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fix test

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

updating structure of default

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>

fix model id creation

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-10-20 14:50:57 -04:00
parent b3addc94d1
commit 7ffd20d112
10 changed files with 119 additions and 62 deletions
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9
tests/integration/vector_io/test_openai_vector_stores.py
View file

@ -8,9 +8,8 @@ import time
from io import BytesIO
import pytest
from llama_stack_client import BadRequestError, NotFoundError
from llama_stack_client import BadRequestError
from openai import BadRequestError as OpenAIBadRequestError
from openai import NotFoundError as OpenAINotFoundError
from llama_stack.apis.vector_io import Chunk
from llama_stack.core.library_client import LlamaStackAsLibraryClient
@ -839,7 +838,7 @@ def test_openai_vector_store_list_files_invalid_vector_store(
if isinstance(compat_client, LlamaStackAsLibraryClient):
errors = ValueError
else:
errors = (NotFoundError, OpenAINotFoundError)
errors = (BadRequestError, OpenAIBadRequestError)
with pytest.raises(errors):
compat_client.vector_stores.files.list(vector_store_id="abc123")
@ -1528,11 +1527,11 @@ def test_openai_vector_store_file_batch_error_handling(
batch_id="non_existent_batch_id",
)
# Test operations on non-existent vector store (returns NotFoundError)
# Test operations on non-existent vector store (returns BadRequestError)
if isinstance(compat_client, LlamaStackAsLibraryClient):
vector_store_errors = ValueError
else:
vector_store_errors = (NotFoundError, OpenAINotFoundError)
vector_store_errors = (BadRequestError, OpenAIBadRequestError)
with pytest.raises(vector_store_errors): # Should raise an error for non-existent vector store
compat_client.vector_stores.file_batches.create(

24
tests/unit/core/test_stack_validation.py
View file

@ -11,7 +11,7 @@ from unittest.mock import AsyncMock
import pytest
from llama_stack.apis.models import Model, ModelType
from llama_stack.core.datatypes import StackRunConfig, VectorStoresConfig
from llama_stack.core.datatypes import DefaultEmbeddingModel, StackRunConfig, VectorStoresConfig
from llama_stack.core.stack import validate_vector_stores_config
from llama_stack.providers.datatypes import Api
@ -20,7 +20,15 @@ class TestVectorStoresValidation:
async def test_validate_missing_model(self):
"""Test validation fails when model not found."""
run_config = StackRunConfig(
image_name="test", providers={}, vector_stores=VectorStoresConfig(embedding_model_id="missing")
image_name="test",
providers={},
vector_stores=VectorStoresConfig(
default_provider_id="faiss",
default_embedding_model=DefaultEmbeddingModel(
provider_id="p",
model_id="missing",
),
),
)
mock_models = AsyncMock()
mock_models.list_models.return_value = []
@ -31,12 +39,20 @@ class TestVectorStoresValidation:
async def test_validate_success(self):
"""Test validation passes with valid model."""
run_config = StackRunConfig(
image_name="test", providers={}, vector_stores=VectorStoresConfig(embedding_model_id="valid")
image_name="test",
providers={},
vector_stores=VectorStoresConfig(
default_provider_id="faiss",
default_embedding_model=DefaultEmbeddingModel(
provider_id="p",
model_id="valid",
),
),
)
mock_models = AsyncMock()
mock_models.list_models.return_value = [
Model(
identifier="valid",
identifier="p/valid", # Must match provider_id/model_id format
model_type=ModelType.embedding,
metadata={"embedding_dimension": 768},
provider_id="p",