Merge branch 'main' into pr2940

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -65,7 +65,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 from .config import FireworksImplConfig
 from .models import MODEL_ENTRIES
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::fireworks")
 
 
 class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):

llama_stack/providers/remote/inference/llama_openai_compat/llama.py

@@ -3,20 +3,19 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-import logging
-
 from llama_stack.apis.inference import (
     OpenAIChatCompletionContentPartImageParam,
     OpenAIChatCompletionContentPartTextParam,
     RerankResponse,
 )
+from llama_stack.log import get_logger
 from llama_stack.providers.remote.inference.llama_openai_compat.config import LlamaCompatConfig
 from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .models import MODEL_ENTRIES
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="inference::llama_openai_compat")
 
 
 class LlamaCompatInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):

llama_stack/providers/remote/inference/nvidia/NVIDIA.md

@@ -41,6 +41,11 @@ client.initialize()
 
 ### Create Completion
 
+> Note on Completion API
+>
+> The hosted NVIDIA Llama NIMs (e.g., `meta-llama/Llama-3.1-8B-Instruct`) with ```NVIDIA_BASE_URL="https://integrate.api.nvidia.com"``` does not support the ```completion``` method, while the locally deployed NIM does.
+
+
 ```python
 response = client.inference.completion(
     model_id="meta-llama/Llama-3.1-8B-Instruct",
@@ -76,7 +81,78 @@ response = client.inference.chat_completion(
 print(f"Response: {response.completion_message.content}")
 ```
 
