feat: módulo embedding — encode, model CRUD, stores sqlvec y usearch

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>

python/functions/embedding/embedding_save_model.md

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+---
+name: embedding_save_model
+kind: function
+lang: py
+domain: infra
+version: "1.0.0"
+purity: impure
+signature: "def embedding_save_model(model_id: str, path: str) -> str"
+description: "Descarga modelo de embeddings de HuggingFace y lo guarda en path local para carga rapida sin red."
+tags: [embedding, model, save, huggingface, e5, python]
+uses_functions: []
+uses_types: []
+returns: []
+returns_optional: false
+error_type: "error_go_core"
+imports: [sentence_transformers]
+tested: false
+tests: []
+test_file_path: ""
+file_path: "python/functions/embedding/model.py"
+---
+
+## Ejemplo
+
+```python
+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.