feat(engram): rank-interleave the semantic leg into recall; embed the corpus
Replaces the score-fusion first cut with rank fusion, which is what the data called for. nomic's cosine scale is compressed (true matches 0.55-0.70, unrelated pairs 0.35-0.50), so an additive blend of cosine onto token-coverage is dominated by whichever leg has the wider spread. Alternation is invariant to both scales: L1, S1, L2, S2, ... deduped, capped at limit Lexical ranking is left byte-identical; the semantic ranking is computed beside it and admitted only above ENGRAM_EMBED_SEED_MIN (0.60) — Will's existing seed floor, no new tuning constant. That floor is what keeps the nonsense controls clean: a query with no real match must not be answered with its neighbours. embed-corpus.py / merge-corpus.py produce the derived corpus the semantic leg needs (76,986 vectors, nomic-embed-text, 0 failures, 11 min). Zero of 78,791 nodes carried an embedding before this; the field round-tripped through the snapshot but nothing ever wrote it. MEASURED, 38-query gold set, paired against the SAME derived corpus so the comparison isolates the code change: hit@5 34.3% -> 51.4% paraphrase 0.0% -> 38.5% MRR@10 0.294 -> 0.387 superseded 1/3 -> 2/3 outranks recall@10 33.3% -> 50.5% latency p50 1146 -> 1220ms (1.06x) exact_rare 100% -> 100% phrase 85.7% -> 85.7% nonsense 2/3 -> 2/3 6 queries fixed, 0 broken, McNemar exact p=0.0312, 0 drift across repeats. Regression guards all held. Contrast PR #135, which swapped the read path to spreading activation wholesale: phrase 85.7 -> 28.6, latency 2.81x. Correct mechanism, wrong substrate. The substrate is now present. Restores engram claim 24 (previously 0% honoured). Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
This commit is contained in:
Neuron
@@ -0,0 +1,145 @@
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{
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"baseline": "baseline-embcorpus",
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"candidate": "hybrid-semantic",
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"n_shared_queries": 38,
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"fixed_by_candidate": [
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"q15",
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"q16",
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"q20",
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"q21",
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"q26",
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"q37"
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],
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"broken_by_candidate": [],
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"discordant": 6,
|
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"net_queries": 6,
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"mcnemar_exact_p": 0.03125,
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"min_detectable_swing_queries": 6,
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"observed_run_to_run_drift_queries": 0,
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"noise_floor_queries": 6,
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"verdict": "candidate better",
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"baseline_aggregate": {
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"n_queries": 38,
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"n_scored": 35,
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"hit@5": 0.34285714285714286,
|
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"recall@5": 0.26947278911564626,
|
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"recall@10": 0.3333333333333333,
|
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"precision@5": 0.12000000000000001,
|
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"mrr@10": 0.2943197278911564,
|
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"nonsense_clean": "2/3",
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"superseded_outranks": "1/3",
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"latency_ms_p50": 1145.9,
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"latency_ms_p95": 1574.3,
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"latency_ms_max": 1634.2,
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"errors": 0,
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"by_category": {
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"associative": {
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"n": 6,
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"hit@5": 0.0,
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"recall@5": 0.0,
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"recall@10": 0.0,
|
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"mrr@10": 0.0
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},
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"exact_rare": {
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"n": 6,
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"hit@5": 1.0,
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"recall@5": 1.0,
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"recall@10": 1.0,
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"mrr@10": 1.0
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},
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"nonsense": {
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"n": 3,
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"clean": 2,
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"avg_false_positives": 3.3333333333333335
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},
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"paraphrase": {
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"n": 13,
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"hit@5": 0.0,
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"recall@5": 0.0,
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"recall@10": 0.0,
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"mrr@10": 0.0
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},
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"phrase": {
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"n": 7,
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"hit@5": 0.8571428571428571,
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"recall@5": 0.4902210884353741,
|
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"recall@10": 0.6666666666666666,
|
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"mrr@10": 0.5965986394557822
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},
|
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"superseded": {
|
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"n": 3,
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"hit@5": 0.0,
|
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"recall@5": 0.0,
|
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"recall@10": 0.3333333333333333,
|
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"mrr@10": 0.041666666666666664,
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"outranks": 1
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}
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}
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},
