mirror of
https://github.com/langchain-ai/langgraph.git
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786 lines
25 KiB
Python
786 lines
25 KiB
Python
"""Test suite for create_react_agent with structured output response_format permutations."""
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from dataclasses import dataclass
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from typing import Union
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import pytest
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from langchain_core.messages import HumanMessage
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from pydantic import BaseModel, Field
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from typing_extensions import TypedDict
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from langgraph.prebuilt import create_react_agent
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from langgraph.prebuilt.responses import (
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MultipleStructuredOutputsError,
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NativeOutput,
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StructuredOutputParsingError,
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ToolOutput,
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)
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from tests.model import FakeToolCallingModel
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try:
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from langchain_openai import ChatOpenAI
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except ImportError:
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skip_openai_integration_tests = True
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else:
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skip_openai_integration_tests = False
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# Test data models
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class WeatherBaseModel(BaseModel):
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"""Weather response."""
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temperature: float = Field(description="The temperature in fahrenheit")
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condition: str = Field(description="Weather condition")
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@dataclass
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class WeatherDataclass:
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"""Weather response."""
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temperature: float
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condition: str
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class WeatherTypedDict(TypedDict):
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"""Weather response."""
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temperature: float
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condition: str
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weather_json_schema = {
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"type": "object",
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"properties": {
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"temperature": {"type": "number", "description": "Temperature in fahrenheit"},
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"condition": {"type": "string", "description": "Weather condition"},
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},
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"title": "weather_schema",
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"required": ["temperature", "condition"],
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}
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class LocationResponse(BaseModel):
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city: str = Field(description="The city name")
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country: str = Field(description="The country name")
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class LocationTypedDict(TypedDict):
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city: str
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country: str
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location_json_schema = {
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"type": "object",
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"properties": {
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"city": {"type": "string", "description": "The city name"},
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"country": {"type": "string", "description": "The country name"},
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},
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"title": "location_schema",
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"required": ["city", "country"],
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}
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def get_weather() -> str:
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"""Get the weather."""
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return "The weather is sunny and 75°F."
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def get_location() -> str:
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"""Get the current location."""
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return "You are in New York, USA."
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# Standardized test data
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WEATHER_DATA = {"temperature": 75.0, "condition": "sunny"}
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LOCATION_DATA = {"city": "New York", "country": "USA"}
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# Standardized expected responses
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EXPECTED_WEATHER_PYDANTIC = WeatherBaseModel(**WEATHER_DATA)
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EXPECTED_WEATHER_DATACLASS = WeatherDataclass(**WEATHER_DATA)
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EXPECTED_WEATHER_DICT: WeatherTypedDict = {"temperature": 75.0, "condition": "sunny"}
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EXPECTED_LOCATION = LocationResponse(**LOCATION_DATA)
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EXPECTED_LOCATION_DICT: LocationTypedDict = {"city": "New York", "country": "USA"}
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class TestResponseFormatAsModel:
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def test_pydantic_model(self) -> None:
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"""Test response_format as Pydantic model."""
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tool_calls = [
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[{"args": {}, "id": "1", "name": "get_weather"}],
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[
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{
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"name": "WeatherBaseModel",
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"id": "2",
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"args": WEATHER_DATA,
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}
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(model, [get_weather], response_format=WeatherBaseModel)
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response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
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assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
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assert len(response["messages"]) == 5
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def test_dataclass(self) -> None:
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"""Test response_format as dataclass."""
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tool_calls = [
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[{"args": {}, "id": "1", "name": "get_weather"}],
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[
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{
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"name": "WeatherDataclass",
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"id": "2",
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"args": WEATHER_DATA,
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}
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(model, [get_weather], response_format=WeatherDataclass)
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response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
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assert response["structured_response"] == EXPECTED_WEATHER_DATACLASS
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assert len(response["messages"]) == 5
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def test_typed_dict(self) -> None:
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"""Test response_format as TypedDict."""
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tool_calls = [
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[{"args": {}, "id": "1", "name": "get_weather"}],
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[
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{
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"name": "WeatherTypedDict",
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"id": "2",
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"args": WEATHER_DATA,
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}
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(model, [get_weather], response_format=WeatherTypedDict)
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response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
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assert response["structured_response"] == EXPECTED_WEATHER_DICT
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assert len(response["messages"]) == 5
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def test_json_schema(self) -> None:
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"""Test response_format as JSON schema."""
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tool_calls = [
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[{"args": {}, "id": "1", "name": "get_weather"}],
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[
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{
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"name": "weather_schema",
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"id": "2",
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"args": WEATHER_DATA,
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}
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(model, [get_weather], response_format=weather_json_schema)
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response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
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assert response["structured_response"] == EXPECTED_WEATHER_DICT
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assert len(response["messages"]) == 5
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class TestResponseFormatAsToolOutput:
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def test_pydantic_model(self) -> None:
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"""Test response_format as ToolOutput with Pydantic model."""
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tool_calls = [
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[{"args": {}, "id": "1", "name": "get_weather"}],
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[
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{
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"name": "WeatherBaseModel",
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"id": "2",
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"args": WEATHER_DATA,
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}
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(
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model, [get_weather], response_format=ToolOutput(WeatherBaseModel)
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)
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response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
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assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
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assert len(response["messages"]) == 5
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def test_dataclass(self) -> None:
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"""Test response_format as ToolOutput with dataclass."""
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tool_calls = [
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[{"args": {}, "id": "1", "name": "get_weather"}],
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[
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{
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"name": "WeatherDataclass",
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"id": "2",
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"args": WEATHER_DATA,
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}
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(
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model, [get_weather], response_format=ToolOutput(WeatherDataclass)
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)
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response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
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assert response["structured_response"] == EXPECTED_WEATHER_DATACLASS
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assert len(response["messages"]) == 5
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def test_typed_dict(self) -> None:
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"""Test response_format as ToolOutput with TypedDict."""
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tool_calls = [
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[{"args": {}, "id": "1", "name": "get_weather"}],
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[
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{
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"name": "WeatherTypedDict",
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"id": "2",
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"args": WEATHER_DATA,
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}
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(
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model, [get_weather], response_format=ToolOutput(WeatherTypedDict)
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)
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response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
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assert response["structured_response"] == EXPECTED_WEATHER_DICT
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assert len(response["messages"]) == 5
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def test_json_schema(self) -> None:
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"""Test response_format as ToolOutput with JSON schema."""
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tool_calls = [
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[{"args": {}, "id": "1", "name": "get_weather"}],
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[
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{
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"name": "weather_schema",
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"id": "2",
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"args": WEATHER_DATA,
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}
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(
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model, [get_weather], response_format=ToolOutput(weather_json_schema)
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)
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response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
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assert response["structured_response"] == EXPECTED_WEATHER_DICT
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assert len(response["messages"]) == 5
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def test_union_of_json_schemas(self) -> None:
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"""Test response_format as ToolOutput with union of JSON schemas."""
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tool_calls = [
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[{"args": {}, "id": "1", "name": "get_weather"}],
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[
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{
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"name": "weather_schema",
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"id": "2",
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"args": WEATHER_DATA,
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}
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(
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model,
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[get_weather, get_location],
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response_format=ToolOutput(
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{"oneOf": [weather_json_schema, location_json_schema]}
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),
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)
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response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
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assert response["structured_response"] == EXPECTED_WEATHER_DICT
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assert len(response["messages"]) == 5
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# Test with LocationResponse
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tool_calls_location = [
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[{"args": {}, "id": "1", "name": "get_location"}],
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[
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{
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"name": "location_schema",
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"id": "2",
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"args": LOCATION_DATA,
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}
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],
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]
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model_location = FakeToolCallingModel(tool_calls=tool_calls_location)
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agent_location = create_react_agent(
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model_location,
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[get_weather, get_location],
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response_format=ToolOutput(
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{"oneOf": [weather_json_schema, location_json_schema]}
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),
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)
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response_location = agent_location.invoke(
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{"messages": [HumanMessage("Where am I?")]}
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)
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assert response_location["structured_response"] == EXPECTED_LOCATION_DICT
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assert len(response_location["messages"]) == 5
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def test_union_of_types(self) -> None:
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"""Test response_format as ToolOutput with Union of various types."""
