File size: 15,223 Bytes
dbb04e4 c3a3710 dbb04e4 | 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 | """
End-to-End Tests for MnemoCore
===============================
Tests the complete cognitive memory pipeline:
store → query → feedback → consolidation
These tests run fully offline using the mock infrastructure from conftest.py.
No live Redis or Qdrant required.
SEGMENT 3.4 – End-to-end tests (AGENT_MASTER_PLAN)
"""
import os
import pytest
import pytest_asyncio
from mnemocore.core.config import get_config, reset_config
from mnemocore.core.engine import HAIMEngine
from mnemocore.core.binary_hdv import BinaryHDV
# =============================================================================
# Fixtures
# =============================================================================
@pytest.fixture
def isolated_engine(tmp_path):
"""
Create a fully isolated HAIMEngine with a temp data directory.
No live services required — uses local file-based tier only.
Key settings:
- HAIM_HOT_LTP_THRESHOLD_MIN=0.0 → prevents immediate HOT→WARM demotion
(new memories have LTP ~0.55, below the default threshold of 0.7)
- HAIM_HOT_MAX_MEMORIES=10000 → prevents eviction during tests
"""
from mnemocore.core.hnsw_index import HNSWIndexManager
HNSWIndexManager._instance = None
reset_config()
data_dir = tmp_path / "data"
data_dir.mkdir()
os.environ["HAIM_DATA_DIR"] = str(data_dir)
os.environ["HAIM_MEMORY_FILE"] = str(data_dir / "memory.jsonl")
os.environ["HAIM_CODEBOOK_FILE"] = str(data_dir / "codebook.json")
os.environ["HAIM_SYNAPSES_FILE"] = str(data_dir / "synapses.json")
os.environ["HAIM_WARM_MMAP_DIR"] = str(data_dir / "warm")
os.environ["HAIM_COLD_ARCHIVE_DIR"] = str(data_dir / "cold")
os.environ["HAIM_ENCODING_MODE"] = "binary"
os.environ["HAIM_DIMENSIONALITY"] = "1024"
# Prevent HOT→WARM demotion: new memories have LTP ~0.55,
# below the default threshold of 0.7, causing immediate demotion.
# _build_tier("hot", ...) uses prefix TIERS_HOT, so env var is HAIM_TIERS_HOT_*
os.environ["HAIM_TIERS_HOT_LTP_THRESHOLD_MIN"] = "0.0"
os.environ["HAIM_TIERS_HOT_MAX_MEMORIES"] = "10000"
reset_config()
engine = HAIMEngine()
yield engine
# Cleanup env
for key in [
"HAIM_DATA_DIR", "HAIM_MEMORY_FILE", "HAIM_CODEBOOK_FILE",
"HAIM_SYNAPSES_FILE", "HAIM_WARM_MMAP_DIR", "HAIM_COLD_ARCHIVE_DIR",
"HAIM_ENCODING_MODE", "HAIM_DIMENSIONALITY",
"HAIM_TIERS_HOT_LTP_THRESHOLD_MIN", "HAIM_TIERS_HOT_MAX_MEMORIES",
]:
os.environ.pop(key, None)
reset_config()
# =============================================================================
# Test 1: Complete Store → Query Cycle
# =============================================================================
@pytest.mark.asyncio
async def test_complete_store_query_cycle(isolated_engine):
"""
Full pipeline: store a memory, then query for it.
The stored memory should appear as the top result.
"""
await isolated_engine.initialize()
# Store a distinctive memory
content = "The mitochondria is the powerhouse of the cell"
memory_id = await isolated_engine.store(content)
assert isinstance(memory_id, str)
assert len(memory_id) == 36 # UUID format
# Query with the same content — should be top result
results = await isolated_engine.query(content, top_k=5)
assert len(results) > 0
top_id, top_score = results[0]
assert top_id == memory_id
assert top_score > 0.5 # High similarity for identical content
@pytest.mark.asyncio
async def test_store_multiple_query_returns_most_relevant(isolated_engine):
"""
Store multiple memories, query for one specific topic.
The most semantically relevant memory should rank highest.
