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codex-py/codex/quality.py
Tarik Moussa c70ff9aa1e feat(quality): store real section titles for .tex chunks (R-F)
Add quality.section_label(title, content): for section-aware .tex ingest, keep the controlled bucket (intro/theorem/proof/abstract/bibliography) when the real \section heading maps to one, else store the cleaned title verbatim (e.g. 'preliminaries', 'rigidity') instead of collapsing to 'body'. Math papers title most sections descriptively, so R-C's vocab-only mapping left ~90% as 'body'; this lifts the section signal toward near-full. .pdf/.txt/run_quality_pass keep content-based classify_section unchanged. Safe because the section column is write-only (no consumer filters on the vocab).

New _clean_title (strip LaTeX/numbering, lower-case, truncate). Tests: section_label bucket/verbatim/fallback paths + _clean_title; .tex ingest stores a descriptive heading as its title. Full suite 377 passed; ruff + mypy clean.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-06-18 09:03:46 +02:00

226 lines
8.1 KiB
Python

"""Chunk quality filtering and section classification (F-16).
Three independent quality signals are applied at ingest time:
1. **Length** — structural; no content required.
2. **Alpha-ratio** — OCR artefacts have high non-alpha character density.
3. **Bib-score** — DOI + "et al." + (YYYY) patterns co-occur almost only
in reference list entries.
Section classification is rule-based (no LLM) and runs on the first 200
characters of each chunk. The retroactive ``run_quality_pass`` function
can be called from the CLI to back-fill existing chunks.
"""
from __future__ import annotations
import logging
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from codex.config import Settings
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Section classification patterns (checked in order; first match wins)
# ---------------------------------------------------------------------------
_SECTION_PATTERNS: list[tuple[str, re.Pattern[str]]] = [
("abstract", re.compile(r"^\s*(abstract|zusammenfassung|r[eé]sum[eé])\b", re.I)),
("intro", re.compile(r"^\s*(introduction|einleitung|1\.\s)", re.I)),
("theorem", re.compile(r"^\s*(theorem|lemma|proposition|corollary|definition)\b", re.I)),
("proof", re.compile(r"^\s*(proof\b|beweis\b|proof\s+of\b)", re.I)),
("bibliography", re.compile(r"^\s*(references|bibliography|bibliographie|literatur)\b", re.I)),
]
_DOI_RE = re.compile(r"10\.\d{4,}/\S+")
_ET_AL_RE = re.compile(r"\bet\s+al\b", re.I)
_YEAR_BRACKETS_RE = re.compile(r"\(\d{4}\)")
# ---------------------------------------------------------------------------
# Bibliography heuristic score
# ---------------------------------------------------------------------------
def _bib_score(text: str) -> float:
"""Return a [0..1] heuristic for how bibliography-like a chunk is.
Three signals contribute:
* DOI occurrences (weight 3)
* "et al." occurrences (weight 2)
* year-in-brackets occurrences (weight 1)
The raw signal is normalised against a rough word-count proxy so that
long chunks with occasional references don't get flagged.
"""
if not text:
return 0.0
word_count = max(len(text.split()), 1)
doi_hits = len(_DOI_RE.findall(text))
etal_hits = len(_ET_AL_RE.findall(text))
year_hits = len(_YEAR_BRACKETS_RE.findall(text))
raw = (doi_hits * 3 + etal_hits * 2 + year_hits) / max(word_count / 10, 1)
return min(raw, 1.0)
# ---------------------------------------------------------------------------
# Quality predicate
# ---------------------------------------------------------------------------
def is_quality_chunk(text: str, *, settings: Settings) -> bool:
"""Return True when *text* passes all three quality thresholds.
Checks (all configurable via :class:`codex.config.Settings`):
1. ``chunk_min_chars`` — character count floor.
2. ``chunk_min_alpha_ratio`` — minimum fraction of alphabetic chars.
3. ``chunk_max_bib_score`` — bibliography heuristic ceiling.
"""
if len(text) < settings.chunk_min_chars:
return False
alpha_ratio = sum(c.isalpha() for c in text) / max(len(text), 1)
if alpha_ratio < settings.chunk_min_alpha_ratio:
return False
return _bib_score(text) <= settings.chunk_max_bib_score
