egutierrez
f4d9d09575
Funciones Python para embeddings: carga/guardado de modelos, encoding de texto, y almacenamiento/búsqueda vectorial con sqlite-vec y usearch. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
935 B
935 B
name, kind, lang, domain, version, purity, signature, description, tags, uses_functions, uses_types, returns, returns_optional, error_type, imports, tested, tests, test_file_path, file_path
| name | kind | lang | domain | version | purity | signature | description | tags | uses_functions | uses_types | returns | returns_optional | error_type | imports | tested | tests | test_file_path | file_path | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| embedding_save_model | function | py | infra | 1.0.0 | impure | def embedding_save_model(model_id: str, path: str) -> str | Descarga modelo de embeddings de HuggingFace y lo guarda en path local para carga rapida sin red. |
|
false | error_go_core |
|
false | python/functions/embedding/model.py |
Ejemplo
path = embedding_save_model("intfloat/multilingual-e5-small", ".local/models/e5-small")
# path = "/home/lucas/fn_registry/.local/models/e5-small"
Notas
El modelo se guarda en formato sentence-transformers (safetensors + tokenizer). Para multilingual-e5-small ocupa ~465 MB en disco. Carga local es ~2.3x mas rapida que desde HF cache.