"""
精準測試 Context Optimizer - 智能上下文優化
"""
from boring.intelligence.context_optimizer import ContextOptimizer, ContextSection, ContextStats
class TestContextSection:
"""測試 ContextSection 數據結構"""
def test_section_initialization(self):
"""測試 section 初始化"""
section = ContextSection(
content="def test():\n pass",
source="test.py",
priority=0.8,
token_count=10,
content_hash="abc123",
section_type="code",
)
assert section.content == "def test():\n pass"
assert section.source == "test.py"
assert section.priority == 0.8
assert section.token_count == 10
assert section.section_type == "code"
def test_section_priority_range(self):
"""測試 priority 範圍"""
high_priority = ContextSection(
content="ERROR",
source="error",
priority=1.0,
token_count=5,
content_hash="err",
section_type="error",
)
low_priority = ContextSection(
content="docs",
source="readme",
priority=0.1,
token_count=100,
content_hash="doc",
section_type="doc",
)
assert high_priority.priority == 1.0
assert low_priority.priority == 0.1
def test_section_types(self):
"""測試不同類型的 section"""
types = ["code", "error", "doc", "rag"]
for section_type in types:
section = ContextSection(
content=f"{section_type} content",
source=f"{section_type}.txt",
priority=0.5,
token_count=10,
content_hash=section_type,
section_type=section_type,
)
assert section.section_type == section_type
class TestContextStats:
"""測試 ContextStats 統計數據"""
def test_stats_initialization(self):
"""測試統計數據初始化"""
stats = ContextStats(
original_tokens=1000,
optimized_tokens=600,
compression_ratio=0.6,
sections_removed=3,
duplicates_merged=2,
total_sections=10,
)
assert stats.original_tokens == 1000
assert stats.optimized_tokens == 600
assert stats.compression_ratio == 0.6
assert stats.sections_removed == 3
assert stats.duplicates_merged == 2
def test_compression_calculation(self):
"""測試壓縮率計算"""
stats = ContextStats(
original_tokens=2000,
optimized_tokens=1000,
compression_ratio=0.5,
sections_removed=5,
duplicates_merged=3,
total_sections=15,
)
# 驗證壓縮率
expected_ratio = stats.optimized_tokens / stats.original_tokens
assert abs(stats.compression_ratio - expected_ratio) < 0.01
class TestContextOptimizer:
"""測試 ContextOptimizer 核心功能"""
def test_optimizer_initialization(self):
"""測試優化器初始化"""
optimizer = ContextOptimizer(max_tokens=8000)
assert optimizer.max_tokens == 8000
assert hasattr(optimizer, "sections")
def test_optimizer_custom_max_tokens(self):
"""測試自定義 token 限制"""
optimizer = ContextOptimizer(max_tokens=4000)
assert optimizer.max_tokens == 4000
optimizer2 = ContextOptimizer(max_tokens=16000)
assert optimizer2.max_tokens == 16000
def test_add_section(self):
"""測試添加 section"""
optimizer = ContextOptimizer(max_tokens=8000)
if hasattr(optimizer, "add_section"):
optimizer.add_section(content="def hello(): pass", source="hello.py", priority=0.8)
assert len(optimizer.sections) >= 1
def test_priority_based_selection(self):
"""測試基於優先級的選擇"""
optimizer = ContextOptimizer(max_tokens=100)
if hasattr(optimizer, "add_section"):
# 添加不同優先級的 sections
optimizer.add_section("High priority", "error", priority=1.0)
optimizer.add_section("Low priority", "doc", priority=0.1)
optimizer.add_section("Medium priority", "code", priority=0.5)
if hasattr(optimizer, "optimize"):
result, stats = optimizer.optimize()
# 高優先級內容應該被保留
assert isinstance(result, str)
assert isinstance(stats, (dict, ContextStats))
def test_deduplication(self):
"""測試內容去重"""
optimizer = ContextOptimizer(max_tokens=8000)
if hasattr(optimizer, "add_section"):
# 添加重複內容
optimizer.add_section("Same content", "file1.py", priority=0.5)
optimizer.add_section("Same content", "file2.py", priority=0.5)
optimizer.add_section("Different content", "file3.py", priority=0.5)
if hasattr(optimizer, "optimize"):
result, stats = optimizer.optimize()
# 驗證去重
if isinstance(stats, ContextStats):
assert stats.duplicates_merged >= 0
def test_token_limit_enforcement(self):
"""測試 token 限制強制執行"""
optimizer = ContextOptimizer(max_tokens=50)
if hasattr(optimizer, "add_section"):
# 添加超過限制的內容
for i in range(10):
optimizer.add_section(
content="x" * 100, # 大量內容
source=f"file{i}.py",
priority=0.5,
)
if hasattr(optimizer, "optimize"):
result, stats = optimizer.optimize()
# 優化後應該在限制內
if isinstance(stats, ContextStats):
assert stats.optimized_tokens <= optimizer.max_tokens * 1.1
def test_section_type_handling(self):
"""測試不同 section 類型的處理"""
optimizer = ContextOptimizer(max_tokens=8000)
if hasattr(optimizer, "add_section"):
section_types = ["code", "error", "doc", "rag"]
for section_type in section_types:
optimizer.add_section(
content=f"{section_type} content",
source=f"{section_type}.txt",
priority=0.7,
section_type=section_type,
)
def test_importance_keywords_detection(self):
"""測試重要關鍵字檢測"""
optimizer = ContextOptimizer(max_tokens=8000)
# 驗證重要關鍵字定義
assert hasattr(optimizer, "IMPORTANCE_KEYWORDS")
assert "error" in optimizer.IMPORTANCE_KEYWORDS
assert "critical" in optimizer.IMPORTANCE_KEYWORDS
def test_semantic_deduplication_option(self):
"""測試語義去重選項"""
optimizer_with_semantic = ContextOptimizer(max_tokens=8000, enable_semantic_dedup=True)
optimizer_without_semantic = ContextOptimizer(max_tokens=8000, enable_semantic_dedup=False)
assert optimizer_with_semantic.enable_semantic_dedup is True
assert optimizer_without_semantic.enable_semantic_dedup is False
def test_empty_optimization(self):
"""測試空內容優化"""
optimizer = ContextOptimizer(max_tokens=8000)
if hasattr(optimizer, "optimize"):
result, stats = optimizer.optimize()
# 空內容應該返回空結果
assert isinstance(result, str)
assert len(result) == 0 or result == ""
def test_single_section_optimization(self):
"""測試單個 section 優化"""
optimizer = ContextOptimizer(max_tokens=1000)
if hasattr(optimizer, "add_section") and hasattr(optimizer, "optimize"):
optimizer.add_section(content="print('Hello, World!')", source="main.py", priority=0.8)
result, stats = optimizer.optimize()
assert "Hello" in result or isinstance(result, str)
def test_compression_ratio_calculation(self):
"""測試壓縮率計算"""
optimizer = ContextOptimizer(max_tokens=100)
if hasattr(optimizer, "add_section") and hasattr(optimizer, "optimize"):
# 添加大量內容
for i in range(5):
optimizer.add_section(content="x" * 200, source=f"file{i}.py", priority=0.5)
result, stats = optimizer.optimize()
if isinstance(stats, ContextStats):
assert 0 <= stats.compression_ratio <= 1.0
