Merge branch 'main' into max_infer_iters

docs/getting_started.ipynb

@@ -1145,6 +1145,7 @@
         }
       ],
       "source": [
+        "# NBVAL_SKIP\n",
         "from pydantic import BaseModel\n",
         "\n",
         "\n",
@@ -2885,7 +2886,6 @@
         }
       ],
       "source": [
-        "# NBVAL_SKIP\n",
         "from llama_stack_client.lib.agents.agent import Agent\n",
         "from llama_stack_client.lib.agents.event_logger import EventLogger\n",
         "from llama_stack_client.types.agent_create_params import AgentConfig\n",
@@ -4326,7 +4326,7 @@
       "provenance": []
     },
     "kernelspec": {
-      "display_name": "toolchain",
+      "display_name": "master",
       "language": "python",
       "name": "python3"
     },

docs/openapi_generator/generate.py

@@ -55,6 +55,7 @@ def main(output_dir: str):
                 a set of endpoints and their corresponding interfaces that are tailored to
                 best leverage Llama Models.""",
             ),
+            include_standard_error_responses=True,
         ),
     )
 

docs/openapi_generator/pyopenapi/generator.py

@@ -10,6 +10,7 @@ import typing
 from dataclasses import make_dataclass
 from typing import Any, Dict, Set, Union
 
+from llama_stack.apis.datatypes import Error
 from llama_stack.strong_typing.core import JsonType
 from llama_stack.strong_typing.docstring import Docstring, parse_type
 from llama_stack.strong_typing.inspection import (
@@ -435,6 +436,75 @@ class Generator:
         self.schema_builder = SchemaBuilder(schema_generator)
         self.responses = {}
         
+        # Create standard error responses
+        self._create_standard_error_responses()
+
+    def _create_standard_error_responses(self) -> None:
+        """
+        Creates standard error responses that can be reused across operations.
+        These will be added to the components.responses section of the OpenAPI document.
+        """
+        # Get the Error schema
+        error_schema = self.schema_builder.classdef_to_ref(Error)
+        
+        # Create standard error responses
+        self.responses["BadRequest400"] = Response(
+            description="The request was invalid or malformed",
+            content={
+                "application/json": MediaType(
+                    schema=error_schema,
+                    example={
+                        "status": 400,
+                        "title": "Bad Request",
+                        "detail": "The request was invalid or malformed",
+                    }
+                )
+            }
+        )
+        
+        self.responses["TooManyRequests429"] = Response(
+            description="The client has sent too many requests in a given amount of time",
+            content={
+                "application/json": MediaType(
+                    schema=error_schema,
+                    example={
+                        "status": 429,
+                        "title": "Too Many Requests",
+                        "detail": "You have exceeded the rate limit. Please try again later.",
+                    }
+                )
+            }
+        )
+        
+        self.responses["InternalServerError500"] = Response(
+            description="The server encountered an unexpected error",
+            content={
+                "application/json": MediaType(
+                    schema=error_schema,
+                    example={
+                        "status": 500,
+                        "title": "Internal Server Error",
+                        "detail": "An unexpected error occurred. Our team has been notified.",
+                    }
+                )
+            }
+        )
+        
+        # Add a default error response for any unhandled error cases
+        self.responses["DefaultError"] = Response(
+            description="An unexpected error occurred",
+            content={
+                "application/json": MediaType(
+                    schema=error_schema,
+                    example={
+                        "status": 0,
+                        "title": "Error",
+                        "detail": "An unexpected error occurred",
+                    }
+                )
+            }
+        )
+
     def _build_type_tag(self, ref: str, schema: Schema) -> Tag:
         # Don't include schema definition in the tag description because for one,
         # it is not very valuable and for another, it causes string formatting
@@ -649,6 +719,18 @@ class Generator:
             responses.update(response_builder.build_response(response_options))
 
         assert len(responses.keys()) > 0, f"No responses found for {op.name}"
+        
+        # Add standard error response references
+        if self.options.include_standard_error_responses:
+            if "400" not in responses:
+                responses["400"] = ResponseRef("BadRequest400")
+            if "429" not in responses:
+                responses["429"] = ResponseRef("TooManyRequests429")
+            if "500" not in responses:
+                responses["500"] = ResponseRef("InternalServerError500")
+            if "default" not in responses:
+                responses["default"] = ResponseRef("DefaultError")
+        
         if op.event_type is not None:
             builder = ContentBuilder(self.schema_builder)
             callbacks = {

docs/openapi_generator/pyopenapi/options.py

@@ -35,6 +35,7 @@ class Options:
     :param error_wrapper: True if errors are encapsulated in an error object wrapper.
     :param property_description_fun: Custom transformation function to apply to class property documentation strings.
     :param captions: User-defined captions for sections such as "Operations" or "Types", and (if applicable) groups of extra types.
+    :param include_standard_error_responses: Whether to include standard error responses (400, 429, 500, 503) in all operations.
     """
 
     server: Server
@@ -52,6 +53,7 @@ class Options:
     error_wrapper: bool = False
     property_description_fun: Optional[Callable[[type, str, str], str]] = None
     captions: Optional[Dict[str, str]] = None
+    include_standard_error_responses: bool = True
 
     default_captions: ClassVar[Dict[str, str]] = {
         "Operations": "Operations",

docs/source/distributions/building_distro.md

@@ -106,7 +106,7 @@ It would be best to start with a template and understand the structure of the co
 llama stack build
 
 > Enter a name for your Llama Stack (e.g. my-local-stack): my-stack
-> Enter the image type you want your Llama Stack to be built as (container or conda): conda
+> Enter the image type you want your Llama Stack to be built as (container or conda or venv): conda
 
 Llama Stack is composed of several APIs working together. Let's select
 the provider types (implementations) you want to use for these APIs.
@@ -187,7 +187,7 @@ usage: llama stack run [-h] [--port PORT] [--image-name IMAGE_NAME] [--disable-i
                        [--tls-certfile TLS_CERTFILE] [--image-type {conda,container,venv}]
                        config
 
-start the server for a Llama Stack Distribution. You should have already built (or downloaded) and configured the distribution.
+Start the server for a Llama Stack Distribution. You should have already built (or downloaded) and configured the distribution.
 
 positional arguments:
   config                Path to config file to use for the run

docs/source/distributions/self_hosted_distro/meta-reference-gpu.md

@@ -41,12 +41,31 @@ The following environment variables can be configured:
 
 ## Prerequisite: Downloading Models
 
-Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
+Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
 
 ```
-$ ls ~/.llama/checkpoints
+$ llama model list --downloaded
-Llama3.1-8B           Llama3.2-11B-Vision-Instruct  Llama3.2-1B-Instruct  Llama3.2-90B-Vision-Instruct  Llama-Guard-3-8B
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
-Llama3.1-8B-Instruct  Llama3.2-1B                   Llama3.2-3B-Instruct  Llama-Guard-3-1B              Prompt-Guard-86M
+┃ Model                                   ┃ Size     ┃ Modified Time       ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
+│ Llama3.2-1B-Instruct:int4-qlora-eo8     │ 1.53 GB  │ 2025-02-26 11:22:28 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-1B                             │ 2.31 GB  │ 2025-02-18 21:48:52 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Prompt-Guard-86M                        │ 0.02 GB  │ 2025-02-26 11:29:28 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-3B-Instruct:int4-spinquant-eo8 │ 3.69 GB  │ 2025-02-26 11:37:41 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-3B                             │ 5.99 GB  │ 2025-02-18 21:51:26 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.1-8B                             │ 14.97 GB │ 2025-02-16 10:36:37 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-1B-Instruct:int4-spinquant-eo8 │ 1.51 GB  │ 2025-02-26 11:35:02 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama-Guard-3-1B                        │ 2.80 GB  │ 2025-02-26 11:20:46 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama-Guard-3-1B:int4                   │ 0.43 GB  │ 2025-02-26 11:33:33 │
+└─────────────────────────────────────────┴──────────┴─────────────────────┘
 ```
 
 ## Running the Distribution

docs/source/distributions/self_hosted_distro/meta-reference-quantized-gpu.md

@@ -41,12 +41,31 @@ The following environment variables can be configured:
 
 ## Prerequisite: Downloading Models
 
-Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
+Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
 
 ```
-$ ls ~/.llama/checkpoints
+$ llama model list --downloaded
-Llama3.1-8B           Llama3.2-11B-Vision-Instruct  Llama3.2-1B-Instruct  Llama3.2-90B-Vision-Instruct  Llama-Guard-3-8B
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
-Llama3.1-8B-Instruct  Llama3.2-1B                   Llama3.2-3B-Instruct  Llama-Guard-3-1B              Prompt-Guard-86M
+┃ Model                                   ┃ Size     ┃ Modified Time       ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
+│ Llama3.2-1B-Instruct:int4-qlora-eo8     │ 1.53 GB  │ 2025-02-26 11:22:28 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-1B                             │ 2.31 GB  │ 2025-02-18 21:48:52 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Prompt-Guard-86M                        │ 0.02 GB  │ 2025-02-26 11:29:28 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-3B-Instruct:int4-spinquant-eo8 │ 3.69 GB  │ 2025-02-26 11:37:41 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-3B                             │ 5.99 GB  │ 2025-02-18 21:51:26 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.1-8B                             │ 14.97 GB │ 2025-02-16 10:36:37 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-1B-Instruct:int4-spinquant-eo8 │ 1.51 GB  │ 2025-02-26 11:35:02 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama-Guard-3-1B                        │ 2.80 GB  │ 2025-02-26 11:20:46 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama-Guard-3-1B:int4                   │ 0.43 GB  │ 2025-02-26 11:33:33 │
+└─────────────────────────────────────────┴──────────┴─────────────────────┘
 ```
 
 ## Running the Distribution

docs/source/getting_started/index.md

@@ -38,7 +38,7 @@ The API is **exactly identical** for both clients.
 :::{dropdown} Starting up the Llama Stack server
 The Llama Stack server can be configured flexibly so you can mix-and-match various providers for its individual API components -- beyond Inference, these include Vector IO, Agents, Telemetry, Evals, Post Training, etc.
 
