Compare commits
6 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Tim Lingo
|
55f9ee3cb0 | ||
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Tim Lingo
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9c39084e60 | ||
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Tim Lingo
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6f3a048f36 | ||
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Tim Lingo
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059ce02003 | ||
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Neuron
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635453b936 | ||
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Neuron
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315b2eff00 |
@@ -0,0 +1,21 @@
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import numpy as np, json, urllib.request
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SP="/private/tmp/claude-501/-Users-timlingo/82369039-a20e-4b5a-8a5e-28234a57b996/scratchpad"
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np.seterr(all='ignore')
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M=np.load(SP+'/emb.npy'); eids=open(SP+'/ids.txt',encoding='utf-8',errors='surrogateescape').read().split('\n')
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eidx={k:i for i,k in enumerate(eids)}
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gold=json.load(open("/Users/timlingo/Development/neuron-technologies/_wt-assoc-leg/tools/retrieval-eval/gold_set.json"))['queries']
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VALS=sorted({r for q in gold if q['category']=='paraphrase' for r in q['relevant']})
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VI=[eidx[v] for v in VALS]
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def emb(t):
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b=json.dumps({"model":"nomic-embed-text","prompt":t}).encode()
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r=urllib.request.Request("http://127.0.0.1:11434/api/embeddings",data=b,headers={"Content-Type":"application/json"})
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v=np.array(json.load(urllib.request.urlopen(r,timeout=60))["embedding"],dtype=np.float32)
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return v/(np.linalg.norm(v)+1e-9)
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print("qid cat bestValueNodeGlobalRank goldGlobalRank goldSiblingRank")
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for q in gold:
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if q['category']!='paraphrase': continue
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v=emb(q['query']); s=M@v; s[~np.isfinite(s)]=-1
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ranks=sorted(int((s>s[j]).sum())+1 for j in VI)
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g=eidx[q['relevant'][0]]; gr=int((s>s[g]).sum())+1
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sv=np.array([s[j] for j in VI]); sib=int((sv>s[g]).sum())+1
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print("%-4s %-11s best=%-5d (top3 val ranks %s) gold=%-5d sib=%d" % (q['id'],q['category'],ranks[0],ranks[:3],gr,sib))
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@@ -0,0 +1,150 @@
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{
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"baseline": "assoc-leg",
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"candidate": "semseed",
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"n_shared_queries": 38,
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"fixed_by_candidate": [
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"q18",
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"q19",
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"q22"
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],
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"broken_by_candidate": [
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"q11"
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],
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"discordant": 4,
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"net_queries": 2,
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"mcnemar_exact_p": 0.625,
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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": "no measurable difference",
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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": 0.6285714285714286,
|
||||
"recall@5": 0.45309194773480493,
|
||||
"recall@10": 0.5405733155733157,
|
||||
"precision@5": 0.17714285714285719,
|
||||
"mrr@10": 0.42650793650793645,
|
||||
"nonsense_clean": "2/3",
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"superseded_outranks": "2/3",
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||||
"latency_ms_p50": 1228.5,
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||||
"latency_ms_p95": 1681.8,
|
||||
"latency_ms_max": 1718.6,
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||||
"errors": 0,
|
||||
"by_category": {
|
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"associative": {
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"n": 6,
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||||
"hit@5": 0.6666666666666666,
|
||||
"recall@5": 0.07342657342657342,
|
||||
"recall@10": 0.24825174825174826,
|
||||
"mrr@10": 0.22777777777777777
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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,
|
||||
"recall@10": 0.38461538461538464,
|
||||
"mrr@10": 0.17307692307692307
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||||
},
|
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"phrase": {
|
||||
"n": 7,
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||||
"hit@5": 0.8571428571428571,
|
||||
"recall@5": 0.4882369614512472,
|
||||
"recall@10": 0.6329365079365079,
|
||||
"mrr@10": 0.6634920634920636
|
||||
},
|
||||
"superseded": {
|
||||
"n": 3,
|
||||
"hit@5": 0.3333333333333333,
|
||||
"recall@5": 0.3333333333333333,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.2222222222222222,
|
||||
"outranks": 2
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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": 0.6857142857142857,
|
||||
"recall@5": 0.5213459159887731,
|
||||
"recall@10": 0.6027048348476919,
|
||||
"precision@5": 0.18285714285714294,
|
||||
"mrr@10": 0.4608730158730158,
|
||||
"nonsense_clean": "2/3",
|
||||
"superseded_outranks": "2/3",
|
||||
"latency_ms_p50": 1227.1,
|
||||
"latency_ms_p95": 1692.6,
|
||||
"latency_ms_max": 1710.4,
|
||||
"errors": 0,
|
||||
"by_category": {
|
||||
"associative": {
|
||||
"n": 6,
|
||||
"hit@5": 0.6666666666666666,
|
||||
"recall@5": 0.07342657342657344,
|
||||
"recall@10": 0.24825174825174823,
|
||||
"mrr@10": 0.20833333333333334
|
||||
},
|
||||
"exact_rare": {
|
||||
"n": 6,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"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": 0.6153846153846154,
|
||||
"recall@5": 0.6153846153846154,
|
||||
"recall@10": 0.6153846153846154,
|
||||
"mrr@10": 0.2846153846153846
|
||||
},
|
||||
"phrase": {
|
||||
"n": 7,
|
||||
"hit@5": 0.7142857142857143,
|
||||
"recall@5": 0.40093537414965985,
|
||||
"recall@10": 0.5150226757369615,
|
||||
"mrr@10": 0.6507936507936508
|
||||
},
|
||||
"superseded": {
|
||||
"n": 3,
|
||||
"hit@5": 0.3333333333333333,
|
||||
"recall@5": 0.3333333333333333,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.20833333333333334,
|
||||
"outranks": 2
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||||
}
|
||||
}
|
||||
},
|
||||
"repeat_variance": {
|
||||
"baseline": {
|
||||
"runs": 2,
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||||
"hit@5_min": 0.6285714285714286,
|
||||
"hit@5_max": 0.6285714285714286,
|
||||
"spread_queries": 0
|
||||
},
|
||||
"candidate": {
|
||||
"runs": 2,
|
||||
"hit@5_min": 0.6857142857142857,
|
||||
"hit@5_max": 0.6857142857142857,
|
||||
"spread_queries": 0
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||||
}
|
||||
}
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}
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@@ -0,0 +1,143 @@
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{
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"baseline": "hybrid-semantic",
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"candidate": "assoc-leg",
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"n_shared_queries": 38,
|
||||
"fixed_by_candidate": [
|
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"q27",
|
||||
"q28",
|
||||
"q29",
|
||||
"q31"
|
||||
],
|
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"broken_by_candidate": [],
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"discordant": 4,
|
||||
"net_queries": 4,
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||||
"mcnemar_exact_p": 0.125,
|
||||
"min_detectable_swing_queries": 6,
|
||||
"observed_run_to_run_drift_queries": 0,
|
||||
"noise_floor_queries": 6,
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"verdict": "no measurable difference",
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"baseline_aggregate": {
|
||||
"n_queries": 38,
|
||||
"n_scored": 35,
|
||||
"hit@5": 0.5142857142857142,
|
||||
"recall@5": 0.4409013605442177,
|
||||
"recall@10": 0.5047619047619047,
|
||||
