mcp-run-python

from __future__ import annotations as _annotations import asyncio import functools import inspect import re import time import uuid from collections.abc import AsyncIterable, AsyncIterator, Awaitable, Callable, Iterable, Iterator from contextlib import asynccontextmanager, suppress from dataclasses import dataclass, fields, is_dataclass 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 anyio.to_thread import run_sync from pydantic import BaseModel, TypeAdapter from pydantic.json_schema import JsonSchemaValue from typing_extensions import ( ParamSpec, TypeIs, is_typeddict, ) from typing_inspection import typing_objects from typing_inspection.introspection import is_union_origin from pydantic_graph._utils import AbstractSpan from . import exceptions AbstractSpan = AbstractSpan if TYPE_CHECKING: from pydantic_ai.agent import AgentRun, AgentRunResult from pydantic_graph import GraphRun, GraphRunResult from . import messages as _messages from .tools import ObjectJsonSchema _P = ParamSpec('_P') _R = TypeVar('_R') async def run_in_executor(func: Callable[_P, _R], *args: _P.args, **kwargs: _P.kwargs) -> _R: wrapped_func = partial(func, *args, **kwargs) return await run_sync(wrapped_func) def is_model_like(type_: Any) -> bool: """Check if something is a pydantic model, dataclass or typedict. These should all generate a JSON Schema with `{"type": "object"}` and therefore be usable directly as function parameters. """ return ( isinstance(type_, type) and not isinstance(type_, GenericAlias) and ( issubclass(type_, BaseModel) or is_dataclass(type_) # pyright: ignore[reportUnknownArgumentType] or is_typeddict(type_) # pyright: ignore[reportUnknownArgumentType] or getattr(type_, '__is_model_like__', False) # pyright: ignore[reportUnknownArgumentType] ) ) def check_object_json_schema(schema: JsonSchemaValue) -> ObjectJsonSchema: from .exceptions import UserError if schema.get('type') == 'object': return schema elif ref := schema.get('$ref'): prefix = '#/$defs/' # Return the referenced schema unless it contains additional nested references. if ( ref.startswith(prefix) and (resolved := schema.get('$defs', {}).get(ref[len(prefix) :])) and resolved.get('type') == 'object' and not _contains_ref(resolved) ): return resolved return schema else: raise UserError('Schema must be an object') def _contains_ref(obj: JsonSchemaValue | list[JsonSchemaValue]) -> bool: """Recursively check if an object contains any $ref keys.""" items: Iterable[JsonSchemaValue] if isinstance(obj, dict): if '$ref' in obj: return True items = obj.values() else: items = obj return any(isinstance(item, dict | list) and _contains_ref(item) for item in items) # pyright: ignore[reportUnknownArgumentType] T = TypeVar('T') @dataclass class Some(Generic[T]): """Analogous to Rust's `Option::Some` type.""" value: T Option: TypeAlias = Some[T] | None """Analogous to Rust's `Option` type, usage: `Option[Thing]` is equivalent to `Some[Thing] | None`.""" class Unset: """A singleton to represent an unset value.""" pass UNSET = Unset() def is_set(t_or_unset: T | Unset) -> TypeGuard[T]: return t_or_unset is not UNSET @asynccontextmanager async def group_by_temporal( aiterable: AsyncIterable[T], soft_max_interval: float | None ) -> AsyncIterator[AsyncIterable[list[T]]]: """Group items from an async iterable into lists based on time interval between them. Effectively, this debounces the iterator. This returns a context manager usable as an iterator so any pending tasks can be cancelled if an error occurs during iteration. Usage: ```python async with group_by_temporal(yield_groups(), 0.1) as groups_iter: async for groups in groups_iter: print(groups) ``` Args: aiterable: The async iterable to group. soft_max_interval: Maximum interval over which to group items, this should avoid a trickle of items causing a group to never be yielded. It's a soft max in the sense that once we're over this time, we yield items as soon as `aiter.__anext__()` returns. If `None`, no grouping/debouncing is performed Returns: A context manager usable as an async iterable of lists of items produced by the input async iterable. """ if soft_max_interval is None: async def async_iter_groups_noop() -> AsyncIterator[list[T]]: async for item in aiterable: yield [item] yield async_iter_groups_noop() return # we might wait for the next item more than once, so we store the task to await next time task: asyncio.Task[T] | None = None async def async_iter_groups() -> AsyncIterator[list[T]]: nonlocal task assert soft_max_interval is not None and soft_max_interval >= 0, 'soft_max_interval must be a positive number' buffer: list[T] = [] group_start_time = time.monotonic() aiterator = aiterable.