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fix: Fix embedding model listing and usage for watsonx

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Signed-off-by: Bill Murdock <bmurdock@redhat.com>
This commit is contained in:
Bill Murdock 2025-10-03 16:16:55 -04:00
parent 999c28e809
commit ecafe40a84
2 changed files with 36 additions and 26 deletions
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56
llama_stack/providers/remote/inference/watsonx/watsonx.py
View file

@ -56,15 +56,40 @@ class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
async def list_models(self) -> list[Model] | None:
models = []
for model_spec in self._get_model_specs():
models.append(
Model(
identifier=model_spec["model_id"],
provider_resource_id=f"{self.__provider_id__}/{model_spec['model_id']}",
provider_id=self.__provider_id__,
metadata={},
model_type=ModelType.llm,
functions = [f['id'] for f in model_spec.get("functions", [])]
# Format: {"embedding_dimension": 1536, "context_length": 8192}
# Example of an embedding model:
# {'model_id': 'ibm/granite-embedding-278m-multilingual',
# 'label': 'granite-embedding-278m-multilingual',
# 'model_limits': {'max_sequence_length': 512, 'embedding_dimension': 768},
# ...
if "embedding" in functions:
embedding_dimension = model_spec["model_limits"]["embedding_dimension"]
context_length = model_spec["model_limits"]["max_sequence_length"]
embedding_metadata = {
"embedding_dimension": embedding_dimension,
"context_length": context_length,
}
models.append(
Model(
identifier=model_spec["model_id"],
provider_resource_id=f"{self.__provider_id__}/{model_spec['model_id']}",
provider_id=self.__provider_id__,
metadata=embedding_metadata,
model_type=ModelType.embedding,
)
)
if "text_chat" in functions:
models.append(
Model(
identifier=model_spec["model_id"],
provider_resource_id=f"{self.__provider_id__}/{model_spec['model_id']}",
provider_id=self.__provider_id__,
metadata={},
model_type=ModelType.llm,
)
)
)
return models
# LiteLLM provides methods to list models for many providers, but not for watsonx.ai.
@ -91,18 +116,3 @@ class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
if "resources" not in response_data:
raise ValueError("Resources not found in response")
return response_data["resources"]
# TO DO: Delete the test main method.
if __name__ == "__main__":
config = WatsonXConfig(url="https://us-south.ml.cloud.ibm.com", api_key="xxx", project_id="xxx", timeout=60)
adapter = WatsonXInferenceAdapter(config)
model_specs = adapter._get_model_specs()
models = asyncio.run(adapter.list_models())
for model in models:
print(model.identifier)
print(model.provider_resource_id)
print(model.provider_id)
print(model.metadata)
print(model.model_type)
print("--------------------------------")

6
llama_stack/providers/utils/inference/openai_compat.py
View file

@ -1405,7 +1405,7 @@ def prepare_openai_embeddings_params(
def b64_encode_openai_embeddings_response(
response_data: dict, encoding_format: str | None = "float"
response_data: list[dict], encoding_format: str | None = "float"
) -> list[OpenAIEmbeddingData]:
"""
Process the OpenAI embeddings response to encode the embeddings in base64 format if specified.
@ -1414,12 +1414,12 @@ def b64_encode_openai_embeddings_response(
for i, embedding_data in enumerate(response_data):
if encoding_format == "base64":
byte_array = bytearray()
for embedding_value in embedding_data.embedding:
for embedding_value in embedding_data["embedding"]:
byte_array.extend(struct.pack("f", float(embedding_value)))
response_embedding = base64.b64encode(byte_array).decode("utf-8")
else:
response_embedding = embedding_data.embedding
response_embedding = embedding_data["embedding"]
data.append(
OpenAIEmbeddingData(
embedding=response_embedding,