"precision@5": 0.15428571428571433,
|
||||
"mrr@10": 0.38746031746031745,
|
||||
"nonsense_clean": "2/3",
|
||||
"superseded_outranks": "2/3",
|
||||
"latency_ms_p50": 1219.7,
|
||||
"latency_ms_p95": 1667.1,
|
||||
"latency_ms_max": 1720.2,
|
||||
"errors": 0,
|
||||
"by_category": {
|
||||
"associative": {
|
||||
"n": 6,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"mrr@10": 0.0
|
||||
},
|
||||
"exact_rare": {
|
||||
"n": 6,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
"nonsense": {
|
||||
"n": 3,
|
||||
"clean": 2,
|
||||
"avg_false_positives": 3.3333333333333335
|
||||
},
|
||||
"paraphrase": {
|
||||
"n": 13,
|
||||
"hit@5": 0.38461538461538464,
|
||||
"recall@5": 0.38461538461538464,
|
||||
"recall@10": 0.38461538461538464,
|
||||
"mrr@10": 0.17307692307692307
|
||||
},
|
||||
"phrase": {
|
||||
"n": 7,
|
||||
"hit@5": 0.8571428571428571,
|
||||
"recall@5": 0.4902210884353741,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.6634920634920636
|
||||
},
|
||||
"superseded": {
|
||||
"n": 3,
|
||||
"hit@5": 0.3333333333333333,
|
||||
"recall@5": 0.3333333333333333,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.2222222222222222,
|
||||
"outranks": 2
|
||||
}
|
||||
}
|
||||
},
|
||||
"candidate_aggregate": {
|
||||
"n_queries": 38,
|
||||
"n_scored": 35,
|
||||
"hit@5": 0.6285714285714286,
|
||||
"recall@5": 0.45309194773480493,
|
||||
"recall@10": 0.5405733155733157,
|
||||
"precision@5": 0.17714285714285719,
|
||||
"mrr@10": 0.42650793650793645,
|
||||
"nonsense_clean": "2/3",
|
||||
"superseded_outranks": "2/3",
|
||||
"latency_ms_p50": 1228.5,
|
||||
"latency_ms_p95": 1681.8,
|
||||
"latency_ms_max": 1718.6,
|
||||
"errors": 0,
|
||||
"by_category": {
|
||||
"associative": {
|
||||
"n": 6,
|
||||
"hit@5": 0.6666666666666666,
|
||||
"recall@5": 0.07342657342657342,
|
||||
"recall@10": 0.24825174825174826,
|
||||
"mrr@10": 0.22777777777777777
|
||||
},
|
||||
"exact_rare": {
|
||||
"n": 6,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
"nonsense": {
|
||||
"n": 3,
|
||||
"clean": 2,
|
||||
"avg_false_positives": 3.3333333333333335
|
||||
},
|
||||
"paraphrase": {
|
||||
"n": 13,
|
||||
"hit@5": 0.38461538461538464,
|
||||
"recall@5": 0.38461538461538464,
|
||||
"recall@10": 0.38461538461538464,
|
||||
"mrr@10": 0.17307692307692307
|
||||
},
|
||||
"phrase": {
|
||||
"n": 7,
|
||||
"hit@5": 0.8571428571428571,
|
||||
"recall@5": 0.4882369614512472,
|
||||
"recall@10": 0.6329365079365079,
|
||||
"mrr@10": 0.6634920634920636
|
||||
},
|
||||
"superseded": {
|
||||
"n": 3,
|
||||
"hit@5": 0.3333333333333333,
|
||||
"recall@5": 0.3333333333333333,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.2222222222222222,
|
||||
"outranks": 2
|
||||
}
|
||||
}
|
||||
},
|
||||
"repeat_variance": {
|
||||
"baseline": {
|
||||
"runs": 2,
|
||||
"hit@5_min": 0.5142857142857142,
|
||||
"hit@5_max": 0.5142857142857142,
|
||||
"spread_queries": 0
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,154 @@
|
||||
{
|
||||
"baseline": "hybrid-semantic",
|
||||
"candidate": "semseed",
|
||||
"n_shared_queries": 38,
|
||||
"fixed_by_candidate": [
|
||||
"q18",
|
||||
"q19",
|
||||
"q22",
|
||||
"q27",
|
||||
"q28",
|
||||
"q29",
|
||||
"q31"
|
||||
],
|
||||
"broken_by_candidate": [
|
||||
"q11"
|
||||
],
|
||||
"discordant": 8,
|
||||
"net_queries": 6,
|
||||
"mcnemar_exact_p": 0.0703125,
|
||||
"min_detectable_swing_queries": 6,
|
||||
"observed_run_to_run_drift_queries": 0,
|
||||
"noise_floor_queries": 6,
|
||||
"verdict": "candidate better",
|
||||
"baseline_aggregate": {
|
||||
"n_queries": 38,
|
||||
"n_scored": 35,
|
||||
"hit@5": 0.5142857142857142,
|
||||
"recall@5": 0.4409013605442177,
|
||||
"recall@10": 0.5047619047619047,
|
||||
"precision@5": 0.15428571428571433,
|
||||
"mrr@10": 0.38746031746031745,
|
||||
"nonsense_clean": "2/3",
|
||||
"superseded_outranks": "2/3",
|
||||
"latency_ms_p50": 1219.7,
|
||||
"latency_ms_p95": 1667.1,
|
||||
"latency_ms_max": 1720.2,
|
||||
"errors": 0,
|
||||
"by_category": {
|
||||
"associative": {
|
||||
"n": 6,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"mrr@10": 0.0
|
||||
},
|
||||
"exact_rare": {
|
||||
"n": 6,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
"nonsense": {
|
||||
"n": 3,
|
||||
"clean": 2,
|
||||
"avg_false_positives": 3.3333333333333335
|
||||
},
|
||||
"paraphrase": {
|
||||
"n": 13,
|
||||
"hit@5": 0.38461538461538464,
|
||||
"recall@5": 0.38461538461538464,
|
||||
"recall@10": 0.38461538461538464,
|
||||
"mrr@10": 0.17307692307692307
|
||||
},
|
||||
"phrase": {
|
||||
"n": 7,
|
||||
"hit@5": 0.8571428571428571,
|
||||
"recall@5": 0.4902210884353741,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.6634920634920636
|
||||
},
|
||||
"superseded": {
|
||||
"n": 3,
|
||||
"hit@5": 0.3333333333333333,
|
||||
"recall@5": 0.3333333333333333,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.2222222222222222,
|
||||
"outranks": 2
|
||||
}
|
||||
}
|
||||
},
|
||||
"candidate_aggregate": {
|
||||
"n_queries": 38,
|
||||
"n_scored": 35,
|
||||
"hit@5": 0.6857142857142857,
|
||||
"recall@5": 0.5213459159887731,
|
||||
"recall@10": 0.6027048348476919,
|
||||
"precision@5": 0.18285714285714294,
|
||||
"mrr@10": 0.4608730158730158,
|
||||
"nonsense_clean": "2/3",
|
||||
"superseded_outranks": "2/3",
|
||||
"latency_ms_p50": 1227.1,
|
||||
"latency_ms_p95": 1692.6,
|
||||
"latency_ms_max": 1710.4,
|
||||
"errors": 0,
|
||||
"by_category": {
|
||||
"associative": {
|
||||
"n": 6,
|
||||
"hit@5": 0.6666666666666666,
|
||||
"recall@5": 0.07342657342657344,
|
||||
"recall@10": 0.24825174825174823,
|
||||
"mrr@10": 0.20833333333333334
|
||||
},
|
||||
"exact_rare": {
|
||||
"n": 6,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
"nonsense": {
|
||||
"n": 3,
|
||||
"clean": 2,
|
||||
"avg_false_positives": 3.3333333333333335
|
||||
},
|
||||
"paraphrase": {
|
||||
"n": 13,
|
||||
"hit@5": 0.6153846153846154,
|
||||
"recall@5": 0.6153846153846154,
|
||||
"recall@10": 0.6153846153846154,
|
||||
"mrr@10": 0.2846153846153846
|
||||
},
|
||||
"phrase": {
|
||||
"n": 7,
|
||||
"hit@5": 0.7142857142857143,
|
||||
"recall@5": 0.40093537414965985,
|
||||
"recall@10": 0.5150226757369615,
|
||||
"mrr@10": 0.6507936507936508
|
||||
},
|
||||
"superseded": {
|
||||
"n": 3,
|
||||
"hit@5": 0.3333333333333333,
|
||||
"recall@5": 0.3333333333333333,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.20833333333333334,
|
||||
"outranks": 2
|
||||
}
|
||||
}
|
||||
},
|
||||
"repeat_variance": {
|
||||
"baseline": {
|
||||
"runs": 2,
|
||||
"hit@5_min": 0.5142857142857142,
|
||||
"hit@5_max": 0.5142857142857142,
|
||||
"spread_queries": 0
|
||||
},
|
||||
"candidate": {
|
||||
"runs": 2,
|
||||
"hit@5_min": 0.6857142857142857,
|
||||
"hit@5_max": 0.6857142857142857,
|
||||
"spread_queries": 0
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,145 @@
|
||||
{
|
||||
"baseline": "baseline-embcorpus",
|
||||
"candidate": "hybrid-semantic",
|
||||
"n_shared_queries": 38,
|
||||
"fixed_by_candidate": [
|
||||
"q15",
|
||||
"q16",
|
||||
"q20",
|
||||
"q21",
|
||||
"q26",
|
||||
"q37"
|
||||
],
|
||||
"broken_by_candidate": [],
|
||||
"discordant": 6,
|
||||
"net_queries": 6,
|
||||
"mcnemar_exact_p": 0.03125,
|
||||
"min_detectable_swing_queries": 6,
|
||||
"observed_run_to_run_drift_queries": 0,
|
||||
"noise_floor_queries": 6,
|
||||
"verdict": "candidate better",
|
||||
"baseline_aggregate": {
|
||||
"n_queries": 38,
|
||||
"n_scored": 35,
|
||||
"hit@5": 0.34285714285714286,
|
||||
"recall@5": 0.26947278911564626,
|
||||
"recall@10": 0.3333333333333333,
|
||||
"precision@5": 0.12000000000000001,
|
||||
"mrr@10": 0.2943197278911564,
|
||||
"nonsense_clean": "2/3",
|
||||
"superseded_outranks": "1/3",
|
||||
"latency_ms_p50": 1145.9,
|
||||
"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
|
||||
},
|
||||
"exact_rare": {
|
||||
"n": 6,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
"nonsense": {
|
||||
"n": 3,
|
||||
"clean": 2,
|
||||
"avg_false_positives": 3.3333333333333335
|
||||
},
|
||||
"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,
|
||||
"outranks": 1
|
||||
}
|
||||
}
|
||||
},
|
||||
"candidate_aggregate": {
|
||||
"n_queries": 38,
|
||||
"n_scored": 35,
|
||||
"hit@5": 0.5142857142857142,
|
||||
"recall@5": 0.4409013605442177,
|
||||
"recall@10": 0.5047619047619047,
|
||||
"precision@5": 0.15428571428571433,
|
||||
"mrr@10": 0.38746031746031745,
|
||||
"nonsense_clean": "2/3",
|
||||
"superseded_outranks": "2/3",
|
||||
"latency_ms_p50": 1219.7,
|
||||
"latency_ms_p95": 1667.1,
|
||||
"latency_ms_max": 1720.2,
|
||||
"errors": 0,
|
||||
"by_category": {
|
||||
"associative": {
|
||||
"n": 6,
|
||||
"hit@5": 0.0,
|
||||
"recall@5": 0.0,
|
||||
"recall@10": 0.0,
|
||||
"mrr@10": 0.0
|
||||
},
|
||||
"exact_rare": {
|
||||
"n": 6,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
"nonsense": {
|
||||
"n": 3,
|
||||
"clean": 2,
|
||||
"avg_false_positives": 3.3333333333333335
|
||||
},
|
||||
"paraphrase": {
|
||||
"n": 13,
|
||||
"hit@5": 0.38461538461538464,
|
||||
"recall@5": 0.38461538461538464,
|
||||
"recall@10": 0.38461538461538464,
|
||||
"mrr@10": 0.17307692307692307
|
||||
},
|
||||
"phrase": {
|
||||
"n": 7,
|
||||
"hit@5": 0.8571428571428571,
|
||||
"recall@5": 0.4902210884353741,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.6634920634920636
|
||||
},
|
||||
"superseded": {
|
||||
"n": 3,
|
||||
"hit@5": 0.3333333333333333,
