Clean up debug test files
🤖 Generated with Claude Code
Co-Authored-By: Claude <noreply@anthropic.com>
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佐藤優一
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#!/usr/bin/env python
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"""Test the actual market analyst test."""
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import pytest
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from tests.conftest import MockResult
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# Import test fixtures
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pytest_plugins = ["tests.conftest"]
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def test_debug():
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from tests.conftest import mock_llm, mock_toolkit, sample_agent_state
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from tradingagents.agents.analysts.market_analyst import create_market_analyst
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# Create fixtures
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llm = mock_llm()
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toolkit = mock_toolkit()
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state = sample_agent_state()
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# Setup like the test does
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toolkit.config = {"online_tools": False}
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mock_result = MockResult(content="Market analysis complete", tool_calls=[])
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llm._chain_mock.invoke.return_value = mock_result
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print(f"llm: {llm}")
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print(f"llm._chain_mock: {llm._chain_mock}")
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print(f"llm._chain_mock.invoke: {llm._chain_mock.invoke}")
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print(f"llm._chain_mock.invoke.return_value: {llm._chain_mock.invoke.return_value}")
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analyst_node = create_market_analyst(llm, toolkit)
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# Execute
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result = analyst_node(state)
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print(f"Result messages: {result['messages']}")
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print(f"Expected: {[mock_result]}")
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print(f"Are they equal? {result['messages'] == [mock_result]}")
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if __name__ == "__main__":
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test_debug()
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#!/usr/bin/env python
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"""Test the actual market analyst test."""
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from unittest.mock import Mock
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from tests.conftest import MockResult
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from tradingagents.agents.analysts.market_analyst import create_market_analyst
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# Recreate the mock_llm fixture logic
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mock = Mock()
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mock.model_name = "test-model"
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# Create a default mock result with proper tool_calls
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default_result = MockResult()
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# Create a chain mock (what prompt | llm.bind_tools(tools) returns)
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chain_mock = Mock()
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chain_mock.invoke = Mock(return_value=default_result)
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# Store the chain_mock on the mock_llm so tests can configure it
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mock._chain_mock = chain_mock
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# Mock the bind_tools to return a mock that handles piping
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bound_tools_mock = Mock()
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# Handle the pipe operation (prompt | llm.bind_tools(tools))
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def handle_pipe(self, other):
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# Return the chain_mock that tests can configure
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print(f"handle_pipe called with self={self}, other={other}")
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print(f"Returning chain_mock: {chain_mock}")
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return chain_mock
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bound_tools_mock.__ror__ = handle_pipe # Right-side or (other | bound_tools_mock)
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mock.bind_tools = Mock(return_value=bound_tools_mock)
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# Create toolkit
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toolkit = Mock()
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toolkit.config = {"online_tools": False}
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# Set up toolkit methods with proper name attributes
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toolkit.get_YFin_data = Mock()
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toolkit.get_YFin_data.name = "get_YFin_data"
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toolkit.get_stockstats_indicators_report = Mock()
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toolkit.get_stockstats_indicators_report.name = "get_stockstats_indicators_report"
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# Setup like the test does
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mock_result = MockResult(content="Market analysis complete", tool_calls=[])
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mock._chain_mock.invoke.return_value = mock_result
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# Create state
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state = {
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"company_of_interest": "AAPL",
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"trade_date": "2024-05-10",
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"messages": [],
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}
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print(f"mock: {mock}")
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print(f"mock._chain_mock: {mock._chain_mock}")
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print(f"mock._chain_mock.invoke: {mock._chain_mock.invoke}")
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print(f"mock._chain_mock.invoke.return_value: {mock._chain_mock.invoke.return_value}")
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analyst_node = create_market_analyst(mock, toolkit)
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# Execute
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result = analyst_node(state)
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print(f"Result messages: {result['messages']}")
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print(f"Expected: {[mock_result]}")
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print(f"First message: {result['messages'][0]}")
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print(f"Are they equal? {result['messages'] == [mock_result]}")
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print(f"First item equal? {result['messages'][0] == mock_result}")
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print(f"Are they the same object? {result['messages'][0] is mock_result}")
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#!/usr/bin/env python
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"""Debug script to understand the mock chain behavior."""
