Add cache optimization module with scalable dynamic content detection
Implements a comprehensive cache optimization layer for LLM providers:
- Provider-specific optimizers (Anthropic, OpenAI, Google) with distinct
caching strategies: explicit breakpoints, prefix stabilization, and
CachedContent API respectively
- Scalable dynamic content detector using three strategies:
1. Structural detection: "Label: value" patterns (language-agnostic)
2. Entropy-based detection: high-entropy strings (IDs, tokens, hashes)
3. Universal patterns: ISO 8601, UUIDs, JWTs, hex hashes
- NO hardcoded locale-specific patterns (no month names, etc.)
- Semantic caching layer with LRU eviction and TTL support
- Plugin registry for provider selection and custom optimizers
- 131 tests, real-world benchmarks showing 20-55% compression at <0.3ms
2026-01-07 14:07:49 -08:00
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"""Tests for OpenAICacheOptimizer."""
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import pytest
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2026-01-10 15:33:44 -08:00
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from headroom.cache import CacheConfig, OpenAICacheOptimizer, OptimizationContext
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Add cache optimization module with scalable dynamic content detection
Implements a comprehensive cache optimization layer for LLM providers:
- Provider-specific optimizers (Anthropic, OpenAI, Google) with distinct
caching strategies: explicit breakpoints, prefix stabilization, and
CachedContent API respectively
- Scalable dynamic content detector using three strategies:
1. Structural detection: "Label: value" patterns (language-agnostic)
2. Entropy-based detection: high-entropy strings (IDs, tokens, hashes)
3. Universal patterns: ISO 8601, UUIDs, JWTs, hex hashes
- NO hardcoded locale-specific patterns (no month names, etc.)
- Semantic caching layer with LRU eviction and TTL support
- Plugin registry for provider selection and custom optimizers
- 131 tests, real-world benchmarks showing 20-55% compression at <0.3ms
2026-01-07 14:07:49 -08:00
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from headroom.cache.base import CacheStrategy
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class TestOpenAICacheOptimizer:
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"""Test OpenAICacheOptimizer functionality."""
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@pytest.fixture
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def optimizer(self):
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"""Create optimizer instance."""
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return OpenAICacheOptimizer()
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@pytest.fixture
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def context(self):
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"""Create optimization context."""
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return OptimizationContext(
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provider="openai",
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model="gpt-4",
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)
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def test_optimizer_properties(self, optimizer):
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"""Test optimizer properties."""
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assert optimizer.name == "openai-prefix-stabilizer"
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assert optimizer.provider == "openai"
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assert optimizer.strategy == CacheStrategy.PREFIX_STABILIZATION
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def test_optimize_simple_messages(self, optimizer, context):
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"""Test optimizing simple messages."""
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello!"},
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]
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result = optimizer.optimize(messages, context)
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assert result.messages is not None
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assert len(result.messages) == 2
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assert result.metrics.stable_prefix_hash != ""
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def test_date_extraction(self, optimizer, context):
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"""Test that dates are extracted from system prompt."""
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messages = [
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{
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"role": "system",
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"content": "Today is January 7, 2026. You are a helpful assistant.",
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},
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{"role": "user", "content": "Hello!"},
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]
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result = optimizer.optimize(messages, context)
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# Check that date was extracted and moved
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system_content = result.messages[0]["content"]
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# The date should be moved to a dynamic section at the end
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assert "You are a helpful assistant" in system_content
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def test_whitespace_normalization(self, optimizer, context):
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"""Test whitespace normalization."""
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messages = [
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{
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"role": "system",
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"content": "You are a helpful assistant.",
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},
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{"role": "user", "content": "Hello!"},
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]
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result = optimizer.optimize(messages, context)
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# Whitespace should be normalized
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system_content = result.messages[0]["content"]
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assert " " not in system_content # Multiple spaces collapsed
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def test_optimize_disabled(self, context):
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"""Test optimization when disabled."""
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config = CacheConfig(enabled=False)
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optimizer = OpenAICacheOptimizer(config)
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messages = [
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{"role": "system", "content": "Test"},
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{"role": "user", "content": "Hello!"},
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]
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result = optimizer.optimize(messages, context)
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assert result.transforms_applied == []
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def test_prefix_stability_tracking(self, optimizer, context):
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"""Test that prefix stability is tracked."""
