fn_registry/python/functions/embedding/embedding_search_sqlvec.md

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_search_sqlvec	function	py	infra	1.0.0	impure	def embedding_search_sqlvec(db_path: str, table: str, query_embedding: list, k: int = 10) -> list	Busca los k vecinos mas cercanos en tabla sqlite-vec. Retorna rowids y distancias ordenados.	embedding sqlite vector search retrieval sqlite-vec python				false	error_go_core	sqlite3 sqlite_vec numpy	false			python/functions/embedding/sqlvec.py

Ejemplo

model = embedding_load_model(".local/models/e5-small")
q_emb = embedding_encode(model, ["¿Que es machine learning?"], mode="query")[0]

results = embedding_search_sqlvec("vectors.db", "doc_embeddings", q_emb, k=5)
# [{"rowid": 0, "distance": 0.23}, {"rowid": 1, "distance": 0.45}, ...]

Notas

Busqueda brute-force (exacta, no aproximada). Para 50k vectores tarda ~19ms/query. El campo distance es distancia coseno (menor = mas similar) porque los embeddings estan normalizados. Cold start rapido (~18ms) porque SQLite no carga todo el indice a RAM.