Spaces:
Running
Running
File size: 21,170 Bytes
7a05808 e4a41fa dd832fe e4a41fa 7a05808 e4a41fa 7a05808 dd832fe 7a05808 dd832fe 7a05808 dd832fe 7a05808 dd832fe 7a05808 dd832fe 7a05808 dd832fe 313fe01 dd832fe 313fe01 dd832fe 313fe01 dd832fe 313fe01 dd832fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 | """Tests for HeadroomClient cache optimizer integration."""
import os
import tempfile
from dataclasses import dataclass
from unittest.mock import MagicMock, patch
import pytest
from headroom import (
AnthropicCacheOptimizer,
HeadroomClient,
)
from headroom.cache.base import CacheMetrics, CacheResult
@pytest.fixture
def temp_db():
"""Create a temporary database file."""
fd, path = tempfile.mkstemp(suffix=".db")
os.close(fd)
yield f"sqlite:///{path}"
if os.path.exists(path):
os.unlink(path)
class MockTokenCounter:
"""Mock token counter for testing."""
def count_text(self, text: str) -> int:
"""Count tokens in text (required by Tokenizer interface)."""
return len(text) // 4
def count_tokens(self, text: str) -> int:
"""Alias for count_text."""
return self.count_text(text)
def count_message(self, message: dict) -> int:
"""Count tokens in a single message."""
content = message.get("content", "")
if isinstance(content, str):
return len(content) // 4
elif isinstance(content, list):
total = 0
for block in content:
if isinstance(block, dict):
total += len(block.get("text", "")) // 4
return total
return 0
def count_messages(self, messages: list) -> int:
"""Count tokens in messages."""
return sum(self.count_message(msg) for msg in messages)
class MockAnthropicProvider:
"""Mock Anthropic provider for testing."""
name = "anthropic"
def get_token_counter(self, model: str):
return MockTokenCounter()
def get_context_limit(self, model: str) -> int:
return 200000
class MockOpenAIProvider:
"""Mock OpenAI provider for testing."""
name = "openai"
def get_token_counter(self, model: str):
return MockTokenCounter()
def get_context_limit(self, model: str) -> int:
return 128000
# Mock response classes for testing (avoid MagicMock in sqlite)
@dataclass
class MockTextBlock:
"""Mock text block for Anthropic response."""
type: str = "text"
text: str = "Hello!"
@dataclass
class MockUsage:
"""Mock usage for Anthropic response."""
input_tokens: int = 100
output_tokens: int = 20
@dataclass
class MockAnthropicResponse:
"""Mock Anthropic API response."""
content: list = None
usage: MockUsage = None
model: str = "claude-sonnet-4-20250514"
id: str = "msg_123"
stop_reason: str = "end_turn"
def __post_init__(self):
if self.content is None:
self.content = [MockTextBlock()]
if self.usage is None:
self.usage = MockUsage()
class TestHeadroomClientCacheIntegration:
"""Test HeadroomClient cache optimizer integration."""
def test_auto_detect_anthropic_optimizer(self, temp_db):
"""Test that Anthropic optimizer is auto-detected."""
mock_client = MagicMock()
provider = MockAnthropicProvider()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
enable_cache_optimizer=True,
)
assert client._cache_optimizer is not None
assert client._cache_optimizer.name == "anthropic-cache-optimizer"
def test_auto_detect_openai_optimizer(self, temp_db):
"""Test that OpenAI optimizer is auto-detected."""
mock_client = MagicMock()
provider = MockOpenAIProvider()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
enable_cache_optimizer=True,
)
assert client._cache_optimizer is not None
assert client._cache_optimizer.name == "openai-prefix-stabilizer"
def test_custom_optimizer(self, temp_db):
"""Test using a custom optimizer."""
mock_client = MagicMock()
provider = MockAnthropicProvider()
custom_optimizer = AnthropicCacheOptimizer()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
cache_optimizer=custom_optimizer,
)
assert client._cache_optimizer is custom_optimizer
def test_disable_cache_optimizer(self, temp_db):
"""Test disabling cache optimizer."""
