Traditional keyword search stops at words. Semantic search understands meaning.
On most Drupal sites today, a user searching for “child education rights” might only get results that contain those exact words. But what if the best article uses the phrase “legal protections for minors”? That’s where Semantic Search comes in.
Semantic Search goes beyond literal matches. It uses language models and vector databases like Milvus or Pinecone to understand context and intent, delivering results that make sense to the user. Think of how Google now understands that a search for “best laptop for travel” might prioritise battery life and weight, not just articles with the word “travel.”
By connecting Semantic Search with Drupal’s Search API module, site builders can offer smarter search experiences across government portals, large publishing sites, nonprofit knowledge hubs, or healthcare platforms—anywhere users need quick, intelligent access to information.
Drupal AI ecosystem's AI search module
The Drupal AI Ecosystem's AI Search module makes this powerful Semantic Search functionality possible. As part of Drupal’s growing AI ecosystem, this module integrates seamlessly with Drupal’s existing architecture, enabling developers and site builders to harness the full potential of AI-powered search without needing to build complex solutions from scratch.
By leveraging tools like vector databases (e.g., Milvus or Pinecone) and the Search API module, the AI Search module provides an intuitive way to implement Semantic Search on your Drupal site. Whether you’re running a small website or a large enterprise platform, this module ensures that your users enjoy smarter, faster, and more relevant search experiences.
What is Semantic search?
Semantic Search goes beyond simple keyword matching. It understands the meaning behind user queries and matches them with content that aligns with their intent.
For example, if a user searches for "How to bake a cake," Semantic Search doesn’t just look for pages containing those exact words; it also considers related concepts like "dessert recipes," "baking tips," or "cake ingredients."
In Drupal, Semantic Search is powered by vector embeddings, which represent content as numerical vectors in a multi-dimensional space. These embeddings are stored in vector databases like Milvus or Pinecone, enabling fast and accurate similarity searches.
How Semantic search works in Drupal
To implement Semantic Search in Drupal, you can use the Search API module to connect to a vector database and index your content. Here’s how it works:
- Add a New Server: Configure a new server in the Search API module to connect to your chosen vector database (e.g., Milvus or Pinecone).
- Index Your Data: Select the fields you want to index, categorising them into different types:
- Main content: The primary body of your content, broken into chunks for efficient processing.
- Contextual content: Additional details like titles, summaries, or authors that provide context to each chunk.
- Filterable attributes: Metadata like dates or taxonomy terms used to pre-filter results before performing a vector search.
- Ignore: Fields you don’t want to include in the index.
Once indexed, the vector database processes the data and creates embeddings, enabling semantic matching during user queries.
To get started with Semantic Search in Drupal, you can explore these community-driven projects:
- AI Module: Drupal AI Project
- Vector Database Integration: AI Vector DB Provider for Milvus
These modules make it easier to integrate advanced AI capabilities into your Drupal site.
Benefits of Semantic search over traditional search
While traditional search engines like Apache Solr or database searches rely on keyword matching and ranking algorithms, Semantic Search offers several advantages:
1. Understanding user intent
- Traditional search often struggles with ambiguous queries or synonyms. For example, searching for "Apple" might return results about the fruit instead of the tech company.
- Semantic Search uses natural language understanding (NLU) to interpret the meaning behind queries, ensuring accurate results regardless of phrasing.
2. Context-aware results
- By incorporating contextual content (e.g., titles, summaries, authors), Semantic Search provides richer and more relevant results. For instance, a query about "climate change" will prioritise articles tagged with environmental topics rather than unrelated mentions.
3. Faster and Smarter searches
- Vector databases like Milvus and Pinecone are optimised for similarity searches, making them faster and more scalable than traditional databases. This is especially critical for large datasets or complex content structures.
4. Personalisation and filtering
- Semantic Search allows you to attach filterable attributes to records, enabling advanced filtering options like date ranges, categories, or user roles. This ensures users only see results that matter to them.
5. Future-proof technology
- As AI continues to evolve, Semantic Search will become even smarter, integrating with technologies like voice assistants, chatbots, and predictive analytics. Drupal’s flexibility ensures you’re ready for these advancements.
Comparison: Semantic search vs. traditional search
| Feature | Semantic search | Traditional search |
|---|---|---|
| Search approach | Understands the meaning and intent behind queries using natural language processing. | Relies on exact keyword matches or simple ranking algorithms. |
| Context awareness | Incorporates contextual details (e.g., titles, summaries, metadata) for richer results. | Ignores context; focuses solely on matching keywords in content. |
| Handling ambiguity | Can interpret ambiguous queries (e.g., "Apple" as a fruit vs. a tech company). | Struggles with ambiguity; often returns irrelevant results. |
| Scalability & performance | Optimised for large datasets using vector databases for fast similarity searches. | May struggle with scalability and performance for complex queries. |
Real-world use cases
Semantic Search has transformative potential across various industries:
- E-Commerce Websites: Customers can find products using conversational queries like "Show me eco-friendly sneakers under $100."
- Educational Platforms: Students locate resources, courses, or materials with ease, even when using vague or incomplete queries.
- Healthcare Websites: Patients find relevant health articles, FAQS, or services by asking questions in plain language.
- Corporate Intranets: Employees quickly locate internal documents, policies, or contact information, even without knowing the exact keywords.
Semantic search in action
Let’s consider an example:
- Query in Thai: “Kaeng Khiao Wan" (Green Curry in Thai)
- Traditional Keyword Search: Might return results based solely on the phrase "Kaeng Khiao Wan" without understanding its broader context.
- Semantic Search: Understands that the user is looking for recipes, ingredients, or cultural insights related to green curry, delivering richer and more relevant results.
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Conclusion
Instead of matching exact keywords, Semantic Search uses vector databases like Milvus or Pinecone to understand what users are really asking. It considers intent, phrasing, and related concepts, helping people find the right content even if they don’t use the exact terms.
When paired with Drupal’s Search API, Semantic Search can support faceted filters, multilingual queries, and complex taxonomies with more relevance and less friction. For content-heavy sites like legal portals, digital libraries, or nonprofit platforms, it means faster answers, fewer dead ends, and better usability.
Want to make your Drupal search smarter? Start by integrating a vector store and configuring your Search API to support embeddings. The result is a search experience that actually understands your content and your users.
For more information and to try it locally, explore these resources: