Изграждане на агентни конвейери за интелигентна обработка на документи: създаване на научни фигури с AutoFigure
В този урок разглеждаме AutoFigure като практичен инструментариум за генериране на научни фигури директно от текстови описания, съдържание, подобно на научна статия, и структурирани методологични обяснения. В този урок настройваме цялата среда на AutoFigure, отстраняваме проблеми със зависимости като съвместимостта с Pillow и подготвяме необходимите инструменти за визуализиране на SVG и PNG изходи. След това създаваме персонализирана референтна фигура, конфигурираме работен процес за генериране чрез API и използваме AutoFigure, за да преобразуваме подробен pipeline за агентно разузнаване на документи в научна диаграма в стил публикация. По пътя тестваме и офлайн визуализиране на SVG, преглеждаме генерираните файлове, създаваме примерна статия и PDF и експортираме крайните резултати в галерия за повторна употреба и zip архив.
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import os
import sys
import json
import time
import glob
import shutil
import textwrap
import subprocess
import importlib
from pathlib import Path
from getpass import getpass
REPO_URL = "https://github.com/ResearAI/AutoFigure.git"
REPO_DIR = Path("/content/AutoFigure")
OUTPUT_ROOT = Path("/content/autofigure_colab_outputs")
PROVIDER = os.environ.get("AUTOFIGURE_PROVIDER", "openrouter")
DEFAULT_MODELS = {
"openrouter": "google/gemini-3.1-pro-preview",
"gemini": "gemini-3.1-pro-preview",
"bianxie": "gemini-3.1-pro-preview",
}
GENERATION_MODEL = os.environ.get(
"AUTOFIGURE_MODEL",
DEFAULT_MODELS.get(PROVIDER, "google/gemini-3.1-pro-preview")
)
MAX_ITERATIONS = int(os.environ.get("AUTOFIGURE_MAX_ITERATIONS", "1"))
QUALITY_THRESHOLD = float(os.environ.get("AUTOFIGURE_QUALITY_THRESHOLD", "8.5"))
RUN_TEXT_TO_FIGURE = True
RUN_PAPER_TO_FIGURE = False
RUN_MXGRAPH_DEMO = False
RUN_IMAGE_ENHANCEMENT = False
TEXT_OUTPUT_FORMAT = "svg"
MXGRAPH_OUTPUT_FORMAT = "mxgraphxml"
ART_STYLE = (
"clean publication-ready scientific illustration, precise alignment, subtle shadows, "
"clear academic typography, high contrast, minimal clutter"
)
FIGURE_DESCRIPTION = """
Create a publication-ready scientific method figure for an agentic long-document intelligence system.
The figure should explain the following pipeline in a left-to-right architecture:
1. Long documents enter the system. They may be PDFs, scanned reports, markdown files, tables, or mixed-layout documents.
2. A document normalization layer extracts raw text, section hierarchy, tables, figures, and metadata.
3. A routing planner decides whether each section should go to summarization, field extraction, table reconstruction, visual analysis, or citation grounding.
4. Specialized expert modules process the routed chunks:
- Summarizer expert creates hierarchical summaries.
- Extraction expert returns JSON fields.
- Table expert reconstructs exact tables.
- Visual expert describes charts and diagrams.
- Citation expert links claims to evidence spans.
5. A low-cost orchestration layer selects smaller or larger LLMs depending on complexity, confidence, and budget.
6. A verification layer checks schema validity, source grounding, table consistency, and confidence.
7. The final output is an analyst-ready workspace containing a summary, extracted fields, exact tables, cited answers, and audit logs.
Design requirements:
- Use a wide 16:9 layout.
- Use clear module boxes, arrows, and labels.
- Add small callouts for cost control, confidence scoring, and auditability.
- Avoid decorative clutter.
- Make the flow understandable for a finance or enterprise document intelligence audience.
"""
MINI_PAPER_MARKDOWN = """
# Efficient Agentic Document Intelligence for Long Financial Reports
## Abstract
We propose an agentic document intelligence architecture for extracting summaries, facts, tables,
and grounded answers from long, heterogeneous financial documents.
