Embedding Dimension
Embedding Dimension is the coordinate length of the vector used to represent data items in a latent space (e.g. OpenAI's text-embedding-3-small uses 1536 dimensions). It determines the detail capacity of the semantic space.
Frequently Asked Questions
Does a higher embedding dimension guarantee better retrieval?▼
Generally yes, as it allows mapping more complex semantic nuances. However, it increases vector database search latency and storage footprint.
Can embedding dimensions be modified after index creation?▼
No, a vector database index must be configured with a fixed dimension size that matches the embedding model's output length.
Quick Facts
- CategoryMathematical Foundations
- Key ApplicationVector database configuration, RAG vector index setups, and model design.
Coverage Trend12 Weeks
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