ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter5/paper-to-ppt/paper_source.py

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+"""Pinned real-paper preparation for Experiment 5-4.
+
+The canonical campaign uses the published PDF, extracts its text directly, and
+renders three original paper figures from declared PDF page rectangles.  The
+resulting manifest makes it possible to prove that the images in the Slidev
+deck came from the source PDF rather than from programmatic stand-ins.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import shutil
+import urllib.request
+from pathlib import Path
+
+import fitz
+from PIL import Image
+
+
+PAPER = {
+    "title": "Attention Is All You Need",
+    "authors": "Ashish Vaswani et al.",
+    "arxiv_id": "1706.03762",
+    "pdf_url": "https://arxiv.org/pdf/1706.03762",
+    "pdf_sha256": "bdfaa68d8984f0dc02beaca527b76f207d99b666d31d1da728ee0728182df697",
+}
+
+# Coordinates are in PDF points and were registered against the pinned PDF.
+# They isolate the published figure itself (rather than surrounding body text)
+# so labels remain legible after a 16:9 slide render. Provenance does not rely
+# on pixels from the caption: the manifest records the source page, crop
+# rectangle, published figure label/caption, PDF hash, and extracted hash.
+VISUALS = [
+    {
+        "filename": "paper_figure_1_transformer.png",
+        "pdf_page": 3,
+        "source_label": "Figure 1",
+        "caption": "The Transformer model architecture.",
+        "rect": [92, 60, 520, 405],
+        "rotation_degrees": 0,
+    },
+    {
+        "filename": "paper_figure_3_long_distance.png",
+        "pdf_page": 13,
+        "source_label": "Figure 3 (long-distance dependency focus)",
+        "caption": "Published encoder attention linking 'making' to 'more difficult'.",
+        "rect": [190, 88, 425, 311],
+        "rotation_degrees": 90,
+    },
+    {
+        "filename": "paper_figure_4_anaphora.png",
+        "pdf_page": 14,
+        "source_label": "Figure 4 (lower panel, anaphora focus)",
+        "caption": "Published attention from 'its' to 'Law' and 'application'.",
+        "rect": [92, 360, 310, 610],
+        "rotation_degrees": 90,
+    },
+]
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def _download(url: str, destination: Path) -> None:
+    request = urllib.request.Request(url, headers={"User-Agent": "ai-agent-book/5-4"})
+    with urllib.request.urlopen(request, timeout=120) as response:
+        destination.write_bytes(response.read())
+
+
+def prepare_real_paper(run_dir: str | Path, public_dir: str | Path) -> dict:
+    run_dir = Path(run_dir)
+    public_dir = Path(public_dir)
+    source_dir = run_dir / "source"
+    visual_dir = source_dir / "source_visuals"
+    source_dir.mkdir(parents=True, exist_ok=True)
+    visual_dir.mkdir(parents=True, exist_ok=True)
+    public_dir.mkdir(parents=True, exist_ok=True)
+
+    pdf_path = source_dir / "1706.03762.pdf"
+    if not pdf_path.exists():
+        _download(PAPER["pdf_url"], pdf_path)
+    observed_pdf_hash = sha256(pdf_path)
+    if observed_pdf_hash != PAPER["pdf_sha256"]:
+        raise ValueError(
+            f"source PDF hash mismatch: expected {PAPER['pdf_sha256']}, "
+            f"observed {observed_pdf_hash}"
+        )
+
+    document = fitz.open(pdf_path)
+    page_text = []
+    for page_index, page in enumerate(document):
+        page_text.append(f"\n\n## PDF page {page_index + 1}\n\n{page.get_text('text')}")
+    text_path = source_dir / "paper_text.md"
+    text_path.write_text(
+        f"# {PAPER['title']}\n\nAuthors: {PAPER['authors']}\n" + "".join(page_text),
+        encoding="utf-8",
+    )
+
+    manifest_rows = []
+    figure_descriptions = {}
+    for visual in VISUALS:
+        page = document[visual["pdf_page"] - 1]
+        rect = fitz.Rect(visual["rect"])
+        if not page.rect.contains(rect):
+            raise ValueError(f"visual crop is outside page bounds: {visual}")
+        pixmap = page.get_pixmap(matrix=fitz.Matrix(2.5, 2.5), clip=rect, alpha=False)
+        extracted_path = visual_dir / visual["filename"]
+        pixmap.save(extracted_path)
+        if visual.get("rotation_degrees"):
+            # The published attention labels run vertically. A lossless
+            # quarter-turn makes those original pixels audience-readable on a
+            # landscape slide; the transform is explicit in the manifest.
+            with Image.open(extracted_path) as source_image:
+                rotated = source_image.rotate(
+                    -int(visual["rotation_degrees"]), expand=True
+                )
+                rotated.save(extracted_path)
+        public_path = public_dir / visual["filename"]
+        shutil.copyfile(extracted_path, public_path)
+        row = {
+            **visual,
+            "sha256": sha256(extracted_path),
+            "bytes": extracted_path.stat().st_size,
+            "public_copy_sha256": sha256(public_path),
+        }
+        manifest_rows.append(row)
+        figure_descriptions[visual["filename"]] = (
+            f"{visual['source_label']} from PDF page {visual['pdf_page']}: "
+            f"{visual['caption']}"
+        )
+    document.close()
+
+    manifest = {
+        "paper": {**PAPER, "observed_pdf_sha256": observed_pdf_hash},
+        "paper_text": {
+            "path": str(text_path),
+            "sha256": sha256(text_path),
+            "characters": len(text_path.read_text(encoding="utf-8")),
+        },
+        "visuals": manifest_rows,
+    }
+    manifest_path = visual_dir / "manifest.json"
+    manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8")
+    return {
+        "paper_text": text_path.read_text(encoding="utf-8"),
+        "figures": figure_descriptions,
+        "manifest": manifest,
+        "manifest_path": str(manifest_path),
+        "pdf_path": str(pdf_path),
+    }