## Method
Our method first normalizes each incoming document into a structured document graph. The graph
contains section nodes, paragraph nodes, table nodes, figure nodes, and metadata nodes. A routing
planner assigns each node to a specialized expert according to modality, complexity, and required
output schema.
The system uses five experts. The summarization expert produces hierarchical summaries from
section-level chunks. The extraction expert fills strict JSON schemas for entities, dates, risks,
financial metrics, and obligations. The table expert reconstructs exact tables and validates row-column
alignment. The visual expert describes charts and diagrams. The citation expert maps every generated
claim to source spans.
A budget-aware orchestration layer selects model size dynamically. Simple chunks are processed by
low-cost models, while complex chunks are escalated to stronger models. A verification layer then
checks schema validity, citation support, numerical consistency, and table integrity. Failed checks are
routed back for repair.
## Experiments
We evaluate on financial filings and analyst reports using extraction accuracy, grounding precision,
table reconstruction quality, and total inference cost.
"""
def run(cmd, cwd=None, check=True, quiet=False):
print(f"\n$ {cmd}")
process = subprocess.run(
cmd,
shell=True,
cwd=str(cwd) if cwd else None,
text=True,
stdout=subprocess.PIPE if quiet else None,
stderr=subprocess.STDOUT if quiet else None,
)
if quiet and process.stdout:
print(process.stdout[-5000:])
if check and process.returncode != 0:
raise RuntimeError(f"Command failed with exit code {process.returncode}: {cmd}")
return process
def heading(title):
print("\n" + "=" * 100)
print(title)
print("=" * 100)
def safe_read(path, max_chars=2500):
path = Path(path)
if not path.exists():
return ""
text = path.read_text(encoding="utf-8", errors="ignore")
return text[:max_chars] + ("\n... [truncated]" if len(text) > max_chars else "")
def clear_loaded_modules(prefixes):
for name in list(sys.modules):
if any(name == prefix or name.startswith(prefix + ".") for prefix in prefixes):
del sys.modules[name]
def get_colab_secret(names):
try:
from google.colab import userdata
for name in names:
try:
value = userdata.get(name)
if value:
return value
except Exception:
pass
except Exception:
pass
return None
def collect_api_key(provider):
env_candidates = [
"AUTOFIGURE_API_KEY",
"OPENROUTER_API_KEY",
"GOOGLE_API_KEY",
"GEMINI_API_KEY",
"BIANXIE_API_KEY",
]
for key_name in env_candidates:
value = os.environ.get(key_name)
if value:
print(f"Using API key from environment variable: {key_name}")
return value
secret_candidates = {
"openrouter": ["AUTOFIGURE_API_KEY", "OPENROUTER_API_KEY"],
"gemini": ["AUTOFIGURE_API_KEY", "GOOGLE_API_KEY", "GEMINI_API_KEY"],
"bianxie": ["AUTOFIGURE_API_KEY", "BIANXIE_API_KEY"],
}.get(provider, ["AUTOFIGURE_API_KEY"])
value = get_colab_secret(secret_candidates)
if value:
print("Using API key from Colab Secrets.")
return value
value = getpass(f"Paste your {provider} API key, or press Enter to skip cloud generation: ").strip()
return value
Започваме с импортирането и дефинирането на основните пътища, настройките на доставчика, конфигурацията на модела и опциите на урока. Подготвяме и подробното описание на фигурата и съдържанието на примерната статия, които по-късно използваме за генериране с AutoFigure. След това създаваме помощни функции за изпълнение на команди, отпечатване на заглавия на секции, безопасно четене на файлове, изчистване на заредени модули и сигурно събиране на API ключове.