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"candidate_aggregate": {
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"n_queries": 38,
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"n_scored": 35,
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"hit@5": 0.5142857142857142,
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"recall@5": 0.4409013605442177,
|
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"recall@10": 0.5047619047619047,
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"precision@5": 0.15428571428571433,
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"mrr@10": 0.38746031746031745,
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"nonsense_clean": "2/3",
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"superseded_outranks": "2/3",
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"latency_ms_p50": 1219.7,
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"latency_ms_p95": 1667.1,
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"latency_ms_max": 1720.2,
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"errors": 0,
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"by_category": {
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"associative": {
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"n": 6,
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"hit@5": 0.0,
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"recall@5": 0.0,
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"recall@10": 0.0,
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"mrr@10": 0.0
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},
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"exact_rare": {
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"n": 6,
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"hit@5": 1.0,
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"recall@5": 1.0,
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"recall@10": 1.0,
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"mrr@10": 1.0
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},
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"nonsense": {
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"n": 3,
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"clean": 2,
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"avg_false_positives": 3.3333333333333335
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},
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"paraphrase": {
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"n": 13,
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"hit@5": 0.38461538461538464,
|
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"recall@5": 0.38461538461538464,
|
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"recall@10": 0.38461538461538464,
|
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"mrr@10": 0.17307692307692307
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},
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"phrase": {
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"n": 7,
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"hit@5": 0.8571428571428571,
|
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"recall@5": 0.4902210884353741,
|
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"recall@10": 0.6666666666666666,
|
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"mrr@10": 0.6634920634920636
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},
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"superseded": {
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"n": 3,
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"hit@5": 0.3333333333333333,
|
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"recall@5": 0.3333333333333333,
|
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"recall@10": 0.6666666666666666,
|
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"mrr@10": 0.2222222222222222,
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"outranks": 2
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}
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}
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},
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"repeat_variance": {
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"candidate": {
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"runs": 2,
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"hit@5_min": 0.5142857142857142,
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"hit@5_max": 0.5142857142857142,
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"spread_queries": 0
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}
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}
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}
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@@ -0,0 +1,43 @@
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import json,sys,time,urllib.request,threading,queue
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SRC="/Users/timlingo/neuron-memory-backups/snapshot-pre-repair-20260806.json"
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OUT=sys.argv[1]
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URL="http://127.0.0.1:11434/api/embeddings"; MODEL="nomic-embed-text"
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MAXB=2000 # ENGRAM_EMBED_MAX_CHARS, applied to bytes as the C code does
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d=json.load(open(SRC,encoding='utf-8',errors='surrogateescape'))
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tasks=[]
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for n in d["nodes"]:
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c=n.get("content") or ""; t=n.get("node_type") or ""
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if len(c)<8: continue # eg_embed_eligible
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if t in ("InternalStateEvent","Tag"): continue
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b=c.encode('utf-8',errors='surrogateescape')[:MAXB]
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tasks.append((n.get("id") or "", b.decode('utf-8',errors='replace')))
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del d
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print("tasks",len(tasks),flush=True)
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q=queue.Queue(); [q.put(t) for t in tasks]
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lock=threading.Lock(); f=open(OUT,"w",encoding="utf-8",errors="surrogateescape"); done=[0]; t0=time.time(); fails=[0]
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def work():
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while True:
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try: nid,txt=q.get_nowait()
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except queue.Empty: return
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v=None
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for attempt in range(3):
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try:
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body=json.dumps({"model":MODEL,"prompt":txt}).encode()
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r=urllib.request.Request(URL,data=body,headers={"Content-Type":"application/json"})
