chore: more mypy fixes (#2029)

# What does this PR do?

Mainly tried to cover the entire llama_stack/apis directory, we only
have one left. Some excludes were just noop.

Signed-off-by: Sébastien Han <seb@redhat.com>

llama_stack/apis/agents/agents.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import sys
 from collections.abc import AsyncIterator
 from datetime import datetime
 from enum import Enum
@@ -35,6 +36,14 @@ from .openai_responses import (
     OpenAIResponseObjectStream,
 )
 
+# TODO: use enum.StrEnum when we drop support for python 3.10
+if sys.version_info >= (3, 11):
+    from enum import StrEnum
+else:
+
+    class StrEnum(str, Enum):
+        """Backport of StrEnum for Python 3.10 and below."""
+
 
 class Attachment(BaseModel):
     """An attachment to an agent turn.
@@ -73,7 +82,7 @@ class StepCommon(BaseModel):
     completed_at: datetime | None = None
 
 
-class StepType(Enum):
+class StepType(StrEnum):
     """Type of the step in an agent turn.
 
     :cvar inference: The step is an inference step that calls an LLM.
@@ -97,7 +106,7 @@ class InferenceStep(StepCommon):
 
     model_config = ConfigDict(protected_namespaces=())
 
-    step_type: Literal[StepType.inference.value] = StepType.inference.value
+    step_type: Literal[StepType.inference] = StepType.inference
     model_response: CompletionMessage
 
 
@@ -109,7 +118,7 @@ class ToolExecutionStep(StepCommon):
     :param tool_responses: The tool responses from the tool calls.
     """
 
-    step_type: Literal[StepType.tool_execution.value] = StepType.tool_execution.value
+    step_type: Literal[StepType.tool_execution] = StepType.tool_execution
     tool_calls: list[ToolCall]
     tool_responses: list[ToolResponse]
 
@@ -121,7 +130,7 @@ class ShieldCallStep(StepCommon):
     :param violation: The violation from the shield call.
     """
 
-    step_type: Literal[StepType.shield_call.value] = StepType.shield_call.value
+    step_type: Literal[StepType.shield_call] = StepType.shield_call
     violation: SafetyViolation | None
 
 
@@ -133,7 +142,7 @@ class MemoryRetrievalStep(StepCommon):
     :param inserted_context: The context retrieved from the vector databases.
     """
 
-    step_type: Literal[StepType.memory_retrieval.value] = StepType.memory_retrieval.value
+    step_type: Literal[StepType.memory_retrieval] = StepType.memory_retrieval
     # TODO: should this be List[str]?
     vector_db_ids: str
     inserted_context: InterleavedContent
@@ -154,7 +163,7 @@ class Turn(BaseModel):
     input_messages: list[UserMessage | ToolResponseMessage]
     steps: list[Step]
     output_message: CompletionMessage
-    output_attachments: list[Attachment] | None = Field(default_factory=list)
+    output_attachments: list[Attachment] | None = Field(default_factory=lambda: [])
 
     started_at: datetime
     completed_at: datetime | None = None
@@ -182,10 +191,10 @@ register_schema(AgentToolGroup, name="AgentTool")
 class AgentConfigCommon(BaseModel):
     sampling_params: SamplingParams | None = Field(default_factory=SamplingParams)
 
-    input_shields: list[str] | None = Field(default_factory=list)
-    output_shields: list[str] | None = Field(default_factory=list)
-    toolgroups: list[AgentToolGroup] | None = Field(default_factory=list)
-    client_tools: list[ToolDef] | None = Field(default_factory=list)
+    input_shields: list[str] | None = Field(default_factory=lambda: [])
+    output_shields: list[str] | None = Field(default_factory=lambda: [])
+    toolgroups: list[AgentToolGroup] | None = Field(default_factory=lambda: [])
+    client_tools: list[ToolDef] | None = Field(default_factory=lambda: [])
     tool_choice: ToolChoice | None = Field(default=None, deprecated="use tool_config instead")
     tool_prompt_format: ToolPromptFormat | None = Field(default=None, deprecated="use tool_config instead")
     tool_config: ToolConfig | None = Field(default=None)
@@ -246,7 +255,7 @@ class AgentConfigOverridablePerTurn(AgentConfigCommon):
     instructions: str | None = None
 
