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8457533c69
* Replace Pydantic Settings with TypedDict + load_settings() - Remove pydantic-settings dependency, add python-dotenv - Delete _pydantic.py (AFBaseSettings, HTTPsUrl) - Add _settings.py with generic load_settings() function, SecretString, type coercion, and Required field validation (SettingNotFoundError) - Convert all 13 settings classes from AFBaseSettings subclasses to TypedDict definitions with load_settings() calls - Update all consumers from attribute access to dict access - Add 20 unit tests for load_settings() covering basic loading, dotenv, SecretString, type coercion, and required field validation - Update all existing tests for new settings patterns * Fix mypy type errors from settings conversion - Fix str | None attribute access in responses_client (walrus operator) - Fix SecretString | None narrowing in bedrock (type: ignore after guard) - Convert _context_provider.py attribute access to dict access (missed file) - Fix endpoint type narrowing in search_provider and context_provider - Fix purview: str | None .rstrip(), int | None defaults, urlparse bytes * Address PR review: required_fields param, type validation, fixes - Move required field validation from TypedDict annotations (Required) to a required_fields parameter on load_settings(), enabling runtime decisions about which fields are required - Remove Required imports and restore from __future__ import annotations in ollama and foundry_local - Add _check_override_type() for deterministic ServiceInitializationError on invalid override types (e.g. dict passed for str field) - Fix all multi-exception test catches back to single exception type - Fix Ollama host=None: use .get() so None is passed through to SDK default - Fix Purview processor: use explicit is-None checks instead of or operator - Remove unused BaseModel import from openai/_shared.py - Add 4 new tests (24 total): required_fields param, type validation * Fix type validation: allow int for float fields _check_override_type now permits int values for float-typed fields, matching Python's standard numeric promotion behavior. * fix: wrap urlparse arg with str() to fix mypy bytes endswith error
136 lines
4.3 KiB
Python
136 lines
4.3 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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from __future__ import annotations
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from unittest.mock import MagicMock
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import pytest
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from agent_framework import (
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ChatOptions,
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Content,
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FunctionTool,
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Message,
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)
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from agent_framework._settings import load_settings
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from pydantic import BaseModel
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from agent_framework_bedrock._chat_client import BedrockChatClient, BedrockSettings
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class _WeatherArgs(BaseModel):
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location: str
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def _build_client() -> BedrockChatClient:
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fake_runtime = MagicMock()
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fake_runtime.converse.return_value = {}
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return BedrockChatClient(model_id="test-model", client=fake_runtime)
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def _dummy_weather(location: str) -> str: # pragma: no cover - helper
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return f"Weather in {location}"
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def test_settings_load_from_environment(monkeypatch: pytest.MonkeyPatch) -> None:
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monkeypatch.setenv("BEDROCK_REGION", "us-west-2")
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monkeypatch.setenv("BEDROCK_CHAT_MODEL_ID", "anthropic.claude-v2")
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settings = load_settings(BedrockSettings, env_prefix="BEDROCK_")
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assert settings["region"] == "us-west-2"
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assert settings["chat_model_id"] == "anthropic.claude-v2"
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def test_build_request_includes_tool_config() -> None:
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client = _build_client()
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tool = FunctionTool(name="get_weather", description="desc", func=_dummy_weather, input_model=_WeatherArgs)
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options = {
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"tools": [tool],
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"tool_choice": {"mode": "required", "required_function_name": "get_weather"},
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}
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messages = [Message(role="user", contents=[Content.from_text(text="hi")])]
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request = client._prepare_options(messages, options)
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assert request["toolConfig"]["tools"][0]["toolSpec"]["name"] == "get_weather"
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assert request["toolConfig"]["toolChoice"] == {"tool": {"name": "get_weather"}}
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def test_build_request_serializes_tool_history() -> None:
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client = _build_client()
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options: ChatOptions = {}
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messages = [
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Message(role="user", contents=[Content.from_text(text="how's weather?")]),
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Message(
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role="assistant",
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contents=[
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Content.from_function_call(call_id="call-1", name="get_weather", arguments='{"location": "SEA"}')
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],
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),
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Message(
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role="tool",
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contents=[Content.from_function_result(call_id="call-1", result={"answer": "72F"})],
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),
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]
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request = client._prepare_options(messages, options)
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assistant_block = request["messages"][1]["content"][0]["toolUse"]
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result_block = request["messages"][2]["content"][0]["toolResult"]
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assert assistant_block["name"] == "get_weather"
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assert assistant_block["input"] == {"location": "SEA"}
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assert result_block["toolUseId"] == "call-1"
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assert result_block["content"][0]["json"] == {"answer": "72F"}
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def test_process_response_parses_tool_use_and_result() -> None:
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client = _build_client()
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response = {
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"modelId": "model",
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"output": {
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"message": {
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"id": "msg-1",
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"content": [
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{"toolUse": {"toolUseId": "call-1", "name": "get_weather", "input": {"location": "NYC"}}},
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{"text": "Calling tool"},
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],
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},
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"completionReason": "tool_use",
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},
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}
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chat_response = client._process_converse_response(response)
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contents = chat_response.messages[0].contents
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assert contents[0].type == "function_call"
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assert contents[0].name == "get_weather"
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assert contents[1].type == "text"
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assert chat_response.finish_reason == client._map_finish_reason("tool_use")
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def test_process_response_parses_tool_result() -> None:
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client = _build_client()
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response = {
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"modelId": "model",
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"output": {
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"message": {
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"id": "msg-2",
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"content": [
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{
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"toolResult": {
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"toolUseId": "call-1",
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"status": "success",
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"content": [{"json": {"answer": 42}}],
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}
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}
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],
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},
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"completionReason": "end_turn",
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},
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}
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chat_response = client._process_converse_response(response)
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contents = chat_response.messages[0].contents
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assert contents[0].type == "function_result"
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assert contents[0].result == {"answer": 42}
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