mock_client = MagicMock()
provider = MockAnthropicProvider()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
enable_cache_optimizer=False,
)
assert client._cache_optimizer is None
def test_semantic_cache_layer_creation(self, temp_db):
"""Test semantic cache layer is created when enabled."""
mock_client = MagicMock()
provider = MockAnthropicProvider()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
enable_cache_optimizer=True,
enable_semantic_cache=True,
)
assert client._semantic_cache_layer is not None
assert client._cache_optimizer is not None
def test_extract_query_from_string_content(self, temp_db):
"""Test query extraction from string content."""
mock_client = MagicMock()
provider = MockAnthropicProvider()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
)
messages = [
{"role": "system", "content": "You are helpful."},
{"role": "user", "content": "What is 2+2?"},
]
query = client._extract_query(messages)
assert query == "What is 2+2?"
def test_extract_query_from_content_blocks(self, temp_db):
"""Test query extraction from content block format."""
mock_client = MagicMock()
provider = MockAnthropicProvider()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
)
messages = [
{"role": "system", "content": "You are helpful."},
{
"role": "user",
"content": [{"type": "text", "text": "What is 2+2?"}],
},
]
query = client._extract_query(messages)
assert query == "What is 2+2?"
def test_extract_query_last_user_message(self, temp_db):
"""Test that query extraction uses last user message."""
mock_client = MagicMock()
provider = MockAnthropicProvider()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
)
messages = [
{"role": "user", "content": "First question"},
{"role": "assistant", "content": "First answer"},
{"role": "user", "content": "Second question"},
]
query = client._extract_query(messages)
assert query == "Second question"
def test_config_propagation(self, temp_db):
"""Test that config is properly propagated."""
mock_client = MagicMock()
provider = MockAnthropicProvider()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
enable_cache_optimizer=True,
enable_semantic_cache=True,
)
assert client._config.cache_optimizer.enabled is True
assert client._config.cache_optimizer.enable_semantic_cache is True
class TestCacheOptimizerInvocation:
"""Test that cache optimizer is actually INVOKED during chat completion.
These tests catch bugs where the optimizer is assigned but never called
in the production code path.
"""
@patch("headroom.storage.sqlite.SQLiteStorage.save")
def test_optimizer_optimize_is_called_during_chat(self, mock_save, temp_db):
"""CRITICAL: Verify optimizer.optimize() is called during chat completion.
This test catches the gap where tests verify assignment but not invocation.
Note: Cache optimizer is only invoked in OPTIMIZE mode, not AUDIT mode (the default).
"""
from headroom import HeadroomMode
# Use module-level mock classes to avoid sqlite issues with MagicMock
mock_client = MagicMock()
mock_client.messages.create.return_value = MockAnthropicResponse()
provider = MockAnthropicProvider()
# Create a spy optimizer to track calls
real_optimizer = AnthropicCacheOptimizer()
spy_optimize = MagicMock(
return_value=CacheResult(
messages=[{"role": "user", "content": "test"}],
metrics=CacheMetrics(
cacheable_tokens=100,
breakpoints_inserted=1,
estimated_cache_hit=False,
estimated_savings_percent=0.0,
),
transforms_applied=["test_transform"],
)
)
real_optimizer.optimize = spy_optimize
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
cache_optimizer=real_optimizer,
)
# Make a chat completion call in OPTIMIZE mode (cache optimizer only runs in OPTIMIZE mode)
messages = [
{"role": "user", "content": "Hello, how are you?"},
]
client.chat.completions.create(
model="claude-sonnet-4-20250514",
messages=messages,
max_tokens=100,
headroom_mode=HeadroomMode.OPTIMIZE,
)
# CRITICAL: Verify optimizer.optimize() was actually called
assert spy_optimize.called, (
"Cache optimizer.optimize() should be called during chat completion. "
"If this fails, the optimizer is assigned but never invoked."
