431 lines
14 KiB
Python
431 lines
14 KiB
Python
"""配置管理模块的单元测试。"""
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import os
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import json
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import pytest
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from pathlib import Path
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from unittest.mock import patch
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from src.config import (
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LLMConfig,
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PerformanceConfig,
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OutputConfig,
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Config,
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get_config,
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set_config,
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load_config_from_env,
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load_config_from_file
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)
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class TestLLMConfig:
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"""测试 LLM 配置。"""
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def test_default_config(self):
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"""测试默认配置。"""
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config = LLMConfig(api_key="test_key")
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assert config.provider == "openai"
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assert config.api_key == "test_key"
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assert config.base_url == "https://api.openai.com/v1"
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assert config.model == "gpt-4"
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assert config.timeout == 120
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assert config.max_retries == 3
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assert config.temperature == 0.7
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assert config.max_tokens is None
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def test_custom_config(self):
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"""测试自定义配置。"""
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config = LLMConfig(
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provider="gemini",
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api_key="gemini_key",
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base_url="https://gemini.api",
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model="gemini-pro",
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timeout=60,
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max_retries=5,
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temperature=0.5,
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max_tokens=1000
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)
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assert config.provider == "gemini"
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assert config.api_key == "gemini_key"
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assert config.base_url == "https://gemini.api"
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assert config.model == "gemini-pro"
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assert config.timeout == 60
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assert config.max_retries == 5
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assert config.temperature == 0.5
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assert config.max_tokens == 1000
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def test_empty_api_key(self):
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"""测试空 API key。"""
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with pytest.raises(ValueError, match="API key 不能为空"):
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LLMConfig(api_key="")
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def test_invalid_provider(self):
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"""测试无效的 provider。"""
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with pytest.raises(ValueError, match="不支持的 LLM provider"):
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LLMConfig(api_key="test", provider="invalid")
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def test_invalid_timeout(self):
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"""测试无效的 timeout。"""
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with pytest.raises(ValueError, match="timeout 必须大于 0"):
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LLMConfig(api_key="test", timeout=0)
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def test_invalid_max_retries(self):
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"""测试无效的 max_retries。"""
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with pytest.raises(ValueError, match="max_retries 不能为负数"):
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LLMConfig(api_key="test", max_retries=-1)
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class TestPerformanceConfig:
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"""测试性能配置。"""
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def test_default_config(self):
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"""测试默认配置。"""
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config = PerformanceConfig()
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assert config.agent_max_rounds == 20
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assert config.agent_timeout == 300
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assert config.tool_max_query_rows == 10000
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assert config.tool_execution_timeout == 60
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assert config.data_max_rows == 1000000
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assert config.data_sample_threshold == 1000000
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assert config.max_concurrent_tasks == 1
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def test_custom_config(self):
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"""测试自定义配置。"""
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config = PerformanceConfig(
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agent_max_rounds=10,
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agent_timeout=600,
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tool_max_query_rows=5000,
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tool_execution_timeout=30,
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data_max_rows=500000,
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data_sample_threshold=500000,
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max_concurrent_tasks=2
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)
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assert config.agent_max_rounds == 10
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assert config.agent_timeout == 600
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assert config.tool_max_query_rows == 5000
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assert config.tool_execution_timeout == 30
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assert config.data_max_rows == 500000
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assert config.data_sample_threshold == 500000
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assert config.max_concurrent_tasks == 2
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def test_invalid_agent_max_rounds(self):
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"""测试无效的 agent_max_rounds。"""
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with pytest.raises(ValueError, match="agent_max_rounds 必须大于 0"):
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PerformanceConfig(agent_max_rounds=0)
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def test_invalid_tool_max_query_rows(self):
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"""测试无效的 tool_max_query_rows。"""
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with pytest.raises(ValueError, match="tool_max_query_rows 必须大于 0"):
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PerformanceConfig(tool_max_query_rows=-1)
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class TestOutputConfig:
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"""测试输出配置。"""
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def test_default_config(self):
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"""测试默认配置。"""
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config = OutputConfig()
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assert config.output_dir == "output"
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assert config.log_dir == "output"
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assert config.chart_dir == str(Path("output") / "charts")
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assert config.report_filename == "analysis_report.md"
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assert config.log_level == "INFO"
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assert config.log_to_file is True
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assert config.log_to_console is True