-To get started quickly, we provide various container images for the server component that work with different inference providers out of the box. For this guide, we will use `llamastack/distribution-ollama` as the container image.
+To get started quickly, we provide various container images for the server component that work with different inference providers out of the box. For this guide, we will use `llamastack/distribution-ollama` as the container image. If you'd like to build your own image or customize the configurations, please check out [this guide](../references/index.md).
 
 Lets setup some environment variables that we will use in the rest of the guide.
 ```bash

docs/source/references/llama_cli_reference/download_models.md

@@ -129,3 +129,35 @@ llama download --source huggingface --model-id Prompt-Guard-86M --ignore-pattern
 **Important:** Set your environment variable `HF_TOKEN` or pass in `--hf-token` to the command to validate your access. You can find your token at [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens).
 
 > **Tip:** Default for `llama download` is to run with `--ignore-patterns *.safetensors` since we use the `.pth` files in the `original` folder. For Llama Guard and Prompt Guard, however, we need safetensors. Hence, please run with `--ignore-patterns original` so that safetensors are downloaded and `.pth` files are ignored.
+
+## List the downloaded models
+
+To list the downloaded models with the following command:
+```
+llama model list --downloaded
+```
+
+You should see a table like this:
+```
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
+┃ Model                                   ┃ Size     ┃ Modified Time       ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
+│ Llama3.2-1B-Instruct:int4-qlora-eo8     │ 1.53 GB  │ 2025-02-26 11:22:28 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-1B                             │ 2.31 GB  │ 2025-02-18 21:48:52 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Prompt-Guard-86M                        │ 0.02 GB  │ 2025-02-26 11:29:28 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-3B-Instruct:int4-spinquant-eo8 │ 3.69 GB  │ 2025-02-26 11:37:41 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-3B                             │ 5.99 GB  │ 2025-02-18 21:51:26 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.1-8B                             │ 14.97 GB │ 2025-02-16 10:36:37 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-1B-Instruct:int4-spinquant-eo8 │ 1.51 GB  │ 2025-02-26 11:35:02 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama-Guard-3-1B                        │ 2.80 GB  │ 2025-02-26 11:20:46 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama-Guard-3-1B:int4                   │ 0.43 GB  │ 2025-02-26 11:33:33 │
+└─────────────────────────────────────────┴──────────┴─────────────────────┘
+```

docs/source/references/llama_cli_reference/index.md

@@ -154,6 +154,38 @@ llama download --source huggingface --model-id Prompt-Guard-86M --ignore-pattern
 
 > **Tip:** Default for `llama download` is to run with `--ignore-patterns *.safetensors` since we use the `.pth` files in the `original` folder. For Llama Guard and Prompt Guard, however, we need safetensors. Hence, please run with `--ignore-patterns original` so that safetensors are downloaded and `.pth` files are ignored.
 
+## List the downloaded models
+
+To list the downloaded models with the following command:
+```
+llama model list --downloaded
+```
+
+You should see a table like this:
+```
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
+┃ Model                                   ┃ Size     ┃ Modified Time       ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
+│ Llama3.2-1B-Instruct:int4-qlora-eo8     │ 1.53 GB  │ 2025-02-26 11:22:28 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-1B                             │ 2.31 GB  │ 2025-02-18 21:48:52 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Prompt-Guard-86M                        │ 0.02 GB  │ 2025-02-26 11:29:28 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-3B-Instruct:int4-spinquant-eo8 │ 3.69 GB  │ 2025-02-26 11:37:41 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-3B                             │ 5.99 GB  │ 2025-02-18 21:51:26 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.1-8B                             │ 14.97 GB │ 2025-02-16 10:36:37 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-1B-Instruct:int4-spinquant-eo8 │ 1.51 GB  │ 2025-02-26 11:35:02 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama-Guard-3-1B                        │ 2.80 GB  │ 2025-02-26 11:20:46 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama-Guard-3-1B:int4                   │ 0.43 GB  │ 2025-02-26 11:33:33 │
+└─────────────────────────────────────────┴──────────┴─────────────────────┘
+```
+
 
 ## Understand the models
 The `llama model` command helps you explore the model’s interface.

llama_stack/apis/datatypes.py

@@ -5,6 +5,9 @@
 # the root directory of this source tree.
 
 from enum import Enum
+from typing import Optional
+
+from pydantic import BaseModel
 
 from llama_stack.schema_utils import json_schema_type
 
@@ -33,3 +36,20 @@ class Api(Enum):
 
     # built-in API
     inspect = "inspect"
+
+
+@json_schema_type
+class Error(BaseModel):
+    """
+    Error response from the API. Roughly follows RFC 7807.
+
+    :param status: HTTP status code
+    :param title: Error title, a short summary of the error which is invariant for an error type
+    :param detail: Error detail, a longer human-readable description of the error
+    :param instance: (Optional) A URL which can be used to retrieve more information about the specific occurrence of the error
+    """
+
+    status: int
+    title: str
+    detail: str
+    instance: Optional[str] = None

llama_stack/cli/model/prompt_format.py

@@ -9,6 +9,7 @@ import textwrap
 from io import StringIO
 
 from llama_stack.cli.subcommand import Subcommand
+from llama_stack.cli.table import print_table
 from llama_stack.models.llama.datatypes import CoreModelId, ModelFamily, is_multimodal, model_family
 
 
@@ -48,7 +49,26 @@ class ModelPromptFormat(Subcommand):
         supported_model_ids = [
             m for m in CoreModelId if model_family(m) in {ModelFamily.llama3_1, ModelFamily.llama3_2}
         ]
-        model_str = "\n".join([m.value for m in supported_model_ids])
+
+        model_list = [m.value for m in supported_model_ids]
+        model_str = "\n".join(model_list)
+
+        if args.list:
+            headers = ["Model(s)"]
+            rows = []
+            for m in model_list:
+                rows.append(
+                    [
+                        m,
+                    ]
+                )
+            print_table(
+                rows,
+                headers,
+                separate_rows=True,
+            )
+            return
+
         try:
             model_id = CoreModelId(args.model_name)
         except ValueError:

llama_stack/cli/stack/_build.py

@@ -141,7 +141,7 @@ def run_stack_build_command(args: argparse.Namespace) -> None:
                 completer=WordCompleter(available_providers),
                 complete_while_typing=True,
                 validator=Validator.from_callable(
-                    lambda x: x in available_providers,
+                    lambda x: x in available_providers,  # noqa: B023 - see https://github.com/astral-sh/ruff/issues/7847
                     error_message="Invalid provider, use <TAB> to see options",
                 ),
             )

llama_stack/cli/tests/test_stack_config.py

@@ -112,7 +112,7 @@ def test_parse_and_maybe_upgrade_config_old_format(old_config):
 
     inference_providers = result.providers["inference"]
     assert len(inference_providers) == 2
-    assert set(x.provider_id for x in inference_providers) == {
+    assert {x.provider_id for x in inference_providers} == {
         "remote::ollama-00",
         "meta-reference-01",
     }

llama_stack/distribution/build.py

@@ -15,7 +15,6 @@ from termcolor import cprint
 
 from llama_stack.distribution.datatypes import BuildConfig, Provider
 from llama_stack.distribution.distribution import get_provider_registry
-from llama_stack.distribution.utils.config_dirs import BUILDS_BASE_DIR
 from llama_stack.distribution.utils.exec import run_command, run_with_pty
 from llama_stack.distribution.utils.image_types import ImageType
 from llama_stack.providers.datatypes import Api
@@ -103,8 +102,6 @@ def build_image(
             template_or_config,
             image_name,
             container_base,
-            str(build_file_path),
-            str(BUILDS_BASE_DIR / ImageType.container.value),
             " ".join(normal_deps),
         ]
     elif build_config.image_type == ImageType.conda.value:

llama_stack/distribution/build_conda_env.sh

@@ -52,7 +52,7 @@ ensure_conda_env_python310() {
   local python_version="3.10"
 
   # Check if conda command is available
-  if ! command -v conda &>/dev/null; then
+  if ! is_command_available conda; then
     printf "${RED}Error: conda command not found. Is Conda installed and in your PATH?${NC}" >&2
     exit 1
   fi

llama_stack/distribution/build_container.sh

@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # All rights reserved.
@@ -20,26 +20,27 @@ UV_HTTP_TIMEOUT=${UV_HTTP_TIMEOUT:-500}
 # mounting is not supported by docker buildx, so we use COPY instead
 USE_COPY_NOT_MOUNT=${USE_COPY_NOT_MOUNT:-}
 
-if [ "$#" -lt 6 ]; then
+if [ "$#" -lt 4 ]; then
   # This only works for templates
-  echo "Usage: $0 <template_or_config> <image_name> <container_base> <build_file_path> <host_build_dir> <pip_dependencies> [<special_pip_deps>]" >&2
+  echo "Usage: $0 <template_or_config> <image_name> <container_base> <pip_dependencies> [<special_pip_deps>]" >&2
   exit 1
 fi
 
 set -euo pipefail
 
 template_or_config="$1"
-image_name="$2"
+shift
-container_base="$3"
+image_name="$1"
-build_file_path="$4"
+shift
-host_build_dir="$5"
+container_base="$1"
-pip_dependencies="$6"
+shift
-special_pip_deps="${7:-}"
+pip_dependencies="$1"
+shift
+special_pip_deps="${1:-}"
 
 
 # Define color codes
 RED='\033[0;31m'
-GREEN='\033[0;32m'
 NC='\033[0m' # No Color
 
 CONTAINER_BINARY=${CONTAINER_BINARY:-docker}
@@ -47,8 +48,10 @@ CONTAINER_OPTS=${CONTAINER_OPTS:-}
 
 TEMP_DIR=$(mktemp -d)
 
+SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
+source "$SCRIPT_DIR/common.sh"
+
 add_to_container() {
-  local input
   output_file="$TEMP_DIR/Containerfile"
   if [ -t 0 ]; then
     printf '%s\n' "$1" >>"$output_file"
@@ -58,15 +61,21 @@ add_to_container() {
   fi
 }
 
+# Check if container command is available
+if ! is_command_available $CONTAINER_BINARY; then
+  printf "${RED}Error: ${CONTAINER_BINARY} command not found. Is ${CONTAINER_BINARY} installed and in your PATH?${NC}" >&2
+  exit 1
+fi
+
 # Update and install UBI9 components if UBI9 base image is used
 if [[ $container_base == *"registry.access.redhat.com/ubi9"* ]]; then
   add_to_container << EOF
 FROM $container_base
 WORKDIR /app
 
-RUN microdnf -y update && microdnf install -y iputils net-tools wget \
+RUN dnf -y update && dnf install -y iputils net-tools wget \
     vim-minimal python3.11 python3.11-pip python3.11-wheel \
-    python3.11-setuptools && ln -s /bin/pip3.11 /bin/pip && ln -s /bin/python3.11 /bin/python && microdnf clean all
+    python3.11-setuptools && ln -s /bin/pip3.11 /bin/pip && ln -s /bin/python3.11 /bin/python && dnf clean all
 
 ENV UV_SYSTEM_PYTHON=1
 RUN pip install uv
@@ -165,6 +174,11 @@ EOF
   fi
 fi
 
+# remove uv after installation
+  add_to_container << EOF
+RUN pip uninstall -y uv
+EOF
+
 # if template_or_config ends with .yaml, it is not a template and we should not use the --template flag
 if [[ "$template_or_config" != *.yaml ]]; then
   add_to_container << EOF
@@ -185,26 +199,31 @@ RUN mkdir -p /.llama /.cache
 RUN chmod -R g+rw /app /.llama /.cache
 EOF
 
-printf "Containerfile created successfully in $TEMP_DIR/Containerfile\n\n"
+printf "Containerfile created successfully in %s/Containerfile\n\n" "$TEMP_DIR"
-cat $TEMP_DIR/Containerfile
+cat "$TEMP_DIR"/Containerfile
 printf "\n"
 
-mounts=""
+# Start building the CLI arguments
+CLI_ARGS=()
+
+# Read CONTAINER_OPTS and put it in an array
+read -ra CLI_ARGS <<< "$CONTAINER_OPTS"
+
 if [ "$USE_COPY_NOT_MOUNT" != "true" ]; then
   if [ -n "$LLAMA_STACK_DIR" ]; then
-    mounts="$mounts -v $(readlink -f $LLAMA_STACK_DIR):$stack_mount"
+    CLI_ARGS+=("-v" "$(readlink -f "$LLAMA_STACK_DIR"):$stack_mount")
   fi
   if [ -n "$LLAMA_MODELS_DIR" ]; then
-    mounts="$mounts -v $(readlink -f $LLAMA_MODELS_DIR):$models_mount"
+    CLI_ARGS+=("-v" "$(readlink -f "$LLAMA_MODELS_DIR"):$models_mount")
   fi
   if [ -n "$LLAMA_STACK_CLIENT_DIR" ]; then
-    mounts="$mounts -v $(readlink -f $LLAMA_STACK_CLIENT_DIR):$client_mount"
+    CLI_ARGS+=("-v" "$(readlink -f "$LLAMA_STACK_CLIENT_DIR"):$client_mount")
   fi
 fi
 
-if command -v selinuxenabled &>/dev/null && selinuxenabled; then
+if is_command_available selinuxenabled && selinuxenabled; then
   # Disable SELinux labels -- we don't want to relabel the llama-stack source dir
-  CONTAINER_OPTS="$CONTAINER_OPTS --security-opt label=disable"
+  CLI_ARGS+=("--security-opt" "label=disable")
 fi
 
 # Set version tag based on PyPI version
@@ -225,11 +244,11 @@ image_tag="$image_name:$version_tag"
 # Detect platform architecture
 ARCH=$(uname -m)
 if [ -n "$BUILD_PLATFORM" ]; then
-  PLATFORM="--platform $BUILD_PLATFORM"
+  CLI_ARGS+=("--platform $BUILD_PLATFORM")
 elif [ "$ARCH" = "arm64" ] || [ "$ARCH" = "aarch64" ]; then
-  PLATFORM="--platform linux/arm64"
+  CLI_ARGS+=("--platform" "linux/arm64")
 elif [ "$ARCH" = "x86_64" ]; then
-  PLATFORM="--platform linux/amd64"
+  CLI_ARGS+=("--platform" "linux/amd64")
 else
   echo "Unsupported architecture: $ARCH"
   exit 1
@@ -238,8 +257,13 @@ fi
 echo "PWD: $(pwd)"
 echo "Containerfile: $TEMP_DIR/Containerfile"
 set -x
-$CONTAINER_BINARY build $CONTAINER_OPTS $PLATFORM -t $image_tag \
+
-  -f "$TEMP_DIR/Containerfile" "." $mounts --progress=plain
+$CONTAINER_BINARY build \
+  "${CLI_ARGS[@]}" \
+  -t "$image_tag" \
+  -f "$TEMP_DIR/Containerfile" \
+  "." \
+  --progress=plain
 
 # clean up tmp/configs
 set +x

llama_stack/distribution/distribution.py

@@ -13,7 +13,7 @@ from llama_stack.providers.datatypes import Api, ProviderSpec
 
 
 def stack_apis() -> List[Api]:
-    return [v for v in Api]
+    return list(Api)
 
 
 class AutoRoutedApiInfo(BaseModel):
@@ -55,7 +55,7 @@ def builtin_automatically_routed_apis() -> List[AutoRoutedApiInfo]:
 
 
 def providable_apis() -> List[Api]:
-    routing_table_apis = set(x.routing_table_api for x in builtin_automatically_routed_apis())
+    routing_table_apis = {x.routing_table_api for x in builtin_automatically_routed_apis()}
     return [api for api in Api if api not in routing_table_apis and api != Api.inspect]
 
 

llama_stack/distribution/resolver.py

@@ -115,8 +115,8 @@ async def resolve_impls(
     - flatmaps, sorts and resolves the providers in dependency order
     - for each API, produces either a (local, passthrough or router) implementation
     """
-    routing_table_apis = set(x.routing_table_api for x in builtin_automatically_routed_apis())
+    routing_table_apis = {x.routing_table_api for x in builtin_automatically_routed_apis()}
-    router_apis = set(x.router_api for x in builtin_automatically_routed_apis())
+    router_apis = {x.router_api for x in builtin_automatically_routed_apis()}
 
     providers_with_specs = {}
 

llama_stack/distribution/routers/routing_tables.py

@@ -318,13 +318,6 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
                 )
         model = await self.get_object_by_identifier("model", embedding_model)
         if model is None:
-            if embedding_model == "all-MiniLM-L6-v2":
-                raise ValueError(
-                    "Embeddings are now served via Inference providers. "
-                    "Please upgrade your run.yaml to include inline::sentence-transformer as an additional inference provider. "
-                    "See https://github.com/meta-llama/llama-stack/blob/main/llama_stack/templates/together/run.yaml for an example."
-                )
-            else:
             raise ValueError(f"Model {embedding_model} not found")
         if model.model_type != ModelType.embedding:
             raise ValueError(f"Model {embedding_model} is not an embedding model")

llama_stack/distribution/ui/page/playground/rag.py

@@ -134,7 +134,7 @@ def rag_chat_page():
             dict(
                 name="builtin::rag/knowledge_search",
                 args={
-                    "vector_db_ids": [vector_db_id for vector_db_id in selected_vector_dbs],
+                    "vector_db_ids": list(selected_vector_dbs),
                 },
             )
         ],

llama_stack/distribution/utils/exec.py

@@ -46,7 +46,7 @@ def formulate_run_args(image_type, image_name, config, template_name) -> list:
             conda_env_info = json.loads(subprocess.check_output(["conda", "info", "--envs", "--json"]).decode())
             envs = conda_env_info["envs"]
             for envpath in envs:
-                if envpath.endswith(env_name):
+                if os.path.basename(envpath) == env_name:
                     return envpath
             return None
 

llama_stack/models/llama/llama3/prompt_templates/system_prompts.py

@@ -226,10 +226,9 @@ class FunctionTagCustomToolGenerator(PromptTemplateGeneratorBase):
 class PythonListCustomToolGenerator(PromptTemplateGeneratorBase):  # noqa: N801
     DEFAULT_PROMPT = textwrap.dedent(
         """
+        You are a helpful assistant. You have access to functions, but you should only use them if they are required.
         You are an expert in composing functions. You are given a question and a set of possible functions.
-        Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
+        Based on the question, you may or may not need to make one function/tool call to achieve the purpose.
-        If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
-        also point it out. You should only return the function call in tools call sections.
 
         {{ function_description }}
         """.strip("\n")

llama_stack/providers/inline/agents/meta_reference/agent_instance.py

@@ -611,8 +611,17 @@ class ChatAgent(ShieldRunnerMixin):
                     if event.stop_reason is not None:
                         stop_reason = event.stop_reason
                 span.set_attribute("stop_reason", stop_reason)
-                span.set_attribute("input", [m.model_dump_json() for m in input_messages])
+                span.set_attribute(
-                span.set_attribute("output", f"content: {content} tool_calls: {tool_calls}")
+                    "input",
+                    json.dumps([json.loads(m.model_dump_json()) for m in input_messages]),
+                )
+                output_attr = json.dumps(
+                    {
+                        "content": content,
+                        "tool_calls": [json.loads(t.model_dump_json()) for t in tool_calls],
+                    }
+                )
+                span.set_attribute("output", output_attr)
 
             n_iter += 1
             await self.storage.set_num_infer_iters_in_turn(session_id, turn_id, n_iter)
@@ -796,10 +805,10 @@ class ChatAgent(ShieldRunnerMixin):
         self, toolgroups_for_turn: Optional[List[AgentToolGroup]] = None
     ) -> Tuple[List[ToolDefinition], Dict[str, str]]:
         # Determine which tools to include
-        agent_config_toolgroups = set(
+        agent_config_toolgroups = {
-            (toolgroup.name if isinstance(toolgroup, AgentToolGroupWithArgs) else toolgroup)
+            toolgroup.name if isinstance(toolgroup, AgentToolGroupWithArgs) else toolgroup
             for toolgroup in self.agent_config.toolgroups
-        )
+        }
         toolgroups_for_turn_set = (
             agent_config_toolgroups
             if toolgroups_for_turn is None

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

@@ -3,6 +3,7 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+import json
 from typing import Any, Dict, List, Optional
 
 from tqdm import tqdm
@@ -86,7 +87,6 @@ class MetaReferenceEvalImpl(
     ) -> Job:
         task_def = self.benchmarks[benchmark_id]
         dataset_id = task_def.dataset_id
-        candidate = task_config.eval_candidate
         scoring_functions = task_def.scoring_functions
         dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
         validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.eval.value))
@@ -117,7 +117,7 @@ class MetaReferenceEvalImpl(
