Bump Pyright and Ruff

docs/graph.md

@@ -379,7 +379,7 @@ class Email:
 @dataclass
 class State:
     user: User
-    write_agent_messages: list[ModelMessage] = field(default_factory=list)
+    write_agent_messages: list[ModelMessage] = field(default_factory=list[ModelMessage])
 
 
 email_writer_agent = Agent(
@@ -669,8 +669,8 @@ ask_agent = Agent('openai:gpt-4o', output_type=str, instrument=True)
 @dataclass
 class QuestionState:
     question: str | None = None
-    ask_agent_messages: list[ModelMessage] = field(default_factory=list)
-    evaluate_agent_messages: list[ModelMessage] = field(default_factory=list)
+    ask_agent_messages: list[ModelMessage] = field(default_factory=list[ModelMessage])
+    evaluate_agent_messages: list[ModelMessage] = field(default_factory=list[ModelMessage])
 
 
 @dataclass
@@ -912,8 +912,8 @@ ask_agent = Agent('openai:gpt-4o', output_type=str, instrument=True)
 @dataclass
 class QuestionState:
     question: str | None = None
-    ask_agent_messages: list[ModelMessage] = field(default_factory=list)
-    evaluate_agent_messages: list[ModelMessage] = field(default_factory=list)
+    ask_agent_messages: list[ModelMessage] = field(default_factory=list[ModelMessage])
+    evaluate_agent_messages: list[ModelMessage] = field(default_factory=list[ModelMessage])
 
 
 @dataclass

examples/pydantic_ai_examples/ag_ui/api/agentic_generative_ui.py

@@ -26,7 +26,9 @@ class Step(BaseModel):
 class Plan(BaseModel):
     """Represents a plan with multiple steps."""
 
-    steps: list[Step] = Field(default_factory=list, description='The steps in the plan')
+    steps: list[Step] = Field(
+        default_factory=list[Step], description='The steps in the plan'
+    )
 
 
 class JSONPatchOp(BaseModel):

examples/pydantic_ai_examples/ag_ui/api/shared_state.py

@@ -61,18 +61,18 @@ class Recipe(BaseModel):
         description='The skill level required for the recipe',
     )
     special_preferences: list[SpecialPreferences] = Field(
-        default_factory=list,
+        default_factory=list[SpecialPreferences],
         description='Any special preferences for the recipe',
     )
     cooking_time: CookingTime = Field(
         default=CookingTime.FIVE_MIN, description='The cooking time of the recipe'
     )
     ingredients: list[Ingredient] = Field(
-        default_factory=list,
+        default_factory=list[Ingredient],
         description='Ingredients for the recipe',
     )
     instructions: list[str] = Field(
-        default_factory=list, description='Instructions for the recipe'
+        default_factory=list[str], description='Instructions for the recipe'
     )
 
 

examples/pydantic_ai_examples/data_analyst.py

@@ -1,4 +1,5 @@
 from dataclasses import dataclass, field
+from typing import cast
 
 import datasets
 import duckdb
@@ -9,7 +10,7 @@
 
 @dataclass
 class AnalystAgentDeps:
-    output: dict[str, pd.DataFrame] = field(default_factory=dict)
+    output: dict[str, pd.DataFrame] = field(default_factory=dict[str, pd.DataFrame])
 
     def store(self, value: pd.DataFrame) -> str:
         """Store the output in deps and return the reference such as Out[1] to be used by the LLM."""
@@ -47,7 +48,7 @@ def load_dataset(
     """
     # begin load data from hf
     builder = datasets.load_dataset_builder(path)  # pyright: ignore[reportUnknownMemberType]
-    splits: dict[str, datasets.SplitInfo] = builder.info.splits or {}  # pyright: ignore[reportUnknownMemberType]
+    splits = cast(dict[str, datasets.SplitInfo], builder.info.splits or {})  # pyright: ignore[reportUnknownMemberType]
     if split not in splits:
         raise ModelRetry(
             f'{split} is not valid for dataset {path}. Valid splits are {",".join(splits.keys())}'

examples/pydantic_ai_examples/question_graph.py

@@ -32,8 +32,10 @@
 @dataclass
 class QuestionState:
     question: str | None = None
-    ask_agent_messages: list[ModelMessage] = field(default_factory=list)
-    evaluate_agent_messages: list[ModelMessage] = field(default_factory=list)
+    ask_agent_messages: list[ModelMessage] = field(default_factory=list[ModelMessage])
+    evaluate_agent_messages: list[ModelMessage] = field(
+        default_factory=list[ModelMessage]
+    )
 
