What is Vector Search? Why Vector search engines are on the rise
How often have you searched for something, but you're not sure what it's called? Maybe you know what something does or have a description, but without the right keywords, you often keep searching. Vector search can remove this limitation, allowing you to search for what you mean.
What is Vector Search?
Vector search is a way of finding information based on the meaning and context of words, rather than exact search terms or keywords. Traditional search engines search a list of keywords and find exact matches. Vector search uses mathematical models like machine learning to convert words and phrases into vectors - points in a multidimensional space - making it possible to find search results that are similar in meaning, even if they do not exactly match the search terms.
Imagine you are searching for "healthy snacks for kids." A traditional search engine would only find pages with the exact words or phrases you entered. Vector search can yield results like "nutritious snacks for toddlers" or "child-friendly, healthy snack ideas," because it understands that these terms are similar in content.
Google Trends clearly shows the rising interest in Vector Search
AI applications dependent on search
Due to AI, Vector Search is increasingly emerging because generative AI is often combined with search. The knowledge of a Large Language Model is limited to the training data. By combining both technologies, an LLM also gains access to current information based on a search query.
Benefits for end users
- Better relevance: vector search provides results that better match the user's intent. You spend less time sifting through irrelevant results.
- More natural interaction: users can enter queries as they would normally speak or write them, without worrying about specific keywords or exact phrases.
- Discovering information: by showing semantically similar results, vector search helps users discover new and relevant information they might not have found otherwise.
- Multilingual support: vector search can more easily handle queries in different languages, as it universally recognizes the meaning of words.
How does vector search lead to more relevant results?
Semantic understanding
Vector search uses machine learning models like neural networks to understand the meaning of words and sentences. These models are trained on large datasets and can detect subtle differences and similarities between words. This allows search engines to display results that closely match the intent behind the query, even if the exact words do not appear.
Contextual information
Instead of only searching for specific words, vector search considers the context in which those words are used. This means it can distinguish between different meanings of the same word depending on the situation, leading to more accurate search results.
Flexibility
Vector search can handle synonyms and related terms more flexibly. For example, if someone searches for "cheap flights," a vector search can also show results for "affordable flying" and "budget airlines," as it understands the underlying meaning of these terms.
Solutions and technologies
There are various services and SAAS products that implement vector search to improve search experiences. Here are some of the most well-known:
Algolia
Algolia is a powerful search and discovery API that helps developers integrate fast and relevant search into their applications. Algolia uses both traditional search methods and vector search to ensure fast and relevant results.
Elastic
Elastic (or Elasticsearch) is a popular open-source search engine widely used by large tech companies to search big data. Elastic offers various algorithms, including Vector. An interesting detail is that Elastic is based in Amsterdam.
MeiliSearch
Meilisearch is a cost-effective search solution focused on providing relevant search results. Although it is primarily known for its traditional search capabilities, MeiliSearch is rapidly expanding with advanced features, including vector search.
Pinecone
Pinecone is a high-performance vector database designed to be combined with AI.
The future of search is hybrid
Vector search is redefining the way we find information by understanding queries based on meaning and context. This leads to more relevant and useful search results, a more natural interaction for users, and improved content discovery.
However, traditional algorithms remain just as important, as keyword search works better when you know exactly what you're looking for.
With solutions like Algolia, Elastic, and Meilisearch at the forefront, it is easier than ever to integrate these advanced search capabilities into various applications and platforms.
Vector search finds results on your website that are similar in meaning, even if they do not exactly match the search term.