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with urllib.request.urlopen(r,timeout=120) as fh: v=json.load(fh)["embedding"]
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break
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except Exception as e:
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if attempt==2:
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with lock: fails[0]+=1
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time.sleep(0.5)
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with lock:
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if v: f.write(nid+"\t"+",".join("%.5g"%x for x in v)+"\n")
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done[0]+=1
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if done[0]%2000==0:
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el=time.time()-t0
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print("%d/%d %.1f/s eta %.1fmin fails=%d"%(done[0],len(tasks),done[0]/el,(len(tasks)-done[0])/(done[0]/el)/60,fails[0]),flush=True)
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f.flush()
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ths=[threading.Thread(target=work) for _ in range(8)]
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[t.start() for t in ths]; [t.join() for t in ths]
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f.close()
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print("DONE",done[0],"fails",fails[0],"secs %.1f"%(time.time()-t0),flush=True)
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@@ -0,0 +1,17 @@
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import json,sys
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SRC="/Users/timlingo/neuron-memory-backups/snapshot-pre-repair-20260806.json"
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TSV,OUT=sys.argv[1],sys.argv[2]
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emb={}
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for line in open(TSV,encoding='utf-8',errors='surrogateescape'):
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p=line.rstrip("\n").rsplit("\t",1)
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if len(p)==2 and p[1].count(",")>100: emb[p[0]]=p[1]
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print("vectors",len(emb),flush=True)
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d=json.load(open(SRC,encoding='utf-8',errors='surrogateescape'))
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hit=0
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for n in d["nodes"]:
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v=emb.get(n.get("id") or "")
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if v: n["emb"]=v; hit+=1
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print("attached",hit,"of",len(d["nodes"]),flush=True)
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with open(OUT,"w",encoding='utf-8',errors='surrogateescape') as f:
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json.dump(d,f,ensure_ascii=False)
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print("wrote",OUT,flush=True)
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@@ -0,0 +1,945 @@
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{
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"label": "baseline-embcorpus",
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"soul_binary": "/private/tmp/claude-501/-Users-timlingo/82369039-a20e-4b5a-8a5e-28234a57b996/scratchpad/soul-baseline",
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"soul_md5": "5cc9521734907cf2da30f0af94498c06",
|
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"corpus": "/private/tmp/claude-501/-Users-timlingo/82369039-a20e-4b5a-8a5e-28234a57b996/scratchpad/corpus-embedded.json",
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"corpus_nodes": 78768,
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"corpus_edges": 14214,
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"gold_set": "/Users/timlingo/Development/neuron-technologies/_wt-eval/tools/retrieval-eval/gold_set.json",
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"limit": 10,
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"port": 7893,
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"wall_clock_s": 48.4,
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"child_pid": 85995,
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"child_confirmed_dead": true,
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"aggregate": {
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"n_queries": 38,
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"n_scored": 35,
|
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"hit@5": 0.34285714285714286,
|
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"recall@5": 0.26947278911564626,
|
||||
"recall@10": 0.3333333333333333,
|
||||
"precision@5": 0.12000000000000001,
|
||||
"mrr@10": 0.2943197278911564,
|
||||
"nonsense_clean": "2/3",
|
||||
"superseded_outranks": "1/3",
|
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"latency_ms_p50": 1145.9,
|
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"latency_ms_p95": 1574.3,
|
||||
"latency_ms_max": 1634.2,
|
||||
"errors": 0,
|
||||
"by_category": {
|
||||
"associative": {
|
||||
"n": 6,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"mrr@10": 0.0
|
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},
|
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"exact_rare": {
|
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"n": 6,
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"hit@5": 1.0,
|
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"recall@5": 1.0,
|
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"recall@10": 1.0,
|
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"mrr@10": 1.0
|
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},
|
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"nonsense": {
|
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"n": 3,
|
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"clean": 2,
|
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"avg_false_positives": 3.3333333333333335
|
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},
|
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"paraphrase": {
|
||||
"n": 13,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"mrr@10": 0.0
|
||||
},
|
||||
"phrase": {
|
||||
"n": 7,
|
||||
"hit@5": 0.8571428571428571,
|
||||
"recall@5": 0.4902210884353741,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.5965986394557822
|
||||
},
|
||||
"superseded": {
|
||||
"n": 3,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.3333333333333333,
|
||||
"mrr@10": 0.041666666666666664,
|
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"outranks": 1
|
||||
}
|
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}
|
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},
|
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"rows": [
|
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{
|
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"id": "q01",
|
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"category": "exact_rare",
|
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"query": "unjailbreakable",
|
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"returned": [
|
||||
"mem-7f61beb4-271c-4feb-9f6e-1c9c837a6226"
|
||||
],
|
||||
"n_returned": 1,
|
||||
"latency_ms": 229.0,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.2,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
{
|
||||
"id": "q02",
|
||||
"category": "exact_rare",
|
||||
"query": "engram-migrate",
|
||||
"returned": [
|
||||
"mem-6fdf6545-5e1a-43a9-8bdc-d2cd248146a5"
|
||||
],
|
||||
"n_returned": 1,
|
||||
"latency_ms": 262.1,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.2,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
{
|
||||
"id": "q03",
|
||||
"category": "exact_rare",
|
||||
"query": "cartabandonedevent",
|
||||
"returned": [
|
||||
"mem-1ba7c67d-85b9-4c2e-9fe2-39f8b0477091"
|
||||
],
|
||||
"n_returned": 1,
|
||||
"latency_ms": 230.1,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.2,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
{
|
||||
"id": "q04",
|
||||
"category": "exact_rare",
|
||||
"query": "pre-apprenticeship",
|
||||
"returned": [
|
||||
"mem-89c02aae-d3ca-43f9-9e5d-eb369896276c"
|
||||
],
|
||||
"n_returned": 1,
|
||||
"latency_ms": 229.2,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.2,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
{
|
||||
"id": "q05",
|
||||
"category": "exact_rare",
|
||||
"query": "inferencenodemanager",
|
||||
"returned": [
|
||||
"mem-73969486-143f-4431-b5e6-6845d1cc9848"
|
||||
],
|
||||
"n_returned": 1,
|
||||
"latency_ms": 250.4,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.2,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
{
|
||||
"id": "q06",
|
||||
"category": "exact_rare",
|
||||
"query": "clear-eyed",
|
||||
"returned": [
|
||||
"knw-c72597c5-c23d-4c08-8e9e-996dadf26a99"
|
||||
],
|
||||
"n_returned": 1,
|
||||
"latency_ms": 227.7,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.2,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
{
|
||||
"id": "q07",
|
||||
"category": "phrase",
|
||||
"query": "patterns not returns",
|
||||
"returned": [
|
||||
"kn-57b4c5e7-40c6-4c90-bf14-71841b0081d4",
|
||||
"?ǚ?7??????",
|
||||
"mem-a4a9dfc3-e40b-49b3-b1e1-060e8be2f482",
|
||||
"kn-5584ef9c-7f9d-4d7c-a10a-4ee6bc5cf356",
|
||||
"015644f5-8194-4af0-800d-dd4a0cd71396",
|
||||
"????7???Ջ3",
|
||||
"kn-d97920d0-1649-4223-9508-c0bb621e7fc0",
|
||||
"art-ee615cdb-e599-423d-9a4d-977859390ed3",
|
||||
"art-d24fd6dd-2cda-4eed-92f3-67b535a0d71b",
|
||||
"7?e?7???\f3?"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 532.2,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.2,
|
||||
"mrr@10": 0.3333333333333333
|
||||
},
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
"? ?}&?#??X\b",
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||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"? ?}&?#??X\b",
|
||||
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|
||||
"? ?}&?#??X\b",
|
||||
"? ?}&?#??X\b",
|
||||
"? ?}&?#??X\b"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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{
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||||
"id": "q12",
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||||
"category": "phrase",
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||||
"query": "Directed Autonomous Runtime Modification",
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
"? ?}&?#??X\b",
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||||
"? ?}&?#??X\b",
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"? ?}&?#??X\b",
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||||
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||||
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||||
"? ?}&?#??X\b",
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||||
"? ?}&?#??X\b",
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||||
"? ?}&?#??X\b"
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||||
],
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
"query": "cheerfulness you arrive at instead of assuming",
|
||||
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|
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|
||||
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
"category": "associative",
|
||||
"query": "Grandma Lucas stroke February 2006 goodbye window",
|
||||
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|
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|
||||
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
|
||||
"?ǚ?7??????"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 1499.4,
|
||||
"error": null,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"precision@5": 0.0,
|
||||
"mrr@10": 0.0
|
||||
},
|
||||
{
|
||||
"id": "q28",
|
||||
"category": "associative",
|
||||
"query": "Marines hernia sepsis medical ward",
|
||||
"returned": [
|
||||
"art-79042b8b-6192-440f-90b0-60708f7e6325",
|
||||
"kn-58874a74-b96f-4883-9e08-45707f4bd3ee",
|
||||
"bl-33ecccc2-e37f-43db-91b3-c2a86f08aaac",
|
||||
"art-ddfcd045-2c3b-4a1e-9966-fec5ce44e1dd",
|
||||
"art-e0bdf5d8-d163-491f-b649-453fee8b721d",
|
||||
"7?e?7???\f3?",
|
||||
"? ?}&?#??X\b",
|
||||
"? ?}&?#??X\b",
|
||||
"kn-9397c74b-35f3-4428-b4b0-5123353bbcd1",
|
||||
"?of?7???"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 1167.2,
|
||||
"error": null,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"precision@5": 0.0,
|
||||
"mrr@10": 0.0
|
||||
},
|
||||
{
|
||||
"id": "q29",
|
||||
"category": "associative",
|
||||
"query": "Sarah Bishop Dyer trailer performance",
|
||||
"returned": [
|
||||
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e",
|
||||
"? ?}&?#??X\b",
|
||||
"? ?}&?#??X\b",
|
||||
"? ?}&?#??X\b",
|
||||
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
|
||||
"7?e?7???\f3?",
|
||||
"?of?7???",
|
||||
"art-79042b8b-6192-440f-90b0-60708f7e6325",
|
||||
"art-ddfcd045-2c3b-4a1e-9966-fec5ce44e1dd",
|
||||
"kn-58874a74-b96f-4883-9e08-45707f4bd3ee"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 1245.4,
|
||||
"error": null,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"precision@5": 0.0,
|
||||
"mrr@10": 0.0
|
||||
},
|
||||
{
|
||||
"id": "q30",
|
||||
"category": "associative",
|
||||
"query": "Swarm Architecture containment lateral worker",
|
||||
"returned": [
|
||||
"art-ee615cdb-e599-423d-9a4d-977859390ed3",
|
||||
"bl-9bde67c1-f0ba-4c3a-8fe5-de0deee0ce43",
|
||||
"kn-b36902cc-0b05-44ba-9aa7-800e5dea9ca9",
|
||||
"8cbb60c5-4999-4ec1-8682-2592aedc4249",
|
||||
"kn-6f248a50-355b-47bb-aec8-e0e646a9b077",
|
||||
"bl-0fac287f-f4c0-4f15-bc4d-ff7f8a7af3ae",
|
||||
"bl-8c2d5f51-3ccd-4c2e-848a-eb60d90a3b98",
|
||||
"7?e?7???\f3?",
|
||||
"kn-a5b3d0ac-f6a1-49a4-aebb-b8b4cd67fe83",
|
||||
"bl-2121fdb9-796a-427e-b9b5-651f4388ea16"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 1219.7,
|
||||
"error": null,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"precision@5": 0.0,
|
||||
"mrr@10": 0.0
|
||||
},
|
||||
{
|
||||
"id": "q31",
|
||||
"category": "associative",
|
||||
"query": "hope won inside the narrative preface",
|
||||
"returned": [
|
||||
"kn-e0423482-cfa5-4796-8689-8495c93b66bc",
|
||||
"bl-2b00aeb0-c0fa-4a9f-8f30-4207e98b3d52",
|
||||
"kn-5b606390-a52d-4ca2-8e0e-eba141d13440",
|
||||
"knw-4aebd815-4eaf-49d7-954b-03595f3d48be",
|
||||
"knw-9e74ee95-ba7d-49b1-9262-977eae9729d1",
|
||||
"knw-d357b6bb-ad8a-4791-b516-426aea45fa5b",
|
||||
"7?e?7???\f3?",
|
||||
"knw-ed33e669-0790-44cb-a036-958d605c6fea",
|
||||
"rQ??m?;?x?'"
