Quick takeaways
- Embedding models turn text into vectors so retrieval can find semantically similar passages.
- API models are faster to deploy; open models give control and lower per-token cost at scale.
- Higher dimensions improve expressiveness but increase storage and compute. Match dimensions to your vector database limits.
Embeddings explained for builders
AssemblyAI explains text embeddings, similarity search, and how embedding models power retrieval.
What embedding models do
An embedding model maps words, sentences, or documents into a dense vector of numbers. Similar meanings end up close together in that vector space. In RAG, this lets a query find relevant chunks even when the wording differs.
Good embeddings are the foundation of semantic search. If your embeddings do not capture the meaning of your documents, retrieval fails before the generation model gets involved.
Model landscape
| Family | Provider | Best for | Notes |
|---|---|---|---|
| OpenAI text-embedding | OpenAI | Fast, high-quality retrieval | API-only; good default for many RAG systems. |
| Cohere Embed | Cohere | Multilingual and enterprise search | Strong clustering and classification support. |
| sentence-transformers | Open source | Self-hosting and customization | Large selection of pre-trained models. |
| BGE | BAAI / open source | Strong retrieval benchmarks | Popular in open RAG pipelines. |
| E5 | Microsoft / open source | Instruction-tuned retrieval | Good when queries need task-specific guidance. |
API vs open models
For many teams, the right path is to start with an API model, measure retrieval quality, and then consider self-hosting once volume and cost justify it.
Dimension trade-offs
Embedding dimensions range from a few hundred to several thousand. More dimensions can carry richer meaning, but they also increase storage, indexing time, and query cost.
- Small dimensions (256-512): Lower storage and faster search. Useful for large-scale or memory-constrained systems.
- Medium dimensions (768-1024): Common default. Good balance of quality and cost.
- Large dimensions (1536+): Higher expressiveness. Check whether your vector database and budget can handle them.
Some models let you truncate dimensions with modest quality loss. Test retrieval with a few sizes before committing.
Use cases
Multimodal embeddings
Multimodal embedding models handle text, images, or audio in the same vector space. They are useful when your knowledge base includes slides, diagrams, product photos, or video transcripts alongside documents.
- OpenAI CLIP-style models: Good for image-text alignment.
- Multimodal API embeddings: Simpler but vendor-dependent.
- Open multimodal models: More setup but stronger control.
Start with multimodal embeddings only when search across media types is a real requirement. Otherwise, text embeddings keep the stack simpler.
Evaluation
Test embeddings on your own documents and queries. Public benchmarks are a starting point; your domain language and chunking choices matter more.
- Build a small set of real questions and mark the ideal chunks.
- Index the same documents with each embedding model.
- Measure recall at k, precision, and mean reciprocal rank.
- Compare cost and latency at your expected volume.
Without AI vs. with AI
| Task | Without AI | With AI |
|---|---|---|
| Text matching | Search relies on exact keywords and Boolean queries. | Embeddings capture meaning, so search finds related text even with different words. |
| Model choice | Teams default to the largest embedding model without testing. | Models are chosen by retrieval quality, cost, latency, and dimension constraints. |
| Hosting | Every team self-hosts embeddings regardless of scale or expertise. | APIs are used to ship quickly; self-hosting is added when volume justifies it. |
| Dimensions | The highest dimension model is chosen assuming more is always better. | Dimensions are balanced against storage, indexing speed, and retrieval quality. |
| Evaluation | Embeddings are judged by public benchmark rank. | Retrieval is measured on the team's own documents and real queries. |
FAQ
Do I need the largest embedding model?
Usually no. A well-sized model that matches your data and vector database often performs better in practice than the largest option.
Can I mix embedding models in one system?
Technically yes, but it complicates the stack. Most teams standardize on one model per index.
Should I fine-tune an embedding model?
Consider fine-tuning when your domain vocabulary is very specific and off-the-shelf retrieval is clearly underperforming.
How do embeddings affect cost?
API embeddings charge per token. Open models shift cost to infrastructure. Storage and query cost also grow with dimension and vector count.
What is the difference between embeddings and re-rankers?
Embeddings retrieve candidate passages quickly. Re-rankers score a smaller set of candidates more accurately.
Which model works best with which vector database?
Most modern vector databases accept any embedding vector. The main constraint is dimension size, not model family.
What is the difference between an embedding model and a language model?
An embedding model outputs a fixed-size vector representing text. A language model generates text. They are often used together in RAG.
Can I use the same embedding model for search and clustering?
Sometimes. Search and clustering have different requirements, so test the model on both tasks if you need both.
How do multilingual embeddings work?
They map text in different languages into the same vector space, enabling cross-lingual semantic search.
Should I truncate embedding dimensions?
Some models support truncation with modest quality loss. Test retrieval before and after truncation on your data.