Merge branch 'main' into store_registeration_bug_fix

docs/_static/llama-stack-spec.html

@@ -1380,6 +1380,40 @@
                         }
                     }
                 ]
+            },
+            "delete": {
+                "responses": {
+                    "200": {
+                        "description": "OK"
+                    },
+                    "400": {
+                        "$ref": "#/components/responses/BadRequest400"
+                    },
+                    "429": {
+                        "$ref": "#/components/responses/TooManyRequests429"
+                    },
+                    "500": {
+                        "$ref": "#/components/responses/InternalServerError500"
+                    },
+                    "default": {
+                        "$ref": "#/components/responses/DefaultError"
+                    }
+                },
+                "tags": [
+                    "Benchmarks"
+                ],
+                "description": "Unregister a benchmark.",
+                "parameters": [
+                    {
+                        "name": "benchmark_id",
+                        "in": "path",
+                        "description": "The ID of the benchmark to unregister.",
+                        "required": true,
+                        "schema": {
+                            "type": "string"
+                        }
+                    }
+                ]
             }
         },
         "/v1/openai/v1/chat/completions/{completion_id}": {
@@ -1620,6 +1654,40 @@
                         }
                     }
                 ]
+            },
+            "delete": {
+                "responses": {
+                    "200": {
+                        "description": "OK"
+                    },
+                    "400": {
+                        "$ref": "#/components/responses/BadRequest400"
+                    },
+                    "429": {
+                        "$ref": "#/components/responses/TooManyRequests429"
+                    },
+                    "500": {
+                        "$ref": "#/components/responses/InternalServerError500"
+                    },
+                    "default": {
+                        "$ref": "#/components/responses/DefaultError"
+                    }
+                },
+                "tags": [
+                    "ScoringFunctions"
+                ],
+                "description": "Unregister a scoring function.",
+                "parameters": [
+                    {
+                        "name": "scoring_fn_id",
+                        "in": "path",
+                        "description": "The ID of the scoring function to unregister.",
+                        "required": true,
+                        "schema": {
+                            "type": "string"
+                        }
+                    }
+                ]
             }
         },
         "/v1/shields/{identifier}": {

docs/_static/llama-stack-spec.yaml

@@ -954,6 +954,30 @@ paths:
           required: true
           schema:
             type: string
+    delete:
+      responses:
+        '200':
+          description: OK
+        '400':
+          $ref: '#/components/responses/BadRequest400'
+        '429':
+          $ref: >-
+            #/components/responses/TooManyRequests429
+        '500':
+          $ref: >-
+            #/components/responses/InternalServerError500
+        default:
+          $ref: '#/components/responses/DefaultError'
+      tags:
+        - Benchmarks
+      description: Unregister a benchmark.
+      parameters:
+        - name: benchmark_id
+          in: path
+          description: The ID of the benchmark to unregister.
+          required: true
+          schema:
+            type: string
   /v1/openai/v1/chat/completions/{completion_id}:
     get:
       responses:
@@ -1119,6 +1143,31 @@ paths:
           required: true
           schema:
             type: string
+    delete:
+      responses:
+        '200':
+          description: OK
+        '400':
+          $ref: '#/components/responses/BadRequest400'
+        '429':
+          $ref: >-
+            #/components/responses/TooManyRequests429
+        '500':
+          $ref: >-
+            #/components/responses/InternalServerError500
+        default:
+          $ref: '#/components/responses/DefaultError'
+      tags:
+        - ScoringFunctions
+      description: Unregister a scoring function.
+      parameters:
+        - name: scoring_fn_id
+          in: path
+          description: >-
+            The ID of the scoring function to unregister.
+          required: true
+          schema:
+            type: string
   /v1/shields/{identifier}:
     get:
       responses:

docs/source/apis/api_leveling.md

@@ -0,0 +1,94 @@
+# Llama Stack API Stability Leveling
+
+In order to provide a stable experience in Llama Stack, the various APIs need different stability levels indicating the level of support, backwards compatability, and overall production readiness.
+
+## Different Levels
+
+### v1alpha
+
+- Little to no expectation of support between versions
+- Breaking changes are permitted
+- Datatypes and parameters can break
+- Routes can be added and removed
+
+#### Graduation Criteria
+
+- an API can graduate from `v1alpha` to `v1beta` if the team has identified the extent of the non-optional routes and the shape of their parameters/return types for the API eg. `/v1/openai/chat/completions`. Optional types can change.
+- CRUD must stay stable once in `v1beta`. This is a commitment to backward compatibility, guaranteeing that most code you write against the v1beta version will not break during future updates. We may make additive changes (like adding a new, optional field to a response), but we will not make breaking changes (like renaming an existing "modelName" field to "name", changing an ID's data type from an integer to a string, or altering an endpoint URL).
+- for OpenAI APIs, a comparison to the OpenAI spec for the specific API can be done to ensure completeness.
+
+### v1beta
+
+- API routes remain consistent between versions
+- Parameters and return types are not ensured between versions
+- API, besides minor fixes and adjustments, should be _almost_ v1. Changes should not be drastic.
+
+#### Graduation Criteria
+
+- an API can graduate from `v1beta` to `v1` if the API surface and datatypes are complete as identified by the team. The parameters and return types that are mandatory for each route are stable. All aspects of graduating from `v1alpha1` to `v1beta` apply as well.
+- Optional parameters, routes, or parts of the return type can be added after graduating to `v1`
+
+### v1 (stable)
+
+- Considered stable
+- Backwards compatible between Z-streams
+  - Y-stream breaking changes must go through the proper approval and announcement process.
+- Datatypes for a route and its return types cannot change between Z-streams
+  - Y-stream datatype changes should be sparing, unless the changes are additional net-new parameters
+- Must have proper conformance testing as outlined in https://github.com/llamastack/llama-stack/issues/3237
+
+### v2+ (Major Versions)
+
+Introducing a new major version like `/v2` is a significant and disruptive event that should be treated as a last resort. It is reserved for essential changes to a stable `/v1` API that are fundamentally backward-incompatible and cannot be implemented through additive, non-breaking changes or breaking changes across X/Y-Stream releases (x.y.z).
+
+If a `/v2` version is deemed absolutely necessary, it must adhere to the following protocol to ensure a sane and predictable transition for users:
+
+#### Lifecycle Progression
+
+ A new major version must follow the same stability lifecycle as `/v1`. It will be introduced as `/v2alpha`, mature to `/v2beta`, and finally become stable as `/v2`.
+
+#### Coexistence:
+
+The new `/v2` API must be introduced alongside the existing `/v1` API and run in parallel. It must not replace the `/v1` API immediately.
+
+#### Deprecation Policy:
+
+When a `/v2` API is introduced, a clear and generous deprecation policy for the `/v1` API must be published simultaneously. This policy must outline the timeline for the eventual removal of the `/v1` API, giving users ample time to migrate.
+
+### API Stability vs. Provider Stability
+
+The leveling introduced in this document relates to the stability of the API and not specifically the providers within the API.
+
+Providers can iterate as much as they want on functionality as long as they work within the bounds of an API. If they need to change the API, then the API should not be `/v1`, or those breaking changes can only happen on a y-stream release basis.
+
+### Approval and Announcement Process for Breaking Changes
+
+- **PR Labeling**: Any pull request that introduces a breaking API change must be clearly labeled with `breaking-change`.
+- **PR Title/Commit**: Any pull request that introduces a breaking API change must contain `BREAKING CHANGE` in the title and commit footer. Alternatively, the commit can include `!`, eg. `feat(api)!: title goes here` This is outlined in the [conventional commits documentation](https://www.conventionalcommits.org/en/v1.0.0/#specification)
+- **Maintainer Review**: At least one maintainer must explicitly acknowledge the breaking change during review by applying the `breaking-change` label. An approval must come with this label or the acknowledgement this label has already been applied.
+- **Announcement**: Breaking changes require inclusion in release notes and, if applicable, a separate communication (e.g., Discord, Github Issues, or GitHub Discussions) prior to release.
+
+If a PR has proper approvals, labels, and commit/title hygiene, the failing API conformance tests will be bypassed.
+
+
+## Enforcement
+
+### Migration of API routes under `/v1alpha`, `/v1beta`, and `/v1`
+
+Instead of placing every API under `/v1`, any API that is not fully stable or complete should go under `/v1alpha` or `/v1beta`. For example, at the time of this writing,  `post_training` belongs here, as well as any OpenAI-compatible API whose surface does not exactly match the upstream OpenAI API it mimics.
+
+This migration is crucial as we get Llama Stack in the hands of users who intend to productize various APIs. A clear view of what is stable and what is actively being developed will enable users to pick and choose various APIs to build their products on.
+
+This migration will be a breaking change for any API moving out of `/v1`. Ideally, this should happen before 0.3.0 and especially 1.0.0.
+
+### `x-stability` tags in the OpenAPI spec for oasdiff
+
+`x-stability` tags allow tools like oasdiff to enforce different rules for different stability levels; these tags should match the routes: [oasdiff stability](https://github.com/oasdiff/oasdiff/blob/main/docs/STABILITY.md)
+
+### Testing
+
+The testing of each stable API is already outlined in [issue #3237](https://github.com/llamastack/llama-stack/issues/3237) and is being worked on. These sorts of conformance tests should apply primarily to `/v1` APIs only, with `/v1alpha` and `/v1beta` having any tests the maintainers see fit as well as basic testing to ensure the routing works properly.
+
+### New APIs going forward
+
+Any subsequently introduced APIs should be introduced as `/v1alpha`

docs/source/providers/inference/remote_watsonx.md

@@ -9,8 +9,8 @@ IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform
 | Field | Type | Required | Default | Description |
 |-------|------|----------|---------|-------------|
 | `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, only needed of using the hosted service |
-| `project_id` | `str \| None` | No |  | The Project ID key, only needed of using the hosted service |
+| `api_key` | `pydantic.types.SecretStr \| None` | No |  | The watsonx API key |
+| `project_id` | `str \| None` | No |  | The Project ID key |
 | `timeout` | `<class 'int'>` | No | 60 | Timeout for the HTTP requests |
 