 
-class AgentTurnResponseEventType(Enum):
+class AgentTurnResponseEventType(StrEnum):
     step_start = "step_start"
     step_complete = "step_complete"
     step_progress = "step_progress"
@@ -258,15 +267,15 @@ class AgentTurnResponseEventType(Enum):
 
 @json_schema_type
 class AgentTurnResponseStepStartPayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.step_start.value] = AgentTurnResponseEventType.step_start.value
+    event_type: Literal[AgentTurnResponseEventType.step_start] = AgentTurnResponseEventType.step_start
     step_type: StepType
     step_id: str
-    metadata: dict[str, Any] | None = Field(default_factory=dict)
+    metadata: dict[str, Any] | None = Field(default_factory=lambda: {})
 
 
 @json_schema_type
 class AgentTurnResponseStepCompletePayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.step_complete.value] = AgentTurnResponseEventType.step_complete.value
+    event_type: Literal[AgentTurnResponseEventType.step_complete] = AgentTurnResponseEventType.step_complete
     step_type: StepType
     step_id: str
     step_details: Step
@@ -276,7 +285,7 @@ class AgentTurnResponseStepCompletePayload(BaseModel):
 class AgentTurnResponseStepProgressPayload(BaseModel):
     model_config = ConfigDict(protected_namespaces=())
 
-    event_type: Literal[AgentTurnResponseEventType.step_progress.value] = AgentTurnResponseEventType.step_progress.value
+    event_type: Literal[AgentTurnResponseEventType.step_progress] = AgentTurnResponseEventType.step_progress
     step_type: StepType
     step_id: str
 
@@ -285,21 +294,19 @@ class AgentTurnResponseStepProgressPayload(BaseModel):
 
 @json_schema_type
 class AgentTurnResponseTurnStartPayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.turn_start.value] = AgentTurnResponseEventType.turn_start.value
+    event_type: Literal[AgentTurnResponseEventType.turn_start] = AgentTurnResponseEventType.turn_start
     turn_id: str
 
 
 @json_schema_type
 class AgentTurnResponseTurnCompletePayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.turn_complete.value] = AgentTurnResponseEventType.turn_complete.value
+    event_type: Literal[AgentTurnResponseEventType.turn_complete] = AgentTurnResponseEventType.turn_complete
     turn: Turn
 
 
 @json_schema_type
 class AgentTurnResponseTurnAwaitingInputPayload(BaseModel):
-    event_type: Literal[AgentTurnResponseEventType.turn_awaiting_input.value] = (
-        AgentTurnResponseEventType.turn_awaiting_input.value
-    )
+    event_type: Literal[AgentTurnResponseEventType.turn_awaiting_input] = AgentTurnResponseEventType.turn_awaiting_input
     turn: Turn
 
 
@@ -341,7 +348,7 @@ class AgentTurnCreateRequest(AgentConfigOverridablePerTurn):
     messages: list[UserMessage | ToolResponseMessage]
 
     documents: list[Document] | None = None
-    toolgroups: list[AgentToolGroup] | None = None
+    toolgroups: list[AgentToolGroup] | None = Field(default_factory=lambda: [])
 
     stream: bool | None = False
     tool_config: ToolConfig | None = None

llama_stack/apis/benchmarks/benchmarks.py

@@ -22,14 +22,14 @@ class CommonBenchmarkFields(BaseModel):
 
 @json_schema_type
 class Benchmark(CommonBenchmarkFields, Resource):
-    type: Literal[ResourceType.benchmark.value] = ResourceType.benchmark.value
+    type: Literal[ResourceType.benchmark] = ResourceType.benchmark
 
