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Vector Search

Search that retrieves items by similarity between numerical vector representations.

한국어 English
#Embedding#RAG#Retrieval#Vector Search
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English updated 8월 29, 2026 Source article updated 8월 29, 2026 1 sources
This English page is a curated translation layer linked to the Korean source article. Community changes are currently made on the Korean source, where the full revision history and anonymous edit trail are preserved.
Vector search retrieves records whose embeddings are close to a query embedding. It is commonly used when semantic similarity matters more than exact keyword overlap.

How it works

A system first creates embeddings for documents or items and stores them in a vector index. At query time it embeds the query and performs exact or approximate nearest-neighbor search using a chosen similarity metric.

Why it matters

Vector search is a core component of many semantic-search and RAG systems. Good retrieval still depends on chunking, embedding quality, metadata filtering, index configuration, and evaluation.

Related concepts

See also Embedding, Retrieval-Augmented Generation (RAG), and Latency and Throughput.

SOURCES

Sources

  1. Billion-scale similarity search with GPUsFacebook AI Research / arXiv
    Open source ↗

KNOWLEDGE LINKS

Embedding → Retrieval-Augmented Generation (RAG) →

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Related articles

Latency and ThroughputTwo core serving metrics describing response delay and the amount of work a system completes over time.GPUA highly parallel processor widely used to train and run modern machine-learning models.Context WindowThe amount of tokenized input and generated text a model can consider within one inference context.KV CacheA cache of attention keys and values used to avoid recomputing earlier context during autoregressive generation.InferenceThe process of running a trained model on new input to produce predictions, embeddings, or generated output.Knowledge DistillationA training method in which a smaller student model learns from the outputs or representations of a larger teacher model.
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