Embeddings integrate with vector databases to enable fast and efficient similarity searches. A vector database is designed to store high-dimensional vectors (like embeddings) and perform operations like nearest neighbor search, which finds the most similar vectors to a given query vector. Once embeddings are generated for your data (e.g., text, images, or products), they are stored in the vector database, where they can be indexed for fast retrieval.
For example, in a product recommendation system, embeddings for products can be stored in a vector database. When a user queries the system with an item or search term, the system generates the embedding for the query and retrieves the most similar items by performing a nearest neighbor search in the database. Vector databases support efficient search algorithms like Approximate Nearest Neighbor (ANN), which significantly speeds up the search process for high-dimensional data.
Some popular vector databases, like FAISS, Pinecone, or Milvus, are optimized for working with embeddings, enabling scalable and low-latency similarity search. These databases also provide indexing techniques to handle large-scale data efficiently. By integrating embeddings with vector databases, organizations can create fast, scalable, and high-performance search systems for a variety of applications, from image retrieval to recommendation engines.