         generations = []
         for i, x in tqdm(enumerate(input_rows)):
             assert ColumnName.chat_completion_input.value in x, "Invalid input row"
-            input_messages = eval(str(x[ColumnName.chat_completion_input.value]))
+            input_messages = json.loads(x[ColumnName.chat_completion_input.value])
             input_messages = [UserMessage(**x) for x in input_messages]
 
             # NOTE: only single-turn agent generation is supported. Create a new session for each input row
@@ -159,7 +159,7 @@ class MetaReferenceEvalImpl(
         generations = []
         for x in tqdm(input_rows):
             if ColumnName.completion_input.value in x:
-                input_content = eval(str(x[ColumnName.completion_input.value]))
+                input_content = json.loads(x[ColumnName.completion_input.value])
                 response = await self.inference_api.completion(
                     model=candidate.model,
                     content=input_content,
@@ -167,9 +167,8 @@ class MetaReferenceEvalImpl(
                 )
                 generations.append({ColumnName.generated_answer.value: response.completion_message.content})
             elif ColumnName.chat_completion_input.value in x:
-                chat_completion_input_str = str(x[ColumnName.chat_completion_input.value])
+                chat_completion_input_json = json.loads(x[ColumnName.chat_completion_input.value])
-                input_messages = eval(chat_completion_input_str)
+                input_messages = [UserMessage(**x) for x in chat_completion_input_json]
-                input_messages = [UserMessage(**x) for x in input_messages]
                 messages = []
                 if candidate.system_message:
                     messages.append(candidate.system_message)

llama_stack/providers/inline/inference/meta_reference/inference.py

@@ -208,7 +208,6 @@ class MetaReferenceInferenceImpl(
             logprobs = []
             stop_reason = None
 
-            tokenizer = self.generator.formatter.tokenizer
             for token_result in self.generator.completion(request):
                 tokens.append(token_result.token)
                 if token_result.text == "<|eot_id|>":

llama_stack/providers/inline/inference/meta_reference/parallel_utils.py

@@ -207,7 +207,7 @@ def maybe_parse_message(maybe_json: Optional[str]) -> Optional[ProcessingMessage
         return parse_message(maybe_json)
     except json.JSONDecodeError:
         return None
-    except ValueError as e:
+    except ValueError:
         return None
 
 
@@ -352,7 +352,7 @@ class ModelParallelProcessGroup:
                 if isinstance(obj, TaskResponse):
                     yield obj.result
 
-        except GeneratorExit as e:
+        except GeneratorExit:
             self.request_socket.send(encode_msg(CancelSentinel()))
             while True:
                 obj_json = self.request_socket.send()

llama_stack/providers/inline/inference/meta_reference/quantization/fp8_txest_disabled.py

@@ -7,6 +7,9 @@
 # Copyright (c) Meta Platforms, Inc. and affiliates.
 # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement.
 
+# The file gets a special treatment for now?
+# ruff: noqa: N803
+
 import unittest
 
 import torch

llama_stack/providers/inline/post_training/torchtune/datasets/format_adapter.py

@@ -10,16 +10,19 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import json
 from typing import Any, Mapping
 
 from llama_stack.providers.utils.common.data_schema_validator import ColumnName
 
 
-def llama_stack_instruct_to_torchtune_instruct(sample: Mapping[str, Any]) -> Mapping[str, Any]:
+def llama_stack_instruct_to_torchtune_instruct(
+    sample: Mapping[str, Any],
+) -> Mapping[str, Any]:
     assert ColumnName.chat_completion_input.value in sample and ColumnName.expected_answer.value in sample, (
         "Invalid input row"
     )
-    input_messages = eval(str(sample[ColumnName.chat_completion_input.value]))
+    input_messages = json.loads(sample[ColumnName.chat_completion_input.value])
 
     assert len(input_messages) == 1, "llama stack intruct dataset format only supports 1 user message"
     input_message = input_messages[0]
@@ -37,7 +40,7 @@ def llama_stack_instruct_to_torchtune_instruct(sample: Mapping[str, Any]) -> Map
 def llama_stack_chat_to_torchtune_chat(sample: Mapping[str, Any]) -> Mapping[str, Any]:
     assert ColumnName.dialog.value in sample, "Invalid input row"
     role_map = {"user": "human", "assistant": "gpt"}
-    dialog = eval(str(sample[ColumnName.dialog.value]))
+    dialog = json.loads(sample[ColumnName.dialog.value])
 
     assert len(dialog) > 1, "dialog must have at least 2 messagse"
     roles = []

llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py

@@ -264,7 +264,7 @@ class LoraFinetuningSingleDevice:
             )
 
         self.adapter_params = get_adapter_params(model)
-        self._is_dora = any(["magnitude" in k for k in self.adapter_params.keys()])
+        self._is_dora = any("magnitude" in k for k in self.adapter_params.keys())
 
         set_trainable_params(model, self.adapter_params)
 

llama_stack/providers/inline/scoring/braintrust/braintrust.py

@@ -133,7 +133,7 @@ class BraintrustScoringImpl(
     async def shutdown(self) -> None: ...
 
     async def list_scoring_functions(self) -> List[ScoringFn]:
-        scoring_fn_defs_list = [x for x in self.supported_fn_defs_registry.values()]
+        scoring_fn_defs_list = list(self.supported_fn_defs_registry.values())
         for f in scoring_fn_defs_list:
             assert f.identifier.startswith("braintrust"), (
                 "All braintrust scoring fn must have identifier prefixed with 'braintrust'! "

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

@@ -198,7 +198,7 @@ class NVIDIAInferenceAdapter(Inference, ModelRegistryHelper):
         tool_config: Optional[ToolConfig] = None,
     ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
         if tool_prompt_format:
-            warnings.warn("tool_prompt_format is not supported by NVIDIA NIM, ignoring")
+            warnings.warn("tool_prompt_format is not supported by NVIDIA NIM, ignoring", stacklevel=2)
 
         await check_health(self._config)  # this raises errors
 

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

@@ -106,7 +106,7 @@ async def convert_chat_completion_request(
             payload.update(temperature=strategy.temperature)
         elif isinstance(strategy, TopKSamplingStrategy):
             if strategy.top_k != -1 and strategy.top_k < 1:
-                warnings.warn("top_k must be -1 or >= 1")
+                warnings.warn("top_k must be -1 or >= 1", stacklevel=2)
             nvext.update(top_k=strategy.top_k)
         elif isinstance(strategy, GreedySamplingStrategy):
             nvext.update(top_k=-1)
@@ -168,7 +168,7 @@ def convert_completion_request(
             payload.update(top_p=request.sampling_params.top_p)
         elif request.sampling_params.strategy == "top_k":
             if request.sampling_params.top_k != -1 and request.sampling_params.top_k < 1:
-                warnings.warn("top_k must be -1 or >= 1")
+                warnings.warn("top_k must be -1 or >= 1", stacklevel=2)
             nvext.update(top_k=request.sampling_params.top_k)
         elif request.sampling_params.strategy == "greedy":
             nvext.update(top_k=-1)

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

@@ -270,6 +270,12 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         tool_config: Optional[ToolConfig] = None,
     ) -> AsyncGenerator:
         model = await self.model_store.get_model(model_id)
+        # This is to be consistent with OpenAI API and support vLLM <= v0.6.3
+        # References:
+        #   * https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice
+        #   * https://github.com/vllm-project/vllm/pull/10000
+        if not tools and tool_config is not None:
+            tool_config.tool_choice = ToolChoice.none
         request = ChatCompletionRequest(
             model=model.provider_resource_id,
             messages=messages,

llama_stack/providers/tests/eval/test_eval.py

@@ -39,12 +39,11 @@ class Testeval:
 
     @pytest.mark.asyncio
     async def test_eval_evaluate_rows(self, eval_stack, inference_model, judge_model):
-        eval_impl, benchmarks_impl, datasetio_impl, datasets_impl, models_impl = (
+        eval_impl, benchmarks_impl, datasetio_impl, datasets_impl = (
             eval_stack[Api.eval],
             eval_stack[Api.benchmarks],
             eval_stack[Api.datasetio],
             eval_stack[Api.datasets],
-            eval_stack[Api.models],
         )
 
         await register_dataset(datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval")
@@ -92,11 +91,10 @@ class Testeval:
 
     @pytest.mark.asyncio
     async def test_eval_run_eval(self, eval_stack, inference_model, judge_model):
-        eval_impl, benchmarks_impl, datasets_impl, models_impl = (
+        eval_impl, benchmarks_impl, datasets_impl = (
             eval_stack[Api.eval],
             eval_stack[Api.benchmarks],
             eval_stack[Api.datasets],
-            eval_stack[Api.models],
         )
 
         await register_dataset(datasets_impl, for_generation=True, dataset_id="test_dataset_for_eval")
@@ -131,11 +129,10 @@ class Testeval:
 
     @pytest.mark.asyncio
     async def test_eval_run_benchmark_eval(self, eval_stack, inference_model):
-        eval_impl, benchmarks_impl, datasets_impl, models_impl = (
+        eval_impl, benchmarks_impl, datasets_impl = (
             eval_stack[Api.eval],
             eval_stack[Api.benchmarks],
             eval_stack[Api.datasets],
-            eval_stack[Api.models],
         )
 
         response = await datasets_impl.list_datasets()

llama_stack/providers/tests/report.py

@@ -18,8 +18,7 @@ from llama_stack.models.llama.sku_list import all_registered_models
 INFERENCE_APIS = ["chat_completion"]
 FUNCTIONALITIES = ["streaming", "structured_output", "tool_calling"]
 SUPPORTED_MODELS = {
-    "ollama": set(
+    "ollama": {
-        [
         CoreModelId.llama3_1_8b_instruct.value,
         CoreModelId.llama3_1_8b_instruct.value,
         CoreModelId.llama3_1_70b_instruct.value,
@@ -37,10 +36,8 @@ SUPPORTED_MODELS = {
         CoreModelId.llama3_3_70b_instruct.value,
         CoreModelId.llama_guard_3_8b.value,
