1df9be6563
- codex/parsing/mathpix.py: pix2tex (local, CPU) primary + MathPix API optional; bbox heuristic h>15px, math-char-count>5; singleton model cache - codex/parsing/figures.py: pymupdf embedded-image extraction → PNG; caption detection via proximity + "Figure/Fig./Abbildung" prefix - codex/models.py: FormulaChunk + FigureChunk dataclasses (R-10/R-11) - codex/ingest.py: --rich flag wires formula+figure extraction post-ingest - codex/cli.py: search_app sub-typer (paper + formula subcommands), --rich flag on ingest; wiki_app from F-12 preserved intact - codex/config.py: mathpix_app_id/key, pix2tex_fallback, figures_dir - infra/schema.sql: formulas + figures tables with HNSW pgvector indexes - pyproject.toml: pymupdf>=1.24, pix2tex>=0.1.4 - tests/parsing/test_mathpix.py + test_figures.py: 31 tests (mock pix2tex + MathPix HTTP, real pymupdf on synthetic PDF) Gate: 158 passed, ruff clean, mypy clean (20 files) Requirements: R-10 R-11 R-12 R-13 R-14 → done Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
262 lines
10 KiB
Python
262 lines
10 KiB
Python
"""End-to-end idempotent ingest pipeline for a single paper."""
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from __future__ import annotations
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import contextlib
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import logging
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from dataclasses import dataclass
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from pathlib import Path
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import numpy as np
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from codex.config import get_settings
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from codex.db import get_conn
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from codex.embed import get_embedder
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from codex.models import Citation, Paper
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from codex.sources import openalex, semanticscholar
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logger = logging.getLogger(__name__)
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@dataclass
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class IngestResult:
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paper_id: str
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chunks_upserted: int
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citations_upserted: int
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formulas_upserted: int = 0
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figures_upserted: int = 0
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def ingest_paper(
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paper_id: str,
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source_path: str | None = None,
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rich: bool = False,
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) -> IngestResult:
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"""Idempotent ingest of one paper.
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Parameters
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----------
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paper_id:
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An arXiv ID (``"2301.07041"``), DOI (``"10.1145/…"``), or
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OpenAlex W-ID (``"W2741809807"``).
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source_path:
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Optional path to a local ``.tex`` or ``.pdf`` file.
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If given, the file is parsed into text chunks and stored.
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Supports:
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- ``.tex`` → :func:`codex.parsing.tex.latex_to_text` + chunk
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- ``.pdf`` → :func:`codex.parsing.nougat.pdf_to_markdown` + chunk
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+ :func:`codex.parsing.grobid.extract_references` for refs
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rich:
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When True and *source_path* is a PDF, also extract formulas via
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:func:`codex.parsing.mathpix.extract_formulas` and figures via
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:func:`codex.parsing.figures.extract_figures` (F-09).
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Returns
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-------
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IngestResult
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Counts of upserted chunks, citations, formulas, and figures.
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"""
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# ---------------------------------------------------------------
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# 1. Fetch metadata (OpenAlex primary, SemanticScholar fallback)
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# ---------------------------------------------------------------
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paper: Paper | None = openalex.fetch_paper(paper_id)
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if paper is None:
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# Detect arXiv IDs: numeric pattern like "2301.07041" or "arxiv:..." prefix
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pid_lower = paper_id.lower()
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looks_like_arxiv = pid_lower.startswith("arxiv:") or (
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len(paper_id) > 4 and paper_id[4:5] == "." and paper_id[:4].isdigit()
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)
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if looks_like_arxiv:
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arxiv_key = paper_id if pid_lower.startswith("arxiv:") else f"arXiv:{paper_id}"
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with contextlib.suppress(Exception):
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semanticscholar.fetch_references(arxiv_key)
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paper = Paper(id=paper_id, title="")
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else:
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raise ValueError(f"Paper not found: {paper_id}")
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if paper is None:
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raise ValueError(f"Paper not found: {paper_id}")
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# ---------------------------------------------------------------
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# 2. Embed abstract (dense only — schema has one vector column)
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# ---------------------------------------------------------------
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embedder = get_embedder()
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abstract_text = paper.abstract or ""
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if abstract_text:
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abstract_emb_arr: np.ndarray = embedder.encode_dense([abstract_text])
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abstract_emb: list[float] = abstract_emb_arr[0].tolist()
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else:
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dim = embedder.dim
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abstract_emb = [0.0] * dim
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# ---------------------------------------------------------------
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# 3. Upsert paper (INSERT … ON CONFLICT (id) DO UPDATE SET …)
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# ---------------------------------------------------------------
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with get_conn() as conn:
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conn.execute(
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"""
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INSERT INTO papers (id, openalex_id, bibkey, title, authors, year, abstract,
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source_path, abstract_emb)
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VALUES (%(id)s, %(openalex_id)s, %(bibkey)s, %(title)s, %(authors)s, %(year)s,
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%(abstract)s, %(source_path)s, %(abstract_emb)s)
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ON CONFLICT (id) DO UPDATE SET
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openalex_id = EXCLUDED.openalex_id,
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title = EXCLUDED.title,
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authors = EXCLUDED.authors,
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year = EXCLUDED.year,
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abstract = EXCLUDED.abstract,
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source_path = COALESCE(EXCLUDED.source_path, papers.source_path),
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abstract_emb = EXCLUDED.abstract_emb
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""",
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{
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"id": paper.id,
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"openalex_id": paper.openalex_id,
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"bibkey": paper.bibkey,
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"title": paper.title,
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"authors": paper.authors,
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"year": paper.year,
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"abstract": paper.abstract,
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"source_path": source_path,
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"abstract_emb": abstract_emb,
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},