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# Test with WeatherBaseModel
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tool_calls = [
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[{"args": {}, "id": "1", "name": "get_weather"}],
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[
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{
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"name": "WeatherBaseModel",
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"id": "2",
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"args": WEATHER_DATA,
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}
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],
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]
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model = FakeToolCallingModel[Union[WeatherBaseModel, LocationResponse]](
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tool_calls=tool_calls
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)
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agent = create_react_agent(
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model,
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[get_weather, get_location],
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response_format=ToolOutput(Union[WeatherBaseModel, LocationResponse]),
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)
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response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
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assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
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assert len(response["messages"]) == 5
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# Test with LocationResponse
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tool_calls_location = [
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[{"args": {}, "id": "1", "name": "get_location"}],
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[
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{
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"name": "LocationResponse",
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"id": "2",
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"args": LOCATION_DATA,
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}
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],
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]
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model_location = FakeToolCallingModel(tool_calls=tool_calls_location)
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agent_location = create_react_agent(
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model_location,
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[get_weather, get_location],
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response_format=ToolOutput(Union[WeatherBaseModel, LocationResponse]),
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)
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response_location = agent_location.invoke(
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{"messages": [HumanMessage("Where am I?")]}
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)
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assert response_location["structured_response"] == EXPECTED_LOCATION
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assert len(response_location["messages"]) == 5
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def test_multiple_structured_outputs_error_without_retry(self) -> None:
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"""Test that MultipleStructuredOutputsError is raised when model returns multiple structured tool calls without retry."""
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tool_calls = [
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[
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{
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"name": "WeatherBaseModel",
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"id": "1",
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"args": WEATHER_DATA,
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},
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{
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"name": "LocationResponse",
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"id": "2",
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"args": LOCATION_DATA,
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},
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(
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model,
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[],
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response_format=ToolOutput(
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Union[WeatherBaseModel, LocationResponse],
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handle_errors=False,
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),
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)
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with pytest.raises(
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MultipleStructuredOutputsError,
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match=".*WeatherBaseModel.*LocationResponse.*",
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):
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agent.invoke({"messages": [HumanMessage("Give me weather and location")]})
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def test_multiple_structured_outputs_with_retry(self) -> None:
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"""Test that retry handles multiple structured output tool calls."""
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tool_calls = [
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[
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{
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"name": "WeatherBaseModel",
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"id": "1",
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"args": WEATHER_DATA,
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},
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{
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"name": "LocationResponse",
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"id": "2",
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"args": LOCATION_DATA,
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},
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],
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[
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{
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"name": "WeatherBaseModel",
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"id": "3",
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"args": WEATHER_DATA,
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},
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(
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model,
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[],
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response_format=ToolOutput(
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Union[WeatherBaseModel, LocationResponse],
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handle_errors=True,
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),
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)
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response = agent.invoke({"messages": [HumanMessage("Give me weather")]})
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# HumanMessage, AIMessage, ToolMessage, ToolMessage, AI, ToolMessage
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assert len(response["messages"]) == 6
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assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
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def test_structured_output_parsing_error_without_retry(self) -> None:
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"""Test that StructuredOutputParsingError is raised when tool args fail to parse without retry."""
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tool_calls = [
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[
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{
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"name": "WeatherBaseModel",
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"id": "1",
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"args": {"invalid": "data"},
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},
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(
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model,
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[],
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response_format=ToolOutput(
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WeatherBaseModel,
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handle_errors=False,
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),
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)
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with pytest.raises(
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StructuredOutputParsingError,
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match=".*WeatherBaseModel.*",
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):
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agent.invoke({"messages": [HumanMessage("What's the weather?")]})
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def test_structured_output_parsing_error_with_retry(self) -> None:
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"""Test that retry handles parsing errors for structured output."""
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tool_calls = [
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[
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{
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"name": "WeatherBaseModel",
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"id": "1",
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"args": {"invalid": "data"},
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},
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],
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[
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{
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"name": "WeatherBaseModel",
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"id": "2",
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"args": WEATHER_DATA,
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},
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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agent = create_react_agent(
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model,
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[],
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response_format=ToolOutput(
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WeatherBaseModel,
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handle_errors=(StructuredOutputParsingError,),
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),
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)
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response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
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# HumanMessage, AIMessage, ToolMessage, AIMessage, ToolMessage
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assert len(response["messages"]) == 5
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assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
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def test_retry_with_custom_function(self) -> None:
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"""Test retry with custom message generation."""