         CoreModelId.llama_guard_3_1b.value,
-        ]
+    },
-    ),
+    "fireworks": {
-    "fireworks": set(
-        [
         CoreModelId.llama3_1_8b_instruct.value,
         CoreModelId.llama3_1_70b_instruct.value,
         CoreModelId.llama3_1_405b_instruct.value,
@@ -51,10 +48,8 @@ SUPPORTED_MODELS = {
         CoreModelId.llama3_3_70b_instruct.value,
         CoreModelId.llama_guard_3_8b.value,
         CoreModelId.llama_guard_3_11b_vision.value,
-        ]
+    },
-    ),
+    "together": {
-    "together": set(
-        [
         CoreModelId.llama3_1_8b_instruct.value,
         CoreModelId.llama3_1_70b_instruct.value,
         CoreModelId.llama3_1_405b_instruct.value,
@@ -64,8 +59,7 @@ SUPPORTED_MODELS = {
         CoreModelId.llama3_3_70b_instruct.value,
         CoreModelId.llama_guard_3_8b.value,
         CoreModelId.llama_guard_3_11b_vision.value,
-        ]
+    },
-    ),
 }
 
 

llama_stack/providers/tests/scoring/test_scoring.py

@@ -45,13 +45,11 @@ class TestScoring:
             scoring_functions_impl,
             datasetio_impl,
             datasets_impl,
-            models_impl,
         ) = (
             scoring_stack[Api.scoring],
             scoring_stack[Api.scoring_functions],
             scoring_stack[Api.datasetio],
             scoring_stack[Api.datasets],
-            scoring_stack[Api.models],
         )
         scoring_fns_list = await scoring_functions_impl.list_scoring_functions()
         provider_id = scoring_fns_list[0].provider_id
@@ -102,13 +100,11 @@ class TestScoring:
             scoring_functions_impl,
             datasetio_impl,
             datasets_impl,
-            models_impl,
         ) = (
             scoring_stack[Api.scoring],
             scoring_stack[Api.scoring_functions],
             scoring_stack[Api.datasetio],
             scoring_stack[Api.datasets],
-            scoring_stack[Api.models],
         )
         await register_dataset(datasets_impl, for_rag=True)
         response = await datasets_impl.list_datasets()
@@ -163,13 +159,11 @@ class TestScoring:
             scoring_functions_impl,
             datasetio_impl,
             datasets_impl,
-            models_impl,
         ) = (
             scoring_stack[Api.scoring],
             scoring_stack[Api.scoring_functions],
             scoring_stack[Api.datasetio],
             scoring_stack[Api.datasets],
-            scoring_stack[Api.models],
         )
         await register_dataset(datasets_impl, for_rag=True)
         rows = await datasetio_impl.get_rows_paginated(

llama_stack/providers/utils/inference/openai_compat.py

@@ -6,7 +6,7 @@
 import json
 import logging
 import warnings
-from typing import AsyncGenerator, Dict, Generator, Iterable, List, Optional, Union
+from typing import AsyncGenerator, Dict, Iterable, List, Optional, Union
 
 from openai import AsyncStream
 from openai.types.chat import (
@@ -605,7 +605,7 @@ def convert_tool_call(
             tool_name=tool_call.function.name,
             arguments=json.loads(tool_call.function.arguments),
         )
-    except Exception as e:
+    except Exception:
         return UnparseableToolCall(
             call_id=tool_call.id or "",
             tool_name=tool_call.function.name or "",
@@ -841,14 +841,13 @@ async def convert_openai_chat_completion_stream(
     Convert a stream of OpenAI chat completion chunks into a stream
     of ChatCompletionResponseStreamChunk.
     """
-
+    yield ChatCompletionResponseStreamChunk(
-    # generate a stream of ChatCompletionResponseEventType: start -> progress -> progress -> ...
+        event=ChatCompletionResponseEvent(
-    def _event_type_generator() -> Generator[ChatCompletionResponseEventType, None, None]:
+            event_type=ChatCompletionResponseEventType.start,
-        yield ChatCompletionResponseEventType.start
+            delta=TextDelta(text=""),
-        while True:
+        )
-            yield ChatCompletionResponseEventType.progress
+    )
-
+    event_type = ChatCompletionResponseEventType.progress
-    event_type = _event_type_generator()
 
     stop_reason = None
     toolcall_buffer = {}
@@ -868,7 +867,7 @@ async def convert_openai_chat_completion_stream(
             if choice.delta.content:
                 yield ChatCompletionResponseStreamChunk(
                     event=ChatCompletionResponseEvent(
-                        event_type=next(event_type),
+                        event_type=event_type,
                         delta=TextDelta(text=choice.delta.content),
                         logprobs=_convert_openai_logprobs(logprobs),
                     )
@@ -877,7 +876,9 @@ async def convert_openai_chat_completion_stream(
             # it is possible to have parallel tool calls in stream, but
             # ChatCompletionResponseEvent only supports one per stream
             if len(choice.delta.tool_calls) > 1:
-                warnings.warn("multiple tool calls found in a single delta, using the first, ignoring the rest")
+                warnings.warn(
+                    "multiple tool calls found in a single delta, using the first, ignoring the rest", stacklevel=2
+                )
 
             if not enable_incremental_tool_calls:
                 yield ChatCompletionResponseStreamChunk(
@@ -909,7 +910,7 @@ async def convert_openai_chat_completion_stream(
                 toolcall_buffer["content"] += delta
                 yield ChatCompletionResponseStreamChunk(
                     event=ChatCompletionResponseEvent(
-                        event_type=next(event_type),
+                        event_type=event_type,
                         delta=ToolCallDelta(
                             tool_call=delta,
                             parse_status=ToolCallParseStatus.in_progress,
@@ -920,7 +921,7 @@ async def convert_openai_chat_completion_stream(
         else:
             yield ChatCompletionResponseStreamChunk(
                 event=ChatCompletionResponseEvent(
-                    event_type=next(event_type),
+                    event_type=event_type,
                     delta=TextDelta(text=choice.delta.content or ""),
                     logprobs=_convert_openai_logprobs(logprobs),
                 )
@@ -931,7 +932,7 @@ async def convert_openai_chat_completion_stream(
         toolcall_buffer["content"] += delta
         yield ChatCompletionResponseStreamChunk(
             event=ChatCompletionResponseEvent(
-                event_type=next(event_type),
+                event_type=event_type,
                 delta=ToolCallDelta(
                     tool_call=delta,
                     parse_status=ToolCallParseStatus.in_progress,

llama_stack/providers/utils/kvstore/redis/redis.py

@@ -36,7 +36,7 @@ class RedisKVStoreImpl(KVStore):
         value = await self.redis.get(key)
         if value is None:
             return None
-        ttl = await self.redis.ttl(key)
+        await self.redis.ttl(key)
         return value
 
     async def delete(self, key: str) -> None:

llama_stack/providers/utils/scoring/aggregation_utils.py

@@ -32,7 +32,7 @@ def aggregate_categorical_count(
     scoring_results: List[ScoringResultRow],
 ) -> Dict[str, Any]:
     scores = [str(r["score"]) for r in scoring_results]
-    unique_scores = sorted(list(set(scores)))
+    unique_scores = sorted(set(scores))
     return {"categorical_count": {s: scores.count(s) for s in unique_scores}}
 
 

llama_stack/providers/utils/scoring/base_scoring_fn.py

@@ -66,7 +66,7 @@ class RegisteredBaseScoringFn(BaseScoringFn):
         return self.__class__.__name__
 
     def get_supported_scoring_fn_defs(self) -> List[ScoringFn]:
-        return [x for x in self.supported_fn_defs_registry.values()]
+        return list(self.supported_fn_defs_registry.values())
 
     def register_scoring_fn_def(self, scoring_fn: ScoringFn) -> None:
         if scoring_fn.identifier in self.supported_fn_defs_registry:

llama_stack/providers/utils/telemetry/trace_protocol.py

@@ -6,6 +6,7 @@
 
 import asyncio
 import inspect
+import json
 from functools import wraps
 from typing import Any, AsyncGenerator, Callable, Type, TypeVar
 
@@ -17,6 +18,10 @@ T = TypeVar("T")
 
 
 def serialize_value(value: Any) -> Primitive:
+    return str(_prepare_for_json(value))
+
+
+def _prepare_for_json(value: Any) -> str:
     """Serialize a single value into JSON-compatible format."""
     if value is None:
         return ""
@@ -25,8 +30,16 @@ def serialize_value(value: Any) -> Primitive:
     elif hasattr(value, "_name_"):
         return value._name_
     elif isinstance(value, BaseModel):
-        return value.model_dump_json()
+        return json.loads(value.model_dump_json())
+    elif isinstance(value, (list, tuple, set)):
+        return [_prepare_for_json(item) for item in value]
+    elif isinstance(value, dict):
+        return {str(k): _prepare_for_json(v) for k, v in value.items()}
     else:
+        try:
+            json.dumps(value)
+            return value
+        except Exception:
             return str(value)
 
 
@@ -104,7 +117,8 @@ def trace_protocol(cls: Type[T]) -> Type[T]:
                     result = method(self, *args, **kwargs)
                     span.set_attribute("output", serialize_value(result))
                     return result
-                except Exception as _e:
+                except Exception as e:
+                    span.set_attribute("error", str(e))
                     raise
 
         if is_async_gen:

llama_stack/scripts/distro_codegen.py

@@ -99,7 +99,7 @@ def collect_template_dependencies(template_dir: Path) -> tuple[str | None, list[
             template = template_func()
             normal_deps, special_deps = get_provider_dependencies(template.providers)
             # Combine all dependencies in order: normal deps, special deps, server deps
-            all_deps = sorted(list(set(normal_deps + SERVER_DEPENDENCIES))) + sorted(list(set(special_deps)))
+            all_deps = sorted(set(normal_deps + SERVER_DEPENDENCIES)) + sorted(set(special_deps))
 
             return template.name, all_deps
     except Exception:

llama_stack/templates/meta-reference-gpu/doc_template.md

@@ -29,12 +29,31 @@ The following environment variables can be configured:
 
 ## Prerequisite: Downloading Models
 
-Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
+Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
 
