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#1	"""
#2	BEAM Working Memory Scale Benchmark — demonstrates working_memory recall
#3	stays fast as working_memory grows, thanks to FTS5 fast path.
#4	Run: PYTHONPATH=. python tests/benchmark_beam_working_memory.py
#5	"""
#6
#7	import time
#8	import tempfile
#9	from pathlib import Path
#10
#11	from mnemosyne.core.beam import BeamMemory
#12
#13
#14	def benchmark():
#15	with tempfile.TemporaryDirectory() as tmpdir:
#16	db_path = Path(tmpdir) / "wm_scale.db"
#17	beam = BeamMemory(session_id="wm_scale", db_path=db_path)
#18
#19	sizes = [100, 1000, 5000, 10000]
#20	print("🔍 BEAM Working Memory Recall Latency vs Corpus Size")
#21	print("-" * 55)
#22
#23	cumulative = 0
#24	for size in sizes:
#25	print(f"Inserting batch to reach {size} working memories...")
#26	t0 = time.time()
#27	batch = size - cumulative
#28	items = [
#29	{
#30	"content": f"Working memory item {cumulative + i}: concept {i % 100} in domain {(cumulative + i) % 10}",
#31	"source": "conversation",
#32	"importance": 0.5
#33	}
#34	for i in range(batch)
#35	]
#36	beam.remember_batch(items)
#37	insert_sec = time.time() - t0
#38	cumulative = size
#39	print(f" Batch insert ({batch} items): {insert_sec:.1f}s")
#40
#41	# Benchmark recall
#42	queries = ["concept 42", "domain 7", "nonexistent xyz"]
#43	for q in queries:
#44	times = []
#45	for _ in range(10):
#46	t0 = time.time()
#47	results = beam.recall(q, top_k=5)
#48	times.append((time.time() - t0) * 1000)
#49	avg = sum(times) / len(times)
#50	p95 = sorted(times)[int(len(times) * 0.95)]
#51	print(f" WM={size:5d} \| Query='{q[:20]:<20}' \| {avg:.2f}ms avg \| {p95:.2f}ms p95")
#52
#53	wm_stats = beam.get_working_stats()
#54	ep_stats = beam.get_episodic_stats()
#55	print(f"\n📊 Final working memory: {wm_stats['total']} items")
#56	print(f"📊 Final episodic memory: {ep_stats['total']} items \| vectors: {ep_stats['vectors']} \| vec_type: {ep_stats.get('vec_type', 'none')}")
#57
#58
#59	if __name__ == "__main__":
#60	benchmark()
#61

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