By configuring these settings, you can ensure a smoother and more intelligent search experience for your users, especially in scenarios where keyword-based search comes up empty.
Vector Search - Overview
Vector search is a type of search technology that finds results based on meaning rather than exact words. It uses language models to understand the context and semantics of your query, allowing it to match 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 it does not rely on general-purpose AI or large language models (LLMs).
All processing is done locally on our servers, and 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 used 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.