 ```
-$ ls ~/.llama/checkpoints
+$ llama model list --downloaded
-Llama3.1-8B           Llama3.2-11B-Vision-Instruct  Llama3.2-1B-Instruct  Llama3.2-90B-Vision-Instruct  Llama-Guard-3-8B
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
-Llama3.1-8B-Instruct  Llama3.2-1B                   Llama3.2-3B-Instruct  Llama-Guard-3-1B              Prompt-Guard-86M
+┃ Model                                   ┃ Size     ┃ Modified Time       ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
+│ Llama3.2-1B-Instruct:int4-qlora-eo8     │ 1.53 GB  │ 2025-02-26 11:22:28 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-1B                             │ 2.31 GB  │ 2025-02-18 21:48:52 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Prompt-Guard-86M                        │ 0.02 GB  │ 2025-02-26 11:29:28 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-3B-Instruct:int4-spinquant-eo8 │ 3.69 GB  │ 2025-02-26 11:37:41 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-3B                             │ 5.99 GB  │ 2025-02-18 21:51:26 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.1-8B                             │ 14.97 GB │ 2025-02-16 10:36:37 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-1B-Instruct:int4-spinquant-eo8 │ 1.51 GB  │ 2025-02-26 11:35:02 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama-Guard-3-1B                        │ 2.80 GB  │ 2025-02-26 11:20:46 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama-Guard-3-1B:int4                   │ 0.43 GB  │ 2025-02-26 11:33:33 │
+└─────────────────────────────────────────┴──────────┴─────────────────────┘
 ```
 
 ## Running the Distribution

llama_stack/templates/meta-reference-quantized-gpu/doc_template.md

@@ -31,12 +31,31 @@ The following environment variables can be configured:
 
 ## Prerequisite: Downloading Models
 
-Please make sure you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
+Please use `llama model list --downloaded` to check that you have llama model checkpoints downloaded in `~/.llama` before proceeding. See [installation guide](https://llama-stack.readthedocs.io/en/latest/references/llama_cli_reference/download_models.html) here to download the models. Run `llama model list` to see the available models to download, and `llama model download` to download the checkpoints.
 