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def display_file_if_possible(path, title=None):
path = Path(path) if path else None
if not path or not path.exists():
print(f"Missing file: {path}")
return
try:
from IPython.display import display, Image as IPImage, SVG, Markdown
if title:
display(Markdown(f"### {title}"))
suffix = path.suffix.lower()
if suffix == ".png":
display(IPImage(filename=str(path)))
elif suffix == ".svg":
display(SVG(filename=str(path)))
elif suffix in [".json", ".md", ".txt", ".drawio"]:
print(safe_read(path, max_chars=5000))
else:
print(path)
except Exception as exc:
print(f"Could not display {path}: {exc}")
def make_output_gallery(output_dir):
output_dir = Path(output_dir)
gallery_path = output_dir / "gallery.html"
blocks = []
for p in sorted(output_dir.rglob("*.png")):
rel = p.relative_to(output_dir)
blocks.append(f"""
<div class="card">
<h3>{rel}</h3>
<img src="{rel}" />
</div>
""")
for p in sorted(output_dir.rglob("*.svg")):
rel = p.relative_to(output_dir)
svg_text = p.read_text(encoding="utf-8", errors="ignore")
blocks.append(f"""
<div class="card">
<h3>{rel}</h3>
<div class="svgbox">{svg_text}</div>
</div>
""")
for p in sorted(output_dir.rglob("*.drawio")):
rel = p.relative_to(output_dir)
code = p.read_text(encoding="utf-8", errors="ignore")[:4000]
blocks.append(f"""
<div class="card">
<h3>{rel}</h3>
<p>Editable draw.io mxGraph XML file.</p>
<pre>{code}</pre>
</div>
""")
for p in sorted(output_dir.rglob("generation_report.json")):
rel = p.relative_to(output_dir)
try:
report_text = json.dumps(json.loads(p.read_text(encoding="utf-8")), indent=2)[:7000]
except Exception:
report_text = p.read_text(encoding="utf-8", errors="ignore")[:7000]
blocks.append(f"""
<div class="card">
<h3>{rel}</h3>
<pre>{report_text}</pre>
</div>
""")
html = f"""
<!doctype html>
<html>
<head>
<meta charset="utf-8">
<title>AutoFigure Colab Gallery</title>
<style>
body {{
font-family: Arial, sans-serif;
margin: 24px;
background: #f7f7f7;
}}
h1 {{
margin-bottom: 8px;
}}
.card {{
background: white;
padding: 18px;
margin: 18px 0;
border-radius: 14px;
box-shadow: 0 2px 16px rgba(0,0,0,0.08);
}}
img {{
max-width: 100%;
border: 1px solid #ddd;
border-radius: 10px;
}}
.svgbox {{
border: 1px solid #ddd;
border-radius: 10px;
padding: 8px;
overflow: auto;
}}
pre {{
white-space: pre-wrap;
word-break: break-word;
max-height: 520px;
overflow: auto;
background: #fafafa;
padding: 12px;
border-radius: 10px;
}}
</style>
</head>
<body>
<h1>AutoFigure Colab Gallery</h1>
{''.join(blocks)}
</body>
</html>
"""
gallery_path.write_text(html, encoding="utf-8")
return gallery_path
def summarize_generation_result(result, label):
print("\n" + "-" * 100)
print(label)
print("-" * 100)
print(f"Success: {result.success}")
print(f"Final score: {result.final_score}")
print(f"Iterations used: {result.iterations_used}")
print(f"SVG path: {result.svg_path}")
print(f"mxGraph path: {result.mxgraph_path}")
print(f"Preview path: {result.preview_path}")
print(f"Enhanced path: {result.enhanced_path}")
print(f"Enhanced paths: {result.enhanced_paths}")
print(f"Error: {result.error}")
if result.logs:
print("\nRecent logs:")
for log in result.logs[-20:]:
print(f"- {log}")
display_file_if_possible(result.preview_path, f"{label}: PNG Preview")
if result.svg_path:
display_file_if_possible(result.svg_path, f"{label}: SVG")
if result.mxgraph_path:
display_file_if_possible(result.mxgraph_path, f"{label}: mxGraph XML")
report_candidates = []
for candidate in [result.svg_path, result.mxgraph_path, result.preview_path]:
if candidate:
report_candidates.append(Path(candidate).parent / "generation_report.json")
for report_path in report_candidates:
if report_path.exists():
print("\nGeneration report preview:")
print(safe_read(report_path, max_chars=6000))
try:
import pandas as pd
from IPython.display import display
report = json.loads(report_path.read_text(encoding="utf-8"))
rows = []
for row in report.get("iteration_history", []):
rows.append({
"iteration": row.get("iteration"),
"quality_score": row.get("quality_score"),
"improvement": row.get("improvement"),
"has_critique": row.get("critique") is not None,
})
if rows:
display(pd.DataFrame(rows))
except Exception as exc:
print(f"Could not tabulate report: {exc}")
break
Дефинираме помощни функции, които ни позволяват да показваме генерирани файлове директно в Colab, включително PNG, SVG, JSON, Markdown, текстови и draw.io изходи. Създаваме и генератор на HTML галерия, за да могат всички изходи от AutoFigure да бъдат преглеждани на една организирана страница. След това добавяме функция за обобщаване на резултатите, която отпечатва метаданните за генерирането, показва визуализации и представя отчета за итерациите в четим формат.