 
 @dataclass

pydantic_ai_slim/pydantic_ai/_agent_graph.py

@@ -166,12 +166,16 @@ class UserPromptNode(AgentNode[DepsT, NodeRunEndT]):
     deferred_tool_results: DeferredToolResults | None = None
 
     instructions: str | None = None
-    instructions_functions: list[_system_prompt.SystemPromptRunner[DepsT]] = dataclasses.field(default_factory=list)
+    instructions_functions: list[_system_prompt.SystemPromptRunner[DepsT]] = dataclasses.field(
+        default_factory=list[_system_prompt.SystemPromptRunner[DepsT]]
+    )
 
     system_prompts: tuple[str, ...] = dataclasses.field(default_factory=tuple)
-    system_prompt_functions: list[_system_prompt.SystemPromptRunner[DepsT]] = dataclasses.field(default_factory=list)
+    system_prompt_functions: list[_system_prompt.SystemPromptRunner[DepsT]] = dataclasses.field(
+        default_factory=list[_system_prompt.SystemPromptRunner[DepsT]]
+    )
     system_prompt_dynamic_functions: dict[str, _system_prompt.SystemPromptRunner[DepsT]] = dataclasses.field(
-        default_factory=dict
+        default_factory=dict[str, _system_prompt.SystemPromptRunner[DepsT]]
     )
 
     async def run(  # noqa: C901
@@ -929,6 +933,14 @@ async def handle_call_or_result(
 
             pending = tasks
             while pending:
+                pending = cast(
+                    list[
+                        Task[
+                            tuple[_messages.ToolReturnPart | _messages.RetryPromptPart, _messages.UserPromptPart | None]
+                        ]
+                    ],
+                    pending,
+                )
                 done, pending = await asyncio.wait(pending, return_when=asyncio.FIRST_COMPLETED)
                 for task in done:
                     index = tasks.index(task)
@@ -1125,8 +1137,7 @@ async def _process_message_history(
             if takes_ctx:
                 messages = await processor(run_context, messages)
             else:
-                async_processor = cast(_HistoryProcessorAsync, processor)
-                messages = await async_processor(messages)
+                messages = await processor(messages)
         else:
             if takes_ctx:
                 sync_processor_with_ctx = cast(_HistoryProcessorSyncWithCtx[DepsT], processor)

pydantic_ai_slim/pydantic_ai/_function_schema.py

@@ -42,7 +42,7 @@ class FunctionSchema:
     takes_ctx: bool
     is_async: bool
     single_arg_name: str | None = None
-    positional_fields: list[str] = field(default_factory=list)
+    positional_fields: list[str] = field(default_factory=list[str])
     var_positional_field: str | None = None
 
     async def call(self, args_dict: dict[str, Any], ctx: RunContext[Any]) -> Any:

pydantic_ai_slim/pydantic_ai/_output.py

@@ -269,10 +269,11 @@ def build(  # noqa: C901
         if len(outputs) == 0 and allows_deferred_tools:
             raise UserError('At least one output type must be provided other than `DeferredToolRequests`.')
 
-        if output := next((output for output in outputs if isinstance(output, NativeOutput)), None):
+        if output := next((output for output in outputs if isinstance(output, NativeOutput)), None):  # pyright: ignore[reportUnknownVariableType,reportUnknownArgumentType]
             if len(outputs) > 1:
                 raise UserError('`NativeOutput` must be the only output type.')  # pragma: no cover
 
+            output = cast(NativeOutput[OutputDataT], output)
             return NativeOutputSchema(
                 processor=cls._build_processor(
                     _flatten_output_spec(output.outputs),
@@ -282,10 +283,11 @@ def build(  # noqa: C901
                 ),
                 allows_deferred_tools=allows_deferred_tools,
             )
-        elif output := next((output for output in outputs if isinstance(output, PromptedOutput)), None):
+        elif output := next((output for output in outputs if isinstance(output, PromptedOutput)), None):  # pyright: ignore[reportUnknownVariableType,reportUnknownArgumentType]
             if len(outputs) > 1:
                 raise UserError('`PromptedOutput` must be the only output type.')  # pragma: no cover
 