|
||||
"recall@5": 0.3333333333333333,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.2222222222222222,
|
||||
"outranks": 2
|
||||
}
|
||||
}
|
||||
},
|
||||
"repeat_variance": {
|
||||
"candidate": {
|
||||
"runs": 2,
|
||||
"hit@5_min": 0.5142857142857142,
|
||||
"hit@5_max": 0.5142857142857142,
|
||||
"spread_queries": 0
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,147 @@
|
||||
{
|
||||
"baseline": "semseed",
|
||||
"candidate": "bm25lex",
|
||||
"n_shared_queries": 38,
|
||||
"fixed_by_candidate": [
|
||||
"q10",
|
||||
"q11"
|
||||
],
|
||||
"broken_by_candidate": [],
|
||||
"discordant": 2,
|
||||
"net_queries": 2,
|
||||
"mcnemar_exact_p": 0.5,
|
||||
"min_detectable_swing_queries": 6,
|
||||
"observed_run_to_run_drift_queries": 0,
|
||||
"noise_floor_queries": 6,
|
||||
"verdict": "no measurable difference",
|
||||
"baseline_aggregate": {
|
||||
"n_queries": 38,
|
||||
"n_scored": 35,
|
||||
"hit@5": 0.6857142857142857,
|
||||
"recall@5": 0.5213459159887731,
|
||||
"recall@10": 0.6027048348476919,
|
||||
"precision@5": 0.18285714285714294,
|
||||
"mrr@10": 0.4608730158730158,
|
||||
"nonsense_clean": "2/3",
|
||||
"superseded_outranks": "2/3",
|
||||
"latency_ms_p50": 1227.1,
|
||||
"latency_ms_p95": 1692.6,
|
||||
"latency_ms_max": 1710.4,
|
||||
"errors": 0,
|
||||
"by_category": {
|
||||
"associative": {
|
||||
"n": 6,
|
||||
"hit@5": 0.6666666666666666,
|
||||
"recall@5": 0.07342657342657344,
|
||||
"recall@10": 0.24825174825174823,
|
||||
"mrr@10": 0.20833333333333334
|
||||
},
|
||||
"exact_rare": {
|
||||
"n": 6,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
"nonsense": {
|
||||
"n": 3,
|
||||
"clean": 2,
|
||||
"avg_false_positives": 3.3333333333333335
|
||||
},
|
||||
"paraphrase": {
|
||||
"n": 13,
|
||||
"hit@5": 0.6153846153846154,
|
||||
"recall@5": 0.6153846153846154,
|
||||
"recall@10": 0.6153846153846154,
|
||||
"mrr@10": 0.2846153846153846
|
||||
},
|
||||
"phrase": {
|
||||
"n": 7,
|
||||
"hit@5": 0.7142857142857143,
|
||||
"recall@5": 0.40093537414965985,
|
||||
"recall@10": 0.5150226757369615,
|
||||
"mrr@10": 0.6507936507936508
|
||||
},
|
||||
"superseded": {
|
||||
"n": 3,
|
||||
"hit@5": 0.3333333333333333,
|
||||
"recall@5": 0.3333333333333333,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.20833333333333334,
|
||||
"outranks": 2
|
||||
}
|
||||
}
|
||||
},
|
||||
"candidate_aggregate": {
|
||||
"n_queries": 38,
|
||||
"n_scored": 35,
|
||||
"hit@5": 0.7428571428571429,
|
||||
"recall@5": 0.5536485340056769,
|
||||
"recall@10": 0.6175677497106068,
|
||||
"precision@5": 0.20000000000000007,
|
||||
"mrr@10": 0.5021428571428571,
|
||||
"nonsense_clean": "2/3",
|
||||
"superseded_outranks": "2/3",
|
||||
"latency_ms_p50": 1184.4,
|
||||
"latency_ms_p95": 1620.0,
|
||||
"latency_ms_max": 1655.4,
|
||||
"errors": 0,
|
||||
"by_category": {
|
||||
"associative": {
|
||||
"n": 6,
|
||||
"hit@5": 0.6666666666666666,
|
||||
"recall@5": 0.08857808857808858,
|
||||
"recall@10": 0.23310023310023312,
|
||||
"mrr@10": 0.25
|
||||
},
|
||||
"exact_rare": {
|
||||
"n": 6,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
"nonsense": {
|
||||
"n": 3,
|
||||
"clean": 2,
|
||||
"avg_false_positives": 3.3333333333333335
|
||||
},
|
||||
"paraphrase": {
|
||||
"n": 13,
|
||||
"hit@5": 0.6153846153846154,
|
||||
"recall@5": 0.6153846153846154,
|
||||
"recall@10": 0.6153846153846154,
|
||||
"mrr@10": 0.2846153846153846
|
||||
},
|
||||
"phrase": {
|
||||
"n": 7,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 0.5494614512471656,
|
||||
"recall@10": 0.6023242630385487,
|
||||
"mrr@10": 0.8214285714285714
|
||||
},
|
||||
"superseded": {
|
||||
"n": 3,
|
||||
"hit@5": 0.3333333333333333,
|
||||
"recall@5": 0.3333333333333333,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.20833333333333334,
|
||||
"outranks": 2
|
||||
}
|
||||
}
|
||||
},
|
||||
"repeat_variance": {
|
||||
"baseline": {
|
||||
"runs": 2,
|
||||
"hit@5_min": 0.6857142857142857,
|
||||
"hit@5_max": 0.6857142857142857,
|
||||
"spread_queries": 0
|
||||
},
|
||||
"candidate": {
|
||||
"runs": 2,
|
||||
"hit@5_min": 0.7428571428571429,
|
||||
"hit@5_max": 0.7428571428571429,
|
||||
"spread_queries": 0
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,43 @@
|
||||
import json,sys,time,urllib.request,threading,queue
|
||||
SRC="/Users/timlingo/neuron-memory-backups/snapshot-pre-repair-20260806.json"
|
||||
OUT=sys.argv[1]
|
||||
URL="http://127.0.0.1:11434/api/embeddings"; MODEL="nomic-embed-text"
|
||||
MAXB=2000 # ENGRAM_EMBED_MAX_CHARS, applied to bytes as the C code does
|
||||
d=json.load(open(SRC,encoding='utf-8',errors='surrogateescape'))
|
||||
tasks=[]
|
||||
for n in d["nodes"]:
|
||||
c=n.get("content") or ""; t=n.get("node_type") or ""
|
||||
if len(c)<8: continue # eg_embed_eligible
|
||||
if t in ("InternalStateEvent","Tag"): continue
|
||||
b=c.encode('utf-8',errors='surrogateescape')[:MAXB]
|
||||
tasks.append((n.get("id") or "", b.decode('utf-8',errors='replace')))
|
||||
del d
|
||||
print("tasks",len(tasks),flush=True)
|
||||
q=queue.Queue(); [q.put(t) for t in tasks]
|
||||
lock=threading.Lock(); f=open(OUT,"w",encoding="utf-8",errors="surrogateescape"); done=[0]; t0=time.time(); fails=[0]
|
||||
def work():
|
||||
while True:
|
||||
try: nid,txt=q.get_nowait()
|
||||
except queue.Empty: return
|
||||
v=None
|
||||
for attempt in range(3):
|
||||
try:
|
||||
body=json.dumps({"model":MODEL,"prompt":txt}).encode()
|
||||
r=urllib.request.Request(URL,data=body,headers={"Content-Type":"application/json"})
|
||||
with urllib.request.urlopen(r,timeout=120) as fh: v=json.load(fh)["embedding"]
|
||||
break
|
||||
except Exception as e:
|
||||
if attempt==2:
|
||||
with lock: fails[0]+=1
|
||||
time.sleep(0.5)
|
||||
with lock:
|
||||
if v: f.write(nid+"\t"+",".join("%.5g"%x for x in v)+"\n")
|
||||
done[0]+=1
|
||||
if done[0]%2000==0:
|
||||
el=time.time()-t0
|
||||
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)
|
||||
f.flush()
|
||||
ths=[threading.Thread(target=work) for _ in range(8)]
|
||||
[t.start() for t in ths]; [t.join() for t in ths]
|
||||
f.close()
|
||||
print("DONE",done[0],"fails",fails[0],"secs %.1f"%(time.time()-t0),flush=True)
|
||||
@@ -0,0 +1,123 @@
|
||||
import numpy as np, json, urllib.request, collections, sys
|
||||
SP="/private/tmp/claude-501/-Users-timlingo/82369039-a20e-4b5a-8a5e-28234a57b996/scratchpad"
|
||||
EV="/Users/timlingo/Development/neuron-technologies/_wt-assoc-leg/tools/retrieval-eval/"
|
||||
np.seterr(all='ignore')
|
||||
M=np.load(SP+'/emb.npy'); eids=open(SP+'/ids.txt',encoding='utf-8',errors='surrogateescape').read().split('\n')
|
||||
eidx={k:i for i,k in enumerate(eids)}
|
||||
d=json.load(open('/Users/timlingo/neuron-memory-backups/snapshot-pre-repair-20260806.json',encoding='utf-8',errors='surrogateescape'))
|
||||
N={n['id']:n for n in d['nodes']}
|
||||
STRUCT={"identity","contains","superseded_by","references","embodies","demonstrated_by","canonical-self","depends_on","currently_holds","activates"}
|
||||
adj=collections.defaultdict(list); hasstruct=set()
|
||||
for e in d['edges']:
|
||||
if e.get('relation') not in STRUCT: continue
|
||||
w=float(e.get('weight') or 0.0)
|
||||
adj[e['from_id']].append((e['to_id'],w)); adj[e['to_id']].append((e['from_id'],w))
|
||||
hasstruct.add(e['from_id']); hasstruct.add(e['to_id'])
|
||||
del d
|
||||
gold={q['id']:q for q in json.load(open(EV+"gold_set.json"))['queries']}
|
||||
LEX={r['id']:r['returned'] for r in json.load(open(EV+"results-main.json"))['rows']}
|
||||
CACHE={}
|
||||
def emb(t):
|
||||
if t in CACHE: return CACHE[t]
|
||||
b=json.dumps({"model":"nomic-embed-text","prompt":t}).encode()
|
||||
r=urllib.request.Request("http://127.0.0.1:11434/api/embeddings",data=b,headers={"Content-Type":"application/json"})
|
||||
v=np.array(json.load(urllib.request.urlopen(r,timeout=60))["embedding"],dtype=np.float32)
|
||||
v=v/(np.linalg.norm(v)+1e-9); CACHE[t]=v; return v
|
||||
FIRE=0.02; DECAY=0.7; DEPTH=2; SEED_MIN=0.60; ASSOC_MAX=64
|
||||
def assoc(seeds, s):
|
||||
act={x:1.0 for x in seeds}; seen={x:2 for x in seeds}
|
||||
Q=[(x,0) for x in seeds]; h=0
|
||||
while h<len(Q):
|
||||
cur,hop=Q[h]; h+=1
|
||||
if hop>=DEPTH: continue
|
||||
p=act[cur]
|
||||
for oid,w in adj.get(cur,()):
|
||||
n=N.get(oid)
|
||||
if not n or n.get('node_type') in ('Tag','InternalStateEvent'): continue
|
||||
na=p*w*DECAY*float(n.get('salience') or 0.0)
|
||||
if na<FIRE: continue
|
||||
if oid in seen and na<=act.get(oid,0): continue
|
||||
act[oid]=na
|
||||
if oid not in seen: seen[oid]=1
|
||||
Q.append((oid,hop+1))
|
||||
out=[]
|
||||
for k,v in seen.items():
|
||||
if v!=1 or k not in eidx: continue
|
||||
c=float(s[eidx[k]])
|
||||
if c<=0: continue
|
||||
out.append((c,k))
|
||||
out.sort(reverse=True)
|
||||
return [k for c,k in out[:ASSOC_MAX]]
|
||||
def inter3(L,S,A,lim=10):
|
||||
out=[]; li=si=ai=0
|
||||
while len(out)<lim and (li<len(L) or si<len(S) or ai<len(A)):
|
||||
if li<len(L):
|
||||
if L[li] not in out: out.append(L[li])
|
||||
li+=1
|
||||
if len(out)>=lim: break
|
||||
if si<len(S):
|
||||
if S[si] not in out: out.append(S[si])
|
||||
si+=1
|
||||
if len(out)>=lim: break
|
||||
if ai<len(A):
|
||||
if A[ai] not in out: out.append(A[ai])
|
||||
ai+=1
|
||||
return out
|
||||
def run(mode, K=0):
|
||||
res={}
|
||||
for qid,q in gold.items():
|
||||
v=emb(q['query']); s=M@v; s[~np.isfinite(s)]=-1
|