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello!"},
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]
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# First call
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2026-01-10 15:33:44 -08:00
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optimizer.optimize(messages, context)
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Add cache optimization module with scalable dynamic content detection
Implements a comprehensive cache optimization layer for LLM providers:
- Provider-specific optimizers (Anthropic, OpenAI, Google) with distinct
caching strategies: explicit breakpoints, prefix stabilization, and
CachedContent API respectively
- Scalable dynamic content detector using three strategies:
1. Structural detection: "Label: value" patterns (language-agnostic)
2. Entropy-based detection: high-entropy strings (IDs, tokens, hashes)
3. Universal patterns: ISO 8601, UUIDs, JWTs, hex hashes
- NO hardcoded locale-specific patterns (no month names, etc.)
- Semantic caching layer with LRU eviction and TTL support
- Plugin registry for provider selection and custom optimizers
- 131 tests, real-world benchmarks showing 20-55% compression at <0.3ms
2026-01-07 14:07:49 -08:00
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# Second call with same messages
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result2 = optimizer.optimize(messages, context)
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# Second call should detect stable prefix
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assert result2.metrics.estimated_cache_hit is True
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assert result2.metrics.prefix_changed_from_previous is False
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def test_prefix_change_detection(self, optimizer, context):
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"""Test detection of prefix changes."""
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messages1 = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello!"},
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]
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messages2 = [
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{"role": "system", "content": "You are a different assistant."},
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{"role": "user", "content": "Hello!"},
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]
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2026-01-10 15:33:44 -08:00
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optimizer.optimize(messages1, context)
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Add cache optimization module with scalable dynamic content detection
Implements a comprehensive cache optimization layer for LLM providers:
- Provider-specific optimizers (Anthropic, OpenAI, Google) with distinct
caching strategies: explicit breakpoints, prefix stabilization, and
CachedContent API respectively
- Scalable dynamic content detector using three strategies:
1. Structural detection: "Label: value" patterns (language-agnostic)
2. Entropy-based detection: high-entropy strings (IDs, tokens, hashes)
3. Universal patterns: ISO 8601, UUIDs, JWTs, hex hashes
- NO hardcoded locale-specific patterns (no month names, etc.)
- Semantic caching layer with LRU eviction and TTL support
- Plugin registry for provider selection and custom optimizers
- 131 tests, real-world benchmarks showing 20-55% compression at <0.3ms
2026-01-07 14:07:49 -08:00
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result2 = optimizer.optimize(messages2, context)
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# Second call should detect prefix change
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assert result2.metrics.prefix_changed_from_previous is True
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def test_token_threshold_warning(self, optimizer, context):
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"""Test warning when below token threshold."""
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messages = [
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{"role": "system", "content": "Short."},
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{"role": "user", "content": "Hi"},
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]
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result = optimizer.optimize(messages, context)
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# Should have warning about being below threshold
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assert any("1024" in w for w in result.warnings)
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def test_estimate_savings_below_threshold(self, optimizer, context):
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"""Test savings estimation below threshold."""
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messages = [
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{"role": "system", "content": "Short system prompt."},
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{"role": "user", "content": "Hello!"},
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]
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savings = optimizer.estimate_savings(messages, context)
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assert savings == 0.0 # Below threshold
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def test_estimate_savings_above_threshold(self, optimizer, context):
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"""Test savings estimation above threshold."""
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messages = [
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{"role": "system", "content": "You are helpful. " * 500},
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{"role": "user", "content": "Hello!"},
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]
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# First call to establish baseline
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optimizer.optimize(messages, context)
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# Second call should show savings
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savings = optimizer.estimate_savings(messages, context)
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assert savings > 0.0
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def test_uuid_pattern_detection(self, optimizer, context):
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"""Test detection of UUIDs in content."""
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messages = [
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{
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"role": "system",
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"content": "Request ID: 12345678-1234-1234-1234-123456789012. Be helpful.",
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},
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{"role": "user", "content": "Hello!"},
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]
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result = optimizer.optimize(messages, context)
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# UUID should be detected as dynamic content
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assert result.metrics.stable_prefix_hash != ""
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def test_content_block_format(self, optimizer, context):
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"""Test handling of content block format."""
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messages = [
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{
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"role": "system",
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"content": [{"type": "text", "text": "You are helpful."}],
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},
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{"role": "user", "content": "Hello!"},
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]
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result = optimizer.optimize(messages, context)
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assert result.messages is not None
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def test_metrics_recording(self, optimizer, context):
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"""Test that metrics are recorded."""
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messages = [
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{"role": "system", "content": "You are helpful."},
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{"role": "user", "content": "Hello!"},
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]
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optimizer.optimize(messages, context)
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metrics = optimizer.get_metrics()
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assert metrics.stable_prefix_hash != ""
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