     @property
     def benchmark_id(self) -> str:
         return self.identifier
 
     @property
-    def provider_benchmark_id(self) -> str:
+    def provider_benchmark_id(self) -> str | None:
         return self.provider_resource_id
 
 

llama_stack/apis/common/content_types.py

@@ -28,7 +28,7 @@ class _URLOrData(BaseModel):
 
     url: URL | None = None
     # data is a base64 encoded string, hint with contentEncoding=base64
-    data: str | None = Field(contentEncoding="base64", default=None)
+    data: str | None = Field(default=None, json_schema_extra={"contentEncoding": "base64"})
 
     @model_validator(mode="before")
     @classmethod

llama_stack/apis/datasets/datasets.py

@@ -106,14 +106,14 @@ class CommonDatasetFields(BaseModel):
 
 @json_schema_type
 class Dataset(CommonDatasetFields, Resource):
-    type: Literal[ResourceType.dataset.value] = ResourceType.dataset.value
+    type: Literal[ResourceType.dataset] = ResourceType.dataset
 
     @property
     def dataset_id(self) -> str:
         return self.identifier
 
     @property
-    def provider_dataset_id(self) -> str:
+    def provider_dataset_id(self) -> str | None:
         return self.provider_resource_id
 
 

llama_stack/apis/inference/inference.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import sys
 from collections.abc import AsyncIterator
 from enum import Enum
 from typing import (
@@ -35,6 +36,16 @@ register_schema(ToolCall)
 register_schema(ToolParamDefinition)
 register_schema(ToolDefinition)
 
+# TODO: use enum.StrEnum when we drop support for python 3.10
+if sys.version_info >= (3, 11):
+    from enum import StrEnum
+else:
+
+    class StrEnum(str, Enum):
+        """Backport of StrEnum for Python 3.10 and below."""
+
+        pass
+
 
 @json_schema_type
 class GreedySamplingStrategy(BaseModel):
@@ -187,7 +198,7 @@ class CompletionMessage(BaseModel):
     role: Literal["assistant"] = "assistant"
     content: InterleavedContent
     stop_reason: StopReason
-    tool_calls: list[ToolCall] | None = Field(default_factory=list)
+    tool_calls: list[ToolCall] | None = Field(default_factory=lambda: [])
 
 
 Message = Annotated[
@@ -267,7 +278,7 @@ class ChatCompletionResponseEvent(BaseModel):
     stop_reason: StopReason | None = None
 
 
-class ResponseFormatType(Enum):
+class ResponseFormatType(StrEnum):
     """Types of formats for structured (guided) decoding.
 
     :cvar json_schema: Response should conform to a JSON schema. In a Python SDK, this is often a `pydantic` model.
@@ -286,7 +297,7 @@ 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] = ResponseFormatType.json_schema.value
+    type: Literal[ResponseFormatType.json_schema] = ResponseFormatType.json_schema
     json_schema: dict[str, Any]
 
 
@@ -298,7 +309,7 @@ class GrammarResponseFormat(BaseModel):
     :param bnf: The BNF grammar specification the response should conform to
     """
 
-    type: Literal[ResponseFormatType.grammar.value] = ResponseFormatType.grammar.value
+    type: Literal[ResponseFormatType.grammar] = ResponseFormatType.grammar
     bnf: dict[str, Any]
 
 
@@ -394,7 +405,7 @@ class ChatCompletionRequest(BaseModel):
     messages: list[Message]
     sampling_params: SamplingParams | None = Field(default_factory=SamplingParams)
 
-    tools: list[ToolDefinition] | None = Field(default_factory=list)
+    tools: list[ToolDefinition] | None = Field(default_factory=lambda: [])
     tool_config: ToolConfig | None = Field(default_factory=ToolConfig)
 
     response_format: ResponseFormat | None = None
@@ -567,14 +578,14 @@ class OpenAIResponseFormatText(BaseModel):
 @json_schema_type
 class OpenAIJSONSchema(TypedDict, total=False):
     name: str
-    description: str | None = None
-    strict: bool | None = None
+    description: str | None
+    strict: bool | None
 
