Spaces:
Build error
Build error
File size: 14,809 Bytes
c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 e4a41fa c1feb60 | 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 | """Integration tests for the full TOIN feedback loop.
Tests the complete flow:
1. SmartCrusher compresses data and records compression event
2. compression_store stores with correct tool_signature_hash
3. User retrieves cached data (triggering feedback)
4. TOIN learns from retrieval event
5. Future compressions get improved recommendations
"""
import json
import tempfile
from pathlib import Path
import pytest
from headroom.cache.compression_store import (
get_compression_store,
reset_compression_store,
)
from headroom.config import CCRConfig
from headroom.telemetry import ToolSignature
from headroom.telemetry.toin import (
TOINConfig,
ToolIntelligenceNetwork,
get_toin,
reset_toin,
)
from headroom.transforms.smart_crusher import (
SmartCrusher,
SmartCrusherConfig,
)
@pytest.fixture
def fresh_toin():
"""Create a fresh TOIN instance with temporary storage."""
reset_toin()
with tempfile.TemporaryDirectory() as tmpdir:
storage_path = str(Path(tmpdir) / "toin.json")
toin = get_toin(
TOINConfig(
storage_path=storage_path,
auto_save_interval=0, # No auto-persist during tests
)
)
yield toin
reset_toin()
@pytest.fixture
def fresh_store():
"""Create a fresh compression store."""
reset_compression_store()
store = get_compression_store(max_entries=100, default_ttl=300)
yield store
reset_compression_store()
class TestTOINIntegration:
"""Test the full TOIN feedback loop."""
def test_compression_records_correct_hash(self, fresh_toin, fresh_store):
"""Test that SmartCrusher records the correct tool_signature_hash in store."""
# Create test data
items = [{"id": i, "score": 100 - i, "name": f"Item {i}"} for i in range(50)]
content = json.dumps(items)
# Create a SmartCrusher with CCR enabled
ccr_config = CCRConfig(enabled=True, inject_retrieval_marker=False)
crusher = SmartCrusher(
SmartCrusherConfig(max_items_after_crush=10),
ccr_config=ccr_config,
)
# Compress the content
crushed, was_modified, info = crusher._smart_crush_content(content, tool_name="test_tool")
assert was_modified, "Content should be modified by compression"
# Verify the store has an entry with the correct tool_signature_hash
stats = fresh_store.get_stats()
assert stats["entry_count"] >= 1, "Store should have at least one entry"
# Get the entry and verify it has tool_signature_hash
# We need to find the hash key from the store
entries = list(fresh_store._store.values())
assert len(entries) >= 1, "Should have at least one entry"
entry = entries[0]
assert entry.tool_signature_hash is not None, "Entry should have tool_signature_hash"
assert entry.compression_strategy is not None, "Entry should have compression_strategy"
# Verify the hash matches what ToolSignature would generate
expected_signature = ToolSignature.from_items(items)
assert entry.tool_signature_hash == expected_signature.structure_hash, (
"Stored hash should match ToolSignature.structure_hash"
)
def test_retrieval_updates_toin_strategy_success(self, fresh_toin, fresh_store):
"""Test that retrieval events update TOIN strategy success rates."""
# Create test data
items = [{"id": i, "score": 100 - i, "name": f"Item {i}"} for i in range(50)]
signature = ToolSignature.from_items(items)
# Record some compressions with fresh_toin
for _ in range(5):
fresh_toin.record_compression(
tool_signature=signature,
original_count=50,
compressed_count=10,
original_tokens=5000,
compressed_tokens=1000,
strategy="smart_sample",
)
# Get initial strategy success rate
pattern = fresh_toin._patterns.get(signature.structure_hash)
assert pattern is not None, "Pattern should exist after compressions"
initial_rate = pattern.strategy_success_rates.get("smart_sample", 1.0)
assert initial_rate > 0, "Initial success rate should be positive"
# Simulate retrieval events (which indicate compression was too aggressive)
for _ in range(3):
fresh_toin.record_retrieval(
tool_signature_hash=signature.structure_hash,
retrieval_type="full",
query="test query",
query_fields=["id"],
strategy="smart_sample",
)
# Verify success rate decreased
final_rate = pattern.strategy_success_rates.get("smart_sample", 1.0)
assert final_rate < initial_rate, (
f"Success rate should decrease after retrievals: {initial_rate} -> {final_rate}"
)
def test_full_feedback_loop(self, fresh_toin, fresh_store):
"""Test the complete feedback loop: compress → retrieve → learn → recommend."""
# Create test data
items = [{"id": i, "score": 100 - i, "name": f"Item {i}"} for i in range(50)]
content = json.dumps(items)
signature = ToolSignature.from_items(items)
# Step 1: Compress with SmartCrusher (records to TOIN)
ccr_config = CCRConfig(enabled=True, inject_retrieval_marker=False)
crusher = SmartCrusher(
SmartCrusherConfig(max_items_after_crush=10, use_feedback_hints=True),
ccr_config=ccr_config,
)
# Multiple compressions to build pattern
for _ in range(5):
crusher._smart_crush_content(content, tool_name="test_tool")
# Step 2: Verify TOIN has a pattern
pattern = fresh_toin._patterns.get(signature.structure_hash)
assert pattern is not None, "TOIN should have a pattern after compressions"
assert pattern.total_compressions >= 5, "Should have recorded 5 compressions"
# Step 3: Simulate retrievals (indicating compression was too aggressive)
# Find the stored entry hash
entries = list(fresh_store._store.values())
assert len(entries) > 0, "Should have cached entries"
# Retrieve multiple times to trigger learning
for entry in entries[:3]:
fresh_store.retrieve(entry.hash, query="find all items")
# Step 4: Verify TOIN learned from retrievals
recommendation = fresh_toin.get_recommendation(signature, "find all items")
# After many retrievals, TOIN should recommend more items
# Default is 15-20, but with high retrieval rate it should go higher
assert recommendation.confidence > 0, "Recommendation should have confidence"
def test_preserve_fields_used_in_compression(self, fresh_toin, fresh_store):
"""Test that TOIN preserve_fields are used during compression planning."""
# Create test data with specific fields
items = [{"id": i, "score": 100 - i, "category": f"cat_{i % 3}"} for i in range(50)]
signature = ToolSignature.from_items(items)
# Record compressions and retrievals that query the "category" field
for _ in range(10):
fresh_toin.record_compression(
tool_signature=signature,
original_count=50,
compressed_count=10,
original_tokens=5000,
compressed_tokens=1000,
strategy="smart_sample",
)
# Record retrievals that query "category"
for _ in range(5):
fresh_toin.record_retrieval(
tool_signature_hash=signature.structure_hash,
retrieval_type="search",
query="category:cat_1",
query_fields=["category"],
strategy="smart_sample",
)
# Get recommendation - should now preserve "category" field
recommendation = fresh_toin.get_recommendation(signature, "find all items in cat_1")
# Verify the recommendation reflects learning
assert recommendation.source in ("local", "network", "default"), (
f"Should have a valid source: {recommendation.source}"
)
def test_instance_id_stable_across_restarts(self):
"""Test that instance_id is stable across restarts."""
reset_toin()
with tempfile.TemporaryDirectory() as tmpdir:
storage_path = str(Path(tmpdir) / "toin_stable.json")
# Create first TOIN instance
toin1 = ToolIntelligenceNetwork(TOINConfig(storage_path=storage_path))
instance_id_1 = toin1._instance_id
# Create second instance with same path (simulating restart)
toin2 = ToolIntelligenceNetwork(TOINConfig(storage_path=storage_path))
instance_id_2 = toin2._instance_id
# Instance IDs should be the same since derived from path
assert instance_id_1 == instance_id_2, (
f"Instance ID should be stable: {instance_id_1} vs {instance_id_2}"
)
def test_atomic_save(self):
"""Test that save() uses atomic writes."""
