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simplyfing

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-07-11 16:25:23 -04:00
parent d361154102
commit cfde4af3ab
5 changed files with 5 additions and 10 deletions
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2
docs/_static/llama-stack-spec.html vendored
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@ -15624,7 +15624,7 @@
}, },
"vector_db_name": { "vector_db_name": {
"type": "string", "type": "string",
"description": "The name of the vector database. :param provider_vector_db_id: The identifier of the vector database in the provider." "description": "The name of the vector database."
}, },
"provider_vector_db_id": { "provider_vector_db_id": {
"type": "string", "type": "string",

4
docs/_static/llama-stack-spec.yaml vendored
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@ -10933,9 +10933,7 @@ components:
description: The identifier of the provider. description: The identifier of the provider.
vector_db_name: vector_db_name:
type: string type: string
description: >- description: The name of the vector database.
The name of the vector database. :param provider_vector_db_id: The identifier
of the vector database in the provider.
provider_vector_db_id: provider_vector_db_id:
type: string type: string
description: >- description: >-

2
llama_stack/apis/vector_dbs/vector_dbs.py
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@ -82,7 +82,7 @@ class VectorDBs(Protocol):
:param embedding_dimension: The dimension of the embedding model. :param embedding_dimension: The dimension of the embedding model.
:param provider_id: The identifier of the provider. :param provider_id: The identifier of the provider.
:param vector_db_name: The name of the vector database. :param vector_db_name: The name of the vector database.
:param provider_vector_db_id: The identifier of the vector database in the provider. :param provider_vector_db_id: The identifier of the vector database in the provider.
:returns: A VectorDB. :returns: A VectorDB.
""" """
... ...

3
llama_stack/providers/utils/memory/openai_vector_store_mixin.py
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@ -8,6 +8,7 @@ import asyncio
import logging import logging
import mimetypes import mimetypes
import time import time
import uuid
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
from typing import Any from typing import Any
@ -148,7 +149,7 @@ class OpenAIVectorStoreMixin(ABC):
"""Creates a vector store.""" """Creates a vector store."""
created_at = int(time.time()) created_at = int(time.time())
if provider_vector_db_id is None: if provider_vector_db_id is None:
raise ValueError("Provider vector DB ID is required") provider_vector_db_id = f"vs_{uuid.uuid4()}"
if provider_id is None: if provider_id is None:
raise ValueError("Provider ID is required") raise ValueError("Provider ID is required")

4
tests/integration/vector_io/test_vector_io.py
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@ -53,7 +53,6 @@ def test_vector_db_retrieve(client_with_empty_registry, embedding_model_id, embe
vector_db_id=vector_db_id, vector_db_id=vector_db_id,
embedding_model=embedding_model_id, embedding_model=embedding_model_id,
embedding_dimension=embedding_dimension, embedding_dimension=embedding_dimension,
provider_vector_db_id=vector_db_id,
) )
# Retrieve the memory bank and validate its properties # Retrieve the memory bank and validate its properties
@ -70,7 +69,6 @@ def test_vector_db_register(client_with_empty_registry, embedding_model_id, embe
vector_db_id=vector_db_id, vector_db_id=vector_db_id,
embedding_model=embedding_model_id, embedding_model=embedding_model_id,
embedding_dimension=embedding_dimension, embedding_dimension=embedding_dimension,
provider_vector_db_id=vector_db_id,
) )
vector_dbs_after_register = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()] vector_dbs_after_register = [vector_db.identifier for vector_db in client_with_empty_registry.vector_dbs.list()]
@ -98,7 +96,6 @@ def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding
vector_db_id=vector_db_id, vector_db_id=vector_db_id,
embedding_model=embedding_model_id, embedding_model=embedding_model_id,
embedding_dimension=embedding_dimension, embedding_dimension=embedding_dimension,
provider_vector_db_id=vector_db_id,
) )
client_with_empty_registry.vector_io.insert( client_with_empty_registry.vector_io.insert(
@ -134,7 +131,6 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e
vector_db_id=vector_db_id, vector_db_id=vector_db_id,
embedding_model=embedding_model_id, embedding_model=embedding_model_id,
embedding_dimension=embedding_dimension, embedding_dimension=embedding_dimension,
provider_vector_db_id=vector_db_id,
) )
chunks_with_embeddings = [ chunks_with_embeddings = [