How Embeddings Represent Meaning
An embedding maps text or another supported input into a numeric vector. Similar meanings may be close in the learned space, but embeddings are not a perfect representation of truth. Query and document vectors must use compatible models and preprocessing. Store the embedding model identifier and dimensions with each indexed vector.
Embeddings support semantic retrieval, clustering, recommendations, and deduplication. They are not encryption and should not be assumed anonymous.
Implementation example
const vector = await embeddingModel.embed("How do I rotate an API key?");
// vector is a numeric array in a model-specific embedding space💡 The vector is useful for similarity search, but individual dimensions are not generally human-interpretable.
Key Takeaways
- Inspect the relevant pipeline stage instead of guessing.
- Preserve source provenance, versions, and access scope.
- Measure quality on representative queries.
Knowledge Check
What is an embedding?