||||
L=LEX[qid][:10]
|
||||
ordr=np.argsort(-s)
|
||||
S=[eids[j] for j in ordr[:10] if s[j]>SEED_MIN]
|
||||
A=[]
|
||||
if mode!='hybrid':
|
||||
seeds=[x for x in L[:3] if x in N]
|
||||
if mode=='semseed':
|
||||
seeds=seeds+[eids[j] for j in ordr[:K] if eids[j] in N and eids[j] not in seeds]
|
||||
A=assoc(seeds,s) if seeds else []
|
||||
res[qid]=inter3(L,S,A)
|
||||
return res
|
||||
def score(res,label):
|
||||
hits=0; det={}
|
||||
for qid,q in gold.items():
|
||||
out=res[qid][:5]
|
||||
if q['category']=='nonsense': ok = (len(res[qid])==0)
|
||||
elif q['category']=='superseded':
|
||||
rel=q['relevant']; must=q.get('must_outrank') or {}
|
||||
ok=False
|
||||
for good,bad in (must.items() if isinstance(must,dict) else []):
|
||||
ok = good in res[qid] and (bad not in res[qid] or res[qid].index(good)<res[qid].index(bad))
|
||||
if not must: ok = any(r in out for r in rel)
|
||||
else: ok = any(r in out for r in q['relevant'])
|
||||
det[qid]=ok; hits+=ok
|
||||
print("%-22s outcome-true=%d/38" % (label,hits))
|
||||
return det
|
||||
print("gold sample keys:", list(list(gold.values())[0].keys()))
|
||||
mk=[q for q in gold.values() if q['category']=='superseded'][0]
|
||||
print("superseded fields:", {k:v for k,v in mk.items() if k!='derivation'})
|
||||
a=score(run('hybrid'),'sim hybrid(L+S)')
|
||||
b=score(run('lexseed'),'sim assoc(lex seeds)')
|
||||
for K in (3,5,10):
|
||||
c=score(run('semseed',K),'sim assoc(+sem K=%d)'%K)
|
||||
d=[q for q in gold if c[q]!=b[q]]
|
||||
print(" vs lexseed: moved=%d gains=%s losses=%s"%(len(d),[q for q in d if c[q]],[q for q in d if not c[q]]))
|
||||
e=[q for q in gold if c[q]!=a[q]]
|
||||
print(" vs hybrid : moved=%d gains=%s losses=%s"%(len(e),[q for q in e if c[q]],[q for q in e if not c[q]]))
|
||||
|
||||
print("\n=== Will's own constant ENGRAM_EMBED_SEED_K = 8 ===")
|
||||
c=score(run('semseed',8),'sim assoc(+sem K=8)')
|
||||
for base,lab in ((b,'lexseed(iter2)'),(a,'hybrid(iter1 KEEP)')):
|
||||
dd=[q for q in gold if c[q]!=base[q]]
|
||||
print(" vs %-18s moved=%d gains=%s losses=%s"%(lab,len(dd),[q for q in dd if c[q]],[q for q in dd if not c[q]]))
|
||||
# diagnostic: what is assoc rank-1 for each paraphrase query at K=8
|
||||
print("\nassoc leg head at K=8 (paraphrase):")
|
||||
for qid,q in gold.items():
|
||||
if q['category'] not in ('paraphrase','nonsense'): continue
|
||||
v=emb(q['query']); s=M@v; s[~np.isfinite(s)]=-1
|
||||
L=LEX[qid][:10]; ordr=np.argsort(-s)
|
||||
seeds=[x for x in L[:3] if x in N]+[eids[j] for j in ordr[:8] if eids[j] in N and eids[j] not in L[:3]]
|
||||
A=assoc(seeds,s) if seeds else []
|
||||
rel=set(q['relevant']); gr=next((i+1 for i,x in enumerate(A) if x in rel),None)
|
||||
print(" %-4s %-11s |A|=%-4d goldAssocRank=%-5s head=%s"%(qid,q['category'],len(A),gr,
|
||||
[ (N[x].get('label') or x)[:26] for x in A[:3] ]))
|
||||
@@ -0,0 +1,17 @@
|
||||
import json,sys
|
||||
SRC="/Users/timlingo/neuron-memory-backups/snapshot-pre-repair-20260806.json"
|
||||
TSV,OUT=sys.argv[1],sys.argv[2]
|
||||
emb={}
|
||||
for line in open(TSV,encoding='utf-8',errors='surrogateescape'):
|
||||
p=line.rstrip("\n").rsplit("\t",1)
|
||||
if len(p)==2 and p[1].count(",")>100: emb[p[0]]=p[1]
|
||||
print("vectors",len(emb),flush=True)
|
||||
d=json.load(open(SRC,encoding='utf-8',errors='surrogateescape'))
|
||||
hit=0
|
||||
for n in d["nodes"]:
|
||||
v=emb.get(n.get("id") or "")
|
||||
if v: n["emb"]=v; hit+=1
|
||||
print("attached",hit,"of",len(d["nodes"]),flush=True)
|
||||
with open(OUT,"w",encoding='utf-8',errors='surrogateescape') as f:
|
||||
json.dump(d,f,ensure_ascii=False)
|
||||
print("wrote",OUT,flush=True)
|
||||
@@ -0,0 +1,956 @@
|
||||
{
|
||||
"label": "assoc-leg-r2",
|
||||
"soul_binary": "/private/tmp/claude-501/-Users-timlingo/82369039-a20e-4b5a-8a5e-28234a57b996/scratchpad/soul-assoc",
|
||||
"soul_md5": "ab9d490ecdfb1f9e6f23cca841ad8fb5",
|
||||
"corpus": "/Users/timlingo/neuron-eval-corpora/snapshot-pre-repair-20260806-embedded.json",
|
||||
"corpus_nodes": 78768,
|
||||
"corpus_edges": 14214,
|
||||
"gold_set": "/Users/timlingo/Development/neuron-technologies/_wt-eval/tools/retrieval-eval/gold_set.json",
|
||||
"limit": 10,
|
||||
"port": 7894,
|
||||
"wall_clock_s": 51.8,
|
||||
"child_pid": 87150,
|
||||
"child_confirmed_dead": true,
|
||||
"aggregate": {
|
||||
"n_queries": 38,
|
||||
"n_scored": 35,
|
||||
"hit@5": 0.6285714285714286,
|
||||
"recall@5": 0.45309194773480493,
|
||||
"recall@10": 0.5405733155733157,
|
||||
"precision@5": 0.17714285714285719,
|
||||
"mrr@10": 0.42650793650793645,
|
||||
"nonsense_clean": "2/3",
|
||||
"superseded_outranks": "2/3",
|
||||
"latency_ms_p50": 1223.5,
|
||||
"latency_ms_p95": 1676.1,
|
||||
"latency_ms_max": 1740.6,
|
||||
"errors": 0,
|
||||
"by_category": {
|
||||
"associative": {
|
||||
"n": 6,
|
||||
"hit@5": 0.6666666666666666,
|
||||
"recall@5": 0.07342657342657342,
|
||||
"recall@10": 0.24825174825174826,
|
||||
"mrr@10": 0.22777777777777777
|
||||
},
|
||||
"exact_rare": {
|
||||
"n": 6,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
"nonsense": {
|
||||
"n": 3,
|
||||
"clean": 2,
|
||||
"avg_false_positives": 3.3333333333333335
|
||||
},
|
||||
"paraphrase": {
|
||||
"n": 13,
|
||||
"hit@5": 0.38461538461538464,
|
||||
"recall@5": 0.38461538461538464,
|
||||
"recall@10": 0.38461538461538464,
|
||||
"mrr@10": 0.17307692307692307
|
||||
},
|
||||
"phrase": {
|
||||
"n": 7,
|
||||
"hit@5": 0.8571428571428571,
|
||||
"recall@5": 0.4882369614512472,
|
||||
"recall@10": 0.6329365079365079,
|
||||
"mrr@10": 0.6634920634920636
|
||||
},
|
||||
"superseded": {
|
||||
"n": 3,
|
||||
"hit@5": 0.3333333333333333,
|
||||
"recall@5": 0.3333333333333333,
|
||||
"recall@10": 0.6666666666666666,
|
||||
"mrr@10": 0.2222222222222222,
|
||||
"outranks": 2
|
||||
}
|
||||
}
|
||||
},
|
||||
"rows": [
|
||||
{
|
||||
"id": "q01",
|
||||
"category": "exact_rare",
|
||||
"query": "unjailbreakable",
|
||||
"returned": [
|
||||
"mem-7f61beb4-271c-4feb-9f6e-1c9c837a6226"
|
||||
],
|
||||
"n_returned": 1,
|
||||
"latency_ms": 306.5,
|
||||
"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",
|
||||
"mem-22fe5ec8-ae0d-4583-a05c-d1ef50353257",
|
||||
"project-engram",
|
||||
"project-engram-lang",
|
||||
"mem-60778715-758c-4677-933d-fc39b8f94152",
|
||||
"ctx-89a2",
|
||||
"bl-13babd0c-582e-4e28-a9e4-a77e65925e5d",
|
||||
"870ede67-3454-4e00-9988-46cb13a8a4e2",
|
||||
"bl-3e433255-3710-49fc-a093-c25e71de2ccb",
|
||||
"mem-235a7657-d49e-467e-9f69-f4c3d5f6bd48"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 331.6,
|
||||
"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": 292.8,
|
||||
"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": 309.0,
|
||||
"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",
|
||||
"bl-c1765767-3e27-449a-8c94-10411d1eb7c0",
|
||||
"project-Add_inference_url_config_to_Neuron_MCP__Route_summarization_gen_tasks_to_Pantheon__keep_frontier_for_complex_reasoning_"
|
||||
],
|
||||
"n_returned": 3,
|
||||
"latency_ms": 331.0,
|
||||
"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": 295.7,
|
||||
"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": [
|
||||
"mem-a4a9dfc3-e40b-49b3-b1e1-060e8be2f482",
|
||||
"kn-58874a74-b96f-4883-9e08-45707f4bd3ee",
|
||||
"????7???Ջ3",
|
||||
"kn-363f4976-6946-4b4d-b51b-8a2b0f5aef25",
|
||||
"015644f5-8194-4af0-800d-dd4a0cd71396",
|
||||
"kn-6061318f-046b-4935-907d-8eafdce14930",
|
||||
"art-ee615cdb-e599-423d-9a4d-977859390ed3",
|
||||
"ctx-63e3",
|
||||
"?ǚ?7??????",
|
||||
"knw-d357b6bb-ad8a-4791-b516-426aea45fa5b"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 613.3,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 1.0,
|
||||
"recall@10": 1.0,
|
||||
"precision@5": 0.2,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
{
|
||||
"id": "q08",
|
||||
"category": "phrase",
|
||||
"query": "thirty moves",
|
||||
"returned": [
|
||||
"knw-4aebd815-4eaf-49d7-954b-03595f3d48be",
|
||||
"knw-7902acca-604e-409b-8faf-ad85424211d0",
|
||||
"knw-d357b6bb-ad8a-4791-b516-426aea45fa5b",
|
||||
"knw-0087493b-25cd-45b0-bf46-c078c5b49718",
|
||||
"knw-2c46cfb4-6d4e-4822-8a1a-7d743c1e4329",
|
||||
"knw-ed33e669-0790-44cb-a036-958d605c6fea",
|
||||
"art-4a99aa1a-489b-4b43-958b-25217adb1aad",
|
||||
"knw-920c891f-bb8c-48c4-9afc-018ef12dcdc4",
|
||||
"knw-f671966c-3387-4848-abca-b5deec122e00",
|
||||
"knw-e94982a2-358d-4f2f-af31-8ee0fcec07c6"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 538.1,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 0.1875,
|
||||
"recall@10": 0.375,
|
||||
"precision@5": 0.6,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
{
|
||||
"id": "q09",
|
||||
"category": "phrase",
|
||||
"query": "Grandma Lucas",
|
||||
"returned": [
|
||||
"art-0f0277a1-4a8e-4645-95dd-fa379976f31c",
|
||||
"kn-db9f141b-dbe3-4037-92e0-4bb9be0e5e6e",
|
||||
"kn-a99cefe3-5e83-4050-98d8-6c69f57c7c71",
|
||||
"kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8",
|
||||
"? ?}&?#??X\b",
|
||||
"kn-6061318f-046b-4935-907d-8eafdce14930",
|
||||
"? ?}&?#??X\b",
|
||||
"kn-0bb4f021-56de-4947-a35b-a37209e7ba21",
|
||||
"? ?}&?#??X\b",
|
||||
"kn-58874a74-b96f-4883-9e08-45707f4bd3ee"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 553.2,
|
||||
"error": null,
|
||||
"hit@5": 1.0,
|
||||
"recall@5": 0.4444444444444444,
|
||||
"recall@10": 0.4444444444444444,
|
||||
"precision@5": 0.8,
|
||||
"mrr@10": 1.0
|
||||
},
|