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def test_custom_config(self):
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"""测试自定义配置。"""
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config = OutputConfig(
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output_dir="results",
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log_dir="logs",
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chart_dir="charts",
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report_filename="report.md",
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log_level="DEBUG",
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log_to_file=False,
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log_to_console=True
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)
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assert config.output_dir == "results"
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assert config.log_dir == "logs"
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assert config.chart_dir == "charts"
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assert config.report_filename == "report.md"
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assert config.log_level == "DEBUG"
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assert config.log_to_file is False
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assert config.log_to_console is True
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def test_invalid_log_level(self):
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"""测试无效的 log_level。"""
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with pytest.raises(ValueError, match="不支持的 log_level"):
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OutputConfig(log_level="INVALID")
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def test_get_paths(self):
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"""测试路径获取方法。"""
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config = OutputConfig(
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output_dir="results",
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log_dir="logs",
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chart_dir="charts"
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)
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assert config.get_output_path() == Path("results")
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assert config.get_log_path() == Path("logs")
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assert config.get_chart_path() == Path("charts")
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assert config.get_report_path() == Path("results/analysis_report.md")
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class TestConfig:
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"""测试系统配置。"""
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def test_default_config(self):
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"""测试默认配置。"""
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config = Config(
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llm=LLMConfig(api_key="test_key")
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)
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assert config.llm.api_key == "test_key"
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assert config.performance.agent_max_rounds == 20
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assert config.output.output_dir == "output"
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assert config.code_repo_enable_reuse is True
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def test_from_env(self):
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"""测试从环境变量加载配置。"""
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env_vars = {
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"LLM_PROVIDER": "openai",
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"OPENAI_API_KEY": "env_test_key",
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"OPENAI_BASE_URL": "https://test.api",
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"OPENAI_MODEL": "gpt-3.5-turbo",
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"AGENT_MAX_ROUNDS": "15",
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"AGENT_OUTPUT_DIR": "test_output",
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"TOOL_MAX_QUERY_ROWS": "5000",
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"CODE_REPO_ENABLE_REUSE": "false"
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}
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with patch.dict(os.environ, env_vars, clear=True):
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config = Config.from_env()
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assert config.llm.provider == "openai"
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assert config.llm.api_key == "env_test_key"
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assert config.llm.base_url == "https://test.api"
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assert config.llm.model == "gpt-3.5-turbo"
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assert config.performance.agent_max_rounds == 15
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assert config.performance.tool_max_query_rows == 5000
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assert config.output.output_dir == "test_output"
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assert config.code_repo_enable_reuse is False
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def test_from_env_gemini(self):
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"""测试从环境变量加载 Gemini 配置。"""
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env_vars = {
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"LLM_PROVIDER": "gemini",
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"GEMINI_API_KEY": "gemini_key",
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"GEMINI_BASE_URL": "https://gemini.api",
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"GEMINI_MODEL": "gemini-pro"
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}
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with patch.dict(os.environ, env_vars, clear=True):
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config = Config.from_env()
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assert config.llm.provider == "gemini"
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assert config.llm.api_key == "gemini_key"
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assert config.llm.base_url == "https://gemini.api"
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assert config.llm.model == "gemini-pro"
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def test_from_dict(self):
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"""测试从字典加载配置。"""
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config_dict = {
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"llm": {
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"provider": "openai",
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"api_key": "dict_test_key",
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"base_url": "https://dict.api",
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"model": "gpt-4",
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"timeout": 90,
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"max_retries": 2,
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"temperature": 0.5,
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"max_tokens": 2000
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},
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"performance": {
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"agent_max_rounds": 25,
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"tool_max_query_rows": 8000
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},
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"output": {
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"output_dir": "dict_output",
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"log_level": "DEBUG"
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},
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"code_repo_enable_reuse": False
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}
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config = Config.from_dict(config_dict)
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assert config.llm.api_key == "dict_test_key"
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assert config.llm.base_url == "https://dict.api"
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assert config.llm.timeout == 90
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assert config.llm.max_retries == 2
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assert config.llm.temperature == 0.5
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assert config.llm.max_tokens == 2000
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assert config.performance.agent_max_rounds == 25
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assert config.performance.tool_max_query_rows == 8000
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assert config.output.output_dir == "dict_output"
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assert config.output.log_level == "DEBUG"
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assert config.code_repo_enable_reuse is False
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def test_from_file(self, tmp_path):