 ```
-$ ls ~/.llama/checkpoints
+$ llama model list --downloaded
-Llama3.1-8B           Llama3.2-11B-Vision-Instruct  Llama3.2-1B-Instruct  Llama3.2-90B-Vision-Instruct  Llama-Guard-3-8B
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓
-Llama3.1-8B-Instruct  Llama3.2-1B                   Llama3.2-3B-Instruct  Llama-Guard-3-1B              Prompt-Guard-86M
+┃ Model                                   ┃ Size     ┃ Modified Time       ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩
+│ Llama3.2-1B-Instruct:int4-qlora-eo8     │ 1.53 GB  │ 2025-02-26 11:22:28 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-1B                             │ 2.31 GB  │ 2025-02-18 21:48:52 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Prompt-Guard-86M                        │ 0.02 GB  │ 2025-02-26 11:29:28 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-3B-Instruct:int4-spinquant-eo8 │ 3.69 GB  │ 2025-02-26 11:37:41 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-3B                             │ 5.99 GB  │ 2025-02-18 21:51:26 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.1-8B                             │ 14.97 GB │ 2025-02-16 10:36:37 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama3.2-1B-Instruct:int4-spinquant-eo8 │ 1.51 GB  │ 2025-02-26 11:35:02 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama-Guard-3-1B                        │ 2.80 GB  │ 2025-02-26 11:20:46 │
+├─────────────────────────────────────────┼──────────┼─────────────────────┤
+│ Llama-Guard-3-1B:int4                   │ 0.43 GB  │ 2025-02-26 11:33:33 │
+└─────────────────────────────────────────┴──────────┴─────────────────────┘
 ```
 
 ## Running the Distribution

llama_stack/templates/ollama/ollama.py

@@ -93,7 +93,7 @@ def get_distribution_template() -> DistributionTemplate:
                     "inference": [inference_provider],
                     "vector_io": [vector_io_provider_sqlite],
                 },
-                default_models=[inference_model],
+                default_models=[inference_model, embedding_model],
                 default_tool_groups=default_tool_groups,
             ),
             "run-with-safety.yaml": RunConfigSettings(

llama_stack/templates/ollama/run.yaml

@@ -90,6 +90,12 @@ models:
   model_id: ${env.INFERENCE_MODEL}
   provider_id: ollama
   model_type: llm
+- metadata:
+    embedding_dimension: 384
+  model_id: all-MiniLM-L6-v2
+  provider_id: ollama
+  provider_model_id: all-minilm:latest
+  model_type: embedding
 shields: []
 vector_dbs: []
 datasets: []

pyproject.toml

@@ -123,39 +123,16 @@ select = [
     "I",  # isort
 ]
 ignore = [
-    "E203",
+    # The following ignores are desired by the project maintainers.
-    "E305",
+    "E402", # Module level import not at top of file
-    "E402",
+    "E501", # Line too long
-    "E501", # line too long
+    "F405", # Maybe undefined or defined from star import
-    "E721",
+    "C408", # Ignored because we like the dict keyword argument syntax
-    "E741",
+    "N812", # Ignored because import torch.nn.functional as F is PyTorch convention
-    "F405",
+
-    "F841",
-    "C408", # ignored because we like the dict keyword argument syntax
-    "E302",
-    "W291",
-    "E303",
-    "N812", # ignored because import torch.nn.functional as F is PyTorch convention
-    "N817", # ignored because importing using acronyms is convention (DistributedDataParallel as DDP)
-    "E731", # allow usage of assigning lambda expressions
     # These are the additional ones we started ignoring after moving to ruff. We should look into each one of them later.
-    "C901",
+    "C901", # Complexity of the function is too high
-    "C405",
-    "C414",
-    "N803",
-    "N999",
-    "C403",
-    "C416",
-    "B028",
-    "C419",
-    "C401",
-    "B023",
-    # shebang has extra meaning in fbcode lints, so I think it's not worth trying
-    # to line this up with executable bit
-    "EXE001",
-    "N802",   # random naming hints don't need
     # these ignores are from flake8-bugbear; please fix!
-    "B007",
     "B008",
 ]
 

tests/client-sdk/__init__.py

@@ -3,3 +3,4 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+# ruff: noqa: N999

tests/client-sdk/agents/__init__.py

@@ -3,3 +3,4 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+# ruff: noqa: N999

tests/client-sdk/agents/test_agents.py

@@ -4,20 +4,15 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import json
-from typing import Dict, List
 from uuid import uuid4
 
 import pytest
 from llama_stack_client.lib.agents.agent import Agent
-from llama_stack_client.lib.agents.client_tool import ClientTool
+from llama_stack_client.lib.agents.client_tool import client_tool
 from llama_stack_client.lib.agents.event_logger import EventLogger
-from llama_stack_client.types import ToolResponseMessage
 from llama_stack_client.types.agents.turn_create_params import Document as AgentDocument
 from llama_stack_client.types.memory_insert_params import Document
-from llama_stack_client.types.shared.completion_message import CompletionMessage
 from llama_stack_client.types.shared_params.agent_config import AgentConfig, ToolConfig
-from llama_stack_client.types.tool_def_param import Parameter
 
 from llama_stack.apis.agents.agents import (
     AgentConfig as Server__AgentConfig,
@@ -27,56 +22,15 @@ from llama_stack.apis.agents.agents import (
 )
 
 
-class TestClientTool(ClientTool):
+@client_tool
-    """Tool to give boiling point of a liquid
+def get_boiling_point(liquid_name: str, celcius: bool = True) -> int:
-    Returns the correct value for polyjuice in Celcius and Fahrenheit
-    and returns -1 for other liquids
     """
+    Returns the boiling point of a liquid in Celcius or Fahrenheit
 
-    def run(self, messages: List[CompletionMessage]) -> List[ToolResponseMessage]:
+    :param liquid_name: The name of the liquid
-        assert len(messages) == 1, "Expected single message"
+    :param celcius: Whether to return the boiling point in Celcius
-
+    :return: The boiling point of the liquid in Celcius or Fahrenheit
-        message = messages[0]
+    """
-
-        tool_call = message.tool_calls[0]
-
-        try:
-            response = self.run_impl(**tool_call.arguments)
-            response_str = json.dumps(response, ensure_ascii=False)
-        except Exception as e:
-            response_str = f"Error when running tool: {e}"
-
-        message = ToolResponseMessage(
-            role="tool",
-            call_id=tool_call.call_id,
-            tool_name=tool_call.tool_name,
-            content=response_str,
-        )
-        return message
-
-    def get_name(self) -> str:
-        return "get_boiling_point"
-
-    def get_description(self) -> str:
-        return "Get the boiling point of imaginary liquids (eg. polyjuice)"
-
-    def get_params_definition(self) -> Dict[str, Parameter]:
-        return {
-            "liquid_name": Parameter(
-                name="liquid_name",
-                parameter_type="string",
-                description="The name of the liquid",
-                required=True,
-            ),
-            "celcius": Parameter(
-                name="celcius",
-                parameter_type="boolean",
-                description="Whether to return the boiling point in Celcius",
-                required=False,
-            ),
-        }
-
-    def run_impl(self, liquid_name: str, celcius: bool = True) -> int:
     if liquid_name.lower() == "polyjuice":
         if celcius:
             return -100
@@ -298,7 +252,7 @@ def test_code_interpreter_for_attachments(llama_stack_client, agent_config):
 
 
 def test_custom_tool(llama_stack_client, agent_config):
-    client_tool = TestClientTool()
+    client_tool = get_boiling_point
     agent_config = {
         **agent_config,
         "toolgroups": ["builtin::websearch"],
@@ -326,7 +280,7 @@ def test_custom_tool(llama_stack_client, agent_config):
 
 def test_tool_choice(llama_stack_client, agent_config):
     def run_agent(tool_choice):
-        client_tool = TestClientTool()
+        client_tool = get_boiling_point
 
         test_agent_config = {
             **agent_config,
@@ -362,7 +316,7 @@ def test_tool_choice(llama_stack_client, agent_config):
 
 # TODO: fix this flaky test
 def xtest_override_system_message_behavior(llama_stack_client, agent_config):
-    client_tool = TestClientTool()
+    client_tool = get_boiling_point
     agent_config = {
         **agent_config,
         "instructions": "You are a pirate",
@@ -458,7 +412,6 @@ def test_rag_agent(llama_stack_client, agent_config, rag_tool_name):
         vector_db_id=vector_db_id,
         embedding_model="all-MiniLM-L6-v2",
         embedding_dimension=384,
-        provider_id="faiss",
     )
     llama_stack_client.tool_runtime.rag_tool.insert(
         documents=documents,
@@ -587,7 +540,7 @@ def test_rag_and_code_agent(llama_stack_client, agent_config):
 
 
 def test_create_turn_response(llama_stack_client, agent_config):
-    client_tool = TestClientTool()
+    client_tool = get_boiling_point
     agent_config = {
         **agent_config,
         "input_shields": [],

tests/client-sdk/conftest.py

@@ -117,7 +117,7 @@ def client_with_models(llama_stack_client, text_model_id, vision_model_id, embed
     assert len(providers) > 0, "No inference providers found"
     inference_providers = [p.provider_id for p in providers if p.provider_type != "inline::sentence-transformers"]
 
-    model_ids = set(m.identifier for m in client.models.list())
+    model_ids = {m.identifier for m in client.models.list()}
     model_ids.update(m.provider_resource_id for m in client.models.list())
 
     if text_model_id and text_model_id not in model_ids:

tests/client-sdk/inference/__init__.py

@@ -3,3 +3,4 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+# ruff: noqa: N999

tests/client-sdk/inference/test_embedding.py

@@ -75,6 +75,26 @@ DUMMY_IMAGE_URL = ImageContentItem(
     image=ImageContentItemImage(url=ImageContentItemImageURL(uri="https://example.com/image.jpg")), type="image"
 )
 DUMMY_IMAGE_BASE64 = ImageContentItem(image=ImageContentItemImage(data="base64string"), type="image")
+SUPPORTED_PROVIDERS = {"remote::nvidia"}
+MODELS_SUPPORTING_MEDIA = {}
+MODELS_SUPPORTING_OUTPUT_DIMENSION = {"nvidia/llama-3.2-nv-embedqa-1b-v2"}
+MODELS_REQUIRING_TASK_TYPE = {
+    "nvidia/llama-3.2-nv-embedqa-1b-v2",
+    "nvidia/nv-embedqa-e5-v5",
+    "nvidia/nv-embedqa-mistral-7b-v2",
+    "snowflake/arctic-embed-l",
+}
+MODELS_SUPPORTING_TASK_TYPE = MODELS_REQUIRING_TASK_TYPE
+
+
+def default_task_type(model_id):
+    """
+    Some models require a task type parameter. This provides a default value for
+    testing those models.
+    """
+    if model_id in MODELS_REQUIRING_TASK_TYPE:
+        return {"task_type": "query"}
+    return {}
 
 
 @pytest.mark.parametrize(
@@ -88,8 +108,12 @@ DUMMY_IMAGE_BASE64 = ImageContentItem(image=ImageContentItemImage(data="base64st