|
||||
],
|
||||
"n_returned": 9,
|
||||
"latency_ms": 1235.8,
|
||||
"error": null,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"precision@5": 0.0,
|
||||
"mrr@10": 0.0
|
||||
},
|
||||
{
|
||||
"id": "q32",
|
||||
"category": "associative",
|
||||
"query": "man of the house six years old expectation",
|
||||
"returned": [
|
||||
"? ?}&?#??X\b",
|
||||
"kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8",
|
||||
"? ?}&?#??X\b",
|
||||
"? ?}&?#??X\b",
|
||||
"? ?}&?#??X\b",
|
||||
"? ?}&?#??X\b",
|
||||
"kn-57b4c5e7-40c6-4c90-bf14-71841b0081d4",
|
||||
"knw-35940684-abc4-42f0-b942-818f66b1f69a",
|
||||
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
|
||||
"art-2fabd873-d787-49cb-ad30-d4ed9fcff8ef"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 1464.7,
|
||||
"error": null,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"precision@5": 0.0,
|
||||
"mrr@10": 0.0
|
||||
},
|
||||
{
|
||||
"id": "q33",
|
||||
"category": "nonsense",
|
||||
"query": "zqxjvw plimforth grebulon",
|
||||
"returned": [],
|
||||
"n_returned": 0,
|
||||
"latency_ms": 761.0,
|
||||
"error": null,
|
||||
"clean": true,
|
||||
"false_positives": 0
|
||||
},
|
||||
{
|
||||
"id": "q34",
|
||||
"category": "nonsense",
|
||||
"query": "flarnbistle quommetry",
|
||||
"returned": [],
|
||||
"n_returned": 0,
|
||||
"latency_ms": 525.1,
|
||||
"error": null,
|
||||
"clean": true,
|
||||
"false_positives": 0
|
||||
},
|
||||
{
|
||||
"id": "q35",
|
||||
"category": "nonsense",
|
||||
"query": "xxqzzt vurblenacht throom",
|
||||
"returned": [
|
||||
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
|
||||
"?V?",
|
||||
"knw-920c891f-bb8c-48c4-9afc-018ef12dcdc4",
|
||||
"?m?\\}Q??6??",
|
||||
"art-79042b8b-6192-440f-90b0-60708f7e6325",
|
||||
"?m?\\}Q??6??",
|
||||
"art-ddfcd045-2c3b-4a1e-9966-fec5ce44e1dd",
|
||||
"?m?\\}Q??6??",
|
||||
"bl-4476e856-c567-4b49-8ff7-d7dca3e5715e",
|
||||
"?m?\\}Q??6??"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 761.7,
|
||||
"error": null,
|
||||
"clean": false,
|
||||
"false_positives": 10
|
||||
},
|
||||
{
|
||||
"id": "q36",
|
||||
"category": "superseded",
|
||||
"query": "is the self-improvement architecture called DARMA or DHARMA",
|
||||
"returned": [
|
||||
"7?e?7???\f3?",
|
||||
"mem-80d7416b-20e9-48a0-b176-b215527e2f56",
|
||||
"? ?}&?#??X\b",
|
||||
"mem-f3b37427-b7d1-4f7e-b32c-0241a20ce8da",
|
||||
"? ?}&?#??X\b",
|
||||
"kn-b7e98d63-8b83-4911-b4d0-990602a7f575",
|
||||
"?of?7???",
|
||||
"knw-e047bb42-dc5b-4383-9e88-e508dc03abe3",
|
||||
"? ?}&?#??X\b",
|
||||
"bl-5b17bd3b-0c41-46cb-a710-6fa4429692ff"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 1322.6,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.2,
|
||||
"mrr@10": 0.5,
|
||||
"outranks": true,
|
||||
"rank_correct": 2,
|
||||
"rank_stale": 10
|
||||
},
|
||||
{
|
||||
"id": "q37",
|
||||
"category": "superseded",
|
||||
"query": "how many provisional patents does Will actually have",
|
||||
"returned": [
|
||||
"? ?}&?#??X\b",
|
||||
"12082f7e-e320-438b-bd65-083d8259748f",
|
||||
"? ?}&?#??X\b",
|
||||
"527ecb25-2587-47eb-8269-73be2431abd4",
|
||||
"? ?}&?#??X\b",
|
||||
"3cf706a1-3825-45d8-b0a9-06cae6cdf5b8",
|
||||
"? ?}&?#??X\b",
|
||||
"4f698ae6-c40e-464e-9798-50350991a188",
|
||||
"? ?}&?#??X\b",
|
||||
"be3b6036-6eca-44a7-8fdf-37b23edfdfd1"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 1694.6,
|
||||
"error": null,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.0,
|
||||
"mrr@10": 0.16666666666666666,
|
||||
"outranks": true,
|
||||
"rank_correct": 6,
|
||||
"rank_stale": null
|
||||
},
|
||||
{
|
||||
"id": "q38",
|
||||
"category": "superseded",
|
||||
