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update resolver to only pass vector_stores section of run config

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

Using Router only from VectorDBs

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

removing model_api from vector store providers

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

fix test

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

updating integration tests

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

special handling for replay mode for available providers

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-10-16 10:59:01 -04:00
parent 24a1430c8b
commit accc4c437e
46 changed files with 397 additions and 702 deletions
Download patch file Download diff file Expand all files Collapse all files

2
tests/unit/providers/vector_io/conftest.py
View file

@ -144,7 +144,6 @@ async def sqlite_vec_adapter(sqlite_vec_db_path, unique_kvstore_config, mock_inf
config=config,
inference_api=mock_inference_api,
files_api=None,
models_api=None,
)
collection_id = f"sqlite_test_collection_{np.random.randint(1e6)}"
await adapter.initialize()
@ -183,7 +182,6 @@ async def faiss_vec_adapter(unique_kvstore_config, mock_inference_api, embedding
config=config,
inference_api=mock_inference_api,
files_api=None,
models_api=None,
)
await adapter.initialize()
await adapter.register_vector_db(

19
tests/unit/providers/vector_io/test_faiss.py
View file

@ -11,7 +11,6 @@ import numpy as np
import pytest
from llama_stack.apis.files import Files
from llama_stack.apis.models import Models
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
from llama_stack.providers.datatypes import HealthStatus
@ -76,12 +75,6 @@ def mock_files_api():
return mock_api
@pytest.fixture
def mock_models_api():
mock_api = MagicMock(spec=Models)
return mock_api
@pytest.fixture
def faiss_config():
config = MagicMock(spec=FaissVectorIOConfig)
@ -117,7 +110,7 @@ async def test_faiss_query_vector_returns_infinity_when_query_and_embedding_are_
assert response.chunks[1] == sample_chunks[1]
async def test_health_success(mock_models_api):
async def test_health_success():
"""Test that the health check returns OK status when faiss is working correctly."""
# Create a fresh instance of FaissVectorIOAdapter for testing
config = MagicMock()
@ -126,9 +119,7 @@ async def test_health_success(mock_models_api):
with patch("llama_stack.providers.inline.vector_io.faiss.faiss.faiss.IndexFlatL2") as mock_index_flat:
mock_index_flat.return_value = MagicMock()
adapter = FaissVectorIOAdapter(
config=config, inference_api=inference_api, models_api=mock_models_api, files_api=files_api
)
adapter = FaissVectorIOAdapter(config=config, inference_api=inference_api, files_api=files_api)
# Calling the health method directly
response = await adapter.health()
@ -142,7 +133,7 @@ async def test_health_success(mock_models_api):
mock_index_flat.assert_called_once_with(128) # VECTOR_DIMENSION is 128
async def test_health_failure(mock_models_api):
async def test_health_failure():
"""Test that the health check returns ERROR status when faiss encounters an error."""
# Create a fresh instance of FaissVectorIOAdapter for testing
config = MagicMock()
@ -152,9 +143,7 @@ async def test_health_failure(mock_models_api):
with patch("llama_stack.providers.inline.vector_io.faiss.faiss.faiss.IndexFlatL2") as mock_index_flat:
mock_index_flat.side_effect = Exception("Test error")
adapter = FaissVectorIOAdapter(
config=config, inference_api=inference_api, models_api=mock_models_api, files_api=files_api
)
adapter = FaissVectorIOAdapter(config=config, inference_api=inference_api, files_api=files_api)
# Calling the health method directly
response = await adapter.health()

2
tests/unit/providers/vector_io/test_vector_io_openai_vector_stores.py
View file

@ -1162,5 +1162,5 @@ async def test_embedding_config_required_model_missing(vector_io_adapter):
# Test with no embedding model provided
params = OpenAICreateVectorStoreRequestWithExtraBody(name="test_store", metadata={})
with pytest.raises(ValueError, match="embedding_model is required in extra_body when creating a vector store"):
with pytest.raises(ValueError, match="embedding_model is required"):
await vector_io_adapter.openai_create_vector_store(params)