||||
{
|
||||
"id": "q10",
|
||||
"category": "phrase",
|
||||
"query": "Directed Harmonic",
|
||||
"returned": [
|
||||
"ԍ????X????",
|
||||
"project-harmonic-framework",
|
||||
"?ǚ?7??????",
|
||||
"project-harmonic-framework_com",
|
||||
"knw-e24d6339-5ff3-4bed-ba53-707ffd0dc70a",
|
||||
"??????X??2c",
|
||||
"bl-dcee1887-34c4-4ffa-9119-1e291685ba08",
|
||||
"????7???Ջ3",
|
||||
"mem-7eeacad7-d7c2-4c2b-8348-19a59aa6dbaf",
|
||||
"kn-5584ef9c-7f9d-4d7c-a10a-4ee6bc5cf356"
|
||||
],
|
||||
"n_returned": 10,
|
||||
"latency_ms": 527.1,
|
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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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",
|
||||
"? ?}&?#??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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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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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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|
||||
{
|
||||
"id": "q19",
|
||||
"category": "paraphrase",
|
||||
"query": "learning is the wealth creditors cannot seize",
|
||||
"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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||||
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||||
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||||
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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": "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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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
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{
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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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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|
||||
"? ?}&?#??X\b",
|
||||
"kn-e8423822-eacf-4029-aa7b-10d4d28d621e",
|
||||
"? ?}&?#??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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|
||||
"returned": [
|
||||
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|
||||
"?of?7???",
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||||
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{
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||||
"id": "q12",
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||||
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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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||||
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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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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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{
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||||
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||||
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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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|
||||
"query": "Grandma Lucas stroke February 2006 goodbye window",
|
||||
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||||
"kn-eb1b9e18-3dc6-4b9b-9cc6-86e0ae6b6be8",
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||||
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"? ?}&?#??X\b",
|
||||
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||||
],
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"? ?}&?#??X\b",
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},
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"id": "q31",
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"query": "hope won inside the narrative preface",
|
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},
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{
|
||||
"id": "q32",
|
||||
"category": "associative",
|
||||
"query": "man of the house six years old expectation",
|
||||
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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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||||
"id": "q33",
|
||||
"category": "nonsense",
|
||||
"query": "zqxjvw plimforth grebulon",
|
||||
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|
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|
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|
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},
|
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{
|
||||
"id": "q34",
|
||||
"category": "nonsense",
|
||||
"query": "flarnbistle quommetry",
|
||||
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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",
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||||
"?V?",
|
||||
"knw-6b48dce2-f21c-452a-9db5-4e6aa61c87ca",
|
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"knw-920c891f-bb8c-48c4-9afc-018ef12dcdc4",
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"?m?\\}Q??6??",
|
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"art-79042b8b-6192-440f-90b0-60708f7e6325",
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||||
"?m?\\}Q??6??",
|
||||
"knw-723551f5-1950-42a3-8b89-b6a06913cef0",
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||||
"art-ddfcd045-2c3b-4a1e-9966-fec5ce44e1dd"
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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",
|
||||
"? ?}&?#??X\b",
|
||||
"mem-f3b37427-b7d1-4f7e-b32c-0241a20ce8da",
|
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"? ?}&?#??X\b",
|
||||
"kn-b7e98d63-8b83-4911-b4d0-990602a7f575",
|
||||
"?of?7???",
|
||||
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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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{
|
||||
"id": "q37",
|
||||
"category": "superseded",
|
||||
"query": "how many provisional patents does Will actually have",
|
||||
"returned": [
|
||||
"? ?}&?#??X\b",
|
||||
"12082f7e-e320-438b-bd65-083d8259748f",
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"? ?}&?#??X\b",
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"? ?}&?#??X\b",
|
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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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{
|
||||
"id": "q38",
|
||||
"category": "superseded",
|
||||
"query": "is MCP still the live integration layer",
|
||||
"returned": [
|
||||
"kn-5584ef9c-7f9d-4d7c-a10a-4ee6bc5cf356",
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||||
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|
||||
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|
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|
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"? ?}&?#??X\b",
|
||||
"4509ed62-9fb2-48b8-9038-ac569fca9604",
|
||||
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|
||||
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|
||||
],
|
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|
||||
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|
||||
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|
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|
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|
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}
|
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]
|
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}
|
||||
@@ -0,0 +1,142 @@
|
||||
import numpy as np, json, urllib.request, collections, math, re, sys, time
|
||||
SP="/private/tmp/claude-501/-Users-timlingo/82369039-a20e-4b5a-8a5e-28234a57b996/scratchpad"
|
||||
EV="/Users/timlingo/Development/neuron-technologies/_wt-semseed/tools/retrieval-eval/"
|
||||
np.seterr(all='ignore')
|
||||
t0=time.time()
|
||||
M=np.load(SP+'/emb.npy'); eids=open(SP+'/ids.txt',encoding='utf-8',errors='surrogateescape').read().split('\n')
|
||||
eidx={k:i for i,k in enumerate(eids)}
|
||||
d=json.load(open('/Users/timlingo/neuron-memory-backups/snapshot-pre-repair-20260806.json',encoding='utf-8',errors='surrogateescape'))
|
||||
print("loaded corpus %.1fs"%(time.time()-t0),file=sys.stderr)
|
||||
STRUCT={"identity","contains","superseded_by","references","embodies","demonstrated_by","canonical-self","depends_on","currently_holds","activates"}
|
||||
adj=collections.defaultdict(list)
|
||||
for e in d['edges']:
|
||||
if e.get('relation') not in STRUCT: continue
|
||||
w=float(e.get('weight') or 0.0)
|
||||
adj[e['from_id']].append((e['to_id'],w)); adj[e['to_id']].append((e['from_id'],w))
|
||||
nodes=d['nodes']
|
||||
N={n['id']:n for n in nodes}
|
||||
PRINT=re.compile(r'^[\x20-\x7e]+$')
|
||||
ids=[]; hay=[]; dl=[]; sal=[]; addressable=[]
|
||||
for n in nodes:
|
||||
i=n.get('id') or ''
|
||||
h=((n.get('content') or '')+'\x00'+(n.get('label') or '')+'\x00'+(n.get('tags') or '')).lower()
|
||||
ids.append(i); hay.append(h); dl.append(len(h)); sal.append(float(n.get('salience') or 0.0))
|
||||
addressable.append(bool(PRINT.match(i)))
|
||||
del d
|
||||
NN=len(ids); avgdl=sum(dl)/NN
|
||||
print("nodes=%d avgdl=%.0f addressable=%d %.1fs"%(NN,avgdl,sum(addressable),time.time()-t0),file=sys.stderr)
|
||||
gold={q['id']:q for q in json.load(open(EV+"gold_set.json"))['queries']}
|
||||
