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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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78
llama_stack/providers/utils/memory/openai_vector_store_mixin.py
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@ -44,6 +44,7 @@ from llama_stack.apis.vector_io import (
VectorStoreSearchResponse,
VectorStoreSearchResponsePage,
)
from llama_stack.core.datatypes import VectorStoresConfig
from llama_stack.core.id_generation import generate_object_id
from llama_stack.log import get_logger
from llama_stack.providers.utils.kvstore.api import KVStore
@ -81,13 +82,17 @@ class OpenAIVectorStoreMixin(ABC):
# Implementing classes should call super().__init__() in their __init__ method
# to properly initialize the mixin attributes.
def __init__(
self, files_api: Files | None = None, kvstore: KVStore | None = None, models_api: Models | None = None
self,
files_api: Files | None = None,
kvstore: KVStore | None = None,
):
self.openai_vector_stores: dict[str, dict[str, Any]] = {}
self.openai_file_batches: dict[str, dict[str, Any]] = {}
self.files_api = files_api
self.kvstore = kvstore
self.models_api = models_api
# These will be set by implementing classes
self.models_api: Models | None = None
self.vector_stores_config: VectorStoresConfig | None = None
self._last_file_batch_cleanup_time = 0
self._file_batch_tasks: dict[str, asyncio.Task[None]] = {}
@ -474,24 +479,6 @@ class OpenAIVectorStoreMixin(ABC):
store_info = self.openai_vector_stores[vector_db_id]
return VectorStoreObject.model_validate(store_info)
async def _get_embedding_models(self) -> list[Model]:
"""Get list of embedding models from the models API."""
if not self.models_api:
return []
models_response = await self.models_api.list_models()
models_list = models_response.data if hasattr(models_response, "data") else models_response
embedding_models = []
for model in models_list:
if not isinstance(model, Model):
logger.warning(f"Non-Model object found in models list: {type(model)} - {model}")
continue
if model.model_type == "embedding":
embedding_models.append(model)
return embedding_models
async def _get_embedding_dimension_for_model(self, model_id: str) -> int | None:
"""Get embedding dimension for a specific model by looking it up in the models API.
@ -501,9 +488,18 @@ class OpenAIVectorStoreMixin(ABC):
Returns:
The embedding dimension for the model, or None if not found
"""
embedding_models = await self._get_embedding_models()
if not self.models_api:
return None
models_response = await self.models_api.list_models()
models_list = models_response.data if hasattr(models_response, "data") else models_response
for model in models_list:
if not isinstance(model, Model):
continue
if model.model_type != "embedding":
continue
for model in embedding_models:
# Check for exact match first
if model.identifier == model_id:
embedding_dimension = model.metadata.get("embedding_dimension")
@ -523,35 +519,23 @@ class OpenAIVectorStoreMixin(ABC):
return None
async def _get_default_embedding_model_and_dimension(self) -> tuple[str, int] | None:
"""Get default embedding model from the models API.
"""Get default embedding model from vector stores config.
Looks for embedding models marked with default_configured=True in metadata.
Returns None if no default embedding model is found.
Raises ValueError if multiple defaults are found.
Returns None if no vector stores config is provided.
"""
embedding_models = await self._get_embedding_models()
if not self.vector_stores_config:
logger.info("No vector stores config provided")
return None
default_models = []
for model in embedding_models:
if model.metadata.get("default_configured") is True:
default_models.append(model.identifier)
model_id = self.vector_stores_config.default_embedding_model_id
embedding_dimension = await self._get_embedding_dimension_for_model(model_id)
if embedding_dimension is None:
raise ValueError(f"Embedding model '{model_id}' not found or has no embedding_dimension in metadata")
if len(default_models) > 1:
raise ValueError(
f"Multiple embedding models marked as default_configured=True: {default_models}. "
"Only one embedding model can be marked as default."
)
if default_models:
model_id = default_models[0]
embedding_dimension = await self._get_embedding_dimension_for_model(model_id)
if embedding_dimension is None:
raise ValueError(f"Embedding model '{model_id}' has no embedding_dimension in metadata")
logger.info(f"Using default embedding model: {model_id} with dimension {embedding_dimension}")
return model_id, embedding_dimension
logger.debug("No default embedding models found")
return None
logger.debug(
f"Using default embedding model from vector stores config: {model_id} with dimension {embedding_dimension}"
)
return model_id, embedding_dimension
async def openai_list_vector_stores(
self,