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A vector database is a specialised database designed to store, index, and search high-dimensional vectors (embeddings) efficiently. It enables fast similarity searches — finding items whose vector representations are closest to a given query.
Vector databases are critical infrastructure for RAG systems, semantic search, and recommendation engines — they make it possible to search by meaning rather than keywords.
Pinecone is a vector database that companies use to power semantic search — users type a question in natural language and get relevant results even if exact keywords do not match.
Embedding
An embedding is a way of representing data — such as words, sentences, or images — as a list of numbers (a vector) in a continuous space. Items that are semantically similar end up close together in this space, allowing machines to understand relationships between concepts.
Retrieval-Augmented Generation (RAG)
Retrieval-augmented generation is a technique that enhances a language model's responses by first retrieving relevant information from an external knowledge base, then using that information to generate a more accurate and grounded answer. It combines the strengths of search with generative AI.
Recommendation System
A recommendation system is an AI application that suggests relevant items to users based on their past behaviour, preferences, and similarities to other users. It uses techniques like collaborative filtering and content-based filtering to predict what a user will find valuable.
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