"query": "is MCP still the live integration layer",
|
||||
"returned": [
|
||||
"kn-5584ef9c-7f9d-4d7c-a10a-4ee6bc5cf356",
|
||||
"bl-7328cbe3-0200-43c2-88e7-0a164e15fca4",
|
||||
"7?e?7???\f3?",
|
||||
"mem-101e81b4-8097-4749-8d8d-7bb66de34517",
|
||||
"? ?}&?#??X\b",
|
||||
"4509ed62-9fb2-48b8-9038-ac569fca9604",
|
||||
"%???2??jH??",
|
||||
"art-8a0870d5-a716-4672-8094-f7463af1265b",
|
||||
"???Ͼd??W\b?",
|
||||
"bl-556438af-57b2-4bd8-a747-9f868aaee290"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 1041.4,
|
||||
"error": null,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"precision@5": 0.0,
|
||||
"mrr@10": 0.0,
|
||||
"outranks": false,
|
||||
"rank_correct": null,
|
||||
"rank_stale": 4
|
||||
}
|
||||
]
|
||||
}
|
||||
+93
-48
@@ -6099,21 +6099,12 @@ static void engram_bll_parse_access(EngramNode* nn, const char* s) {
|
||||
* propagation loop in engram_activate. 0.25 damps semantically unrelated
|
||||
* branches ~4x without severing them. Unembedded targets are ungated. */
|
||||
#define ENGRAM_QGATE_FLOOR 0.25
|
||||
/* ENGRAM_SEM_WEIGHT: weight of the semantic (cosine) leg in the fused read-path
|
||||
* score, per engram claim 24 — "maintain a vector similarity index over the
|
||||
* semantic embedding vectors of all stored node records, and ... respond to
|
||||
* embedding search queries by returning the node records whose embedding
|
||||
* vectors have the highest cosine similarity to a query vector, independently
|
||||
* of the spreading activation traversal."
|
||||
* The lexical leg contributes distinct-token coverage in [0,1]; the semantic
|
||||
* leg contributes at most ENGRAM_SEM_WEIGHT. Holding this strictly BELOW 1.0
|
||||
* is the safety property: a node matching every query token can never be
|
||||
* displaced by semantics alone, so exact/phrase retrieval cannot regress the
|
||||
* way it did when the read path was swapped wholesale to spreading activation
|
||||
* (PR #135: phrase 85.7% -> 28.6%). Semantics reorders WITHIN and BELOW the
|
||||
* full-lexical-match band, and admits high-cosine nodes the lexical pass
|
||||
* missed entirely. */
|
||||
#define ENGRAM_SEM_WEIGHT 0.90
|
||||
/* The read-path semantic leg (engram claim 24) reuses ENGRAM_EMBED_SEED_MIN
|
||||
* above as its admission floor: a node joins the embedding ranking only if its
|
||||
* query cosine clears the same bar that lets it join the seed set. No new
|
||||
* tuning constant is introduced, and the floor is load-bearing rather than
|
||||
* cosmetic — it is what keeps a query with no real match (the gold set's
|
||||
* nonsense controls) from being answered with its nearest neighbours. */
|
||||
#define ENGRAM_EMBED_MAX_CHARS 2000
|
||||
#define ENGRAM_EMBED_TIMEOUT_MS 4000L
|
||||
#define ENGRAM_EMBED_BREAKER_LIMIT 3
|
||||
@@ -7411,29 +7402,67 @@ static double eg_sem_term(const EngramNode* n, const float* qv, int32_t qdim) {
|
||||
return t > 1.0 ? 1.0 : t;
|
||||
}
|
||||
|
||||
/* Fused rank score: distinct-token coverage in [0,1] plus at most
|
||||
* ENGRAM_SEM_WEIGHT of semantic similarity. With no query embedding this is
|
||||
* sc/ntok, a strictly monotone map of the old integer score, so the ordering
|
||||
* is bit-identical to the pre-semantic ranking. */
|
||||
static double eg_fused_score(int sc, int ntok, double sem) {
|
||||
double f = (double)sc / (double)(ntok > 0 ? ntok : 1);
|
||||
return f + ENGRAM_SEM_WEIGHT * sem;
|
||||
}
|
||||
|
||||
/* Rank entry: fused lexical+semantic score (primary, desc) then salience
|
||||
* (tiebreak, desc). `score` retains the raw distinct-token count for callers
|
||||
* that want the lexical signal on its own. */
|
||||