 ## Sample Configuration

llama_stack/apis/benchmarks/benchmarks.py

@@ -93,3 +93,11 @@ class Benchmarks(Protocol):
         :param metadata: The metadata to use for the benchmark.
         """
         ...
+
+    @webmethod(route="/eval/benchmarks/{benchmark_id}", method="DELETE")
+    async def unregister_benchmark(self, benchmark_id: str) -> None:
+        """Unregister a benchmark.
+
+        :param benchmark_id: The ID of the benchmark to unregister.
+        """
+        ...

llama_stack/apis/scoring_functions/scoring_functions.py

@@ -197,3 +197,11 @@ class ScoringFunctions(Protocol):
         :param params: The parameters for the scoring function for benchmark eval, these can be overridden for app eval.
         """
         ...
+
+    @webmethod(route="/scoring-functions/{scoring_fn_id:path}", method="DELETE")
+    async def unregister_scoring_function(self, scoring_fn_id: str) -> None:
+        """Unregister a scoring function.
+
+        :param scoring_fn_id: The ID of the scoring function to unregister.
+        """
+        ...

llama_stack/core/routing_tables/benchmarks.py

@@ -56,3 +56,7 @@ class BenchmarksRoutingTable(CommonRoutingTableImpl, Benchmarks):
             provider_resource_id=provider_benchmark_id,
         )
         await self.register_object(benchmark)
+
+    async def unregister_benchmark(self, benchmark_id: str) -> None:
+        existing_benchmark = await self.get_benchmark(benchmark_id)
+        await self.unregister_object(existing_benchmark)

llama_stack/core/routing_tables/common.py

@@ -64,6 +64,10 @@ async def unregister_object_from_provider(obj: RoutableObject, p: Any) -> None:
         return await p.unregister_shield(obj.identifier)
     elif api == Api.datasetio:
         return await p.unregister_dataset(obj.identifier)
+    elif api == Api.eval:
+        return await p.unregister_benchmark(obj.identifier)
+    elif api == Api.scoring:
+        return await p.unregister_scoring_function(obj.identifier)
     elif api == Api.tool_runtime:
         return await p.unregister_toolgroup(obj.identifier)
     else:

llama_stack/core/routing_tables/scoring_functions.py

@@ -60,3 +60,7 @@ class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions):
         )
         scoring_fn.provider_id = provider_id
         await self.register_object(scoring_fn)
+
+    async def unregister_scoring_function(self, scoring_fn_id: str) -> None:
+        existing_scoring_fn = await self.get_scoring_function(scoring_fn_id)
+        await self.unregister_object(existing_scoring_fn)

llama_stack/distributions/watsonx/run.yaml

@@ -10,6 +10,7 @@ apis:
 - telemetry
 - tool_runtime
 - vector_io
+- files
 providers:
   inference:
   - provider_id: watsonx
@@ -94,6 +95,14 @@ providers:
     provider_type: inline::rag-runtime
   - provider_id: model-context-protocol
     provider_type: remote::model-context-protocol
+  files:
+  - provider_id: meta-reference-files
+    provider_type: inline::localfs
+    config:
+      storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/watsonx/files}
+      metadata_store:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/files_metadata.db
 metadata_store:
   type: sqlite
   db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/watsonx}/registry.db

llama_stack/distributions/watsonx/watsonx.py

@@ -9,6 +9,7 @@ from pathlib import Path
 from llama_stack.apis.models import ModelType
 from llama_stack.core.datatypes import BuildProvider, ModelInput, Provider, ToolGroupInput
 from llama_stack.distributions.template import DistributionTemplate, RunConfigSettings, get_model_registry
+from llama_stack.providers.inline.files.localfs.config import LocalfsFilesImplConfig
 from llama_stack.providers.inline.inference.sentence_transformers import (
     SentenceTransformersInferenceConfig,
 )
@@ -16,7 +17,7 @@ from llama_stack.providers.remote.inference.watsonx import WatsonXConfig
 from llama_stack.providers.remote.inference.watsonx.models import MODEL_ENTRIES
 
 
-def get_distribution_template() -> DistributionTemplate:
+def get_distribution_template(name: str = "watsonx") -> DistributionTemplate:
     providers = {
         "inference": [
             BuildProvider(provider_type="remote::watsonx"),
@@ -42,6 +43,7 @@ def get_distribution_template() -> DistributionTemplate:
             BuildProvider(provider_type="inline::rag-runtime"),
             BuildProvider(provider_type="remote::model-context-protocol"),
         ],
+        "files": [BuildProvider(provider_type="inline::localfs")],
     }
 
     inference_provider = Provider(
@@ -79,9 +81,14 @@ def get_distribution_template() -> DistributionTemplate:
         },
     )
 
+    files_provider = Provider(
+        provider_id="meta-reference-files",
+        provider_type="inline::localfs",
+        config=LocalfsFilesImplConfig.sample_run_config(f"~/.llama/distributions/{name}"),
+    )
     default_models, _ = get_model_registry(available_models)
     return DistributionTemplate(
-        name="watsonx",
+        name=name,
         distro_type="remote_hosted",
         description="Use watsonx for running LLM inference",
         container_image=None,
@@ -92,6 +99,7 @@ def get_distribution_template() -> DistributionTemplate:
             "run.yaml": RunConfigSettings(
                 provider_overrides={
                     "inference": [inference_provider, embedding_provider],
+                    "files": [files_provider],
                 },
                 default_models=default_models + [embedding_model],
                 default_tool_groups=default_tool_groups,

llama_stack/providers/inline/eval/meta_reference/eval.py

@@ -75,6 +75,13 @@ class MetaReferenceEvalImpl(
         )
         self.benchmarks[task_def.identifier] = task_def
 
+    async def unregister_benchmark(self, benchmark_id: str) -> None:
+        if benchmark_id in self.benchmarks:
+            del self.benchmarks[benchmark_id]
+
+        key = f"{EVAL_TASKS_PREFIX}{benchmark_id}"
+        await self.kvstore.delete(key)
+
     async def run_eval(
         self,
         benchmark_id: str,

llama_stack/providers/inline/scoring/llm_as_judge/scoring.py

@@ -63,6 +63,9 @@ class LlmAsJudgeScoringImpl(
     async def register_scoring_function(self, function_def: ScoringFn) -> None:
         self.llm_as_judge_fn.register_scoring_fn_def(function_def)
 
+    async def unregister_scoring_function(self, scoring_fn_id: str) -> None:
+        self.llm_as_judge_fn.unregister_scoring_fn_def(scoring_fn_id)
+
     async def score_batch(
         self,
         dataset_id: str,

llama_stack/providers/remote/eval/nvidia/eval.py

@@ -51,18 +51,23 @@ class NVIDIAEvalImpl(
 
     async def shutdown(self) -> None: ...
 
-    async def _evaluator_get(self, path):
+    async def _evaluator_get(self, path: str):
         """Helper for making GET requests to the evaluator service."""
         response = requests.get(url=f"{self.config.evaluator_url}{path}")
         response.raise_for_status()
         return response.json()
 
-    async def _evaluator_post(self, path, data):
+    async def _evaluator_post(self, path: str, data: dict[str, Any]):
         """Helper for making POST requests to the evaluator service."""
         response = requests.post(url=f"{self.config.evaluator_url}{path}", json=data)
         response.raise_for_status()
         return response.json()
 
+    async def _evaluator_delete(self, path: str) -> None:
+        """Helper for making DELETE requests to the evaluator service."""
+        response = requests.delete(url=f"{self.config.evaluator_url}{path}")
+        response.raise_for_status()
+
     async def register_benchmark(self, task_def: Benchmark) -> None:
         """Register a benchmark as an evaluation configuration."""
         await self._evaluator_post(
@@ -75,6 +80,10 @@ class NVIDIAEvalImpl(
             },
         )
 
+    async def unregister_benchmark(self, benchmark_id: str) -> None:
+        """Unregister a benchmark evaluation configuration from NeMo Evaluator."""
+        await self._evaluator_delete(f"/v1/evaluation/configs/{DEFAULT_NAMESPACE}/{benchmark_id}")
+
     async def run_eval(
         self,
         benchmark_id: str,

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

@@ -8,6 +8,7 @@
 from collections.abc import AsyncGenerator
 
 from huggingface_hub import AsyncInferenceClient, HfApi
+from pydantic import SecretStr
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -33,6 +34,7 @@ from llama_stack.apis.inference import (
     ToolPromptFormat,
 )
 from llama_stack.apis.models import Model
+from llama_stack.apis.models.models import ModelType
 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
@@ -41,16 +43,15 @@ from llama_stack.providers.utils.inference.model_registry import (
     build_hf_repo_model_entry,
 )
 from llama_stack.providers.utils.inference.openai_compat import (
-    OpenAIChatCompletionToLlamaStackMixin,
     OpenAICompatCompletionChoice,
     OpenAICompatCompletionResponse,
-    OpenAICompletionToLlamaStackMixin,
     get_sampling_options,
     process_chat_completion_response,
     process_chat_completion_stream_response,
     process_completion_response,
     process_completion_stream_response,
 )
+from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_model_input_info,
     completion_request_to_prompt_model_input_info,
@@ -73,26 +74,49 @@ def build_hf_repo_model_entries():
 
 
 class _HfAdapter(
+    OpenAIMixin,
     Inference,
-    OpenAIChatCompletionToLlamaStackMixin,
-    OpenAICompletionToLlamaStackMixin,
     ModelsProtocolPrivate,
 ):
-    client: AsyncInferenceClient
+    url: str
+    api_key: SecretStr
+
+    hf_client: AsyncInferenceClient
     max_tokens: int
     model_id: str
 
+    overwrite_completion_id = True  # TGI always returns id=""
+
     def __init__(self) -> None:
         self.register_helper = ModelRegistryHelper(build_hf_repo_model_entries())
         self.huggingface_repo_to_llama_model_id = {
             model.huggingface_repo: model.descriptor() for model in all_registered_models() if model.huggingface_repo
         }
 
+    def get_api_key(self):
+        return self.api_key.get_secret_value()
+
+    def get_base_url(self):
+        return self.url
+
     async def shutdown(self) -> None:
         pass
 