__aiter__() while True: if group_start_time is None: # group hasn't started, we just wait for the maximum interval wait_time = soft_max_interval else: # wait for the time remaining in the group wait_time = soft_max_interval - (time.monotonic() - group_start_time) # if there's no current task, we get the next one 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] # we use asyncio.wait to avoid cancelling the coroutine if it's not done done, _ = await asyncio.wait((task,), timeout=wait_time) if done: # the one task we waited for completed try: item = done.pop().result() except StopAsyncIteration: # if the task raised StopAsyncIteration, we're done iterating if buffer: yield buffer task = None break else: # we got an item, add it to the buffer and set task to None to get the next item buffer.append(item) task = None # if this is the first item in the group, set the group start time if group_start_time is None: group_start_time = time.monotonic() elif buffer: # otherwise if the task timeout expired and we have items in the buffer, yield the buffer yield buffer # clear the buffer and reset the group start time ready for the next group buffer = [] group_start_time = None try: yield async_iter_groups() finally: # pragma: no cover # after iteration if a tasks still exists, cancel it, this will only happen if an error occurred if task: task.cancel('Cancelling due to error in iterator') with suppress(asyncio.CancelledError): await task def sync_anext(iterator: Iterator[T]) -> T: """Get the next item from a sync iterator, raising `StopAsyncIteration` if it's exhausted. Useful when iterating over a sync iterator in an async context. """ try: return next(iterator) except StopIteration as e: raise StopAsyncIteration() from e def now_utc() -> datetime: return datetime.now(tz=timezone.utc) def guard_tool_call_id( t: _messages.ToolCallPart | _messages.ToolReturnPart | _messages.RetryPromptPart | _messages.BuiltinToolCallPart | _messages.BuiltinToolReturnPart, ) -> str: """Type guard that either returns the tool call id or generates a new one if it's None.""" return t.tool_call_id or generate_tool_call_id() def generate_tool_call_id() -> str: """Generate a tool call id. Ensure that the tool call id is unique. """ return f'pyd_ai_{uuid.uuid4().hex}' class PeekableAsyncStream(Generic[T]): """Wraps an async iterable of type T and allows peeking at the *next* item without consuming it. We only buffer one item at a time (the next item). Once that item is yielded, it is discarded. This is a single-pass stream. """ def __init__(self, source: AsyncIterable[T]): self._source = source self._source_iter: AsyncIterator[T] | None = None self._buffer: T | Unset = UNSET self._exhausted = False async def peek(self) -> T | Unset: """Returns the next item that would be yielded without consuming it. Returns None if the stream is exhausted. """ if self._exhausted: return UNSET # If we already have a buffered item, just return it. if not isinstance(self._buffer, Unset): return self._buffer # Otherwise, we need to fetch the next item from the underlying iterator. if self._source_iter is None: self._source_iter = self._source.__aiter__() try: self._buffer = await self._source_iter.__anext__() except StopAsyncIteration: self._exhausted = True return UNSET return self._buffer async def is_exhausted(self) -> bool: """Returns True if the stream is exhausted, False otherwise.""" return isinstance(await self.peek(), Unset) def __aiter__(self) -> AsyncIterator[T]: # For a single-pass iteration, we can return self as the iterator. return self async def __anext__(self) -> T: """Yields the buffered item if present, otherwise fetches the next item from the underlying source. Raises StopAsyncIteration if the stream is exhausted. """ if self._exhausted: raise StopAsyncIteration # If we have a buffered item, yield it. if not isinstance(self._buffer, Unset): item = self._buffer self._buffer = UNSET return item # Otherwise, fetch the next item from the source. if self._source_iter is None: self._source_iter = self._source.__aiter__() try: return await self._source_iter.__anext__() except StopAsyncIteration: self._exhausted = True raise def get_traceparent(x: AgentRun | AgentRunResult | GraphRun | GraphRunResult) -> str: return x._traceparent(required=False) or '' # type: ignore[reportPrivateUsage] def dataclasses_no_defaults_repr(self: Any) -> str: """Exclude fields with values equal to the field default.""" kv_pairs = ( f'{f.name}={getattr(self, f.name)!r}' for f in fields(self) if f.repr and getattr(self, f.name) != f.default ) return f'{self.