Embedding model · Fast-moving · Intermediate

Text embedding models

Models whose only job is to convert text into vectors for search, clustering and retrieval.

What Text embedding models is

Embedding models are small, fast and specialised. Choice depends on retrieval quality on your data, dimension size, multilingual coverage and whether you can self-host.

How it works

Text is encoded into a fixed-length vector stored in a vector index. Some models support variable dimensions so storage can be traded against accuracy. Switching model means re-embedding the whole corpus.

Why it matters

Retrieval quality in a RAG system depends more on the embedding and reranking choice than on the generation model.

Common uses

→RAG retrieval
→Semantic and multilingual search
→Deduplication
→Clustering and topic analysis

Strengths

✓Cheap and fast
✓Strong open options exist

Watch for

✓Vectors are not portable between models
✓Domain jargon may need fine-tuning

Continue exploring

Embeddings Vector Database Retrieval-Augmented Generation Reranking