Vector Search

Configure these settings to provide a smoother, more intelligent search experience—especially when keyword-based search returns no results.

Overview

Vector search finds results based on meaning, not just exact words. It uses language models to understand context and semantics, matching related terms, synonyms, and natural language.

How does it work?

• Text is converted into vectors: Both the user query and the searchable content are transformed into high-dimensional numeric representations called vectors. These vectors capture the semantic meaning of the text.

• Similarity is measured: Vector search finds content whose vector is most similar to the query’s vector using a proprietary similarity metric.

Privacy & Technology Note

This feature uses advanced vector-based language understanding but does not rely on general-purpose AI or large language models (LLMs). All processing happens on our servers; your data never leaves our infrastructure.

When to Use Vector Search

Use vector search when:

• You want results that match intent, not just exact keywords.

• You support natural language queries, like full sentences or questions.

• Your users often search with synonyms or varying terminology.

• Your users sometimes use mixed languages in queries.

Pros and Cons

Pros:

• Finds relevant results even with vague or unstructured queries.

• Handles synonyms and paraphrases automatically.

• Great for exploratory or conversational search experiences.

Cons:

• Results can be less explainable.

• May need fine-tuning for optimal accuracy and relevance.

For zero-hit fallbacks and hybrid behavior, see:

• Fallback Vector Search

• Hybrid Search