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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
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79
llama_stack/providers/utils/memory/openai_vector_store_mixin.py
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@ -17,7 +17,6 @@ from pydantic import TypeAdapter
from llama_stack.apis.common.errors import VectorStoreNotFoundError
from llama_stack.apis.files import Files, OpenAIFileObject
from llama_stack.apis.models import Model, Models
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import (
Chunk,
@ -44,7 +43,6 @@ 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
@ -90,9 +88,6 @@ class OpenAIVectorStoreMixin(ABC):
self.openai_file_batches: dict[str, dict[str, Any]] = {}
self.files_api = files_api
self.kvstore = kvstore
# 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]] = {}
@ -398,21 +393,7 @@ class OpenAIVectorStoreMixin(ABC):
vector_db_id = provider_vector_db_id or generate_object_id("vector_store", lambda: f"vs_{uuid.uuid4()}")
if embedding_model is None:
result = await self._get_default_embedding_model_and_dimension()
if result is None:
raise ValueError(
"embedding_model is required in extra_body when creating a vector store. "
"No default embedding model could be determined automatically."
)
embedding_model, embedding_dimension = result
elif embedding_dimension is None:
# Embedding model was provided but dimension wasn't, look it up
embedding_dimension = await self._get_embedding_dimension_for_model(embedding_model)
if embedding_dimension is None:
raise ValueError(
f"Could not determine embedding dimension for model '{embedding_model}'. "
"Please provide embedding_dimension in extra_body or ensure the model metadata contains embedding_dimension."
)
raise ValueError("embedding_model is required")
if embedding_dimension is None:
raise ValueError("Embedding dimension is required")
@ -479,64 +460,6 @@ class OpenAIVectorStoreMixin(ABC):
store_info = self.openai_vector_stores[vector_db_id]
return VectorStoreObject.model_validate(store_info)
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.
Args:
model_id: The identifier of the embedding model (supports both prefixed and non-prefixed)
Returns:
The embedding dimension for the model, or None if not found
"""
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
# Check for exact match first
if model.identifier == model_id:
embedding_dimension = model.metadata.get("embedding_dimension")
if embedding_dimension is not None:
return int(embedding_dimension)
else:
logger.warning(f"Model {model_id} found but has no embedding_dimension in metadata")
return None
# Check for prefixed/unprefixed variations
# If model_id is unprefixed, check if it matches the resource_id
if model.provider_resource_id == model_id:
embedding_dimension = model.metadata.get("embedding_dimension")
if embedding_dimension is not None:
return int(embedding_dimension)
return None
async def _get_default_embedding_model_and_dimension(self) -> tuple[str, int] | None:
"""Get default embedding model from vector stores config.
Returns None if no vector stores config is provided.
"""
if not self.vector_stores_config:
logger.info("No vector stores config provided")
return None
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")
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,
limit: int | None = 20,