)
# Verify it was called with the right arguments
call_args = spy_optimize.call_args
assert call_args is not None
optimized_messages, context = call_args[0]
assert len(optimized_messages) >= 1, "Should pass messages to optimizer"
@patch("headroom.storage.sqlite.SQLiteStorage.save")
def test_optimizer_transforms_applied_in_response(self, mock_save, temp_db):
"""Verify optimizer transforms are reported in the response metadata."""
from headroom import HeadroomMode
# Use module-level mock classes to avoid sqlite issues with MagicMock
mock_client = MagicMock()
mock_client.messages.create.return_value = MockAnthropicResponse()
provider = MockAnthropicProvider()
# Create optimizer that applies a transform
real_optimizer = AnthropicCacheOptimizer()
real_optimizer.optimize = MagicMock(
return_value=CacheResult(
messages=[{"role": "user", "content": "test"}],
metrics=CacheMetrics(
cacheable_tokens=500,
breakpoints_inserted=2,
estimated_cache_hit=True,
estimated_savings_percent=0.5,
),
transforms_applied=["add_cache_control"],
)
)
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
cache_optimizer=real_optimizer,
)
messages = [
{"role": "user", "content": "x" * 1000}, # Large message
]
# Use OPTIMIZE mode so cache optimizer is invoked
result = client.chat.completions.create(
model="claude-sonnet-4-20250514",
messages=messages,
max_tokens=100,
headroom_mode=HeadroomMode.OPTIMIZE,
)
# Verify the response includes cache optimizer info
assert hasattr(result, "headroom"), "Response should have headroom metadata"
headroom_meta = result.headroom
# Check that cache optimizer was reported
assert headroom_meta.cache_optimizer_used is not None or any(
"cache_optimizer" in t for t in (headroom_meta.transforms_applied or [])
), "Cache optimizer usage should be reported in metadata"
@patch("headroom.storage.sqlite.SQLiteStorage.save")
def test_optimizer_not_called_in_audit_mode(self, mock_save, temp_db):
"""Verify optimizer is NOT called in AUDIT mode (observe only)."""
from headroom import HeadroomMode
# Use module-level mock classes to avoid sqlite issues with MagicMock
mock_client = MagicMock()
mock_client.messages.create.return_value = MockAnthropicResponse()
provider = MockAnthropicProvider()
spy_optimize = MagicMock(
return_value=CacheResult(
messages=[{"role": "user", "content": "test"}],
metrics=CacheMetrics(),
)
)
real_optimizer = AnthropicCacheOptimizer()
real_optimizer.optimize = spy_optimize
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
cache_optimizer=real_optimizer,
)
messages = [{"role": "user", "content": "Hello"}]
# Make call in AUDIT mode (observe only, no modifications)
client.chat.completions.create(
model="claude-sonnet-4-20250514",
messages=messages,
max_tokens=100,
headroom_mode=HeadroomMode.AUDIT,
)
# Optimizer should NOT be called in AUDIT mode
assert not spy_optimize.called, "Cache optimizer should NOT be called in AUDIT mode"
class TestSemanticCacheIntegration:
"""Test semantic cache integration with HeadroomClient.
These tests verify the full production code path for semantic caching,
including that cache hits actually return cached responses without calling
the underlying API.
"""
@patch("headroom.storage.sqlite.SQLiteStorage.save")
def test_semantic_cache_hit_returns_cached_response_without_api_call(self, mock_save, temp_db):
"""CRITICAL: Verify semantic cache hit returns cached response without API call.
This test catches the gap where semantic cache is enabled but cached
responses are never actually returned (API is always called).
