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.
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) 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 does not always capture exact identifiers, rare names, or required keywords. Hybrid Search combines vector retrieval with lexical search, while Reranking applies a more precise model to the initial candidates.
See also Vector Embedding and Vector Store.
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