+    async def list_models(self) -> list[Model] | None:
+        models = []
+        async for model in self.client.models.list():
+            models.append(
+                Model(
+                    identifier=model.id,
+                    provider_resource_id=model.id,
+                    provider_id=self.__provider_id__,
+                    metadata={},
+                    model_type=ModelType.llm,
+                )
+            )
+        return models
+
     async def register_model(self, model: Model) -> Model:
-        model = await self.register_helper.register_model(model)
         if model.provider_resource_id != self.model_id:
             raise ValueError(
                 f"Model {model.provider_resource_id} does not match the model {self.model_id} served by TGI."
@@ -176,7 +200,7 @@ class _HfAdapter(
         params = await self._get_params_for_completion(request)
 
         async def _generate_and_convert_to_openai_compat():
-            s = await self.client.text_generation(**params)
+            s = await self.hf_client.text_generation(**params)
             async for chunk in s:
                 token_result = chunk.token
                 finish_reason = None
@@ -194,7 +218,7 @@ class _HfAdapter(
 
     async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator:
         params = await self._get_params_for_completion(request)
-        r = await self.client.text_generation(**params)
+        r = await self.hf_client.text_generation(**params)
 
         choice = OpenAICompatCompletionChoice(
             finish_reason=r.details.finish_reason,
@@ -241,7 +265,7 @@ class _HfAdapter(
 
     async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
         params = await self._get_params(request)
-        r = await self.client.text_generation(**params)
+        r = await self.hf_client.text_generation(**params)
 
         choice = OpenAICompatCompletionChoice(
             finish_reason=r.details.finish_reason,
@@ -256,7 +280,7 @@ class _HfAdapter(
         params = await self._get_params(request)
 
         async def _generate_and_convert_to_openai_compat():
-            s = await self.client.text_generation(**params)
+            s = await self.hf_client.text_generation(**params)
             async for chunk in s:
                 token_result = chunk.token
 
@@ -308,18 +332,21 @@ class TGIAdapter(_HfAdapter):
         if not config.url:
             raise ValueError("You must provide a URL in run.yaml (or via the TGI_URL environment variable) to use TGI.")
         log.info(f"Initializing TGI client with url={config.url}")
-        self.client = AsyncInferenceClient(model=config.url, provider="hf-inference")
-        endpoint_info = await self.client.get_endpoint_info()
+        self.hf_client = AsyncInferenceClient(model=config.url, provider="hf-inference")
+        endpoint_info = await self.hf_client.get_endpoint_info()
         self.max_tokens = endpoint_info["max_total_tokens"]
         self.model_id = endpoint_info["model_id"]
+        self.url = f"{config.url.rstrip('/')}/v1"
+        self.api_key = SecretStr("NO_KEY")
 
 
 class InferenceAPIAdapter(_HfAdapter):
     async def initialize(self, config: InferenceAPIImplConfig) -> None:
-        self.client = AsyncInferenceClient(model=config.huggingface_repo, token=config.api_token.get_secret_value())
-        endpoint_info = await self.client.get_endpoint_info()
+        self.hf_client = AsyncInferenceClient(model=config.huggingface_repo, token=config.api_token.get_secret_value())
+        endpoint_info = await self.hf_client.get_endpoint_info()
         self.max_tokens = endpoint_info["max_total_tokens"]
         self.model_id = endpoint_info["model_id"]
+        # TODO: how do we set url for this?
 
 
 class InferenceEndpointAdapter(_HfAdapter):
@@ -331,6 +358,7 @@ class InferenceEndpointAdapter(_HfAdapter):
         endpoint.wait(timeout=60)
 
         # Initialize the adapter
-        self.client = endpoint.async_client
+        self.hf_client = endpoint.async_client
         self.model_id = endpoint.repository
         self.max_tokens = int(endpoint.raw["model"]["image"]["custom"]["env"]["MAX_TOTAL_TOKENS"])
+        # TODO: how do we set url for this?

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

@@ -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.
 
-from llama_stack.apis.models import ModelType
 from llama_stack.models.llama.sku_types import CoreModelId
 from llama_stack.providers.utils.inference.model_registry import (
     ProviderModelEntry,
@@ -21,7 +20,47 @@ SAFETY_MODELS_ENTRIES = [
         CoreModelId.llama_guard_3_11b_vision.value,
     ),
 ]
-MODEL_ENTRIES = [
+
+# source: https://docs.together.ai/docs/serverless-models#embedding-models
+EMBEDDING_MODEL_ENTRIES = {
+    "togethercomputer/m2-bert-80M-32k-retrieval": ProviderModelEntry(
+        provider_model_id="togethercomputer/m2-bert-80M-32k-retrieval",
+        metadata={
+            "embedding_dimension": 768,
+            "context_length": 32768,
+        },
+    ),
+    "BAAI/bge-large-en-v1.5": ProviderModelEntry(
+        provider_model_id="BAAI/bge-large-en-v1.5",
+        metadata={
+            "embedding_dimension": 1024,
+            "context_length": 512,
+        },
+    ),
+    "BAAI/bge-base-en-v1.5": ProviderModelEntry(
+        provider_model_id="BAAI/bge-base-en-v1.5",
+        metadata={
+            "embedding_dimension": 768,
+            "context_length": 512,
+        },
+    ),
+    "Alibaba-NLP/gte-modernbert-base": ProviderModelEntry(
+        provider_model_id="Alibaba-NLP/gte-modernbert-base",
+        metadata={
+            "embedding_dimension": 768,
+            "context_length": 8192,
+        },
+    ),
+    "intfloat/multilingual-e5-large-instruct": ProviderModelEntry(
+        provider_model_id="intfloat/multilingual-e5-large-instruct",
+        metadata={
+            "embedding_dimension": 1024,
+            "context_length": 512,
+        },
+    ),
+}
+MODEL_ENTRIES = (
+    [
         build_hf_repo_model_entry(
             "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
             CoreModelId.llama3_1_8b_instruct.value,
@@ -50,22 +89,6 @@ MODEL_ENTRIES = [
             "meta-llama/Llama-3.3-70B-Instruct-Turbo",
             CoreModelId.llama3_3_70b_instruct.value,
         ),
-    ProviderModelEntry(
-        provider_model_id="togethercomputer/m2-bert-80M-8k-retrieval",
-        model_type=ModelType.embedding,
-        metadata={
-            "embedding_dimension": 768,
-            "context_length": 8192,
-        },
-    ),
-    ProviderModelEntry(
-        provider_model_id="togethercomputer/m2-bert-80M-32k-retrieval",
-        model_type=ModelType.embedding,
-        metadata={
-            "embedding_dimension": 768,
-            "context_length": 32768,
-        },
-    ),
         build_hf_repo_model_entry(
             "meta-llama/Llama-4-Scout-17B-16E-Instruct",
             CoreModelId.llama4_scout_17b_16e_instruct.value,
@@ -74,4 +97,7 @@ MODEL_ENTRIES = [
             "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
             CoreModelId.llama4_maverick_17b_128e_instruct.value,
         ),
-] + SAFETY_MODELS_ENTRIES
+    ]
+    + SAFETY_MODELS_ENTRIES
+    + list(EMBEDDING_MODEL_ENTRIES.values())
+)

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

@@ -4,11 +4,11 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from collections.abc import AsyncGenerator, AsyncIterator
-from typing import Any
+from collections.abc import AsyncGenerator
 
-from openai import AsyncOpenAI
+from openai import NOT_GIVEN, AsyncOpenAI
 from together import AsyncTogether
+from together.constants import BASE_URL
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -23,12 +23,7 @@ from llama_stack.apis.inference import (
     Inference,
     LogProbConfig,
     Message,
-    OpenAIChatCompletion,
-    OpenAIChatCompletionChunk,
-    OpenAICompletion,
     OpenAIEmbeddingsResponse,
-    OpenAIMessageParam,
-    OpenAIResponseFormatParam,
     ResponseFormat,
     ResponseFormatType,
     SamplingParams,
@@ -38,18 +33,20 @@ from llama_stack.apis.inference import (
     ToolDefinition,
     ToolPromptFormat,
 )
+from llama_stack.apis.inference.inference import OpenAIEmbeddingUsage
+from llama_stack.apis.models import Model, ModelType
 from llama_stack.core.request_headers import NeedsRequestProviderData
 from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
 from llama_stack.providers.utils.inference.openai_compat import (
     convert_message_to_openai_dict,
     get_sampling_options,
-    prepare_openai_completion_params,
     process_chat_completion_response,
     process_chat_completion_stream_response,
     process_completion_response,
     process_completion_stream_response,
 )
+from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_prompt,
     completion_request_to_prompt,
@@ -59,15 +56,22 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 )
 
 from .config import TogetherImplConfig
-from .models import MODEL_ENTRIES
+from .models import EMBEDDING_MODEL_ENTRIES, MODEL_ENTRIES
 
 logger = get_logger(name=__name__, category="inference::together")
 
 
-class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):
+class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, NeedsRequestProviderData):
     def __init__(self, config: TogetherImplConfig) -> None:
         ModelRegistryHelper.__init__(self, MODEL_ENTRIES, config.allowed_models)
         self.config = config
+        self._model_cache: dict[str, Model] = {}
+
+    def get_api_key(self):
+        return self.config.api_key.get_secret_value()
+
+    def get_base_url(self):
+        return BASE_URL
 
     async def initialize(self) -> None:
         pass
@@ -255,6 +259,37 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
         embeddings = [item.embedding for item in r.data]
         return EmbeddingsResponse(embeddings=embeddings)
 
+    async def list_models(self) -> list[Model] | None:
+        self._model_cache = {}
+        # Together's /v1/models is not compatible with OpenAI's /v1/models. Together support ticket #13355 -> will not fix, use Together's own client
+        for m in await self._get_client().models.list():
+            if m.type == "embedding":
+                if m.id not in EMBEDDING_MODEL_ENTRIES:
+                    logger.warning(f"Unknown embedding dimension for model {m.id}, skipping.")
+                    continue
+                self._model_cache[m.id] = Model(
+                    provider_id=self.__provider_id__,
+                    provider_resource_id=EMBEDDING_MODEL_ENTRIES[m.id].provider_model_id,
+                    identifier=m.id,
+                    model_type=ModelType.embedding,
+                    metadata=EMBEDDING_MODEL_ENTRIES[m.id].metadata,
+                )
+            else:
+                self._model_cache[m.id] = Model(
+                    provider_id=self.__provider_id__,
+                    provider_resource_id=m.id,
+                    identifier=m.id,
+                    model_type=ModelType.llm,
+                )
+
+        return self._model_cache.values()
+
+    async def should_refresh_models(self) -> bool:
+        return True
+
+    async def check_model_availability(self, model):
+        return model in self._model_cache
+
     async def openai_embeddings(
         self,
         model: str,
@@ -263,125 +298,39 @@ class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProvi
         dimensions: int | None = None,
         user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
-        raise NotImplementedError()
+        """
+        Together's OpenAI-compatible embeddings endpoint is not compatible with
+        the standard OpenAI embeddings endpoint.
 
