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 0.387 superseded 1/3 -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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||||
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||||
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||||
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||||
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||||
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||||
"query": "Marines hernia sepsis medical ward",
|
||||
"returned": [
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||||
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||||
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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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||||
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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": "Swarm Architecture containment lateral worker",
|
||||
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|
||||
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||||
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||||
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||||
"7?e?7???\f3?",
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||||
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||||
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||||
{
|
||||
"id": "q31",
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||||
"category": "associative",
|
||||
"query": "hope won inside the narrative preface",
|
||||
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||||
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||||
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||||
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||||
"knw-9e74ee95-ba7d-49b1-9262-977eae9729d1",
|
||||
"knw-d357b6bb-ad8a-4791-b516-426aea45fa5b",
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||||
"7?e?7???\f3?",
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||||
"knw-ed33e669-0790-44cb-a036-958d605c6fea",
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||||
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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": "q32",
|
||||
"category": "associative",
|
||||
"query": "man of the house six years old expectation",
|
||||
"returned": [
|
||||
"? ?}&?#??X\b",
|
||||
"kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8",
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||||
"? ?}&?#??X\b",
|
||||
"? ?}&?#??X\b",
|
||||
"? ?}&?#??X\b",
|
||||
"? ?}&?#??X\b",
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||||
"kn-57b4c5e7-40c6-4c90-bf14-71841b0081d4",
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||||
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||||
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||||
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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": "q33",
|
||||
"category": "nonsense",
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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},
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||||
{
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||||
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|
||||
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||||
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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": "q35",
|
||||
"category": "nonsense",
|
||||
"query": "xxqzzt vurblenacht throom",
|
||||
"returned": [
|
||||
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
|
||||
"?V?",
|
||||
"knw-920c891f-bb8c-48c4-9afc-018ef12dcdc4",
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"?m?\\}Q??6??",
|
||||
"art-79042b8b-6192-440f-90b0-60708f7e6325",
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||||
"?m?\\}Q??6??",
|
||||
"art-ddfcd045-2c3b-4a1e-9966-fec5ce44e1dd",
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||||
"?m?\\}Q??6??",
|
||||
"bl-4476e856-c567-4b49-8ff7-d7dca3e5715e",
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||||
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|
||||
],
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||||
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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": "q36",
|
||||
"category": "superseded",
|
||||
"query": "is the self-improvement architecture called DARMA or DHARMA",
|
||||
"returned": [
|
||||
"7?e?7???\f3?",
|
||||
"mem-80d7416b-20e9-48a0-b176-b215527e2f56",
|
||||
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|
||||
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|
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"? ?}&?#??X\b",
|
||||
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||||
"?of?7???",
|
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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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||||
"id": "q37",
|
||||
"category": "superseded",
|
||||
"query": "how many provisional patents does Will actually have",
|
||||
"returned": [
|
||||
"? ?}&?#??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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||||
{
|
||||
"id": "q38",
|
||||
"category": "superseded",
|
||||
"query": "is MCP still the live integration layer",
|
||||
"returned": [
|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
"???Ͼd??W\b?",
|
||||
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||||
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}
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||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user