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fix test

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

updating structure of default

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

fix model id creation

Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-10-20 14:50:57 -04:00
parent b3addc94d1
commit 7ffd20d112
10 changed files with 119 additions and 62 deletions
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65
llama_stack/core/stack.py
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@ -135,41 +135,52 @@ async def validate_vector_stores_config(run_config: StackRunConfig, impls: dict[
return
vector_stores_config = run_config.vector_stores
default_model_id = vector_stores_config.embedding_model_id
if Api.models not in impls:
raise ValueError(f"Models API is not available but vector_stores config requires model '{default_model_id}'")
# Validate default embedding model if configured
if vector_stores_config.default_embedding_model:
default_embedding_model = vector_stores_config.default_embedding_model
provider_id = default_embedding_model.provider_id
model_id = default_embedding_model.model_id
# Construct the full model identifier
default_model_id = f"{provider_id}/{model_id}"
models_impl = impls[Api.models]
response = await models_impl.list_models()
models_list = response.data if hasattr(response, "data") else response
if Api.models not in impls:
raise ValueError(
f"Models API is not available but vector_stores config requires model '{default_model_id}'"
)
# find default embedding model
default_model = None
for model in models_list:
if model.identifier == default_model_id:
default_model = model
break
models_impl = impls[Api.models]
response = await models_impl.list_models()
models_list = response.data if hasattr(response, "data") else response
if not default_model:
available_models = [m.identifier for m in models_list if m.model_type == "embedding"]
raise ValueError(
f"Embedding model '{default_model_id}' not found. Available embedding models: {available_models}"
)
# find default embedding model
default_model = None
for model in models_list:
if model.identifier == default_model_id:
default_model = model
break
if default_model.model_type != "embedding":
raise ValueError(f"Model '{default_model_id}' is type '{default_model.model_type}', not 'embedding'")
if not default_model:
available_models = [m.identifier for m in models_list if m.model_type == "embedding"]
raise ValueError(
f"Embedding model '{default_model_id}' not found. Available embedding models: {available_models}"
)
embedding_dimension = default_model.metadata.get("embedding_dimension")
if embedding_dimension is None:
raise ValueError(f"Embedding model '{default_model_id}' is missing 'embedding_dimension' in metadata")
if default_model.model_type != "embedding":
raise ValueError(f"Model '{default_model_id}' is type '{default_model.model_type}', not 'embedding'")
try:
int(embedding_dimension)
except ValueError as err:
raise ValueError(f"Embedding dimension '{embedding_dimension}' cannot be converted to an integer") from err
embedding_dimension = default_model.metadata.get("embedding_dimension")
if embedding_dimension is None:
raise ValueError(f"Embedding model '{default_model_id}' is missing 'embedding_dimension' in metadata")
logger.debug(f"Validated default embedding model: {default_model_id} (dimension: {embedding_dimension})")
try:
int(embedding_dimension)
except ValueError as err:
raise ValueError(f"Embedding dimension '{embedding_dimension}' cannot be converted to an integer") from err
logger.debug(f"Validated default embedding model: {default_model_id} (dimension: {embedding_dimension})")
# If no default embedding model is configured, that's fine - validation passes
class EnvVarError(Exception):