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chore: Support embedding params from metadata
This commit is contained in:
Swapna Lekkala
parent
ef4bc70bbe
commit
a695b4fa84
2 changed files with 230 additions and 2 deletions
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@ -353,9 +353,25 @@ class OpenAIVectorStoreMixin(ABC):
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created_at = int(time.time())
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extra = params.model_extra or {}
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metadata = params.metadata or {}
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provider_vector_db_id = extra.get("provider_vector_db_id")
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embedding_model = extra.get("embedding_model")
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embedding_dimension = extra.get("embedding_dimension")
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# Use embedding info from metadata if available, otherwise from extra_body
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if metadata.get("embedding_model"):
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# If either is in metadata, use metadata as source
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embedding_model = metadata.get("embedding_model")
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embedding_dimension = int(metadata["embedding_dimensions"]) if metadata.get("embedding_dimensions") else 768
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# Check for conflicts with extra_body
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if (extra.get("embedding_model") and extra["embedding_model"] != embedding_model) or (
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extra.get("embedding_dimension") and extra["embedding_dimension"] != embedding_dimension
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):
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raise ValueError("Embedding config inconsistent between metadata and extra_body")
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else:
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embedding_model = extra.get("embedding_model")
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embedding_dimension = extra.get("embedding_dimension", 768)
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# use provider_id set by router; fallback to provider's own ID when used directly via --stack-config
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provider_id = extra.get("provider_id") or getattr(self, "__provider_id__", None)
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# Derive the canonical vector_db_id (allow override, else generate)
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@ -1053,3 +1053,215 @@ async def test_openai_create_vector_store_uses_default_model(vector_io_adapter):
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call_args = vector_io_adapter.register_vector_db.call_args[0][0]
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assert call_args.embedding_model == "default-model"
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assert call_args.embedding_dimension == 512
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async def test_embedding_config_from_metadata(vector_io_adapter):
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"""Test that embedding configuration is correctly extracted from metadata."""
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from llama_stack.apis.vector_io import OpenAICreateVectorStoreRequestWithExtraBody
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# Mock register_vector_db to avoid actual registration
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vector_io_adapter.register_vector_db = AsyncMock()
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# Set provider_id attribute for the adapter
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vector_io_adapter.__provider_id__ = "test_provider"
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# Test with embedding config in metadata
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params = OpenAICreateVectorStoreRequestWithExtraBody(
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name="test_store",
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metadata={
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"embedding_model": "test-embedding-model",
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"embedding_dimensions": "512",
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},
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model_extra={},
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)
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await vector_io_adapter.openai_create_vector_store(params)
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# Verify VectorDB was registered with correct embedding config from metadata
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vector_io_adapter.register_vector_db.assert_called_once()
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call_args = vector_io_adapter.register_vector_db.call_args[0][0]
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assert call_args.embedding_model == "test-embedding-model"
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assert call_args.embedding_dimension == 512
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async def test_embedding_config_from_extra_body(vector_io_adapter):
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"""Test that embedding configuration is correctly extracted from extra_body when metadata is empty."""
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from llama_stack.apis.vector_io import OpenAICreateVectorStoreRequestWithExtraBody
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# Mock register_vector_db to avoid actual registration
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vector_io_adapter.register_vector_db = AsyncMock()
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# Set provider_id attribute for the adapter
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vector_io_adapter.__provider_id__ = "test_provider"
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# Test with embedding config in extra_body only (metadata has no embedding_model)
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params = OpenAICreateVectorStoreRequestWithExtraBody(
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name="test_store",
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metadata={}, # Empty metadata to ensure extra_body is used
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**{
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"embedding_model": "extra-body-model",
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"embedding_dimension": 1024,
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},
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)
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await vector_io_adapter.openai_create_vector_store(params)
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# Verify VectorDB was registered with correct embedding config from extra_body
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vector_io_adapter.register_vector_db.assert_called_once()
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call_args = vector_io_adapter.register_vector_db.call_args[0][0]
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assert call_args.embedding_model == "extra-body-model"
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assert call_args.embedding_dimension == 1024
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async def test_embedding_config_consistency_check_passes(vector_io_adapter):
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"""Test that consistent embedding config in both metadata and extra_body passes validation."""
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from llama_stack.apis.vector_io import OpenAICreateVectorStoreRequestWithExtraBody
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# Mock register_vector_db to avoid actual registration
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vector_io_adapter.register_vector_db = AsyncMock()
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# Set provider_id attribute for the adapter
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vector_io_adapter.__provider_id__ = "test_provider"
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# Test with consistent embedding config in both metadata and extra_body
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params = OpenAICreateVectorStoreRequestWithExtraBody(
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name="test_store",
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metadata={
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"embedding_model": "consistent-model",
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"embedding_dimensions": "768",
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},
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**{
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"embedding_model": "consistent-model",
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"embedding_dimension": 768,
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},
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)
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await vector_io_adapter.openai_create_vector_store(params)
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# Should not raise any error and use metadata config
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vector_io_adapter.register_vector_db.assert_called_once()
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call_args = vector_io_adapter.register_vector_db.call_args[0][0]
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assert call_args.embedding_model == "consistent-model"
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assert call_args.embedding_dimension == 768
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async def test_embedding_config_inconsistency_error_model(vector_io_adapter):
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"""Test that inconsistent embedding model between metadata and extra_body raises error."""
