Fix precommit check after moving to ruff (#927)

Lint check in main branch is failing. This fixes the lint check after we
moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We
need to move to a `ruff.toml` file as well as fixing and ignoring some
additional checks.

Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>

.ruff.toml

@@ -1,7 +1,8 @@
-[flake8]
 # Suggested config from pytorch that we can adapt
-select = B,C,E,F,N,P,T4,W,B9,TOR0,TOR1,TOR2
+lint.select = ["B", "C", "E" , "F" , "N", "W", "B9"]
-max-line-length = 120
+
+line-length = 120
+
 # C408 ignored because we like the dict keyword argument syntax
 # E501 is not flexible enough, we're using B950 instead
 # N812 ignored because import torch.nn.functional as F is PyTorch convention
@@ -9,23 +10,28 @@ max-line-length = 120
 # E731 allow usage of assigning lambda expressions
 # E701 let black auto-format statements on one line
 # E704 let black auto-format statements on one line
-ignore =
+lint.ignore = [
-    E203,E305,E402,E501,E721,E741,F405,F821,F841,F999,W503,W504,C408,E302,W291,E303,N812,N817,E731,E701,E704
+    "E203", "E305", "E402", "E501", "E721", "E741", "F405", "F821", "F841",
+    "C408", "E302", "W291", "E303", "N812", "N817", "E731", "E701",
+    # These are the additional ones we started ignoring after moving to ruff. We should look into each one of them later.
+    "C901", "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,
+    "EXE001",
     # random naming hints don't need
-    N802,
+    "N802",
     # these ignores are from flake8-bugbear; please fix!
-    B007,B008,B950
+    "B007", "B008"
-optional-ascii-coding = True
+]
-exclude =
+
-    ./.git,
+exclude = [
-    ./docs/*,
+    "./.git",
-    ./build,
+    "./docs/*",
-    ./scripts,
+    "./build",
-    ./venv,
+    "./scripts",
-    *.pyi,
+    "./venv",
-    .pre-commit-config.yaml,
+    "*.pyi",
-    *.md,
+    ".pre-commit-config.yaml",
-    .flake8
+    "*.md",
+    ".flake8"
+]

docs/source/building_applications/agent_execution_loop.md

@@ -77,7 +77,7 @@ agent_config = AgentConfig(
     instructions="You are a helpful assistant",
     # Enable both RAG and tool usage
     toolgroups=[
-        {"name": "builtin::rag", "args": {"vector_db_ids": ["my_docs"]}}.
+        {"name": "builtin::rag", "args": {"vector_db_ids": ["my_docs"]}},
         "builtin::code_interpreter",
     ],
     # Configure safety
@@ -86,13 +86,9 @@ agent_config = AgentConfig(
     # Control the inference loop
     max_infer_iters=5,
     sampling_params={
-        "strategy": {
+        "strategy": {"type": "top_p", "temperature": 0.7, "top_p": 0.95},
-            "type": "top_p",
+        "max_tokens": 2048,
-            "temperature": 0.7,
-            "top_p": 0.95
     },
-        "max_tokens": 2048
-    }
 )
 
 agent = Agent(client, agent_config)
@@ -101,11 +97,13 @@ session_id = agent.create_session("monitored_session")
 # Stream the agent's execution steps
 response = agent.create_turn(
     messages=[{"role": "user", "content": "Analyze this code and run it"}],
-    attachments=[{
+    attachments=[
+        {
             "content": "https://raw.githubusercontent.com/example/code.py",
-        "mime_type": "text/plain"
+            "mime_type": "text/plain",
-    }],
+        }
-    session_id=session_id
+    ],
+    session_id=session_id,
 )
 
 # Monitor each step of execution

docs/source/building_applications/evals.md

@@ -15,6 +15,7 @@ This first example walks you through how to evaluate a model candidate served by
 
 ```python
 import datasets
+
 ds = datasets.load_dataset(path="llamastack/mmmu", name="Agriculture", split="dev")
 ds = ds.select_columns(["chat_completion_input", "input_query", "expected_answer"])
 eval_rows = ds.to_pandas().to_dict(orient="records")
@@ -43,7 +44,7 @@ system_message = {
 client.eval_tasks.register(
     eval_task_id="meta-reference::mmmu",
     dataset_id=f"mmmu-{subset}-{split}",
-    scoring_functions=["basic::regex_parser_multiple_choice_answer"]
+    scoring_functions=["basic::regex_parser_multiple_choice_answer"],
 )
 
 response = client.eval.evaluate_rows(
@@ -62,9 +63,9 @@ response = client.eval.evaluate_rows(
                 "max_tokens": 4096,
                 "repeat_penalty": 1.0,
             },
-            "system_message": system_message
+            "system_message": system_message,
-        }
+        },
-    }
+    },
 )
 ```
 
@@ -88,7 +89,7 @@ _ = client.datasets.register(
         "input_query": {"type": "string"},
         "expected_answer": {"type": "string"},
         "chat_completion_input": {"type": "chat_completion_input"},
-    }
+    },
 )
 
 eval_rows = client.datasetio.get_rows_paginated(
@@ -101,7 +102,7 @@ eval_rows = client.datasetio.get_rows_paginated(
 client.eval_tasks.register(
     eval_task_id="meta-reference::simpleqa",
     dataset_id=simpleqa_dataset_id,
-    scoring_functions=["llm-as-judge::405b-simpleqa"]
+    scoring_functions=["llm-as-judge::405b-simpleqa"],
 )
 
 response = client.eval.evaluate_rows(
@@ -120,8 +121,8 @@ response = client.eval.evaluate_rows(
                 "max_tokens": 4096,
                 "repeat_penalty": 1.0,
             },
-        }
+        },
-    }
+    },
 )
 ```
 
@@ -144,14 +145,14 @@ agent_config = {
         {
             "type": "brave_search",
             "engine": "tavily",
-            "api_key": userdata.get("TAVILY_SEARCH_API_KEY")
+            "api_key": userdata.get("TAVILY_SEARCH_API_KEY"),
         }
     ],
     "tool_choice": "auto",
     "tool_prompt_format": "json",
     "input_shields": [],
     "output_shields": [],
-    "enable_session_persistence": False
+    "enable_session_persistence": False,
 }
 
 response = client.eval.evaluate_rows(
@@ -163,7 +164,7 @@ response = client.eval.evaluate_rows(
         "eval_candidate": {
             "type": "agent",
             "config": agent_config,
-        }
+        },
-    }
+    },
 )
 ```

docs/source/building_applications/evaluation.md

@@ -13,7 +13,7 @@ Here's how to set up basic evaluation:
 response = client.eval_tasks.register(
     eval_task_id="my_eval",
     dataset_id="my_dataset",
-    scoring_functions=["accuracy", "relevance"]
+    scoring_functions=["accuracy", "relevance"],
 )
 
 # Run evaluation
@@ -21,16 +21,10 @@ job = client.eval.run_eval(
     task_id="my_eval",
     task_config={
         "type": "app",
-        "eval_candidate": {
+        "eval_candidate": {"type": "agent", "config": agent_config},
-            "type": "agent",
+    },
-            "config": agent_config
-        }
-    }
 )
 
 # Get results
-result = client.eval.job_result(
+result = client.eval.job_result(task_id="my_eval", job_id=job.job_id)
-    task_id="my_eval",
-    job_id=job.job_id
-)
 ```

docs/source/building_applications/rag.md

@@ -34,15 +34,16 @@ chunks = [
     {
         "document_id": "doc1",
         "content": "Your document text here",
-        "mime_type": "text/plain"
+        "mime_type": "text/plain",
     },
-    ...
+    ...,
 ]
 client.vector_io.insert(vector_db_id, chunks)
 
 # You can then query for these chunks
-chunks_response = client.vector_io.query(vector_db_id, query="What do you know about...")
+chunks_response = client.vector_io.query(
-
+    vector_db_id, query="What do you know about..."
+)
 ```
 
 ### Using the RAG Tool
@@ -81,7 +82,6 @@ results = client.tool_runtime.rag_tool.query(
 One of the most powerful patterns is combining agents with RAG capabilities. Here's a complete example:
 
 ```python
-
 # Configure agent with memory
 agent_config = AgentConfig(
     model="Llama3.2-3B-Instruct",
@@ -91,9 +91,9 @@ agent_config = AgentConfig(
             "name": "builtin::rag",
             "args": {
                 "vector_db_ids": [vector_db_id],
+            },
         }
-        }
+    ],
-    ]
 )
 
 agent = Agent(client, agent_config)
@@ -101,25 +101,21 @@ session_id = agent.create_session("rag_session")
 
 # Initial document ingestion
 response = agent.create_turn(
-    messages=[{
+    messages=[
-        "role": "user",
+        {"role": "user", "content": "I am providing some documents for reference."}
-        "content": "I am providing some documents for reference."
+    ],
-    }],
     documents=[
         dict(
             content="https://raw.githubusercontent.com/example/doc.rst",
-            mime_type="text/plain"
+            mime_type="text/plain",
         )
     ],
-    session_id=session_id
+    session_id=session_id,
 )
 
 # Query with RAG
 response = agent.create_turn(
-    messages=[{
+    messages=[{"role": "user", "content": "What are the key topics in the documents?"}],
-        "role": "user",
+    session_id=session_id,
-        "content": "What are the key topics in the documents?"
-    }],
-    session_id=session_id
 )
 ```

docs/source/building_applications/safety.md

@@ -5,15 +5,11 @@ Safety is a critical component of any AI application. Llama Stack provides a Shi
 ```python
 # Register a safety shield
 shield_id = "content_safety"
-client.shields.register(
+client.shields.register(shield_id=shield_id, provider_shield_id="llama-guard-basic")
-    shield_id=shield_id,
-    provider_shield_id="llama-guard-basic"
-)
 
 # Run content through shield
 response = client.safety.run_shield(
-    shield_id=shield_id,
+    shield_id=shield_id, messages=[{"role": "user", "content": "User message here"}]
-    messages=[{"role": "user", "content": "User message here"}]
 )
 
 if response.violation:

docs/source/building_applications/telemetry.md

@@ -8,24 +8,16 @@ The telemetry system supports three main types of events:
 - **Unstructured Log Events**: Free-form log messages with severity levels
 ```python
 unstructured_log_event = UnstructuredLogEvent(
-    message="This is a log message",
+    message="This is a log message", severity=LogSeverity.INFO
-    severity=LogSeverity.INFO
 )
 ```
 - **Metric Events**: Numerical measurements with units
 ```python
-metric_event = MetricEvent(
+metric_event = MetricEvent(metric="my_metric", value=10, unit="count")
-    metric="my_metric",
-    value=10,
-    unit="count"
-)
 ```
 - **Structured Log Events**: System events like span start/end. Extensible to add more structured log types.
 ```python
-structured_log_event = SpanStartPayload(
+structured_log_event = SpanStartPayload(name="my_span", parent_span_id="parent_span_id")
-    name="my_span",
-    parent_span_id="parent_span_id"
-)
 ```
 
 ### Spans and Traces

docs/source/building_applications/tools.md

@@ -35,7 +35,7 @@ Example client SDK call to register a "websearch" toolgroup that is provided by
 client.toolgroups.register(
     toolgroup_id="builtin::websearch",
     provider_id="brave-search",
-    args={"max_results": 5}
+    args={"max_results": 5},
 )
 ```
 
@@ -50,8 +50,7 @@ The Code Interpreter allows execution of Python code within a controlled environ
 ```python
 # Register Code Interpreter tool group
 client.toolgroups.register(
-    toolgroup_id="builtin::code_interpreter",
+    toolgroup_id="builtin::code_interpreter", provider_id="code_interpreter"
-    provider_id="code_interpreter"
 )
 ```
 
@@ -68,16 +67,14 @@ The WolframAlpha tool provides access to computational knowledge through the Wol
 ```python
 # Register WolframAlpha tool group
 client.toolgroups.register(
-    toolgroup_id="builtin::wolfram_alpha",
+    toolgroup_id="builtin::wolfram_alpha", provider_id="wolfram-alpha"
-    provider_id="wolfram-alpha"
 )
 ```
 
 Example usage:
 ```python
 result = client.tool_runtime.invoke_tool(
-    tool_name="wolfram_alpha",
+    tool_name="wolfram_alpha", args={"query": "solve x^2 + 2x + 1 = 0"}
-    args={"query": "solve x^2 + 2x + 1 = 0"}
 )
 ```
 
@@ -90,10 +87,7 @@ The Memory tool enables retrieval of context from various types of memory banks
 client.toolgroups.register(
     toolgroup_id="builtin::memory",
     provider_id="memory",
-    args={
+    args={"max_chunks": 5, "max_tokens_in_context": 4096},
-        "max_chunks": 5,
-        "max_tokens_in_context": 4096
-    }
 )
 ```
 
@@ -136,9 +130,7 @@ config = AgentConfig(
     toolgroups=[
         "builtin::websearch",
     ],
-    client_tools=[
+    client_tools=[ToolDef(name="client_tool", description="Client provided tool")],
-        ToolDef(name="client_tool", description="Client provided tool")
-    ]
 )
 ```
 
@@ -167,9 +159,9 @@ Example tool definition:
             "name": "query",
             "parameter_type": "string",
             "description": "The query to search for",
-            "required": True
+            "required": True,
         }
-    ]
+    ],
 }
 ```
 
@@ -179,8 +171,7 @@ Tools can be invoked using the `invoke_tool` method:
 
 ```python
 result = client.tool_runtime.invoke_tool(
-    tool_name="web_search",
+    tool_name="web_search", kwargs={"query": "What is the capital of France?"}
-    kwargs={"query": "What is the capital of France?"}
 )
 ```
 

docs/source/distributions/importing_as_library.md

@@ -1,9 +1,9 @@
 # Using Llama Stack as a Library
 
 If you are planning to use an external service for Inference (even Ollama or TGI counts as external), it is often easier to use Llama Stack as a library. This avoids the overhead of setting up a server.
-```python
+```bash
 # setup
-pip install llama-stack
+uv pip install llama-stack
 llama stack build --template together --image-type venv
 ```
 
@@ -13,7 +13,7 @@ from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
 client = LlamaStackAsLibraryClient(
     "ollama",
     # provider_data is optional, but if you need to pass in any provider specific data, you can do so here.
-    provider_data = {"tavily_search_api_key": os.environ['TAVILY_SEARCH_API_KEY']}
+    provider_data={"tavily_search_api_key": os.environ["TAVILY_SEARCH_API_KEY"]},
 )
 await client.initialize()
 ```

docs/source/getting_started/index.md

@@ -96,18 +96,26 @@ Here is a simple example to perform chat completions using the SDK.
 ```python
 import os
 
+
 def create_http_client():
     from llama_stack_client import LlamaStackClient
-    return LlamaStackClient(base_url=f"http://localhost:{os.environ['LLAMA_STACK_PORT']}")
+
+    return LlamaStackClient(
+        base_url=f"http://localhost:{os.environ['LLAMA_STACK_PORT']}"
+    )
+
 
 def create_library_client(template="ollama"):
     from llama_stack import LlamaStackAsLibraryClient
+
     client = LlamaStackAsLibraryClient(template)
     client.initialize()
     return client
 
 
-client = create_library_client()  # or create_http_client() depending on the environment you picked
+client = (
+    create_library_client()
+)  # or create_http_client() depending on the environment you picked
 
 # List available models
 models = client.models.list()
@@ -120,8 +128,8 @@ response = client.inference.chat_completion(
     model_id=os.environ["INFERENCE_MODEL"],
     messages=[
         {"role": "system", "content": "You are a helpful assistant."},
-        {"role": "user", "content": "Write a haiku about coding"}
+        {"role": "user", "content": "Write a haiku about coding"},
-    ]
+    ],
 )
 print(response.completion_message.content)
 ```
@@ -139,7 +147,9 @@ from llama_stack_client.lib.agents.event_logger import EventLogger
 from llama_stack_client.types.agent_create_params import AgentConfig
 from llama_stack_client.types import Document
 
-client = create_library_client()  # or create_http_client() depending on the environment you picked
+client = (
+    create_library_client()
+)  # or create_http_client() depending on the environment you picked
 
 # Documents to be used for RAG
 urls = ["chat.rst", "llama3.rst", "datasets.rst", "lora_finetune.rst"]
@@ -174,12 +184,12 @@ agent_config = AgentConfig(
     instructions="You are a helpful assistant",
     enable_session_persistence=False,
     # Define tools available to the agent
-    toolgroups = [
+    toolgroups=[
         {
             "name": "builtin::rag",
-          "args" : {
+            "args": {
                 "vector_db_ids": [vector_db_id],
-          }
+            },
         }
     ],
 )
@@ -193,7 +203,7 @@ user_prompts = [
 
 # Run the agent loop by calling the `create_turn` method
 for prompt in user_prompts:
-    cprint(f'User> {prompt}', 'green')
+    cprint(f"User> {prompt}", "green")
     response = rag_agent.create_turn(
         messages=[{"role": "user", "content": prompt}],
         session_id=session_id,

docs/source/references/evals_reference/index.md

@@ -51,6 +51,7 @@ This first example walks you through how to evaluate a model candidate served by
 
 ```python
 import datasets
+
 ds = datasets.load_dataset(path="llamastack/mmmu", name="Agriculture", split="dev")
 ds = ds.select_columns(["chat_completion_input", "input_query", "expected_answer"])
 eval_rows = ds.to_pandas().to_dict(orient="records")
@@ -79,7 +80,7 @@ system_message = {
 client.eval_tasks.register(
     eval_task_id="meta-reference::mmmu",
     dataset_id=f"mmmu-{subset}-{split}",
-    scoring_functions=["basic::regex_parser_multiple_choice_answer"]
+    scoring_functions=["basic::regex_parser_multiple_choice_answer"],
 )
 
 response = client.eval.evaluate_rows(
@@ -98,9 +99,9 @@ response = client.eval.evaluate_rows(
                 "max_tokens": 4096,
                 "repeat_penalty": 1.0,
             },
-            "system_message": system_message
+            "system_message": system_message,
-        }
+        },
-    }
+    },
 )
 ```
 
@@ -124,7 +125,7 @@ _ = client.datasets.register(
         "input_query": {"type": "string"},
         "expected_answer": {"type": "string"},
         "chat_completion_input": {"type": "chat_completion_input"},
-    }
+    },
 )
 
 eval_rows = client.datasetio.get_rows_paginated(
@@ -137,7 +138,7 @@ eval_rows = client.datasetio.get_rows_paginated(
 client.eval_tasks.register(
     eval_task_id="meta-reference::simpleqa",
     dataset_id=simpleqa_dataset_id,
-    scoring_functions=["llm-as-judge::405b-simpleqa"]
+    scoring_functions=["llm-as-judge::405b-simpleqa"],
 )
 
 response = client.eval.evaluate_rows(
@@ -156,8 +157,8 @@ response = client.eval.evaluate_rows(
                 "max_tokens": 4096,
                 "repeat_penalty": 1.0,
             },
-        }
+        },
-    }
+    },
 )
 ```
 
@@ -180,14 +181,14 @@ agent_config = {
         {
             "type": "brave_search",
             "engine": "tavily",
-            "api_key": userdata.get("TAVILY_SEARCH_API_KEY")
+            "api_key": userdata.get("TAVILY_SEARCH_API_KEY"),
         }
     ],
     "tool_choice": "auto",
     "tool_prompt_format": "json",
     "input_shields": [],
     "output_shields": [],
-    "enable_session_persistence": False
+    "enable_session_persistence": False,
 }
 
 response = client.eval.evaluate_rows(
@@ -199,8 +200,8 @@ response = client.eval.evaluate_rows(
         "eval_candidate": {
             "type": "agent",
             "config": agent_config,
-        }
+        },
-    }
+    },
 )
 ```
 
@@ -237,7 +238,9 @@ GENERATED_RESPONSE: {generated_answer}
 EXPECTED_RESPONSE: {expected_answer}
 """
 
-input_query = "What are the top 5 topics that were explained? Only list succinct bullet points."
+input_query = (
+    "What are the top 5 topics that were explained? Only list succinct bullet points."
+)
 generated_answer = """
 Here are the top 5 topics that were explained in the documentation for Torchtune:
 
@@ -268,7 +271,9 @@ scoring_params = {
     "braintrust::factuality": None,
 }
 
-response = client.scoring.score(input_rows=dataset_rows, scoring_functions=scoring_params)
+response = client.scoring.score(
+    input_rows=dataset_rows, scoring_functions=scoring_params
+)
 ```
 
 ## Running Evaluations via CLI

docs/source/references/python_sdk_reference/index.md

@@ -33,7 +33,11 @@ from llama_stack_client.types import (
 Types:
 
 ```python
-from llama_stack_client.types import ListToolGroupsResponse, ToolGroup, ToolgroupListResponse
+from llama_stack_client.types import (
+    ListToolGroupsResponse,
+    ToolGroup,
+    ToolgroupListResponse,
+)
 ```
 
 Methods:
@@ -444,7 +448,11 @@ Methods:
 Types:
 
 ```python
-from llama_stack_client.types import EvalTask, ListEvalTasksResponse, EvalTaskListResponse
+from llama_stack_client.types import (
+    EvalTask,
+    ListEvalTasksResponse,
+    EvalTaskListResponse,
+)
 ```
 
 Methods:

docs/zero_to_hero_guide/README.md

@@ -224,7 +224,7 @@ client = LlamaStackClient(base_url="http://localhost:5001")
 response = client.inference.chat_completion(
     messages=[
         {"role": "system", "content": "You are a friendly assistant."},
-        {"role": "user", "content": "Write a two-sentence poem about llama."}
+        {"role": "user", "content": "Write a two-sentence poem about llama."},
     ],
     model_id=INFERENCE_MODEL,
 )

llama_stack/apis/agents/agents.py

@@ -86,9 +86,7 @@ class ShieldCallStep(StepCommon):
 
 @json_schema_type
 class MemoryRetrievalStep(StepCommon):
-    step_type: Literal[StepType.memory_retrieval.value] = (
+    step_type: Literal[StepType.memory_retrieval.value] = StepType.memory_retrieval.value
-        StepType.memory_retrieval.value
-    )
     vector_db_ids: str
     inserted_context: InterleavedContent
 
@@ -184,9 +182,7 @@ class AgentTurnResponseEventType(Enum):
 
 @json_schema_type
 class AgentTurnResponseStepStartPayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.step_start.value] = (
+    event_type: Literal[AgentTurnResponseEventType.step_start.value] = AgentTurnResponseEventType.step_start.value
-        AgentTurnResponseEventType.step_start.value
-    )
     step_type: StepType
     step_id: str
     metadata: Optional[Dict[str, Any]] = Field(default_factory=dict)
@@ -194,9 +190,7 @@ class AgentTurnResponseStepStartPayload(BaseModel):
 
 @json_schema_type
 class AgentTurnResponseStepCompletePayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.step_complete.value] = (
+    event_type: Literal[AgentTurnResponseEventType.step_complete.value] = AgentTurnResponseEventType.step_complete.value
-        AgentTurnResponseEventType.step_complete.value
-    )
     step_type: StepType
     step_id: str
     step_details: Step
@@ -206,9 +200,7 @@ class AgentTurnResponseStepCompletePayload(BaseModel):
 class AgentTurnResponseStepProgressPayload(BaseModel):
     model_config = ConfigDict(protected_namespaces=())
 
-    event_type: Literal[AgentTurnResponseEventType.step_progress.value] = (
+    event_type: Literal[AgentTurnResponseEventType.step_progress.value] = AgentTurnResponseEventType.step_progress.value
-        AgentTurnResponseEventType.step_progress.value
-    )
     step_type: StepType
     step_id: str
 
@@ -217,17 +209,13 @@ class AgentTurnResponseStepProgressPayload(BaseModel):
 
 @json_schema_type
 class AgentTurnResponseTurnStartPayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.turn_start.value] = (
+    event_type: Literal[AgentTurnResponseEventType.turn_start.value] = AgentTurnResponseEventType.turn_start.value
-        AgentTurnResponseEventType.turn_start.value
-    )
     turn_id: str
 
 
 @json_schema_type
 class AgentTurnResponseTurnCompletePayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.turn_complete.value] = (
+    event_type: Literal[AgentTurnResponseEventType.turn_complete.value] = AgentTurnResponseEventType.turn_complete.value
-        AgentTurnResponseEventType.turn_complete.value
-    )
     turn: Turn
 
 
@@ -329,9 +317,7 @@ class Agents(Protocol):
         toolgroups: Optional[List[AgentToolGroup]] = None,
     ) -> Union[Turn, AsyncIterator[AgentTurnResponseStreamChunk]]: ...
 