# ---------------------------------------------------------------------------
# Section classification
# ---------------------------------------------------------------------------
def classify_section(text: str) -> str:
"""Classify a chunk's section using rule-based regex matching.
Inspects only the first 200 characters. Returns one of:
``abstract``, ``intro``, ``theorem``, ``proof``, ``bibliography``, ``body``.
"""
snippet = text[:200]
for section_name, pattern in _SECTION_PATTERNS:
if pattern.search(snippet):
return section_name
# Fallback: bibliography by DOI density even without a header
if _bib_score(text) > 0.5:
return "bibliography"
return "body"
def _clean_title(title: str) -> str:
"""Normalize a LaTeX ``\\section`` title for use as a section label.
Strips LaTeX commands (``\\emph`` …) and ``{}$``, drops leading section
numbering (``3.2 ``), collapses whitespace, lower-cases, and truncates to
60 chars so the stored ``section`` value is a clean, comparable string.
"""
t = re.sub(r"\\[a-zA-Z]+\*?", " ", title) # LaTeX commands
t = re.sub(r"[{}$]", "", t) # braces / math delimiters
t = re.sub(r"^\s*\d+(?:\.\d+)*\.?\s*", "", t) # leading numbering "3.2 "
return " ".join(t.split()).strip().lower()[:60]
def section_label(title: str | None, content: str) -> str:
"""Label a chunk's section, preferring the real ``\\section`` heading (R-F).
For section-aware ``.tex`` ingest *title* is the real heading: if it maps to a
controlled bucket via :func:`classify_section` (intro / theorem / proof /
abstract / bibliography) that bucket is kept (cross-source consistency);
otherwise the cleaned title itself is stored (e.g. ``"preliminaries"``,
``"rigidity"``) — far more signal than collapsing everything to ``body``.
When *title* is None / blank (the ``.pdf`` / ``.txt`` / ``run_quality_pass``
paths) this falls back to content-based :func:`classify_section` — unchanged
behaviour. Safe to store free-text titles because the ``section`` column is
write-only (no consumer filters on the controlled vocabulary).
"""
if not title or not title.strip():
return classify_section(content)
cleaned = _clean_title(title)
if not cleaned:
return classify_section(content)
bucket = classify_section(cleaned)
return bucket if bucket != "body" else cleaned
# ---------------------------------------------------------------------------
# Batch filter
# ---------------------------------------------------------------------------
def filter_chunks(chunks: list[str], *, settings: Settings) -> list[str]:
"""Return only the chunks that pass all quality filters.
Logs the keep ratio at DEBUG level.
"""
kept = [c for c in chunks if is_quality_chunk(c, settings=settings)]
logger.debug(
"Quality filter: %d/%d chunks kept (%.0f%%)",
len(kept),
len(chunks),
100 * len(kept) / max(len(chunks), 1),
)
return kept
# ---------------------------------------------------------------------------
# Retroactive DB pass
# ---------------------------------------------------------------------------
def run_quality_pass(
*,
paper_id: str | None = None,
conn: Any,
settings: Settings,
) -> dict[str, int]:
"""Apply quality filter + section classification to existing chunks in DB.
For each chunk:
* Fails quality → DELETE.
* Passes quality → UPDATE ``section`` with :func:`classify_section`.
Parameters
----------
paper_id:
When provided, restrict the pass to chunks for this paper only.
conn:
Open psycopg connection (dict-row factory assumed).
settings:
Application settings for quality thresholds.
Returns
-------
dict with keys ``kept``, ``removed``, ``tagged`` (= kept).
"""
if paper_id is not None:
rows = conn.execute(
"SELECT id, content FROM chunks WHERE paper_id = %(pid)s",
{"pid": paper_id},
).fetchall()
else:
rows = conn.execute("SELECT id, content FROM chunks").fetchall()
kept = 0
removed = 0
for row in rows:
chunk_id = row["id"]
content = row["content"]
if not is_quality_chunk(content, settings=settings):
conn.execute("DELETE FROM chunks WHERE id = %(id)s", {"id": chunk_id})
removed += 1
else:
section = classify_section(content)
conn.execute(
"UPDATE chunks SET section = %(section)s WHERE id = %(id)s",
{"section": section, "id": chunk_id},
)
kept += 1
conn.commit()
logger.info("Quality pass done: %d kept, %d removed, %d section-tagged", kept, removed, kept)
return {"kept": kept, "removed": removed, "tagged": kept}