"""
await isolated_engine.initialize()
# Store memories on different topics
id_biology = await isolated_engine.store("Photosynthesis converts sunlight into glucose in plants")
id_physics = await isolated_engine.store("Newton's second law: force equals mass times acceleration")
id_chemistry = await isolated_engine.store("Water molecule consists of two hydrogen and one oxygen atom")
# Query for biology topic
results = await isolated_engine.query("How do plants make food from sunlight?", top_k=5)
assert len(results) > 0
# All stored memories should be retrievable via query
result_ids = [r[0] for r in results]
# At least one of our stored memories should appear in results
stored_ids = {id_biology, id_physics, id_chemistry}
assert len(stored_ids & set(result_ids)) > 0, "At least one stored memory should appear in query results"
# Note: HDV uses hash-based token encoding, not semantic embeddings,
# so cross-topic ranking order is not deterministic.
# =============================================================================
# Test 2: LTP Strength Decay
# =============================================================================
@pytest.mark.asyncio
async def test_ltp_strength_is_positive_after_store(isolated_engine):
"""
Verify that stored memories have positive LTP strength.
Formula: S = I × log(1+A) × e^(-λT)
"""
await isolated_engine.initialize()
memory_id = await isolated_engine.store("Test memory for LTP verification")
node = await isolated_engine.get_memory(memory_id)
assert node is not None
assert hasattr(node, "ltp_strength")
assert node.ltp_strength >= 0.0
@pytest.mark.asyncio
async def test_retrieval_feedback_updates_node(isolated_engine):
"""
Test that recording retrieval feedback (helpful=True) works without error.
The Bayesian LTP updater should be called.
"""
await isolated_engine.initialize()
memory_id = await isolated_engine.store("Memory to receive positive feedback")
# Record positive feedback — should not raise
await isolated_engine.record_retrieval_feedback(memory_id, helpful=True, eig_signal=0.8)
# Node should still be retrievable
node = await isolated_engine.get_memory(memory_id)
assert node is not None
@pytest.mark.asyncio
async def test_negative_feedback_does_not_delete_memory(isolated_engine):
"""
Negative feedback should update reliability but not delete the memory.
"""
await isolated_engine.initialize()
memory_id = await isolated_engine.store("Memory to receive negative feedback")
await isolated_engine.record_retrieval_feedback(memory_id, helpful=False, eig_signal=0.5)
# Memory should still exist
node = await isolated_engine.get_memory(memory_id)
assert node is not None
# =============================================================================
# Test 3: XOR Project Isolation
# =============================================================================
@pytest.mark.asyncio
async def test_xor_project_isolation(isolated_engine):
"""
Memories stored with project_id A should not be the top result
when querying with project_id B (XOR isolation).
"""
await isolated_engine.initialize()
content = "Secret project Alpha data: classified information"
# Store with project A
id_project_a = await isolated_engine.store(
content,
project_id="project_alpha"
)
# Query with project B — should NOT find project A's memory as top result
results_b = await isolated_engine.query(
content,
top_k=5,
project_id="project_beta"
)
# Project A's memory should either not appear, or appear with low score
result_ids = [r[0] for r in results_b]
if id_project_a in result_ids:
# If it appears, its score should be low (XOR mask garbles the vector)
a_score = dict(results_b)[id_project_a]
assert a_score < 0.9, "Cross-project memory should have low similarity score"
@pytest.mark.asyncio
async def test_same_project_query_finds_memory(isolated_engine):
"""
Memories stored with project_id should be findable with the same project_id.
"""
await isolated_engine.initialize()
content = "Project Alpha internal knowledge base entry"
memory_id = await isolated_engine.store(content, project_id="project_alpha")
# Query with same project — should find it
results = await isolated_engine.query(content, top_k=5, project_id="project_alpha")
assert len(results) > 0
top_id, top_score = results[0]
assert top_id == memory_id
assert top_score > 0.5
# =============================================================================
# Test 4: Episodic Chaining
# =============================================================================
@pytest.mark.asyncio
async def test_episodic_chain_links_memories(isolated_engine):
"""
Memories stored sequentially should form an episodic chain
via the previous_id field.
"""
await isolated_engine.initialize()
id_1 = await isolated_engine.store("First memory in the chain")
id_2 = await isolated_engine.store("Second memory in the chain")
id_3 = await isolated_engine.store("Third memory in the chain")
node_2 = await isolated_engine.get_memory(id_2)
node_3 = await isolated_engine.get_memory(id_3)
assert node_2 is not None
assert node_3 is not None
# Each memory should point to the previous one
assert node_2.previous_id == id_1
assert node_3.previous_id == id_2
@pytest.mark.asyncio
async def test_temporal_neighbors_via_include_neighbors(isolated_engine):
"""
Query with include_neighbors=True should return temporal context.