     # Pydantic BaseModel cannot be used with a schema param, since it already
     # has one. And, we don't want to alias here because then have to handle
     # that alias when converting to OpenAI params. So, to support schema,
     # we use a TypedDict.
-    schema: dict[str, Any] | None = None
+    schema: dict[str, Any] | None
 
 
 @json_schema_type

llama_stack/apis/models/models.py

@@ -29,14 +29,14 @@ class ModelType(str, Enum):
 
 @json_schema_type
 class Model(CommonModelFields, Resource):
-    type: Literal[ResourceType.model.value] = ResourceType.model.value
+    type: Literal[ResourceType.model] = ResourceType.model
 
     @property
     def model_id(self) -> str:
         return self.identifier
 
     @property
-    def provider_model_id(self) -> str:
+    def provider_model_id(self) -> str | None:
         return self.provider_resource_id
 
     model_config = ConfigDict(protected_namespaces=())

llama_stack/apis/resource.py

@@ -4,12 +4,23 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+import sys
 from enum import Enum
 
 from pydantic import BaseModel, Field
 
+# TODO: use enum.StrEnum when we drop support for python 3.10
+if sys.version_info >= (3, 11):
+    from enum import StrEnum
+else:
 
-class ResourceType(Enum):
+    class StrEnum(str, Enum):
+        """Backport of StrEnum for Python 3.10 and below."""
+
+        pass
+
+
+class ResourceType(StrEnum):
     model = "model"
     shield = "shield"
     vector_db = "vector_db"
@@ -25,9 +36,9 @@ class Resource(BaseModel):
 
     identifier: str = Field(description="Unique identifier for this resource in llama stack")
 
-    provider_resource_id: str = Field(
-        description="Unique identifier for this resource in the provider",
+    provider_resource_id: str | None = Field(
         default=None,
+        description="Unique identifier for this resource in the provider",
     )
 
     provider_id: str = Field(description="ID of the provider that owns this resource")

llama_stack/apis/safety/safety.py

@@ -53,5 +53,5 @@ class Safety(Protocol):
         self,
         shield_id: str,
         messages: list[Message],
-        params: dict[str, Any] = None,
+        params: dict[str, Any],
     ) -> RunShieldResponse: ...

llama_stack/apis/scoring_functions/scoring_functions.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+# TODO: use enum.StrEnum when we drop support for python 3.10
+import sys
 from enum import Enum
 from typing import (
     Annotated,
@@ -19,18 +21,27 @@ from llama_stack.apis.common.type_system import ParamType
 from llama_stack.apis.resource import Resource, ResourceType
 from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
 
+if sys.version_info >= (3, 11):
+    from enum import StrEnum
+else:
+
+    class StrEnum(str, Enum):
+        """Backport of StrEnum for Python 3.10 and below."""
+
+        pass
+
 
 # Perhaps more structure can be imposed on these functions. Maybe they could be associated
 # with standard metrics so they can be rolled up?
 @json_schema_type
-class ScoringFnParamsType(Enum):
+class ScoringFnParamsType(StrEnum):
     llm_as_judge = "llm_as_judge"
     regex_parser = "regex_parser"
     basic = "basic"
 
 
 @json_schema_type
-class AggregationFunctionType(Enum):
+class AggregationFunctionType(StrEnum):
     average = "average"
     weighted_average = "weighted_average"
     median = "median"
@@ -40,36 +51,36 @@ class AggregationFunctionType(Enum):
 
 @json_schema_type
 class LLMAsJudgeScoringFnParams(BaseModel):
-    type: Literal[ScoringFnParamsType.llm_as_judge.value] = ScoringFnParamsType.llm_as_judge.value
+    type: Literal[ScoringFnParamsType.llm_as_judge] = ScoringFnParamsType.llm_as_judge
     judge_model: str
     prompt_template: str | None = None
-    judge_score_regexes: list[str] | None = Field(
+    judge_score_regexes: list[str] = Field(
         description="Regexes to extract the answer from generated response",
-        default_factory=list,
+        default_factory=lambda: [],
     )
-    aggregation_functions: list[AggregationFunctionType] | None = Field(
+    aggregation_functions: list[AggregationFunctionType] = Field(
         description="Aggregation functions to apply to the scores of each row",
-        default_factory=list,
+        default_factory=lambda: [],
     )
 