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from llama_stack.apis.vector_io import OpenAICreateVectorStoreRequestWithExtraBody
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# Mock register_vector_db to avoid actual registration
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vector_io_adapter.register_vector_db = AsyncMock()
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# Set provider_id attribute for the adapter
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vector_io_adapter.__provider_id__ = "test_provider"
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# Test with inconsistent embedding model
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params = OpenAICreateVectorStoreRequestWithExtraBody(
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name="test_store",
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metadata={
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"embedding_model": "metadata-model",
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"embedding_dimensions": "768",
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},
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**{
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"embedding_model": "extra-body-model",
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"embedding_dimension": 768,
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},
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)
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with pytest.raises(ValueError, match="Embedding config inconsistent between metadata and extra_body"):
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await vector_io_adapter.openai_create_vector_store(params)
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async def test_embedding_config_inconsistency_error_dimension(vector_io_adapter):
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"""Test that inconsistent embedding dimension between metadata and extra_body raises error."""
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from llama_stack.apis.vector_io import OpenAICreateVectorStoreRequestWithExtraBody
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# Mock register_vector_db to avoid actual registration
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vector_io_adapter.register_vector_db = AsyncMock()
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# Set provider_id attribute for the adapter
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vector_io_adapter.__provider_id__ = "test_provider"
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# Test with inconsistent embedding dimension
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params = OpenAICreateVectorStoreRequestWithExtraBody(
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name="test_store",
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metadata={
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"embedding_model": "same-model",
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"embedding_dimensions": "512",
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},
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**{
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"embedding_model": "same-model",
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"embedding_dimension": 1024,
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},
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)
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with pytest.raises(ValueError, match="Embedding config inconsistent between metadata and extra_body"):
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await vector_io_adapter.openai_create_vector_store(params)
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async def test_embedding_config_defaults_when_missing(vector_io_adapter):
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"""Test that embedding dimension defaults to 768 when not provided."""
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from llama_stack.apis.vector_io import OpenAICreateVectorStoreRequestWithExtraBody
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# Mock register_vector_db to avoid actual registration
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vector_io_adapter.register_vector_db = AsyncMock()
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# Set provider_id attribute for the adapter
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vector_io_adapter.__provider_id__ = "test_provider"
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# Test with only embedding model, no dimension (metadata empty to use extra_body)
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params = OpenAICreateVectorStoreRequestWithExtraBody(
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name="test_store",
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metadata={}, # Empty metadata to ensure extra_body is used
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**{
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"embedding_model": "model-without-dimension",
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},
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)
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await vector_io_adapter.openai_create_vector_store(params)
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# Should default to 768 dimensions
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vector_io_adapter.register_vector_db.assert_called_once()
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call_args = vector_io_adapter.register_vector_db.call_args[0][0]
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assert call_args.embedding_model == "model-without-dimension"
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assert call_args.embedding_dimension == 768
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async def test_embedding_config_partial_metadata_consistency(vector_io_adapter):
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"""Test that partial metadata (only model) with extra_body works correctly."""
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from llama_stack.apis.vector_io import OpenAICreateVectorStoreRequestWithExtraBody
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# Mock register_vector_db to avoid actual registration
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vector_io_adapter.register_vector_db = AsyncMock()
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# Set provider_id attribute for the adapter
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vector_io_adapter.__provider_id__ = "test_provider"
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# Test with only embedding model in metadata, dimension in extra_body
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params = OpenAICreateVectorStoreRequestWithExtraBody(
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name="test_store",
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metadata={
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"embedding_model": "metadata-model",
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},
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**{
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"embedding_model": "metadata-model",
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"embedding_dimension": 512,
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},
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)
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await vector_io_adapter.openai_create_vector_store(params)
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# Should use metadata as source (defaults dimension to 768 since not in metadata)
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vector_io_adapter.register_vector_db.assert_called_once()
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call_args = vector_io_adapter.register_vector_db.call_args[0][0]
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assert call_args.embedding_model == "metadata-model"
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assert call_args.embedding_dimension == 768 # Default since not in metadata
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async def test_embedding_config_required_model_missing(vector_io_adapter):
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"""Test that missing embedding model raises error."""
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from llama_stack.apis.vector_io import OpenAICreateVectorStoreRequestWithExtraBody
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# Mock register_vector_db to avoid actual registration
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vector_io_adapter.register_vector_db = AsyncMock()
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# Set provider_id attribute for the adapter
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vector_io_adapter.__provider_id__ = "test_provider"
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# Test with no embedding model provided
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params = OpenAICreateVectorStoreRequestWithExtraBody(name="test_store", metadata={})
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with pytest.raises(ValueError, match="Embedding model is required"):
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await vector_io_adapter.openai_create_vector_store(params)
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