-    @webmethod(
+    @webmethod(route="/agents/{agent_id}/session/{session_id}/turn/{turn_id}", method="GET")
-        route="/agents/{agent_id}/session/{session_id}/turn/{turn_id}", method="GET"
-    )
     async def get_agents_turn(
         self,
         agent_id: str,

llama_stack/apis/agents/event_logger.py

@@ -63,9 +63,7 @@ class EventLogger:
                 if isinstance(chunk, ToolResponseMessage):
                     yield (
                         chunk,
-                        LogEvent(
+                        LogEvent(role="CustomTool", content=chunk.content, color="grey"),
-                            role="CustomTool", content=chunk.content, color="grey"
-                        ),
                     )
                 continue
 
@@ -81,17 +79,12 @@ class EventLogger:
 
             step_type = event.payload.step_type
             # handle safety
-            if (
+            if step_type == StepType.shield_call and event_type == EventType.step_complete.value:
-                step_type == StepType.shield_call
-                and event_type == EventType.step_complete.value
-            ):
                 violation = event.payload.step_details.violation
                 if not violation:
                     yield (
                         event,
-                        LogEvent(
+                        LogEvent(role=step_type, content="No Violation", color="magenta"),
-                            role=step_type, content="No Violation", color="magenta"
-                        ),
                     )
                 else:
                     yield (
@@ -110,9 +103,7 @@ class EventLogger:
                         # TODO: Currently this event is never received
                         yield (
                             event,
-                            LogEvent(
+                            LogEvent(role=step_type, content="", end="", color="yellow"),
-                                role=step_type, content="", end="", color="yellow"
-                            ),
                         )
                     elif event_type == EventType.step_progress.value:
                         # HACK: if previous was not step/event was not inference's step_progress
@@ -125,9 +116,7 @@ class EventLogger:
                         ):
                             yield (
                                 event,
-                                LogEvent(
+                                LogEvent(role=step_type, content="", end="", color="yellow"),
-                                    role=step_type, content="", end="", color="yellow"
-                                ),
                             )
 
                         delta = event.payload.delta
@@ -161,9 +150,7 @@ class EventLogger:
                     if event_type == EventType.step_complete.value:
                         response = event.payload.step_details.model_response
                         if response.tool_calls:
-                            content = ToolUtils.encode_tool_call(
+                            content = ToolUtils.encode_tool_call(response.tool_calls[0], tool_prompt_format)
-                                response.tool_calls[0], tool_prompt_format
-                            )
                         else:
                             content = response.content
                         yield (
@@ -202,10 +189,7 @@ class EventLogger:
                         ),
                     )
 
-            if (
+            if step_type == StepType.memory_retrieval and event_type == EventType.step_complete.value:
-                step_type == StepType.memory_retrieval
-                and event_type == EventType.step_complete.value
-            ):
                 details = event.payload.step_details
                 inserted_context = interleaved_content_as_str(details.inserted_context)
                 content = f"fetched {len(inserted_context)} bytes from {details.vector_db_ids}"

llama_stack/apis/datasetio/datasetio.py

@@ -39,6 +39,4 @@ class DatasetIO(Protocol):
     ) -> PaginatedRowsResult: ...
 
     @webmethod(route="/datasetio/rows", method="POST")
-    async def append_rows(
+    async def append_rows(self, dataset_id: str, rows: List[Dict[str, Any]]) -> None: ...
-        self, dataset_id: str, rows: List[Dict[str, Any]]
-    ) -> None: ...

llama_stack/apis/eval/eval.py

@@ -63,9 +63,7 @@ class AppEvalTaskConfig(BaseModel):
 
 
 EvalTaskConfig = register_schema(
-    Annotated[
+    Annotated[Union[BenchmarkEvalTaskConfig, AppEvalTaskConfig], Field(discriminator="type")],
-        Union[BenchmarkEvalTaskConfig, AppEvalTaskConfig], Field(discriminator="type")
-    ],
     name="EvalTaskConfig",
 )
 

llama_stack/apis/inference/inference.py

@@ -245,9 +245,7 @@ class JsonSchemaResponseFormat(BaseModel):
     :param json_schema: The JSON schema the response should conform to. In a Python SDK, this is often a `pydantic` model.
     """
 
-    type: Literal[ResponseFormatType.json_schema.value] = (
+    type: Literal[ResponseFormatType.json_schema.value] = ResponseFormatType.json_schema.value
-        ResponseFormatType.json_schema.value
-    )
     json_schema: Dict[str, Any]
 
 
@@ -406,9 +404,7 @@ class Inference(Protocol):
         response_format: Optional[ResponseFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
-    ) -> Union[
+    ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
-        ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]
-    ]:
         """Generate a chat completion for the given messages using the specified model.
 
         :param model_id: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint.

llama_stack/apis/post_training/post_training.py

@@ -89,9 +89,7 @@ class QATFinetuningConfig(BaseModel):
 
 
 AlgorithmConfig = register_schema(
-    Annotated[
+    Annotated[Union[LoraFinetuningConfig, QATFinetuningConfig], Field(discriminator="type")],
-        Union[LoraFinetuningConfig, QATFinetuningConfig], Field(discriminator="type")
-    ],
     name="AlgorithmConfig",
 )
 
@@ -204,14 +202,10 @@ class PostTraining(Protocol):
     async def get_training_jobs(self) -> ListPostTrainingJobsResponse: ...
 
     @webmethod(route="/post-training/job/status", method="GET")
-    async def get_training_job_status(
+    async def get_training_job_status(self, job_uuid: str) -> Optional[PostTrainingJobStatusResponse]: ...
-        self, job_uuid: str
-    ) -> Optional[PostTrainingJobStatusResponse]: ...
 
     @webmethod(route="/post-training/job/cancel", method="POST")
     async def cancel_training_job(self, job_uuid: str) -> None: ...
 
     @webmethod(route="/post-training/job/artifacts", method="GET")
-    async def get_training_job_artifacts(
+    async def get_training_job_artifacts(self, job_uuid: str) -> Optional[PostTrainingJobArtifactsResponse]: ...
-        self, job_uuid: str
-    ) -> Optional[PostTrainingJobArtifactsResponse]: ...

llama_stack/apis/resource.py

@@ -23,9 +23,7 @@ class ResourceType(Enum):
 class Resource(BaseModel):
     """Base class for all Llama Stack resources"""
 
-    identifier: str = Field(
+    identifier: str = Field(description="Unique identifier for this resource in llama stack")
-        description="Unique identifier for this resource in llama stack"
-    )
 
     provider_resource_id: str = Field(
         description="Unique identifier for this resource in the provider",
@@ -34,6 +32,4 @@ class Resource(BaseModel):
 
     provider_id: str = Field(description="ID of the provider that owns this resource")
 
-    type: ResourceType = Field(
+    type: ResourceType = Field(description="Type of resource (e.g. 'model', 'shield', 'vector_db', etc.)")
-        description="Type of resource (e.g. 'model', 'shield', 'vector_db', etc.)"
-    )

llama_stack/apis/scoring_functions/scoring_functions.py

@@ -43,9 +43,7 @@ class AggregationFunctionType(Enum):
 
 @json_schema_type
 class LLMAsJudgeScoringFnParams(BaseModel):
-    type: Literal[ScoringFnParamsType.llm_as_judge.value] = (
+    type: Literal[ScoringFnParamsType.llm_as_judge.value] = ScoringFnParamsType.llm_as_judge.value
-        ScoringFnParamsType.llm_as_judge.value
-    )
     judge_model: str
     prompt_template: Optional[str] = None
     judge_score_regexes: Optional[List[str]] = Field(
@@ -60,9 +58,7 @@ class LLMAsJudgeScoringFnParams(BaseModel):
 
 @json_schema_type
 class RegexParserScoringFnParams(BaseModel):
-    type: Literal[ScoringFnParamsType.regex_parser.value] = (
+    type: Literal[ScoringFnParamsType.regex_parser.value] = ScoringFnParamsType.regex_parser.value
-        ScoringFnParamsType.regex_parser.value
-    )
     parsing_regexes: Optional[List[str]] = Field(
         description="Regex to extract the answer from generated response",
         default_factory=list,
@@ -112,9 +108,7 @@ class CommonScoringFnFields(BaseModel):
 
 @json_schema_type
 class ScoringFn(CommonScoringFnFields, Resource):
-    type: Literal[ResourceType.scoring_function.value] = (
+    type: Literal[ResourceType.scoring_function.value] = ResourceType.scoring_function.value
-        ResourceType.scoring_function.value
-    )
 
     @property
     def scoring_fn_id(self) -> str:
@@ -141,9 +135,7 @@ class ScoringFunctions(Protocol):
     async def list_scoring_functions(self) -> ListScoringFunctionsResponse: ...
 
     @webmethod(route="/scoring-functions/{scoring_fn_id}", method="GET")
-    async def get_scoring_function(
+    async def get_scoring_function(self, scoring_fn_id: str, /) -> Optional[ScoringFn]: ...
-        self, scoring_fn_id: str, /
-    ) -> Optional[ScoringFn]: ...
 
     @webmethod(route="/scoring-functions", method="POST")
     async def register_scoring_function(

llama_stack/apis/telemetry/telemetry.py

@@ -102,9 +102,7 @@ class StructuredLogType(Enum):
 
 @json_schema_type
 class SpanStartPayload(BaseModel):
-    type: Literal[StructuredLogType.SPAN_START.value] = (
+    type: Literal[StructuredLogType.SPAN_START.value] = StructuredLogType.SPAN_START.value
-        StructuredLogType.SPAN_START.value
-    )
     name: str
     parent_span_id: Optional[str] = None
 
@@ -190,9 +188,7 @@ class QuerySpanTreeResponse(BaseModel):
 @runtime_checkable
 class Telemetry(Protocol):
     @webmethod(route="/telemetry/events", method="POST")
-    async def log_event(
+    async def log_event(self, event: Event, ttl_seconds: int = DEFAULT_TTL_DAYS * 86400) -> None: ...
-        self, event: Event, ttl_seconds: int = DEFAULT_TTL_DAYS * 86400
-    ) -> None: ...
 
     @webmethod(route="/telemetry/traces", method="GET")
     async def query_traces(

llama_stack/apis/tools/rag_tool.py

@@ -64,9 +64,7 @@ RAGQueryGeneratorConfig = register_schema(
 class RAGQueryConfig(BaseModel):
     # This config defines how a query is generated using the messages
     # for memory bank retrieval.
-    query_generator_config: RAGQueryGeneratorConfig = Field(
+    query_generator_config: RAGQueryGeneratorConfig = Field(default=DefaultRAGQueryGeneratorConfig())
-        default=DefaultRAGQueryGeneratorConfig()
-    )
     max_tokens_in_context: int = 4096
     max_chunks: int = 5
 

llama_stack/apis/tools/tools.py

@@ -150,8 +150,6 @@ class ToolRuntime(Protocol):
     ) -> List[ToolDef]: ...
 
     @webmethod(route="/tool-runtime/invoke", method="POST")
-    async def invoke_tool(
+    async def invoke_tool(self, tool_name: str, kwargs: Dict[str, Any]) -> ToolInvocationResult:
-        self, tool_name: str, kwargs: Dict[str, Any]
-    ) -> ToolInvocationResult:
         """Run a tool with the given arguments"""
         ...

llama_stack/cli/download.py

@@ -147,9 +147,7 @@ class ParallelDownloader:
             "follow_redirects": True,
         }
 
-    async def retry_with_exponential_backoff(
+    async def retry_with_exponential_backoff(self, task: DownloadTask, func, *args, **kwargs):
-        self, task: DownloadTask, func, *args, **kwargs
-    ):
         last_exception = None
         for attempt in range(task.max_retries):
             try:
@@ -166,13 +164,9 @@ class ParallelDownloader:
                     continue
         raise last_exception
 
-    async def get_file_info(
+    async def get_file_info(self, client: httpx.AsyncClient, task: DownloadTask) -> None:
-        self, client: httpx.AsyncClient, task: DownloadTask
-    ) -> None:
         async def _get_info():
-            response = await client.head(
+            response = await client.head(task.url, headers={"Accept-Encoding": "identity"}, **self.client_options)
-                task.url, headers={"Accept-Encoding": "identity"}, **self.client_options
-            )
             response.raise_for_status()
             return response
 
@@ -201,14 +195,10 @@ class ParallelDownloader:
             return False
         return os.path.getsize(task.output_file) == task.total_size
 
-    async def download_chunk(
+    async def download_chunk(self, client: httpx.AsyncClient, task: DownloadTask, start: int, end: int) -> None:
-        self, client: httpx.AsyncClient, task: DownloadTask, start: int, end: int
-    ) -> None:
         async def _download_chunk():
             headers = {"Range": f"bytes={start}-{end}"}
-            async with client.stream(
+            async with client.stream("GET", task.url, headers=headers, **self.client_options) as response:
-                "GET", task.url, headers=headers, **self.client_options
-            ) as response:
                 response.raise_for_status()
 
                 with open(task.output_file, "ab") as file:
@@ -225,8 +215,7 @@ class ParallelDownloader:
             await self.retry_with_exponential_backoff(task, _download_chunk)
         except Exception as e:
             raise DownloadError(
-                f"Failed to download chunk {start}-{end} after "
+                f"Failed to download chunk {start}-{end} after {task.max_retries} attempts: {str(e)}"
-                f"{task.max_retries} attempts: {str(e)}"
             ) from e
 
     async def prepare_download(self, task: DownloadTask) -> None:
@@ -244,9 +233,7 @@ class ParallelDownloader:
                 # Check if file is already downloaded
                 if os.path.exists(task.output_file):
                     if self.verify_file_integrity(task):
-                        self.console.print(
+                        self.console.print(f"[green]Already downloaded {task.output_file}[/green]")
-                            f"[green]Already downloaded {task.output_file}[/green]"
-                        )
                         self.progress.update(task.task_id, completed=task.total_size)
                         return
 
@@ -259,9 +246,7 @@ class ParallelDownloader:
 
                     current_pos = task.downloaded_size
                     while current_pos < task.total_size:
-                        chunk_end = min(
+                        chunk_end = min(current_pos + chunk_size - 1, task.total_size - 1)
-                            current_pos + chunk_size - 1, task.total_size - 1
-                        )
                         chunks.append((current_pos, chunk_end))
                         current_pos = chunk_end + 1
 
@@ -273,18 +258,12 @@ class ParallelDownloader:
                     raise DownloadError(f"Download failed: {str(e)}") from e
 
         except Exception as e:
-            self.progress.update(
+            self.progress.update(task.task_id, description=f"[red]Failed: {task.output_file}[/red]")
-                task.task_id, description=f"[red]Failed: {task.output_file}[/red]"
+            raise DownloadError(f"Download failed for {task.output_file}: {str(e)}") from e
-            )
-            raise DownloadError(
-                f"Download failed for {task.output_file}: {str(e)}"
-            ) from e
 
     def has_disk_space(self, tasks: List[DownloadTask]) -> bool:
         try:
-            total_remaining_size = sum(
+            total_remaining_size = sum(task.total_size - task.downloaded_size for task in tasks)
-                task.total_size - task.downloaded_size for task in tasks
-            )
             dir_path = os.path.dirname(os.path.abspath(tasks[0].output_file))
             free_space = shutil.disk_usage(dir_path).free
 
@@ -314,9 +293,7 @@ class ParallelDownloader:
         with self.progress:
             for task in tasks:
                 desc = f"Downloading {Path(task.output_file).name}"
-                task.task_id = self.progress.add_task(
+                task.task_id = self.progress.add_task(desc, total=task.total_size, completed=task.downloaded_size)
-                    desc, total=task.total_size, completed=task.downloaded_size
-                )
 
             semaphore = asyncio.Semaphore(self.max_concurrent_downloads)
 
@@ -332,9 +309,7 @@ class ParallelDownloader:
         if failed_tasks:
             self.console.print("\n[red]Some downloads failed:[/red]")
             for task, error in failed_tasks:
-                self.console.print(
+                self.console.print(f"[red]- {Path(task.output_file).name}: {error}[/red]")
-                    f"[red]- {Path(task.output_file).name}: {error}[/red]"
-                )
             raise DownloadError(f"{len(failed_tasks)} downloads failed")
 
 
@@ -396,11 +371,7 @@ def _meta_download(
         output_file = str(output_dir / f)
         url = meta_url.replace("*", f"{info.folder}/{f}")
         total_size = info.pth_size if "consolidated" in f else 0
-        tasks.append(
+        tasks.append(DownloadTask(url=url, output_file=output_file, total_size=total_size, max_retries=3))
-            DownloadTask(
-                url=url, output_file=output_file, total_size=total_size, max_retries=3
-            )
-        )
 
     # Initialize and run parallel downloader
     downloader = ParallelDownloader(max_concurrent_downloads=max_concurrent_downloads)
@@ -446,14 +417,10 @@ def _download_from_manifest(manifest_file: str, max_concurrent_downloads: int):
         os.makedirs(output_dir, exist_ok=True)
 
         if any(output_dir.iterdir()):
-            console.print(
+            console.print(f"[yellow]Output directory {output_dir} is not empty.[/yellow]")
-                f"[yellow]Output directory {output_dir} is not empty.[/yellow]"
-            )
 
             while True:
-                resp = input(
+                resp = input("Do you want to (C)ontinue download or (R)estart completely? (continue/restart): ")
-                    "Do you want to (C)ontinue download or (R)estart completely? (continue/restart): "
-                )
                 if resp.lower() in ["restart", "r"]:
                     shutil.rmtree(output_dir)
                     os.makedirs(output_dir, exist_ok=True)
@@ -471,9 +438,7 @@ def _download_from_manifest(manifest_file: str, max_concurrent_downloads: int):
         ]
 
         # Initialize and run parallel downloader
-        downloader = ParallelDownloader(
+        downloader = ParallelDownloader(max_concurrent_downloads=max_concurrent_downloads)
-            max_concurrent_downloads=max_concurrent_downloads
-        )
         asyncio.run(downloader.download_all(tasks))
 
 

llama_stack/cli/model/prompt_format.py

@@ -47,33 +47,20 @@ class ModelPromptFormat(Subcommand):
 
         # Only Llama 3.1 and 3.2 are supported
         supported_model_ids = [
-            m
+            m for m in CoreModelId if model_family(m) in {ModelFamily.llama3_1, ModelFamily.llama3_2}
-            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])
         try:
             model_id = CoreModelId(args.model_name)
         except ValueError:
-            self.parser.error(
+            self.parser.error(f"{args.model_name} is not a valid Model. Choose one from --\n{model_str}")
-                f"{args.model_name} is not a valid Model. Choose one from --\n{model_str}"
-            )
 
         if model_id not in supported_model_ids:
-            self.parser.error(
+            self.parser.error(f"{model_id} is not a valid Model. Choose one from --\n {model_str}")
-                f"{model_id} is not a valid Model. Choose one from --\n {model_str}"
-            )
 
-        llama_3_1_file = (
+        llama_3_1_file = importlib.resources.files("llama_models") / "llama3_1/prompt_format.md"
-            importlib.resources.files("llama_models") / "llama3_1/prompt_format.md"
+        llama_3_2_text_file = importlib.resources.files("llama_models") / "llama3_2/text_prompt_format.md"
-        )
+        llama_3_2_vision_file = importlib.resources.files("llama_models") / "llama3_2/vision_prompt_format.md"
-        llama_3_2_text_file = (
-            importlib.resources.files("llama_models") / "llama3_2/text_prompt_format.md"
-        )
-        llama_3_2_vision_file = (
-            importlib.resources.files("llama_models")
-            / "llama3_2/vision_prompt_format.md"
-        )
         if model_family(model_id) == ModelFamily.llama3_1:
             with importlib.resources.as_file(llama_3_1_file) as f:
                 content = f.open("r").read()

llama_stack/cli/model/safety_models.py

@@ -17,16 +17,12 @@ class PromptGuardModel(BaseModel):
     """Make a 'fake' Model-like object for Prompt Guard. Eventually this will be removed."""
 
     model_id: str = "Prompt-Guard-86M"
-    description: str = (
+    description: str = "Prompt Guard. NOTE: this model will not be provided via `llama` CLI soon."
-        "Prompt Guard. NOTE: this model will not be provided via `llama` CLI soon."
-    )
     is_featured: bool = False
     huggingface_repo: str = "meta-llama/Prompt-Guard-86M"
     max_seq_length: int = 2048
     is_instruct_model: bool = False
-    quantization_format: CheckpointQuantizationFormat = (
+    quantization_format: CheckpointQuantizationFormat = CheckpointQuantizationFormat.bf16
-        CheckpointQuantizationFormat.bf16
-    )
     arch_args: Dict[str, Any] = Field(default_factory=dict)
     recommended_sampling_params: Optional[SamplingParams] = None
 

llama_stack/cli/stack/_build.py

@@ -56,9 +56,7 @@ def available_templates_specs() -> Dict[str, BuildConfig]:
     return template_specs
 
 
-def run_stack_build_command(
+def run_stack_build_command(parser: argparse.ArgumentParser, args: argparse.Namespace) -> None:
-    parser: argparse.ArgumentParser, args: argparse.Namespace
-) -> None:
     if args.list_templates:
         return _run_template_list_cmd()
 
@@ -129,11 +127,7 @@ def run_stack_build_command(
 
         providers = dict()
         for api, providers_for_api in get_provider_registry().items():
-            available_providers = [
+            available_providers = [x for x in providers_for_api.keys() if x not in ("remote", "remote::sample")]
-                x
-                for x in providers_for_api.keys()
-                if x not in ("remote", "remote::sample")
-            ]
             api_provider = prompt(
                 "> Enter provider for API {}: ".format(api.value),
                 completer=WordCompleter(available_providers),
@@ -156,9 +150,7 @@ def run_stack_build_command(
             description=description,
         )
 
-        build_config = BuildConfig(
+        build_config = BuildConfig(image_type=image_type, distribution_spec=distribution_spec)
-            image_type=image_type, distribution_spec=distribution_spec
-        )
     else:
         with open(args.config, "r") as f:
             try:
@@ -179,9 +171,7 @@ def run_stack_build_command(
 
     if args.print_deps_only:
         print(f"# Dependencies for {args.template or args.config or image_name}")
-        normal_deps, special_deps = get_provider_dependencies(
+        normal_deps, special_deps = get_provider_dependencies(build_config.distribution_spec.providers)
-            build_config.distribution_spec.providers
-        )
         normal_deps += SERVER_DEPENDENCIES
         print(f"uv pip install {' '.join(normal_deps)}")
         for special_dep in special_deps:
@@ -206,9 +196,7 @@ def _generate_run_config(
     """
     apis = list(build_config.distribution_spec.providers.keys())
     run_config = StackRunConfig(
-        container_image=(
+        container_image=(image_name if build_config.image_type == ImageType.container.value else None),
-            image_name if build_config.image_type == ImageType.container.value else None
-        ),
         image_name=image_name,
         apis=apis,
         providers={},
@@ -228,13 +216,9 @@ def _generate_run_config(
             if p.deprecation_error:
                 raise InvalidProviderError(p.deprecation_error)
 
-            config_type = instantiate_class_type(
+            config_type = instantiate_class_type(provider_registry[Api(api)][provider_type].config_class)
-                provider_registry[Api(api)][provider_type].config_class
-            )
             if hasattr(config_type, "sample_run_config"):
-                config = config_type.sample_run_config(
+                config = config_type.sample_run_config(__distro_dir__=f"distributions/{image_name}")
-                    __distro_dir__=f"distributions/{image_name}"
-                )
             else:
                 config = {}
 
