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ce3f6c1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 | from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any, Iterable, Mapping, Sequence
import math
import re
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer, ENGLISH_STOP_WORDS
def _clip01(x: float, eps: float = 1e-6) -> float:
return min(max(float(x), eps), 1.0)
def _logsumexp(xs: Sequence[float]) -> float:
if not xs:
return float('-inf')
finite = [x for x in xs if math.isfinite(x)]
if not finite:
return float('-inf')
m = max(finite)
return m + math.log(sum(math.exp(x - m) for x in finite))
@dataclass(frozen=True)
class DocumentRecord:
id: str
text: str
title: str = ""
source: str = "unknown"
metadata: Mapping[str, Any] = field(default_factory=dict)
@property
def retrieval_text(self) -> str:
return (self.title + ". " + self.text).strip(". ")
def source_prior(self) -> float:
md = self.metadata
a = max(float(md.get("reliability_alpha", 1.0)), 1e-6)
b = max(float(md.get("reliability_beta", 1.0)), 1e-6)
reliability = a / (a + b)
authority = _clip01(md.get("authority", 0.5))
freshness = _clip01(md.get("freshness", 0.5))
return reliability * authority * freshness
@dataclass(frozen=True)
class RetrievalHit:
document: DocumentRecord
retrieval_score: float
log_prior: float
log_likelihood: float
log_posterior_unnormalized: float
posterior: float
query_coverage: float
rank: int
def as_dict(self) -> dict[str, Any]:
return {
"id": self.document.id,
"title": self.document.title,
"text": self.document.text,
"source": self.document.source,
"metadata": dict(self.document.metadata),
"retrieval_score": self.retrieval_score,
"log_prior": self.log_prior,
"log_likelihood": self.log_likelihood,
"posterior": self.posterior,
"query_coverage": self.query_coverage,
"rank": self.rank,
}
@dataclass(frozen=True)
class BayesianRetrievalConfig:
candidate_pool: int = 12
likelihood_temperature: float = 2.0
likelihood_floor: float = 0.02
reliability_weight: float = 1.0
authority_weight: float = 1.0
freshness_weight: float = 0.8
prior_floor: float = 1e-4
min_relevance: float = 0.06
min_top_posterior: float = 0.30
min_query_coverage: float = 0.30
min_dominance_ratio: float = 1.20
class BayesianIndex:
"""Generalized-Bayesian reranker over TF-IDF retrieval candidates.
Candidate document hypotheses are scored by
p(d|q) ∝ p(d) exp(tau * sim(q,d)).
p(d) is induced by a Beta source-reliability mean plus authority/freshness
metadata. The exponential similarity term is a Gibbs/generalized-Bayes
likelihood, not claimed to be a literal language-model likelihood.
"""
def __init__(self, documents: Sequence[DocumentRecord], config: BayesianRetrievalConfig | None = None):
if not documents:
raise ValueError("documents must be non-empty")
self.documents = list(documents)
self.config = config or BayesianRetrievalConfig()
self.vectorizer = TfidfVectorizer(
lowercase=True,
stop_words="english",
ngram_range=(1, 2),
sublinear_tf=True,
norm="l2",
)
self.matrix = self.vectorizer.fit_transform([d.retrieval_text for d in self.documents])
def _log_prior(self, d: DocumentRecord) -> float:
md = d.metadata
a = max(float(md.get("reliability_alpha", 1.0)), 1e-6)
b = max(float(md.get("reliability_beta", 1.0)), 1e-6)
rel = _clip01(a / (a + b))
auth = _clip01(md.get("authority", 0.5))
fresh = _clip01(md.get("freshness", 0.5))
cfg = self.config
prior = max(
cfg.prior_floor,
(rel ** cfg.reliability_weight)
* (auth ** cfg.authority_weight)
* (fresh ** cfg.freshness_weight),
)
return math.log(prior)
def lexical_scores(self, query: str) -> np.ndarray:
q = self.vectorizer.transform([str(query)])
# TF-IDF vectors are L2 normalized, so dot product is cosine similarity.
return (self.matrix @ q.T).toarray().ravel()
@staticmethod
def _query_coverage(query: str, text: str) -> float:
qterms = {t for t in re.findall(r"[A-Za-z0-9_-]+", query.lower()) if len(t) > 1 and t not in ENGLISH_STOP_WORDS}
if not qterms:
return 0.0
dterms = set(re.findall(r"[A-Za-z0-9_-]+", text.lower()))
return len(qterms & dterms) / len(qterms)
def search(self, query: str, k: int = 4) -> list[RetrievalHit]:
if k <= 0:
return []
scores = self.lexical_scores(query)
pool_n = min(max(k, self.config.candidate_pool), len(self.documents))