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)
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# ---------------------------------------------------------------
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# 4. Parse + chunk + embed source file (if provided)
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# DELETE existing chunks for this paper first, then bulk INSERT
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# ---------------------------------------------------------------
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chunks_upserted = 0
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pdf_citations: list[Citation] = []
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if source_path is not None:
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from codex.parsing.grobid import extract_references
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from codex.parsing.nougat import pdf_to_markdown
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from codex.parsing.tex import chunk_text, latex_to_text
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suffix = Path(source_path).suffix.lower()
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text = ""
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if suffix == ".tex":
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text = latex_to_text(Path(source_path).read_text())
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elif suffix == ".pdf":
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text = pdf_to_markdown(source_path)
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# Also extract GROBID refs
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grobid_refs = extract_references(source_path)
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for ref in grobid_refs:
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cited_id = ref.get("doi") or ref.get("arxiv_id")
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if cited_id:
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pdf_citations.append(Citation(citing_id=paper.id, cited_id=cited_id))
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else:
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logger.warning("Unbekannter Dateityp: %s — kein Text-Parsing", source_path)
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chunks_text = chunk_text(text) if text else []
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# Delete existing chunks for this paper
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conn.execute("DELETE FROM chunks WHERE paper_id = %s", (paper.id,))
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if chunks_text:
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# Embed all chunks in one batch
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chunk_embeddings = embedder.encode_dense(chunks_text)
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chunk_rows = [
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(paper.id, ord_idx, content, chunk_embeddings[ord_idx].tolist())
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for ord_idx, content in enumerate(chunks_text)
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]
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with conn.cursor() as cur:
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cur.executemany(
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"INSERT INTO chunks (paper_id, ord, content, embedding)"
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" VALUES (%s, %s, %s, %s)",
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chunk_rows,
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)
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chunks_upserted = len(chunk_rows)
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# ---------------------------------------------------------------
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# 5. Fetch citations (OpenAlex if openalex_id, else S2)
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# Insert with ON CONFLICT (citing_id, cited_id) DO NOTHING
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# ---------------------------------------------------------------
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api_citations: list[Citation]
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if paper.openalex_id:
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api_citations = openalex.fetch_citations(paper.openalex_id)
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else:
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api_citations = semanticscholar.fetch_references(paper_id)
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# Merge API citations and GROBID PDF citations; dedup via set
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all_citations_set: set[tuple[str, str]] = set()
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merged_citations: list[Citation] = []
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for cit in api_citations + pdf_citations:
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key = (cit.citing_id, cit.cited_id)
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if key not in all_citations_set:
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all_citations_set.add(key)
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merged_citations.append(cit)
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if merged_citations:
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with conn.cursor() as cur:
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cur.executemany(
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"""
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INSERT INTO citations (citing_id, cited_id, context)
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VALUES (%s, %s, %s)
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ON CONFLICT (citing_id, cited_id) DO NOTHING
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""",
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[(c.citing_id, c.cited_id, c.context) for c in merged_citations],
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)
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citations_upserted = len(merged_citations)
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conn.commit()
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# ---------------------------------------------------------------
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# 6. F-09 Rich Parsing: formulas + figures (PDF only)
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# ---------------------------------------------------------------
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formulas_upserted = 0
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figures_upserted = 0
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if rich and source_path is not None and Path(source_path).suffix.lower() == ".pdf":
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from codex.parsing.figures import extract_figures
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from codex.parsing.mathpix import extract_formulas
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settings = get_settings()
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formulas = extract_formulas(source_path)
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figures = extract_figures(source_path, output_dir=settings.figures_dir)
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if formulas or figures:
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with get_conn() as conn2:
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if formulas:
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with conn2.cursor() as cur:
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cur.executemany(
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"""
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INSERT INTO formulas (paper_id, page, raw_latex, context, eq_label)
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VALUES (%s, %s, %s, %s, %s)
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ON CONFLICT DO NOTHING
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""",
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[
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(f.paper_id, f.page, f.raw_latex, f.context, f.eq_label)
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for f in formulas
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],
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)
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formulas_upserted = len(formulas)
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if figures:
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with conn2.cursor() as cur:
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cur.executemany(
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"""
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INSERT INTO figures (paper_id, page, caption, image_path)
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VALUES (%s, %s, %s, %s)
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ON CONFLICT DO NOTHING
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""",
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[
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(fig.paper_id, fig.page, fig.caption, fig.image_path)
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for fig in figures
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],
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)
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figures_upserted = len(figures)
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conn2.commit()
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return IngestResult(
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paper_id=paper.id,
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chunks_upserted=chunks_upserted,
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citations_upserted=citations_upserted,
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formulas_upserted=formulas_upserted,
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figures_upserted=figures_upserted,
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)
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