@@ -269,9 +253,7 @@ def _run_stack_build_command_from_build_config(
             image_name = f"distribution-{template_name}"
         else:
             if not image_name:
-                raise ValueError(
+                raise ValueError("Please specify an image name when building a container image without a template")
-                    "Please specify an image name when building a container image without a template"
-                )
     elif build_config.image_type == ImageType.conda.value:
         if not image_name:
             raise ValueError("Please specify an image name when building a conda image")
@@ -299,10 +281,7 @@ def _run_stack_build_command_from_build_config(
 
     if template_name:
         # copy run.yaml from template to build_dir instead of generating it again
-        template_path = (
+        template_path = importlib.resources.files("llama_stack") / f"templates/{template_name}/run.yaml"
-            importlib.resources.files("llama_stack")
-            / f"templates/{template_name}/run.yaml"
-        )
         with importlib.resources.as_file(template_path) as path:
             run_config_file = build_dir / f"{template_name}-run.yaml"
             shutil.copy(path, run_config_file)

llama_stack/cli/stack/run.py

@@ -82,31 +82,21 @@ class StackRun(Subcommand):
 
         if not config_file.exists() and not has_yaml_suffix:
             # check if this is a template
-            config_file = (
+            config_file = Path(REPO_ROOT) / "llama_stack" / "templates" / args.config / "run.yaml"
-                Path(REPO_ROOT) / "llama_stack" / "templates" / args.config / "run.yaml"
-            )
             if config_file.exists():
                 template_name = args.config
 
         if not config_file.exists() and not has_yaml_suffix:
             # check if it's a build config saved to conda dir
-            config_file = Path(
+            config_file = Path(BUILDS_BASE_DIR / ImageType.conda.value / f"{args.config}-run.yaml")
-                BUILDS_BASE_DIR / ImageType.conda.value / f"{args.config}-run.yaml"
-            )
 
         if not config_file.exists() and not has_yaml_suffix:
             # check if it's a build config saved to container dir
-            config_file = Path(
+            config_file = Path(BUILDS_BASE_DIR / ImageType.container.value / f"{args.config}-run.yaml")
-                BUILDS_BASE_DIR / ImageType.container.value / f"{args.config}-run.yaml"
-            )
 
         if not config_file.exists() and not has_yaml_suffix:
             # check if it's a build config saved to ~/.llama dir
-            config_file = Path(
+            config_file = Path(DISTRIBS_BASE_DIR / f"llamastack-{args.config}" / f"{args.config}-run.yaml")
-                DISTRIBS_BASE_DIR
-                / f"llamastack-{args.config}"
-                / f"{args.config}-run.yaml"
-            )
 
         if not config_file.exists():
             self.parser.error(
@@ -119,15 +109,8 @@ class StackRun(Subcommand):
         config = parse_and_maybe_upgrade_config(config_dict)
 
         if config.container_image:
-            script = (
+            script = importlib.resources.files("llama_stack") / "distribution/start_container.sh"
-                importlib.resources.files("llama_stack")
+            image_name = f"distribution-{template_name}" if template_name else config.container_image
-                / "distribution/start_container.sh"
-            )
-            image_name = (
-                f"distribution-{template_name}"
-                if template_name
-                else config.container_image
-            )
             run_args = [script, image_name]
         else:
             current_conda_env = os.environ.get("CONDA_DEFAULT_ENV")
@@ -145,11 +128,7 @@ class StackRun(Subcommand):
                 if env_name == "base":
                     return os.environ.get("CONDA_PREFIX")
                 # Get conda environments info
-                conda_env_info = json.loads(
+                conda_env_info = json.loads(subprocess.check_output(["conda", "info", "--envs", "--json"]).decode())
-                    subprocess.check_output(
-                        ["conda", "info", "--envs", "--json"]
-                    ).decode()
-                )
                 envs = conda_env_info["envs"]
                 for envpath in envs:
                     if envpath.endswith(env_name):
@@ -173,10 +152,7 @@ class StackRun(Subcommand):
                 )
                 return
 
-            script = (
+            script = importlib.resources.files("llama_stack") / "distribution/start_conda_env.sh"
-                importlib.resources.files("llama_stack")
-                / "distribution/start_conda_env.sh"
-            )
             run_args = [
                 script,
                 image_name,

llama_stack/cli/table.py

@@ -22,11 +22,7 @@ def format_row(row, col_widths):
             if line.strip() == "":
                 lines.append("")
             else:
-                lines.extend(
+                lines.extend(textwrap.wrap(line, width, break_long_words=False, replace_whitespace=False))
-                    textwrap.wrap(
-                        line, width, break_long_words=False, replace_whitespace=False
-                    )
-                )
         return lines
 
     wrapped = [wrap(item, width) for item, width in zip(row, col_widths)]

llama_stack/cli/tests/test_stack_config.py

@@ -41,9 +41,7 @@ def up_to_date_config():
             - provider_id: provider1
               provider_type: inline::meta-reference
               config: {{}}
-    """.format(
+    """.format(version=LLAMA_STACK_RUN_CONFIG_VERSION, built_at=datetime.now().isoformat())
-            version=LLAMA_STACK_RUN_CONFIG_VERSION, built_at=datetime.now().isoformat()
-        )
     )
 
 
@@ -83,9 +81,7 @@ def old_config():
           telemetry:
             provider_type: noop
             config: {{}}
-    """.format(
+    """.format(built_at=datetime.now().isoformat())
-            built_at=datetime.now().isoformat()
-        )
     )
 
 
@@ -108,10 +104,7 @@ def test_parse_and_maybe_upgrade_config_up_to_date(up_to_date_config):
 def test_parse_and_maybe_upgrade_config_old_format(old_config):
     result = parse_and_maybe_upgrade_config(old_config)
     assert result.version == LLAMA_STACK_RUN_CONFIG_VERSION
-    assert all(
+    assert all(api in result.providers for api in ["inference", "safety", "memory", "telemetry"])
-        api in result.providers
-        for api in ["inference", "safety", "memory", "telemetry"]
-    )
     safety_provider = result.providers["safety"][0]
     assert safety_provider.provider_type == "meta-reference"
     assert "llama_guard_shield" in safety_provider.config

llama_stack/cli/verify_download.py

@@ -72,9 +72,7 @@ def load_checksums(checklist_path: Path) -> Dict[str, str]:
     return checksums
 
 
-def verify_files(
+def verify_files(model_dir: Path, checksums: Dict[str, str], console: Console) -> List[VerificationResult]:
-    model_dir: Path, checksums: Dict[str, str], console: Console
-) -> List[VerificationResult]:
     results = []
 
     with Progress(

llama_stack/distribution/build.py

@@ -58,22 +58,14 @@ def get_provider_dependencies(
     for api_str, provider_or_providers in config_providers.items():
         providers_for_api = all_providers[Api(api_str)]
 
-        providers = (
+        providers = provider_or_providers if isinstance(provider_or_providers, list) else [provider_or_providers]
-            provider_or_providers
-            if isinstance(provider_or_providers, list)
-            else [provider_or_providers]
-        )
 
         for provider in providers:
             # Providers from BuildConfig and RunConfig are subtly different – not great
-            provider_type = (
+            provider_type = provider if isinstance(provider, str) else provider.provider_type
-                provider if isinstance(provider, str) else provider.provider_type
-            )
 
             if provider_type not in providers_for_api:
-                raise ValueError(
+                raise ValueError(f"Provider `{provider}` is not available for API `{api_str}`")
-                    f"Provider `{provider}` is not available for API `{api_str}`"
-                )
 
             provider_spec = providers_for_api[provider_type]
             deps.extend(provider_spec.pip_packages)
@@ -109,19 +101,13 @@ def build_image(
     image_name: str,
     template_or_config: str,
 ):
-    container_base = (
+    container_base = build_config.distribution_spec.container_image or "python:3.10-slim"
-        build_config.distribution_spec.container_image or "python:3.10-slim"
-    )
 
-    normal_deps, special_deps = get_provider_dependencies(
+    normal_deps, special_deps = get_provider_dependencies(build_config.distribution_spec.providers)
-        build_config.distribution_spec.providers
-    )
     normal_deps += SERVER_DEPENDENCIES
 
     if build_config.image_type == ImageType.container.value:
-        script = str(
+        script = str(importlib.resources.files("llama_stack") / "distribution/build_container.sh")
-            importlib.resources.files("llama_stack") / "distribution/build_container.sh"
-        )
         args = [
             script,
             template_or_config,
@@ -132,9 +118,7 @@ def build_image(
             " ".join(normal_deps),
         ]
     elif build_config.image_type == ImageType.conda.value:
-        script = str(
+        script = str(importlib.resources.files("llama_stack") / "distribution/build_conda_env.sh")
-            importlib.resources.files("llama_stack") / "distribution/build_conda_env.sh"
-        )
         args = [
             script,
             str(image_name),
@@ -142,9 +126,7 @@ def build_image(
             " ".join(normal_deps),
         ]
     elif build_config.image_type == ImageType.venv.value:
-        script = str(
+        script = str(importlib.resources.files("llama_stack") / "distribution/build_venv.sh")
-            importlib.resources.files("llama_stack") / "distribution/build_venv.sh"
-        )
         args = [
             script,
             str(image_name),

llama_stack/distribution/client.py

@@ -68,9 +68,7 @@ def create_api_client_class(protocol) -> Type:
                 return_type = None
             else:
                 return_type = extract_non_async_iterator_type(sig.return_annotation)
-                assert return_type, (
+                assert return_type, f"Could not extract return type for {sig.return_annotation}"
-                    f"Could not extract return type for {sig.return_annotation}"
-                )
 
             async with httpx.AsyncClient() as client:
                 params = self.httpx_request_params(method_name, *args, **kwargs)
@@ -87,9 +85,7 @@ def create_api_client_class(protocol) -> Type:
             webmethod, sig = self.routes[method_name]
 
             return_type = extract_async_iterator_type(sig.return_annotation)
-            assert return_type, (
+            assert return_type, f"Could not extract return type for {sig.return_annotation}"
-                f"Could not extract return type for {sig.return_annotation}"
-            )
 
             async with httpx.AsyncClient() as client:
                 params = self.httpx_request_params(method_name, *args, **kwargs)
@@ -204,9 +200,7 @@ async def example(model: str = None):
     if not model:
         model = "Llama3.2-3B-Instruct"
 
-    message = UserMessage(
+    message = UserMessage(content="hello world, write me a 2 sentence poem about the moon")
-        content="hello world, write me a 2 sentence poem about the moon"
-    )
     cprint(f"User>{message.content}", "green")
 
     stream = True

llama_stack/distribution/configure.py

@@ -26,9 +26,7 @@ from llama_stack.providers.datatypes import Api, ProviderSpec
 logger = logging.getLogger(__name__)
 
 
-def configure_single_provider(
+def configure_single_provider(registry: Dict[str, ProviderSpec], provider: Provider) -> Provider:
-    registry: Dict[str, ProviderSpec], provider: Provider
-) -> Provider:
     provider_spec = registry[provider.provider_type]
     config_type = instantiate_class_type(provider_spec.config_class)
     try:
@@ -47,9 +45,7 @@ def configure_single_provider(
     )
 
 
-def configure_api_providers(
+def configure_api_providers(config: StackRunConfig, build_spec: DistributionSpec) -> StackRunConfig:
-    config: StackRunConfig, build_spec: DistributionSpec
-) -> StackRunConfig:
     is_nux = len(config.providers) == 0
 
     if is_nux:
@@ -87,9 +83,7 @@ def configure_api_providers(
             updated_providers = []
             for p in existing_providers:
                 logger.info(f"> Configuring provider `({p.provider_type})`")
-                updated_providers.append(
+                updated_providers.append(configure_single_provider(provider_registry[api], p))
-                    configure_single_provider(provider_registry[api], p)
-                )
                 logger.info("")
         else:
             # we are newly configuring this API
@@ -114,11 +108,7 @@ def configure_api_providers(
                     configure_single_provider(
                         provider_registry[api],
                         Provider(
-                            provider_id=(
+                            provider_id=(f"{provider_type}-{i:02d}" if len(plist) > 1 else provider_type),
-                                f"{provider_type}-{i:02d}"
-                                if len(plist) > 1
-                                else provider_type
-                            ),
                             provider_type=provider_type,
                             config={},
                         ),
@@ -137,11 +127,7 @@ def upgrade_from_routing_table(
     def get_providers(entries):
         return [
             Provider(
-                provider_id=(
+                provider_id=(f"{entry['provider_type']}-{i:02d}" if len(entries) > 1 else entry["provider_type"]),
-                    f"{entry['provider_type']}-{i:02d}"
-                    if len(entries) > 1
-                    else entry["provider_type"]
-                ),
                 provider_type=entry["provider_type"],
                 config=entry["config"],
             )

llama_stack/distribution/datatypes.py

@@ -163,9 +163,7 @@ a default SQLite store will be used.""",
 class BuildConfig(BaseModel):
     version: str = LLAMA_STACK_BUILD_CONFIG_VERSION
 
-    distribution_spec: DistributionSpec = Field(
+    distribution_spec: DistributionSpec = Field(description="The distribution spec to build including API providers. ")
-        description="The distribution spec to build including API providers. "
-    )
     image_type: str = Field(
         default="conda",
         description="Type of package to build (conda | container | venv)",

llama_stack/distribution/distribution.py

@@ -55,9 +55,7 @@ def builtin_automatically_routed_apis() -> List[AutoRoutedApiInfo]:
 
 
 def providable_apis() -> List[Api]:
-    routing_table_apis = set(
+    routing_table_apis = set(x.routing_table_api for x in builtin_automatically_routed_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/library_client.py

@@ -154,9 +154,7 @@ class LlamaStackAsLibraryClient(LlamaStackClient):
 
             def sync_generator():
                 try:
-                    async_stream = loop.run_until_complete(
+                    async_stream = loop.run_until_complete(self.async_client.request(*args, **kwargs))
-                        self.async_client.request(*args, **kwargs)
-                    )
                     while True:
                         chunk = loop.run_until_complete(async_stream.__anext__())
                         yield chunk
@@ -181,9 +179,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         # when using the library client, we should not log to console since many
         # of our logs are intended for server-side usage
         current_sinks = os.environ.get("TELEMETRY_SINKS", "sqlite").split(",")
-        os.environ["TELEMETRY_SINKS"] = ",".join(
+        os.environ["TELEMETRY_SINKS"] = ",".join(sink for sink in current_sinks if sink != "console")
-            sink for sink in current_sinks if sink != "console"
-        )
 
         if config_path_or_template_name.endswith(".yaml"):
             config_path = Path(config_path_or_template_name)
@@ -202,9 +198,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
 
     async def initialize(self):
         try:
-            self.impls = await construct_stack(
+            self.impls = await construct_stack(self.config, self.custom_provider_registry)
-                self.config, self.custom_provider_registry
-            )
         except ModuleNotFoundError as _e:
             cprint(_e.msg, "red")
             cprint(
@@ -247,9 +241,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
                 func = getattr(impl, endpoint.name)
                 if endpoint.method not in endpoint_impls:
                     endpoint_impls[endpoint.method] = {}
-                endpoint_impls[endpoint.method][
+                endpoint_impls[endpoint.method][_convert_path_to_regex(endpoint.route)] = func
-                    _convert_path_to_regex(endpoint.route)
-                ] = func
 
         self.endpoint_impls = endpoint_impls
         return True
@@ -266,9 +258,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
             raise ValueError("Client not initialized")
 
         if self.provider_data:
-            set_request_provider_data(
+            set_request_provider_data({"X-LlamaStack-Provider-Data": json.dumps(self.provider_data)})
-                {"X-LlamaStack-Provider-Data": json.dumps(self.provider_data)}
-            )
 
         if stream:
             response = await self._call_streaming(
@@ -408,9 +398,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         )
         return await response.parse()
 
-    def _convert_body(
+    def _convert_body(self, path: str, method: str, body: Optional[dict] = None) -> dict:
-        self, path: str, method: str, body: Optional[dict] = None
-    ) -> dict:
         if not body:
             return {}
 
@@ -425,7 +413,5 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         for param_name, param in sig.parameters.items():
             if param_name in body:
                 value = body.get(param_name)
-                converted_body[param_name] = convert_to_pydantic(
+                converted_body[param_name] = convert_to_pydantic(param.annotation, value)
-                    param.annotation, value
-                )
         return converted_body

llama_stack/distribution/resolver.py

@@ -115,9 +115,7 @@ 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(
+    routing_table_apis = set(x.routing_table_api for x in builtin_automatically_routed_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())
 
     providers_with_specs = {}
@@ -125,16 +123,12 @@ async def resolve_impls(
     for api_str, providers in run_config.providers.items():
         api = Api(api_str)
         if api in routing_table_apis:
-            raise ValueError(
+            raise ValueError(f"Provider for `{api_str}` is automatically provided and cannot be overridden")
-                f"Provider for `{api_str}` is automatically provided and cannot be overridden"
-            )
 
         specs = {}
         for provider in providers:
             if provider.provider_type not in provider_registry[api]:
-                raise ValueError(
+                raise ValueError(f"Provider `{provider.provider_type}` is not available for API `{api}`")
-                    f"Provider `{provider.provider_type}` is not available for API `{api}`"
-                )
 
             p = provider_registry[api][provider.provider_type]
             if p.deprecation_error:
@@ -145,9 +139,7 @@ async def resolve_impls(
                 log.warning(
                     f"Provider `{provider.provider_type}` for API `{api}` is deprecated and will be removed in a future release: {p.deprecation_warning}",
                 )
-            p.deps__ = [a.value for a in p.api_dependencies] + [
+            p.deps__ = [a.value for a in p.api_dependencies] + [a.value for a in p.optional_api_dependencies]
-                a.value for a in p.optional_api_dependencies
-            ]
             spec = ProviderWithSpec(
                 spec=p,
                 **(provider.model_dump()),
@@ -158,9 +150,7 @@ async def resolve_impls(
         providers_with_specs[key] = specs
 
     apis_to_serve = run_config.apis or set(
-        list(providers_with_specs.keys())
+        list(providers_with_specs.keys()) + [x.value for x in routing_table_apis] + [x.value for x in router_apis]
-        + [x.value for x in routing_table_apis]
-        + [x.value for x in router_apis]
     )
 
     for info in builtin_automatically_routed_apis():
@@ -197,9 +187,7 @@ async def resolve_impls(
             )
         }
 
-    sorted_providers = topological_sort(
+    sorted_providers = topological_sort({k: v.values() for k, v in providers_with_specs.items()})
-        {k: v.values() for k, v in providers_with_specs.items()}
-    )
     apis = [x[1].spec.api for x in sorted_providers]
     sorted_providers.append(
         (
@@ -237,9 +225,7 @@ async def resolve_impls(
 
         inner_impls = {}
         if isinstance(provider.spec, RoutingTableProviderSpec):
-            inner_impls = inner_impls_by_provider_id[
+            inner_impls = inner_impls_by_provider_id[f"inner-{provider.spec.router_api.value}"]
-                f"inner-{provider.spec.router_api.value}"
-            ]
 
         impl = await instantiate_provider(
             provider,
@@ -336,10 +322,7 @@ async def instantiate_provider(
     # TODO: check compliance for special tool groups
     # the impl should be for Api.tool_runtime, the name should be the special tool group, the protocol should be the special tool group protocol
     check_protocol_compliance(impl, protocols[provider_spec.api])
-    if (
+    if not isinstance(provider_spec, AutoRoutedProviderSpec) and provider_spec.api in additional_protocols:
-        not isinstance(provider_spec, AutoRoutedProviderSpec)
-        and provider_spec.api in additional_protocols
-    ):
         additional_api, _, _ = additional_protocols[provider_spec.api]
         check_protocol_compliance(impl, additional_api)
 
@@ -367,19 +350,12 @@ def check_protocol_compliance(obj: Any, protocol: Any) -> None:
                 obj_params = set(obj_sig.parameters)
                 obj_params.discard("self")
                 if not (proto_params <= obj_params):
-                    log.error(
+                    log.error(f"Method {name} incompatible proto: {proto_params} vs. obj: {obj_params}")
-                        f"Method {name} incompatible proto: {proto_params} vs. obj: {obj_params}"
-                    )
                     missing_methods.append((name, "signature_mismatch"))
                 else:
                     # Check if the method is actually implemented in the class
-                    method_owner = next(
+                    method_owner = next((cls for cls in mro if name in cls.__dict__), None)
-                        (cls for cls in mro if name in cls.__dict__), None
+                    if method_owner is None or method_owner.__name__ == protocol.__name__:
-                    )
-                    if (
-                        method_owner is None
-                        or method_owner.__name__ == protocol.__name__
-                    ):
                         missing_methods.append((name, "not_actually_implemented"))
 
     if missing_methods:

llama_stack/distribution/routers/routers.py

@@ -85,9 +85,7 @@ class VectorIORouter(VectorIO):
         chunks: List[Chunk],
         ttl_seconds: Optional[int] = None,
     ) -> None:
-        return await self.routing_table.get_provider_impl(vector_db_id).insert_chunks(
+        return await self.routing_table.get_provider_impl(vector_db_id).insert_chunks(vector_db_id, chunks, ttl_seconds)
-            vector_db_id, chunks, ttl_seconds
-        )
 
     async def query_chunks(
         self,
@@ -95,9 +93,7 @@ class VectorIORouter(VectorIO):
         query: InterleavedContent,
         params: Optional[Dict[str, Any]] = None,
     ) -> QueryChunksResponse:
-        return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(
+        return await self.routing_table.get_provider_impl(vector_db_id).query_chunks(vector_db_id, query, params)
-            vector_db_id, query, params
-        )
 
 
 class InferenceRouter(Inference):
@@ -123,9 +119,7 @@ class InferenceRouter(Inference):
         metadata: Optional[Dict[str, Any]] = None,
         model_type: Optional[ModelType] = None,
     ) -> None:
-        await self.routing_table.register_model(
+        await self.routing_table.register_model(model_id, provider_model_id, provider_id, metadata, model_type)
-            model_id, provider_model_id, provider_id, metadata, model_type
-        )
 
     async def chat_completion(
         self,
@@ -143,9 +137,7 @@ class InferenceRouter(Inference):
         if model is None:
             raise ValueError(f"Model '{model_id}' not found")
         if model.model_type == ModelType.embedding:
-            raise ValueError(
+            raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
-                f"Model '{model_id}' is an embedding model and does not support chat completions"
-            )
         params = dict(
             model_id=model_id,
             messages=messages,
@@ -176,9 +168,7 @@ class InferenceRouter(Inference):
         if model is None:
             raise ValueError(f"Model '{model_id}' not found")
         if model.model_type == ModelType.embedding:
-            raise ValueError(
+            raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
-                f"Model '{model_id}' is an embedding model and does not support chat completions"
-            )
         provider = self.routing_table.get_provider_impl(model_id)
         params = dict(
             model_id=model_id,
@@ -202,9 +192,7 @@ class InferenceRouter(Inference):
         if model is None:
             raise ValueError(f"Model '{model_id}' not found")
         if model.model_type == ModelType.llm:
-            raise ValueError(
+            raise ValueError(f"Model '{model_id}' is an LLM model and does not support embeddings")
-                f"Model '{model_id}' is an LLM model and does not support embeddings"
-            )
         return await self.routing_table.get_provider_impl(model_id).embeddings(
             model_id=model_id,
             contents=contents,
@@ -231,9 +219,7 @@ class SafetyRouter(Safety):
         provider_id: Optional[str] = None,
         params: Optional[Dict[str, Any]] = None,
     ) -> Shield:
-        return await self.routing_table.register_shield(
+        return await self.routing_table.register_shield(shield_id, provider_shield_id, provider_id, params)
-            shield_id, provider_shield_id, provider_id, params
-        )
 