 
 @json_schema_type
 class RegexParserScoringFnParams(BaseModel):
-    type: Literal[ScoringFnParamsType.regex_parser.value] = ScoringFnParamsType.regex_parser.value
-    parsing_regexes: list[str] | None = Field(
+    type: Literal[ScoringFnParamsType.regex_parser] = ScoringFnParamsType.regex_parser
+    parsing_regexes: list[str] = Field(
         description="Regex to extract the answer from generated response",
-        default_factory=list,
+        default_factory=lambda: [],
     )
-    aggregation_functions: list[AggregationFunctionType] | None = Field(
+    aggregation_functions: list[AggregationFunctionType] = Field(
         description="Aggregation functions to apply to the scores of each row",
-        default_factory=list,
+        default_factory=lambda: [],
     )
 
 
 @json_schema_type
 class BasicScoringFnParams(BaseModel):
-    type: Literal[ScoringFnParamsType.basic.value] = ScoringFnParamsType.basic.value
-    aggregation_functions: list[AggregationFunctionType] | None = Field(
+    type: Literal[ScoringFnParamsType.basic] = ScoringFnParamsType.basic
+    aggregation_functions: list[AggregationFunctionType] = Field(
         description="Aggregation functions to apply to the scores of each row",
         default_factory=list,
     )
@@ -99,14 +110,14 @@ class CommonScoringFnFields(BaseModel):
 
 @json_schema_type
 class ScoringFn(CommonScoringFnFields, Resource):
-    type: Literal[ResourceType.scoring_function.value] = ResourceType.scoring_function.value
+    type: Literal[ResourceType.scoring_function] = ResourceType.scoring_function
 
     @property
     def scoring_fn_id(self) -> str:
         return self.identifier
 
     @property
-    def provider_scoring_fn_id(self) -> str:
+    def provider_scoring_fn_id(self) -> str | None:
         return self.provider_resource_id
 
 

llama_stack/apis/shields/shields.py

@@ -21,14 +21,14 @@ class CommonShieldFields(BaseModel):
 class Shield(CommonShieldFields, Resource):
     """A safety shield resource that can be used to check content"""
 
-    type: Literal[ResourceType.shield.value] = ResourceType.shield.value
+    type: Literal[ResourceType.shield] = ResourceType.shield
 
     @property
     def shield_id(self) -> str:
         return self.identifier
 
     @property
-    def provider_shield_id(self) -> str:
+    def provider_shield_id(self) -> str | None:
         return self.provider_resource_id
 
 

llama_stack/apis/telemetry/telemetry.py

@@ -37,7 +37,7 @@ class Span(BaseModel):
     name: str
     start_time: datetime
     end_time: datetime | None = None
-    attributes: dict[str, Any] | None = Field(default_factory=dict)
+    attributes: dict[str, Any] | None = Field(default_factory=lambda: {})
 
     def set_attribute(self, key: str, value: Any):
         if self.attributes is None:
@@ -74,19 +74,19 @@ class EventCommon(BaseModel):
     trace_id: str
     span_id: str
     timestamp: datetime
-    attributes: dict[str, Primitive] | None = Field(default_factory=dict)
+    attributes: dict[str, Primitive] | None = Field(default_factory=lambda: {})
 
 
 @json_schema_type
 class UnstructuredLogEvent(EventCommon):
-    type: Literal[EventType.UNSTRUCTURED_LOG.value] = EventType.UNSTRUCTURED_LOG.value
+    type: Literal[EventType.UNSTRUCTURED_LOG] = EventType.UNSTRUCTURED_LOG
     message: str
     severity: LogSeverity
 