"""
await isolated_engine.initialize()
id_1 = await isolated_engine.store("Context before the target memory")
id_target = await isolated_engine.store("Target memory to query for")
id_3 = await isolated_engine.store("Context after the target memory")
results = await isolated_engine.query(
"Target memory to query for",
top_k=5,
include_neighbors=True,
)
result_ids = [r[0] for r in results]
# Target should be in results
assert id_target in result_ids
# =============================================================================
# Test 5: Redis Fallback (engine works without Redis)
# =============================================================================
@pytest.mark.asyncio
async def test_engine_works_without_redis(isolated_engine):
"""
Engine should function correctly even when Redis is unavailable.
The tier_manager uses local in-memory storage as fallback.
"""
await isolated_engine.initialize()
# No Redis configured — engine should still work
memory_id = await isolated_engine.store("Memory stored without Redis")
assert memory_id is not None
results = await isolated_engine.query("Memory stored without Redis", top_k=3)
assert len(results) > 0
assert results[0][0] == memory_id
# =============================================================================
# Test 6: Qdrant Fallback (engine works without Qdrant)
# =============================================================================
@pytest.mark.asyncio
async def test_engine_works_without_qdrant(isolated_engine):
"""
Engine should function correctly even when Qdrant is unavailable.
The tier_manager uses local FAISS/in-memory HOT tier as fallback.
"""
await isolated_engine.initialize()
# Qdrant not configured — engine should still work via HOT tier
memory_id = await isolated_engine.store("Memory stored without Qdrant")
assert memory_id is not None
node = await isolated_engine.get_memory(memory_id)
assert node is not None
assert node.content == "Memory stored without Qdrant"
# =============================================================================
# Test 7: Delete Memory
# =============================================================================
@pytest.mark.asyncio
async def test_delete_removes_memory_from_results(isolated_engine):
"""
After deleting a memory, it should not appear in query results.
"""
await isolated_engine.initialize()
content = "Memory that will be deleted"
memory_id = await isolated_engine.store(content)
# Verify it exists
node = await isolated_engine.get_memory(memory_id)
assert node is not None
# Delete it
await isolated_engine.delete_memory(memory_id)
# Should no longer be retrievable
node_after = await isolated_engine.get_memory(memory_id)
assert node_after is None
# =============================================================================
# Test 8: Stats Endpoint
# =============================================================================
@pytest.mark.asyncio
async def test_get_stats_returns_valid_structure(isolated_engine):
"""
get_stats() should return a dict with expected keys.
"""
await isolated_engine.initialize()
await isolated_engine.store("Memory for stats test")
stats = await isolated_engine.get_stats()
assert isinstance(stats, dict)
assert "engine_version" in stats
assert "dimension" in stats
assert "tiers" in stats
assert "synapses_count" in stats
assert "timestamp" in stats
# =============================================================================
# Test 9: Synapse Binding
# =============================================================================
@pytest.mark.asyncio
async def test_bind_memories_creates_synapse(isolated_engine):
"""
bind_memories() should create a synaptic connection between two nodes.
"""
await isolated_engine.initialize()
id_a = await isolated_engine.store("Memory A about machine learning")
id_b = await isolated_engine.store("Memory B about neural networks")
await isolated_engine.bind_memories(id_a, id_b, success=True)
stats = await isolated_engine.get_stats()
assert stats["synapses_count"] >= 1
@pytest.mark.asyncio
async def test_associative_jump_finds_bound_memory(isolated_engine):
"""
After binding two memories, querying for one should surface the other
via associative spreading.
"""
await isolated_engine.initialize()
id_a = await isolated_engine.store("Concept Alpha: quantum entanglement")
id_b = await isolated_engine.store("Concept Beta: spooky action at a distance")
# Bind them explicitly
await isolated_engine.bind_memories(id_a, id_b, success=True)
# Query for A — B should appear via associative jump
results = await isolated_engine.query(
"Concept Alpha: quantum entanglement",
top_k=5,
associative_jump=True,
)
result_ids = [r[0] for r in results]
assert id_a in result_ids # Direct match
# B may appear via associative spreading
# (not guaranteed if score is too low, but no error should occur)
|