     async def run_shield(
         self,
@@ -268,9 +254,7 @@ class DatasetIORouter(DatasetIO):
         page_token: Optional[str] = None,
         filter_condition: Optional[str] = None,
     ) -> PaginatedRowsResult:
-        return await self.routing_table.get_provider_impl(
+        return await self.routing_table.get_provider_impl(dataset_id).get_rows_paginated(
-            dataset_id
-        ).get_rows_paginated(
             dataset_id=dataset_id,
             rows_in_page=rows_in_page,
             page_token=page_token,
@@ -305,9 +289,7 @@ class ScoringRouter(Scoring):
     ) -> ScoreBatchResponse:
         res = {}
         for fn_identifier in scoring_functions.keys():
-            score_response = await self.routing_table.get_provider_impl(
+            score_response = await self.routing_table.get_provider_impl(fn_identifier).score_batch(
-                fn_identifier
-            ).score_batch(
                 dataset_id=dataset_id,
                 scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
             )
@@ -328,9 +310,7 @@ class ScoringRouter(Scoring):
         res = {}
         # look up and map each scoring function to its provider impl
         for fn_identifier in scoring_functions.keys():
-            score_response = await self.routing_table.get_provider_impl(
+            score_response = await self.routing_table.get_provider_impl(fn_identifier).score(
-                fn_identifier
-            ).score(
                 input_rows=input_rows,
                 scoring_functions={fn_identifier: scoring_functions[fn_identifier]},
             )
@@ -381,9 +361,7 @@ class EvalRouter(Eval):
         task_id: str,
         job_id: str,
     ) -> Optional[JobStatus]:
-        return await self.routing_table.get_provider_impl(task_id).job_status(
+        return await self.routing_table.get_provider_impl(task_id).job_status(task_id, job_id)
-            task_id, job_id
-        )
 
     async def job_cancel(
         self,
@@ -420,9 +398,9 @@ class ToolRuntimeRouter(ToolRuntime):
             vector_db_ids: List[str],
             query_config: Optional[RAGQueryConfig] = None,
         ) -> RAGQueryResult:
-            return await self.routing_table.get_provider_impl(
+            return await self.routing_table.get_provider_impl("query_from_memory").query(
-                "query_from_memory"
+                content, vector_db_ids, query_config
-            ).query(content, vector_db_ids, query_config)
+            )
 
         async def insert(
             self,
@@ -430,9 +408,9 @@ class ToolRuntimeRouter(ToolRuntime):
             vector_db_id: str,
             chunk_size_in_tokens: int = 512,
         ) -> None:
-            return await self.routing_table.get_provider_impl(
+            return await self.routing_table.get_provider_impl("insert_into_memory").insert(
-                "insert_into_memory"
+                documents, vector_db_id, chunk_size_in_tokens
-            ).insert(documents, vector_db_id, chunk_size_in_tokens)
+            )
 
     def __init__(
         self,
@@ -460,6 +438,4 @@ class ToolRuntimeRouter(ToolRuntime):
     async def list_runtime_tools(
         self, tool_group_id: Optional[str] = None, mcp_endpoint: Optional[URL] = None
     ) -> List[ToolDef]:
-        return await self.routing_table.get_provider_impl(tool_group_id).list_tools(
+        return await self.routing_table.get_provider_impl(tool_group_id).list_tools(tool_group_id, mcp_endpoint)
-            tool_group_id, mcp_endpoint
-        )

llama_stack/distribution/routers/routing_tables.py

@@ -94,9 +94,7 @@ class CommonRoutingTableImpl(RoutingTable):
         self.dist_registry = dist_registry
 
     async def initialize(self) -> None:
-        async def add_objects(
+        async def add_objects(objs: List[RoutableObjectWithProvider], provider_id: str, cls) -> None:
-            objs: List[RoutableObjectWithProvider], provider_id: str, cls
-        ) -> None:
             for obj in objs:
                 if cls is None:
                     obj.provider_id = provider_id
@@ -131,9 +129,7 @@ class CommonRoutingTableImpl(RoutingTable):
         for p in self.impls_by_provider_id.values():
             await p.shutdown()
 
-    def get_provider_impl(
+    def get_provider_impl(self, routing_key: str, provider_id: Optional[str] = None) -> Any:
-        self, routing_key: str, provider_id: Optional[str] = None
-    ) -> Any:
         def apiname_object():
             if isinstance(self, ModelsRoutingTable):
                 return ("Inference", "model")
@@ -171,9 +167,7 @@ class CommonRoutingTableImpl(RoutingTable):
 
         raise ValueError(f"Provider not found for `{routing_key}`")
 
-    async def get_object_by_identifier(
+    async def get_object_by_identifier(self, type: str, identifier: str) -> Optional[RoutableObjectWithProvider]:
-        self, type: str, identifier: str
-    ) -> Optional[RoutableObjectWithProvider]:
         # Get from disk registry
         obj = await self.dist_registry.get(type, identifier)
         if not obj:
@@ -183,13 +177,9 @@ class CommonRoutingTableImpl(RoutingTable):
 
     async def unregister_object(self, obj: RoutableObjectWithProvider) -> None:
         await self.dist_registry.delete(obj.type, obj.identifier)
-        await unregister_object_from_provider(
+        await unregister_object_from_provider(obj, self.impls_by_provider_id[obj.provider_id])
-            obj, self.impls_by_provider_id[obj.provider_id]
-        )
 
-    async def register_object(
+    async def register_object(self, obj: RoutableObjectWithProvider) -> RoutableObjectWithProvider:
-        self, obj: RoutableObjectWithProvider
-    ) -> RoutableObjectWithProvider:
         # if provider_id is not specified, pick an arbitrary one from existing entries
         if not obj.provider_id and len(self.impls_by_provider_id) > 0:
             obj.provider_id = list(self.impls_by_provider_id.keys())[0]
@@ -244,9 +234,7 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
         if model_type is None:
             model_type = ModelType.llm
         if "embedding_dimension" not in metadata and model_type == ModelType.embedding:
-            raise ValueError(
+            raise ValueError("Embedding model must have an embedding dimension in its metadata")
-                "Embedding model must have an embedding dimension in its metadata"
-            )
         model = Model(
             identifier=model_id,
             provider_resource_id=provider_model_id,
@@ -266,9 +254,7 @@ class ModelsRoutingTable(CommonRoutingTableImpl, Models):
 
 class ShieldsRoutingTable(CommonRoutingTableImpl, Shields):
     async def list_shields(self) -> ListShieldsResponse:
-        return ListShieldsResponse(
+        return ListShieldsResponse(data=await self.get_all_with_type(ResourceType.shield.value))
-            data=await self.get_all_with_type(ResourceType.shield.value)
-        )
 
     async def get_shield(self, identifier: str) -> Optional[Shield]:
         return await self.get_object_by_identifier("shield", identifier)
@@ -340,9 +326,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         if model.model_type != ModelType.embedding:
             raise ValueError(f"Model {embedding_model} is not an embedding model")
         if "embedding_dimension" not in model.metadata:
-            raise ValueError(
+            raise ValueError(f"Model {embedding_model} does not have an embedding dimension")
-                f"Model {embedding_model} does not have an embedding dimension"
-            )
         vector_db_data = {
             "identifier": vector_db_id,
             "type": ResourceType.vector_db.value,
@@ -364,9 +348,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
 
 class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
     async def list_datasets(self) -> ListDatasetsResponse:
-        return ListDatasetsResponse(
+        return ListDatasetsResponse(data=await self.get_all_with_type(ResourceType.dataset.value))
-            data=await self.get_all_with_type(ResourceType.dataset.value)
-        )
 
     async def get_dataset(self, dataset_id: str) -> Optional[Dataset]:
         return await self.get_object_by_identifier("dataset", dataset_id)
@@ -411,9 +393,7 @@ class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):
 
 class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions):
     async def list_scoring_functions(self) -> ListScoringFunctionsResponse:
-        return ListScoringFunctionsResponse(
+        return ListScoringFunctionsResponse(data=await self.get_all_with_type(ResourceType.scoring_function.value))
-            data=await self.get_all_with_type(ResourceType.scoring_function.value)
-        )
 
     async def get_scoring_function(self, scoring_fn_id: str) -> Optional[ScoringFn]:
         return await self.get_object_by_identifier("scoring_function", scoring_fn_id)
@@ -510,12 +490,8 @@ class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups):
         args: Optional[Dict[str, Any]] = None,
     ) -> None:
         tools = []
-        tool_defs = await self.impls_by_provider_id[provider_id].list_runtime_tools(
+        tool_defs = await self.impls_by_provider_id[provider_id].list_runtime_tools(toolgroup_id, mcp_endpoint)
-            toolgroup_id, mcp_endpoint
+        tool_host = ToolHost.model_context_protocol if mcp_endpoint else ToolHost.distribution
-        )
-        tool_host = (
-            ToolHost.model_context_protocol if mcp_endpoint else ToolHost.distribution
-        )
 
         for tool_def in tool_defs:
             tools.append(

llama_stack/distribution/server/endpoints.py

@@ -43,9 +43,7 @@ def get_all_api_endpoints() -> Dict[Api, List[ApiEndpoint]]:
         if api == Api.tool_runtime:
             for tool_group in SpecialToolGroup:
                 sub_protocol = toolgroup_protocols[tool_group]
-                sub_protocol_methods = inspect.getmembers(
+                sub_protocol_methods = inspect.getmembers(sub_protocol, predicate=inspect.isfunction)
-                    sub_protocol, predicate=inspect.isfunction
-                )
                 for name, method in sub_protocol_methods:
                     if not hasattr(method, "__webmethod__"):
                         continue

llama_stack/distribution/server/server.py

@@ -76,9 +76,7 @@ async def global_exception_handler(request: Request, exc: Exception):
     traceback.print_exception(exc)
     http_exc = translate_exception(exc)
 
-    return JSONResponse(
+    return JSONResponse(status_code=http_exc.status_code, content={"error": {"detail": http_exc.detail}})
-        status_code=http_exc.status_code, content={"error": {"detail": http_exc.detail}}
-    )
 
 
 def translate_exception(exc: Exception) -> Union[HTTPException, RequestValidationError]:
@@ -178,9 +176,7 @@ def create_dynamic_typed_route(func: Any, method: str, route: str):
         is_streaming = is_streaming_request(func.__name__, request, **kwargs)
         try:
             if is_streaming:
-                return StreamingResponse(
+                return StreamingResponse(sse_generator(func(**kwargs)), media_type="text/event-stream")
-                    sse_generator(func(**kwargs)), media_type="text/event-stream"
-                )
             else:
                 value = func(**kwargs)
                 return await maybe_await(value)
@@ -190,11 +186,7 @@ def create_dynamic_typed_route(func: Any, method: str, route: str):
 
     sig = inspect.signature(func)
 
-    new_params = [
+    new_params = [inspect.Parameter("request", inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=Request)]
-        inspect.Parameter(
-            "request", inspect.Parameter.POSITIONAL_OR_KEYWORD, annotation=Request
-        )
-    ]
     new_params.extend(sig.parameters.values())
 
     path_params = extract_path_params(route)
@@ -202,15 +194,9 @@ def create_dynamic_typed_route(func: Any, method: str, route: str):
         # Annotate parameters that are in the path with Path(...) and others with Body(...)
         new_params = [new_params[0]] + [
             (
-                param.replace(
+                param.replace(annotation=Annotated[param.annotation, FastapiPath(..., title=param.name)])
-                    annotation=Annotated[
-                        param.annotation, FastapiPath(..., title=param.name)
-                    ]
-                )
                 if param.name in path_params
-                else param.replace(
+                else param.replace(annotation=Annotated[param.annotation, Body(..., embed=True)])
-                    annotation=Annotated[param.annotation, Body(..., embed=True)]
-                )
             )
             for param in new_params[1:]
         ]
@@ -244,12 +230,8 @@ class ClientVersionMiddleware:
             client_version = headers.get(b"x-llamastack-client-version", b"").decode()
             if client_version:
                 try:
-                    client_version_parts = tuple(
+                    client_version_parts = tuple(map(int, client_version.split(".")[:2]))
-                        map(int, client_version.split(".")[:2])
+                    server_version_parts = tuple(map(int, self.server_version.split(".")[:2]))
-                    )
-                    server_version_parts = tuple(
-                        map(int, self.server_version.split(".")[:2])
-                    )
                     if client_version_parts != server_version_parts:
 
                         async def send_version_error(send):
@@ -267,9 +249,7 @@ class ClientVersionMiddleware:
                                     }
                                 }
                             ).encode()
-                            await send(
+                            await send({"type": "http.response.body", "body": error_msg})
-                                {"type": "http.response.body", "body": error_msg}
-                            )
 
                         return await send_version_error(send)
                 except (ValueError, IndexError):
@@ -296,9 +276,7 @@ def main():
         default=int(os.getenv("LLAMA_STACK_PORT", 8321)),
         help="Port to listen on",
     )
-    parser.add_argument(
+    parser.add_argument("--disable-ipv6", action="store_true", help="Whether to disable IPv6 support")
-        "--disable-ipv6", action="store_true", help="Whether to disable IPv6 support"
-    )
     parser.add_argument(
         "--env",
         action="append",
@@ -323,9 +301,7 @@ def main():
             raise ValueError(f"Config file {config_file} does not exist")
         print(f"Using config file: {config_file}")
     elif args.template:
-        config_file = (
+        config_file = Path(REPO_ROOT) / "llama_stack" / "templates" / args.template / "run.yaml"
-            Path(REPO_ROOT) / "llama_stack" / "templates" / args.template / "run.yaml"
-        )
         if not config_file.exists():
             raise ValueError(f"Template {args.template} does not exist")
         print(f"Using template {args.template} config file: {config_file}")
@@ -383,9 +359,7 @@ def main():
             impl_method = getattr(impl, endpoint.name)
 
             with warnings.catch_warnings():
-                warnings.filterwarnings(
+                warnings.filterwarnings("ignore", category=UserWarning, module="pydantic._internal._fields")
-                    "ignore", category=UserWarning, module="pydantic._internal._fields"
-                )
                 getattr(app, endpoint.method)(endpoint.route, response_model=None)(
                     create_dynamic_typed_route(
                         impl_method,
@@ -416,9 +390,7 @@ def main():
 
 def extract_path_params(route: str) -> List[str]:
     segments = route.split("/")
-    params = [
+    params = [seg[1:-1] for seg in segments if seg.startswith("{") and seg.endswith("}")]
-        seg[1:-1] for seg in segments if seg.startswith("{") and seg.endswith("}")
-    ]
     return params
 
 

llama_stack/distribution/stack.py

@@ -110,9 +110,7 @@ class EnvVarError(Exception):
     def __init__(self, var_name: str, path: str = ""):
         self.var_name = var_name
         self.path = path
-        super().__init__(
+        super().__init__(f"Environment variable '{var_name}' not set or empty{f' at {path}' if path else ''}")
-            f"Environment variable '{var_name}' not set or empty{f' at {path}' if path else ''}"
-        )
 
 
 def redact_sensitive_fields(data: Dict[str, Any]) -> Dict[str, Any]:
@@ -187,9 +185,7 @@ def validate_env_pair(env_pair: str) -> tuple[str, str]:
         if not key:
             raise ValueError(f"Empty key in environment variable pair: {env_pair}")
         if not all(c.isalnum() or c == "_" for c in key):
-            raise ValueError(
+            raise ValueError(f"Key must contain only alphanumeric characters and underscores: {key}")
-                f"Key must contain only alphanumeric characters and underscores: {key}"
-            )
         return key, value
     except ValueError as e:
         raise ValueError(
@@ -202,20 +198,14 @@ def validate_env_pair(env_pair: str) -> tuple[str, str]:
 async def construct_stack(
     run_config: StackRunConfig, provider_registry: Optional[ProviderRegistry] = None
 ) -> Dict[Api, Any]:
-    dist_registry, _ = await create_dist_registry(
+    dist_registry, _ = await create_dist_registry(run_config.metadata_store, run_config.image_name)
-        run_config.metadata_store, run_config.image_name
+    impls = await resolve_impls(run_config, provider_registry or get_provider_registry(), dist_registry)
-    )
-    impls = await resolve_impls(
-        run_config, provider_registry or get_provider_registry(), dist_registry
-    )
     await register_resources(run_config, impls)
     return impls
 
 
 def get_stack_run_config_from_template(template: str) -> StackRunConfig:
-    template_path = (
+    template_path = importlib.resources.files("llama_stack") / f"templates/{template}/run.yaml"
-        importlib.resources.files("llama_stack") / f"templates/{template}/run.yaml"
-    )
 
     with importlib.resources.as_file(template_path) as path:
         if not path.exists():

llama_stack/distribution/store/registry.py

@@ -25,9 +25,7 @@ class DistributionRegistry(Protocol):
 
     def get_cached(self, identifier: str) -> Optional[RoutableObjectWithProvider]: ...
 
-    async def update(
+    async def update(self, obj: RoutableObjectWithProvider) -> RoutableObjectWithProvider: ...
-        self, obj: RoutableObjectWithProvider
-    ) -> RoutableObjectWithProvider: ...
 
     async def register(self, obj: RoutableObjectWithProvider) -> bool: ...
 
@@ -61,9 +59,7 @@ class DiskDistributionRegistry(DistributionRegistry):
     async def initialize(self) -> None:
         pass
 
-    def get_cached(
+    def get_cached(self, type: str, identifier: str) -> Optional[RoutableObjectWithProvider]:
-        self, type: str, identifier: str
-    ) -> Optional[RoutableObjectWithProvider]:
         # Disk registry does not have a cache
         raise NotImplementedError("Disk registry does not have a cache")
 
@@ -72,12 +68,8 @@ class DiskDistributionRegistry(DistributionRegistry):
         values = await self.kvstore.range(start_key, end_key)
         return _parse_registry_values(values)
 
-    async def get(
+    async def get(self, type: str, identifier: str) -> Optional[RoutableObjectWithProvider]:
-        self, type: str, identifier: str
+        json_str = await self.kvstore.get(KEY_FORMAT.format(type=type, identifier=identifier))
-    ) -> Optional[RoutableObjectWithProvider]:
-        json_str = await self.kvstore.get(
-            KEY_FORMAT.format(type=type, identifier=identifier)
-        )
         if not json_str:
             return None
 
@@ -143,9 +135,7 @@ class CachedDiskDistributionRegistry(DiskDistributionRegistry):
     async def initialize(self) -> None:
         await self._ensure_initialized()
 
-    def get_cached(
+    def get_cached(self, type: str, identifier: str) -> Optional[RoutableObjectWithProvider]:
-        self, type: str, identifier: str
-    ) -> Optional[RoutableObjectWithProvider]:
         return self.cache.get((type, identifier), None)
 
     async def get_all(self) -> List[RoutableObjectWithProvider]:
@@ -153,9 +143,7 @@ class CachedDiskDistributionRegistry(DiskDistributionRegistry):
         async with self._locked_cache() as cache:
             return list(cache.values())
 
-    async def get(
+    async def get(self, type: str, identifier: str) -> Optional[RoutableObjectWithProvider]:
-        self, type: str, identifier: str
-    ) -> Optional[RoutableObjectWithProvider]:
         await self._ensure_initialized()
         cache_key = (type, identifier)
 
@@ -197,9 +185,7 @@ async def create_dist_registry(
         dist_kvstore = await kvstore_impl(metadata_store)
     else:
         dist_kvstore = await kvstore_impl(
-            SqliteKVStoreConfig(
+            SqliteKVStoreConfig(db_path=(DISTRIBS_BASE_DIR / image_name / "kvstore.db").as_posix())
-                db_path=(DISTRIBS_BASE_DIR / image_name / "kvstore.db").as_posix()
-            )
         )
     dist_registry = CachedDiskDistributionRegistry(dist_kvstore)
     await dist_registry.initialize()

llama_stack/distribution/store/tests/test_registry.py

@@ -161,9 +161,7 @@ async def test_duplicate_provider_registration(config):
 
     result = await cached_registry.get("vector_db", "test_vector_db_2")
     assert result is not None
-    assert (
+    assert result.embedding_model == original_vector_db.embedding_model  # Original values preserved
-        result.embedding_model == original_vector_db.embedding_model
-    )  # Original values preserved
 
 
 @pytest.mark.asyncio
@@ -193,14 +191,9 @@ async def test_get_all_objects(config):
 
     # Verify each vector_db was stored correctly
     for original_vector_db in test_vector_dbs:
-        matching_vector_dbs = [
+        matching_vector_dbs = [v for v in all_results if v.identifier == original_vector_db.identifier]
-            v for v in all_results if v.identifier == original_vector_db.identifier
-        ]
         assert len(matching_vector_dbs) == 1
         stored_vector_db = matching_vector_dbs[0]
         assert stored_vector_db.embedding_model == original_vector_db.embedding_model
         assert stored_vector_db.provider_id == original_vector_db.provider_id
-        assert (
+        assert stored_vector_db.embedding_dimension == original_vector_db.embedding_dimension
-            stored_vector_db.embedding_dimension
-            == original_vector_db.embedding_dimension
-        )

llama_stack/distribution/ui/app.py

@@ -22,15 +22,11 @@ def main():
     )
 
     # Playground pages
-    chat_page = st.Page(
+    chat_page = st.Page("page/playground/chat.py", title="Chat", icon="💬", default=True)
-        "page/playground/chat.py", title="Chat", icon="💬", default=True
-    )
     rag_page = st.Page("page/playground/rag.py", title="RAG", icon="💬", default=False)
 
     # Distribution pages
-    resources_page = st.Page(
+    resources_page = st.Page("page/distribution/resources.py", title="Resources", icon="🔍", default=False)
-        "page/distribution/resources.py", title="Resources", icon="🔍", default=False
-    )
     provider_page = st.Page(
         "page/distribution/providers.py",
         title="API Providers",

llama_stack/distribution/ui/modules/api.py

@@ -23,15 +23,11 @@ class LlamaStackApi:
             },
         )
 
-    def run_scoring(
+    def run_scoring(self, row, scoring_function_ids: list[str], scoring_params: Optional[dict]):
-        self, row, scoring_function_ids: list[str], scoring_params: Optional[dict]
-    ):
         """Run scoring on a single row"""
         if not scoring_params:
             scoring_params = {fn_id: None for fn_id in scoring_function_ids}
-        return self.client.scoring.score(
+        return self.client.scoring.score(input_rows=[row], scoring_functions=scoring_params)
-            input_rows=[row], scoring_functions=scoring_params
-        )
 
 
 llama_stack_api = LlamaStackApi()

llama_stack/distribution/ui/page/distribution/datasets.py

@@ -11,9 +11,7 @@ from modules.api import llama_stack_api
 def datasets():
     st.header("Datasets")
 
-    datasets_info = {
+    datasets_info = {d.identifier: d.to_dict() for d in llama_stack_api.client.datasets.list()}
-        d.identifier: d.to_dict() for d in llama_stack_api.client.datasets.list()
-    }
     if len(datasets_info) > 0:
         selected_dataset = st.selectbox("Select a dataset", list(datasets_info.keys()))
         st.json(datasets_info[selected_dataset], expanded=True)

llama_stack/distribution/ui/page/distribution/eval_tasks.py

@@ -12,12 +12,8 @@ def eval_tasks():
     # Eval Tasks Section
     st.header("Eval Tasks")
 
-    eval_tasks_info = {
+    eval_tasks_info = {d.identifier: d.to_dict() for d in llama_stack_api.client.eval_tasks.list()}
-        d.identifier: d.to_dict() for d in llama_stack_api.client.eval_tasks.list()
-    }
 
     if len(eval_tasks_info) > 0:
-        selected_eval_task = st.selectbox(
+        selected_eval_task = st.selectbox("Select an eval task", list(eval_tasks_info.keys()), key="eval_task_inspect")
-            "Select an eval task", list(eval_tasks_info.keys()), key="eval_task_inspect"
-        )
         st.json(eval_tasks_info[selected_eval_task], expanded=True)

llama_stack/distribution/ui/page/distribution/models.py

@@ -11,9 +11,7 @@ from modules.api import llama_stack_api
 def models():
     # Models Section
     st.header("Models")
-    models_info = {
+    models_info = {m.identifier: m.to_dict() for m in llama_stack_api.client.models.list()}
-        m.identifier: m.to_dict() for m in llama_stack_api.client.models.list()
-    }
 
     selected_model = st.selectbox("Select a model", list(models_info.keys()))
     st.json(models_info[selected_model])

llama_stack/distribution/ui/page/distribution/scoring_functions.py

@@ -11,12 +11,7 @@ from modules.api import llama_stack_api
 def scoring_functions():
     st.header("Scoring Functions")
 