 
 @json_schema_type
 class MetricEvent(EventCommon):
-    type: Literal[EventType.METRIC.value] = EventType.METRIC.value
+    type: Literal[EventType.METRIC] = EventType.METRIC
     metric: str  # this would be an enum
     value: int | float
     unit: str
@@ -131,14 +131,14 @@ class StructuredLogType(Enum):
 
 @json_schema_type
 class SpanStartPayload(BaseModel):
-    type: Literal[StructuredLogType.SPAN_START.value] = StructuredLogType.SPAN_START.value
+    type: Literal[StructuredLogType.SPAN_START] = StructuredLogType.SPAN_START
     name: str
     parent_span_id: str | None = None
 
 
 @json_schema_type
 class SpanEndPayload(BaseModel):
-    type: Literal[StructuredLogType.SPAN_END.value] = StructuredLogType.SPAN_END.value
+    type: Literal[StructuredLogType.SPAN_END] = StructuredLogType.SPAN_END
     status: SpanStatus
 
 
@@ -151,7 +151,7 @@ register_schema(StructuredLogPayload, name="StructuredLogPayload")
 
 @json_schema_type
 class StructuredLogEvent(EventCommon):
-    type: Literal[EventType.STRUCTURED_LOG.value] = EventType.STRUCTURED_LOG.value
+    type: Literal[EventType.STRUCTURED_LOG] = EventType.STRUCTURED_LOG
     payload: StructuredLogPayload
 
 

llama_stack/apis/tools/tools.py

@@ -36,7 +36,7 @@ class ToolHost(Enum):
 
 @json_schema_type
 class Tool(Resource):
-    type: Literal[ResourceType.tool.value] = ResourceType.tool.value
+    type: Literal[ResourceType.tool] = ResourceType.tool
     toolgroup_id: str
     tool_host: ToolHost
     description: str
@@ -62,7 +62,7 @@ class ToolGroupInput(BaseModel):
 
 @json_schema_type
 class ToolGroup(Resource):
-    type: Literal[ResourceType.tool_group.value] = ResourceType.tool_group.value
+    type: Literal[ResourceType.tool_group] = ResourceType.tool_group
     mcp_endpoint: URL | None = None
     args: dict[str, Any] | None = None
 

llama_stack/apis/vector_dbs/vector_dbs.py

@@ -15,7 +15,7 @@ from llama_stack.schema_utils import json_schema_type, webmethod
 
 @json_schema_type
 class VectorDB(Resource):
-    type: Literal[ResourceType.vector_db.value] = ResourceType.vector_db.value
+    type: Literal[ResourceType.vector_db] = ResourceType.vector_db
 
     embedding_model: str
     embedding_dimension: int
@@ -25,7 +25,7 @@ class VectorDB(Resource):
         return self.identifier
 
     @property
-    def provider_vector_db_id(self) -> str:
+    def provider_vector_db_id(self) -> str | None:
         return self.provider_resource_id
 
 

llama_stack/cli/llama.py

@@ -38,7 +38,10 @@ class LlamaCLIParser:
         print_subcommand_description(self.parser, subparsers)
 
     def parse_args(self) -> argparse.Namespace:
-        return self.parser.parse_args()
+        args = self.parser.parse_args()
+        if not isinstance(args, argparse.Namespace):
+            raise TypeError(f"Expected argparse.Namespace, got {type(args)}")
+        return args
 
     def run(self, args: argparse.Namespace) -> None:
         args.func(args)

llama_stack/cli/stack/list_providers.py

@@ -46,7 +46,7 @@ class StackListProviders(Subcommand):
         else:
             providers = [(k.value, prov) for k, prov in all_providers.items()]
 
-        providers = [p for api, p in providers if api in self.providable_apis]
+        providers = [(api, p) for api, p in providers if api in self.providable_apis]
 
         # eventually, this should query a registry at llama.meta.com/llamastack/distributions
         headers = [
@@ -57,7 +57,7 @@ class StackListProviders(Subcommand):
 
         rows = []
 
