ReadonlydimensionsThe dimensionality of vectors this embedder produces.
ReadonlyrelevanceThe cosine similarity at which this model's results turn from unrelated to relevant, used as the admission threshold when MemoryConfig.relevanceThreshold is not set. Declare it for a custom embedder once you have measured it; leave it out to use the library default.
StaticcreateLoad the model, resolving its dimension by a probe embed. The first call downloads the model
into cacheDir; every later call loads it from there, offline.
OptionalmodelName?: stringA fastembed-js model id. Defaults to DEFAULT_FASTEMBED_MODEL. Only
the default model carries a calibrated relevanceThreshold; set one in your config for others.
OptionalcacheDir?: stringWhere model files are kept. Defaults to FASTEMBED_CACHE_PATH, else
~/.cache/actrone-memory/fastembed, never the current directory.
Embed a single text into a dense vector.
In-process ONNX dense embedder via
fastembed(fastembed-js, onnxruntime, no torch/GPU). The preferred "real" recall tier: local-first, zero-egress after a one-time model download (about 130 MB), no API key. Default modelbge-small-en-v1.5(384-dim), the same model the Python library uses.fastembedis an optional peer: install it (npm i fastembed) andMemoryManager.create()picks it up automatically; without it buildLocalEmbedder degrades to the dependency-free lexical LocalEmbedder.