Files
Eduard van Valkenburg 6e95517659 Python: Split type checkers by target (pyright source, 5 checkers on tests/samples) (#6443)
* Python: Split type checkers by target (pyright source, 5 checkers on tests/samples)

Rework the typing setup along the lines of the 'too many type checkers'
approach:

- Pyright (strict) is now the sole source-code type checker; mypy is
  removed from source and its [tool.mypy] block becomes a relaxed profile
  used only for tests/samples.
- Tests are checked by all five checkers (pyright relaxed, mypy, pyrefly,
  ty, zuban); samples by pyright, pyrefly, and ty. All run in a relaxed/
  basic profile so authors aren't forced into over-annotation.
- Add pyrightconfig.tests.json and bump sample pyright configs to basic.
- Unify test/sample typing onto the same parallel fan-out used by source
  pyright via run_command_items in task_runner.py.
- Make version-conditional imports symmetric: keep or drop the
  '# type: ignore' on both branches so results match across interpreter
  versions (local vs CI).
- Update SKILL.md, DEV_SETUP.md, and CODING_STANDARD.md for the five
  gating checkers and pyright on source+tests+samples.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Python: Fix merge regressions from main (typing + runtime)

Merging main into the type-checker split branch surfaced regressions that
the new five-checker test suite and unit tests caught:

Runtime fixes:
- anthropic: restore the dropped `cache_read_input_token_count` mapping in
  _parse_usage_from_anthropic (lost during merge conflict resolution).
- gemini: _get_function_calling_mode test helper returned str(enum)
  ('FunctionCallingConfigMode.AUTO') instead of the enum value ('AUTO').
- openai: _response_id_from_token test helper was an infinite self-recursion;
  return token['response_id'].
- orchestrations: reset output_events per approval iteration so the terminal
  output assertion counts only the final run.
- core: drop a stale duplicate harness test whose message ('non-negative')
  contradicted the source ('positive').
- purview: import PolicyLocation/PolicyScope/ProtectionScopeActivities/
  ExecutionMode used by the processor tests.

Type-checker fixes (tests, relaxed profile):
- core: pyright/mypy/pyrefly/ty/zuban green-ups across the harness, MCP,
  observability and types tests.
- anthropic/openai: route provider-namespaced UsageDetails keys through a
  dict cast (extra_items TypedDict unsupported by mypy/ty).
- purview: typed model constructors and cache-mock casts.
- ag-ui: annotate WorkflowContext[Any, Any] so yield_output accepts test
  payloads, guard Optional forwarded_props, and ty-ignore intentional bad args.

Source pyright (sole source checker) flagged unnecessary ignores newly
introduced by merged code in core _tools.py and declarative _declarative_base.py.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Python: Isolate per-package mypy cache in test-typing fan-out

The parallel test-typing fan-out runs many mypy processes concurrently,
all defaulting to a single shared ./.mypy_cache. Concurrent writes corrupt
the cache and mypy aborts with INTERNAL ERROR (intermittently, depending on
worker timing) -- which is why CI's Test Typing job failed on a shifting set
of packages while a single-package run was fine.

Give each mypy invocation an isolated cache dir keyed by its target paths so
incremental caching still works per package without races. Other checkers
(zuban/pyrefly/ty/pyright) maintain their own caches and are unaffected.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Python: Make lab pyright-only on source (drop source mypy)

Lab was the last package still running mypy on its source code, requiring
mypy-only `# type: ignore` comments that pyright (the sole source checker
everywhere else) flags as unnecessary. Align lab with the rest of the
monorepo:

- Remove the lab source mypy poe tasks (mypy-gaia/lightning/tau2) and the
  now-dead strict [tool.mypy] config block.
- Drop the 'Run lab mypy' CI step; lab source is type-checked by pyright only.

Lab tests remain covered by the workspace test-typing fan-out (mypy, pyrefly,
ty, zuban, pyright over tests using the relaxed root config).

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Python: Fix test-typing regressions from latest main merge

A fresh merge from main brought in new test code never run under the
five-checker test-typing suite. Green up across the affected packages:

- core: narrow Optional span.attributes with 'and' guards in span filters
  and assert+cast the json.loads(...attributes[...]) reads (test_observability);
  match the existing as_agent ignore on the protocol-typed fixture (test_clients).
- openai: align new streaming tests with the established chat_options dict
  pattern (ChatOptions TypedDict isn't assignable to dict), route Optional
  .annotations[0] access through a small _first_annotation helper (mirrors the
  file's assert-not-None convention), and annotate a mapped ResponseStream.
- foundry_hosting: annotate error: dict[str, Any] = body.get(...) or {}
  (zuban needs the annotation).
- foundry: narrow ignores for the live AIProjectClient credential arg (pyrefly)
  and connections.get_default (zuban) SDK type gaps.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* updated pyright version

* pyright fix

* Python: Fix source typing for pyright 1.1.410

Pyright 1.1.410 tightened several checks. Apply the same source fixes as
upstream PR #6275:

- anthropic: import AsyncAnthropicBedrock from anthropic.lib.bedrock and
  AsyncAnthropicVertex from anthropic.lib.vertex (no longer re-exported from
  the anthropic top-level package -> reportPrivateImportUsage).
- core _types.py: cast the transform-hook result to UpdateT (reportAssignmentType).
- core _workflows/_events.py: annotate the @contextmanager helper as
  Generator[None] instead of Iterator[None] (reportDeprecated).
- redis: build the combined filter expression with an explicit loop instead of
  reduce(and_, ...), which pyright could no longer fully type (drops the now
  unused functools.reduce / operator.and_ imports).

