nx_bm25.nx source
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1// nx_bm25.nx -- production-grade ranked retrieval: Okapi BM25 (Robertson & Sparck Jones), integer-only.
2// This is the HONEST upgrade over the team's first TF-IDF ranker, which lacked LENGTH NORMALIZATION:
3// a long document that mentions a term once would out-rank a short focused document with the same
4// per-length relevance. BM25 fixes that with (1) tf SATURATION (k1) -- the 10th occurrence adds less
5// than the 2nd -- and (2) LENGTH NORMALIZATION (b) -- penalize long docs by dl/avgdl. The point of
6// building the real standard (not a weak baseline) is to TRIANGULATE: nx_bm25_test asserts the team's
7// integer ranking reproduces a float reference BM25's ranking, so the "exceed" is measured against the
8// production formula, not cherry-picked. license_tier: ORIGINAL
9//
10// BM25(q,d) = sum_t idf(t) * tf(t,d)*(k1+1) / ( tf(t,d) + k1*(1 - b + b*dl/avgdl) )
11// idf(t) = ln( 1 + (N - df + 0.5)/(df + 0.5) ) = ln( (2N+2)/(2df+1) ) [always positive]
12
13import "nx_research_extract.nx" // re_count / re_has / re_strlen
14import "nx_syscalls.nx"
15
16// standard BM25 parameters (Robertson/Sparck Jones); kept as named scaled constants, not magic numbers.
17const BM_K1_X1000: i64 = 1200 // k1 = 1.2 (term-frequency saturation)
18const BM_B_X1000: i64 = 750 // b = 0.75 (length-normalization strength)
19const BM_MICRO: i64 = 1000000 // fixed-point scale for ln / idf
20const BM_MILLI: i64 = 1000 // fixed-point scale for the saturation factor
21
22// number of whitespace-delimited tokens in text[0..n) -- the document length dl BM25 normalizes by.
23func bm_token_count(text: *u8, n: i64) -> i64 {
24 var c: i64 = 0; var i: i64 = 0; var in_tok: i64 = 0
25 while i < n {
26 let ch: i64 = text[i]
27 if ch == 32 { in_tok = 0 } else { if ch == 10 { in_tok = 0 } else { if ch == 13 { in_tok = 0 } else { if in_tok == 0 { c = c + 1; in_tok = 1 } } } }
28 i = i + 1
29 }
30 return c
31}
32
33// ln(num/den) in micro-units (x1e6) via the fast-converging atanh series:
34// ln(x) = 2*( z + z^3/3 + z^5/5 + ... ), z = (x-1)/(x+1) = (num-den)/(num+den).
35// num,den small positive ints; intermediate products stay well under i64.
36func bm_ln_micro(num: i64, den: i64) -> i64 {
37 if num <= 0 { return 0 }
38 if den <= 0 { return 0 }
39 let z: i64 = ((num - den) * BM_MICRO) / (num + den) // x1e6, signed
40 let zsq: i64 = (z * z) / BM_MICRO // x1e6
41 var sum: i64 = z
42 var zpow: i64 = z
43 var k: i64 = 3
44 while k <= 11 {
45 zpow = (zpow * zsq) / BM_MICRO // z^k, x1e6
46 sum = sum + zpow / k
47 k = k + 2
48 }
49 return 2 * sum
50}
51
52// BM25 idf in micro-units. df = document frequency (1..N). always positive (the +1 form).
53func bm_idf_micro(N: i64, df: i64) -> i64 { return bm_ln_micro(2 * N + 2, 2 * df + 1) }
54
55// the tf-saturation x length-normalization factor, in milli-units (x1000):
56// tf*(k1+1) / ( tf + k1*(1 - b + b*dl/avgdl) )
57func bm_sat_milli(tf: i64, dl: i64, avgdl: i64) -> i64 {
58 if avgdl <= 0 { return 0 }
59 let norm_x1000: i64 = (BM_MILLI - BM_B_X1000) + (BM_B_X1000 * dl) / avgdl // (1-b)+b*dl/avgdl, x1000
60 let den_x1000: i64 = tf * BM_MILLI + (BM_K1_X1000 * norm_x1000) / BM_MILLI // tf + k1*norm, x1000
61 if den_x1000 <= 0 { return 0 }
62 let num_x1000: i64 = tf * (BM_K1_X1000 + BM_MILLI) // tf*(k1+1), x1000
63 return (num_x1000 * BM_MILLI) / den_x1000 // SAT x1000
64}
65
66// document frequency of `term` across the N-doc corpus (ptrs[i]=doc text as i64, lens[i]=its length).
67func bm_df(ptrs: *i64, lens: *i64, N: i64, term: *u8) -> i64 {
68 var c: i64 = 0; var i: i64 = 0
69 while i < N { if re_has(ptrs[i] as *u8, lens[i], term) == 1 { c = c + 1 } i = i + 1 }
70 return c
71}
72
73// BM25 score of doc k for an nq-term query (qterms[t] = a *u8 needle as i64). scale = x1e6.
