Embeddings are numerical vector representations of text that capture semantic meaning. Similar concepts have similar vectors, enabling semantic search and comparison.
How Embeddings Work
- Text is converted to a fixed-size vector (e.g., 1536 dimensions)
- Similar meanings produce similar vectors
- Cosine similarity measures relatedness
- Stored in vector databases for fast retrieval
Embedding Use Cases
- Semantic search
- RAG document retrieval
- Clustering and classification
- Recommendation systems
Which embedding model should I use?
OpenAI's text-embedding-3-small is popular. For open-source, consider sentence-transformers. Match embedding model to your use case and latency requirements.
Practical example
A search system maps a query and document passages to numeric vectors for similarity retrieval.
What to check
Evaluate relevant-document retrieval on a labeled set, and version the embedding model with its stored vectors.
Limits and tradeoffs
Similarity is not proof of factual agreement, user authorization, or relevance to the current task.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.