-        specs = [spec for p in providers for spec in p.values()]
+        specs = [spec for api, p in providers for spec in p.values()]
         for spec in specs:
             if spec.is_sample:
                 continue
@@ -65,7 +65,7 @@ class StackListProviders(Subcommand):
                 [
                     spec.api.value,
                     spec.provider_type,
-                    ",".join(spec.pip_packages),
+                    ",".join(spec.pip_packages) if hasattr(spec, "pip_packages") else "",
                 ]
             )
         print_table(

llama_stack/distribution/configure.py

@@ -73,11 +73,7 @@ def configure_api_providers(config: StackRunConfig, build_spec: DistributionSpec
 
         existing_providers = config.providers.get(api_str, [])
         if existing_providers:
-            logger.info(
-                f"Re-configuring existing providers for API `{api_str}`...",
-                "green",
-                attrs=["bold"],
-            )
+            logger.info(f"Re-configuring existing providers for API `{api_str}`...")
             updated_providers = []
             for p in existing_providers:
                 logger.info(f"> Configuring provider `({p.provider_type})`")
@@ -91,7 +87,7 @@ def configure_api_providers(config: StackRunConfig, build_spec: DistributionSpec
             if not plist:
                 raise ValueError(f"No provider configured for API {api_str}?")
 
-            logger.info(f"Configuring API `{api_str}`...", "green", attrs=["bold"])
+            logger.info(f"Configuring API `{api_str}`...")
             updated_providers = []
             for i, provider_type in enumerate(plist):
                 if i >= 1:

llama_stack/distribution/library_client.py

@@ -30,7 +30,7 @@ from termcolor import cprint
 
 from llama_stack.distribution.build import print_pip_install_help
 from llama_stack.distribution.configure import parse_and_maybe_upgrade_config
-from llama_stack.distribution.datatypes import Api
+from llama_stack.distribution.datatypes import Api, BuildConfig, DistributionSpec
 from llama_stack.distribution.request_headers import (
     PROVIDER_DATA_VAR,
     request_provider_data_context,
@@ -216,7 +216,18 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
                 "yellow",
             )
             if self.config_path_or_template_name.endswith(".yaml"):
-                print_pip_install_help(self.config.providers)
+                # Convert Provider objects to their types
+                provider_types: dict[str, str | list[str]] = {}
+                for api, providers in self.config.providers.items():
+                    types = [p.provider_type for p in providers]
+                    # Convert single-item lists to strings
+                    provider_types[api] = types[0] if len(types) == 1 else types
+                build_config = BuildConfig(
+                    distribution_spec=DistributionSpec(
+                        providers=provider_types,
+                    ),
+                )
+                print_pip_install_help(build_config)
             else:
                 prefix = "!" if in_notebook() else ""
                 cprint(

llama_stack/distribution/request_headers.py

@@ -44,7 +44,8 @@ class RequestProviderDataContext(AbstractContextManager):
 class NeedsRequestProviderData:
     def get_request_provider_data(self) -> Any:
         spec = self.__provider_spec__
-        assert spec, f"Provider spec not set on {self.__class__}"
+        if not spec:
+            raise ValueError(f"Provider spec not set on {self.__class__}")
 
         provider_type = spec.provider_type
         validator_class = spec.provider_data_validator

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

@@ -124,7 +124,7 @@ if prompt := st.chat_input("Example: What is Llama Stack?"):
                 message_placeholder.markdown(full_response + "▌")
             message_placeholder.markdown(full_response)
         else:
-            full_response = response
-            message_placeholder.markdown(full_response.completion_message.content)
+            full_response = response.completion_message.content
+            message_placeholder.markdown(full_response)
 
         st.session_state.messages.append({"role": "assistant", "content": full_response})

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

@@ -245,7 +245,7 @@ class PythonListCustomToolGenerator(PromptTemplateGeneratorBase):  # noqa: N801
             {"function_description": self._gen_function_description(custom_tools)},
         )
 