         "list[text]",
     ],
 )
-def test_embedding_text(llama_stack_client, embedding_model_id, contents):
+def test_embedding_text(llama_stack_client, embedding_model_id, contents, inference_provider_type):
-    response = llama_stack_client.inference.embeddings(model_id=embedding_model_id, contents=contents)
+    if inference_provider_type not in SUPPORTED_PROVIDERS:
+        pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
+    response = llama_stack_client.inference.embeddings(
+        model_id=embedding_model_id, contents=contents, **default_task_type(embedding_model_id)
+    )
     assert isinstance(response, EmbeddingsResponse)
     assert len(response.embeddings) == sum(len(content) if isinstance(content, list) else 1 for content in contents)
     assert isinstance(response.embeddings[0], list)
@@ -107,9 +131,14 @@ def test_embedding_text(llama_stack_client, embedding_model_id, contents):
         "list[url,string,base64,text]",
     ],
 )
-@pytest.mark.xfail(reason="Media is not supported")
+def test_embedding_image(llama_stack_client, embedding_model_id, contents, inference_provider_type):
-def test_embedding_image(llama_stack_client, embedding_model_id, contents):
+    if inference_provider_type not in SUPPORTED_PROVIDERS:
-    response = llama_stack_client.inference.embeddings(model_id=embedding_model_id, contents=contents)
+        pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
+    if embedding_model_id not in MODELS_SUPPORTING_MEDIA:
+        pytest.xfail(f"{embedding_model_id} doesn't support media")
+    response = llama_stack_client.inference.embeddings(
+        model_id=embedding_model_id, contents=contents, **default_task_type(embedding_model_id)
+    )
     assert isinstance(response, EmbeddingsResponse)
     assert len(response.embeddings) == sum(len(content) if isinstance(content, list) else 1 for content in contents)
     assert isinstance(response.embeddings[0], list)
@@ -134,9 +163,16 @@ def test_embedding_image(llama_stack_client, embedding_model_id, contents):
         "short",
     ],
 )
-def test_embedding_truncation(llama_stack_client, embedding_model_id, text_truncation, contents):
+def test_embedding_truncation(
+    llama_stack_client, embedding_model_id, text_truncation, contents, inference_provider_type
+):
+    if inference_provider_type not in SUPPORTED_PROVIDERS:
+        pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
     response = llama_stack_client.inference.embeddings(
-        model_id=embedding_model_id, contents=contents, text_truncation=text_truncation
+        model_id=embedding_model_id,
+        contents=contents,
+        text_truncation=text_truncation,
+        **default_task_type(embedding_model_id),
     )
     assert isinstance(response, EmbeddingsResponse)
     assert len(response.embeddings) == 1
@@ -162,25 +198,43 @@ def test_embedding_truncation(llama_stack_client, embedding_model_id, text_trunc
         "long-str",
     ],
 )
-def test_embedding_truncation_error(llama_stack_client, embedding_model_id, text_truncation, contents):
+def test_embedding_truncation_error(
-    with pytest.raises(BadRequestError) as excinfo:
+    llama_stack_client, embedding_model_id, text_truncation, contents, inference_provider_type
+):
+    if inference_provider_type not in SUPPORTED_PROVIDERS:
+        pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
+    with pytest.raises(BadRequestError):
         llama_stack_client.inference.embeddings(
-            model_id=embedding_model_id, contents=[DUMMY_LONG_TEXT], text_truncation=text_truncation
+            model_id=embedding_model_id,
+            contents=[DUMMY_LONG_TEXT],
+            text_truncation=text_truncation,
+            **default_task_type(embedding_model_id),
         )
 
 
-@pytest.mark.xfail(reason="Only valid for model supporting dimension reduction")
+def test_embedding_output_dimension(llama_stack_client, embedding_model_id, inference_provider_type):
-def test_embedding_output_dimension(llama_stack_client, embedding_model_id):
+    if inference_provider_type not in SUPPORTED_PROVIDERS:
-    base_response = llama_stack_client.inference.embeddings(model_id=embedding_model_id, contents=[DUMMY_STRING])
+        pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
+    if embedding_model_id not in MODELS_SUPPORTING_OUTPUT_DIMENSION:
+        pytest.xfail(f"{embedding_model_id} doesn't support output_dimension")
+    base_response = llama_stack_client.inference.embeddings(
+        model_id=embedding_model_id, contents=[DUMMY_STRING], **default_task_type(embedding_model_id)
+    )
     test_response = llama_stack_client.inference.embeddings(
-        model_id=embedding_model_id, contents=[DUMMY_STRING], output_dimension=32
+        model_id=embedding_model_id,
+        contents=[DUMMY_STRING],
+        **default_task_type(embedding_model_id),
+        output_dimension=32,
     )
     assert len(base_response.embeddings[0]) != len(test_response.embeddings[0])
     assert len(test_response.embeddings[0]) == 32
 
 
-@pytest.mark.xfail(reason="Only valid for model supporting task type")
+def test_embedding_task_type(llama_stack_client, embedding_model_id, inference_provider_type):
-def test_embedding_task_type(llama_stack_client, embedding_model_id):
+    if inference_provider_type not in SUPPORTED_PROVIDERS:
+        pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
+    if embedding_model_id not in MODELS_SUPPORTING_TASK_TYPE:
+        pytest.xfail(f"{embedding_model_id} doesn't support task_type")
     query_embedding = llama_stack_client.inference.embeddings(
         model_id=embedding_model_id, contents=[DUMMY_STRING], task_type="query"
     )
@@ -199,9 +253,14 @@ def test_embedding_task_type(llama_stack_client, embedding_model_id):
         "start",
     ],
 )
-def test_embedding_text_truncation(llama_stack_client, embedding_model_id, text_truncation):
+def test_embedding_text_truncation(llama_stack_client, embedding_model_id, text_truncation, inference_provider_type):
+    if inference_provider_type not in SUPPORTED_PROVIDERS:
+        pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
     response = llama_stack_client.inference.embeddings(
-        model_id=embedding_model_id, contents=[DUMMY_STRING], text_truncation=text_truncation
+        model_id=embedding_model_id,
+        contents=[DUMMY_STRING],
+        text_truncation=text_truncation,
+        **default_task_type(embedding_model_id),
     )
     assert isinstance(response, EmbeddingsResponse)
     assert len(response.embeddings) == 1
@@ -219,8 +278,15 @@ def test_embedding_text_truncation(llama_stack_client, embedding_model_id, text_
         "right",
     ],
 )
-def test_embedding_text_truncation_error(llama_stack_client, embedding_model_id, text_truncation):
+def test_embedding_text_truncation_error(
-    with pytest.raises(BadRequestError) as excinfo:
+    llama_stack_client, embedding_model_id, text_truncation, inference_provider_type
+):
+    if inference_provider_type not in SUPPORTED_PROVIDERS:
+        pytest.xfail(f"{inference_provider_type} doesn't support embedding model yet")
+    with pytest.raises(BadRequestError):
         llama_stack_client.inference.embeddings(
-            model_id=embedding_model_id, contents=[DUMMY_STRING], text_truncation=text_truncation
+            model_id=embedding_model_id,
+            contents=[DUMMY_STRING],
+            text_truncation=text_truncation,
+            **default_task_type(embedding_model_id),
         )

tests/client-sdk/inference/test_text_inference.py

@@ -139,7 +139,7 @@ def test_text_completion_log_probs_streaming(client_with_models, text_model_id,
             "top_k": 1,
         },
     )
-    streamed_content = [chunk for chunk in response]
+    streamed_content = list(response)
     for chunk in streamed_content:
         if chunk.delta:  # if there's a token, we expect logprobs
             assert chunk.logprobs, "Logprobs should not be empty"
@@ -405,7 +405,7 @@ def test_text_chat_completion_tool_calling_tools_not_in_request(
                 assert delta.tool_call.tool_name == "get_object_namespace_list"
             if delta.type == "tool_call" and delta.parse_status == "failed":
                 # expect raw message that failed to parse in tool_call
-                assert type(delta.tool_call) == str
+                assert isinstance(delta.tool_call, str)
                 assert len(delta.tool_call) > 0
     else:
         for tc in response.completion_message.tool_calls:

tests/client-sdk/report.py

@@ -42,8 +42,7 @@ def featured_models():
 
 
 SUPPORTED_MODELS = {
-    "ollama": set(
+    "ollama": {
-        [
         CoreModelId.llama3_1_8b_instruct.value,
         CoreModelId.llama3_1_8b_instruct.value,
         CoreModelId.llama3_1_70b_instruct.value,
@@ -61,10 +60,9 @@ SUPPORTED_MODELS = {
         CoreModelId.llama3_3_70b_instruct.value,
         CoreModelId.llama_guard_3_8b.value,
         CoreModelId.llama_guard_3_1b.value,
-        ]
+    },
-    ),
+    "tgi": {model.core_model_id.value for model in all_registered_models() if model.huggingface_repo},
-    "tgi": set([model.core_model_id.value for model in all_registered_models() if model.huggingface_repo]),
+    "vllm": {model.core_model_id.value for model in all_registered_models() if model.huggingface_repo},
-    "vllm": set([model.core_model_id.value for model in all_registered_models() if model.huggingface_repo]),
 }
 
 

tests/client-sdk/safety/__init__.py

@@ -3,3 +3,4 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+# ruff: noqa: N999

tests/client-sdk/safety/test_safety.py

@@ -42,7 +42,7 @@ def code_scanner_shield_id(available_shields):
 
 @pytest.fixture(scope="session")
 def model_providers(llama_stack_client):
-    return set([x.provider_id for x in llama_stack_client.providers.list() if x.api == "inference"])
+    return {x.provider_id for x in llama_stack_client.providers.list() if x.api == "inference"}
 
 
 def test_unsafe_examples(llama_stack_client, llama_guard_text_shield_id):

tests/client-sdk/tool_runtime/test_rag_tool.py

@@ -24,7 +24,6 @@ def single_entry_vector_db_registry(llama_stack_client, empty_vector_db_registry
         vector_db_id=vector_db_id,
         embedding_model="all-MiniLM-L6-v2",
         embedding_dimension=384,
-        provider_id="faiss",
     )
     vector_dbs = [vector_db.identifier for vector_db in llama_stack_client.vector_dbs.list()]
     return vector_dbs
@@ -121,7 +120,6 @@ def test_vector_db_insert_from_url_and_query(llama_stack_client, empty_vector_db
         vector_db_id=vector_db_id,
         embedding_model="all-MiniLM-L6-v2",
         embedding_dimension=384,
-        provider_id="faiss",
     )
 
     # list to check memory bank is successfully registered

tests/client-sdk/vector_io/__init__.py

@@ -3,3 +3,4 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
+# ruff: noqa: N999