A numerical representation of data (text, images, etc.) in a multi-dimensional vector space. Embeddings capture semantic meaning, allowing similar concepts to be mathematically close together.
A specialized database designed to store and efficiently search high-dimensional vectors (embeddings). Vector databases are essential for RAG systems, similarity search, and recommendation engines.
A technique that enhances LLM responses by retrieving relevant information from external knowledge sources before generating a response. RAG improves accuracy, reduces hallucinations, and enables LLMs to access up-to-date information.
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