LEXMAIN={r['id']:r['returned'] for r in json.load(open(EV+"results-main.json"),) ['rows']} if False else {r['id']:r['returned'] for r in json.load(open(EV+"results-main.json",encoding='utf-8',errors='surrogateescape'))['rows']}
|
||||
CACHE={}
|
||||
def emb(t):
|
||||
if t in CACHE: return CACHE[t]
|
||||
b=json.dumps({"model":"nomic-embed-text","prompt":t}).encode()
|
||||
r=urllib.request.Request("http://127.0.0.1:11434/api/embeddings",data=b,headers={"Content-Type":"application/json"})
|
||||
v=np.array(json.load(urllib.request.urlopen(r,timeout=60))["embedding"],dtype=np.float32)
|
||||
v=v/(np.linalg.norm(v)+1e-9); CACHE[t]=v; return v
|
||||
K1,B=1.2,0.75
|
||||
def lexleg(query, mode, guard, lim=10):
|
||||
toks=[]
|
||||
for w in query.split():
|
||||
wl=w.lower()
|
||||
if wl not in toks: toks.append(wl)
|
||||
nt=len(toks)
|
||||
masks=[]; df=[0]*nt
|
||||
for i in range(NN):
|
||||
if guard and not addressable[i]: continue
|
||||
h=hay[i]; m=0; sc=0
|
||||
for t in range(nt):
|
||||
if toks[t] in h: m|=(1<<t); sc+=1; df[t]+=1
|
||||
if sc: masks.append((i,m,sc))
|
||||
if mode=='tokcount':
|
||||
masks.sort(key=lambda x:(-x[2], -sal[x[0]]))
|
||||
return [ids[i] for i,m,sc in masks[:lim]]
|
||||
idf=[math.log(1.0+(NN-df[t]+0.5)/(df[t]+0.5)) for t in range(nt)]
|
||||
scored=[]
|
||||
for i,m,sc in masks:
|
||||
norm=1.0-B+B*dl[i]/avgdl
|
||||
s=0.0
|
||||
for t in range(nt):
|
||||
if m&(1<<t): s+=idf[t]*(K1+1.0)/(1.0+K1*norm)
|
||||
scored.append((s,i))
|
||||
scored.sort(key=lambda x:(-x[0], -sal[x[1]]))
|
||||
return [ids[i] for s,i in scored[:lim]]
|
||||
FIRE=0.02; DECAY=0.7; DEPTH=2; SEED_MIN=0.60; ASSOC_MAX=64
|
||||
def assoc(seeds, s):
|
||||
act={x:1.0 for x in seeds}; seen={x:2 for x in seeds}
|
||||
Q=[(x,0) for x in seeds]; h=0
|
||||
while h<len(Q):
|
||||
cur,hop=Q[h]; h+=1
|
||||
if hop>=DEPTH: continue
|
||||
p=act[cur]
|
||||
for oid,w in adj.get(cur,()):
|
||||
n=N.get(oid)
|
||||
if not n or n.get('node_type') in ('Tag','InternalStateEvent'): continue
|
||||
na=p*w*DECAY*float(n.get('salience') or 0.0)
|
||||
if na<FIRE: continue
|
||||
if oid in seen and na<=act.get(oid,0): continue
|
||||
act[oid]=na
|
||||
if oid not in seen: seen[oid]=1
|
||||
Q.append((oid,hop+1))
|
||||
out=[]
|
||||
for k,v in seen.items():
|
||||
if v!=1 or k not in eidx: continue
|
||||
c=float(s[eidx[k]])
|
||||
if c<=0: continue
|
||||
out.append((c,k))
|
||||
out.sort(reverse=True)
|
||||
return [k for c,k in out[:ASSOC_MAX]]
|
||||
def inter3(L,S,A,lim=10):
|
||||
out=[]; li=si=ai=0
|
||||
while len(out)<lim and (li<len(L) or si<len(S) or ai<len(A)):
|
||||
if li<len(L):
|
||||
if L[li] not in out: out.append(L[li])
|
||||
li+=1
|
||||
if len(out)>=lim: break
|
||||
if si<len(S):
|
||||
if S[si] not in out: out.append(S[si])
|
||||
si+=1
|
||||
if len(out)>=lim: break
|
||||
if ai<len(A):
|
||||
if A[ai] not in out: out.append(A[ai])
|
||||
ai+=1
|
||||
return out
|
||||
def run(mode, guard, use_main_lex=False):
|
||||
res={}; legs={}
|
||||
for qid,q in gold.items():
|
||||
v=emb(q['query']); s=M@v; s[~np.isfinite(s)]=-1
|
||||
L = LEXMAIN[qid][:10] if use_main_lex else lexleg(q['query'], mode, guard)
|
||||
ordr=np.argsort(-s)
|
||||
S=[eids[j] for j in ordr[:10] if s[j]>SEED_MIN]
|
||||
if guard: S=[x for x in S if PRINT.match(x or '')]
|
||||
seeds=[x for x in L[:3] if x in N]
|
||||
seeds=seeds+[eids[j] for j in ordr[:8] if eids[j] in N and eids[j] not in seeds and (not guard or PRINT.match(eids[j] or ''))]
|
||||
A=assoc(seeds,s) if seeds else []
|
||||
if guard: A=[x for x in A if PRINT.match(x or '')]
|
||||
res[qid]=inter3(L,S,A); legs[qid]=(L,S,A)
|
||||
return res,legs
|
||||
def score(res,label,verbose=False):
|
||||
det={}
|
||||
for qid,q in gold.items():
|
||||
out=res[qid][:5]
|
||||
if q['category']=='nonsense': ok=(len(res[qid])==0)
|
||||
elif q['category']=='superseded':
|
||||
must=q.get('must_outrank') or {}; ok=False
|
||||
for good,bad in (must.items() if isinstance(must,dict) else []):
|
||||
ok = good in res[qid] and (bad not in res[qid] or res[qid].index(good)<res[qid].index(bad))
|
||||
if not must: ok=any(r in out for r in q['relevant'])
|
||||
else: ok=any(r in out for r in q['relevant'])
|
||||
det[qid]=ok
|
||||
print("%-28s outcome-true=%d/38"%(label,sum(det.values())))
|
||||
return det
|
||||
if __name__=="__main__":
|
||||
base,_=run('tokcount',False,use_main_lex=True); b=score(base,'BASELINE semseed(real lex)')
|
||||
variants=[('tokcount',False,'replica: tokcount,noguard'),
|
||||
('tokcount',True ,'A: tokcount + idguard'),
|
||||
('bm25', False,'B: bm25 only'),
|
||||
('bm25', True ,'C: bm25 + idguard')]
|
||||
dets={}
|
||||
for m,g,lab in variants:
|
||||
r,_=run(m,g); dets[lab]=score(r,lab)
|
||||
dd=[q for q in gold if dets[lab][q]!=b[q]]
|
||||
print(" vs BASELINE moved=%d gains=%s losses=%s"%(len(dd),[q for q in dd if dets[lab][q]],[q for q in dd if not dets[lab][q]]))
|
||||
@@ -0,0 +1,31 @@
|
||||
import numpy as np, json, urllib.request
|
||||
SP="/private/tmp/claude-501/-Users-timlingo/82369039-a20e-4b5a-8a5e-28234a57b996/scratchpad"
|
||||
M=np.load(SP+'/emb.npy'); ids=open(SP+'/ids.txt',encoding='utf-8',errors='surrogateescape').read().split('\n')
|
||||
np.seterr(all='ignore')
|
||||
bad=~np.isfinite(M).all(axis=1)
|
||||
M[bad]=0.0
|
||||
print("non-finite rows zeroed:",int(bad.sum()))
|
||||
idx={k:i for i,k in enumerate(ids)}
|
||||
gold=json.load(open("/Users/timlingo/Development/neuron-technologies/_wt-assoc-leg/tools/retrieval-eval/gold_set.json"))['queries']
|
||||
def emb(t):
|
||||
b=json.dumps({"model":"nomic-embed-text","prompt":t}).encode()
|
||||
r=urllib.request.Request("http://127.0.0.1:11434/api/embeddings",data=b,headers={"Content-Type":"application/json"})
|
||||
v=np.array(json.load(urllib.request.urlopen(r,timeout=60))["embedding"],dtype=np.float32)
|
||||
return v/(np.linalg.norm(v)+1e-9)
|
||||
out={}
|
||||
for q in gold:
|
||||
v=emb(q['query']); s=M@v
|
||||
s=s[np.isfinite(s)]
|
||||
mu=float(s.mean()); sd=float(s.std())
|
||||
top=np.sort(s)[::-1][:10]
|
||||
z=[(float(t)-mu)/sd for t in top]
|
||||
grank=[]
|
||||
for rel in q['relevant']:
|
||||
if rel in idx:
|
||||
j=idx[rel]; grank.append((int((M@v > (M@v)[j]).sum())+1, round(float((M@v)[j]),3)))
|
||||
grank.sort()
|
||||
out[q['id']]=dict(cat=q['category'],mu=round(mu,3),sd=round(sd,4),top1=round(float(top[0]),3),
|
||||
z1=round(z[0],2),z3=round(z[2],2),z5=round(z[4],2),gold=grank[:1])
|
||||
print("%s %-11s mu=%.3f sd=%.4f top1=%.3f z1=%5.2f z3=%5.2f z5=%5.2f gold=%s"%(
|
||||
q['id'],q['category'],mu,sd,top[0],z[0],z[2],z[4],grank[:1]))
|
||||
json.dump(out,open(SP+'/zprobe.json','w'),indent=1)
|
||||
+461
-14
@@ -6099,6 +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
|
||||
/* 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
|
||||
@@ -7381,9 +7387,73 @@ static int engram_node_match_score(const EngramNode* n,
|
||||
return score;
|
||||
}
|
||||
|
||||
/* Same match test as engram_node_match_score, but returns the SET of matched
|
||||
* query tokens as a bitmask instead of only their count. ENGRAM_MAX_QTOKENS is
|
||||
* 32, so one uint32 covers every token the tokenizer can produce. The mask is
|
||||
* what lets the caller accumulate a per-token document frequency in the SAME
|
||||
* pass that finds the hits — no second scan of the corpus. */
|
||||
static uint32_t engram_node_match_mask(const EngramNode* n,
|
||||
char toks[][ENGRAM_QTOK_LEN], int ntok) {
|
||||
uint32_t m = 0;
|
||||
for (int t = 0; t < ntok && t < 32; t++) {
|
||||
if (istr_contains(n->content, toks[t]) ||
|
||||
istr_contains(n->label, toks[t]) ||
|
||||
istr_contains(n->tags, toks[t]))
|
||||
m |= (uint32_t)1u << t;
|
||||
}
|
||||
return m;
|
||||
}
|
||||
|
||||
/* Searchable byte length of a node: the same three fields the match test
|
||||
* reads. Used as the BM25 document length so a long node does not out-match a
|
||||
* short one merely by containing more text. */
|
||||
static double engram_node_len(const EngramNode* n) {
|
||||
double l = 0.0;
|
||||
if (n->content) l += (double)strlen(n->content);
|
||||
if (n->label) l += (double)strlen(n->label);
|
||||
if (n->tags) l += (double)strlen(n->tags);
|
||||
return l;
|
||||
}
|
||||
|
||||
/* Addressability guard. Claim 23 stores node records under a key encoding the
|
||||
* node identifier, claim 12 deduplicates merged results by node identifier,
|
||||
* and claim 27's competition map is indexed by node identifier — every one of
|
||||
* those requires the identifier to be a usable string. This corpus contains
|
||||
* records whose id field is binary garbage (a save-side corruption); they are
|
||||
* unfetchable by any caller, so returning one wastes a result slot. Printable
|
||||
* ASCII, non-empty, is the whole test. */
|
||||
static int eg_node_addressable(const EngramNode* n) {
|
||||
const unsigned char* p = (const unsigned char*)n->id;
|
||||
if (!p || !*p) return 0;
|
||||
for (; *p; p++) if (*p < 0x20 || *p > 0x7e) return 0;
|
||||
return 1;
|
||||
}
|
||||
|
||||
/* Semantic leg of the read path (engram claim 24). Returns the query/target
|
||||
* cosine renormalized onto [0,1] over the band [ENGRAM_EMBED_SEED_MIN, 1.0],
|
||||
* and exactly 0.0 when the pair is not comparable (no query embedding, target
|
||||
* unembedded, dim mismatch) or falls at/below the seed floor. Claim 32's
|
||||
* clamp-at-zero is subsumed: nothing below the floor can contribute.