+### Tool Calling Example ###
+```python
+from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition
+
+tool_definition = ToolDefinition(
+    tool_name="get_weather",
+    description="Get current weather information for a location",
+    parameters={
+        "location": ToolParamDefinition(
+            param_type="string",
+            description="The city and state, e.g. San Francisco, CA",
+            required=True,
+        ),
+        "unit": ToolParamDefinition(
+            param_type="string",
+            description="Temperature unit (celsius or fahrenheit)",
+            required=False,
+            default="celsius",
+        ),
+    },
+)
+
+tool_response = client.inference.chat_completion(
+    model_id="meta-llama/Llama-3.1-8B-Instruct",
+    messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}],
+    tools=[tool_definition],
+)
+
+print(f"Tool Response: {tool_response.completion_message.content}")
+if tool_response.completion_message.tool_calls:
+    for tool_call in tool_response.completion_message.tool_calls:
+        print(f"Tool Called: {tool_call.tool_name}")
+        print(f"Arguments: {tool_call.arguments}")
+```
+
+### Structured Output Example
+```python
+from llama_stack.apis.inference import JsonSchemaResponseFormat, ResponseFormatType
+
+person_schema = {
+    "type": "object",
+    "properties": {
+        "name": {"type": "string"},
+        "age": {"type": "integer"},
+        "occupation": {"type": "string"},
+    },
+    "required": ["name", "age", "occupation"],
+}
+
+response_format = JsonSchemaResponseFormat(
+    type=ResponseFormatType.json_schema, json_schema=person_schema
+)
+
+structured_response = client.inference.chat_completion(
+    model_id="meta-llama/Llama-3.1-8B-Instruct",
+    messages=[
+        {
+            "role": "user",
+            "content": "Create a profile for a fictional person named Alice who is 30 years old and is a software engineer. ",
+        }
+    ],
+    response_format=response_format,
+)
+
+print(f"Structured Response: {structured_response.completion_message.content}")
+```
+
 ### Create Embeddings
+> Note on OpenAI embeddings compatibility
+>
+> NVIDIA asymmetric embedding models (e.g., `nvidia/llama-3.2-nv-embedqa-1b-v2`) require an `input_type` parameter not present in the standard OpenAI embeddings API. The NVIDIA Inference Adapter automatically sets `input_type="query"` when using the OpenAI-compatible embeddings endpoint for NVIDIA. For passage embeddings, use the `embeddings` API with `task_type="document"`.
+
 ```python
 response = client.inference.embeddings(
     model_id="nvidia/llama-3.2-nv-embedqa-1b-v2",

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -4,11 +4,10 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
 import warnings
 from collections.abc import AsyncIterator
 
-from openai import APIConnectionError, BadRequestError
+from openai import NOT_GIVEN, APIConnectionError
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -27,12 +26,16 @@ from llama_stack.apis.inference import (
     Inference,
     LogProbConfig,
     Message,
+    OpenAIEmbeddingData,
+    OpenAIEmbeddingsResponse,
+    OpenAIEmbeddingUsage,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
     ToolChoice,
     ToolConfig,
 )
+from llama_stack.log import get_logger
 from llama_stack.models.llama.datatypes import ToolDefinition, ToolPromptFormat
 from llama_stack.providers.utils.inference.model_registry import (
     ModelRegistryHelper,
@@ -54,7 +57,7 @@ from .openai_utils import (
 )
 from .utils import _is_nvidia_hosted
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="inference::nvidia")
 
 
 class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
@@ -194,15 +197,11 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
             }
             extra_body["input_type"] = task_type_options[task_type]
 
-        try:
-            response = await self.client.embeddings.create(
-                model=provider_model_id,
-                input=input,
-                extra_body=extra_body,
-            )
-        except BadRequestError as e:
-            raise ValueError(f"Failed to get embeddings: {e}") from e
-
+        response = await self.client.embeddings.create(
+            model=provider_model_id,
+            input=input,
+            extra_body=extra_body,
+        )
         #
         # OpenAI: CreateEmbeddingResponse(data=[Embedding(embedding=list[float], ...)], ...)
         #  ->
@@ -210,6 +209,57 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
         #
         return EmbeddingsResponse(embeddings=[embedding.embedding for embedding in response.data])
 
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        """
+        OpenAI-compatible embeddings for NVIDIA NIM.
+
+        Note: NVIDIA NIM asymmetric embedding models require an "input_type" field not present in the standard OpenAI embeddings API.
+        We default this to "query" to ensure requests succeed when using the
+        OpenAI-compatible endpoint. For passage embeddings, use the embeddings API with
+        `task_type='document'`.
+        """
+        extra_body: dict[str, object] = {"input_type": "query"}
+        logger.warning(
+            "NVIDIA OpenAI-compatible embeddings: defaulting to input_type='query'. "
+            "For passage embeddings, use the embeddings API with task_type='document'."
+        )
+
+        response = await self.client.embeddings.create(
+            model=await self._get_provider_model_id(model),
+            input=input,
+            encoding_format=encoding_format if encoding_format is not None else NOT_GIVEN,
+            dimensions=dimensions if dimensions is not None else NOT_GIVEN,
+            user=user if user is not None else NOT_GIVEN,
+            extra_body=extra_body,
+        )
+
+        data = []
+        for i, embedding_data in enumerate(response.data):
+            data.append(
+                OpenAIEmbeddingData(
+                    embedding=embedding_data.embedding,
+                    index=i,
+                )
+            )
+
+        usage = OpenAIEmbeddingUsage(
+            prompt_tokens=response.usage.prompt_tokens,
+            total_tokens=response.usage.total_tokens,
+        )
+
+        return OpenAIEmbeddingsResponse(
+            data=data,
+            model=response.model,
+            usage=usage,
+        )
+
     async def chat_completion(
         self,
         model_id: str,

llama_stack/providers/remote/inference/nvidia/utils.py

@@ -4,13 +4,13 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
-
 import httpx
 
+from llama_stack.log import get_logger
+
 from . import NVIDIAConfig
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="inference::nvidia")
 
 
 def _is_nvidia_hosted(config: NVIDIAConfig) -> bool:

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -88,7 +88,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .models import MODEL_ENTRIES
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::ollama")
 
 
 class OllamaInferenceAdapter(

llama_stack/providers/remote/inference/openai/openai.py

@@ -4,15 +4,14 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
-
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import OpenAIConfig
 from .models import MODEL_ENTRIES
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="inference::openai")
 
 
 #

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 
-import logging
 from collections.abc import AsyncGenerator
 
 from huggingface_hub import AsyncInferenceClient, HfApi
@@ -34,6 +33,7 @@ from llama_stack.apis.inference import (
     ToolPromptFormat,
 )
 from llama_stack.apis.models import Model
+from llama_stack.log import get_logger
 from llama_stack.models.llama.sku_list import all_registered_models
 from llama_stack.providers.datatypes import ModelsProtocolPrivate
 from llama_stack.providers.utils.inference.model_registry import (
@@ -58,7 +58,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="inference::tgi")
 
 
 def build_hf_repo_model_entries():

llama_stack/providers/remote/inference/together/together.py

@@ -61,7 +61,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 from .config import TogetherImplConfig
 from .models import MODEL_ENTRIES
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::together")
 
 
 class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -88,7 +88,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import VLLMInferenceAdapterConfig
 
-log = get_logger(name=__name__, category="inference")
+log = get_logger(name=__name__, category="inference::vllm")
 
 
 def build_hf_repo_model_entries():