LearnAI

Navigation

Introduction to AI

Machine Learning

Deep Learning

NLP & LLMs

Computer Vision

Generative AI

Reinforcement Learning

AI Ethics & Governance

Tools & Frameworks

General

Introduction to Vector Search

Vector search is a technique used to find similar items based on their content rather than exact matches. It is a core component of many AI applications, including recommendation systems, image search, and Retrieval-Augmented Generation (RAG).

How Vector Search Works

Instead of matching keywords, vector search transforms data (text, images, audio) into numerical representations called embeddings. These embeddings are high-dimensional vectors that capture the semantic meaning of the data.

  1. Embedding: Data is passed through a model (like an LLM) to generate a vector.
  2. Indexing: Vectors are stored in a specialized database called a vector database.
  3. Querying: A search query is also converted into a vector.
  4. Similarity Search: The system calculates the distance (e.g., Cosine Similarity or Euclidean Distance) between the query vector and the stored vectors to find the nearest neighbors.
  • Semantic Understanding: Finds results that are contextually related even if they don’t share keywords.
  • Multimodal: Can compare different types of data (e.g., searching images using text).
  • Scalability: Optimized algorithms like HNSW (Hierarchical Navigable Small World) allow for fast searching across millions of vectors.