How AI-Powered Vector Search Enhances Performance and User Experience at Scale

In our previous article, we introduced the concept of semantic search in Drupal 11 using Solr. At its core, semantic search allows websites to go beyond traditional keyword matching by understanding the intent and meaning behind user queries.

This represents a significant step forward for organizations managing large volumes of content. By leveraging dense vector search, teams can deliver more relevant search results without relying on manual synonym curation, complex taxonomies, or boost rules.

What Is Semantic Seach and Why It Matters

With semantic search powered by dense vectors, users can find the most relevant content even when the exact words in their query do not appear on the page. For example, a search for "cattle grazing" may surface content on "livestock management," because the system recognizes their semantic similarity.

Without this capability, many of these searches would either return no results or miss the most valuable content entirely, discouraging your users. Imagine how many times you have visited a website and tried to search for something, but you aren’t exactly sure what you are looking for. Unless you type in the exact word or phrase, you may not find it. You may not even know what phrase to use, but you may be able to describe it. With similarity search and retrieval-augmented generation (RAG), even vague queries can yield accurate results, helping reduce “dead-end” searches and improving user satisfaction.

Demonstrating Real-World Impact

To show how this works in practice, we have included a short video demonstration. It highlights multiple search phrases that do not appear verbatim in the content—yet the system consistently returns accurate results. This illustrates the value proposition of semantic search: enabling users to find what they need, not just what they literally type.

This approach is particularly impactful for organizations with thousands of pages of content. Instead of asking content management teams to maintain extensive synonym lists, abbreviations, or boost values within Solr, semantic search uses dense vector similarity to do the heavy lifting automatically.

The result? Less manual effort for teams and a more intuitive search experience for end users.

Optimizing for Performance

As we continue to enhance this solution, performance optimization remains a top priority. One key improvement underway is the ability to store previously searched phrases and sentences along with their vector values.

This allows the system to respond to repeat queries more quickly, ensuring users get results as fast as possible without re-computing similarity each time. The vectorized version of a phrase will not change (unless you choose to modify your embedding model size), so generating it repeatedly for the same phrase is unnecessary, especially at query time for site users.

It is also important to note this is not just a conceptual solution—we’re already running it in production on Acquia Cloud using Drupal 11 for new projects in 2025.

This demonstrates both the feasibility and enterprise readiness of semantic search with Solr.

In our next post, we’ll dive deeper into performance strategies for managing API load, limits and cost, to ensure semantic search remains scalable and cost-effective.

In Conclusion: Smarter Search at Scale

Semantic search with dense vectors in Solr marks a major leap forward for content-rich organizations looking to provide a better user experience. By enabling relevant results even when exact keywords are absent, it reduces the burden on content managers while improving the search experience for site visitors.

This capability is available today in Drupal 11 through the AI module and the Search API Solr Dense Vector module, which we have contributed to the Drupal community. Together, these tools empower organizations to deliver more effective and user-friendly search experiences at scale.