Indexing in a vector database involves organizing and structuring data to enable efficient similarity searches. The process begins by transforming raw data, such as text or images, into vector embeddings using machine learning models. These embeddings are high-dimensional vectors that capture the semantic meaning of the data. Once generated, these vectors are stored in the database.
To facilitate fast retrieval, the database employs indexing algorithms like the Hierarchical Navigable Small World (HNSW) algorithm or Approximate Nearest Neighbors (ANN). These algorithms create a data structure that allows for efficient searching and retrieval of similar items based on vector similarity. The index helps in partitioning the data space, reducing computational cost and improving search speed.
During a query, the database compares the query vector with the indexed vectors to find the nearest neighbors, effectively identifying items that are semantically similar. This process is crucial for applications like natural language processing and image recognition, where understanding the context and meaning is essential.