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"""测试从文件加载配置。"""
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config_file = tmp_path / "test_config.json"
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config_dict = {
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"llm": {
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"provider": "openai",
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"api_key": "file_test_key",
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"model": "gpt-4"
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},
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"performance": {
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"agent_max_rounds": 30
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}
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}
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with open(config_file, 'w') as f:
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json.dump(config_dict, f)
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config = Config.from_file(str(config_file))
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assert config.llm.api_key == "file_test_key"
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assert config.llm.model == "gpt-4"
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assert config.performance.agent_max_rounds == 30
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def test_from_file_not_found(self):
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"""测试加载不存在的配置文件。"""
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with pytest.raises(FileNotFoundError):
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Config.from_file("nonexistent.json")
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def test_to_dict(self):
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"""测试转换为字典。"""
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config = Config(
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llm=LLMConfig(
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api_key="test_key",
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model="gpt-4"
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),
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performance=PerformanceConfig(
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agent_max_rounds=15
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),
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output=OutputConfig(
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output_dir="test_output"
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)
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)
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config_dict = config.to_dict()
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assert config_dict["llm"]["api_key"] == "***" # API key 应该被隐藏
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assert config_dict["llm"]["model"] == "gpt-4"
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assert config_dict["performance"]["agent_max_rounds"] == 15
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assert config_dict["output"]["output_dir"] == "test_output"
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def test_save_to_file(self, tmp_path):
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"""测试保存配置到文件。"""
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config_file = tmp_path / "saved_config.json"
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config = Config(
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llm=LLMConfig(api_key="test_key"),
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performance=PerformanceConfig(agent_max_rounds=15)
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)
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config.save_to_file(str(config_file))
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assert config_file.exists()
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with open(config_file, 'r') as f:
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saved_dict = json.load(f)
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assert saved_dict["llm"]["api_key"] == "***"
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assert saved_dict["performance"]["agent_max_rounds"] == 15
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def test_validate_success(self):
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"""测试配置验证成功。"""
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config = Config(
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llm=LLMConfig(api_key="test_key")
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)
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assert config.validate() is True
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def test_validate_missing_api_key(self):
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"""测试配置验证失败（缺少 API key）。"""
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config = Config(
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llm=LLMConfig(api_key="test_key")
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)
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config.llm.api_key = "" # 手动清空
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assert config.validate() is False
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class TestGlobalConfig:
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"""测试全局配置管理。"""
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def test_get_config(self):
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"""测试获取全局配置。"""
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# 重置全局配置
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set_config(None)
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# 模拟环境变量
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env_vars = {
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"OPENAI_API_KEY": "global_test_key"
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}
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with patch.dict(os.environ, env_vars, clear=True):
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config = get_config()
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assert config is not None
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assert config.llm.api_key == "global_test_key"
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def test_set_config(self):
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"""测试设置全局配置。"""
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custom_config = Config(
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llm=LLMConfig(api_key="custom_key")
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)
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set_config(custom_config)
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config = get_config()
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assert config.llm.api_key == "custom_key"
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def test_load_config_from_env(self):
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"""测试从环境变量加载全局配置。"""
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env_vars = {
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"OPENAI_API_KEY": "env_global_key",
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"AGENT_MAX_ROUNDS": "25"
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}
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with patch.dict(os.environ, env_vars, clear=True):
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config = load_config_from_env()
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assert config.llm.api_key == "env_global_key"
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assert config.performance.agent_max_rounds == 25
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# 验证全局配置已更新
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global_config = get_config()
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assert global_config.llm.api_key == "env_global_key"
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def test_load_config_from_file(self, tmp_path):
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"""测试从文件加载全局配置。"""
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config_file = tmp_path / "global_config.json"
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config_dict = {
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"llm": {
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"provider": "openai",
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"api_key": "file_global_key",
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"model": "gpt-4"
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}
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}
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with open(config_file, 'w') as f:
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json.dump(config_dict, f)
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config = load_config_from_file(str(config_file))
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assert config.llm.api_key == "file_global_key"
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# 验证全局配置已更新
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global_config = get_config()
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assert global_config.llm.api_key == "file_global_key"
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