--- ## Highlights Vector embeddings describe complex data objects as numeric values in hundreds or thousands of different dimensions. ([View Highlight](https://read.readwise.io/read/01h00mvndss69z6f0k26ehvztk)) They index vectors for easy search and retrieval by comparing values and finding those that are most similar to one another ([View Highlight](https://read.readwise.io/read/01h00mwjzswmb2b3mn4shbgn2r)) When we say that vector databases index [vector embeddings](https://www.pinecone.io/learn/vector-embeddings/), we mean that they organize them in a way that we can compare any vector to one another or to the vector of a search query. ([View Highlight](https://read.readwise.io/read/01h00mxzrn4zckf128pg5xrrdr)) Vector search enables users to describe what they want to find without having to know which keywords or metadata classifications are ascribed to the stored objects. ([View Highlight](https://read.readwise.io/read/01h00mynpdzn06g4cy7b63p321)) Different use cases require the prioritization of accuracy, latency, or memory usage which can be fine-tuned using different algorithms. ([View Highlight](https://read.readwise.io/read/01h00n19ntzvqdbjy8m6ed716y)) there are also similarity and distance metrics. These metrics are what measure the relevance/similarity between vector embeddings. ([View Highlight](https://read.readwise.io/read/01h00n1rsc13850rx74j8rca2a)) Euclidean distance, cosine similarity, and dot products. ([View Highlight](https://read.readwise.io/read/01h00n1zaehn3831rj339jn4zq)) Traditional nearest neighbor search is problematic for large indexes as they require a comparison between the search query and every indexed vector. Comparing every vector takes time. ([View Highlight](https://read.readwise.io/read/01h00n3bqhf7428cmv0nss2hym)) Approximate Nearest Neighbor (ANN) search circumvents this problem by approximating and retrieving a best guess of most similar vectors. While ANN does not guarantee to return the exact closest match, it balances very good precision with very fast performance. ([View Highlight](https://read.readwise.io/read/01h00n67177fw9xhvz5fv296kj))