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tool_calls = [
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[
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{
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"name": "WeatherBaseModel",
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"id": "1",
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"args": WEATHER_DATA,
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},
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{
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"name": "LocationResponse",
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"id": "2",
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"args": LOCATION_DATA,
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},
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],
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[
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{
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"name": "WeatherBaseModel",
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"id": "3",
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"args": WEATHER_DATA,
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},
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],
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]
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model = FakeToolCallingModel(tool_calls=tool_calls)
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def custom_message(exception: Exception) -> str:
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if isinstance(exception, MultipleStructuredOutputsError):
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return "Custom error: Multiple outputs not allowed"
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return "Custom error"
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|
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agent = create_react_agent(
|
|
model,
|
|
[],
|
|
response_format=ToolOutput(
|
|
Union[WeatherBaseModel, LocationResponse],
|
|
handle_errors=custom_message,
|
|
),
|
|
)
|
|
|
|
response = agent.invoke({"messages": [HumanMessage("Give me weather")]})
|
|
|
|
# HumanMessage, AIMessage, ToolMessage, ToolMessage, AI, ToolMessage
|
|
assert len(response["messages"]) == 6
|
|
assert (
|
|
response["messages"][2].content
|
|
== "Custom error: Multiple outputs not allowed"
|
|
)
|
|
assert (
|
|
response["messages"][3].content
|
|
== "Custom error: Multiple outputs not allowed"
|
|
)
|
|
assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
|
|
|
|
def test_retry_with_custom_string_message(self) -> None:
|
|
"""Test retry with custom static string message."""
|
|
tool_calls = [
|
|
[
|
|
{
|
|
"name": "WeatherBaseModel",
|
|
"id": "1",
|
|
"args": {"invalid": "data"},
|
|
},
|
|
],
|
|
[
|
|
{
|
|
"name": "WeatherBaseModel",
|
|
"id": "2",
|
|
"args": WEATHER_DATA,
|
|
},
|
|
],
|
|
]
|
|
|
|
model = FakeToolCallingModel(tool_calls=tool_calls)
|
|
|
|
agent = create_react_agent(
|
|
model,
|
|
[],
|
|
response_format=ToolOutput(
|
|
WeatherBaseModel,
|
|
handle_errors="Please provide valid weather data with temperature and condition.",
|
|
),
|
|
)
|
|
|
|
response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
|
|
|
|
assert len(response["messages"]) == 5
|
|
assert (
|
|
response["messages"][2].content
|
|
== "Please provide valid weather data with temperature and condition."
|
|
)
|
|
assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
|
|
|
|
|
|
class TestResponseFormatAsNativeOutput:
|
|
def test_pydantic_model(self) -> None:
|
|
"""Test response_format as NativeOutput with Pydantic model."""
|
|
tool_calls = [
|
|
[{"args": {}, "id": "1", "name": "get_weather"}],
|
|
]
|
|
|
|
model = FakeToolCallingModel[WeatherBaseModel](
|
|
tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_PYDANTIC
|
|
)
|
|
|
|
agent = create_react_agent(
|
|
model, [get_weather], response_format=NativeOutput(WeatherBaseModel)
|
|
)
|
|
response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
|
|
|
|
assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
|
|
assert len(response["messages"]) == 4
|
|
|
|
def test_dataclass(self) -> None:
|
|
"""Test response_format as NativeOutput with dataclass."""
|
|
tool_calls = [
|
|
[{"args": {}, "id": "1", "name": "get_weather"}],
|
|
]
|
|
|
|
model = FakeToolCallingModel[WeatherDataclass](
|
|
tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_DATACLASS
|
|
)
|
|
|
|
agent = create_react_agent(
|
|
model, [get_weather], response_format=NativeOutput(WeatherDataclass)
|
|
)
|
|
response = agent.invoke(
|
|
{"messages": [HumanMessage("What's the weather?")]},
|
|
)
|
|
|
|
assert response["structured_response"] == EXPECTED_WEATHER_DATACLASS
|
|
assert len(response["messages"]) == 4
|
|
|
|
def test_typed_dict(self) -> None:
|
|
"""Test response_format as NativeOutput with TypedDict."""
|
|
tool_calls = [
|
|
[{"args": {}, "id": "1", "name": "get_weather"}],
|
|
]
|
|
|
|
model = FakeToolCallingModel[WeatherTypedDict](
|
|
tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_DICT
|
|
)
|
|
|
|
agent = create_react_agent(
|
|
model, [get_weather], response_format=NativeOutput(WeatherTypedDict)
|
|
)
|
|
response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
|
|
|
|
assert response["structured_response"] == EXPECTED_WEATHER_DICT
|
|
assert len(response["messages"]) == 4
|
|
|
|
def test_json_schema(self) -> None:
|
|
"""Test response_format as NativeOutput with JSON schema."""