typedef struct { int64_t idx; int score; double fused; double salience; } EngramRankEntry;
|
||||
/* Rank entry: distinct-token match count (primary, desc) then salience
|
||||
* (tiebreak, desc). The lexical leg is deliberately left EXACTLY as it was —
|
||||
* the semantic leg is a second ranking merged beside it, never a reweighting
|
||||
* of this one. */
|
||||
typedef struct { int64_t idx; int score; double salience; } EngramRankEntry;
|
||||
static int engram_rank_cmp(const void* a, const void* b) {
|
||||
const EngramRankEntry* ea = (const EngramRankEntry*)a;
|
||||
const EngramRankEntry* eb = (const EngramRankEntry*)b;
|
||||
if (ea->fused < eb->fused) return 1; /* desc */
|
||||
if (ea->fused > eb->fused) return -1;
|
||||
if (ea->score != eb->score) return eb->score - ea->score; /* desc */
|
||||
if (ea->salience < eb->salience) return 1;
|
||||
if (ea->salience > eb->salience) return -1;
|
||||
return 0;
|
||||
}
|
||||
|
||||
/* Semantic rank entry: node index and its renormalized query similarity,
|
||||
* ordered by similarity desc. This is the claim-24 "embedding search"
|
||||
* ranking, computed independently of the lexical one. */
|
||||
typedef struct { int64_t idx; double sem; } EngramSemEntry;
|
||||
static int engram_sem_cmp(const void* a, const void* b) {
|
||||
const EngramSemEntry* ea = (const EngramSemEntry*)a;
|
||||
const EngramSemEntry* eb = (const EngramSemEntry*)b;
|
||||
if (ea->sem < eb->sem) return 1; /* desc */
|
||||
if (ea->sem > eb->sem) return -1;
|
||||
return 0;
|
||||
}
|
||||
|
||||
/* Merge the two rankings by strict alternation, lexical first:
|
||||
* L1, S1, L2, S2, L3, ... deduplicated by node index, capped at lim.
|
||||
*
|
||||
* Rank fusion, not score fusion. nomic's cosine scale is compressed (real
|
||||
* matches land ~0.55-0.70 while unrelated pairs sit ~0.35-0.50), so any
|
||||
* additive blend of a cosine onto a token-coverage score is dominated by
|
||||
* whichever leg happens to have the wider spread. Alternation is invariant to
|
||||
* both scales: it asks each leg for its next best answer in turn.
|
||||
*
|
||||
* Position 1 is always the top lexical hit, so a query whose answer the
|
||||
* lexical leg already ranks first cannot be displaced — exact-token retrieval
|
||||
* is structurally safe. The cost is bounded and explicit: a lexical hit at
|
||||
* rank r lands at output position 2r-1. */
|
||||
static int64_t engram_interleave(const EngramRankEntry* L, int64_t nL,
|
||||
const EngramSemEntry* S, int64_t nS,
|
||||
int64_t lim, int64_t* out) {
|
||||
int64_t no = 0, li = 0, si = 0;
|
||||
while (no < lim && (li < nL || si < nS)) {
|
||||
if (li < nL) {
|
||||
int dup = 0;
|
||||
for (int64_t k = 0; k < no; k++) if (out[k] == L[li].idx) { dup = 1; break; }
|
||||
if (!dup) out[no++] = L[li].idx;
|
||||
li++;
|
||||
}
|
||||
if (no >= lim) break;
|
||||
if (si < nS) {
|
||||
int dup = 0;
|
||||
for (int64_t k = 0; k < no; k++) if (out[k] == S[si].idx) { dup = 1; break; }
|
||||
if (!dup) out[no++] = S[si].idx;
|
||||
si++;
|
||||
}
|
||||
}
|
||||
return no;
|
||||
}
|
||||
|
||||
el_val_t engram_search(el_val_t query, el_val_t limit) {
|
||||
EngramStore* g = engram_get();
|
||||
const char* q = EL_CSTR(query);
|
||||
@@ -7450,6 +7479,8 @@ el_val_t engram_search(el_val_t query, el_val_t limit) {
|
||||
* NULL (embedder down / circuit breaker open) => pure lexical, as before. */
|
||||
int32_t qdim = 0;
|
||||
float* qv = eg_embed_fetch(q, &qdim);
|
||||
EngramSemEntry* sem = qv ? malloc((size_t)g->node_count * sizeof(EngramSemEntry)) : NULL;
|
||||
int64_t nsem = 0;
|
||||
int64_t nhits = 0;
|
||||
for (int64_t i = 0; i < g->node_count; i++) {
|
||||
EngramNode* n = &g->nodes[i];
|
||||