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from unittest.mock import Mock
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from tests.conftest import MockResult
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# Recreate the mock_llm setup
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mock = Mock()
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mock.model_name = "test-model"
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# Create a default mock result with proper tool_calls
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default_result = MockResult()
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# Mock the bind_tools to return a mock that handles piping
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bound_mock = Mock()
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bound_mock.invoke = Mock(return_value=default_result)
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# Handle the pipe operation (prompt | llm.bind_tools(tools))
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def handle_pipe(self, other):
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# Return a mock that will use the bound_mock's invoke method
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pipe_result = Mock()
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pipe_result.invoke = bound_mock.invoke
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return pipe_result
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bound_mock.__ror__ = handle_pipe
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mock.bind_tools.return_value = bound_mock
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# Now simulate what a test does
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mock_result = MockResult(content="Test content", tool_calls=[])
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mock.bind_tools.return_value.invoke.return_value = mock_result
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# Simulate what the code does
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prompt = Mock() # Simulate a prompt
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tools = [] # Simulate tools
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chain = prompt | mock.bind_tools(tools)
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# Invoke the chain
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result = chain.invoke([])
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print(f"Expected: {mock_result}")
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print(f"Got: {result}")
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print(f"Are they the same? {result is mock_result}")
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print(f"bound_mock.invoke: {bound_mock.invoke}")
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print(f"chain.invoke: {chain.invoke}")
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print(f"Are invoke methods the same? {chain.invoke is bound_mock.invoke}")
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#!/usr/bin/env python
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"""Debug script to understand the mock chain behavior."""
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from unittest.mock import Mock, patch
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from tests.conftest import MockResult
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# Let's create a test to see what happens
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def test_mock():
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mock_llm = Mock()
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mock_llm.model_name = "test-model"
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# Create a mock result
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test_result = MockResult(content="Test content", tool_calls=[])
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# Create the chain mock (what prompt | llm.bind_tools(tools) returns)
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chain_mock = Mock()
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chain_mock.invoke = Mock(return_value=test_result)
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# Make bind_tools return a mock that when piped, returns our chain_mock
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bound_tools_mock = Mock()
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# This is the key: when something is piped to bound_tools_mock,
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# it should return our chain_mock
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def pipe_handler(self, other):
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return chain_mock
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bound_tools_mock.__ror__ = pipe_handler
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mock_llm.bind_tools = Mock(return_value=bound_tools_mock)
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# Now simulate what the production code does
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prompt = Mock()
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tools = []
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# This is what happens in the actual code
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chain = prompt | mock_llm.bind_tools(tools)
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result = chain.invoke([])
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print(f"Expected: {test_result}")
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print(f"Got: {result}")
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print(f"Are they the same? {result is test_result}")
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test_mock()
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#!/usr/bin/env python
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"""Debug script to test our mock setup."""
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from unittest.mock import Mock
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from tests.conftest import MockResult
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# Recreate our fixture setup
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mock = Mock()
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mock.model_name = "test-model"
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# Create a default mock result with proper tool_calls
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default_result = MockResult()
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# Create a chain mock (what prompt | llm.bind_tools(tools) returns)
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chain_mock = Mock()
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chain_mock.invoke = Mock(return_value=default_result)
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# Store the chain_mock on the mock_llm so tests can configure it
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mock._chain_mock = chain_mock
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# Mock the bind_tools to return a mock that handles piping
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bound_tools_mock = Mock()
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# Handle the pipe operation (prompt | llm.bind_tools(tools))
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def handle_pipe(self, other):
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# Return the chain_mock that tests can configure
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return chain_mock
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bound_tools_mock.__ror__ = handle_pipe # Right-side or (other | bound_tools_mock)
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mock.bind_tools = Mock(return_value=bound_tools_mock)
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# Now simulate what a test does
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mock_result = MockResult(content="Test content", tool_calls=[])
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mock._chain_mock.invoke.return_value = mock_result
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# Simulate what the production code does
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prompt = Mock()
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tools = []
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# This is what happens in the actual code:
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# chain = prompt | llm.bind_tools(tools)
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print(f"bind_tools is called with tools: {tools}")
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bound_result = mock.bind_tools(tools)
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print(f"bind_tools returns: {bound_result}")
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print(f"Has __ror__? {hasattr(bound_result, '__ror__')}")
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chain = prompt | bound_result
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print(f"chain is: {chain}")
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print(f"chain_mock is: {chain_mock}")
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print(f"Are they the same? {chain is chain_mock}")
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result = chain.invoke([])
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print(f"Result: {result}")
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print(f"Expected: {mock_result}")
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print(f"Are they the same? {result is mock_result}")
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