Tim Lingo
6f3a048f36
engram_assoc_leg previously took its seeds only from the top-3 LEXICAL hits. For a paraphrase query the lexical hits are noise by construction, so the walk never reached the neighbourhood that holds the answer. This adds the seeding pass Will documents at el_runtime.c l.6082 — "Semantic seeding (HippoRAG pattern, use similarity twice): the query is embedded, the top-K nodes by cosine join the seed set" — using his own ENGRAM_EMBED_SEED_K (8). Similarity is now used twice, coherently: cosine picks where to STAND in the graph, the structural-relation walk decides what is REACHABLE, and cosine orders what was reached (iteration 2's finding, unchanged). The seed list is deliberately NOT floored at ENGRAM_EMBED_SEED_MIN. Measured over all 38 gold queries: true paraphrase targets score cosine 0.46-0.66 and the three nonsense controls' own nearest neighbours score 0.55/0.60/0.62 — the distributions OVERLAP, so no absolute cosine floor separates signal from gibberish. The gate that works is reachability: gibberish's nearest neighbours carry no structural edge, so its graph leg is empty and the controls hold. The raw top-K is selected inside the existing scoring pass, so the cosine is computed exactly once per node: no extra corpus pass, no extra embed round-trip, latency flat (p50 1220 -> 1227 ms, 1.01x). Measured vs the certified baseline feat/hybrid-semantic-recall, embedded corpus, 2 runs each, zero run-to-run drift on both sides: hit@5 51.4% -> 68.6% MRR@10 0.387 -> 0.461 paraphrase 38.5% -> 61.5% associative 0% -> 66.7% exact_rare 100% held, nonsense 2/3 held, superseded 2/3 held phrase 85.7% -> 71.4% (q11, the known rank-5 rotation tax) net +6 queries (7 fixed / 1 broken), McNemar p=0.0703
22 lines
1.3 KiB
Python
22 lines
1.3 KiB
Python
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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