@@ -7459,25 +7490,29 @@ el_val_t engram_search(el_val_t query, el_val_t limit) {
|
||||
* + engram_compile_layered_json — that's the legitimate path. */
|
||||
if (engram_layer_is_transparent(n->layer_id)) continue;
|
||||
int sc = engram_node_match_score(n, toks, ntok);
|
||||
double sem = eg_sem_term(n, qv, qdim);
|
||||
/* Union, not replacement: a node enters the candidate set on EITHER
|
||||
* leg. sc == 0 && sem > 0 is the embedding-search half of claim 24 —
|
||||
* nodes the lexical pass cannot see at all. */
|
||||
if (sc > 0 || sem > 0.0) {
|
||||
if (sc > 0) {
|
||||
hits[nhits].idx = i;
|
||||
hits[nhits].score = sc;
|
||||
hits[nhits].fused = eg_fused_score(sc, ntok, sem);
|
||||
hits[nhits].salience = n->salience;
|
||||
nhits++;
|
||||
}
|
||||
if (sem) {
|
||||
double sv = eg_sem_term(n, qv, qdim);
|
||||
if (sv > 0.0) { sem[nsem].idx = i; sem[nsem].sem = sv; nsem++; }
|
||||
}
|
||||
}
|
||||
/* Rank by fused score (desc) then salience (desc), then cap. */
|
||||
/* Rank each leg independently, then alternate between them. */
|
||||
qsort(hits, (size_t)nhits, sizeof(EngramRankEntry), engram_rank_cmp);
|
||||
int64_t end = nhits < lim ? nhits : lim;
|
||||
for (int64_t k = 0; k < end; k++) {
|
||||
lst = el_list_append(lst, engram_node_to_map(&g->nodes[hits[k].idx]));
|
||||
if (sem) qsort(sem, (size_t)nsem, sizeof(EngramSemEntry), engram_sem_cmp);
|
||||
int64_t* order = malloc((size_t)lim * sizeof(int64_t));
|
||||
if (order) {
|
||||
int64_t no = engram_interleave(hits, nhits, sem, nsem, lim, order);
|
||||
for (int64_t k = 0; k < no; k++)
|
||||
lst = el_list_append(lst, engram_node_to_map(&g->nodes[order[k]]));
|
||||
free(order);
|
||||
}
|
||||
free(hits);
|
||||
free(sem);
|
||||
free(qv);
|
||||
return lst;
|
||||
}
|
||||
@@ -9316,29 +9351,39 @@ el_val_t engram_search_json(el_val_t query, el_val_t limit) {
|
||||
* to be observable to the MCP wrapper and the app. */
|
||||
int32_t qdim = 0;
|
||||
float* qv = eg_embed_fetch(q, &qdim);
|
||||
EngramSemEntry* sem = qv ? malloc((size_t)g->node_count * sizeof(EngramSemEntry)) : NULL;
|
||||
int64_t nsem = 0;
|
||||
int64_t nhits = 0;
|
||||
for (int64_t i = 0; i < g->node_count; i++) {
|
||||
EngramNode* n = &g->nodes[i];
|
||||
/* Filter transparent layers — same as engram_search. */
|
||||
if (engram_layer_is_transparent(n->layer_id)) continue;
|
||||
int sc = engram_node_match_score(n, toks, ntok);
|
||||
double sem = eg_sem_term(n, qv, qdim);
|
||||
if (sc > 0 || sem > 0.0) {
|
||||
if (sc > 0) {
|
||||
hits[nhits].idx = i;
|
||||
hits[nhits].score = sc;
|
||||
hits[nhits].fused = eg_fused_score(sc, ntok, sem);
|
||||
hits[nhits].salience = n->salience;
|
||||
nhits++;
|
||||
}
|
||||
if (sem) {
|
||||
double sv = eg_sem_term(n, qv, qdim);
|
||||
if (sv > 0.0) { sem[nsem].idx = i; sem[nsem].sem = sv; nsem++; }
|
||||
}
|
||||
}
|
||||
qsort(hits, (size_t)nhits, sizeof(EngramRankEntry), engram_rank_cmp);
|
||||
int64_t end = nhits < lim ? nhits : lim;
|
||||
for (int64_t k = 0; k < end; k++) {
|
||||
if (!first) jb_putc(&b, ',');
|
||||
engram_emit_node_json(&b, &g->nodes[hits[k].idx], 0);
|
||||
first = 0;
|
||||
if (sem) qsort(sem, (size_t)nsem, sizeof(EngramSemEntry), engram_sem_cmp);
|
||||
int64_t* order = malloc((size_t)lim * sizeof(int64_t));
|
||||
if (order) {
|
||||
int64_t no = engram_interleave(hits, nhits, sem, nsem, lim, order);
|
||||
for (int64_t k = 0; k < no; k++) {
|
||||
if (!first) jb_putc(&b, ',');
|
||||
engram_emit_node_json(&b, &g->nodes[order[k]], 0);
|
||||
first = 0;
|
||||
}
|
||||
free(order);
|
||||
}
|
||||
free(hits);
|
||||
free(sem);
|
||||
free(qv);
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user