# Stable sort makes benchmark results reproducible under ties.
cand = np.argsort(-scores, kind="stable")[:pool_n]
logu: list[float] = []
rows: list[tuple[int, float, float, float]] = []
for i in cand:
d = self.documents[int(i)]
s = max(float(scores[int(i)]), 0.0)
lp = self._log_prior(d)
ll = self.config.likelihood_temperature * math.log(s + self.config.likelihood_floor)
u = lp + ll
rows.append((int(i), s, lp, ll))
logu.append(u)
lse = _logsumexp(logu)
probs = [math.exp(u - lse) if math.isfinite(lse) else 1.0 / len(logu) for u in logu]
ranked = sorted(
zip(rows, probs),
key=lambda rp: (rp[1], rp[0][1]),
reverse=True,
)
hits: list[RetrievalHit] = []
for rank, ((i, s, lp, ll), pr) in enumerate(ranked[:k], start=1):
hits.append(RetrievalHit(
document=self.documents[i],
retrieval_score=s,
log_prior=lp,
log_likelihood=ll,
log_posterior_unnormalized=lp + ll,
posterior=float(pr),
query_coverage=self._query_coverage(query, self.documents[i].retrieval_text),
rank=rank,
))
return hits
def should_abstain(self, hits: Sequence[RetrievalHit]) -> bool:
if not hits:
return True
top = hits[0]
if (
top.retrieval_score < self.config.min_relevance
or top.posterior < self.config.min_top_posterior
or top.query_coverage < self.config.min_query_coverage
):
return True
if len(hits) > 1:
second = hits[1]
dominance = top.posterior / max(second.posterior, 1e-12)
if dominance < self.config.min_dominance_ratio:
return True
return False
class ExtractiveGroundedGenerator:
"""Dependency-free grounded answer synthesizer for tests and offline use.
It selects the query-relevant sentence from the highest-posterior evidence
and returns that evidence sentence verbatim with its document id. It never
invents content not present in the retrieved context.
"""
abstain_text = "I do not know based on the provided documents."
@staticmethod
def _sentences(text: str) -> list[str]:
parts = re.split(r"(?<=[.!?])\s+|\n+", text.strip())
return [p.strip() for p in parts if p.strip()]
def generate(self, question: str, hits: Sequence[RetrievalHit]) -> tuple[str, list[str]]:
if not hits:
return self.abstain_text, []
# The posterior ranking has already selected the evidence hypothesis.
top = hits[0]
sents = self._sentences(top.document.text)
if not sents:
return self.abstain_text, []
if len(sents) == 1:
sentence = sents[0]
else:
v = TfidfVectorizer(lowercase=True, stop_words="english", ngram_range=(1, 2))
mat = v.fit_transform([question] + sents)
q = mat[0]
sims = (mat[1:] @ q.T).toarray().ravel()
sentence = sents[int(np.argmax(sims))]
return f"{sentence} [{top.document.id}]", [top.document.id]
class PlainTfidfIndex:
"""Conventional lexical baseline used by the end-to-end RAG benchmark."""
def __init__(self, documents: Sequence[DocumentRecord]):
self.documents = list(documents)
self.vectorizer = TfidfVectorizer(lowercase=True, stop_words="english", ngram_range=(1, 2), sublinear_tf=True, norm="l2")
self.matrix = self.vectorizer.fit_transform([d.retrieval_text for d in self.documents])
@staticmethod
def _query_coverage(query: str, text: str) -> float:
qterms = {t for t in re.findall(r"[A-Za-z0-9_-]+", query.lower()) if len(t) > 1 and t not in ENGLISH_STOP_WORDS}
if not qterms:
return 0.0
dterms = set(re.findall(r"[A-Za-z0-9_-]+", text.lower()))
return len(qterms & dterms) / len(qterms)
def search(self, query: str, k: int = 4) -> list[RetrievalHit]:
q = self.vectorizer.transform([query])
scores = (self.matrix @ q.T).toarray().ravel()
order = np.argsort(-scores, kind="stable")[:k]
# Baseline assigns normalized positive retrieval mass only for reporting.
vals = [max(float(scores[int(i)]), 0.0) for i in order]
z = sum(vals) or 1.0
return [RetrievalHit(self.documents[int(i)], float(scores[int(i)]), 0.0, 0.0, 0.0, vals[j]/z, 0.0, j+1) for j, i in enumerate(order)]
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