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b68816f f07443e b68816f | 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 | """The four extraction branches. This is the only stage that costs money.
**One call per TERM CLUSTER** — not per mention, not per chunk. That is the
whole economic argument for clustering: 200 mentions of "PA" cost one call, not
200. It is also what makes conflict detection possible, since contradictory
definitions can only be compared when they arrive together.
Each branch returns `(entry, usage)`, with `None` for the entry when the
response fails schema validation. A failed parse is not an exception: one bad
response must not abort a corpus-scale run that has already paid for parsing.
"""
from __future__ import annotations
from ...middlewares.logging import get_logger
from ..ids import brief_id, formula_id, rule_id, term_id
from ..models import (
CallUsage,
Chunk,
DocumentBrief,
FormulaEntry,
FormulaVariable,
GlossaryEntry,
KeyParameter,
Provenance,
RuleCandidate,
RuleEntry,
TermCluster,
)
from ..rank import top_k
from ..settings import EVIDENCE_K
from .base import evidence_block, load_prompt
from .schemas import FormulaDraft, GlossaryDraft, RuleDraft, SummaryDraft, schema_for
logger = get_logger("knowledge_extract")
def _prov(draft_prov, doc_id: str, chunk_id: str | None = None) -> Provenance:
return Provenance(
doc_id=doc_id,
span=draft_prov.span,
page=draft_prov.page,
section_no=draft_prov.section_no,
chunk_id=chunk_id,
)
def _evidence_for(
cluster: TermCluster, chunks: list[Chunk], k: int, round_index: int
) -> tuple[list[Chunk], list[float]]:
by_id = {c.chunk_id: c for c in chunks}
# `chunks` is passed so a linked partner (0.4.0 table pointers) joins the
# window. `service` passes it to `top_k` too, so the prompt and the span-check
# source stay identical — that equality is load-bearing, not incidental.
ids = top_k(cluster, k=k, round_index=round_index, chunks=chunks)
# Ranked scores only. A partner was LINKED in, not ranked in, so it has no
# score — `evidence_block` shows none for it, which is the honest rendering.
scores = cluster.evidence_scores[round_index * k : round_index * k + k]
return [by_id[i] for i in ids if i in by_id], scores
# ── glossary ────────────────────────────────────────────────────────────
def build_glossary_prompt(
cluster: TermCluster, chunks: list[Chunk], k: int = EVIDENCE_K, round_index: int = 0
) -> tuple[str, str]:
evidence, scores = _evidence_for(cluster, chunks, k, round_index)
user = (
f"CANDIDATE TERM: {cluster.canonical}\n"
f"KNOWN VARIANTS: {', '.join(cluster.variants)}\n"
f"MENTION COUNT: {cluster.mention_count}\n\n"
+ evidence_block(evidence, scores)
)
return load_prompt("glossary"), user
def extract_glossary(
cluster: TermCluster,
chunks: list[Chunk],
extractor,
doc_id: str,
k: int = EVIDENCE_K,
round_index: int = 0,
) -> tuple[GlossaryEntry | None, CallUsage]:
system, user = build_glossary_prompt(cluster, chunks, k, round_index)
result = extractor.complete(
"glossary", system, user, schema_for("glossary"), "GlossaryEntry"
)
try:
draft = GlossaryDraft.model_validate(result.data)
except Exception as exc:
logger.warning(
"glossary draft invalid", cluster=cluster.canonical, error=repr(exc)
)
return None, result.usage
evidence, _ = _evidence_for(cluster, chunks, k, round_index)
entry = GlossaryEntry(
# Keyed on the CLUSTER canonical, not on what the model returned as
# `term`: the cluster is the stable thing across runs, and the model is
# free to answer "PA" or "Physical Availability" for the same cluster.
term_id=term_id(doc_id, cluster.canonical),
term=draft.term,
full_name=draft.full_name,
source_wording=_heading_wording(cluster, evidence) or draft.source_wording,
definition=draft.definition,
subdomain_tags=draft.subdomain_tags,
mention_count=cluster.mention_count,
provenance=_prov(
draft.provenance, doc_id, evidence[0].chunk_id if evidence else None
),
)
return entry, result.usage
def _heading_wording(cluster: TermCluster, evidence: list[Chunk]) -> str | None:
"""The verbatim heading of the evidence chunk whose title names this term.