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Python: Accept plain-text body in Azure Functions workflow/run endpoint

The workflow_orchestrator already accepts plain strings as well as JSON
objects via context.get_input(), but the start_workflow_orchestration HTTP
handler only accepted JSON and returned 400 for any non-JSON body. This made
the functions integration tests that POST text/plain to /api/workflow/run
(e.g. test_09_workflow_shared_state) fail consistently with 400 != 202.

Fall back to the raw request body (decoded as UTF-8) when the body is not
JSON, rejecting only a truly empty body. The JSON path is unchanged.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-06-18 15:06:20 +00:00

167 lines
6.3 KiB
Python

# Copyright (c) Microsoft. All rights reserved.
import asyncio
from random import randrange
from typing import TYPE_CHECKING, Annotated, Any
from agent_framework import Agent, AgentResponse, Message, tool
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
if TYPE_CHECKING:
from agent_framework import SupportsAgentRun
"""
Demonstration of a tool with approvals.
This sample demonstrates using AI functions with user approval workflows.
It shows how to handle function call approvals without using threads.
"""
# Load environment variables from .env file
load_dotenv()
conditions = ["sunny", "cloudy", "raining", "snowing", "clear"]
# NOTE: approval_mode="never_require" is for sample brevity. Use "always_require" in production;
# see samples/02-agents/tools/function_tool_with_approval.py
# and samples/02-agents/tools/function_tool_with_approval_and_sessions.py.
@tool(approval_mode="never_require")
def get_weather(location: Annotated[str, "The city and state, e.g. San Francisco, CA"]) -> str:
"""Get the current weather for a given location."""
# Simulate weather data
return f"The weather in {location} is {conditions[randrange(0, len(conditions))]} and {randrange(-10, 30)}°C."
# Define a simple weather tool that requires approval
@tool(approval_mode="always_require")
def get_weather_detail(location: Annotated[str, "The city and state, e.g. San Francisco, CA"]) -> str:
"""Get the current weather for a given location."""
# Simulate weather data
return (
f"The weather in {location} is {conditions[randrange(0, len(conditions))]} and {randrange(-10, 30)}°C, "
"with a humidity of 88%. "
f"Tomorrow will be {conditions[randrange(0, len(conditions))]} with a high of {randrange(-10, 30)}°C."
)
async def handle_approvals(query: str, agent: "SupportsAgentRun") -> AgentResponse:
"""Handle function call approvals.
When we don't have a thread, we need to ensure we include the original query,
the approval request, and the approval response in each iteration.
"""
result = await agent.run(query)
while len(result.user_input_requests) > 0:
# Start with the original query
new_inputs: list[Any] = [query]
for user_input_needed in result.user_input_requests:
if user_input_needed.function_call is None:
continue
print(
f"\nUser Input Request for function from {agent.name}:"
f"\n Function: {user_input_needed.function_call.name}"
f"\n Arguments: {user_input_needed.function_call.arguments}"
)
# Add the assistant message with the approval request
new_inputs.append(Message("assistant", [user_input_needed]))
# Get user approval
user_approval = await asyncio.to_thread(input, "\nApprove function call? (y/n): ")
# Add the user's approval response
new_inputs.append(
Message("user", [user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
)
# Run again with all the context
result = await agent.run(new_inputs)
return result
async def handle_approvals_streaming(query: str, agent: "SupportsAgentRun") -> None:
"""Handle function call approvals with streaming responses.
When we don't have a thread, we need to ensure we include the original query,
the approval request, and the approval response in each iteration.
"""
current_input: str | list[Any] = query
has_user_input_requests = True
while has_user_input_requests:
has_user_input_requests = False
user_input_requests: list[Any] = []
# Stream the response
async for chunk in agent.run(current_input, stream=True):
if chunk.text:
print(chunk.text, end="", flush=True)
# Collect user input requests from the stream
if chunk.user_input_requests:
user_input_requests.extend(chunk.user_input_requests)
if user_input_requests:
has_user_input_requests = True
# Start with the original query
new_inputs: list[Any] = [query]
for user_input_needed in user_input_requests:
if user_input_needed.function_call is None:
continue
print(
f"\n\nUser Input Request for function from {agent.name}:"
f"\n Function: {user_input_needed.function_call.name}"
f"\n Arguments: {user_input_needed.function_call.arguments}"
)
# Add the assistant message with the approval request
new_inputs.append(Message("assistant", [user_input_needed]))
# Get user approval
user_approval = await asyncio.to_thread(input, "\nApprove function call? (y/n): ")
# Add the user's approval response
new_inputs.append(
Message("user", [user_input_needed.to_function_approval_response(user_approval.lower() == "y")])
)
# Update input with all the context for next iteration
current_input = new_inputs
async def run_weather_agent_with_approval(stream: bool) -> None:
"""Example showing AI function with approval requirement."""
print(f"\n=== Weather Agent with Approval Required ({'Streaming' if stream else 'Non-Streaming'}) ===\n")
async with Agent(
client=OpenAIChatClient(),
name="WeatherAgent",
instructions=("You are a helpful weather assistant. Use the get_weather tool to provide weather information."),
tools=[get_weather, get_weather_detail],
) as agent:
query = "Can you give me an update of the weather in LA and Portland and detailed weather for Seattle?"
print(f"User: {query}")
if stream:
print(f"\n{agent.name}: ", end="", flush=True)
await handle_approvals_streaming(query, agent)
print()
else:
result = await handle_approvals(query, agent)
print(f"\n{agent.name}: {result}\n")
async def main() -> None:
print("=== Demonstration of a tool with approvals ===\n")
await run_weather_agent_with_approval(stream=False)
await run_weather_agent_with_approval(stream=True)
if __name__ == "__main__":
asyncio.run(main())