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important fix

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This commit is contained in:
Ashwin Bharambe 2025-10-20 19:54:25 -07:00
parent 86926ee339
commit 016388b788
5 changed files with 14 additions and 41 deletions
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42
llama_stack/core/routing_tables/vector_stores.py
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@ -67,44 +67,16 @@ class VectorStoresRoutingTable(CommonRoutingTableImpl):
raise ModelNotFoundError(embedding_model)
if model.model_type != ModelType.embedding:
raise ModelTypeError(embedding_model, model.model_type, ModelType.embedding)
if "embedding_dimension" not in model.metadata:
raise ValueError(f"Model {embedding_model} does not have an embedding dimension")
try:
provider = self.impls_by_provider_id[provider_id]
except KeyError:
available_providers = list(self.impls_by_provider_id.keys())
raise ValueError(
f"Provider '{provider_id}' not found in routing table. Available providers: {available_providers}"
) from None
logger.warning(
"VectorStore is being deprecated in future releases in favor of VectorStore. Please migrate your usage accordingly."
)
request = OpenAICreateVectorStoreRequestWithExtraBody(
name=vector_store_name or vector_store_id,
embedding_model=embedding_model,
embedding_dimension=model.metadata["embedding_dimension"],
vector_store = VectorStoreWithOwner(
identifier=vector_store_id,
type=ResourceType.vector_store.value,
provider_id=provider_id,
provider_vector_store_id=provider_vector_store_id,
provider_resource_id=provider_vector_store_id,
embedding_model=embedding_model,
embedding_dimension=embedding_dimension,
vector_store_name=vector_store_name,
)
vector_store = await provider.openai_create_vector_store(request)
vector_store_id = vector_store.id
actual_provider_vector_store_id = provider_vector_store_id or vector_store_id
logger.warning(
f"Ignoring vector_store_id {vector_store_id} and using vector_store_id {vector_store_id} instead. Setting VectorStore {vector_store_id} to VectorStore.vector_store_name"
)
vector_store_data = {
"identifier": vector_store_id,
"type": ResourceType.vector_store.value,
"provider_id": provider_id,
"provider_resource_id": actual_provider_vector_store_id,
"embedding_model": embedding_model,
"embedding_dimension": model.metadata["embedding_dimension"],
"vector_store_name": vector_store.name,
}
vector_store = TypeAdapter(VectorStoreWithOwner).validate_python(vector_store_data)
await self.register_object(vector_store)
return vector_store