"""End-to-end idempotent ingest pipeline for a single paper.""" from __future__ import annotations import contextlib import logging from dataclasses import dataclass from pathlib import Path import numpy as np from codex.config import get_settings from codex.db import get_conn from codex.embed import get_embedder from codex.models import Citation, Paper from codex.sources import openalex, semanticscholar logger = logging.getLogger(__name__) @dataclass class IngestResult: paper_id: str chunks_upserted: int citations_upserted: int formulas_upserted: int = 0 figures_upserted: int = 0 def ingest_paper( paper_id: str, source_path: str | None = None, rich: bool = False, ) -> IngestResult: """Idempotent ingest of one paper. Parameters ---------- paper_id: An arXiv ID (``"2301.07041"``), DOI (``"10.1145/…"``), or OpenAlex W-ID (``"W2741809807"``). source_path: Optional path to a local ``.tex`` or ``.pdf`` file. If given, the file is parsed into text chunks and stored. Supports: - ``.tex`` → :func:`codex.parsing.tex.latex_to_text` + chunk - ``.pdf`` → :func:`codex.parsing.nougat.pdf_to_markdown` + chunk + :func:`codex.parsing.grobid.extract_references` for refs rich: When True and *source_path* is a PDF, also extract formulas via :func:`codex.parsing.mathpix.extract_formulas` and figures via :func:`codex.parsing.figures.extract_figures` (F-09). Returns ------- IngestResult Counts of upserted chunks, citations, formulas, and figures. """ # --------------------------------------------------------------- # 1. Fetch metadata (OpenAlex primary, SemanticScholar fallback) # --------------------------------------------------------------- paper: Paper | None = openalex.fetch_paper(paper_id) if paper is None: # Detect arXiv IDs: numeric pattern like "2301.07041" or "arxiv:..." prefix pid_lower = paper_id.lower() looks_like_arxiv = pid_lower.startswith("arxiv:") or ( len(paper_id) > 4 and paper_id[4:5] == "." and paper_id[:4].isdigit() ) if looks_like_arxiv: arxiv_key = paper_id if pid_lower.startswith("arxiv:") else f"arXiv:{paper_id}" with contextlib.suppress(Exception): semanticscholar.fetch_references(arxiv_key) paper = Paper(id=paper_id, title="") else: raise ValueError(f"Paper not found: {paper_id}") if paper is None: raise ValueError(f"Paper not found: {paper_id}") # --------------------------------------------------------------- # 2. Embed abstract (dense only — schema has one vector column) # --------------------------------------------------------------- embedder = get_embedder() abstract_text = paper.abstract or "" if abstract_text: abstract_emb_arr: np.ndarray = embedder.encode_dense([abstract_text]) abstract_emb: list[float] = abstract_emb_arr[0].tolist() else: dim = embedder.dim abstract_emb = [0.0] * dim # --------------------------------------------------------------- # 3. Upsert paper (INSERT … ON CONFLICT (id) DO UPDATE SET …) # --------------------------------------------------------------- with get_conn() as conn: conn.execute( """ INSERT INTO papers (id, openalex_id, bibkey, title, authors, year, abstract, source_path, abstract_emb) VALUES (%(id)s, %(openalex_id)s, %(bibkey)s, %(title)s, %(authors)s, %(year)s, %(abstract)s, %(source_path)s, %(abstract_emb)s) ON CONFLICT (id) DO UPDATE SET openalex_id = EXCLUDED.openalex_id, title = EXCLUDED.title, authors = EXCLUDED.authors, year = EXCLUDED.year, abstract = EXCLUDED.abstract, source_path = COALESCE(EXCLUDED.source_path, papers.source_path), abstract_emb = EXCLUDED.abstract_emb """, { "id": paper.id, "openalex_id": paper.openalex_id, "bibkey": paper.bibkey, "title": paper.title, "authors": paper.authors, "year": paper.year, "abstract": paper.abstract, "source_path": source_path, "abstract_emb": abstract_emb, }, ) # --------------------------------------------------------------- # 4. Parse + chunk + embed source file (if provided) # DELETE