reset_toin()
with tempfile.TemporaryDirectory() as tmpdir:
storage_path = str(Path(tmpdir) / "toin_atomic.json")
toin = ToolIntelligenceNetwork(TOINConfig(storage_path=storage_path))
# Create some data
items = [{"id": i, "name": f"test_{i}"} for i in range(10)]
signature = ToolSignature.from_items(items)
toin.record_compression(
tool_signature=signature,
original_count=10,
compressed_count=5,
original_tokens=1000,
compressed_tokens=500,
strategy="smart_sample",
)
# Save
toin.save()
# Verify file exists and is valid JSON
saved_path = Path(storage_path)
assert saved_path.exists(), "Save file should exist"
with open(saved_path) as f:
data = json.load(f)
assert "patterns" in data, "Saved data should have patterns"
assert len(data["patterns"]) > 0, "Should have at least one pattern"
def test_query_patterns_merged_on_import(self):
"""Test that query patterns are merged when importing patterns."""
reset_toin()
with tempfile.TemporaryDirectory() as tmpdir:
storage_path = str(Path(tmpdir) / "toin_merge.json")
toin = ToolIntelligenceNetwork(TOINConfig(storage_path=storage_path))
# Create local pattern
items = [{"id": i, "name": f"test_{i}"} for i in range(10)]
signature = ToolSignature.from_items(items)
toin.record_compression(
tool_signature=signature,
original_count=10,
compressed_count=5,
original_tokens=1000,
compressed_tokens=500,
strategy="smart_sample",
)
# Record local query pattern
toin.record_retrieval(
tool_signature_hash=signature.structure_hash,
retrieval_type="search",
query="local_pattern:value",
query_fields=["local_pattern"],
strategy="smart_sample",
)
# Create import data with different query pattern
import_data = {
"patterns": {
signature.structure_hash: {
"tool_signature_hash": signature.structure_hash,
"total_compressions": 100,
"total_retrievals": 20,
"avg_original_count": 50,
"avg_compressed_count": 10,
"avg_compression_ratio": 0.2,
"retrieval_rate": 0.2,
"common_query_patterns": ["imported_pattern:x"],
"strategy_success_rates": {"smart_sample": 0.8},
"preserve_fields": ["imported_field"],
"user_count": 50,
"last_updated": 1234567890.0,
}
}
}
# Import patterns
toin.import_patterns(import_data)
# Verify patterns were merged
pattern = toin._patterns.get(signature.structure_hash)
assert pattern is not None, "Pattern should exist after merge"
# Check that both query patterns exist
assert "imported_pattern:x" in pattern.common_query_patterns, (
"Imported query pattern should be present"
)
class TestStoreToTOINHash:
"""Test the hash correlation between compression_store and TOIN."""
def test_hash_matches_between_store_and_toin(self, fresh_toin, fresh_store):
"""Test that the hash stored in compression_store matches TOIN events."""
# Use 50 items with score field to ensure compression threshold is met
items = [{"id": i, "score": 100 - i, "name": f"Item {i}"} for i in range(50)]
content = json.dumps(items)
signature = ToolSignature.from_items(items)
# Compress with aggressive settings to ensure items are actually reduced
ccr_config = CCRConfig(enabled=True, inject_retrieval_marker=False)
crusher = SmartCrusher(
SmartCrusherConfig(max_items_after_crush=10),
ccr_config=ccr_config,
)
crushed, was_modified, info = crusher._smart_crush_content(content, tool_name="hash_test")
# Verify compression actually happened
assert was_modified, f"Content should be modified by compression: {info}"
# Get the stored hash
entries = list(fresh_store._store.values())
assert len(entries) >= 1, (
f"Should have stored entry. Modified: {was_modified}, Info: {info}"
)
stored_hash = entries[0].tool_signature_hash
# Verify it matches ToolSignature
assert stored_hash == signature.structure_hash, (
f"Store hash {stored_hash} should match signature {signature.structure_hash}"
)
# Verify TOIN can receive events for this hash
fresh_toin.record_compression(
tool_signature=signature,
original_count=50,
compressed_count=10,
original_tokens=5000,
compressed_tokens=1000,
strategy="smart_sample",
)
pattern = fresh_toin._patterns.get(signature.structure_hash)
assert pattern is not None, "TOIN should have pattern for same hash"
|