Models
22+ local embedding models
embedcache ships the FastEmbed catalogue — over 22 ONNX models that run on CPU. Choose a model per request, or restrict the set with ENABLED_MODELS.
A representative slice
These five are the models you will reach for most often. The full list of 22+ is in the FastEmbed
catalogue and is reported by GET /v1/params for your build.
| Model | Dimensions | Notes |
|---|---|---|
| AllMiniLML6V2 | 384 | Fast, general purpose |
| BGESmallENV15 | 384 | Best quality/speed balance |
| BGEBaseENV15 | 768 | Higher quality |
| BGELargeENV15 | 1024 | Highest quality among bundled English models |
| MultilingualE5Base | 768 | 100+ languages |
Choosing a model
Higher-dimension models (BGE base/large) generally capture more nuance at the cost of larger
vectors and slower inference. BGESmallENV15 is a solid default for the
quality/speed balance; AllMiniLML6V2 is the fast option.
Multilingual
MultilingualE5Base covers 100+ languages in 768 dimensions. Reach for it when your
corpus is not English-only.
Because vectors are keyed by content and model id, mixing models is safe — each model's output is cached separately. See how it works.