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heading("1. Installing AutoFigure and Colab dependencies")
OUTPUT_ROOT.mkdir(parents=True, exist_ok=True)
run("apt-get update -qq", quiet=True)
run(
"apt-get install -y -qq "
"libcairo2 libpango-1.0-0 libpangocairo-1.0-0 "
"libgdk-pixbuf-2.0-0 libffi-dev shared-mime-info",
quiet=True,
)
clear_loaded_modules(["PIL", "autofigure"])
run(f"{sys.executable} -m pip install -q -U pip 'setuptools<82' wheel jedi", quiet=True)
run(
f"{sys.executable} -m pip install -q --force-reinstall --no-cache-dir "
f"'Pillow==11.3.0'",
quiet=True,
)
if REPO_DIR.exists():
print(f"Repository already exists at {REPO_DIR}. Pulling latest main branch.")
run("git fetch origin main", cwd=REPO_DIR, quiet=True)
run("git checkout main", cwd=REPO_DIR, quiet=True)
run("git pull --ff-only origin main", cwd=REPO_DIR, check=False, quiet=True)
else:
run(f"git clone {REPO_URL} {REPO_DIR}", quiet=True)
run(
f"{sys.executable} -m pip install -q -e '.[pdf,web]' "
f"reportlab pandas 'Pillow==11.3.0'",
cwd=REPO_DIR,
quiet=True,
)
run(
f"{sys.executable} -m pip install -q --force-reinstall --no-cache-dir "
f"'Pillow==11.3.0'",
quiet=True,
)
clear_loaded_modules(["PIL", "autofigure"])
try:
from PIL import Image, ImageDraw, ImageFont
print(f"Pillow imported successfully. Version: {Image.__version__}")
except Exception as exc:
print("Pillow import still failed after reinstall.")
print("Run Runtime -> Restart runtime, then rerun this full cell.")
raise exc
if RUN_MXGRAPH_DEMO:
run(f"{sys.executable} -m playwright install chromium", quiet=True)
sys.path.insert(0, str(REPO_DIR))
heading("2. Importing AutoFigure SDK")
from autofigure import AutoFigureAgent, Config
from autofigure.generator import (
validate_code_syntax,
code_to_png,
get_initial_prompt_template,
)
from autofigure.extractor import MethodologyExtractor
print("AutoFigure imported successfully.")
