An embedding is a numeric vector that represents the meaning of text. Similar ideas tend to produce nearby vectors, even when they use different words.
For RAG, the system embeds document chunks during ingestion and stores their vectors. It embeds the user's question with the same model, compares that query vector with stored vectors, and returns nearby chunks. This enables semantic search across vocabulary differences.
Embeddings are central but not sufficient. They can miss exact names, numbers, or rare terms, so keyword search and reranking often help. The embedding model, chunking method, metadata, and evaluation data all affect retrieval quality.
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