-    async def openai_completion(
-        self,
-        model: str,
-        prompt: str | list[str] | list[int] | list[list[int]],
-        best_of: int | None = None,
-        echo: bool | None = None,
-        frequency_penalty: float | None = None,
-        logit_bias: dict[str, float] | None = None,
-        logprobs: bool | None = None,
-        max_tokens: int | None = None,
-        n: int | None = None,
-        presence_penalty: float | None = None,
-        seed: int | None = None,
-        stop: str | list[str] | None = None,
-        stream: bool | None = None,
-        stream_options: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        top_p: float | None = None,
-        user: str | None = None,
-        guided_choice: list[str] | None = None,
-        prompt_logprobs: int | None = None,
-        suffix: str | None = None,
-    ) -> OpenAICompletion:
-        model_obj = await self.model_store.get_model(model)
-        params = await prepare_openai_completion_params(
-            model=model_obj.provider_resource_id,
-            prompt=prompt,
-            best_of=best_of,
-            echo=echo,
-            frequency_penalty=frequency_penalty,
-            logit_bias=logit_bias,
-            logprobs=logprobs,
-            max_tokens=max_tokens,
-            n=n,
-            presence_penalty=presence_penalty,
-            seed=seed,
-            stop=stop,
-            stream=stream,
-            stream_options=stream_options,
-            temperature=temperature,
-            top_p=top_p,
-            user=user,
+        The endpoint -
+         - does not return usage information
+         - does not support user param, returns 400 Unrecognized request arguments supplied: user
+         - does not support dimensions param, returns 400 Unrecognized request arguments supplied: dimensions
+         - does not support encoding_format param, always returns floats, never base64
+        """
+        # Together support ticket #13332 -> will not fix
+        if user is not None:
+            raise ValueError("Together's embeddings endpoint does not support user param.")
+        # Together support ticket #13333 -> escalated
+        if dimensions is not None:
+            raise ValueError("Together's embeddings endpoint does not support dimensions param.")
+        # Together support ticket #13331 -> will not fix, compute client side
+        if encoding_format not in (None, NOT_GIVEN, "float"):
+            raise ValueError("Together's embeddings endpoint only supports encoding_format='float'.")
+
+        response = await self.client.embeddings.create(
+            model=await self._get_provider_model_id(model),
+            input=input,
         )
-        return await self._get_openai_client().completions.create(**params)  # type: ignore
 
-    async def openai_chat_completion(
-        self,
-        model: str,
-        messages: list[OpenAIMessageParam],
-        frequency_penalty: float | None = None,
-        function_call: str | dict[str, Any] | None = None,
-        functions: list[dict[str, Any]] | None = None,
-        logit_bias: dict[str, float] | None = None,
-        logprobs: bool | None = None,
-        max_completion_tokens: int | None = None,
-        max_tokens: int | None = None,
-        n: int | None = None,
-        parallel_tool_calls: bool | None = None,
-        presence_penalty: float | None = None,
-        response_format: OpenAIResponseFormatParam | None = None,
-        seed: int | None = None,
-        stop: str | list[str] | None = None,
-        stream: bool | None = None,
-        stream_options: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        tool_choice: str | dict[str, Any] | None = None,
-        tools: list[dict[str, Any]] | None = None,
-        top_logprobs: int | None = None,
-        top_p: float | None = None,
-        user: str | None = None,
-    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
-        model_obj = await self.model_store.get_model(model)
-        params = await prepare_openai_completion_params(
-            model=model_obj.provider_resource_id,
-            messages=messages,
-            frequency_penalty=frequency_penalty,
-            function_call=function_call,
-            functions=functions,
-            logit_bias=logit_bias,
-            logprobs=logprobs,
-            max_completion_tokens=max_completion_tokens,
-            max_tokens=max_tokens,
-            n=n,
-            parallel_tool_calls=parallel_tool_calls,
-            presence_penalty=presence_penalty,
-            response_format=response_format,
-            seed=seed,
-            stop=stop,
-            stream=stream,
-            stream_options=stream_options,
-            temperature=temperature,
-            tool_choice=tool_choice,
-            tools=tools,
-            top_logprobs=top_logprobs,
-            top_p=top_p,
-            user=user,
+        response.model = model  # return the user the same model id they provided, avoid exposing the provider model id
+
+        # Together support ticket #13330 -> escalated
+        #  - togethercomputer/m2-bert-80M-32k-retrieval *does not* return usage information
+        if not hasattr(response, "usage") or response.usage is None:
+            logger.warning(
+                f"Together's embedding endpoint for {model} did not return usage information, substituting -1s."
             )
-        if params.get("stream", False):
-            return self._stream_openai_chat_completion(params)
-        return await self._get_openai_client().chat.completions.create(**params)  # type: ignore
+            response.usage = OpenAIEmbeddingUsage(prompt_tokens=-1, total_tokens=-1)
 
-    async def _stream_openai_chat_completion(self, params: dict) -> AsyncGenerator:
-        # together.ai sometimes adds usage data to the stream, even if include_usage is False
-        # This causes an unexpected final chunk with empty choices array to be sent
-        # to clients that may not handle it gracefully.
-        include_usage = False
-        if params.get("stream_options", None):
-            include_usage = params["stream_options"].get("include_usage", False)
-        stream = await self._get_openai_client().chat.completions.create(**params)
-
-        seen_finish_reason = False
-        async for chunk in stream:
-            # Final usage chunk with no choices that the user didn't request, so discard
-            if not include_usage and seen_finish_reason and len(chunk.choices) == 0:
-                break
-            yield chunk
-            for choice in chunk.choices:
-                if choice.finish_reason:
-                    seen_finish_reason = True
-                    break
+        return response

llama_stack/providers/remote/inference/watsonx/config.py

@@ -26,11 +26,11 @@ class WatsonXConfig(BaseModel):
     )
     api_key: SecretStr | None = Field(
         default_factory=lambda: os.getenv("WATSONX_API_KEY"),
-        description="The watsonx API key, only needed of using the hosted service",
+        description="The watsonx API key",
     )
     project_id: str | None = Field(
         default_factory=lambda: os.getenv("WATSONX_PROJECT_ID"),
-        description="The Project ID key, only needed of using the hosted service",
+        description="The Project ID key",
     )
     timeout: int = Field(
         default=60,

llama_stack/providers/remote/inference/watsonx/watsonx.py

@@ -38,6 +38,7 @@ from llama_stack.apis.inference import (
     TopKSamplingStrategy,
     TopPSamplingStrategy,
 )
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper
 from llama_stack.providers.utils.inference.openai_compat import (
     OpenAICompatCompletionChoice,
@@ -57,14 +58,29 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 from . import WatsonXConfig
 from .models import MODEL_ENTRIES
 
+logger = get_logger(name=__name__, category="inference::watsonx")
+
+
+# Note on structured output
+# WatsonX returns responses with a json embedded into a string.
+# Examples:
+
+# ChatCompletionResponse(completion_message=CompletionMessage(content='```json\n{\n
+# "first_name": "Michael",\n  "last_name": "Jordan",\n'...)
+# Not even a valid JSON, but we can still extract the JSON from the content
+
+# CompletionResponse(content=' \nThe best answer is $\\boxed{\\{"name": "Michael Jordan",
+# "year_born": "1963", "year_retired": "2003"\\}}$')
+# Find the start of the boxed content
+
 
 class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
     def __init__(self, config: WatsonXConfig) -> None:
         ModelRegistryHelper.__init__(self, MODEL_ENTRIES)
 
-        print(f"Initializing watsonx InferenceAdapter({config.url})...")
-
+        logger.info(f"Initializing watsonx InferenceAdapter({config.url})...")
         self._config = config
+        self._openai_client: AsyncOpenAI | None = None
 
         self._project_id = self._config.project_id
 

llama_stack/providers/utils/inference/openai_mixin.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import uuid
 from abc import ABC, abstractmethod
 from collections.abc import AsyncIterator
 from typing import Any
@@ -43,6 +44,12 @@ class OpenAIMixin(ABC):
       The model_store is set in routing_tables/common.py during provider initialization.
     """
 
+    # Allow subclasses to control whether to overwrite the 'id' field in OpenAI responses
+    # is overwritten with a client-side generated id.
+    #
+    # This is useful for providers that do not return a unique id in the response.
+    overwrite_completion_id: bool = False
+
     @abstractmethod
     def get_api_key(self) -> str:
         """
@@ -110,6 +117,23 @@ class OpenAIMixin(ABC):
             raise ValueError(f"Model {model} has no provider_resource_id")
         return model_obj.provider_resource_id
 
+    async def _maybe_overwrite_id(self, resp: Any, stream: bool | None) -> Any:
+        if not self.overwrite_completion_id:
+            return resp
+
+        new_id = f"cltsd-{uuid.uuid4()}"
+        if stream:
+
+            async def _gen():
+                async for chunk in resp:
+                    chunk.id = new_id
+                    yield chunk
+
+            return _gen()
+        else:
+            resp.id = new_id
+            return resp
+
     async def openai_completion(
         self,
         model: str,
@@ -147,7 +171,7 @@ class OpenAIMixin(ABC):
             extra_body["guided_choice"] = guided_choice
 
         # TODO: fix openai_completion to return type compatible with OpenAI's API response
-        return await self.client.completions.create(  # type: ignore[no-any-return]
+        resp = await self.client.completions.create(
             **await prepare_openai_completion_params(
                 model=await self._get_provider_model_id(model),
                 prompt=prompt,
@@ -171,6 +195,8 @@ class OpenAIMixin(ABC):
             extra_body=extra_body,
         )
 