|
||||
* A 0.0 return makes the fused score collapse to the lexical score, which is
|
||||
* why a dead embedder degrades to the historical behaviour exactly. */
|
||||
static double eg_sem_term(const EngramNode* n, const float* qv, int32_t qdim) {
|
||||
if (!qv || qdim <= 0 || !n->emb || n->emb_dim != qdim) return 0.0;
|
||||
double c = eg_cosine(n->emb, qv, qdim);
|
||||
if (c <= ENGRAM_EMBED_SEED_MIN) return 0.0;
|
||||
double t = (c - ENGRAM_EMBED_SEED_MIN) / (1.0 - ENGRAM_EMBED_SEED_MIN);
|
||||
return t > 1.0 ? 1.0 : t;
|
||||
}
|
||||
|
||||
/* Rank entry: distinct-token match count (primary, desc) then salience
|
||||
* (tiebreak, desc). */
|
||||
typedef struct { int64_t idx; int score; double salience; } EngramRankEntry;
|
||||
* (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;
|
||||
uint32_t mask; /* which query tokens matched (BM25 leg) */
|
||||
double len; /* searchable byte length (BM25 leg) */
|
||||
double w; /* BM25-shaped weighted score */
|
||||
} EngramRankEntry;
|
||||
static int engram_rank_cmp(const void* a, const void* b) {
|
||||
const EngramRankEntry* ea = (const EngramRankEntry*)a;
|
||||
const EngramRankEntry* eb = (const EngramRankEntry*)b;
|
||||
@@ -7393,6 +7463,269 @@ static int engram_rank_cmp(const void* a, const void* b) {
|
||||
return 0;
|
||||
}
|
||||
|
||||
/* BM25-shaped ordering for the read path's lexical leg: rare-term weight and
|
||||
* length normalisation instead of a raw distinct-token count. Salience stays
|
||||
* the tiebreak, exactly as in engram_rank_cmp. */
|
||||
#define ENGRAM_BM25_K1 1.2
|
||||
#define ENGRAM_BM25_B 0.75
|
||||
static int engram_rank_w_cmp(const void* a, const void* b) {
|
||||
const EngramRankEntry* ea = (const EngramRankEntry*)a;
|
||||
const EngramRankEntry* eb = (const EngramRankEntry*)b;
|
||||
if (ea->w < eb->w) return 1; /* desc */
|
||||
if (ea->w > eb->w) return -1;
|
||||
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;
|
||||
}
|
||||
|
||||
/* ── Associative leg (claim 10 typed relations + claim 1 activation) ────────
|
||||
*
|
||||
* WHY THIS EXISTS. engram_search_json has two legs, and neither can reach a
|
||||
* node that shares no token with the query and no embedding neighbourhood
|
||||
* with it. The route the design reserves for that case is the graph: a node
|
||||
* is reachable because it is STRUCTURALLY associated with something the query
|
||||
* did hit. Nothing on the recall path consults an edge today.
|
||||
*
|
||||
* WHY NOT engram_activate. Wiring recall wholesale to activation was measured
|
||||
* (PR #135) and lost 57 points of phrase accuracy. The failure was one of
|
||||
* RANK, not reach: activation seeds on every token-matching node, so a 2-hop
|
||||
* associate at strength ~0.06 never outranks thousands of 1-hop neighbours of
|
||||
* strong lexical seeds. So this is a separate, small, ranked list that is
|
||||
* merged BESIDE the other two, exactly as the semantic leg is.
|
||||
*
|
||||
* TYPED RELATIONS (06-claims.md claim 10). Expansion follows only relations
|
||||
* that assert a structural claim about meaning. The corpus is 4,915 `tagged`
|
||||
* and 3,767 `triggers-safety` edges against 475 `identity` and 11 `contains`;
|
||||
* walking the bulk relations turns any seed into a flood (measured: 1,387
|
||||
* candidates from one seed) while the structural ones stay in the low tens.
|
||||
* This is the first code on the read path to branch on a relation type at all.
|
||||
*
|
||||
* DIRECTION. Edges are walked in BOTH directions. Claim 23 requires the store
|
||||
* to keep reverse edge records keyed by target id, and the adjacency index
|
||||
* already materialises them (adj_to). It has to be both: every value node in
|
||||
* this corpus has exactly ONE inbound edge (hub -> value) and no outbound
|
||||
* structural edge at all, so a forward-only walk from a value node reaches
|
||||
* nothing. Note this is an extension of the traversal as literally specified
|
||||
* (05-detailed-description l.66 says "all outgoing edges"); the reverse index
|
||||
* is designed and stored, but the description does not say the walk reads it.
|
||||
*/
|
||||
#define ENGRAM_ASSOC_SEEDS 3 /* top-N lexical hits form the context */
|
||||
#define ENGRAM_ASSOC_DEPTH 2 /* seed -> hub -> sibling */
|
||||
#define ENGRAM_ASSOC_FIRE 0.02 /* same firing threshold as engram_activate */
|
||||
#define ENGRAM_ASSOC_MAX 64 /* cap on candidates carried forward */
|
||||
|
||||
static int eg_rel_is_structural(const char* r) {
|
||||
if (!r || !*r) return 0;
|
||||
static const char* ok[] = {
|
||||
"identity", "contains", "superseded_by", "references", "embodies",
|
||||
"demonstrated_by", "canonical-self", "depends_on", "currently_holds",
|
||||
"activates", NULL
|
||||
};
|
||||
for (int i = 0; ok[i]; i++) if (strcmp(r, ok[i]) == 0) return 1;
|
||||
return 0;
|
||||
}
|
||||
|
||||
/* Nodes that are index artefacts rather than recallable content. Same
|
||||
* exclusion eg_embed_eligible() already applies when deciding what deserves an
|
||||
* embedding, reused here so the associative leg cannot surface — or relay
|
||||
* through — a Tag. Relaying through them is what makes a graph walk explode:
|
||||
* tag-tier_note alone has 187 members. */
|
||||
static int eg_assoc_excluded(const EngramNode* n) {
|
||||
if (!n->node_type) return 0;
|
||||
return strcmp(n->node_type, "Tag") == 0
|
||||
|| strcmp(n->node_type, "InternalStateEvent") == 0;
|
||||
}
|
||||
|
||||
/* Breadth-first structural expansion from `seeds`, then ORDER BY query
|
||||
* similarity. Activation decides REACHABILITY (the conjunctive prune of
|
||||
* claim 1: parent strength x edge weight x target salience, cut at the firing
|
||||
* threshold); cosine decides ORDER within what was reached. Ranking the
|
||||
* neighbourhood by activation alone does not work and the reason is
|
||||
* structural: every identity edge in this corpus carries weight 0.5 and every
|
||||
* value node salience 0.7, so the activation product degenerates into a
|
||||
* function of hop count and ranks the relay hub above all of its own
|
||||
* children. Composing the two is mine, not Will's — the description ranks the
|
||||
* activation result set by strength (l.78). */
|
||||
/* Semantic seeding of the graph leg — the HippoRAG pass Will documents at
|
||||
* l.6082: "the query is embedded, the top-K nodes by cosine >= SEED_MIN join
|
||||
* the seed set", using his own ENGRAM_EMBED_SEED_K (8). It is "similarity used
|
||||
* twice, coherently": cosine picks where to STAND in the graph, the structural
|
||||
* walk decides what is REACHABLE from there, and cosine then ORDERS what was
|
||||
* reached (iteration-2's finding, kept intact).
|
||||
*
|
||||
* Why the seed list is NOT floored at ENGRAM_EMBED_SEED_MIN here. That
|
||||
* constant is calibrated for a cosine scale this corpus does not have: with
|
||||
* nomic-embed-text every true paraphrase target measures 0.46-0.66, and the
|
||||
* three nonsense controls' own nearest neighbours measure 0.55/0.60/0.62 —
|
||||
* they OVERLAP, so no absolute cosine floor separates signal from gibberish
|
||||
* (measured, all 38 queries). Iteration 2 established the same thing one step
|
||||
* later in the pipeline: applying the floor to graph CANDIDATES removed every
|
||||
* gain, because within a structurally-reached neighbourhood relative cosine
|
||||
* still discriminates below the absolute threshold. The gate that actually
|
||||
* works is reachability — eg_rel_is_structural() plus the firing threshold.
|
||||
* A semantically-near node with no structural attachment expands to nothing
|
||||
* and contributes nothing, which is exactly what happens to gibberish: the
|
||||
* nearest neighbours of q33/q34 are unattached, so their graph leg is empty.
|
||||
*/
|
||||
static int64_t engram_assoc_leg(EngramStore* g,
|
||||
const EngramRankEntry* L, int64_t nL,
|
||||
const int64_t* semseed, int64_t nsemseed,
|
||||
const float* qv, int32_t qdim,
|
||||
EngramSemEntry* out, int64_t out_cap) {
|
||||
if (!g || nL <= 0 || !qv || qdim <= 0 || out_cap <= 0) return 0;
|
||||
if (g->adj_dirty || !g->adj_from || !g->adj_to) engram_adj_rebuild(g);
|
||||
if (!g->adj_from || !g->adj_to) return 0;
|
||||
|
||||
double* act = calloc((size_t)g->node_count, sizeof(double));
|
||||
char* seen = calloc((size_t)g->node_count, sizeof(char));
|
||||
int64_t* q = malloc((size_t)ENGRAM_ASSOC_MAX * 4 * sizeof(int64_t));
|
||||
int64_t* hop = malloc((size_t)ENGRAM_ASSOC_MAX * 4 * sizeof(int64_t));
|
||||
if (!act || !seen || !q || !hop) { free(act); free(seen); free(q); free(hop); return 0; }
|
||||
int64_t qcap = ENGRAM_ASSOC_MAX * 4, qh = 0, qt = 0;
|
||||
|
||||
int64_t nseed = nL < ENGRAM_ASSOC_SEEDS ? nL : ENGRAM_ASSOC_SEEDS;
|
||||
for (int64_t s = 0; s < nseed; s++) {
|
||||
int64_t idx = L[s].idx;
|
||||
if (idx < 0 || idx >= g->node_count) continue;
|
||||
act[idx] = 1.0; seen[idx] = 2; /* 2 = seed: never a result */
|
||||
if (qt < qcap) { q[qt] = idx; hop[qt] = 0; qt++; }
|
||||
}
|
||||
/* ...and the semantic seeds, on the same footing (strength 1.0, hop 0). */
|
||||
for (int64_t s = 0; s < nsemseed; s++) {
|
||||
int64_t idx = semseed[s];
|
||||
if (idx < 0 || idx >= g->node_count) continue;
|
||||
if (seen[idx]) continue;
|
||||
act[idx] = 1.0; seen[idx] = 2;
|
||||
if (qt < qcap) { q[qt] = idx; hop[qt] = 0; qt++; }
|
||||
}
|
||||
|
||||
const double SPREAD_DECAY = 0.7;
|
||||
while (qh < qt) {
|
||||
int64_t cur = q[qh]; int64_t h = hop[qh]; qh++;
|
||||
if (h >= ENGRAM_ASSOC_DEPTH) continue;
|
||||
double parent = act[cur];
|
||||
int from_len = g->adj_from_len[cur];
|
||||
int to_len = g->adj_to_len[cur];
|
||||
for (int scan = 0; scan < from_len + to_len; scan++) {
|
||||
int64_t ei = (scan < from_len) ? g->adj_from[cur][scan]
|
||||
: g->adj_to[cur][scan - from_len];
|
||||
EngramEdge* e = &g->edges[ei];
|
||||
if (!eg_rel_is_structural(e->relation)) continue;
|
||||
int64_t oi = (scan < from_len) ? engram_idmap_get(g, e->to_id)
|
||||
: engram_idmap_get(g, e->from_id);
|
||||
if (oi < 0 || oi >= g->node_count) continue;
|
||||
EngramNode* on = &g->nodes[oi];
|
||||
if (eg_assoc_excluded(on)) continue;
|
||||
double na = parent * e->weight * SPREAD_DECAY * on->salience;
|
||||
if (na < ENGRAM_ASSOC_FIRE) continue;
|
||||
if (seen[oi] && na <= act[oi]) continue;
|
||||
act[oi] = na;
|
||||
if (!seen[oi]) seen[oi] = 1;
|
||||
if (qt < qcap) { q[qt] = oi; hop[qt] = h + 1; qt++; }
|
||||
}
|
||||
}
|
||||
|
||||
int64_t n = 0;
|
||||
for (int64_t i = 0; i < g->node_count && n < out_cap; i++) {
|
||||
if (seen[i] != 1) continue; /* skip unreached and seeds */
|
||||
if (engram_layer_is_transparent(g->nodes[i].layer_id)) continue;
|
||||
EngramNode* nd = &g->nodes[i];
|
||||
if (!eg_node_addressable(nd)) continue; /* unfetchable record */
|
||||
if (!nd->emb || nd->emb_dim != qdim) continue;
|
||||
double c = eg_cosine(nd->emb, qv, qdim);
|
||||
if (c <= 0.0) continue;
|
||||
out[n].idx = i; out[n].sem = c; n++;
|
||||
}
|
||||
qsort(out, (size_t)n, sizeof(EngramSemEntry), engram_sem_cmp);
|
||||
free(act); free(seen); free(q); free(hop);
|
||||
return n;
|
||||
}
|
||||
|
||||
/* Three-leg merge: lexical, semantic, associative — strict rotation,
|
||||
* L1, S1, A1, L2, S2, A2, ... deduplicated, capped at lim.
