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chore: Updating how default embedding model is set in stack

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

# Conflicts:
#	.github/workflows/integration-vector-io-tests.yml
#	llama_stack/distributions/ci-tests/run.yaml
#	llama_stack/distributions/starter-gpu/run.yaml
#	llama_stack/distributions/starter/run.yaml
#	llama_stack/distributions/template.py
#	llama_stack/providers/utils/memory/openai_vector_store_mixin.py
This commit is contained in:
Francisco Javier Arceo 2025-10-15 17:15:43 -04:00
parent cd152f4240
commit 24a1430c8b
32 changed files with 276 additions and 265 deletions
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104
tests/unit/core/test_stack_validation.py
View file

@ -4,90 +4,44 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
"""
Unit tests for Stack validation functions.
"""
"""Unit tests for Stack validation functions."""
from unittest.mock import AsyncMock
import pytest
from llama_stack.apis.models import Model, ModelType
from llama_stack.core.stack import validate_default_embedding_model
from llama_stack.core.datatypes import StackRunConfig, VectorStoresConfig
from llama_stack.core.stack import validate_vector_stores_config
from llama_stack.providers.datatypes import Api
class TestStackValidation:
"""Test Stack validation functions."""
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(default_embedding_model_id="missing")
)
mock_models = AsyncMock()
mock_models.list_models.return_value = []
@pytest.mark.parametrize(
"models,should_raise",
[
([], False), # No models
(
[
Model(
identifier="emb1",
model_type=ModelType.embedding,
metadata={"default_configured": True},
provider_id="p",
provider_resource_id="emb1",
)
],
False,
), # Single default
(
[
Model(
identifier="emb1",
model_type=ModelType.embedding,
metadata={"default_configured": True},
provider_id="p",
provider_resource_id="emb1",
),
Model(
identifier="emb2",
model_type=ModelType.embedding,
metadata={"default_configured": True},
provider_id="p",
provider_resource_id="emb2",
),
],
True,
), # Multiple defaults
(
[
Model(
identifier="emb1",
model_type=ModelType.embedding,
metadata={"default_configured": True},
provider_id="p",
provider_resource_id="emb1",
),
Model(
identifier="llm1",
model_type=ModelType.llm,
metadata={"default_configured": True},
provider_id="p",
provider_resource_id="llm1",
),
],
False,
), # Ignores non-embedding
],
)
async def test_validate_default_embedding_model(self, models, should_raise):
"""Test validation with various model configurations."""
mock_models_impl = AsyncMock()
mock_models_impl.list_models.return_value = models
impls = {Api.models: mock_models_impl}
with pytest.raises(ValueError, match="not found"):
await validate_vector_stores_config(run_config, {Api.models: mock_models})
if should_raise:
with pytest.raises(ValueError, match="Multiple embedding models marked as default_configured=True"):
await validate_default_embedding_model(impls)
else:
await validate_default_embedding_model(impls)
async def test_validate_success(self):
"""Test validation passes with valid model."""
run_config = StackRunConfig(
image_name="test", providers={}, vector_stores=VectorStoresConfig(default_embedding_model_id="valid")
)
mock_models = AsyncMock()
mock_models.list_models.return_value = [
Model(
identifier="valid",
model_type=ModelType.embedding,
metadata={"embedding_dimension": 768},
provider_id="p",
provider_resource_id="valid",
)
]
async def test_validate_default_embedding_model_no_models_api(self):
"""Test validation when models API is not available."""
await validate_default_embedding_model({})
await validate_vector_stores_config(run_config, {Api.models: mock_models})