---
title: "Vector Search"
description: "Vector search is a method used in information retrieval and Machine Learning (ML) to find and rank items based on their vector representations in a multi-dimensional space. In the context of Large Language Models (LLMs), vector search involves encoding data, such as text or images, into high-dimensional vectors using techniques like embeddings. These vectors capture semantic meaning, allowing for efficient similarity searches and comparisons."
canonical_url: "https://promptmetheus.com/resources/llm-knowledge-base/vector-search"
---

> Index of every page on this site: [llms.txt](https://promptmetheus.com/llms.txt)

Vector search is a method used in information retrieval and [Machine Learning (ML)](https://promptmetheus.com/resources/llm-knowledge-base/machine-learning-ml) to find and rank items based on their vector representations in a multi-dimensional space. In the context of [Large Language Models (LLMs)](https://promptmetheus.com/resources/llm-knowledge-base/large-language-model-llm), vector search involves encoding data, such as text or images, into high-dimensional vectors using techniques like [embeddings](https://promptmetheus.com/resources/llm-knowledge-base/vector-embedding). These vectors capture semantic meaning, allowing for efficient similarity searches and comparisons.

The process typically involves transforming input data into vectors using models like word2vec, BERT, or other neural network-based embeddings. Once data is represented as vectors, vector search algorithms, such as k-nearest neighbors (k-NN) or approximate nearest neighbor (ANN) methods, are employed to identify the most similar vectors within a dataset. This approach is crucial for applications like recommendation systems, semantic search, and [Natural Language Processing (NLP)](https://promptmetheus.com/resources/llm-knowledge-base/natural-language-processing-nlp) tasks, where understanding the contextual similarity between data points is essential.

Vector search is particularly advantageous in handling large-scale datasets, as it enables fast and scalable retrieval of relevant information by leveraging advanced indexing techniques and optimized data structures.

Vector similarity depends on the selected [embedding distance metrics](https://promptmetheus.com/resources/llm-knowledge-base/embedding-distance-metrics), such as cosine distance, Euclidean distance, or dot product. It does not always capture exact identifiers, rare names, or required keywords. [Hybrid Search](https://promptmetheus.com/resources/llm-knowledge-base/hybrid-search) combines vector retrieval with lexical search, while [Reranking](https://promptmetheus.com/resources/llm-knowledge-base/reranking) applies a more precise model to the initial candidates.

See also [Vector Embedding](https://promptmetheus.com/resources/llm-knowledge-base/vector-embedding) and [Vector Store](https://promptmetheus.com/resources/llm-knowledge-base/vector-store).

## Sitemap

See the full [sitemap](https://promptmetheus.com/sitemap.md) for all pages.