A database optimized for storing and searching high-dimensional vectors (embeddings).
A database optimized for storing and searching high-dimensional vectors (embeddings). When you build a RAG system, you store document embeddings in a vector database and search it with query embeddings. Pinecone, Weaviate, Chroma, and pgvector are popular options. Essential infrastructure for AI applications.
A dense numerical representation of data (words, images, etc.
Retrieval-Augmented Generation.
Search that understands meaning and intent rather than just matching keywords.
A mathematical function applied to a neuron's output that introduces non-linearity into the network.
An optimization algorithm that combines the best parts of two other methods — AdaGrad and RMSProp.
Artificial General Intelligence.
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