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feat: configure vector-io provider with an embedding model

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Signed-off-by: Mustafa Elbehery <melbeher@redhat.com>
This commit is contained in:
Mustafa Elbehery 2025-07-31 13:07:03 +02:00
parent 1f0766308d
commit d8f013b35a
29 changed files with 228 additions and 24 deletions
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16
llama_stack/core/routers/vector_io.py
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@ -79,13 +79,23 @@ class VectorIORouter(VectorIO):
async def register_vector_db(
self,
vector_db_id: str,
embedding_model: str,
embedding_dimension: int | None = 384,
embedding_model: str | None = None,
embedding_dimension: int | None = None,
provider_id: str | None = None,
vector_db_name: str | None = None,
provider_vector_db_id: str | None = None,
) -> None:
logger.debug(f"VectorIORouter.register_vector_db: {vector_db_id}, {embedding_model}")
logger.debug(
f"VectorIORouter.register_vector_db: {vector_db_id}, embedding_model={embedding_model}, provider_id={provider_id}"
)
# If embedding model/dimension not provided, they will be applied by the provider
# from its embedding configuration during register_vector_db call
if embedding_model is None and embedding_dimension is None:
# Use fallback defaults if no provider defaults are available
embedding_model = embedding_model or "all-MiniLM-L6-v2"
embedding_dimension = embedding_dimension or 384
await self.routing_table.register_vector_db(
vector_db_id,
embedding_model,