Vector Search: What Is Vector Search and How Does it Work?

Neural search and Grounded Generation, often referred to as Retrieval Augmented Generation, have gained significant attention for their applications, with vector search being a pivotal component in these systems. Vector search involves retrieving relevant data from a vast collection based on their vector representations, which are mathematical constructs capturing the semantic essence of input data like text, images, and more. These vectors, or embeddings, are used to find semantically similar data by measuring the distance between them, with dense vectors typically used for semantic search and sparse vectors for lexical search. Vector search faces challenges such as handling large data sets, maintaining low latency, and optimizing computational resources, with popular libraries like FAISS and ANNOY facilitating these processes. Techniques like Inverted File Index (IVF), Hierarchical Navigable Small Worlds (HNSW), and quantization are employed to enhance the efficiency of vector search, each offering different trade-offs between accuracy and resource demands. Vector databases, such as Milvus and Weaviate, provide specialized storage and search capabilities, while solutions like Vectara integrate these components, offering a secure, scalable, and user-friendly platform for neural information retrieval and generation tasks.