Vector Search
Vector search enhances the search experience by finding results based on meaning, not just exact keywords. It helps users discover relevant content even when traditional keyword search fails.
Overview
Unlike keyword search, which relies on exact word matches, vector search represents text as high-dimensional vectors (also known as embeddings) that capture context and semantics. This allows the system to surface results that align with the intent of the query, even when different words, synonyms, or natural phrasing are used.
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: The system compares vectors and returns the results most similar to the query using a proprietary similarity metric.
Privacy & Technology Note
Powered by 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 Vector Search Helps
Vector search is especially valuable when:
You want results that match intent, not just exact keywords.
Queries are written in natural language, such as full sentences or questions.
Users often search with synonyms, related terms, or varied terminology.
Queries may contain typos, spelling errors, or other mistakes.
Queries may include mixed languages.
Pros and Cons
Pros:
Finds relevant results even with vague, unstructured, or error-prone queries.
Handles synonyms and paraphrases automatically.
Great for exploratory or conversational search experiences.
Cons:
Results can be less explainable.
Accuracy and relevance may require fine-tuning.
Vector search is available in two modes:
Fallback Vector Search – used when keyword search returns no results.
Hybrid Search – combines keyword and vector search for broader coverage.
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