Preferred over whatever the model chose to quote, because the section
heading is where the document formally names the term. Measured on the
reference standard: the model quoted "Physical Availability (PA)" from the
page-1 intro — a real verbatim quote — while the section itself is headed
"Physical **of** Availability (PA)". Both occur in the document; only the
heading form reveals that the two disagree.
Recording the literal form is a locked decision: the discrepancy belongs to
the expert, not to us. Taking it deterministically rather than asking the
model to volunteer it means it cannot be normalised away.
"""
from ..cluster.normalize import normalize
from ..rank.evidence import _word_match
variants = [normalize(v) for v in cluster.variants]
for chunk in evidence:
heading = chunk.heading
if heading and any(_word_match(v, normalize(heading)) for v in variants):
return heading
return None
# ── rule of thumb ───────────────────────────────────────────────────────
def extract_rule(
candidate: RuleCandidate, chunk: Chunk, extractor, doc_id: str
) -> tuple[RuleEntry | None, CallUsage]:
user = (
f"CUE: {candidate.cue}\n\n"
+ evidence_block([chunk])
+ f"\n\nFOCUS PASSAGE:\n{candidate.snippet}"
)
result = extractor.complete(
"rule", load_prompt("rule"), user, schema_for("rule"), "RuleEntry"
)
try:
draft = RuleDraft.model_validate(result.data)
except Exception as exc:
logger.warning("rule draft invalid", chunk_id=candidate.chunk_id, error=repr(exc))
return None, result.usage
return (
RuleEntry(
# From the cue's position in the document, never from the model.
rule_id=rule_id(doc_id, candidate.chunk_id, candidate.char_start),
rule_type=draft.rule_type,
statement=draft.statement,
condition=draft.condition,
consequence=draft.consequence,
provenance=_prov(draft.provenance, doc_id, chunk.chunk_id),
),
result.usage,
)
# ── formula ─────────────────────────────────────────────────────────────
def extract_formula(
chunk: Chunk, extractor, doc_id: str
) -> tuple[FormulaEntry | None, CallUsage]:
user = evidence_block([chunk])
result = extractor.complete(
"formula", load_prompt("formula"), user, schema_for("formula"), "FormulaEntry"
)
try:
draft = FormulaDraft.model_validate(result.data)
except Exception as exc:
logger.warning("formula draft invalid", chunk_id=chunk.chunk_id, error=repr(exc))
return None, result.usage
return (
FormulaEntry(
formula_id=formula_id(
doc_id, draft.name, draft.formula_latex, chunk.chunk_id
),
name=draft.name,
formula_latex=draft.formula_latex,
# `term_id` is left null here and resolved by `link.py`, which is the
# only place that can see the glossary.
variables=[
FormulaVariable(symbol=v.symbol, meaning=v.meaning) for v in draft.variables
],
unit=draft.unit,
provenance=_prov(draft.provenance, doc_id, chunk.chunk_id),
),
result.usage,
)
# ── summary ─────────────────────────────────────────────────────────────
def extract_summary(
chunks: list[Chunk], extractor, doc_id: str
) -> tuple[DocumentBrief | None, CallUsage]:
"""Whole-document summary — the quiet cost risk. Few calls, but a large
share of all input tokens, because summarisation cannot be filtered: it
needs the whole document.
It is also the only branch that cannot be span-checked at all. A plausible
summary is indistinguishable from a correct one, which is exactly why it
belongs on a larger tier as soon as one exists.
"""
user = evidence_block(chunks)
result = extractor.complete(
"summary", load_prompt("summary"), user, schema_for("summary"), "DomainContext"
)
try:
draft = SummaryDraft.model_validate(result.data)
except Exception as exc:
logger.warning("summary draft invalid", error=repr(exc))
return None, result.usage
return (
DocumentBrief(
brief_id=brief_id(doc_id),
title=draft.title,
purpose_verbatim=draft.purpose_verbatim,
# `outline` is derived from the chunks by the caller, not asked of
# the model — see `service._document_outline`.
key_parameters=[
KeyParameter(surface=surface) for surface in draft.key_parameters
],
provenance=_prov(draft.provenance, doc_id),
),
result.usage,
)
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