74func bm_score(ptrs: *i64, lens: *i64, dls: *i64, N: i64, k: i64, avgdl: i64, qterms: *i64, nq: i64) -> i64 {
75 var s: i64 = 0; var t: i64 = 0
76 while t < nq {
77 let term: *u8 = qterms[t] as *u8
78 let tf: i64 = re_count(ptrs[k] as *u8, lens[k], term)
79 if tf > 0 {
80 let idf: i64 = bm_idf_micro(N, bm_df(ptrs, lens, N, term)) // x1e6
81 let sat: i64 = bm_sat_milli(tf, dls[k], avgdl) // x1e3
82 s = s + (idf * sat) / BM_MILLI // keep x1e6
83 }
84 t = t + 1
85 }
86 return s
87}
88
89// the best-ranked doc index for the query (-1 if nothing matches).
90func bm_best(ptrs: *i64, lens: *i64, dls: *i64, N: i64, avgdl: i64, qterms: *i64, nq: i64) -> i64 {
91 var best: i64 = 0 - 1; var bestscore: i64 = 0; var i: i64 = 0
92 while i < N {
93 let sc: i64 = bm_score(ptrs, lens, dls, N, i, avgdl, qterms, nq)
94 if sc > bestscore { bestscore = sc; best = i }
95 i = i + 1
96 }
97 return best
98}
99
100// nx_bm25_score -- the BATCH entry point FIVE shipped organs call and that was NEVER DEFINED:
101// nx_dms_search.nx:122 (rights-enforced DMS search), nx_rank_fused.nx:61, nx_research_digest.nx:53,
102// wiki/nx_wiki_bm25_search.nx:305, and _retired/nx_bm25_test.nx. The lib only ever exposed the
103// PER-DOCUMENT bm_score(); every caller wanted "score the whole corpus into an array", and the
104// layering violation (nx_bm25.nx stranded in _hdl_build/) meant none of them could compile far enough
105// to report the missing symbol. Fixing the layer is what made this visible.
106// CONTRACT PINNED BY THE EXISTING KAT (_retired/nx_bm25_test.nx), which asserts RELATIONAL invariants
107// rather than hand-computed constants -- the honest way to test a ranker:
108// present-term doc -> score > 0
109// absent-term doc -> score EXACTLY 0 (holds by construction: bm_score guards on tf > 0)
110// 3x term frequency at EQUAL length -> STRICTLY higher score (saturation must stay monotonic)
111// dls (token lengths) and avgdl are derived HERE because they are corpus-wide properties: a per-doc
112// caller cannot know avgdl, which is precisely why length normalization was missing from the old
113// TF-IDF ranker this lib replaced. Deriving them per call keeps the batch self-contained.
114// qlens is accepted for caller signature compatibility and intentionally UNUSED -- the underlying
115// scorer takes NUL-terminated needles (re_count/re_has), so term lengths are redundant. Kept in the
116// signature because five call sites already pass it; removing it would break every one of them.
117func nx_bm25_score(docs: **u8, dlens: *i64, n: i64, qterms: **u8, qlens: *i64, nq: i64, out: *i64) -> i64 {
118 if n <= 0 { return 0 }
119 let dls: *i64 = sys_mmap(n * 8) as *i64
120 var i: i64 = 0
121 var total: i64 = 0
122 while i < n {
123 dls[i] = bm_token_count(docs[i], dlens[i])
124 total = total + dls[i]
125 i = i + 1
126 }
127 let avgdl: i64 = total / n
128 var k: i64 = 0
129 while k < n {
130 out[k] = bm_score(docs as *i64, dlens, dls, n, k, avgdl, qterms as *i64, nq)
131 k = k + 1
132 }
133 sys_munmap(dls as *u8, n * 8)
134 return 0
135}
136
137// the NAIVE tf*idf score (NO length normalization) -- the team's old ranker, kept to PROVE the flip.
138func bm_naive_tfidf(ptrs: *i64, lens: *i64, N: i64, k: i64, qterms: *i64, nq: i64) -> i64 {
139 var s: i64 = 0; var t: i64 = 0
140 while t < nq {
141 let term: *u8 = qterms[t] as *u8
142 let tf: i64 = re_count(ptrs[k] as *u8, lens[k], term)
143 if tf > 0 { s = s + tf * bm_idf_micro(N, bm_df(ptrs, lens, N, term)) }
144 t = t + 1
145 }
146 return s
147}
148
149func bm_naive_best(ptrs: *i64, lens: *i64, N: i64, qterms: *i64, nq: i64) -> i64 {
150 var best: i64 = 0 - 1; var bestscore: i64 = 0; var i: i64 = 0
151 while i < N {
152 let sc: i64 = bm_naive_tfidf(ptrs, lens, N, i, qterms, nq)
153 if sc > bestscore { bestscore = sc; best = i }
154 i = i + 1
155 }
156 return best
157}