+        return await self._maybe_overwrite_id(resp, stream)  # type: ignore[no-any-return]
+
     async def openai_chat_completion(
         self,
         model: str,
@@ -200,8 +226,7 @@ class OpenAIMixin(ABC):
         """
         Direct OpenAI chat completion API call.
         """
-        # Type ignore because return types are compatible
-        return await self.client.chat.completions.create(  # type: ignore[no-any-return]
+        resp = await self.client.chat.completions.create(
             **await prepare_openai_completion_params(
                 model=await self._get_provider_model_id(model),
                 messages=messages,
@@ -229,6 +254,8 @@ class OpenAIMixin(ABC):
             )
         )
 
+        return await self._maybe_overwrite_id(resp, stream)  # type: ignore[no-any-return]
+
     async def openai_embeddings(
         self,
         model: str,

llama_stack/testing/inference_recorder.py

@@ -7,7 +7,6 @@
 from __future__ import annotations  # for forward references
 
 import hashlib
-import inspect
 import json
 import os
 from collections.abc import Generator
@@ -243,11 +242,10 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
     global _current_mode, _current_storage
 
     if _current_mode == InferenceMode.LIVE or _current_storage is None:
-        # Normal operation
-        if inspect.iscoroutinefunction(original_method):
-            return await original_method(self, *args, **kwargs)
-        else:
+        if endpoint == "/v1/models":
             return original_method(self, *args, **kwargs)
+        else:
+            return await original_method(self, *args, **kwargs)
 
     # Get base URL based on client type
     if client_type == "openai":
@@ -298,10 +296,10 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
             )
 
     elif _current_mode == InferenceMode.RECORD:
-        if inspect.iscoroutinefunction(original_method):
-            response = await original_method(self, *args, **kwargs)
-        else:
+        if endpoint == "/v1/models":
             response = original_method(self, *args, **kwargs)
+        else:
+            response = await original_method(self, *args, **kwargs)
 
         # we want to store the result of the iterator, not the iterator itself
         if endpoint == "/v1/models":

llama_stack/ui/package-lock.json

@@ -18,7 +18,7 @@
         "class-variance-authority": "^0.7.1",
         "clsx": "^2.1.1",
         "framer-motion": "^12.23.12",
-        "llama-stack-client": "^0.2.21",
+        "llama-stack-client": "^0.2.22",
         "lucide-react": "^0.542.0",
         "next": "15.5.3",
         "next-auth": "^4.24.11",
@@ -10314,9 +10314,9 @@
       "license": "MIT"
     },
     "node_modules/llama-stack-client": {
-      "version": "0.2.21",
-      "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.21.tgz",
-      "integrity": "sha512-rjU2Vx5xStxDYavU8K1An/SYXiQQjroLcK98B+p0Paz/a7OgRao2S0YwvThJjPUyChY4fO03UIXP9LpmHqlXWQ==",
+      "version": "0.2.22",
+      "resolved": "https://registry.npmjs.org/llama-stack-client/-/llama-stack-client-0.2.22.tgz",
+      "integrity": "sha512-7aW3UQj5MwjV73Brd+yQ1e4W1W33nhozyeHM5tzOgbsVZ88tL78JNiNvyFqDR5w6V9XO4/uSGGiQVG6v83yR4w==",
       "license": "MIT",
       "dependencies": {
         "@types/node": "^18.11.18",

llama_stack/ui/package.json

@@ -23,7 +23,7 @@
     "class-variance-authority": "^0.7.1",
     "clsx": "^2.1.1",
     "framer-motion": "^12.23.12",
-    "llama-stack-client": "^0.2.21",
+    "llama-stack-client": "^0.2.22",
     "lucide-react": "^0.542.0",
     "next": "15.5.3",
     "next-auth": "^4.24.11",

pyproject.toml

@@ -7,7 +7,7 @@ required-version = ">=0.7.0"
 
 [project]
 name = "llama_stack"
-version = "0.2.21"
+version = "0.2.22"
 authors = [{ name = "Meta Llama", email = "llama-oss@meta.com" }]
 description = "Llama Stack"
 readme = "README.md"
@@ -31,12 +31,12 @@ dependencies = [
     "huggingface-hub>=0.34.0,<1.0",
     "jinja2>=3.1.6",
     "jsonschema",
-    "llama-stack-client>=0.2.21",
+    "llama-stack-client>=0.2.22",
     "openai>=1.100.0",                                # for expires_after support
     "prompt-toolkit",
     "python-dotenv",
     "python-jose[cryptography]",
-    "pydantic>=2",
+    "pydantic>=2.11.9",
     "rich",
     "starlette",
     "termcolor",
@@ -55,7 +55,7 @@ dependencies = [
 ui = [
     "streamlit",
     "pandas",
-    "llama-stack-client>=0.2.21",
+    "llama-stack-client>=0.2.22",
     "streamlit-option-menu",
 ]
 
@@ -141,7 +141,7 @@ docs = [
     "sphinxcontrib.openapi",
     "requests",
 ]
-codegen = ["rich", "pydantic", "jinja2>=3.1.6"]
+codegen = ["rich", "pydantic>=2.11.9", "jinja2>=3.1.6"]
 benchmark = [
     "locust>=2.39.1",
 ]

tests/integration/inference/test_openai_completion.py

@@ -48,7 +48,6 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id)
         "remote::nvidia",
         "remote::runpod",
         "remote::sambanova",
-        "remote::tgi",
         "remote::vertexai",
         # {"error":{"message":"Unknown request URL: GET /openai/v1/completions. Please check the URL for typos,
         # or see the docs at https://console.groq.com/docs/","type":"invalid_request_error","code":"unknown_url"}}
@@ -59,6 +58,7 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id)
         #  does not work with the specified model, gpt-5-mini. Please choose different model and try
         #  again. You can learn more about which models can be used with each operation here:
         #  https://go.microsoft.com/fwlink/?linkid=2197993.'}}"}
+        "remote::watsonx",  # return 404 when hitting the /openai/v1 endpoint
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.")
 
@@ -96,6 +96,8 @@ def skip_if_doesnt_support_n(client_with_models, model_id):
         "remote::vertexai",
         #  Error code: 400 - [{'error': {'code': 400, 'message': 'Unable to submit request because candidateCount must be 1 but
         #  the entered value was 2. Update the candidateCount value and try again.', 'status': 'INVALID_ARGUMENT'}
+        "remote::tgi",  # TGI ignores n param silently
+        "remote::together",  # `n` > 1 is not supported when streaming tokens. Please disable `stream`
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.")
 
@@ -110,7 +112,7 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode
         "remote::cerebras",
         "remote::databricks",
         "remote::runpod",
-        "remote::tgi",
+        "remote::watsonx",  # watsonx returns 404 when hitting the /openai/v1 endpoint
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI chat completions.")
 

tests/integration/inference/test_openai_embeddings.py

@@ -29,9 +29,35 @@ def provider_from_model(client_with_models, model_id):
     return providers[provider_id]
 
 
-def skip_if_model_doesnt_support_variable_dimensions(model_id):
-    if "text-embedding-3" not in model_id:
-        pytest.skip("{model_id} does not support variable output embedding dimensions")
+def skip_if_model_doesnt_support_user_param(client, model_id):
+    provider = provider_from_model(client, model_id)
+    if provider.provider_type in (
+        "remote::together",  # service returns 400
+    ):
+        pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support user param.")
+
+
+def skip_if_model_doesnt_support_encoding_format_base64(client, model_id):
+    provider = provider_from_model(client, model_id)
+    if provider.provider_type in (
+        "remote::together",  # param silently ignored, always returns floats
+    ):
+        pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support encoding_format='base64'.")
+
+
+def skip_if_model_doesnt_support_variable_dimensions(client_with_models, model_id):
+    provider = provider_from_model(client_with_models, model_id)
+    if provider.provider_type in (
+        "remote::together",  # returns 400
+        "inline::sentence-transformers",
+    ):
+        pytest.skip(
+            f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions."
+        )
+    if provider.provider_type == "remote::openai" and "text-embedding-3" not in model_id:
+        pytest.skip(
+            f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions."
+        )
 
 
 @pytest.fixture(params=["openai_client", "llama_stack_client"])
@@ -92,6 +118,7 @@ def test_openai_embeddings_multiple_strings(compat_client, client_with_models, e
     response = compat_client.embeddings.create(
         model=embedding_model_id,
         input=input_texts,
+        encoding_format="float",
     )
 
     assert response.object == "list"
@@ -127,7 +154,7 @@ def test_openai_embeddings_with_encoding_format_float(compat_client, client_with
 def test_openai_embeddings_with_dimensions(compat_client, client_with_models, embedding_model_id):
     """Test OpenAI embeddings endpoint with custom dimensions parameter."""
     skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
-    skip_if_model_doesnt_support_variable_dimensions(embedding_model_id)
+    skip_if_model_doesnt_support_variable_dimensions(client_with_models, embedding_model_id)
 
     input_text = "Test dimensions parameter"
     dimensions = 16
@@ -148,6 +175,7 @@ def test_openai_embeddings_with_dimensions(compat_client, client_with_models, em
 def test_openai_embeddings_with_user_parameter(compat_client, client_with_models, embedding_model_id):
     """Test OpenAI embeddings endpoint with user parameter."""
     skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
+    skip_if_model_doesnt_support_user_param(client_with_models, embedding_model_id)
 
     input_text = "Test user parameter"
     user_id = "test-user-123"
@@ -196,11 +224,13 @@ def test_openai_embeddings_different_inputs_different_outputs(compat_client, cli
     response1 = compat_client.embeddings.create(
         model=embedding_model_id,
         input=input_text1,
+        encoding_format="float",
     )
 
     response2 = compat_client.embeddings.create(
         model=embedding_model_id,
         input=input_text2,
+        encoding_format="float",
     )
 
