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AI-powered vector search finds conceptually related results by turning content and a query into numerical vectors, then retrieving records whose vectors are close under a chosen mathematical measure. It can match different wording—such as a search for “vacation rules” with an “annual leave policy”—but it does not understand language as a person does, and it does not make exact keyword search obsolete.
What an embedding represents
An embedding is a list of numbers produced by a model from an input such as text, an image, or another supported type of content. It represents learned patterns or features of that input in a high-dimensional space. It is not a dictionary definition, a list of keywords, or a guarantee that the model has captured every nuance.
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For a document collection, a system can create an embedding for each record—or for smaller chunks of longer documents—and store it alongside the original content. When someone searches, the system embeds the query in a compatible representation. The original records remain attached to their vectors so the system can return useful content rather than just coordinates.
How vector search retrieves results
- Represent the collection. An embedding model converts each searchable record or document chunk into a vector.
- Organize the vectors. A vector index supports retrieval, often alongside metadata that can be used to filter records.
- Embed the query. The search query is converted into a vector compatible with the indexed vectors. Vectors from unrelated model spaces should not be treated as directly comparable.
- Measure closeness. The search system applies a selected distance or similarity measure to identify nearby candidates.
- Return and refine. The system returns the records associated with those candidates and may apply filters, additional ranking, or keyword matching. In retrieval-augmented generation (RAG), selected retrieved content can then be passed to a language model as context.
This is why “finds meaning” is shorthand: the model produces a representation, and the search system ranks records by a mathematical relationship between representations. Whether the results are actually useful depends on the model, data, metric, filters, index, and ranking choices.
What “similar” means
Vector search does not use one universal definition of closeness. It uses a selected metric. OpenSearch documentation lists cosine similarity, Euclidean distance, Manhattan distance, inner product, and Hamming distance among its supported spaces.
- Cosine similarity compares the angle, or direction, between vectors and deemphasizes their magnitude. The OpenSearch Documentation describes it this way: “Cosine similarity: Measures the angle between vectors, focusing on direction rather than magnitude.”
- Euclidean distance measures straight-line distance between points and is sensitive to vector magnitude.
- Inner product uses the vectors’ dot product as a similarity measure.
A score only has meaning within the model and metric setup that produced it. There is no universal score threshold that proves two items mean the same thing, and geometric closeness is not identical to human judgment.
Keyword, semantic, and hybrid search
| Search approach | What it matches | Where it helps |
|---|---|---|
| Keyword or lexical search | Literal terms and other textual signals | Exact phrases, names, model numbers, codes, and other identifiers |
| Semantic or vector search | Related representations, which can reflect meaning despite different wording | Natural-language queries where relevant content may use different terms |
| Hybrid search | A combination of lexical and vector retrieval | Queries that may need both conceptual matches and exact-token hits |
For example, Elastic uses “vacation rules” and “annual leave policy” to illustrate how semantic search can retrieve a relevant result even when the query and document do not share the same phrase. A keyword search can still be the better signal when the searcher needs a particular product code, quoted wording, or person’s name. Semantic search can also miss rare terms, identifiers, domain-specific meanings, or distinctions that the chosen model does not represent well. Hybrid search combines the two approaches and is a sensible starting point for mixed queries, but the best setup depends on the collection and what counts as a relevant result.
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With exact k-nearest-neighbor search, the system compares the query against all indexed vectors to identify the true nearest neighbors under the chosen metric. This can be computationally expensive for a large collection. Approximate nearest-neighbor (ANN) methods use an index to reduce the amount of work and can improve retrieval performance, but the returned candidates may differ from the exact nearest neighbors.
Google Cloud’s BigQuery documentation notes that using a vector index enables approximate nearest-neighbor search and reduces recall relative to brute-force search, which can return exact results. This is a performance-versus-recall trade-off, not a rule that approximate search is always inaccurate or that exact search is always impractical. The right choice depends on the workload and the cost of missing a closer match.
Where vector search is used
- Semantic retrieval and RAG: find passages relevant to a natural-language question and, in a separate downstream step, provide them as context to a language model.
- Recommendations and product substitutes: retrieve items similar to a product, user preference, or other represented input. Retrieval is one part of the recommendation system; it does not by itself decide what to recommend.
- Image retrieval: find images with similar representations, when the model and search system support the relevant image workflow.
- Logs and clustering: find similar records or group represented items for investigation and analysis.
These uses share the same basic idea—represent items, compare vectors, and retrieve candidates—but the embedding model, filters, and later ranking need to suit the particular data and task.
Rank #4
What to evaluate in an implementation
There is no universally best vector database or search service. Evaluate the whole retrieval pipeline against the needs of the application:
- Model fit: Does the embedding model suit the content, language, and task? Are its supported data types and vector dimensions appropriate?
- Relevance: Do the nearest results match the way users phrase queries, including exact identifiers and specialized terminology? Test with representative queries and judge results against the application’s definition of relevance.
- Search behavior: Can the system use exact or approximate search as needed? What are the consequences for recall, latency, and resource use?
- Controls and ranking: Does it support metadata filters, hybrid search, or reranking where the application needs them?
- Operations: Consider the collection’s scale, memory needs, index updates, hosting model, integration with the existing database or search stack, and ongoing operational complexity.
Vector proximity is a ranking signal, not a fact-check. A nearby result may be irrelevant, outdated, or incorrect; applications that need reliable answers should assess retrieved content and use appropriate downstream validation.
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