When OpenAI launched ChatGPT and the world fell in love with conversational search, it felt like traditional search was on the way out. After all, why bother with clunky keyword filters when you can ask natural language questions and get precise, semantically relevant results?
But for companies trying to implement that same experience on their own data, the reality quickly sets in: pure vector search doesn't get the job done. Not on its own.
Semantic search is powerful, but it’s incomplete. The most effective search systems — especially in real-world business environments — are hybrid. They combine the nuance of vectors with the precision of good old-fashioned token based search. This matters even more when you're building AI-powered systems. Whether you're designing a chatbot, training a recommendation model or creating a new internal search experience, hybrid search unlocks accuracy and relevance.
The problem with vector search alone
Imagine you’re running a support platform and a customer types:"How can I cancel my subscription without losing my data?"
A vector search engine transforms this sentence into an embedding — a high-dimensional vector that represents its meaning and then compares it to similar vectors across your documentation.
That works beautifully when you have semantically rich content and good training data. But what if your articles don’t use the word “cancel” but say “terminate” instead? What if they mention “data retention” in an unrelated legal context? Or worse: what if they don’t contain those ideas at all?
Semantic search can retrieve conceptually similar content, but it doesn’t understand your business. It can’t apply filters. It doesn’t enforce precision. And it doesn't help when a customer types something as specific as a product name, a user ID or a serial number. You need keyword-based search to anchor the conversation in reality.
Why keyword-based search still matters
Token-based search, like traditional full-text or inverted index search, is great at finding exact matches. It can filter by field, respect access permissions and surface results that match explicit business rules. It excels in structured or semi-structured environments, where customers expect deterministic answers.
Let’s go back to the support example. If a customer searches for:
“cancel pro subscription keep user_1243’s project data”
You probably want:
An exact match on “cancel”
A filter for the “pro” subscription tier
A scoped search on the specific user or project name
No amount of fuzzy, semantic reasoning will guarantee that level of control. That’s where hybrid search comes in.
What real hybrid search looks like
Hybrid search isn’t just vector search with a fallback to keyword search. It’s a true fusion of both approaches, so that results are ranked, filtered and retrieved using a combination of semantics and syntax.
If you're building AI-powered products, like generative search, intelligent assistants or search-enhanced machine learning workflows, hybrid search gives you the context-rich data pipeline you need.
Here’s what it takes to implement a real hybrid search system:
Embed your unstructured contentUse a transformer model to turn your documents, articles, product descriptions and user reviews into dense vector embeddings. This powers your semantic search capabilities.
Index structured metadata for traditional searchKeep your relational fields, like category, SKU, user ID, access level and timestamps in a token-based index. These fields allow for filtering, sorting and precision targeting.
Create a unified query layerBuild or use a system that supports both types of queries: an AI database that combines vector search similarity with keyword-based search relevance, boosting and re-ranking as needed.
Rank results using a hybrid scoring algorithmCombine the vector similarity score with classic token match scores (like TF-IDF or BM25). Decide how much weight to give each and test it in real-world queries.
Apply filters and facets using structured logicUsers often want to search semantically but still filter by product version, price range, language or department. That’s only possible if you integrate keyword-aware filters.
Tune and monitor your search feedback loopHybrid systems are dynamic. Use search logs, user clicks and feedback to tune the balance between token-based and semantic relevance over time.
Why AI needs hybrid search
If you're building a database for AI-powered apps, hybrid search isn't just a nice-to-have, it’s a foundational feature. AI workflows depend on accurate context. From Retrieval-Augmented Generation (RAG) pipelines to smart document classification to customer-facing chatbots, the most effective systems don’t rely on one method of search. They use both.
Hybrid search helps your models retrieve better context, which makes inference more accurate and interactions more natural. It also ensures that when users ask for something specific, like a product ID or invoice number, you can respond with certainty.
What SingleStore brings to the table
At SingleStore, we’ve made it easier than ever to build hybrid search into every part of your application stack. Our distributed SQL engine acts as a high-performance AI database that supports both vector search and token-based search in a single system. That means you can run semantic search and keyword-based search side-by-side, on the same data, using SQL.
Whether you're supporting enterprise machine learning teams, powering real-time AI agents or simply building a better search bar, SingleStore gives you the unified, fast foundation to make hybrid search a reality.