     embedding1 = response1.data[0].embedding
@@ -214,7 +244,8 @@ def test_openai_embeddings_different_inputs_different_outputs(compat_client, cli
 def test_openai_embeddings_with_encoding_format_base64(compat_client, client_with_models, embedding_model_id):
     """Test OpenAI embeddings endpoint with base64 encoding format."""
     skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
-    skip_if_model_doesnt_support_variable_dimensions(embedding_model_id)
+    skip_if_model_doesnt_support_encoding_format_base64(client_with_models, embedding_model_id)
+    skip_if_model_doesnt_support_variable_dimensions(client_with_models, embedding_model_id)
 
     input_text = "Test base64 encoding format"
     dimensions = 12
@@ -247,6 +278,7 @@ def test_openai_embeddings_with_encoding_format_base64(compat_client, client_wit
 def test_openai_embeddings_base64_batch_processing(compat_client, client_with_models, embedding_model_id):
     """Test OpenAI embeddings endpoint with base64 encoding for batch processing."""
     skip_if_model_doesnt_support_openai_embeddings(client_with_models, embedding_model_id)
+    skip_if_model_doesnt_support_encoding_format_base64(client_with_models, embedding_model_id)
 
     input_texts = ["First text for base64", "Second text for base64", "Third text for base64"]
 

tests/integration/inference/test_text_inference.py

@@ -45,7 +45,7 @@ def skip_if_model_doesnt_support_json_schema_structured_output(client_with_model
     provider_id = models[model_id].provider_id
     providers = {p.provider_id: p for p in client_with_models.providers.list()}
     provider = providers[provider_id]
-    if provider.provider_type in ("remote::sambanova", "remote::azure"):
+    if provider.provider_type in ("remote::sambanova", "remote::azure", "remote::watsonx"):
         pytest.skip(
             f"Model {model_id} hosted by {provider.provider_type} doesn't support json_schema structured output"
         )
@@ -211,6 +211,7 @@ def test_text_completion_log_probs_streaming(client_with_models, text_model_id,
 )
 def test_text_completion_structured_output(client_with_models, text_model_id, test_case):
     skip_if_model_doesnt_support_completion(client_with_models, text_model_id)
+    skip_if_model_doesnt_support_json_schema_structured_output(client_with_models, text_model_id)
 
     class AnswerFormat(BaseModel):
         name: str

tests/integration/recordings/responses/07c5fa34d9ca.json

@@ -0,0 +1,800 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "togethercomputer/m2-bert-80M-32k-retrieval",
+      "input": "Test encoding format"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "togethercomputer/m2-bert-80M-32k-retrieval"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
+      "__data__": {
+        "data": [
+          {
+            "embedding": [
+              -0.011256923,
+              0.0037174695,
+              0.047607094,
+              -0.03605117,
+              0.022678856,
+              0.0022196341,
+              0.008172763,
+              -0.07876377,
+              -0.012652523,
+              -0.124776885,
+              -0.07201225,
+              0.011470616,
+              0.020233244,
+              -0.03953407,
+              0.017867543,
+              -0.07615726,
+              0.015161683,
+              0.01493531,
+              0.0021282644,
+              0.02805457,
+              0.0008320583,
+              0.022922216,
+              0.049158294,
+              -0.03197842,
+              0.020910429,
+              0.03798574,
+              0.032469492,
+              0.009267314,
+              0.0883011,
+              0.0032435523,
+              0.013633923,
+              0.0457091,
+              -0.022143621,
+              -0.0007423012,
+              -0.03613117,
+              0.052107,
+              0.02962152,
+              0.045084383,
+              0.044733327,
+              0.11753868,
+              0.05730107,
+              0.026509244,
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+              0.030301955,
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+              0.01714287,
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+              0.042824704,
+              0.04082185,
+              0.030668061,
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+              0.008288249,
+              0.0325246,
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tests/integration/recordings/responses/0c1f45455d3b.json

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tests/integration/recordings/responses/17030e75309f.json

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tests/integration/recordings/responses/27463384d1a3.json

@@ -0,0 +1,56 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8080/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "Qwen/Qwen3-0.6B",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Hello, world!"
+        }
+      ],
+      "stream": false
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "Qwen/Qwen3-0.6B"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "<think>\nOkay, the user just said \"Hello, world!\" so I need to respond in a friendly way. My prompt says to respond in the same style, so I should start with \"Hello, world!\" but maybe add some helpful information. Let me think. Since the user is probably testing or just sharing, a simple \"Hello, world!\" with a question would be best for user interaction. I'll make sure to keep it positive and open-ended.\n</think>\n\nHello, world! \ud83d\ude0a What do you need today?",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
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+              "tool_calls": null
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+        "model": "Qwen/Qwen3-0.6B",
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+        "system_fingerprint": "3.3.5-dev0-sha-1b90c50",
+        "usage": {
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+          "prompt_tokens": 12,
+          "total_tokens": 120,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/4ca6152a0eb8.json

@@ -0,0 +1,59 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Which planet has rings around it with a name starting with letter S?"
+        }
+      ],
+      "stream": false
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "oBUtaEp-62bZhn-9801a2718d0ed123",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "The planet with rings around it that starts with the letter S is Saturn. Saturn's ring system is one of the most prominent and well-known in our solar system.",
+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": []
+            },
+            "seed": 2387155844510162400
+          }
+        ],
+        "created": 1758039032,
+        "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
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+          "prompt_tokens": 49,
+          "total_tokens": 83,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null,
+          "cached_tokens": 0
+        },
+        "prompt": []
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/565b1072cb9d.json

@@ -0,0 +1,46 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/completions",
+    "headers": {},
+    "body": {
+      "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+      "prompt": "Respond to this question and explain your answer. Complete the sentence using one word: Roses are red, violets are ",
+      "stream": false,
+      "extra_body": {}
+    },
+    "endpoint": "/v1/completions",
+    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.completion.Completion",
+      "__data__": {
+        "id": "oBUswCe-62bZhn-98019f663cac0f68",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "text": " _______________________. \n\n## Step 1: Identify the traditional completion of the sentence.\nThe traditional completion of the sentence \"Roses are red, violets are...\" is based on a well-known poem.\n\n## Step 2: Recall the poem.\nThe poem states, \"Roses are red, violets are blue...\"\n\n## Step 3: Determine the word that completes the sentence.\nBased on the poem, the word that completes the sentence is \"blue\".\n\nThe final answer is: $\\boxed{blue}$",
+            "seed": 4892505926413923000
+          }
+        ],
+        "created": 1758038908,
+        "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+        "object": "text.completion",
+        "system_fingerprint": null,
+        "usage": {
+          "completion_tokens": 106,
+          "prompt_tokens": 25,
+          "total_tokens": 131,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null,
+          "cached_tokens": 0
+        },
+        "prompt": []
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/6730dcde0b73.json

@@ -0,0 +1,756 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Hello, world!"
+        }
+      ],
+      "stream": true
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
+  },
+  "response": {
+    "body": [
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "oBUtTzC-62bZhn-9801a1ee1bea25d8",
+          "choices": [
+            {
+              "delta": {
+                "content": "Hello",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null,
+                "token_id": 9906
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null,
+              "text": "Hello",
+              "seed": null
+            }
+          ],
+          "created": 1758039011,
+          "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": null,
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "oBUtTzC-62bZhn-9801a1ee1bea25d8",
+          "choices": [
+            {
+              "delta": {
+                "content": "!",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
+                "tool_calls": null,
+                "token_id": null
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null,
+              "text": "!",
+              "seed": null
+            }
+          ],
+          "created": 1758039011,
+          "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+          "object": "chat.completion.chunk",
+          "service_tier": null,
+          "system_fingerprint": null,
+          "usage": null
+        }
+      },
+      {
+        "__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
+        "__data__": {
+          "id": "oBUtTzC-62bZhn-9801a1ee1bea25d8",
+          "choices": [
+            {
+              "delta": {
+                "content": " It",
+                "function_call": null,
+                "refusal": null,
+                "role": "assistant",
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+                "token_id": 1102
+              },
+              "finish_reason": null,
+              "index": 0,
+              "logprobs": null,
+              "text": " It",
+              "seed": null
+            }
+          ],
+          "created": 1758039011,
+          "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
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tests/integration/recordings/responses/6857b19d3f0a.json

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+}

tests/integration/recordings/responses/6c4e2e207e8a.json

@@ -0,0 +1,59 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Which planet do humans live on?"
+        }
+      ],
+      "stream": false
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "oBUtMpf-62bZhn-9801a16bc8d642d3",
+        "choices": [
+          {
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+            "index": 0,
+            "logprobs": null,
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+              "refusal": null,
+              "role": "assistant",
+              "annotations": null,
+              "audio": null,
+              "function_call": null,
+              "tool_calls": []
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+        "created": 1758038990,
+        "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+        "object": "chat.completion",
+        "service_tier": null,
+        "system_fingerprint": null,
+        "usage": {
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+          "prompt_tokens": 42,
+          "total_tokens": 48,
+          "completion_tokens_details": null,
+          "prompt_tokens_details": null,
+          "cached_tokens": 0
+        },
+        "prompt": []
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/72e075bf28e8.json

@@ -0,0 +1,800 @@
+{
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+    "url": "https://api.together.xyz/v1/v1/embeddings",
+    "headers": {},
+    "body": {
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+      "input": "Hello, world!"
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+    "model": "togethercomputer/m2-bert-80M-32k-retrieval"
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tests/integration/recordings/responses/7ef63231b9f8.json

@@ -0,0 +1,56 @@
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+}

tests/integration/recordings/responses/894fdacb1cfa.json

@@ -0,0 +1,176 @@
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tests/integration/recordings/responses/a98eecadddc8.json

@@ -0,0 +1,366 @@
+{
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+    "headers": {},
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+}

tests/integration/recordings/responses/b9f6e724ae06.json

@@ -0,0 +1,976 @@
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+    "body": {
+      "model": "Qwen/Qwen3-0.6B",
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tests/integration/recordings/responses/cf55f983d1ff.json

