test: add background node unit test (#198)

* test: add background node unit test

Change-Id: Ia99f5a1687464387dcb01bbee04deaa371c6e490

* test: add background node unit test

Change-Id: I9aabcf02ff04fda40c56f3ea22abe6b8f93bf9b6

* test: fix test error

Change-Id: I3997dc53a2cfaa35501a1fbda5902ee15528124e

* test: fix unit test error

Change-Id: If4c4cd10673e76a30945674c7cda198aeabf28d0

* test: fix unit test error

Change-Id: I3dd7a6179132e5497a30ada443d88de0c47af3d4
This commit is contained in:
laundry
2025-05-20 14:25:35 +08:00
committed by GitHub
parent 8bbcdbe4de
commit 55ce399969
2 changed files with 132 additions and 3 deletions

View File

@@ -44,13 +44,12 @@ def get_llm_by_type(
return llm
# Initialize LLMs for different purposes - now these will be cached
basic_llm = get_llm_by_type("basic")
# In the future, we will use reasoning_llm and vl_llm for different purposes
# reasoning_llm = get_llm_by_type("reasoning")
# vl_llm = get_llm_by_type("vision")
if __name__ == "__main__":
# Initialize LLMs for different purposes - now these will be cached
basic_llm = get_llm_by_type("basic")
print(basic_llm.invoke("Hello"))

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@@ -0,0 +1,130 @@
import json
import pytest
from unittest.mock import patch, MagicMock
# 在这里 mock 掉 get_llm_by_type避免 ValueError
with patch("src.llms.llm.get_llm_by_type", return_value=MagicMock()):
from langgraph.types import Command
from src.graph.nodes import background_investigation_node
from src.config import SearchEngine
from langchain_core.messages import HumanMessage
# Mock data
MOCK_SEARCH_RESULTS = [
{"title": "Test Title 1", "content": "Test Content 1"},
{"title": "Test Title 2", "content": "Test Content 2"},
]
@pytest.fixture
def mock_state():
return {
"messages": [HumanMessage(content="test query")],
"background_investigation_results": None,
}
@pytest.fixture
def mock_configurable():
mock = MagicMock()
mock.max_search_results = 5
return mock
@pytest.fixture
def mock_config():
# 你可以根据实际需要返回一个 MagicMock 或 dict
return MagicMock()
@pytest.fixture
def patch_config_from_runnable_config(mock_configurable):
with patch(
"src.graph.nodes.Configuration.from_runnable_config",
return_value=mock_configurable,
):
yield
@pytest.fixture
def mock_tavily_search():
with patch("src.graph.nodes.LoggedTavilySearch") as mock:
instance = mock.return_value
instance.invoke.return_value = [
{"title": "Test Title 1", "content": "Test Content 1"},
{"title": "Test Title 2", "content": "Test Content 2"},
]
yield mock
@pytest.fixture
def mock_web_search_tool():
with patch("src.graph.nodes.get_web_search_tool") as mock:
instance = mock.return_value
instance.invoke.return_value = [
{"title": "Test Title 1", "content": "Test Content 1"},
{"title": "Test Title 2", "content": "Test Content 2"},
]
yield mock
@pytest.mark.parametrize("search_engine", [SearchEngine.TAVILY, "other"])
def test_background_investigation_node_tavily(
mock_state,
mock_tavily_search,
mock_web_search_tool,
search_engine,
patch_config_from_runnable_config,
mock_config,
):
"""Test background_investigation_node with Tavily search engine"""
with patch("src.graph.nodes.SELECTED_SEARCH_ENGINE", search_engine):
result = background_investigation_node(mock_state, mock_config)
# Verify the result structure
assert isinstance(result, Command)
assert result.goto == "planner"
# Verify the update contains background_investigation_results
update = result.update
assert "background_investigation_results" in update
# Parse and verify the JSON content
results = json.loads(update["background_investigation_results"])
assert isinstance(results, list)
if search_engine == SearchEngine.TAVILY:
mock_tavily_search.return_value.invoke.assert_called_once_with(
{"query": "test query"}
)
assert len(results) == 2
assert results[0]["title"] == "Test Title 1"
assert results[0]["content"] == "Test Content 1"
else:
mock_web_search_tool.return_value.invoke.assert_called_once_with(
"test query"
)
assert len(results) == 2
def test_background_investigation_node_malformed_response(
mock_state, mock_tavily_search, patch_config_from_runnable_config, mock_config
):
"""Test background_investigation_node with malformed Tavily response"""
with patch("src.graph.nodes.SELECTED_SEARCH_ENGINE", SearchEngine.TAVILY):
# Mock a malformed response
mock_tavily_search.return_value.invoke.return_value = "invalid response"
result = background_investigation_node(mock_state, mock_config)
# Verify the result structure
assert isinstance(result, Command)
assert result.goto == "planner"
# Verify the update contains background_investigation_results
update = result.update
assert "background_investigation_results" in update
# Parse and verify the JSON content
results = json.loads(update["background_investigation_results"])
assert results is None