+            output = cast(PromptedOutput[OutputDataT], output)
             return PromptedOutputSchema(
                 processor=cls._build_processor(
                     _flatten_output_spec(output.outputs),
@@ -303,9 +305,9 @@ def build(  # noqa: C901
             if output is str:
                 text_outputs.append(cast(type[str], output))
             elif isinstance(output, TextOutput):
-                text_outputs.append(output)
+                text_outputs.append(cast(TextOutput[OutputDataT], output))
             elif isinstance(output, ToolOutput):
-                tool_outputs.append(output)
+                tool_outputs.append(cast(ToolOutput[OutputDataT], output))
             elif isinstance(output, NativeOutput):
                 # We can never get here because this is checked for above.
                 raise UserError('`NativeOutput` must be the only output type.')  # pragma: no cover
@@ -936,6 +938,7 @@ def build(
             description = None
             strict = None
             if isinstance(output, ToolOutput):
+                output = cast(ToolOutput[OutputDataT], output)
                 # do we need to error on conflicts here? (DavidM): If this is internal maybe doesn't matter, if public, use overloads
                 name = output.name
                 description = output.description
@@ -1033,7 +1036,7 @@ def _flatten_output_spec(output_spec: OutputSpec[T]) -> Sequence[_OutputSpecItem
 def _flatten_output_spec(output_spec: OutputSpec[T]) -> Sequence[_OutputSpecItem[T]]:
     outputs: Sequence[OutputSpec[T]]
     if isinstance(output_spec, Sequence):
-        outputs = output_spec
+        outputs = cast(Sequence[OutputSpec[T]], output_spec)
     else:
         outputs = (output_spec,)
 

pydantic_ai_slim/pydantic_ai/_parts_manager.py

@@ -55,9 +55,9 @@ class ModelResponsePartsManager:
     Parts are generally added and/or updated by providing deltas, which are tracked by vendor-specific IDs.
     """
 
-    _parts: list[ManagedPart] = field(default_factory=list, init=False)
+    _parts: list[ManagedPart] = field(default_factory=list[ManagedPart], init=False)
     """A list of parts (text or tool calls) that make up the current state of the model's response."""
-    _vendor_id_to_part_index: dict[VendorId, int] = field(default_factory=dict, init=False)
+    _vendor_id_to_part_index: dict[VendorId, int] = field(default_factory=dict[VendorId, int], init=False)
     """Maps a vendor's "part" ID (if provided) to the index in `_parts` where that part resides."""
 
     def get_parts(self) -> list[ModelResponsePart]:

pydantic_ai_slim/pydantic_ai/_run_context.py

@@ -32,15 +32,15 @@ class RunContext(Generic[AgentDepsT]):
     """LLM usage associated with the run."""
     prompt: str | Sequence[_messages.UserContent] | None = None
     """The original user prompt passed to the run."""
-    messages: list[_messages.ModelMessage] = field(default_factory=list)
+    messages: list[_messages.ModelMessage] = field(default_factory=list[_messages.ModelMessage])
     """Messages exchanged in the conversation so far."""
     tracer: Tracer = field(default_factory=NoOpTracer)
     """The tracer to use for tracing the run."""
     trace_include_content: bool = False
     """Whether to include the content of the messages in the trace."""
     instrumentation_version: int = DEFAULT_INSTRUMENTATION_VERSION
     """Instrumentation settings version, if instrumentation is enabled."""
-    retries: dict[str, int] = field(default_factory=dict)
+    retries: dict[str, int] = field(default_factory=dict[str, int])
     """Number of retries for each tool so far."""
     tool_call_id: str | None = None
     """The ID of the tool call."""

pydantic_ai_slim/pydantic_ai/_tool_manager.py

@@ -33,7 +33,7 @@ class ToolManager(Generic[AgentDepsT]):
     """The agent run context for a specific run step."""
     tools: dict[str, ToolsetTool[AgentDepsT]] | None = None
     """The cached tools for this run step."""
-    failed_tools: set[str] = field(default_factory=set)
+    failed_tools: set[str] = field(default_factory=set[str])
     """Names of tools that failed in this run step."""
 