@@ -0,0 +1,84 @@
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+}

tests/integration/recordings/responses/d85689907fec.json

@@ -0,0 +1,350 @@
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+  }
+}

tests/integration/recordings/responses/e08e01e5652a.json

@@ -0,0 +1,56 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://localhost:8080/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "Qwen/Qwen3-0.6B",
+      "messages": [
+        {
+          "role": "user",
+          "content": "Which planet do humans live on?"
+        }
+      ],
+      "stream": false
+    },
+    "endpoint": "/v1/chat/completions",
+    "model": "Qwen/Qwen3-0.6B"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.chat.chat_completion.ChatCompletion",
+      "__data__": {
+        "id": "",
+        "choices": [
+          {
+            "finish_reason": "stop",
+            "index": 0,
+            "logprobs": null,
+            "message": {
+              "content": "<think>\nOkay, the user is asking which planet humans live on. I need to make sure I answer this accurately. First, I should recall what I know about our solar system. The Earth is our home, and it's in our solar system. There are eight planets in total, right? Let me check that. Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and Neptune. Yep, that's the list.\n\nBut wait, the user might be confusing Earth with Mars. I should clarify that Earth is the only planet known to support life. The other planets are mostly gas giants and have no liquid water, so they don't support life as Earth does. So the answer should be Earth. I should also mention that although there are other planets, none have liquid water, which makes the answer more complete.\n\nI need to make sure there are no alternatives. Maybe some people might think Mars, but I know that's not the case. Also, it's good to mention that life on Earth is closely linked to the presence of water, which is why Earth is our only planet with that characteristic. That way, the answer is not only accurate but also informative.\n</think>\n\nHumans live on **Earth**, the planet that supports life as we know it. The Earth is the only known planet in our solar system where liquid water exists and where life can occur. Other planets are considered \"gas giants\" or \"ice giants\" due to their extreme conditions and lack of liquid water, making them inhospitable for life.",
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+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/f0bbea34c5cc.json

@@ -0,0 +1,611 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "https://api.together.xyz/v1/v1/chat/completions",
+    "headers": {},
+    "body": {
+      "model": "meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
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+}

tests/integration/scoring/test_scoring.py

@@ -9,6 +9,7 @@ from pathlib import Path
 
 import pandas as pd
 import pytest
+import requests
 
 
 @pytest.fixture
@@ -77,7 +78,46 @@ def test_scoring_functions_register(
     assert len(list_response) > 0
     assert any(x.identifier == sample_scoring_fn_id for x in list_response)
 
-    # TODO: add unregister api for scoring functions
+
+def test_scoring_functions_unregister(
+    llama_stack_client,
+    sample_scoring_fn_id,
+    judge_model_id,
+    sample_judge_prompt_template,
+):
+    llm_as_judge_provider = [
+        x
+        for x in llama_stack_client.providers.list()
+        if x.api == "scoring" and x.provider_type == "inline::llm-as-judge"
+    ]
+    if len(llm_as_judge_provider) == 0:
+        pytest.skip("No llm-as-judge provider found, cannot test unregister")
+
+    llm_as_judge_provider_id = llm_as_judge_provider[0].provider_id
+
+    # Register first
+    register_scoring_function(
+        llama_stack_client,
+        llm_as_judge_provider_id,
+        sample_scoring_fn_id,
+        judge_model_id,
+        sample_judge_prompt_template,
+    )
+
+    # Ensure it is present
+    list_response = llama_stack_client.scoring_functions.list()
+    assert any(x.identifier == sample_scoring_fn_id for x in list_response)
+
+    # Unregister scoring fn
+    try:
+        base_url = llama_stack_client.base_url
+    except AttributeError:
+        pytest.skip("No server base_url available; cannot test HTTP unregister in library mode")
+
+    resp = requests.delete(f"{base_url}/v1/scoring-functions/{sample_scoring_fn_id}", timeout=30)
+    assert resp.status_code in (200, 204)
+    list_after = llama_stack_client.scoring_functions.list()
+    assert all(x.identifier != sample_scoring_fn_id for x in list_after)
 
 
 @pytest.mark.parametrize("scoring_fn_id", ["basic::equality"])

tests/integration/suites.py

@@ -90,6 +90,24 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
             "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
         },
     ),
+    "tgi": Setup(
+        name="tgi",
+        description="Text Generation Inference (TGI) provider with a text model",
+        env={
+            "TGI_URL": "http://localhost:8080",
+        },
+        defaults={
+            "text_model": "tgi/Qwen/Qwen3-0.6B",
+        },
+    ),
+    "together": Setup(
+        name="together",
+        description="Together computer models",
+        defaults={
+            "text_model": "together/meta-llama/Llama-3.3-70B-Instruct-Turbo-Free",
+            "embedding_model": "together/togethercomputer/m2-bert-80M-32k-retrieval",
+        },
+    ),
 }
 
 

tests/unit/distribution/routers/test_routing_tables.py

@@ -105,6 +105,9 @@ class ScoringFunctionsImpl(Impl):
     async def register_scoring_function(self, scoring_fn):
         return scoring_fn
 
+    async def unregister_scoring_function(self, scoring_fn_id: str):
+        return scoring_fn_id
+
 
 class BenchmarksImpl(Impl):
     def __init__(self):
@@ -113,6 +116,9 @@ class BenchmarksImpl(Impl):
     async def register_benchmark(self, benchmark):
         return benchmark
 
+    async def unregister_benchmark(self, benchmark_id: str):
+        return benchmark_id
+
 
 class ToolGroupsImpl(Impl):
     def __init__(self):
@@ -330,6 +336,13 @@ async def test_scoring_functions_routing_table(cached_disk_dist_registry):
     assert "test-scoring-fn" in scoring_fn_ids
     assert "test-scoring-fn-2" in scoring_fn_ids
 
+    # Unregister scoring functions and verify listing
+    for i in range(len(scoring_functions.data)):
+        await table.unregister_scoring_function(scoring_functions.data[i].scoring_fn_id)
+
+    scoring_functions_list_after_deletion = await table.list_scoring_functions()
+    assert len(scoring_functions_list_after_deletion.data) == 0
+
 
 async def test_benchmarks_routing_table(cached_disk_dist_registry):
     table = BenchmarksRoutingTable({"test_provider": BenchmarksImpl()}, cached_disk_dist_registry, {})
@@ -347,6 +360,15 @@ async def test_benchmarks_routing_table(cached_disk_dist_registry):
     benchmark_ids = {b.identifier for b in benchmarks.data}
     assert "test-benchmark" in benchmark_ids
 
+    # Unregister the benchmark and verify removal
+    await table.unregister_benchmark(benchmark_id="test-benchmark")
+    benchmarks_after = await table.list_benchmarks()
+    assert len(benchmarks_after.data) == 0
+
+    # Unregistering a non-existent benchmark should raise a clear error
+    with pytest.raises(ValueError, match="Benchmark 'dummy_benchmark' not found"):
+        await table.unregister_benchmark(benchmark_id="dummy_benchmark")
+
 
 async def test_tool_groups_routing_table(cached_disk_dist_registry):
     table = ToolGroupsRoutingTable({"test_provider": ToolGroupsImpl()}, cached_disk_dist_registry, {})

tests/unit/distribution/test_inference_recordings.py

@@ -155,16 +155,10 @@ class TestInferenceRecording:
 
     async def test_recording_mode(self, temp_storage_dir, real_openai_chat_response):
         """Test that recording mode captures and stores responses."""
-
-        async def mock_create(*args, **kwargs):
-            return real_openai_chat_response
-
         temp_storage_dir = temp_storage_dir / "test_recording_mode"
-        with patch(
-            "openai.resources.chat.completions.AsyncCompletions.create", new_callable=AsyncMock, side_effect=mock_create
-        ):
         with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)):
             client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
+            client.chat.completions._post = AsyncMock(return_value=real_openai_chat_response)
 
             response = await client.chat.completions.create(
                 model="llama3.2:3b",
@@ -176,6 +170,7 @@ class TestInferenceRecording:
 
             # Verify the response was returned correctly
             assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking."
+            client.chat.completions._post.assert_called_once()
 
         # Verify recording was stored
         storage = ResponseStorage(temp_storage_dir)
@@ -183,17 +178,11 @@ class TestInferenceRecording:
 
     async def test_replay_mode(self, temp_storage_dir, real_openai_chat_response):
         """Test that replay mode returns stored responses without making real calls."""
-
-        async def mock_create(*args, **kwargs):
-            return real_openai_chat_response
-
         temp_storage_dir = temp_storage_dir / "test_replay_mode"
         # First, record a response
-        with patch(
-            "openai.resources.chat.completions.AsyncCompletions.create", new_callable=AsyncMock, side_effect=mock_create
-        ):
         with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)):
             client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
+            client.chat.completions._post = AsyncMock(return_value=real_openai_chat_response)
 
             response = await client.chat.completions.create(
                 model="llama3.2:3b",
@@ -202,11 +191,12 @@ class TestInferenceRecording:
                 max_tokens=50,
                 user=NOT_GIVEN,
             )
+            client.chat.completions._post.assert_called_once()
 
         # Now test replay mode - should not call the original method
-        with patch("openai.resources.chat.completions.AsyncCompletions.create") as mock_create_patch:
         with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)):
             client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
+            client.chat.completions._post = AsyncMock(return_value=real_openai_chat_response)
 
             response = await client.chat.completions.create(
                 model="llama3.2:3b",
@@ -219,7 +209,7 @@ class TestInferenceRecording:
             assert response.choices[0].message.content == "Hello! I'm doing well, thank you for asking."
 
             # Verify the original method was NOT called
-                mock_create_patch.assert_not_called()
+            client.chat.completions._post.assert_not_called()
 
     async def test_replay_mode_models(self, temp_storage_dir):
         """Test that replay mode returns stored responses without making real model listing calls."""
@@ -272,16 +262,23 @@ class TestInferenceRecording:
     async def test_embeddings_recording(self, temp_storage_dir, real_embeddings_response):
         """Test recording and replay of embeddings calls."""
 