-    def _gen_function_description(self, custom_tools: list[ToolDefinition]) -> PromptTemplate:
+    def _gen_function_description(self, custom_tools: list[ToolDefinition]) -> str:
         template_str = textwrap.dedent(
             """
             Here is a list of functions in JSON format that you can invoke.
@@ -286,10 +286,12 @@ class PythonListCustomToolGenerator(PromptTemplateGeneratorBase):  # noqa: N801
 
             """
         )
-        return PromptTemplate(
+        template = PromptTemplate(
             template_str.strip("\n"),
             {"tools": [t.model_dump() for t in custom_tools]},
-        ).render()
+        )
+        rendered: str = template.render()
+        return rendered
 
     def data_examples(self) -> list[list[ToolDefinition]]:
         return [

llama_stack/models/llama/sku_list.py

@@ -948,6 +948,8 @@ def llama_meta_net_info(model: Model) -> LlamaDownloadInfo:
     elif model.core_model_id == CoreModelId.llama_guard_2_8b:
         folder = "llama-guard-2"
     else:
+        if model.huggingface_repo is None:
+            raise ValueError(f"Model {model.core_model_id} has no huggingface_repo set")
         folder = model.huggingface_repo.split("/")[-1]
         if "Llama-2" in folder:
             folder = folder.lower()
@@ -1024,3 +1026,4 @@ def llama_meta_pth_size(model: Model) -> int:
                 return 54121549657
             else:
                 return 100426653046
+    return 0

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

@@ -139,6 +139,8 @@ class OllamaInferenceAdapter(
         if sampling_params is None:
             sampling_params = SamplingParams()
         model = await self._get_model(model_id)
+        if model.provider_resource_id is None:
+            raise ValueError(f"Model {model_id} has no provider_resource_id set")
         request = CompletionRequest(
             model=model.provider_resource_id,
             content=content,
@@ -202,6 +204,8 @@ class OllamaInferenceAdapter(
         if sampling_params is None:
             sampling_params = SamplingParams()
         model = await self._get_model(model_id)
+        if model.provider_resource_id is None:
+            raise ValueError(f"Model {model_id} has no provider_resource_id set")
         request = ChatCompletionRequest(
             model=model.provider_resource_id,
             messages=messages,
@@ -346,6 +350,8 @@ class OllamaInferenceAdapter(
         #  - models not currently running are run by the ollama server as needed
         response = await self.client.list()
         available_models = [m["model"] for m in response["models"]]
+        if model.provider_resource_id is None:
+            raise ValueError("Model provider_resource_id cannot be None")
         provider_resource_id = self.register_helper.get_provider_model_id(model.provider_resource_id)
         if provider_resource_id is None:
             provider_resource_id = model.provider_resource_id

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

@@ -272,6 +272,8 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         if sampling_params is None:
             sampling_params = SamplingParams()
         model = await self._get_model(model_id)
+        if model.provider_resource_id is None:
+            raise ValueError(f"Model {model_id} has no provider_resource_id set")
         request = CompletionRequest(
             model=model.provider_resource_id,
             content=content,
@@ -302,6 +304,8 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
         if sampling_params is None:
             sampling_params = SamplingParams()
         model = await self._get_model(model_id)
+        if model.provider_resource_id is None:
+            raise ValueError(f"Model {model_id} has no provider_resource_id set")
         # This is to be consistent with OpenAI API and support vLLM <= v0.6.3
         # References:
         #   * https://platform.openai.com/docs/api-reference/chat/create#chat-create-tool_choice

llama_stack/providers/utils/inference/prompt_adapter.py

@@ -382,7 +382,7 @@ def augment_messages_for_tools_llama_3_1(
 
     messages.append(SystemMessage(content=sys_content))
 
-    has_custom_tools = any(isinstance(dfn.tool_name, str) for dfn in request.tools)
+    has_custom_tools = request.tools is not None and any(isinstance(dfn.tool_name, str) for dfn in request.tools)
     if has_custom_tools:
         fmt = request.tool_config.tool_prompt_format or ToolPromptFormat.json
         if fmt == ToolPromptFormat.json: