Embeddings
Transforming text into a numeric vector that captures its semantic meaning. Two similar texts have close vectors in embedding space. The foundation of RAG and semantic search.
Strengths
- Search by meaning rather than keywords, much more relevant results
Limitations
- Generation cost: each text must be vectorized via API (OpenAI, Voyage, etc.)
Best for
- Understanding how semantic search and RAG work