-        async def mock_create(*args, **kwargs):
-            return real_embeddings_response
+        # baseline - mock works without recording
+        client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
+        client.embeddings._post = AsyncMock(return_value=real_embeddings_response)
+        response = await client.embeddings.create(
+            model=real_embeddings_response.model,
+            input=["Hello world", "Test embedding"],
+            encoding_format=NOT_GIVEN,
+        )
+        assert len(response.data) == 2
+        assert response.data[0].embedding == [0.1, 0.2, 0.3]
+        client.embeddings._post.assert_called_once()
 
         temp_storage_dir = temp_storage_dir / "test_embeddings_recording"
         # Record
-        with patch(
-            "openai.resources.embeddings.AsyncEmbeddings.create", new_callable=AsyncMock, side_effect=mock_create
-        ):
         with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)):
             client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
+            client.embeddings._post = AsyncMock(return_value=real_embeddings_response)
 
             response = await client.embeddings.create(
                 model=real_embeddings_response.model,
@@ -294,9 +291,9 @@ class TestInferenceRecording:
             assert len(response.data) == 2
 
         # Replay
-        with patch("openai.resources.embeddings.AsyncEmbeddings.create") as mock_create_patch:
         with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)):
             client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
+            client.embeddings._post = AsyncMock(return_value=real_embeddings_response)
 
             response = await client.embeddings.create(
                 model=real_embeddings_response.model,
@@ -308,7 +305,7 @@ class TestInferenceRecording:
             assert response.data[0].embedding == [0.1, 0.2, 0.3]
 
             # Verify original method was not called
-                mock_create_patch.assert_not_called()
+            client.embeddings._post.assert_not_called()
 
     async def test_completions_recording(self, temp_storage_dir):
         real_completions_response = OpenAICompletion(
@@ -326,17 +323,25 @@ class TestInferenceRecording:
             ],
         )
 
-        async def mock_create(*args, **kwargs):
-            return real_completions_response
-
         temp_storage_dir = temp_storage_dir / "test_completions_recording"
 
+        # baseline - mock works without recording
+        client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
+        client.completions._post = AsyncMock(return_value=real_completions_response)
+        response = await client.completions.create(
+            model=real_completions_response.model,
+            prompt="Hello, how are you?",
+            temperature=0.7,
+            max_tokens=50,
+            user=NOT_GIVEN,
+        )
+        assert response.choices[0].text == real_completions_response.choices[0].text
+        client.completions._post.assert_called_once()
+
         # Record
-        with patch(
-            "openai.resources.completions.AsyncCompletions.create", new_callable=AsyncMock, side_effect=mock_create
-        ):
         with inference_recording(mode=InferenceMode.RECORD, storage_dir=str(temp_storage_dir)):
             client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
+            client.completions._post = AsyncMock(return_value=real_completions_response)
 
             response = await client.completions.create(
                 model=real_completions_response.model,
@@ -347,11 +352,12 @@ class TestInferenceRecording:
             )
 
             assert response.choices[0].text == real_completions_response.choices[0].text
+            client.completions._post.assert_called_once()
 
         # Replay
-        with patch("openai.resources.completions.AsyncCompletions.create") as mock_create_patch:
         with inference_recording(mode=InferenceMode.REPLAY, storage_dir=str(temp_storage_dir)):
             client = AsyncOpenAI(base_url="http://localhost:11434/v1", api_key="test")
+            client.completions._post = AsyncMock(return_value=real_completions_response)
             response = await client.completions.create(
                 model=real_completions_response.model,
                 prompt="Hello, how are you?",
@@ -359,7 +365,7 @@ class TestInferenceRecording:
                 max_tokens=50,
             )
             assert response.choices[0].text == real_completions_response.choices[0].text
-                mock_create_patch.assert_not_called()
+            client.completions._post.assert_not_called()
 
     async def test_live_mode(self, real_openai_chat_response):
         """Test that live mode passes through to original methods."""

tests/unit/providers/nvidia/test_eval.py

@@ -52,14 +52,19 @@ class TestNVIDIAEvalImpl(unittest.TestCase):
         self.evaluator_post_patcher = patch(
             "llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_post"
         )
+        self.evaluator_delete_patcher = patch(
+            "llama_stack.providers.remote.eval.nvidia.eval.NVIDIAEvalImpl._evaluator_delete"
+        )
 
         self.mock_evaluator_get = self.evaluator_get_patcher.start()
         self.mock_evaluator_post = self.evaluator_post_patcher.start()
+        self.mock_evaluator_delete = self.evaluator_delete_patcher.start()
 
     def tearDown(self):
         """Clean up after each test."""
         self.evaluator_get_patcher.stop()
         self.evaluator_post_patcher.stop()
+        self.evaluator_delete_patcher.stop()
 
     def _assert_request_body(self, expected_json):
         """Helper method to verify request body in Evaluator POST request is correct"""
@@ -115,6 +120,13 @@ class TestNVIDIAEvalImpl(unittest.TestCase):
         self.mock_evaluator_post.assert_called_once()
         self._assert_request_body({"namespace": benchmark.provider_id, "name": benchmark.identifier, **eval_config})
 
+    def test_unregister_benchmark(self):
+        # Unregister the benchmark
+        self.run_async(self.eval_impl.unregister_benchmark(benchmark_id=MOCK_BENCHMARK_ID))
+
+        # Verify the Evaluator API was called correctly
+        self.mock_evaluator_delete.assert_called_once_with(f"/v1/evaluation/configs/nvidia/{MOCK_BENCHMARK_ID}")
+
     def test_run_eval(self):
         benchmark_config = BenchmarkConfig(
             eval_candidate=ModelCandidate(

uv.lock

@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
     "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
@@ -1749,7 +1749,7 @@ wheels = [
 
 [[package]]
 name = "llama-stack"
-version = "0.2.21"
+version = "0.2.22"
 source = { editable = "." }
 dependencies = [
     { name = "aiohttp" },
@@ -1885,15 +1885,15 @@ requires-dist = [
     { name = "huggingface-hub", specifier = ">=0.34.0,<1.0" },
     { name = "jinja2", specifier = ">=3.1.6" },
     { name = "jsonschema" },
-    { name = "llama-stack-client", specifier = ">=0.2.21" },
-    { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.21" },
+    { name = "llama-stack-client", specifier = ">=0.2.22" },
+    { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.22" },
     { name = "openai", specifier = ">=1.100.0" },
     { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" },
     { name = "opentelemetry-sdk", specifier = ">=1.30.0" },
     { name = "pandas", marker = "extra == 'ui'" },
     { name = "pillow" },
     { name = "prompt-toolkit" },
-    { name = "pydantic", specifier = ">=2" },
+    { name = "pydantic", specifier = ">=2.11.9" },
     { name = "python-dotenv" },
     { name = "python-jose", extras = ["cryptography"] },
     { name = "python-multipart", specifier = ">=0.0.20" },
@@ -1911,7 +1911,7 @@ provides-extras = ["ui"]
 benchmark = [{ name = "locust", specifier = ">=2.39.1" }]
 codegen = [
     { name = "jinja2", specifier = ">=3.1.6" },
-    { name = "pydantic" },
+    { name = "pydantic", specifier = ">=2.11.9" },
     { name = "rich" },
 ]
 dev = [
@@ -1993,7 +1993,7 @@ unit = [
 
 [[package]]
 name = "llama-stack-client"
-version = "0.2.21"
+version = "0.2.22"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -2012,9 +2012,9 @@ dependencies = [
     { name = "tqdm" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f1/d3/8c50561d167f1e9b601b8fffe852b44c1ff97aaa6db6cdedd611d9e02a65/llama_stack_client-0.2.21.tar.gz", hash = "sha256:bd931fdcadedec5ccdbaa3c54d0c17761af1c227711ad6150dc0dd33d7b66ce2", size = 318319, upload-time = "2025-09-08T22:26:57.668Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/60/80/4260816bfaaa889d515206c9df4906d08d405bf94c9b4d1be399b1923e46/llama_stack_client-0.2.22.tar.gz", hash = "sha256:9a0bc756b91ebd539858eeaf1f231c5e5c6900e1ea4fcced726c6717f3d27ca7", size = 318309, upload-time = "2025-09-16T19:43:33.212Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/02/77/dadc682046a2c7ad68be8d2d2afac7007bf4d22efb0d3929d85ab9706ffe/llama_stack_client-0.2.21-py3-none-any.whl", hash = "sha256:adba82fdf18ab3b8ac218cedba4927bd5d26c23c2318e75c8763a44bb6b40693", size = 369902, upload-time = "2025-09-08T22:26:56.308Z" },
+    { url = "https://files.pythonhosted.org/packages/d1/8e/1ebf6ac0dbb62b81038e856ed00768e283d927b14fcd614e3018a227092b/llama_stack_client-0.2.22-py3-none-any.whl", hash = "sha256:b260d73aec56fcfd8fa601b3b34c2f83c4fbcfb7261a246b02bbdf6c2da184fe", size = 369901, upload-time = "2025-09-16T19:43:32.089Z" },
 ]
 
 [[package]]
@@ -3393,7 +3393,7 @@ wheels = [
 
 [[package]]
 name = "pydantic"
-version = "2.11.7"
+version = "2.11.9"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "annotated-types" },
@@ -3401,9 +3401,9 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "typing-inspection" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/00/dd/4325abf92c39ba8623b5af936ddb36ffcfe0beae70405d456ab1fb2f5b8c/pydantic-2.11.7.tar.gz", hash = "sha256:d989c3c6cb79469287b1569f7447a17848c998458d49ebe294e975b9baf0f0db", size = 788350, upload-time = "2025-06-14T08:33:17.137Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/ff/5d/09a551ba512d7ca404d785072700d3f6727a02f6f3c24ecfd081c7cf0aa8/pydantic-2.11.9.tar.gz", hash = "sha256:6b8ffda597a14812a7975c90b82a8a2e777d9257aba3453f973acd3c032a18e2", size = 788495, upload-time = "2025-09-13T11:26:39.325Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/6a/c0/ec2b1c8712ca690e5d61979dee872603e92b8a32f94cc1b72d53beab008a/pydantic-2.11.7-py3-none-any.whl", hash = "sha256:dde5df002701f6de26248661f6835bbe296a47bf73990135c7d07ce741b9623b", size = 444782, upload-time = "2025-06-14T08:33:14.905Z" },
+    { url = "https://files.pythonhosted.org/packages/3e/d3/108f2006987c58e76691d5ae5d200dd3e0f532cb4e5fa3560751c3a1feba/pydantic-2.11.9-py3-none-any.whl", hash = "sha256:c42dd626f5cfc1c6950ce6205ea58c93efa406da65f479dcb4029d5934857da2", size = 444855, upload-time = "2025-09-13T11:26:36.909Z" },
 ]
 
 [[package]]