What Are Embeddings?
An embedding maps text or another supported input into a numeric vector intended to represent semantic features. Similar meanings may be near one another in the embedding space, depending on the model and training. Embeddings enable semantic retrieval, clustering, deduplication, recommendations, and classification features.
Do not assume embeddings are facts, explanations, or guarantees of relevance. Their quality depends on the embedding model, domain, language, chunking, query form, and retrieval method. Keep model identity and dimensions consistent within an index, and plan re-embedding when changing models.
Diagram
Embedding pipeline
Document text -> normalization -> embedding model -> vector + metadata
Query text -> same embedding space -> nearest-neighbor search
Nearest chunks -> ranking/filtering -> application context💡 Queries and documents should use compatible embedding models and preprocessing. Store document IDs, chunk IDs, access-control metadata, and version information alongside vectors.
Key Takeaways
- Use compatible embedding models for queries and indexed documents.
- Track model and document versions.
- Evaluate retrieval quality on representative queries.
Knowledge Check
What does an embedding primarily represent?