilingual corpora, allowing them to understand and match meaning regardless of the language. These capabilities rely on LLM language models, which are adapted to multilingual data sets.

Does vector search replace classic search?

No, it complements it. Vector search is added to the existing index (keywords, attributes) to improve relevance. Classic search handles precise filters, while AI—including generative AI—manages the understanding of meaning and the links between products via recommendation systems.

Does vector search require a lot of specific e-commerce data?

The AI engine is trained on general data, but it adapts over time based on interactions with your catalog's results. This approach relies on varied data sets and can leverage open source models to boost performance.

What are the technical prerequisites for implementing vector search?

The technical complexity is managed by the ElasticSuite/Gally tool. You only need a well-structured catalog; the AI infrastructure and vector management are handled by the platform, including the numerical representations required for processing.

AI and Search Feature

Vector Search: AI at the Service of Meaning

Vector search uses AI to analyze your queries and your products, which are represented as vectors. It improves the relevance of results, facilitates similarity searches, and accelerates product discovery in real time, offering a fluid and intelligent e-commerce experience.

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Ai Search

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Features
Site search
AI-Powered Vector Search

Semantic Understanding

AI Understands the Intent and Meaning Behind Searches

Unlike traditional searches, vector search does not focus on exact words, but on vectors of meaning. For example, if a customer enters “a cheap blue evening dress,” the AI analyzes the query vector and immediately finds relevant products, even if the descriptions use different terms.

How Vector Search Transforms E-commerce

The vector search engine converts the user’s query and each product sheet into a query vector within a multi-dimensional space. Thanks to vector representations, the search compares similar vectors and finds the products closest to the query vector in real time. It thus returns results based on meaning and not on exact words. This approach allows for the use of advanced search methods, including hybrid search, to enrich the user experience.

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ROI

AI: An Immediate Growth Lever

Improving search translates directly into increased sales. Vector search is the most cost-effective investment for results relevance.

Drastic Reduction of "Zero Results"

By understanding meaning and intent thanks to AI, the engine avoids failures related to exact keywords. Vector search significantly reduces these situations.

Natural Product Discovery

Customers easily find relevant products they wouldn’t have thought of. Vector search maximizes your catalog visibility.

Competitive Advantage

Offering advanced semantic search positions your site as a benchmark in user experience and strengthens your position against the competition.

FAQ

FAQ: Vector Search and AI

Everything you need to know about vector search, AI, and search results.
Read the User Guide or contact us for assistance.