existing chunks for this paper first, then bulk INSERT # --------------------------------------------------------------- chunks_upserted = 0 pdf_citations: list[Citation] = [] if source_path is not None: from codex.parsing.grobid import extract_references from codex.parsing.nougat import pdf_to_markdown from codex.parsing.tex import chunk_text, latex_to_text suffix = Path(source_path).suffix.lower() text = "" if suffix == ".tex": text = latex_to_text(Path(source_path).read_text()) elif suffix == ".pdf": text = pdf_to_markdown(source_path) # Also extract GROBID refs grobid_refs = extract_references(source_path) for ref in grobid_refs: cited_id = ref.get("doi") or ref.get("arxiv_id") if cited_id: pdf_citations.append(Citation(citing_id=paper.id, cited_id=cited_id)) else: logger.warning("Unbekannter Dateityp: %s — kein Text-Parsing", source_path) chunks_text = chunk_text(text) if text else [] # Delete existing chunks for this paper conn.execute("DELETE FROM chunks WHERE paper_id = %s", (paper.id,)) if chunks_text: # Embed all chunks in one batch chunk_embeddings = embedder.encode_dense(chunks_text) chunk_rows = [ (paper.id, ord_idx, content, chunk_embeddings[ord_idx].tolist()) for ord_idx, content in enumerate(chunks_text) ] with conn.cursor() as cur: cur.executemany( "INSERT INTO chunks (paper_id, ord, content, embedding)" " VALUES (%s, %s, %s, %s)", chunk_rows, ) chunks_upserted = len(chunk_rows) # --------------------------------------------------------------- # 5. Fetch citations (OpenAlex if openalex_id, else S2) # Insert with ON CONFLICT (citing_id, cited_id) DO NOTHING # --------------------------------------------------------------- api_citations: list[Citation] if paper.openalex_id: api_citations = openalex.fetch_citations(paper.openalex_id) else: api_citations = semanticscholar.fetch_references(paper_id) # Merge API citations and GROBID PDF citations; dedup via set all_citations_set: set[tuple[str, str]] = set() merged_citations: list[Citation] = [] for cit in api_citations + pdf_citations: key = (cit.citing_id, cit.cited_id) if key not in all_citations_set: all_citations_set.add(key) merged_citations.append(cit) if merged_citations: with conn.cursor() as cur: cur.executemany( """ INSERT INTO citations (citing_id, cited_id, context) VALUES (%s, %s, %s) ON CONFLICT (citing_id, cited_id) DO NOTHING """, [(c.citing_id, c.cited_id, c.context) for c in merged_citations], ) citations_upserted = len(merged_citations) conn.commit() # --------------------------------------------------------------- # 6. F-09 Rich Parsing: formulas + figures (PDF only) # --------------------------------------------------------------- formulas_upserted = 0 figures_upserted = 0 if rich and source_path is not None and Path(source_path).suffix.lower() == ".pdf": from codex.parsing.figures import extract_figures from codex.parsing.mathpix import extract_formulas settings = get_settings() formulas = extract_formulas(source_path) figures = extract_figures(source_path, output_dir=settings.figures_dir) if formulas or figures: with get_conn() as conn2: if formulas: with conn2.cursor() as cur: cur.executemany( """ INSERT INTO formulas (paper_id, page, raw_latex, context, eq_label) VALUES (%s, %s, %s, %s, %s) ON CONFLICT DO NOTHING """, [ (f.paper_id, f.page, f.raw_latex, f.context, f.eq_label) for f in formulas ], ) formulas_upserted = len(formulas) if figures: with conn2.cursor() as cur: cur.executemany( """ INSERT INTO figures (paper_id, page, caption, image_path) VALUES (%s, %s, %s, %s) ON CONFLICT DO NOTHING """, [ (fig.paper_id, fig.page, fig.caption, fig.image_path) for fig in figures ], ) figures_upserted = len(figures) conn2.commit() return IngestResult( paper_id=paper.id, chunks_upserted=chunks_upserted, citations_upserted=citations_upserted, formulas_upserted=formulas_upserted, figures_upserted=figures_upserted, )