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4
docs/docs/providers/inference/remote_watsonx.mdx
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@ -16,8 +16,8 @@ IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform
|-------|------|----------|---------|-------------|
| `allowed_models` | `list[str \| None` | No | | List of models that should be registered with the model registry. If None, all models are allowed. |
| `url` | `<class 'str'>` | No | https://us-south.ml.cloud.ibm.com | A base url for accessing the watsonx.ai |
| `api_key` | `pydantic.types.SecretStr \| None` | No | | The watsonx API key |
| `project_id` | `str \| None` | No | | The Project ID key |
| `api_key` | `pydantic.types.SecretStr \| None` | No | | The watsonx.ai API key |
| `project_id` | `str \| None` | No | | The watsonx.ai project ID |
| `timeout` | `<class 'int'>` | No | 60 | Timeout for the HTTP requests |
## Sample Configuration

7
llama_stack/providers/remote/inference/watsonx/watsonx.py
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@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import asyncio
from typing import Any
import requests
@ -56,14 +55,14 @@ class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
async def list_models(self) -> list[Model] | None:
models = []
for model_spec in self._get_model_specs():
functions = [f['id'] for f in model_spec.get("functions", [])]
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',
# '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"]