|
|
tool_calls = [
|
|
[{"args": {}, "id": "1", "name": "get_weather"}],
|
|
]
|
|
|
|
model = FakeToolCallingModel[dict](
|
|
tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_DICT
|
|
)
|
|
|
|
agent = create_react_agent(
|
|
model, [get_weather], response_format=NativeOutput(weather_json_schema)
|
|
)
|
|
response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
|
|
|
|
assert response["structured_response"] == EXPECTED_WEATHER_DICT
|
|
assert len(response["messages"]) == 4
|
|
|
|
|
|
def test_union_of_types() -> None:
|
|
"""Test response_format as NativeOutput with Union (if supported)."""
|
|
tool_calls = [
|
|
[{"args": {}, "id": "1", "name": "get_weather"}],
|
|
[
|
|
{
|
|
"name": "WeatherBaseModel",
|
|
"id": "2",
|
|
"args": WEATHER_DATA,
|
|
}
|
|
],
|
|
]
|
|
|
|
model = FakeToolCallingModel[Union[WeatherBaseModel, LocationResponse]](
|
|
tool_calls=tool_calls, structured_response=EXPECTED_WEATHER_PYDANTIC
|
|
)
|
|
|
|
agent = create_react_agent(
|
|
model,
|
|
[get_weather, get_location],
|
|
response_format=ToolOutput(Union[WeatherBaseModel, LocationResponse]),
|
|
)
|
|
response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
|
|
|
|
assert response["structured_response"] == EXPECTED_WEATHER_PYDANTIC
|
|
assert len(response["messages"]) == 5
|
|
|
|
|
|
@pytest.mark.skipif(
|
|
skip_openai_integration_tests, reason="OpenAI integration tests are disabled."
|
|
)
|
|
def test_inference_to_native_output() -> None:
|
|
"""Test that native output is inferred when a model supports it."""
|
|
model = ChatOpenAI(model="gpt-5")
|
|
agent = create_react_agent(
|
|
model,
|
|
prompt="You are a helpful weather assistant. Please call the get_weather tool, then use the WeatherReport tool to generate the final response.",
|
|
tools=[get_weather],
|
|
response_format=WeatherBaseModel,
|
|
)
|
|
response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
|
|
|
|
assert isinstance(response["structured_response"], WeatherBaseModel)
|
|
assert response["structured_response"].temperature == 75.0
|
|
assert response["structured_response"].condition.lower() == "sunny"
|
|
assert len(response["messages"]) == 4
|
|
|
|
assert [m.type for m in response["messages"]] == [
|
|
"human", # "What's the weather?"
|
|
"ai", # "What's the weather?"
|
|
"tool", # "The weather is sunny and 75°F."
|
|
"ai", # structured response
|
|
]
|
|
|
|
|
|
@pytest.mark.skipif(
|
|
skip_openai_integration_tests, reason="OpenAI integration tests are disabled."
|
|
)
|
|
def test_inference_to_tool_output() -> None:
|
|
"""Test that tool output is inferred when a model supports it."""
|
|
model = ChatOpenAI(model="gpt-4")
|
|
agent = create_react_agent(
|
|
model,
|
|
prompt="You are a helpful weather assistant. Please call the get_weather tool, then use the WeatherReport tool to generate the final response.",
|
|
tools=[get_weather],
|
|
response_format=ToolOutput(WeatherBaseModel),
|
|
)
|
|
response = agent.invoke({"messages": [HumanMessage("What's the weather?")]})
|
|
|
|
assert isinstance(response["structured_response"], WeatherBaseModel)
|
|
assert response["structured_response"].temperature == 75.0
|
|
assert response["structured_response"].condition.lower() == "sunny"
|
|
assert len(response["messages"]) == 5
|
|
|
|
assert [m.type for m in response["messages"]] == [
|
|
"human", # "What's the weather?"
|
|
"ai", # "What's the weather?"
|
|
"tool", # "The weather is sunny and 75°F."
|
|
"ai", # structured response
|
|
"tool", # artificial tool message
|
|
]
|