-    scoring_functions_info = {
+    scoring_functions_info = {s.identifier: s.to_dict() for s in llama_stack_api.client.scoring_functions.list()}
-        s.identifier: s.to_dict()
-        for s in llama_stack_api.client.scoring_functions.list()
-    }
 
-    selected_scoring_function = st.selectbox(
+    selected_scoring_function = st.selectbox("Select a scoring function", list(scoring_functions_info.keys()))
-        "Select a scoring function", list(scoring_functions_info.keys())
-    )
     st.json(scoring_functions_info[selected_scoring_function], expanded=True)

llama_stack/distribution/ui/page/distribution/shields.py

@@ -12,9 +12,7 @@ def shields():
     # Shields Section
     st.header("Shields")
 
-    shields_info = {
+    shields_info = {s.identifier: s.to_dict() for s in llama_stack_api.client.shields.list()}
-        s.identifier: s.to_dict() for s in llama_stack_api.client.shields.list()
-    }
 
     selected_shield = st.selectbox("Select a shield", list(shields_info.keys()))
     st.json(shields_info[selected_shield])

llama_stack/distribution/ui/page/distribution/vector_dbs.py

@@ -10,14 +10,10 @@ from modules.api import llama_stack_api
 
 def vector_dbs():
     st.header("Vector Databases")
-    vector_dbs_info = {
+    vector_dbs_info = {v.identifier: v.to_dict() for v in llama_stack_api.client.vector_dbs.list()}
-        v.identifier: v.to_dict() for v in llama_stack_api.client.vector_dbs.list()
-    }
 
     if len(vector_dbs_info) > 0:
-        selected_vector_db = st.selectbox(
+        selected_vector_db = st.selectbox("Select a vector database", list(vector_dbs_info.keys()))
-            "Select a vector database", list(vector_dbs_info.keys())
-        )
         st.json(vector_dbs_info[selected_vector_db])
     else:
         st.info("No vector databases found")

llama_stack/distribution/ui/page/evaluations/app_eval.py

@@ -14,7 +14,6 @@ from modules.utils import process_dataset
 
 
 def application_evaluation_page():
-
     st.set_page_config(page_title="Evaluations (Scoring)", page_icon="🦙")
     st.title("📊 Evaluations (Scoring)")
 
@@ -83,9 +82,7 @@ def application_evaluation_page():
                         try:
                             new_params[param_name] = json.loads(value)
                         except json.JSONDecodeError:
-                            st.error(
+                            st.error(f"Invalid JSON for **{param_name}** in {scoring_fn_id}")
-                                f"Invalid JSON for **{param_name}** in {scoring_fn_id}"
-                            )
 
                 st.json(new_params)
             scoring_params[scoring_fn_id] = new_params
@@ -128,9 +125,7 @@ def application_evaluation_page():
                     output_res[fn_id].append(score_res.results[fn_id].score_rows[0])
 
                 # Display current row results using separate containers
-                progress_text_container.write(
+                progress_text_container.write(f"Expand to see current processed result ({i + 1} / {len(rows)})")
-                    f"Expand to see current processed result ({i + 1} / {len(rows)})"
-                )
                 results_container.json(
                     score_res.to_json(),
                     expanded=2,

llama_stack/distribution/ui/page/evaluations/native_eval.py

@@ -195,7 +195,6 @@ def run_evaluation_3():
 
     # Add run button and handle evaluation
     if st.button("Run Evaluation"):
-
         progress_text = "Running evaluation..."
         progress_bar = st.progress(0, text=progress_text)
         rows = rows.rows
@@ -233,9 +232,7 @@ def run_evaluation_3():
                     output_res[scoring_fn] = []
                 output_res[scoring_fn].append(eval_res.scores[scoring_fn].score_rows[0])
 
-            progress_text_container.write(
+            progress_text_container.write(f"Expand to see current processed result ({i + 1} / {len(rows)})")
-                f"Expand to see current processed result ({i + 1} / {len(rows)})"
-            )
             results_container.json(eval_res, expanded=2)
 
         progress_bar.progress(1.0, text="Evaluation complete!")
@@ -247,7 +244,6 @@ def run_evaluation_3():
 
 
 def native_evaluation_page():
-
     st.set_page_config(page_title="Evaluations (Generation + Scoring)", page_icon="🦙")
     st.title("📊 Evaluations (Generation + Scoring)")
 

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

@@ -11,9 +11,7 @@ from modules.api import llama_stack_api
 with st.sidebar:
     st.header("Configuration")
     available_models = llama_stack_api.client.models.list()
-    available_models = [
+    available_models = [model.identifier for model in available_models if model.model_type == "llm"]
-        model.identifier for model in available_models if model.model_type == "llm"
-    ]
     selected_model = st.selectbox(
         "Choose a model",
         available_models,
@@ -128,6 +126,4 @@ if prompt := st.chat_input("Example: What is Llama Stack?"):
             full_response = response
             message_placeholder.markdown(full_response.completion_message.content)
 
-        st.session_state.messages.append(
+        st.session_state.messages.append({"role": "assistant", "content": full_response})
-            {"role": "assistant", "content": full_response}
-        )

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

@@ -74,9 +74,7 @@ def rag_chat_page():
         )
 
         available_models = llama_stack_api.client.models.list()
-        available_models = [
+        available_models = [model.identifier for model in available_models if model.model_type == "llm"]
-            model.identifier for model in available_models if model.model_type == "llm"
-        ]
         selected_model = st.selectbox(
             "Choose a model",
             available_models,
@@ -137,9 +135,7 @@ def rag_chat_page():
             dict(
                 name="builtin::rag",
                 args={
-                    "vector_db_ids": [
+                    "vector_db_ids": [vector_db_id for vector_db_id in selected_vector_dbs],
-                        vector_db_id for vector_db_id in selected_vector_dbs
-                    ],
                 },
             )
         ],
@@ -186,9 +182,7 @@ def rag_chat_page():
                     message_placeholder.markdown(full_response + "▌")
             message_placeholder.markdown(full_response)
 
-            st.session_state.messages.append(
+            st.session_state.messages.append({"role": "assistant", "content": full_response})
-                {"role": "assistant", "content": full_response}
-            )
 
 
 rag_chat_page()

llama_stack/distribution/utils/config_dirs.py

@@ -8,9 +8,7 @@ import os
 from pathlib import Path
 
 
-LLAMA_STACK_CONFIG_DIR = Path(
+LLAMA_STACK_CONFIG_DIR = Path(os.getenv("LLAMA_STACK_CONFIG_DIR", os.path.expanduser("~/.llama/")))
-    os.getenv("LLAMA_STACK_CONFIG_DIR", os.path.expanduser("~/.llama/"))
-)
 
 DISTRIBS_BASE_DIR = LLAMA_STACK_CONFIG_DIR / "distributions"
 

llama_stack/distribution/utils/prompt_for_config.py

@@ -31,15 +31,11 @@ def is_list_of_primitives(field_type):
 
 
 def is_basemodel_without_fields(typ):
-    return (
+    return inspect.isclass(typ) and issubclass(typ, BaseModel) and len(typ.__fields__) == 0
-        inspect.isclass(typ) and issubclass(typ, BaseModel) and len(typ.__fields__) == 0
-    )
 
 
 def can_recurse(typ):
-    return (
+    return inspect.isclass(typ) and issubclass(typ, BaseModel) and len(typ.__fields__) > 0
-        inspect.isclass(typ) and issubclass(typ, BaseModel) and len(typ.__fields__) > 0
-    )
 
 
 def get_literal_values(field):
@@ -72,7 +68,7 @@ def is_discriminated_union(typ) -> bool:
     if isinstance(typ, FieldInfo):
         return typ.discriminator
     else:
-        if not (get_origin(typ) is Annotated):
+        if get_origin(typ) is not Annotated:
             return False
         args = get_args(typ)
         return len(args) >= 2 and args[1].discriminator
@@ -116,9 +112,7 @@ def prompt_for_discriminated_union(
             chosen_type = type_map[discriminator_value]
             log.info(f"\nConfiguring {chosen_type.__name__}:")
 
-            if existing_value and (
+            if existing_value and (getattr(existing_value, discriminator) != discriminator_value):
-                getattr(existing_value, discriminator) != discriminator_value
-            ):
                 existing_value = None
 
             sub_config = prompt_for_config(chosen_type, existing_value)
@@ -134,9 +128,7 @@ def prompt_for_discriminated_union(
 #
 # doesn't support List[nested_class] yet or Dicts of any kind. needs a bunch of
 # unit tests for coverage.
-def prompt_for_config(
+def prompt_for_config(config_type: type[BaseModel], existing_config: Optional[BaseModel] = None) -> BaseModel:
-    config_type: type[BaseModel], existing_config: Optional[BaseModel] = None
-) -> BaseModel:
     """
     Recursively prompt the user for configuration values based on a Pydantic BaseModel.
 
@@ -150,17 +142,11 @@ def prompt_for_config(
 
     for field_name, field in config_type.__fields__.items():
         field_type = field.annotation
-        existing_value = (
+        existing_value = getattr(existing_config, field_name) if existing_config else None
-            getattr(existing_config, field_name) if existing_config else None
-        )
         if existing_value:
             default_value = existing_value
         else:
-            default_value = (
+            default_value = field.default if not isinstance(field.default, PydanticUndefinedType) else None
-                field.default
-                if not isinstance(field.default, PydanticUndefinedType)
-                else None
-            )
         is_required = field.is_required
 
         # Skip fields with Literal type
@@ -183,15 +169,11 @@ def prompt_for_config(
                     config_data[field_name] = validated_value
                     break
                 except KeyError:
-                    log.error(
+                    log.error(f"Invalid choice. Please choose from: {', '.join(e.name for e in field_type)}")
-                        f"Invalid choice. Please choose from: {', '.join(e.name for e in field_type)}"
-                    )
             continue
 
         if is_discriminated_union(field):
-            config_data[field_name] = prompt_for_discriminated_union(
+            config_data[field_name] = prompt_for_discriminated_union(field_name, field, existing_value)
-                field_name, field, existing_value
-            )
             continue
 
         if is_optional(field_type) and can_recurse(get_non_none_type(field_type)):
@@ -202,9 +184,7 @@ def prompt_for_config(
             nested_type = get_non_none_type(field_type)
             log.info(f"Entering sub-configuration for {field_name}:")
             config_data[field_name] = prompt_for_config(nested_type, existing_value)
-        elif is_optional(field_type) and is_discriminated_union(
+        elif is_optional(field_type) and is_discriminated_union(get_non_none_type(field_type)):
-            get_non_none_type(field_type)
-        ):
             prompt = f"Do you want to configure {field_name}? (y/n): "
             if input(prompt).lower() == "n":
                 config_data[field_name] = None
@@ -260,16 +240,12 @@ def prompt_for_config(
                             try:
                                 value = json.loads(user_input)
                                 if not isinstance(value, list):
-                                    raise ValueError(
+                                    raise ValueError("Input must be a JSON-encoded list")
-                                        "Input must be a JSON-encoded list"
-                                    )
                                 element_type = get_args(field_type)[0]
                                 value = [element_type(item) for item in value]
 
                             except json.JSONDecodeError:
-                                log.error(
+                                log.error('Invalid JSON. Please enter a valid JSON-encoded list e.g., ["foo","bar"]')
-                                    'Invalid JSON. Please enter a valid JSON-encoded list e.g., ["foo","bar"]'
-                                )
                                 continue
                             except ValueError as e:
                                 log.error(f"{str(e)}")
@@ -279,20 +255,14 @@ def prompt_for_config(
                             try:
                                 value = json.loads(user_input)
                                 if not isinstance(value, dict):
-                                    raise ValueError(
+                                    raise ValueError("Input must be a JSON-encoded dictionary")
-                                        "Input must be a JSON-encoded dictionary"
-                                    )
 
                             except json.JSONDecodeError:
-                                log.error(
+                                log.error("Invalid JSON. Please enter a valid JSON-encoded dict.")
-                                    "Invalid JSON. Please enter a valid JSON-encoded dict."
-                                )
                                 continue
 
                         # Convert the input to the correct type
-                        elif inspect.isclass(field_type) and issubclass(
+                        elif inspect.isclass(field_type) and issubclass(field_type, BaseModel):
-                            field_type, BaseModel
-                        ):
                             # For nested BaseModels, we assume a dictionary-like string input
                             import ast
 
@@ -301,16 +271,12 @@ def prompt_for_config(
                             value = field_type(user_input)
 
                     except ValueError:
-                        log.error(
+                        log.error(f"Invalid input. Expected type: {getattr(field_type, '__name__', str(field_type))}")
-                            f"Invalid input. Expected type: {getattr(field_type, '__name__', str(field_type))}"
-                        )
                         continue
 
                 try:
                     # Validate the field using our manual validation function
-                    validated_value = manually_validate_field(
+                    validated_value = manually_validate_field(config_type, field_name, value)
-                        config_type, field_name, value
-                    )
                     config_data[field_name] = validated_value
                     break
                 except ValueError as e:

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

@@ -11,9 +11,7 @@ from llama_stack.distribution.datatypes import Api, ProviderSpec
 from .config import MetaReferenceAgentsImplConfig
 
 
-async def get_provider_impl(
+async def get_provider_impl(config: MetaReferenceAgentsImplConfig, deps: Dict[Api, ProviderSpec]):
-    config: MetaReferenceAgentsImplConfig, deps: Dict[Api, ProviderSpec]
-):
     from .agents import MetaReferenceAgentsImpl
 
     impl = MetaReferenceAgentsImpl(

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

@@ -74,9 +74,7 @@ log = logging.getLogger(__name__)
 
 
 def make_random_string(length: int = 8):
-    return "".join(
+    return "".join(secrets.choice(string.ascii_letters + string.digits) for _ in range(length))
-        secrets.choice(string.ascii_letters + string.digits) for _ in range(length)
-    )
 
 
 TOOLS_ATTACHMENT_KEY_REGEX = re.compile(r"__tools_attachment__=(\{.*?\})")
@@ -153,9 +151,7 @@ class ChatAgent(ShieldRunnerMixin):
     async def create_session(self, name: str) -> str:
         return await self.storage.create_session(name)
 
-    async def create_and_execute_turn(
+    async def create_and_execute_turn(self, request: AgentTurnCreateRequest) -> AsyncGenerator:
-        self, request: AgentTurnCreateRequest
-    ) -> AsyncGenerator:
         with tracing.span("create_and_execute_turn") as span:
             span.set_attribute("session_id", request.session_id)
             span.set_attribute("agent_id", self.agent_id)
@@ -206,14 +202,9 @@ class ChatAgent(ShieldRunnerMixin):
                     output_message = chunk
                     continue
 
-                assert isinstance(
+                assert isinstance(chunk, AgentTurnResponseStreamChunk), f"Unexpected type {type(chunk)}"
-                    chunk, AgentTurnResponseStreamChunk
-                ), f"Unexpected type {type(chunk)}"
                 event = chunk.event
-                if (
+                if event.payload.event_type == AgentTurnResponseEventType.step_complete.value:
-                    event.payload.event_type
-                    == AgentTurnResponseEventType.step_complete.value
-                ):
                     steps.append(event.payload.step_details)
 
                 yield chunk
@@ -388,9 +379,7 @@ class ChatAgent(ShieldRunnerMixin):
 
         tool_defs, tool_to_group = await self._get_tool_defs(toolgroups_for_turn)
         if documents:
-            await self.handle_documents(
+            await self.handle_documents(session_id, documents, input_messages, tool_defs)
-                session_id, documents, input_messages, tool_defs
-            )
 
         if RAG_TOOL_GROUP in toolgroups and len(input_messages) > 0:
             with tracing.span(MEMORY_QUERY_TOOL) as span:
@@ -408,9 +397,7 @@ class ChatAgent(ShieldRunnerMixin):
                 vector_db_ids = args.get("vector_db_ids", [])
                 query_config = args.get("query_config")
                 if query_config:
-                    query_config = TypeAdapter(RAGQueryConfig).validate_python(
+                    query_config = TypeAdapter(RAGQueryConfig).validate_python(query_config)
-                        query_config
-                    )
                 else:
                     # handle someone passing an empty dict
                     query_config = RAGQueryConfig()
@@ -438,9 +425,7 @@ class ChatAgent(ShieldRunnerMixin):
                     )
                 )
                 result = await self.tool_runtime_api.rag_tool.query(
-                    content=concat_interleaved_content(
+                    content=concat_interleaved_content([msg.content for msg in input_messages]),
-                        [msg.content for msg in input_messages]
-                    ),
                     vector_db_ids=vector_db_ids,
                     query_config=query_config,
                 )
@@ -472,9 +457,7 @@ class ChatAgent(ShieldRunnerMixin):
                         )
                     )
                 )
-                span.set_attribute(
+                span.set_attribute("input", [m.model_dump_json() for m in input_messages])
-                    "input", [m.model_dump_json() for m in input_messages]
-                )
                 span.set_attribute("output", retrieved_context)
                 span.set_attribute("tool_name", MEMORY_QUERY_TOOL)
 
@@ -511,9 +494,7 @@ class ChatAgent(ShieldRunnerMixin):
                     self.agent_config.model,
                     input_messages,
                     tools=[
-                        tool
+                        tool for tool in tool_defs.values() if tool_to_group.get(tool.tool_name, None) != RAG_TOOL_GROUP
-                        for tool in tool_defs.values()
-                        if tool_to_group.get(tool.tool_name, None) != RAG_TOOL_GROUP
                     ],
                     tool_prompt_format=self.agent_config.tool_prompt_format,
                     response_format=self.agent_config.response_format,
@@ -560,12 +541,8 @@ 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(
+                span.set_attribute("input", [m.model_dump_json() for m in input_messages])
-                    "input", [m.model_dump_json() for m in input_messages]
+                span.set_attribute("output", f"content: {content} tool_calls: {tool_calls}")
-                )
-                span.set_attribute(
-                    "output", f"content: {content} tool_calls: {tool_calls}"
-                )
 
             stop_reason = stop_reason or StopReason.out_of_tokens
 
@@ -667,9 +644,7 @@ class ChatAgent(ShieldRunnerMixin):
                         toolgroup_args,
                         tool_to_group,
                     )
-                    assert (
+                    assert len(result_messages) == 1, "Currently not supporting multiple messages"
-                        len(result_messages) == 1
-                    ), "Currently not supporting multiple messages"
                     result_message = result_messages[0]
                     span.set_attribute("output", result_message.model_dump_json())
 
@@ -697,9 +672,7 @@ class ChatAgent(ShieldRunnerMixin):
                 # TODO: add tool-input touchpoint and a "start" event for this step also
                 # but that needs a lot more refactoring of Tool code potentially
 
-                if out_attachment := _interpret_content_as_attachment(
+                if out_attachment := _interpret_content_as_attachment(result_message.content):
-                    result_message.content
-                ):
                     # NOTE: when we push this message back to the model, the model may ignore the
                     # attached file path etc. since the model is trained to only provide a user message
                     # with the summary. We keep all generated attachments and then attach them to final message
@@ -714,22 +687,14 @@ class ChatAgent(ShieldRunnerMixin):
     ) -> Tuple[Dict[str, ToolDefinition], Dict[str, str]]:
         # Determine which tools to include
         agent_config_toolgroups = set(
-            (
+            (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
             else {
-                (
+                (toolgroup.name if isinstance(toolgroup, AgentToolGroupWithArgs) else toolgroup)
-                    toolgroup.name
-                    if isinstance(toolgroup, AgentToolGroupWithArgs)
-                    else toolgroup
-                )
                 for toolgroup in toolgroups_for_turn
             }
         )
@@ -759,10 +724,7 @@ class ChatAgent(ShieldRunnerMixin):
                 continue
             tools = await self.tool_groups_api.list_tools(toolgroup_id=toolgroup_name)
             for tool_def in tools.data:
-                if (
+                if toolgroup_name.startswith("builtin") and toolgroup_name != RAG_TOOL_GROUP:
-                    toolgroup_name.startswith("builtin")
-                    and toolgroup_name != RAG_TOOL_GROUP
-                ):
                     tool_name = tool_def.identifier
                     built_in_type = BuiltinTool.brave_search
                     if tool_name == "web_search":
@@ -773,9 +735,7 @@ class ChatAgent(ShieldRunnerMixin):
                     if tool_def_map.get(built_in_type, None):
                         raise ValueError(f"Tool {built_in_type} already exists")
 
-                    tool_def_map[built_in_type] = ToolDefinition(
+                    tool_def_map[built_in_type] = ToolDefinition(tool_name=built_in_type)
-                        tool_name=built_in_type
-                    )
                     tool_to_group[built_in_type] = tool_def.toolgroup_id
                     continue
 
@@ -821,9 +781,7 @@ class ChatAgent(ShieldRunnerMixin):
         # Save the contents to a tempdir and use its path as a URL if code interpreter is present
         if code_interpreter_tool:
             for c in content_items:
-                temp_file_path = os.path.join(
+                temp_file_path = os.path.join(self.tempdir, f"{make_random_string()}.txt")
-                    self.tempdir, f"{make_random_string()}.txt"
-                )
                 with open(temp_file_path, "w") as temp_file:
                     temp_file.write(c.content)
                 url_items.append(URL(uri=f"file://{temp_file_path}"))
@@ -849,8 +807,7 @@ class ChatAgent(ShieldRunnerMixin):
             # we try to load the data from the URLs and content items as a message to inference
             # and add it to the last message's context
             input_messages[-1].context = "\n".join(
-                [doc.content for doc in content_items]
+                [doc.content for doc in content_items] + await load_data_from_urls(url_items)
-                + await load_data_from_urls(url_items)
             )
 
     async def _ensure_vector_db(self, session_id: str) -> str:
@@ -874,9 +831,7 @@ class ChatAgent(ShieldRunnerMixin):
 
         return vector_db_id
 
-    async def add_to_session_vector_db(
+    async def add_to_session_vector_db(self, session_id: str, data: List[Document]) -> None:
-        self, session_id: str, data: List[Document]
-    ) -> None:
         vector_db_id = await self._ensure_vector_db(session_id)
         documents = [
             RAGDocument(
@@ -931,11 +886,7 @@ async def attachment_message(tempdir: str, urls: List[URL]) -> ToolResponseMessa
         else:
             raise ValueError(f"Unsupported URL {url}")
 
-        content.append(
+        content.append(TextContentItem(text=f'# There is a file accessible to you at "{filepath}"\n'))
-            TextContentItem(
-                text=f'# There is a file accessible to you at "{filepath}"\n'
-            )
-        )
 
     return ToolResponseMessage(
         call_id="",

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

@@ -94,16 +94,12 @@ class MetaReferenceAgentsImpl(Agents):
         try:
             agent_config = json.loads(agent_config)
         except json.JSONDecodeError as e:
-            raise ValueError(
+            raise ValueError(f"Could not JSON decode agent config for {agent_id}") from e
-                f"Could not JSON decode agent config for {agent_id}"
-            ) from e
 
         try:
             agent_config = AgentConfig(**agent_config)
         except Exception as e:
-            raise ValueError(
+            raise ValueError(f"Could not validate(?) agent config for {agent_id}") from e
-                f"Could not validate(?) agent config for {agent_id}"
-            ) from e
 
         return ChatAgent(
             agent_id=agent_id,
@@ -115,9 +111,7 @@ class MetaReferenceAgentsImpl(Agents):
             tool_runtime_api=self.tool_runtime_api,
             tool_groups_api=self.tool_groups_api,
             persistence_store=(
-                self.persistence_store
+                self.persistence_store if agent_config.enable_session_persistence else self.in_memory_store
-                if agent_config.enable_session_persistence
-                else self.in_memory_store
             ),
         )
 