"""
from headroom import HeadroomMode
# Mock OpenAI-style response (chat.completions.create uses OpenAI API style)
mock_client = MagicMock()
mock_openai_response = MagicMock()
mock_openai_response.choices = [MagicMock(message=MagicMock(content="4"))]
mock_openai_response.usage = MagicMock(
prompt_tokens=10, completion_tokens=5, total_tokens=15
)
mock_openai_response.model = "claude-sonnet-4-20250514"
mock_openai_response.id = "chatcmpl-123"
mock_client.chat.completions.create.return_value = mock_openai_response
provider = MockAnthropicProvider()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
enable_cache_optimizer=True,
enable_semantic_cache=True,
)
messages = [
{"role": "system", "content": "You are helpful."},
{"role": "user", "content": "What is 2+2?"},
]
# First call - should call API and potentially cache
client.chat.completions.create(
model="claude-sonnet-4-20250514",
messages=messages,
max_tokens=100,
headroom_mode=HeadroomMode.OPTIMIZE,
)
first_call_count = mock_client.chat.completions.create.call_count
assert first_call_count == 1, "First call should hit API"
# Manually store response in semantic cache for test
if client._semantic_cache_layer is not None:
from headroom.cache import OptimizationContext
context = OptimizationContext(
provider="anthropic",
model="claude-sonnet-4-20250514",
query="What is 2+2?",
)
client._semantic_cache_layer.store_response(
messages,
{"text": "4", "role": "assistant"},
context,
)
# Second call with same messages - should hit cache, NOT call API
client.chat.completions.create(
model="claude-sonnet-4-20250514",
messages=messages,
max_tokens=100,
headroom_mode=HeadroomMode.OPTIMIZE,
)
second_call_count = mock_client.chat.completions.create.call_count
# If semantic cache is working, API should NOT be called again
assert second_call_count == 1, (
f"Semantic cache hit should NOT call API. "
f"Expected 1 API call, got {second_call_count}. "
"If this fails, cached responses are not being returned."
)
class TestSessionStatsTracking:
"""Test session statistics tracking in HeadroomClient.
These tests verify that session stats are actually updated during
chat completion calls.
"""
@patch("headroom.storage.sqlite.SQLiteStorage.save")
def test_session_stats_incremented_after_request(self, mock_save, temp_db):
"""CRITICAL: Verify session stats are incremented after requests."""
from headroom import HeadroomMode
mock_client = MagicMock()
mock_client.messages.create.return_value = MockAnthropicResponse()
provider = MockAnthropicProvider()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
)
# Get initial stats
initial_stats = client.get_stats()
initial_requests = initial_stats["session"]["requests_total"]
# Make a request in AUDIT mode
messages = [{"role": "user", "content": "Hello"}]
client.chat.completions.create(
model="claude-sonnet-4-20250514",
messages=messages,
max_tokens=100,
headroom_mode=HeadroomMode.AUDIT,
)
# Verify stats were updated
after_stats = client.get_stats()
after_requests = after_stats["session"]["requests_total"]
assert after_requests == initial_requests + 1, (
f"requests_total should increment. Before: {initial_requests}, After: {after_requests}"
)
assert after_stats["session"]["requests_audit"] >= 1, (
"requests_audit should be at least 1 after AUDIT mode request"
)
@patch("headroom.storage.sqlite.SQLiteStorage.save")
def test_session_stats_tracks_optimize_mode(self, mock_save, temp_db):
"""Verify session stats track OPTIMIZE mode requests separately."""
from headroom import HeadroomMode
mock_client = MagicMock()
mock_client.messages.create.return_value = MockAnthropicResponse()
provider = MockAnthropicProvider()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
)
messages = [{"role": "user", "content": "Hello"}]
# Make request in OPTIMIZE mode
client.chat.completions.create(
model="claude-sonnet-4-20250514",
messages=messages,
max_tokens=100,
headroom_mode=HeadroomMode.OPTIMIZE,
)
stats = client.get_stats()
assert stats["session"]["requests_optimized"] >= 1, (
"requests_optimized should be at least 1 after OPTIMIZE mode request"
)
@patch("headroom.storage.sqlite.SQLiteStorage.save")
def test_session_stats_tracks_tokens_saved(self, mock_save, temp_db):
"""Verify session stats track tokens saved."""
from headroom import HeadroomMode
mock_client = MagicMock()
mock_client.messages.create.return_value = MockAnthropicResponse()
provider = MockAnthropicProvider()
client = HeadroomClient(
original_client=mock_client,
provider=provider,
store_url=temp_db,
)
# Create a conversation that will trigger some optimization
messages = [
{"role": "system", "content": "You are helpful. " * 100},
{"role": "user", "content": "Hello"},
]
client.chat.completions.create(
model="claude-sonnet-4-20250514",
messages=messages,
max_tokens=100,
headroom_mode=HeadroomMode.OPTIMIZE,
)
stats = client.get_stats()
# tokens_saved_total should be tracked (may be 0 if no compression)
assert "tokens_saved_total" in stats["session"], (
"Session stats should track tokens_saved_total"
)
|