     @classmethod

pydantic_ai_slim/pydantic_ai/_utils.py

@@ -12,7 +12,7 @@
 from datetime import datetime, timezone
 from functools import partial
 from types import GenericAlias
-from typing import TYPE_CHECKING, Any, Generic, TypeAlias, TypeGuard, TypeVar, get_args, get_origin, overload
+from typing import TYPE_CHECKING, Any, Generic, TypeAlias, TypeGuard, TypeVar, cast, get_args, get_origin, overload
 
 from anyio.to_thread import run_sync
 from pydantic import BaseModel, TypeAdapter
@@ -184,7 +184,7 @@ async def async_iter_groups() -> AsyncIterator[list[T]]:
             if task is None:
                 # aiter.__anext__() returns an Awaitable[T], not a Coroutine which asyncio.create_task expects
                 # so far, this doesn't seem to be a problem
-                task = asyncio.create_task(aiterator.__anext__())  # pyright: ignore[reportArgumentType]
+                task = cast(asyncio.Task[T], asyncio.create_task(aiterator.__anext__()))  # pyright: ignore[reportArgumentType]
 
             # we use asyncio.wait to avoid cancelling the coroutine if it's not done
             done, _ = await asyncio.wait((task,), timeout=wait_time)
@@ -366,7 +366,7 @@ def is_async_callable(obj: Any) -> Any:
     while isinstance(obj, functools.partial):
         obj = obj.func
 
-    return inspect.iscoroutinefunction(obj) or (callable(obj) and inspect.iscoroutinefunction(obj.__call__))  # type: ignore
+    return inspect.iscoroutinefunction(obj) or (callable(obj) and inspect.iscoroutinefunction(obj.__call__))
 
 
 def _update_mapped_json_schema_refs(s: dict[str, Any], name_mapping: dict[str, str]) -> None:
@@ -386,7 +386,7 @@ def _update_mapped_json_schema_refs(s: dict[str, Any], name_mapping: dict[str, s
 
     # Handle arrays
     if 'items' in s and isinstance(s['items'], dict):
-        items: dict[str, Any] = s['items']
+        items = cast(dict[str, Any], s['items'])
         _update_mapped_json_schema_refs(items, name_mapping)
     if 'prefixItems' in s:
         prefix_items: list[dict[str, Any]] = s['prefixItems']

pydantic_ai_slim/pydantic_ai/ag_ui.py

@@ -739,7 +739,7 @@ class _RequestStreamContext:
     message_id: str = ''
     part_end: BaseEvent | None = None
     thinking: bool = False
-    builtin_tool_call_ids: dict[str, str] = field(default_factory=dict)
+    builtin_tool_call_ids: dict[str, str] = field(default_factory=dict[str, str])
 
     def new_message_id(self) -> str:
         """Generate a new message ID for the request stream.

pydantic_ai_slim/pydantic_ai/direct.py

@@ -276,7 +276,7 @@ class StreamedResponseSync:
 
     _async_stream_cm: AbstractAsyncContextManager[StreamedResponse]
     _queue: queue.Queue[messages.ModelResponseStreamEvent | Exception | None] = field(
-        default_factory=queue.Queue, init=False
+        default_factory=queue.Queue[messages.ModelResponseStreamEvent | Exception | None], init=False
     )
     _thread: threading.Thread | None = field(default=None, init=False)
     _stream_response: StreamedResponse | None = field(default=None, init=False)

pydantic_ai_slim/pydantic_ai/durable_exec/temporal/__init__.py

@@ -4,7 +4,7 @@
 from collections.abc import AsyncIterator, Callable, Sequence
 from contextlib import AbstractAsyncContextManager
 from dataclasses import replace
-from typing import Any
+from typing import Any, cast
 
 from pydantic.errors import PydanticUserError
 from temporalio.client import ClientConfig, Plugin as ClientPlugin, WorkflowHistory
@@ -117,7 +117,7 @@ def init_worker_plugin(self, next: WorkerPlugin) -> None:
         self.next_worker_plugin = next
 
     def configure_worker(self, config: WorkerConfig) -> WorkerConfig:
-        activities: Sequence[Callable[..., Any]] = config.get('activities', [])  # pyright: ignore[reportUnknownMemberType]
+        activities = cast(Sequence[Callable[..., Any]], config.get('activities', []))  # pyright: ignore[reportUnknownMemberType]
         # Activities are checked for name conflicts by Temporal.
         config['activities'] = [*activities, *self.agent.temporal_activities]
         return self.next_worker_plugin.configure_worker(config)

pydantic_ai_slim/pydantic_ai/format_prompt.py

@@ -81,11 +81,13 @@ class _ToXml:
     include_field_info: Literal['once'] | bool
     # a map of Pydantic and dataclasses Field paths to their metadata:
     # a field unique string representation and its class
-    _fields_info: dict[str, tuple[str, FieldInfo | ComputedFieldInfo]] = field(default_factory=dict)
+    _fields_info: dict[str, tuple[str, FieldInfo | ComputedFieldInfo]] = field(
+        default_factory=dict[str, tuple[str, FieldInfo | ComputedFieldInfo]]
+    )
     # keep track of fields we have extracted attributes from
-    _included_fields: set[str] = field(default_factory=set)
+    _included_fields: set[str] = field(default_factory=set[str])
     # keep track of class names for dataclasses and Pydantic models, that occur in lists
-    _element_names: dict[str, str] = field(default_factory=dict)
+    _element_names: dict[str, str] = field(default_factory=dict[str, str])
     # flag for parsing dataclasses and Pydantic models once
     _is_info_extracted: bool = False
     _FIELD_ATTRIBUTES = ('title', 'description')

pydantic_ai_slim/pydantic_ai/messages.py

@@ -12,7 +12,7 @@
 import pydantic
 import pydantic_core
 from genai_prices import calc_price, types as genai_types
-from opentelemetry._events import Event  # pyright: ignore[reportPrivateImportUsage]
+from opentelemetry._events import Event
 from typing_extensions import deprecated
 
 from . import _otel_messages, _utils

pydantic_ai_slim/pydantic_ai/models/__init__.py

@@ -293,12 +293,12 @@
 class ModelRequestParameters:
     """Configuration for an agent's request to a model, specifically related to tools and output handling."""
 
-    function_tools: list[ToolDefinition] = field(default_factory=list)
-    builtin_tools: list[AbstractBuiltinTool] = field(default_factory=list)
+    function_tools: list[ToolDefinition] = field(default_factory=list[ToolDefinition])
+    builtin_tools: list[AbstractBuiltinTool] = field(default_factory=list[AbstractBuiltinTool])
 
     output_mode: OutputMode = 'text'
     output_object: OutputObjectDefinition | None = None
-    output_tools: list[ToolDefinition] = field(default_factory=list)
+    output_tools: list[ToolDefinition] = field(default_factory=list[ToolDefinition])
     allow_text_output: bool = True
 
     @cached_property

pydantic_ai_slim/pydantic_ai/models/fallback.py

@@ -3,7 +3,7 @@
 from collections.abc import AsyncIterator, Callable
 from contextlib import AsyncExitStack, asynccontextmanager, suppress
 from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, cast
 
 from opentelemetry.trace import get_current_span
 
@@ -47,6 +47,7 @@ def __init__(
         self.models = [infer_model(default_model), *[infer_model(m) for m in fallback_models]]
 
         if isinstance(fallback_on, tuple):
+            fallback_on = cast(tuple[type[Exception], ...], fallback_on)
             self._fallback_on = _default_fallback_condition_factory(fallback_on)
         else:
             self._fallback_on = fallback_on

pydantic_ai_slim/pydantic_ai/models/instrumented.py

@@ -11,10 +11,10 @@
 
 from genai_prices.types import PriceCalculation
 from opentelemetry._events import (
-    Event,  # pyright: ignore[reportPrivateImportUsage]
-    EventLogger,  # pyright: ignore[reportPrivateImportUsage]
-    EventLoggerProvider,  # pyright: ignore[reportPrivateImportUsage]
-    get_event_logger_provider,  # pyright: ignore[reportPrivateImportUsage]
+    Event,
+    EventLogger,
+    EventLoggerProvider,
+    get_event_logger_provider,
 )
 from opentelemetry.metrics import MeterProvider, get_meter_provider
 from opentelemetry.trace import Span, Tracer, TracerProvider, get_tracer_provider