@@ -168,22 +162,14 @@ class MetaReferenceAgentsImpl(Agents):
         async for event in agent.create_and_execute_turn(request):
             yield event
 
-    async def get_agents_turn(
+    async def get_agents_turn(self, agent_id: str, session_id: str, turn_id: str) -> Turn:
-        self, agent_id: str, session_id: str, turn_id: str
+        turn = await self.persistence_store.get(f"session:{agent_id}:{session_id}:{turn_id}")
-    ) -> Turn:
-        turn = await self.persistence_store.get(
-            f"session:{agent_id}:{session_id}:{turn_id}"
-        )
         turn = json.loads(turn)
         turn = Turn(**turn)
         return turn
 
-    async def get_agents_step(
+    async def get_agents_step(self, agent_id: str, session_id: str, turn_id: str, step_id: str) -> AgentStepResponse:
-        self, agent_id: str, session_id: str, turn_id: str, step_id: str
+        turn = await self.persistence_store.get(f"session:{agent_id}:{session_id}:{turn_id}")
-    ) -> AgentStepResponse:
-        turn = await self.persistence_store.get(
-            f"session:{agent_id}:{session_id}:{turn_id}"
-        )
         turn = json.loads(turn)
         turn = Turn(**turn)
         steps = turn.steps
@@ -203,9 +189,7 @@ class MetaReferenceAgentsImpl(Agents):
         turns = []
         if turn_ids:
             for turn_id in turn_ids:
-                turn = await self.persistence_store.get(
+                turn = await self.persistence_store.get(f"session:{agent_id}:{session_id}:{turn_id}")
-                    f"session:{agent_id}:{session_id}:{turn_id}"
-                )
                 turn = json.loads(turn)
                 turn = Turn(**turn)
                 turns.append(turn)

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

@@ -33,9 +33,7 @@ class ShieldRunnerMixin:
         self.input_shields = input_shields
         self.output_shields = output_shields
 
-    async def run_multiple_shields(
+    async def run_multiple_shields(self, messages: List[Message], identifiers: List[str]) -> None:
-        self, messages: List[Message], identifiers: List[str]
-    ) -> None:
         responses = await asyncio.gather(
             *[
                 self.safety_api.run_shield(

llama_stack/providers/inline/agents/meta_reference/tests/test_chat_agent.py

@@ -64,9 +64,7 @@ class MockInferenceAPI:
         tool_prompt_format: Optional[ToolPromptFormat] = None,
         stream: Optional[bool] = False,
         logprobs: Optional[LogProbConfig] = None,
-    ) -> Union[
+    ) -> Union[ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]]:
-        ChatCompletionResponse, AsyncIterator[ChatCompletionResponseStreamChunk]
-    ]:
         async def stream_response():
             yield ChatCompletionResponseStreamChunk(
                 event=ChatCompletionResponseEvent(
@@ -104,9 +102,7 @@ class MockInferenceAPI:
 
 
 class MockSafetyAPI:
-    async def run_shield(
+    async def run_shield(self, shield_id: str, messages: List[Message]) -> RunShieldResponse:
-        self, shield_id: str, messages: List[Message]
-    ) -> RunShieldResponse:
         return RunShieldResponse(violation=None)
 
 
@@ -129,9 +125,7 @@ class MockVectorIOAPI:
 
 
 class MockToolGroupsAPI:
-    async def register_tool_group(
+    async def register_tool_group(self, toolgroup_id: str, provider_id: str, mcp_endpoint=None, args=None) -> None:
-        self, toolgroup_id: str, provider_id: str, mcp_endpoint=None, args=None
-    ) -> None:
         pass
 
     async def get_tool_group(self, toolgroup_id: str) -> ToolGroup:
@@ -341,26 +335,21 @@ async def test_chat_agent_complex_turn(get_chat_agent):
     assert len(responses) > 0
 
     step_types = [
-        response.event.payload.step_type
+        response.event.payload.step_type for response in responses if hasattr(response.event.payload, "step_type")
-        for response in responses
-        if hasattr(response.event.payload, "step_type")
     ]
 
     assert StepType.shield_call in step_types, "Shield call step is missing"
     assert StepType.inference in step_types, "Inference step is missing"
 
     event_types = [
-        response.event.payload.event_type
+        response.event.payload.event_type for response in responses if hasattr(response.event.payload, "event_type")
-        for response in responses
-        if hasattr(response.event.payload, "event_type")
     ]
     assert "turn_start" in event_types, "Start event is missing"
     assert "turn_complete" in event_types, "Complete event is missing"
 
-    assert any(
+    assert any(isinstance(response.event.payload, AgentTurnResponseTurnCompletePayload) for response in responses), (
-        isinstance(response.event.payload, AgentTurnResponseTurnCompletePayload)
+        "Turn complete event is missing"
-        for response in responses
+    )
-    ), "Turn complete event is missing"
     turn_complete_payload = next(
         response.event.payload
         for response in responses
@@ -380,9 +369,7 @@ async def test_chat_agent_complex_turn(get_chat_agent):
         ([MEMORY_TOOLGROUP, CODE_INTERPRETER_TOOLGROUP], True, True),  # all tools
     ],
 )
-async def test_chat_agent_tools(
+async def test_chat_agent_tools(get_agents_impl, toolgroups, expected_memory, expected_code_interpreter):
-    get_agents_impl, toolgroups, expected_memory, expected_code_interpreter
-):
     impl = await get_agents_impl
     agent_config = AgentConfig(
         model="test_model",

llama_stack/providers/inline/datasetio/localfs/datasetio.py

@@ -172,9 +172,7 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
 
         new_rows_df = pandas.DataFrame(rows)
         new_rows_df = dataset_impl._validate_dataset_schema(new_rows_df)
-        dataset_impl.df = pandas.concat(
+        dataset_impl.df = pandas.concat([dataset_impl.df, new_rows_df], ignore_index=True)
-            [dataset_impl.df, new_rows_df], ignore_index=True
-        )
 
         url = str(dataset_info.dataset_def.url)
         parsed_url = urlparse(url)
@@ -189,12 +187,8 @@ class LocalFSDatasetIOImpl(DatasetIO, DatasetsProtocolPrivate):
                 raise ValueError("Data URL must be a base64-encoded CSV")
 
             csv_buffer = dataset_impl.df.to_csv(index=False)
-            base64_content = base64.b64encode(csv_buffer.encode("utf-8")).decode(
+            base64_content = base64.b64encode(csv_buffer.encode("utf-8")).decode("utf-8")
-                "utf-8"
+            dataset_info.dataset_def.url = URL(uri=f"data:text/csv;base64,{base64_content}")
-            )
-            dataset_info.dataset_def.url = URL(
-                uri=f"data:text/csv;base64,{base64_content}"
-            )
         else:
             raise ValueError(
                 f"Unsupported URL scheme: {parsed_url.scheme}. Only file:// and data: URLs are supported for writing."

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

@@ -91,14 +91,10 @@ class MetaReferenceEvalImpl(
         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(
+        validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.eval.value))
-            dataset_def.dataset_schema, get_valid_schemas(Api.eval.value)
-        )
         all_rows = await self.datasetio_api.get_rows_paginated(
             dataset_id=dataset_id,
-            rows_in_page=(
+            rows_in_page=(-1 if task_config.num_examples is None else task_config.num_examples),
-                -1 if task_config.num_examples is None else task_config.num_examples
-            ),
         )
         res = await self.evaluate_rows(
             task_id=task_id,
@@ -127,9 +123,7 @@ class MetaReferenceEvalImpl(
             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
-            session_create_response = await self.agents_api.create_agent_session(
+            session_create_response = await self.agents_api.create_agent_session(agent_id, f"session-{i}")
-                agent_id, f"session-{i}"
-            )
             session_id = session_create_response.session_id
 
             turn_request = dict(
@@ -138,12 +132,7 @@ class MetaReferenceEvalImpl(
                 messages=input_messages,
                 stream=True,
             )
-            turn_response = [
+            turn_response = [chunk async for chunk in await self.agents_api.create_agent_turn(**turn_request)]
-                chunk
-                async for chunk in await self.agents_api.create_agent_turn(
-                    **turn_request
-                )
-            ]
             final_event = turn_response[-1].event.payload
 
             # check if there's a memory retrieval step and extract the context
@@ -152,14 +141,10 @@ class MetaReferenceEvalImpl(
                 if step.step_type == StepType.tool_execution.value:
                     for tool_response in step.tool_responses:
                         if tool_response.tool_name == MEMORY_QUERY_TOOL:
-                            memory_rag_context = " ".join(
+                            memory_rag_context = " ".join(x.text for x in tool_response.content)
-                                x.text for x in tool_response.content
-                            )
 
             agent_generation = {}
-            agent_generation[ColumnName.generated_answer.value] = (
+            agent_generation[ColumnName.generated_answer.value] = final_event.turn.output_message.content
-                final_event.turn.output_message.content
-            )
             if memory_rag_context:
                 agent_generation[ColumnName.context.value] = memory_rag_context
 
@@ -171,9 +156,7 @@ class MetaReferenceEvalImpl(
         self, input_rows: List[Dict[str, Any]], task_config: EvalTaskConfig
     ) -> List[Dict[str, Any]]:
         candidate = task_config.eval_candidate
-        assert (
+        assert candidate.sampling_params.max_tokens is not None, "SamplingParams.max_tokens must be provided"
-            candidate.sampling_params.max_tokens is not None
-        ), "SamplingParams.max_tokens must be provided"
 
         generations = []
         for x in tqdm(input_rows):
@@ -184,15 +167,9 @@ class MetaReferenceEvalImpl(
                     content=input_content,
                     sampling_params=candidate.sampling_params,
                 )
-                generations.append(
+                generations.append({ColumnName.generated_answer.value: response.completion_message.content})
-                    {
-                        ColumnName.generated_answer.value: response.completion_message.content
-                    }
-                )
             elif ColumnName.chat_completion_input.value in x:
-                chat_completion_input_str = str(
+                chat_completion_input_str = str(x[ColumnName.chat_completion_input.value])
-                    x[ColumnName.chat_completion_input.value]
-                )
                 input_messages = eval(chat_completion_input_str)
                 input_messages = [UserMessage(**x) for x in input_messages]
                 messages = []
@@ -204,11 +181,7 @@ class MetaReferenceEvalImpl(
                     messages=messages,
                     sampling_params=candidate.sampling_params,
                 )
-                generations.append(
+                generations.append({ColumnName.generated_answer.value: response.completion_message.content})
-                    {
-                        ColumnName.generated_answer.value: response.completion_message.content
-                    }
-                )
             else:
                 raise ValueError("Invalid input row")
 
@@ -230,10 +203,7 @@ class MetaReferenceEvalImpl(
             raise ValueError(f"Invalid candidate type: {candidate.type}")
 
         # scoring with generated_answer
-        score_input_rows = [
+        score_input_rows = [input_r | generated_r for input_r, generated_r in zip(input_rows, generations)]
-            input_r | generated_r
-            for input_r, generated_r in zip(input_rows, generations)
-        ]
 
         if task_config.type == "app" and task_config.scoring_params is not None:
             scoring_functions_dict = {
@@ -241,9 +211,7 @@ class MetaReferenceEvalImpl(
                 for scoring_fn_id in scoring_functions
             }
         else:
-            scoring_functions_dict = {
+            scoring_functions_dict = {scoring_fn_id: None for scoring_fn_id in scoring_functions}
-                scoring_fn_id: None for scoring_fn_id in scoring_functions
-            }
 
         score_response = await self.scoring_api.score(
             input_rows=score_input_rows, scoring_functions=scoring_functions_dict

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

@@ -40,9 +40,7 @@ class MetaReferenceInferenceConfig(BaseModel):
         repos = [m.huggingface_repo for m in permitted_models]
         if model not in (descriptors + repos):
             model_list = "\n\t".join(repos)
-            raise ValueError(
+            raise ValueError(f"Unknown model: `{model}`. Choose from [\n\t{model_list}\n]")
-                f"Unknown model: `{model}`. Choose from [\n\t{model_list}\n]"
-            )
         return model
 
     @classmethod

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

@@ -83,9 +83,7 @@ class TokenResult(BaseModel):
 class Llama:
     @staticmethod
     def build(
-        config: Union[
+        config: Union[MetaReferenceInferenceConfig, MetaReferenceQuantizedInferenceConfig],
-            MetaReferenceInferenceConfig, MetaReferenceQuantizedInferenceConfig
-        ],
         model_id: str,
         llama_model: Model,
     ):
@@ -150,9 +148,9 @@ class Llama:
 
         checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
         assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
-        assert model_parallel_size == len(
+        assert model_parallel_size == len(checkpoints), (
-            checkpoints
+            f"Loading a checkpoint for MP={len(checkpoints)} but world size is {model_parallel_size}"
-        ), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {model_parallel_size}"
+        )
         ckpt_path = checkpoints[get_model_parallel_rank()]
         state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True)
         with open(Path(ckpt_dir) / "params.json", "r") as f:
@@ -168,9 +166,9 @@ class Llama:
         )
 
         tokenizer = Tokenizer.get_instance()
-        assert (
+        assert model_args.vocab_size == tokenizer.n_words, (
-            model_args.vocab_size == tokenizer.n_words
+            f"model_args vocab = {model_args.vocab_size} but tokenizer vocab = {tokenizer.n_words}"
-        ), f"model_args vocab = {model_args.vocab_size} but tokenizer vocab = {tokenizer.n_words}"
+        )
 
         if isinstance(config, MetaReferenceQuantizedInferenceConfig):
             if isinstance(config.quantization, Fp8QuantizationConfig):
@@ -193,10 +191,7 @@ class Llama:
                 model = convert_to_int4_quantized_model(model, model_args, config)
                 model.load_state_dict(state_dict, strict=True)
 
-                if (
+                if model_args.quantization_args is not None and model_args.quantization_args.spinquant:
-                    model_args.quantization_args is not None
-                    and model_args.quantization_args.spinquant
-                ):
                     # Add a wrapper for adding hadamard transform for spinquant.
                     # This needs to be done after loading the state dict otherwise an error will be raised while
                     # loading the state dict.
@@ -206,9 +201,7 @@ class Llama:
 
                     add_hadamard_transform_for_spinquant(model)
             else:
-                raise NotImplementedError(
+                raise NotImplementedError("Currently int4 and fp8 are the only supported quantization methods.")
-                    "Currently int4 and fp8 are the only supported quantization methods."
-                )
         else:
             if device == "cuda":
                 if torch.cuda.is_bf16_supported():
@@ -262,10 +255,7 @@ class Llama:
         params = self.model.params
 
         if print_input_tokens:
-            input_tokens = [
+            input_tokens = [self.formatter.vision_token if t == 128256 else t for t in model_input.tokens]
-                self.formatter.vision_token if t == 128256 else t
-                for t in model_input.tokens
-            ]
             log.info("Input to model -> " + self.tokenizer.decode(input_tokens))
         prompt_tokens = [model_input.tokens]
 
@@ -287,13 +277,11 @@ class Llama:
             mask = model_input.vision.mask if model_input.vision is not None else []
 
             # the method works for bsz > 1 so add a batch dimension
-            xattn_caches, cross_attention_masks, full_text_row_masked_out_mask = (
+            xattn_caches, cross_attention_masks, full_text_row_masked_out_mask = self.model.compute_vision_tokens_masks(
-                self.model.compute_vision_tokens_masks(
                 batch_images=[images],
                 batch_masks=[mask],
                 total_len=total_len,
             )
-            )
 
         pad_id = self.tokenizer.pad_id
         tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long)
@@ -340,9 +328,7 @@ class Llama:
 
             next_token = next_token.reshape(-1)
             # only replace token if prompt has already been generated
-            next_token = torch.where(
+            next_token = torch.where(input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token)
-                input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
-            )
             tokens[:, cur_pos] = next_token
 
             target = tokens[:, prev_pos + 1 : cur_pos + 1]
@@ -365,17 +351,11 @@ class Llama:
                     reduction="none",
                     ignore_index=pad_id,
                 )
-            eos_reached |= (~input_text_mask[:, cur_pos]) & (
+            eos_reached |= (~input_text_mask[:, cur_pos]) & (torch.isin(next_token, stop_tokens))
-                torch.isin(next_token, stop_tokens)
-            )
             yield TokenResult(
                 token=next_token[0].item(),
                 text=self.tokenizer.decode(next_token.tolist()),
-                logprobs=(
+                logprobs=(token_logprobs[:, cur_pos : cur_pos + 1][0].tolist() if logprobs else None),
-                    token_logprobs[:, cur_pos : cur_pos + 1][0].tolist()
-                    if logprobs
-                    else None
-                ),
             )
 
             prev_pos = cur_pos
@@ -388,11 +368,7 @@ class Llama:
     ) -> Generator:
         sampling_params = request.sampling_params
         max_gen_len = sampling_params.max_tokens
-        if (
+        if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.model.params.max_seq_len:
-            max_gen_len is None
-            or max_gen_len == 0
-            or max_gen_len >= self.model.params.max_seq_len
-        ):
             max_gen_len = self.model.params.max_seq_len - 1
 
         model_input = self.formatter.encode_content(request.content)
@@ -417,11 +393,7 @@ class Llama:
     ) -> Generator:
         sampling_params = request.sampling_params
         max_gen_len = sampling_params.max_tokens
-        if (
+        if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.model.params.max_seq_len:
-            max_gen_len is None
-            or max_gen_len == 0
-            or max_gen_len >= self.model.params.max_seq_len
-        ):
             max_gen_len = self.model.params.max_seq_len - 1
 
         temperature, top_p = _infer_sampling_params(sampling_params)
@@ -473,9 +445,7 @@ class LogitsProcessor:
         self.token_enforcer = token_enforcer
         self.mask: Optional[torch.Tensor] = None
 
-    def process_logits(
+    def process_logits(self, tokens: torch.Tensor, scores: torch.Tensor) -> torch.Tensor:
-        self, tokens: torch.Tensor, scores: torch.Tensor
-    ) -> torch.Tensor:
         token_sequence = tokens[0, :].tolist()
         allowed_tokens = self.token_enforcer.get_allowed_tokens(token_sequence)
 
@@ -510,9 +480,7 @@ def get_logits_processor(
     return LogitsProcessor(token_enforcer)
 
 
-def _build_regular_tokens_list(
+def _build_regular_tokens_list(tokenizer: Tokenizer, vocab_size: int) -> List[Tuple[int, str, bool]]:
-    tokenizer: Tokenizer, vocab_size: int
-) -> List[Tuple[int, str, bool]]:
     token_0 = tokenizer.encode("0", bos=False, eos=False)[-1]
     regular_tokens = []
 

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

@@ -80,9 +80,7 @@ class MetaReferenceInferenceImpl(
     async def load_model(self, model_id, llama_model) -> None:
         log.info(f"Loading model `{model_id}`")
         if self.config.create_distributed_process_group:
-            self.generator = LlamaModelParallelGenerator(
+            self.generator = LlamaModelParallelGenerator(self.config, model_id, llama_model)
-                self.config, model_id, llama_model
-            )
             self.generator.start()
         else:
             self.generator = Llama.build(self.config, model_id, llama_model)
@@ -100,9 +98,7 @@ class MetaReferenceInferenceImpl(
                 "No avaible model yet, please register your requested model or add your model in the resouces first"
             )
         elif request.model != self.model_id:
-            raise RuntimeError(
+            raise RuntimeError(f"Model mismatch: request model: {request.model} != loaded model: {self.model_id}")
-                f"Model mismatch: request model: {request.model} != loaded model: {self.model_id}"
-            )
 
     async def unregister_model(self, model_id: str) -> None:
         pass
@@ -184,13 +180,7 @@ class MetaReferenceInferenceImpl(
                     if request.logprobs:
                         assert len(token_result.logprobs) == 1
 
-                        logprobs = [
+                        logprobs = [TokenLogProbs(logprobs_by_token={token_result.text: token_result.logprobs[0]})]
-                            TokenLogProbs(
-                                logprobs_by_token={
-                                    token_result.text: token_result.logprobs[0]
-                                }
-                            )
-                        ]
 
                 yield CompletionResponseStreamChunk(
                     delta=text,
@@ -212,9 +202,7 @@ class MetaReferenceInferenceImpl(
             for x in impl():
                 yield x
 
-    async def _nonstream_completion(
+    async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse:
-        self, request: CompletionRequest
-    ) -> CompletionResponse:
         def impl():
             tokens = []
             logprobs = []
@@ -231,13 +219,7 @@ class MetaReferenceInferenceImpl(
                 if request.logprobs:
                     assert len(token_result.logprobs) == 1
 
-                    logprobs.append(
+                    logprobs.append(TokenLogProbs(logprobs_by_token={token_result.text: token_result.logprobs[0]}))
-                        TokenLogProbs(
-                            logprobs_by_token={
-                                token_result.text: token_result.logprobs[0]
-                            }
-                        )
-                    )
 
             if stop_reason is None:
                 stop_reason = StopReason.out_of_tokens
@@ -289,9 +271,7 @@ class MetaReferenceInferenceImpl(
         self.check_model(request)
 
         # augment and rewrite messages depending on the model
-        request.messages = chat_completion_request_to_messages(
+        request.messages = chat_completion_request_to_messages(request, self.llama_model.core_model_id.value)
-            request, self.llama_model.core_model_id.value
-        )
         # download media and convert to raw content so we can send it to the model
         request = await convert_request_to_raw(request)
 
@@ -304,9 +284,7 @@ class MetaReferenceInferenceImpl(
         else:
             return await self._nonstream_chat_completion(request)
 
-    async def _nonstream_chat_completion(
+    async def _nonstream_chat_completion(self, request: ChatCompletionRequest) -> ChatCompletionResponse:
-        self, request: ChatCompletionRequest
-    ) -> ChatCompletionResponse:
         def impl():
             tokens = []
             logprobs = []
@@ -323,20 +301,12 @@ class MetaReferenceInferenceImpl(
                 if request.logprobs:
                     assert len(token_result.logprobs) == 1
 
-                    logprobs.append(
+                    logprobs.append(TokenLogProbs(logprobs_by_token={token_result.text: token_result.logprobs[0]}))
-                        TokenLogProbs(
-                            logprobs_by_token={
-                                token_result.text: token_result.logprobs[0]
-                            }
-                        )
-                    )
 
             if stop_reason is None:
                 stop_reason = StopReason.out_of_tokens
 
-            raw_message = self.generator.formatter.decode_assistant_message(
+            raw_message = self.generator.formatter.decode_assistant_message(tokens, stop_reason)
-                tokens, stop_reason
-            )
             return ChatCompletionResponse(
                 completion_message=CompletionMessage(
                     content=raw_message.content,
@@ -352,9 +322,7 @@ class MetaReferenceInferenceImpl(
         else:
             return impl()
 
-    async def _stream_chat_completion(
+    async def _stream_chat_completion(self, request: ChatCompletionRequest) -> AsyncGenerator:
-        self, request: ChatCompletionRequest
-    ) -> AsyncGenerator:
         def impl():
             yield ChatCompletionResponseStreamChunk(
                 event=ChatCompletionResponseEvent(
@@ -405,13 +373,7 @@ class MetaReferenceInferenceImpl(
                     if request.logprobs:
                         assert len(token_result.logprobs) == 1
 
-                        logprobs.append(
+                        logprobs.append(TokenLogProbs(logprobs_by_token={token_result.text: token_result.logprobs[0]}))
-                            TokenLogProbs(
-                                logprobs_by_token={
-                                    token_result.text: token_result.logprobs[0]
-                                }
-                            )
-                        )
                     yield ChatCompletionResponseStreamChunk(
                         event=ChatCompletionResponseEvent(
                             event_type=ChatCompletionResponseEventType.progress,
@@ -424,9 +386,7 @@ class MetaReferenceInferenceImpl(
             if stop_reason is None:
                 stop_reason = StopReason.out_of_tokens
 
-            message = self.generator.formatter.decode_assistant_message(
+            message = self.generator.formatter.decode_assistant_message(tokens, stop_reason)
-                tokens, stop_reason
-            )
 
             parsed_tool_calls = len(message.tool_calls) > 0
             if ipython and not parsed_tool_calls:

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

@@ -91,9 +91,7 @@ class LlamaModelParallelGenerator:
 
         self.group = ModelParallelProcessGroup(
             model_parallel_size,
-            init_model_cb=partial(
+            init_model_cb=partial(init_model_cb, self.config, self.model_id, self.llama_model),
-                init_model_cb, self.config, self.model_id, self.llama_model
-            ),
         )
         self.group.start()
         return self

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

@@ -55,47 +55,33 @@ class ProcessingMessageName(str, Enum):
 
 
 class ReadyRequest(BaseModel):
-    type: Literal[ProcessingMessageName.ready_request] = (
+    type: Literal[ProcessingMessageName.ready_request] = ProcessingMessageName.ready_request
-        ProcessingMessageName.ready_request
-    )
 
 
 class ReadyResponse(BaseModel):
-    type: Literal[ProcessingMessageName.ready_response] = (
+    type: Literal[ProcessingMessageName.ready_response] = ProcessingMessageName.ready_response
-        ProcessingMessageName.ready_response
-    )
 
 
 class EndSentinel(BaseModel):
-    type: Literal[ProcessingMessageName.end_sentinel] = (
+    type: Literal[ProcessingMessageName.end_sentinel] = ProcessingMessageName.end_sentinel
-        ProcessingMessageName.end_sentinel
-    )
 
 
 class CancelSentinel(BaseModel):
-    type: Literal[ProcessingMessageName.cancel_sentinel] = (
+    type: Literal[ProcessingMessageName.cancel_sentinel] = ProcessingMessageName.cancel_sentinel
-        ProcessingMessageName.cancel_sentinel
-    )
 
 
 class TaskRequest(BaseModel):
-    type: Literal[ProcessingMessageName.task_request] = (
+    type: Literal[ProcessingMessageName.task_request] = ProcessingMessageName.task_request
-        ProcessingMessageName.task_request
-    )
     task: Union[CompletionRequestWithRawContent, ChatCompletionRequestWithRawContent]
 
 
 class TaskResponse(BaseModel):
-    type: Literal[ProcessingMessageName.task_response] = (
+    type: Literal[ProcessingMessageName.task_response] = ProcessingMessageName.task_response
-        ProcessingMessageName.task_response
-    )
     result: TokenResult
 
 
 class ExceptionResponse(BaseModel):
-    type: Literal[ProcessingMessageName.exception_response] = (
+    type: Literal[ProcessingMessageName.exception_response] = ProcessingMessageName.exception_response
-        ProcessingMessageName.exception_response
-    )
     error: str
 
 
@@ -189,9 +175,7 @@ def retrieve_requests(reply_socket_url: str):
                         group=get_model_parallel_group(),
                     )
                     if isinstance(updates[0], CancelSentinel):
-                        log.info(
+                        log.info("quitting generation loop because request was cancelled")
-                            "quitting generation loop because request was cancelled"
-                        )
                         break
 
                 if mp_rank_0():
@@ -350,9 +334,7 @@ class ModelParallelProcessGroup:
 
     def run_inference(
         self,
-        req: Union[
+        req: Union[CompletionRequestWithRawContent, ChatCompletionRequestWithRawContent],
-            CompletionRequestWithRawContent, ChatCompletionRequestWithRawContent
-        ],
     ) -> Generator:
         assert not self.running, "inference already running"
 

llama_stack/providers/inline/inference/meta_reference/quantization/fp8_impls.py

@@ -19,9 +19,7 @@ try:
 
     log.info("Using efficient FP8 operators in FBGEMM.")
 except ImportError:
-    log.error(
+    log.error("No efficient FP8 operators. Please install FBGEMM in fp8_requirements.txt.")
-        "No efficient FP8 operators. Please install FBGEMM in fp8_requirements.txt."
-    )
     raise
 
 import torch
@@ -60,14 +58,8 @@ def ffn_swiglu(
     num_tokens: Optional[Tensor] = None,
     is_memory_bounded: bool = False,
 ) -> Tensor:
-    if (
+    if isinstance(w1, Fp8ScaledWeights) and isinstance(w3, Fp8ScaledWeights) and isinstance(w2, Fp8ScaledWeights):
-        isinstance(w1, Fp8ScaledWeights)
+        return ffn_swiglu_fp8_dynamic(x, w1, w3, w2, w1.activation_scale_ub, num_tokens, is_memory_bounded)
-        and isinstance(w3, Fp8ScaledWeights)
-        and isinstance(w2, Fp8ScaledWeights)
-    ):
-        return ffn_swiglu_fp8_dynamic(
-            x, w1, w3, w2, w1.activation_scale_ub, num_tokens, is_memory_bounded
-        )
 
     (B, T, D) = x.shape  # noqa: N806
     (HD_L, D_) = w1.shape  # noqa: N806
@@ -146,12 +138,8 @@ def fc_fp8_dynamic(
     Single w8a8 fc layer with dynamic row-wise scaling.
     """
     if isinstance(w, Fp8RowwiseWeights):
-        xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_row(
+        xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_row(x, num_tokens, activation_scale_ub)
-            x, num_tokens, activation_scale_ub
+        y = torch.ops.fbgemm.f8f8bf16_rowwise(xq, w.weight, x_scale, w.scale, use_fast_accum=True)
-        )
-        y = torch.ops.fbgemm.f8f8bf16_rowwise(
-            xq, w.weight, x_scale, w.scale, use_fast_accum=True
-        )
     del xq
     return y
 

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

@@ -17,8 +17,7 @@ from torch import Tensor
 
 
 @unittest.skipIf(
-    not torch.cuda.is_available()
+    not torch.cuda.is_available() or torch.cuda.get_device_properties(torch.cuda.current_device()).major < 9,
-    or torch.cuda.get_device_properties(torch.cuda.current_device()).major < 9,
     "Skip when H100 is not available",
 )
 class FP8Tests(unittest.TestCase):

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

@@ -57,9 +57,7 @@ class HadamardModule(torch.nn.Module):
         return x
 
 
-def add_hadamard_transform_for_spinquant(
+def add_hadamard_transform_for_spinquant(model: torch.nn.Module, prefix: str = "") -> None:
-    model: torch.nn.Module, prefix: str = ""
-) -> None:
     """
     Adds a Hadamard transform to the last linear layer of each feedforward network (FFN) in the model.
     This function recursively traverses the model's children and looks for layers that match the pattern
@@ -81,12 +79,8 @@ def add_hadamard_transform_for_spinquant(
     for module_name, module in model.named_children():
         child_full_name = prefix + "." + module_name
         if re.search(pattern_last_linear_ffn, child_full_name):
-            new_module = nn.Sequential(
+            new_module = nn.Sequential(HadamardModule(group_size=module.in_features), module)
-                HadamardModule(group_size=module.in_features), module
-            )
             del module
             setattr(model, module_name, new_module)
         else:
-            add_hadamard_transform_for_spinquant(
+            add_hadamard_transform_for_spinquant(module, (prefix + "." if prefix else prefix) + module_name)
-                module, (prefix + "." if prefix else prefix) + module_name
-            )

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

@@ -63,12 +63,8 @@ def convert_to_fp8_quantized_model(
     # Move weights to GPU with quantization
     if llama_model.quantization_format == CheckpointQuantizationFormat.fp8_mixed.value:
         log.info("Loading fp8 scales...")
-        fp8_scales_path = os.path.join(
+        fp8_scales_path = os.path.join(checkpoint_dir, f"fp8_scales_{get_model_parallel_rank()}.pt")
-            checkpoint_dir, f"fp8_scales_{get_model_parallel_rank()}.pt"
+        assert os.path.isfile(fp8_scales_path), f"fp8_scales_path not found for rank {get_model_parallel_rank()}"
-        )
-        assert os.path.isfile(
-            fp8_scales_path
-        ), f"fp8_scales_path not found for rank {get_model_parallel_rank()}"
         fp8_scales = torch.load(fp8_scales_path, weights_only=True)
 
         for block in model.layers:
@@ -81,9 +77,7 @@ def convert_to_fp8_quantized_model(
                     param = getattr(block.feed_forward, key)
                     param.weight = load_fp8(
                         param.weight,
-                        fp8_scales[
+                        fp8_scales[f"{block.layer_id}_feed_forward.{key}_{get_model_parallel_rank()}"],
-                            f"{block.layer_id}_feed_forward.{key}_{get_model_parallel_rank()}"
-                        ],
                         fp8_activation_scale_ub,
                     )
     else:
@@ -172,9 +166,7 @@ class Int8DynActInt4WeightLinearLoRA(Int8DynActInt4WeightLinear):
         if prefix + "zeros" not in state_dict:
             # Zero-point may not be saved in the state dict. In this case, we assume it's zero.
             assert prefix + "scales" in state_dict
-            state_dict[prefix + "zeros"] = torch.zeros_like(
+            state_dict[prefix + "zeros"] = torch.zeros_like(state_dict[prefix + "scales"])
-                state_dict[prefix + "scales"]
-            )
 
     def forward(self, input_: torch.Tensor) -> torch.Tensor:
         module_out = super().forward(input_)
@@ -229,9 +221,7 @@ class Int8WeightLinear(torch.nn.Linear):
         bias: Whether to use bias.
     """
 
-    def __init__(
+    def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None) -> None:
-        self, in_features: int, out_features: int, bias: bool = True, device=None
-    ) -> None:
         super().__init__(in_features, out_features, bias, device=device)
 
         self._register_load_state_dict_pre_hook(self.load_hook)
@@ -295,9 +285,7 @@ def _prepare_model_int4_weight_int8_dynamic_activation(
             del module
             setattr(model, module_name, quantized_module)
         else:
-            _prepare_model_int4_weight_int8_dynamic_activation(
+            _prepare_model_int4_weight_int8_dynamic_activation(module, group_size, lora_rank, lora_scale)
-                module, group_size, lora_rank, lora_scale
-            )
 
     return model
 
@@ -321,9 +309,7 @@ def convert_to_int4_quantized_model(
 
     group_size = model_args.quantization_args.group_size
     if group_size is None:
-        raise ValueError(
+        raise ValueError("'group_size' cannot be None in 'quantization_args'. Please specify it.")
-            "'group_size' cannot be None in 'quantization_args'. Please specify it."
-        )
 
     if model_args.lora_args is None:
         # Certain quantized models (e.g., SpinQuant) may not have LoRA.
@@ -333,8 +319,6 @@ def convert_to_int4_quantized_model(
         lora_rank = model_args.lora_args.rank
         lora_scale = model_args.lora_args.scale
 
-    _prepare_model_int4_weight_int8_dynamic_activation(
+    _prepare_model_int4_weight_int8_dynamic_activation(model, group_size, lora_rank, lora_scale)
-        model, group_size, lora_rank, lora_scale
-    )
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
     return model.to(device)

llama_stack/providers/inline/inference/meta_reference/quantization/scripts/quantize_checkpoint.py

@@ -76,9 +76,9 @@ def main(
 
         checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
         assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
-        assert model_parallel_size == len(
+        assert model_parallel_size == len(checkpoints), (
-            checkpoints
+            f"Loading a checkpoint for MP={len(checkpoints)} but world size is {model_parallel_size}"
-        ), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {model_parallel_size}"
+        )
         ckpt_path = checkpoints[get_model_parallel_rank()]
         checkpoint = torch.load(ckpt_path, map_location="cpu", weights_only=True)
         with open(Path(ckpt_dir) / "params.json", "r") as f:
@@ -90,9 +90,9 @@ def main(
             **params,
         )
         tokenizer = Tokenizer(model_path=tokenizer_path)
-        assert (
+        assert model_args.vocab_size == tokenizer.n_words, (
-            model_args.vocab_size == tokenizer.n_words
+            f"model_args vocab = {model_args.vocab_size} but tokenizer vocab = {tokenizer.n_words}"
-        ), f"model_args vocab = {model_args.vocab_size} but tokenizer vocab = {tokenizer.n_words}"
+        )
 
         # load on CPU in bf16 so that fp8 conversion does not find an unexpected (fp32, e.g.) datatype
         torch.set_default_tensor_type(torch.BFloat16Tensor)
@@ -106,9 +106,7 @@ def main(
             torch.set_default_tensor_type(torch.cuda.HalfTensor)
 
         log.info(ckpt_path)
-        assert (
+        assert quantized_ckpt_dir is not None, "QUantized checkpoint directory should not be None"
-            quantized_ckpt_dir is not None
-        ), "QUantized checkpoint directory should not be None"
         fp8_scales = {}
         for block in model.layers:
             if isinstance(block, TransformerBlock):
@@ -122,9 +120,7 @@ def main(
                 )
                 with torch.inference_mode():
                     block.feed_forward.w1.weight = Parameter(fp8_weight.weight)
-                fp8_scales[
+                fp8_scales[f"{block.layer_id}_feed_forward.w1_{get_model_parallel_rank()}"] = fp8_weight.scale
-                    f"{block.layer_id}_feed_forward.w1_{get_model_parallel_rank()}"
-                ] = fp8_weight.scale
 
                 fp8_weight = quantize_fp8(
                     block.feed_forward.w3.weight,
@@ -133,9 +129,7 @@ def main(
                 )
                 with torch.inference_mode():
                     block.feed_forward.w3.weight = Parameter(fp8_weight.weight)
-                fp8_scales[
+                fp8_scales[f"{block.layer_id}_feed_forward.w3_{get_model_parallel_rank()}"] = fp8_weight.scale
-                    f"{block.layer_id}_feed_forward.w3_{get_model_parallel_rank()}"
-                ] = fp8_weight.scale
 
                 fp8_weight = quantize_fp8(
                     block.feed_forward.w2.weight,
@@ -144,13 +138,9 @@ def main(
                 )
                 with torch.inference_mode():
                     block.feed_forward.w2.weight = Parameter(fp8_weight.weight)
-                fp8_scales[
+                fp8_scales[f"{block.layer_id}_feed_forward.w2_{get_model_parallel_rank()}"] = fp8_weight.scale
-                    f"{block.layer_id}_feed_forward.w2_{get_model_parallel_rank()}"
-                ] = fp8_weight.scale
 
-        fp8_scales_path = os.path.join(
+        fp8_scales_path = os.path.join(quantized_ckpt_dir, f"fp8_scales_{get_model_parallel_rank()}.pt")
-            quantized_ckpt_dir, f"fp8_scales_{get_model_parallel_rank()}.pt"
-        )
         torch.save(fp8_scales, fp8_scales_path)
 
         ckpt_path = os.path.join(

llama_stack/providers/inline/inference/sentence_transformers/config.py

@@ -10,7 +10,6 @@ from pydantic import BaseModel
 
 
 class SentenceTransformersInferenceConfig(BaseModel):
-
     @classmethod
     def sample_run_config(cls) -> Dict[str, Any]:
         return {}

llama_stack/providers/inline/inference/vllm/config.py

@@ -53,7 +53,5 @@ class VLLMConfig(BaseModel):
         repos = [m.huggingface_repo for m in permitted_models]
         if model not in (descriptors + repos):
             model_list = "\n\t".join(repos)
-            raise ValueError(
+            raise ValueError(f"Unknown model: `{model}`. Choose from [\n\t{model_list}\n]")
-                f"Unknown model: `{model}`. Choose from [\n\t{model_list}\n]"
-            )
         return model

llama_stack/providers/inline/inference/vllm/vllm.py

@@ -176,13 +176,9 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
         log.info("Sampling params: %s", sampling_params)
         request_id = _random_uuid()
 
-        prompt = await chat_completion_request_to_prompt(
+        prompt = await chat_completion_request_to_prompt(request, self.config.model, self.formatter)
-            request, self.config.model, self.formatter
-        )
         vllm_sampling_params = self._sampling_params(request.sampling_params)
-        results_generator = self.engine.generate(
+        results_generator = self.engine.generate(prompt, vllm_sampling_params, request_id)
-            prompt, vllm_sampling_params, request_id
-        )
         if stream:
             return self._stream_chat_completion(request, results_generator)
         else:
@@ -230,12 +226,8 @@ class VLLMInferenceImpl(Inference, ModelsProtocolPrivate):
                 )
 
         stream = _generate_and_convert_to_openai_compat()
-        async for chunk in process_chat_completion_stream_response(
+        async for chunk in process_chat_completion_stream_response(stream, self.formatter):
-            stream, self.formatter
-        ):
             yield chunk
 
-    async def embeddings(
+    async def embeddings(self, model_id: str, contents: List[InterleavedContent]) -> EmbeddingsResponse:
-        self, model_id: str, contents: List[InterleavedContent]
-    ) -> EmbeddingsResponse:
         raise NotImplementedError()

llama_stack/providers/inline/post_training/common/validator.py

@@ -47,6 +47,4 @@ async def validate_input_dataset_schema(
     if dataset_type not in EXPECTED_DATASET_SCHEMA:
         raise ValueError(f"Dataset type {dataset_type} is not supported.")
 
-    validate_dataset_schema(
+    validate_dataset_schema(dataset_def.dataset_schema, EXPECTED_DATASET_SCHEMA[dataset_type])
-        dataset_def.dataset_schema, EXPECTED_DATASET_SCHEMA[dataset_type]
-    )

llama_stack/providers/inline/post_training/torchtune/common/checkpointer.py

@@ -42,9 +42,7 @@ class TorchtuneCheckpointer:
         self._model_type = ModelType[model_type]
         self._output_dir = output_dir
         # get ckpt paths
-        self._checkpoint_path = Path.joinpath(
+        self._checkpoint_path = Path.joinpath(self._checkpoint_dir, self._checkpoint_file)
-            self._checkpoint_dir, self._checkpoint_file
-        )
 
     def load_checkpoint(self) -> Dict[str, Any]:
         """
@@ -57,13 +55,9 @@ class TorchtuneCheckpointer:
                 llama3_vision_meta_to_tune,
             )
 
-            state_dict[training.MODEL_KEY] = llama3_vision_meta_to_tune(
+            state_dict[training.MODEL_KEY] = llama3_vision_meta_to_tune(model_state_dict)
-                model_state_dict
-            )
         else:
-            state_dict[training.MODEL_KEY] = convert_weights.meta_to_tune(
+            state_dict[training.MODEL_KEY] = convert_weights.meta_to_tune(model_state_dict)
-                model_state_dict
-            )
 
         # llama3_2 has tied weights, so we need to remove the output.weight key
         if self._model_type == ModelType.LLAMA3_2:
@@ -82,10 +76,7 @@ class TorchtuneCheckpointer:
         epoch: int,
         adapter_only: bool = False,
     ) -> str:
-        model_file_path = (
+        model_file_path = Path(self._output_dir) / f"{self._model_id}-{self._training_algorithm}-{epoch}"
-            Path(self._output_dir)
-            / f"{self._model_id}-{self._training_algorithm}-{epoch}"
-        )
 
         model_file_path.mkdir(parents=True, exist_ok=True)
 
@@ -116,22 +107,13 @@ class TorchtuneCheckpointer:
                     llama3_vision_tune_to_meta,
                 )
 
-                state_dict[training.MODEL_KEY] = llama3_vision_tune_to_meta(
+                state_dict[training.MODEL_KEY] = llama3_vision_tune_to_meta(model_state_dict)
-                    model_state_dict
-                )
             else:
                 # llama3_2 has tied weights, so we need to add the output.weight key
-                if (
+                if self._model_type == ModelType.LLAMA3_2 and "output.weight" not in model_state_dict:
-                    self._model_type == ModelType.LLAMA3_2
+                    model_state_dict["output.weight"] = model_state_dict["tok_embeddings.weight"]
-                    and "output.weight" not in model_state_dict
-                ):
-                    model_state_dict["output.weight"] = model_state_dict[
-                        "tok_embeddings.weight"
-                    ]
 
-                state_dict[training.MODEL_KEY] = convert_weights.tune_to_meta(
+                state_dict[training.MODEL_KEY] = convert_weights.tune_to_meta(model_state_dict)
-                    model_state_dict
-                )
 
             model_file_name = Path.joinpath(model_file_path, "consolidated.00.pth")
 

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

@@ -15,18 +15,13 @@ from typing import Any, Mapping
 from llama_stack.providers.utils.common.data_schema_validator import ColumnName
 
 
-def llama_stack_instruct_to_torchtune_instruct(
+def llama_stack_instruct_to_torchtune_instruct(sample: Mapping[str, Any]) -> Mapping[str, Any]:
-    sample: Mapping[str, Any]
+    assert ColumnName.chat_completion_input.value in sample and ColumnName.expected_answer.value in sample, (
-) -> Mapping[str, Any]:
+        "Invalid input row"
-    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]))
 
-    assert (
+    assert len(input_messages) == 1, "llama stack intruct dataset format only supports 1 user message"
-        len(input_messages) == 1
-    ), "llama stack intruct dataset format only supports 1 user message"
     input_message = input_messages[0]
 
     assert "content" in input_message, "content not found in input message"
@@ -48,13 +43,9 @@ def llama_stack_chat_to_torchtune_chat(sample: Mapping[str, Any]) -> Mapping[str
     roles = []
     conversations = []
     for message in dialog:
-        assert (
+        assert "role" in message and "content" in message, "role and content must in message"
-            "role" in message and "content" in message
-        ), "role and content must in message"
         roles.append(message["role"])
-        conversations.append(
+        conversations.append({"from": role_map[message["role"]], "value": message["content"]})
-            {"from": role_map[message["role"]], "value": message["content"]}
-        )
 
     assert roles[0] == "user", "first message must be from user"
     assert "assistant" in roles, "at least 1 message should be from assistant"

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

@@ -61,8 +61,7 @@ class SFTDataset(Dataset):
         if not ("tokens" in tokenized_dict and "mask" in tokenized_dict):
             keys_str = ", ".join(tokenized_dict.keys())
             error_message = (
-                "model_transform returned the following keys: "
+                f"model_transform returned the following keys: {keys_str}. Must return 'tokens' and 'mask' as keys."
-                f"{keys_str}. Must return 'tokens' and 'mask' as keys."
             )
             raise ValueError(error_message)
 

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

@@ -119,9 +119,7 @@ class TorchtunePostTrainingImpl:
         return ListPostTrainingJobsResponse(data=self.jobs_list)
 
     @webmethod(route="/post-training/job/status")
-    async def get_training_job_status(
+    async def get_training_job_status(self, job_uuid: str) -> Optional[PostTrainingJobStatusResponse]:
-        self, job_uuid: str
-    ) -> Optional[PostTrainingJobStatusResponse]:
         if job_uuid in self.jobs_status:
             return self.jobs_status[job_uuid]
         return None
@@ -131,12 +129,8 @@ class TorchtunePostTrainingImpl:
         raise NotImplementedError("Job cancel is not implemented yet")
 
     @webmethod(route="/post-training/job/artifacts")
-    async def get_training_job_artifacts(
+    async def get_training_job_artifacts(self, job_uuid: str) -> Optional[PostTrainingJobArtifactsResponse]:
-        self, job_uuid: str
-    ) -> Optional[PostTrainingJobArtifactsResponse]:
         if job_uuid in self.checkpoints_dict:
             checkpoints = self.checkpoints_dict.get(job_uuid, [])
-            return PostTrainingJobArtifactsResponse(
+            return PostTrainingJobArtifactsResponse(job_uuid=job_uuid, checkpoints=checkpoints)
-                job_uuid=job_uuid, checkpoints=checkpoints
-            )
         return None

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

@@ -94,9 +94,7 @@ class LoraFinetuningSingleDevice:
         self.job_uuid = job_uuid
         self.training_config = training_config
         if not isinstance(algorithm_config, LoraFinetuningConfig):
-            raise ValueError(
+            raise ValueError("You need to speicifc LoraFinetuningConfig for LoRA finetuning")
-                "You need to speicifc LoraFinetuningConfig for LoRA finetuning"
-            )
         self.algorithm_config = algorithm_config
         self._device = torchtune_utils.get_device(device="cuda")
         self._dtype = training.get_dtype(training_config.dtype, device=self._device)
@@ -105,10 +103,7 @@ class LoraFinetuningSingleDevice:
         def model_checkpoint_dir(model) -> str:
             checkpoint_dir = Path(model_local_dir(model.descriptor()))
 
-            paths = [
+            paths = [Path(checkpoint_dir / f"consolidated.{ext}") for ext in ["pth", "00.pth"]]
-                Path(checkpoint_dir / f"consolidated.{ext}")
-                for ext in ["pth", "00.pth"]
-            ]
             if not any(p.exists() for p in paths):
                 checkpoint_dir = checkpoint_dir / "original"
 
@@ -123,9 +118,7 @@ class LoraFinetuningSingleDevice:
         else:
             model = resolve_model(self.model_id)
             if model is None:
-                raise ValueError(
+                raise ValueError(f"{self.model_id} not found. Your model id should be in the llama models SKU list")
-                    f"{self.model_id} not found. Your model id should be in the llama models SKU list"
-                )
             self.checkpoint_dir = model_checkpoint_dir(model)
 
         self._output_dir = str(DEFAULT_CHECKPOINT_DIR)
@@ -196,9 +189,7 @@ class LoraFinetuningSingleDevice:
         self._tokenizer = await self._setup_tokenizer()
         log.info("Tokenizer is initialized.")
 
-        self._optimizer = await self._setup_optimizer(
+        self._optimizer = await self._setup_optimizer(optimizer_config=self.training_config.optimizer_config)
-            optimizer_config=self.training_config.optimizer_config
-        )
         log.info("Optimizer is initialized.")
 
         self._loss_fn = CEWithChunkedOutputLoss()
@@ -226,13 +217,8 @@ class LoraFinetuningSingleDevice:
         # by the dataloader and the max_steps_per_epoch param set by the user and is used
         # for logging and tracking training state. This should be computed after the dataloader
         # has been setup
-        self._steps_per_epoch = (
+        self._steps_per_epoch = len(self._training_dataloader) // self._gradient_accumulation_steps
-            len(self._training_dataloader) // self._gradient_accumulation_steps
+        if self.max_steps_per_epoch is not None and self.max_steps_per_epoch < self._steps_per_epoch:
-        )
-        if (
-            self.max_steps_per_epoch is not None
-            and self.max_steps_per_epoch < self._steps_per_epoch
-        ):
             self._steps_per_epoch = self.max_steps_per_epoch
             self.global_step = self.epochs_run * self._steps_per_epoch
 
@@ -246,9 +232,7 @@ class LoraFinetuningSingleDevice:
         log.info("Learning rate scheduler is initialized.")
 
         # Used to ignore labels for loss computation
-        self.ignore_labels_cache = torch.full(
+        self.ignore_labels_cache = torch.full((self._batch_size, 1), self._loss_fn.ignore_index, device=self._device)
-            (self._batch_size, 1), self._loss_fn.ignore_index, device=self._device
-        )
 
     async def _setup_model(
         self,
@@ -282,13 +266,9 @@ class LoraFinetuningSingleDevice:
         set_trainable_params(model, self.adapter_params)
 
         if enable_activation_checkpointing:
-            training.set_activation_checkpointing(
+            training.set_activation_checkpointing(model, auto_wrap_policy={modules.TransformerSelfAttentionLayer})
-                model, auto_wrap_policy={modules.TransformerSelfAttentionLayer}
-            )
 
-        base_missing, base_unexpected = model.load_state_dict(
+        base_missing, base_unexpected = model.load_state_dict(base_model_state_dict, strict=False)
-            base_model_state_dict, strict=False
-        )
 
         # This is for any adapters that need to be initialized after base weights
         # have been loaded (e.g. DoRA).
@@ -297,9 +277,7 @@ class LoraFinetuningSingleDevice:
                 if hasattr(m, "initialize_dora_magnitude"):
                     m.initialize_dora_magnitude()
         if lora_weights_state_dict:
-            lora_missing, lora_unexpected = model.load_state_dict(
+            lora_missing, lora_unexpected = model.load_state_dict(lora_weights_state_dict, strict=False)
-                lora_weights_state_dict, strict=False
-            )
         else:
             lora_missing, lora_unexpected = None, None
         validate_missing_and_unexpected_for_lora(
@@ -313,14 +291,10 @@ class LoraFinetuningSingleDevice:
         )
 
         # Validate model adapter params were loaded in with the expected dtype
-        training.validate_expected_param_dtype(
+        training.validate_expected_param_dtype(self.adapter_params.items(), dtype=self._dtype)
-            self.adapter_params.items(), dtype=self._dtype
-        )
 
         # activation offloading
-        self.activations_handling_ctx = training.get_act_offloading_ctx_manager(
+        self.activations_handling_ctx = training.get_act_offloading_ctx_manager(model, enable_activation_offloading)
-            model, enable_activation_offloading
-        )
 
         memory_stats = training.get_memory_stats(device=self._device)
         training.log_memory_stats(memory_stats)
@@ -456,9 +430,7 @@ class LoraFinetuningSingleDevice:
         # Shift labels to compute loss
         # equivalent to doing labels[..., 1:] and logits[..., :-1, :]
         # But this way we dont need to slice the logits. We just add an ignore index to labels.
-        labels = torch.hstack(
+        labels = torch.hstack((labels[..., 1:], self.ignore_labels_cache[: labels.shape[0]]))
-            (labels[..., 1:], self.ignore_labels_cache[: labels.shape[0]])
-        )
         if not isinstance(logits, list):
             labels = labels.reshape(-1)
             logits = logits.reshape(-1, logits.size(-1))
@@ -487,9 +459,7 @@ class LoraFinetuningSingleDevice:
         for curr_epoch in range(self.epochs_run, self.total_epochs):
             # Update the sampler to ensure data is correctly shuffled across epochs
             # in case shuffle is True
-            metric_logger = DiskLogger(
+            metric_logger = DiskLogger(log_dir=self._output_dir + f"/{self.model_id}-sft-{curr_epoch}")
-                log_dir=self._output_dir + f"/{self.model_id}-sft-{curr_epoch}"
-            )
             self._training_sampler.set_epoch(curr_epoch)
             loss_to_log = 0.0
 
@@ -497,8 +467,7 @@ class LoraFinetuningSingleDevice:
             for idx, batch in enumerate(self._training_dataloader):
                 if (
                     self.max_steps_per_epoch is not None
-                    and (idx // self._gradient_accumulation_steps)
+                    and (idx // self._gradient_accumulation_steps) == self.max_steps_per_epoch
-                    == self.max_steps_per_epoch
                 ):
                     break
 
@@ -506,9 +475,7 @@ class LoraFinetuningSingleDevice:
 
                 # Calculate the number of unmasked tokens in the current batch
                 # and increment the total number of tokens seen in the step
-                current_num_tokens = (
+                current_num_tokens = (batch["labels"] != self._loss_fn.ignore_index).sum()
-                    batch["labels"] != self._loss_fn.ignore_index
-                ).sum()
                 num_tokens += current_num_tokens
 
                 # Loss is normalized by default so we multiply by the number of tokens
@@ -533,9 +500,7 @@ class LoraFinetuningSingleDevice:
                     loss_to_log = running_loss.item() / num_tokens
 
                     pbar.update(1)
-                    pbar.set_description(
+                    pbar.set_description(f"{curr_epoch + 1}|{self.global_step}|Loss: {loss_to_log}")
-                        f"{curr_epoch + 1}|{self.global_step}|Loss: {loss_to_log}"
-                    )
 
                     time_per_step = time.perf_counter() - t0
                     log_dict = {

llama_stack/providers/inline/safety/code_scanner/code_scanner.py

@@ -67,10 +67,6 @@ class MetaReferenceCodeScannerSafetyImpl(Safety):
             violation = SafetyViolation(
                 violation_level=(ViolationLevel.ERROR),
                 user_message="Sorry, I found security concerns in the code.",
-                metadata={
+                metadata={"violation_type": ",".join([issue.pattern_id for issue in result.issues_found])},
-                    "violation_type": ",".join(
-                        [issue.pattern_id for issue in result.issues_found]
-                    )
-                },
             )
         return RunShieldResponse(violation=violation)

llama_stack/providers/inline/safety/llama_guard/__init__.py

@@ -10,9 +10,7 @@ from .config import LlamaGuardConfig
 async def get_provider_impl(config: LlamaGuardConfig, deps):
     from .llama_guard import LlamaGuardSafetyImpl
 
-    assert isinstance(
+    assert isinstance(config, LlamaGuardConfig), f"Unexpected config type: {type(config)}"
-        config, LlamaGuardConfig
-    ), f"Unexpected config type: {type(config)}"
 
     impl = LlamaGuardSafetyImpl(config, deps)
     await impl.initialize()

llama_stack/providers/inline/safety/llama_guard/llama_guard.py

@@ -102,8 +102,7 @@ LLAMA_GUARD_MODEL_IDS = {
 }
 
 MODEL_TO_SAFETY_CATEGORIES_MAP = {
-    "meta-llama/Llama-Guard-3-8B": DEFAULT_LG_V3_SAFETY_CATEGORIES
+    "meta-llama/Llama-Guard-3-8B": DEFAULT_LG_V3_SAFETY_CATEGORIES + [CAT_CODE_INTERPRETER_ABUSE],
-    + [CAT_CODE_INTERPRETER_ABUSE],
     "meta-llama/Llama-Guard-3-1B": DEFAULT_LG_V3_SAFETY_CATEGORIES,
     "meta-llama/Llama-Guard-3-11B-Vision": DEFAULT_LG_V3_SAFETY_CATEGORIES,
 }
@@ -133,9 +132,7 @@ Provide your safety assessment for ONLY THE LAST $agent_type message in the abov
  - If unsafe, a second line must include a comma-separated list of violated categories."""
 
 
-PROMPT_TEMPLATE = Template(
+PROMPT_TEMPLATE = Template(f"{PROMPT_TASK}{SAFETY_CATEGORIES}{PROMPT_CONVERSATION}{PROMPT_INSTRUCTIONS}")
-    f"{PROMPT_TASK}{SAFETY_CATEGORIES}{PROMPT_CONVERSATION}{PROMPT_INSTRUCTIONS}"
-)
 
 
 class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
@@ -233,9 +230,7 @@ class LlamaGuardShield:
         if messages[0].role != Role.user.value:
             raise ValueError("Messages must start with user")
 
-        if len(messages) >= 2 and (
+        if len(messages) >= 2 and (messages[0].role == Role.user.value and messages[1].role == Role.user.value):
-            messages[0].role == Role.user.value and messages[1].role == Role.user.value
-        ):
             messages = messages[1:]
 
         for i in range(1, len(messages)):
@@ -263,10 +258,7 @@ class LlamaGuardShield:
             stream=True,
         ):
             event = chunk.event
-            if (
+            if event.event_type == ChatCompletionResponseEventType.progress and event.delta.type == "text":
-                event.event_type == ChatCompletionResponseEventType.progress
-                and event.delta.type == "text"
-            ):
                 content += event.delta.text
 
         content = content.strip()
@@ -313,10 +305,7 @@ class LlamaGuardShield:
         categories = self.get_safety_categories()
         categories_str = "\n".join(categories)
         conversations_str = "\n\n".join(
-            [
+            [f"{m.role.capitalize()}: {interleaved_content_as_str(m.content)}" for m in messages]
-                f"{m.role.capitalize()}: {interleaved_content_as_str(m.content)}"
-                for m in messages
-            ]
         )
         return PROMPT_TEMPLATE.substitute(
             agent_type=messages[-1].role.capitalize(),

llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py

@@ -46,9 +46,7 @@ class PromptGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
 
     async def register_shield(self, shield: Shield) -> None:
         if shield.provider_resource_id != PROMPT_GUARD_MODEL:
-            raise ValueError(
+            raise ValueError(f"Only {PROMPT_GUARD_MODEL} is supported for Prompt Guard. ")
-                f"Only {PROMPT_GUARD_MODEL} is supported for Prompt Guard. "
-            )
 
     async def run_shield(
         self,
@@ -71,9 +69,7 @@ class PromptGuardShield:
         threshold: float = 0.9,
         temperature: float = 1.0,
     ):
-        assert (
+        assert model_dir is not None, "Must provide a model directory for prompt injection shield"
-            model_dir is not None
-        ), "Must provide a model directory for prompt injection shield"
         if temperature <= 0:
             raise ValueError("Temperature must be greater than 0")
 
@@ -85,9 +81,7 @@ class PromptGuardShield:
 
         # load model and tokenizer
         self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
-        self.model = AutoModelForSequenceClassification.from_pretrained(
+        self.model = AutoModelForSequenceClassification.from_pretrained(model_dir, device_map=self.device)
-            model_dir, device_map=self.device
-        )
 
     async def run(self, messages: List[Message]) -> RunShieldResponse:
         message = messages[-1]
@@ -117,10 +111,7 @@ class PromptGuardShield:
                     "violation_type": f"prompt_injection:embedded={score_embedded},malicious={score_malicious}",
                 },
             )
-        elif (
+        elif self.config.guard_type == PromptGuardType.jailbreak.value and score_malicious > self.threshold:
-            self.config.guard_type == PromptGuardType.jailbreak.value
-            and score_malicious > self.threshold
-        ):
             violation = SafetyViolation(
                 violation_level=ViolationLevel.ERROR,
                 violation_type=f"prompt_injection:malicious={score_malicious}",

llama_stack/providers/inline/scoring/basic/scoring.py

@@ -54,15 +54,11 @@ class BasicScoringImpl(
 
     async def list_scoring_functions(self) -> List[ScoringFn]:
         scoring_fn_defs_list = [
-            fn_def
+            fn_def for impl in self.scoring_fn_id_impls.values() for fn_def in impl.get_supported_scoring_fn_defs()
-            for impl in self.scoring_fn_id_impls.values()
-            for fn_def in impl.get_supported_scoring_fn_defs()
         ]
 
         for f in scoring_fn_defs_list:
-            assert f.identifier.startswith(
+            assert f.identifier.startswith("basic"), "All basic scoring fn must have identifier prefixed with 'basic'! "
-                "basic"
-            ), "All basic scoring fn must have identifier prefixed with 'basic'! "
 
         return scoring_fn_defs_list
 
@@ -76,9 +72,7 @@ class BasicScoringImpl(
         save_results_dataset: bool = False,
     ) -> ScoreBatchResponse:
         dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        validate_dataset_schema(
+        validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value))
-            dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value)
-        )
 
         all_rows = await self.datasetio_api.get_rows_paginated(
             dataset_id=dataset_id,
@@ -108,12 +102,8 @@ class BasicScoringImpl(
                 raise ValueError(f"Scoring function {scoring_fn_id} is not supported.")
             scoring_fn = self.scoring_fn_id_impls[scoring_fn_id]
             scoring_fn_params = scoring_functions.get(scoring_fn_id, None)
-            score_results = await scoring_fn.score(
+            score_results = await scoring_fn.score(input_rows, scoring_fn_id, scoring_fn_params)
-                input_rows, scoring_fn_id, scoring_fn_params
+            agg_results = await scoring_fn.aggregate(score_results, scoring_fn_id, scoring_fn_params)
-            )
-            agg_results = await scoring_fn.aggregate(
-                score_results, scoring_fn_id, scoring_fn_params
-            )
             res[scoring_fn_id] = ScoringResult(
                 score_rows=score_results,
                 aggregated_results=agg_results,

llama_stack/providers/inline/scoring/basic/scoring_fn/equality_scoring_fn.py

@@ -32,9 +32,7 @@ class EqualityScoringFn(RegisteredBaseScoringFn):
         scoring_params: Optional[ScoringFnParams] = None,
     ) -> ScoringResultRow:
         assert "expected_answer" in input_row, "Expected answer not found in input row."
-        assert (
+        assert "generated_answer" in input_row, "Generated answer not found in input row."
-            "generated_answer" in input_row
-        ), "Generated answer not found in input row."
 
         expected_answer = input_row["expected_answer"]
         generated_answer = input_row["generated_answer"]

llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/equality.py

@@ -18,7 +18,5 @@ equality = ScoringFn(
     provider_id="basic",
     provider_resource_id="equality",
     return_type=NumberType(),
-    params=BasicScoringFnParams(
+    params=BasicScoringFnParams(aggregation_functions=[AggregationFunctionType.accuracy]),
-        aggregation_functions=[AggregationFunctionType.accuracy]
-    ),
 )

llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/regex_parser_multiple_choice_answer.py

@@ -55,9 +55,7 @@ MULTILINGUAL_ANSWER_REGEXES = [
     r"Àṣàyàn\s*:",
 ]
 
-MULTILINGUAL_ANSWER_PATTERN_TEMPLATE = (
+MULTILINGUAL_ANSWER_PATTERN_TEMPLATE = r"(?i){}\s*([A-D]|[أ-د]|[অ]|[ব]|[ড]|[ঢ]|[Ａ]|[Ｂ]|[Ｃ]|[Ｄ])"
-    r"(?i){}\s*([A-D]|[أ-د]|[অ]|[ব]|[ড]|[ঢ]|[Ａ]|[Ｂ]|[Ｃ]|[Ｄ])"
-)
 
 regex_parser_multiple_choice_answer = ScoringFn(
     identifier="basic::regex_parser_multiple_choice_answer",
@@ -66,10 +64,7 @@ regex_parser_multiple_choice_answer = ScoringFn(
     provider_id="basic",
     provider_resource_id="regex-parser-multiple-choice-answer",
     params=RegexParserScoringFnParams(
-        parsing_regexes=[
+        parsing_regexes=[MULTILINGUAL_ANSWER_PATTERN_TEMPLATE.format(x) for x in MULTILINGUAL_ANSWER_REGEXES],
-            MULTILINGUAL_ANSWER_PATTERN_TEMPLATE.format(x)
-            for x in MULTILINGUAL_ANSWER_REGEXES
-        ],
         aggregation_functions=[AggregationFunctionType.accuracy],
     ),
 )

llama_stack/providers/inline/scoring/basic/scoring_fn/fn_defs/subset_of.py

@@ -18,7 +18,5 @@ subset_of = ScoringFn(
     return_type=NumberType(),
     provider_id="basic",
     provider_resource_id="subset-of",
-    params=BasicScoringFnParams(
+    params=BasicScoringFnParams(aggregation_functions=[AggregationFunctionType.accuracy]),
-        aggregation_functions=[AggregationFunctionType.accuracy]
-    ),
 )

llama_stack/providers/inline/scoring/basic/scoring_fn/regex_parser_scoring_fn.py

@@ -33,17 +33,14 @@ class RegexParserScoringFn(RegisteredBaseScoringFn):
         scoring_fn_identifier: Optional[str] = None,
         scoring_params: Optional[ScoringFnParams] = None,
     ) -> ScoringResultRow:
-        assert (
+        assert scoring_fn_identifier is not None, "Scoring function identifier not found."
-            scoring_fn_identifier is not None
-        ), "Scoring function identifier not found."
         fn_def = self.supported_fn_defs_registry[scoring_fn_identifier]
         if scoring_params is not None:
             fn_def.params = scoring_params
 
-        assert (
+        assert fn_def.params is not None and fn_def.params.type == ScoringFnParamsType.regex_parser.value, (
-            fn_def.params is not None
+            f"RegexParserScoringFnParams not found for {fn_def}."
-            and fn_def.params.type == ScoringFnParamsType.regex_parser.value
+        )
-        ), f"RegexParserScoringFnParams not found for {fn_def}."
 
         expected_answer = input_row["expected_answer"]
         generated_answer = input_row["generated_answer"]

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

@@ -124,12 +124,10 @@ class BraintrustScoringImpl(
         self.datasets_api = datasets_api
 
         self.braintrust_evaluators = {
-            entry.identifier: entry.evaluator
+            entry.identifier: entry.evaluator for entry in SUPPORTED_BRAINTRUST_SCORING_FN_ENTRY
-            for entry in SUPPORTED_BRAINTRUST_SCORING_FN_ENTRY
         }
         self.supported_fn_defs_registry = {
-            entry.identifier: entry.fn_def
+            entry.identifier: entry.fn_def for entry in SUPPORTED_BRAINTRUST_SCORING_FN_ENTRY
-            for entry in SUPPORTED_BRAINTRUST_SCORING_FN_ENTRY
         }
 
     async def initialize(self) -> None: ...
@@ -139,16 +137,14 @@ class BraintrustScoringImpl(
     async def list_scoring_functions(self) -> List[ScoringFn]:
         scoring_fn_defs_list = [x for x in self.supported_fn_defs_registry.values()]
         for f in scoring_fn_defs_list:
-            assert f.identifier.startswith(
+            assert f.identifier.startswith("braintrust"), (
-                "braintrust"
+                "All braintrust scoring fn must have identifier prefixed with 'braintrust'! "
-            ), "All braintrust scoring fn must have identifier prefixed with 'braintrust'! "
+            )
 
         return scoring_fn_defs_list
 
     async def register_scoring_function(self, scoring_fn: ScoringFn) -> None:
-        raise NotImplementedError(
+        raise NotImplementedError("Registering scoring function not allowed for braintrust provider")
-            "Registering scoring function not allowed for braintrust provider"
-        )
 
     async def set_api_key(self) -> None:
         # api key is in the request headers
@@ -171,17 +167,13 @@ class BraintrustScoringImpl(
         await self.set_api_key()
 
         dataset_def = await self.datasets_api.get_dataset(dataset_id=dataset_id)
-        validate_dataset_schema(
+        validate_dataset_schema(dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value))
-            dataset_def.dataset_schema, get_valid_schemas(Api.scoring.value)
-        )
 
         all_rows = await self.datasetio_api.get_rows_paginated(
             dataset_id=dataset_id,
             rows_in_page=-1,
         )
-        res = await self.score(
+        res = await self.score(input_rows=all_rows.rows, scoring_functions=scoring_functions)
-            input_rows=all_rows.rows, scoring_functions=scoring_functions
-        )
         if save_results_dataset:
             # TODO: persist and register dataset on to server for reading
             # self.datasets_api.register_dataset()
@@ -222,13 +214,8 @@ class BraintrustScoringImpl(
             if scoring_fn_id not in self.supported_fn_defs_registry:
                 raise ValueError(f"Scoring function {scoring_fn_id} is not supported.")
 
-            score_results = [
+            score_results = [await self.score_row(input_row, scoring_fn_id) for input_row in input_rows]
-                await self.score_row(input_row, scoring_fn_id)
+            aggregation_functions = self.supported_fn_defs_registry[scoring_fn_id].params.aggregation_functions
-                for input_row in input_rows
-            ]
-            aggregation_functions = self.supported_fn_defs_registry[
-                scoring_fn_id
-            ].params.aggregation_functions
 
             # override scoring_fn params if provided
             if scoring_functions[scoring_fn_id] is not None:

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_correctness.py

@@ -21,7 +21,5 @@ answer_correctness_fn_def = ScoringFn(
     provider_id="braintrust",
     provider_resource_id="answer-correctness",
     return_type=NumberType(),
-    params=BasicScoringFnParams(
+    params=BasicScoringFnParams(aggregation_functions=[AggregationFunctionType.average]),
-        aggregation_functions=[AggregationFunctionType.average]
-    ),
 )

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_relevancy.py

@@ -20,7 +20,5 @@ answer_relevancy_fn_def = ScoringFn(
     provider_id="braintrust",
     provider_resource_id="answer-relevancy",
     return_type=NumberType(),
-    params=BasicScoringFnParams(
+    params=BasicScoringFnParams(aggregation_functions=[AggregationFunctionType.average]),
-        aggregation_functions=[AggregationFunctionType.average]
-    ),
 )

llama_stack/providers/inline/scoring/braintrust/scoring_fn/fn_defs/answer_similarity.py

@@ -20,7 +20,5 @@ answer_similarity_fn_def = ScoringFn(
     provider_id="braintrust",
     provider_resource_id="answer-similarity",
     return_type=NumberType(),
-    params=BasicScoringFnParams(
+    params=BasicScoringFnParams(aggregation_functions=[AggregationFunctionType.average]),
-        aggregation_functions=[AggregationFunctionType.average]
-    ),
 )