__class__.__qualname__}({", ".join(kv_pairs)})' _datetime_ta = TypeAdapter(datetime) def number_to_datetime(x: int | float) -> datetime: return _datetime_ta.validate_python(x) AwaitableCallable = Callable[..., Awaitable[T]] @overload def is_async_callable(obj: AwaitableCallable[T]) -> TypeIs[AwaitableCallable[T]]: ... @overload def is_async_callable(obj: Any) -> TypeIs[AwaitableCallable[Any]]: ... def is_async_callable(obj: Any) -> Any: """Correctly check if a callable is async. This function was copied from Starlette: https://github.com/encode/starlette/blob/78da9b9e218ab289117df7d62aee200ed4c59617/starlette/_utils.py#L36-L40 """ while isinstance(obj, functools.partial): obj = obj.func return inspect.iscoroutinefunction(obj) or (callable(obj) and inspect.iscoroutinefunction(obj.__call__)) # type: ignore def _update_mapped_json_schema_refs(s: dict[str, Any], name_mapping: dict[str, str]) -> None: """Update $refs in a schema to use the new names from name_mapping.""" if '$ref' in s: ref = s['$ref'] if ref.startswith('#/$defs/'): # pragma: no branch original_name = ref[8:] # Remove '#/$defs/' new_name = name_mapping.get(original_name, original_name) s['$ref'] = f'#/$defs/{new_name}' # Recursively update refs in properties if 'properties' in s: props: dict[str, dict[str, Any]] = s['properties'] for prop in props.values(): _update_mapped_json_schema_refs(prop, name_mapping) # Handle arrays if 'items' in s and isinstance(s['items'], dict): items: dict[str, Any] = s['items'] _update_mapped_json_schema_refs(items, name_mapping) if 'prefixItems' in s: prefix_items: list[dict[str, Any]] = s['prefixItems'] for item in prefix_items: _update_mapped_json_schema_refs(item, name_mapping) # Handle unions for union_type in ['anyOf', 'oneOf']: if union_type in s: union_items: list[dict[str, Any]] = s[union_type] for item in union_items: _update_mapped_json_schema_refs(item, name_mapping) def merge_json_schema_defs(schemas: list[dict[str, Any]]) -> tuple[list[dict[str, Any]], dict[str, dict[str, Any]]]: """Merges the `$defs` from different JSON schemas into a single deduplicated `$defs`, handling name collisions of `$defs` that are not the same, and rewrites `$ref`s to point to the new `$defs`. Returns a tuple of the rewritten schemas and a dictionary of the new `$defs`. """ all_defs: dict[str, dict[str, Any]] = {} rewritten_schemas: list[dict[str, Any]] = [] for schema in schemas: if '$defs' not in schema: rewritten_schemas.append(schema) continue schema = schema.copy() defs = schema.pop('$defs', None) schema_name_mapping: dict[str, str] = {} # Process definitions and build mapping for name, def_schema in defs.items(): if name not in all_defs: all_defs[name] = def_schema schema_name_mapping[name] = name elif def_schema != all_defs[name]: new_name = name if title := schema.get('title'): new_name = f'{title}_{name}' i = 1 original_new_name = new_name new_name = f'{new_name}_{i}' while new_name in all_defs: i += 1 new_name = f'{original_new_name}_{i}' all_defs[new_name] = def_schema schema_name_mapping[name] = new_name _update_mapped_json_schema_refs(schema, schema_name_mapping) rewritten_schemas.append(schema) return rewritten_schemas, all_defs def validate_empty_kwargs(_kwargs: dict[str, Any]) -> None: """Validate that no unknown kwargs remain after processing. Args: _kwargs: Dictionary of remaining kwargs after specific ones have been processed. Raises: UserError: If any unknown kwargs remain. """ if _kwargs: unknown_kwargs = ', '.join(f'`{k}`' for k in _kwargs.keys()) raise exceptions.UserError(f'Unknown keyword arguments: {unknown_kwargs}') def strip_markdown_fences(text: str) -> str: if text.startswith('{'): return text regex = r'```(?:\w+)?\n(\{.*\})\n```' match = re.search(regex, text, re.DOTALL) if match: return match.group(1) return text def _unwrap_annotated(tp: Any) -> Any: origin = get_origin(tp) while typing_objects.is_annotated(origin): tp = tp.__origin__ origin = get_origin(tp) return tp def get_union_args(tp: Any) -> tuple[Any, ...]: """Extract the arguments of a Union type if `tp` is a union, otherwise return an empty tuple.""" if typing_objects.is_typealiastype(tp): tp = tp.__value__ tp = _unwrap_annotated(tp) origin = get_origin(tp) if is_union_origin(origin): return tuple(_unwrap_annotated(arg) for arg in get_args(tp)) else: return ()