|
||||
*
|
||||
* The cost is explicit and worse than the two-leg case: a lexical hit at rank
|
||||
* r lands at output position 3r-2 when both other legs are non-empty. That is
|
||||
* survivable here only because the structural-relation filter leaves the
|
||||
* associative list EMPTY for most queries — a Memory node whose only edges are
|
||||
* `tagged` and `related` expands to nothing, so its ranking is untouched. */
|
||||
static int64_t engram_interleave3(const EngramRankEntry* L, int64_t nL,
|
||||
const EngramSemEntry* S, int64_t nS,
|
||||
const EngramSemEntry* A, int64_t nA,
|
||||
int64_t lim, int64_t* out) {
|
||||
int64_t no = 0, li = 0, si = 0, ai = 0;
|
||||
while (no < lim && (li < nL || si < nS || ai < nA)) {
|
||||
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++;
|
||||
}
|
||||
if (no >= lim) break;
|
||||
if (ai < nA) {
|
||||
int dup = 0;
|
||||
for (int64_t k = 0; k < no; k++) if (out[k] == A[ai].idx) { dup = 1; break; }
|
||||
if (!dup) out[no++] = A[ai].idx;
|
||||
ai++;
|
||||
}
|
||||
}
|
||||
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);
|
||||
@@ -7405,6 +7738,12 @@ el_val_t engram_search(el_val_t query, el_val_t limit) {
|
||||
if (ntok == 0) return lst;
|
||||
EngramRankEntry* hits = malloc((size_t)g->node_count * sizeof(EngramRankEntry));
|
||||
if (!hits) return lst;
|
||||
/* Claim-24 semantic leg: one query embedding, fetched once per search.
|
||||
* 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];
|
||||
@@ -7420,14 +7759,24 @@ el_val_t engram_search(el_val_t query, el_val_t limit) {
|
||||
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 distinct tokens matched (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;
|
||||
}
|
||||
|
||||
@@ -9259,27 +9608,125 @@ el_val_t engram_search_json(el_val_t query, el_val_t limit) {
|
||||
if (ntok > 0) {
|
||||
EngramRankEntry* hits = malloc((size_t)g->node_count * sizeof(EngramRankEntry));
|
||||
if (hits) {
|
||||
/* Claim-24 semantic leg. This is the function /api/neuron/recall
|
||||
* actually reaches (routes.el -> neuron-api.el handle_api_recall),
|
||||
* so the semantic half of the retrieval surface has to land HERE
|
||||
* 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;
|
||||
/* Raw (unfloored) cosine top-K, kept for the graph leg's
|
||||
* semantic seeds. Selected in THIS pass so the cosine is
|
||||
* computed exactly once per node — the seeding costs no extra
|
||||
* pass over the corpus and no extra embed round-trip. */
|
||||
int64_t semseed[ENGRAM_EMBED_SEED_K];
|
||||
double semseedc[ENGRAM_EMBED_SEED_K];
|
||||
int64_t nsemseed = 0;
|
||||
/* BM25 statistics gathered in this same pass: per-token
|
||||
* document frequency, and the corpus mean field length. */
|
||||
int64_t df[ENGRAM_MAX_QTOKENS];
|
||||
for (int t = 0; t < ntok; t++) df[t] = 0;
|
||||
double dl_total = 0.0;
|
||||
int64_t dl_n = 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);
|
||||
if (sc > 0) {
|
||||
/* Unaddressable records cannot be fetched by a caller and
|
||||
* must not consume a result slot (claims 12/23/27). */
|
||||
if (!eg_node_addressable(n)) continue;
|
||||
double dl = engram_node_len(n);
|
||||
dl_total += dl; dl_n++;
|
||||
uint32_t mask = engram_node_match_mask(n, toks, ntok);
|
||||
if (mask) {
|
||||
int sc = 0;
|
||||
for (int t = 0; t < ntok; t++)
|
||||
if (mask & ((uint32_t)1u << t)) { sc++; df[t]++; }
|
||||
hits[nhits].idx = i;
|
||||
hits[nhits].score = sc;
|
||||
hits[nhits].salience = n->salience;
|
||||
hits[nhits].mask = mask;
|
||||
hits[nhits].len = dl;
|
||||
hits[nhits].w = 0.0;
|
||||
nhits++;
|
||||
}
|
||||
if (sem && n->emb && n->emb_dim == qdim) {
|
||||
double c = eg_cosine(n->emb, qv, qdim);
|
||||
/* Semantic leg: identical to eg_sem_term(), which is
|
||||
* left in place and still used by engram_search().
|
||||
* Inlined here only so one cosine serves both uses. */
|
||||
if (c > ENGRAM_EMBED_SEED_MIN) {
|
||||
double sv = (c - ENGRAM_EMBED_SEED_MIN) / (1.0 - ENGRAM_EMBED_SEED_MIN);
|
||||
if (sv > 1.0) sv = 1.0;
|
||||
sem[nsem].idx = i; sem[nsem].sem = sv; nsem++;
|
||||
}
|
||||
/* Graph seeds: top-K by RAW cosine, insertion-ordered. */
|
||||
if (c > 0.0 && (nsemseed < ENGRAM_EMBED_SEED_K
|
||||
|| c > semseedc[nsemseed - 1])) {
|
||||
int64_t p = nsemseed < ENGRAM_EMBED_SEED_K
|
||||
? nsemseed : ENGRAM_EMBED_SEED_K - 1;
|
||||
while (p > 0 && semseedc[p - 1] < c) {
|
||||
semseedc[p] = semseedc[p - 1];
|
||||
semseed[p] = semseed[p - 1];
|
||||
p--;
|
||||
}
|
||||
semseedc[p] = c; semseed[p] = i;
|
||||
if (nsemseed < ENGRAM_EMBED_SEED_K) nsemseed++;
|
||||
}
|
||||
}
|
||||
}
|
||||
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;
|
||||
/* BM25-shaped lexical score. Binary term frequency (the match
|
||||
* primitive is a substring test, not a count), Lucene-form IDF,
|
||||
* and length normalisation over the corpus mean. A token that
|
||||
* occurs in 30,000 nodes now weighs far less than one that
|
||||
* occurs in 1, and a 1.3 MB record no longer out-matches a
|
||||
* 300-byte one by sheer surface area. */
|
||||
double avgdl = dl_n ? (dl_total / (double)dl_n) : 1.0;
|
||||
if (avgdl <= 0.0) avgdl = 1.0;
|
||||
double idf[ENGRAM_MAX_QTOKENS];
|
||||
for (int t = 0; t < ntok; t++) {
|
||||
double dfx = (double)df[t];
|
||||
idf[t] = log(1.0 + ((double)dl_n - dfx + 0.5) / (dfx + 0.5));
|
||||
}
|
||||
for (int64_t h = 0; h < nhits; h++) {
|
||||
double norm = 1.0 - ENGRAM_BM25_B
|
||||
+ ENGRAM_BM25_B * (hits[h].len / avgdl);
|
||||
double s = 0.0;
|
||||
for (int t = 0; t < ntok; t++)
|
||||
if (hits[h].mask & ((uint32_t)1u << t))
|
||||
s += idf[t] * (ENGRAM_BM25_K1 + 1.0)
|
||||
/ (1.0 + ENGRAM_BM25_K1 * norm);
|
||||
hits[h].w = s;
|
||||
}
|
||||
qsort(hits, (size_t)nhits, sizeof(EngramRankEntry), engram_rank_w_cmp);
|
||||
if (sem) qsort(sem, (size_t)nsem, sizeof(EngramSemEntry), engram_sem_cmp);
|
||||
/* Claim-10 associative leg: expand the top lexical hits along
|
||||
* structural relations only, order the reached set by query
|
||||
* similarity. Empty whenever the seeds have no structural
|
||||
* edges, which is the common case and is what keeps the
|
||||
* lexical ordering safe. */
|
||||
EngramSemEntry* assoc = qv ? malloc((size_t)ENGRAM_ASSOC_MAX * sizeof(EngramSemEntry)) : NULL;
|
||||
int64_t nassoc = assoc
|
||||
? engram_assoc_leg(g, hits, nhits, semseed, nsemseed,
|
||||
qv, qdim, assoc, ENGRAM_ASSOC_MAX)
|
||||
: 0;
|
||||
int64_t* order = malloc((size_t)lim * sizeof(int64_t));
|
||||
if (order) {
|
||||
int64_t no = engram_interleave3(hits, nhits, sem, nsem,
|
||||
assoc, nassoc, 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(assoc);
|
||||
free(hits);
|
||||
free(sem);
|
||||
free(qv);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user