print(f"Repository directory: {REPO_DIR}")
print(f"Output root: {OUTPUT_ROOT}")
heading("3. Offline SVG preflight: validation and rendering")
preflight_dir = OUTPUT_ROOT / "00_offline_preflight"
preflight_dir.mkdir(parents=True, exist_ok=True)
sample_svg = """
<svg width="1333" height="750" viewBox="0 0 1333 750" xmlns="http://www.w3.org/2000/svg">
<rect x="0" y="0" width="1333" height="750" fill="#ffffff"/>
<text x="666" y="70" text-anchor="middle" font-family="Arial" font-size="36" font-weight="700" fill="#111111">
AutoFigure Offline Rendering Check
</text>
<rect x="120" y="220" width="250" height="140" rx="18" fill="#f3f3f3" stroke="#111111" stroke-width="3"/>
<text x="245" y="285" text-anchor="middle" font-family="Arial" font-size="24" fill="#111111">Text Prompt</text>
<text x="245" y="325" text-anchor="middle" font-family="Arial" font-size="17" fill="#444444">method description</text>
<line x1="390" y1="290" x2="565" y2="290" stroke="#111111" stroke-width="4" marker-end="url(#arrow)"/>
<rect x="585" y="220" width="250" height="140" rx="18" fill="#f3f3f3" stroke="#111111" stroke-width="3"/>
<text x="710" y="285" text-anchor="middle" font-family="Arial" font-size="24" fill="#111111">AutoFigure</text>
<text x="710" y="325" text-anchor="middle" font-family="Arial" font-size="17" fill="#444444">generate → evaluate → refine</text>
<line x1="855" y1="290" x2="1030" y2="290" stroke="#111111" stroke-width="4" marker-end="url(#arrow)"/>
<rect x="1050" y="220" width="250" height="140" rx="18" fill="#f3f3f3" stroke="#111111" stroke-width="3"/>
<text x="1175" y="285" text-anchor="middle" font-family="Arial" font-size="24" fill="#111111">Figure</text>
<text x="1175" y="325" text-anchor="middle" font-family="Arial" font-size="17" fill="#444444">SVG + PNG output</text>
<defs>
<marker id="arrow" markerWidth="12" markerHeight="12" refX="10" refY="6" orient="auto">
<path d="M2,2 L10,6 L2,10 Z" fill="#111111"/>
</marker>
</defs>
</svg>
""".strip()
is_valid, validation_message = validate_code_syntax(sample_svg, "svg")
print(f"SVG syntax valid: {is_valid}")
print(f"Validation message: {validation_message}")
sample_svg_path = preflight_dir / "offline_preflight.svg"
sample_png_path = preflight_dir / "offline_preflight.png"
sample_svg_path.write_text(sample_svg, encoding="utf-8")
render_ok, processed_svg = code_to_png(
sample_svg,
str(sample_png_path),
attempt_repair=False,
output_format="svg",
)
print(f"Rendered PNG: {render_ok} -> {sample_png_path}")
display_file_if_possible(sample_png_path, "Offline preflight PNG")
Инсталираме необходимите системни пакети, отстраняваме проблемите със съвместимостта на Pillow, клонираме хранилището на AutoFigure и инсталираме SDK заедно с неговите PDF и уеб зависимости. След това импортираме основните класове и помощни инструменти на генератора на AutoFigure, след като потвърдим, че средата е готова. Изпълняваме и тестове за офлайн валидиране на SVG и визуализиране в PNG, за да се уверим, че pipeline-ът за визуализиране работи, преди да направим заявки за генериране чрез API.
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heading("4. Creating a custom reference figure")
reference_dir = OUTPUT_ROOT / "01_custom_references"
reference_dir.mkdir(parents=True, exist_ok=True)
reference_path = reference_dir / "reference_architecture_style.png"
W, H = 1333, 750
img = Image.new("RGB", (W, H), "white")
draw = ImageDraw.Draw(img)
try:
title_font = ImageFont.truetype("DejaVuSans-Bold.ttf", 36)
box_font = ImageFont.truetype("DejaVuSans-Bold.ttf", 24)
small_font = ImageFont.truetype("DejaVuSans.ttf", 18)
except Exception:
title_font = None
box_font = None
small_font = None
draw.text(
(W // 2, 55),
"Reference Layout: Modular Scientific Pipeline",
anchor="mm",
fill="black",
font=title_font,
)
boxes = [
(90, 215, 290, 120, "Input", "documents"),
(365, 215, 290, 120, "Planner", "route by task"),
(640, 215, 290, 120, "Experts", "summary / table / vision"),
(915, 215, 290, 120, "Verifier", "grounded output"),
]
for i, (x, y, bw, bh, title, subtitle) in enumerate(boxes):
draw.rounded_rectangle(
[x, y, x + bw, y + bh],
radius=22,
fill=(245, 245, 245),
outline=(20, 20, 20),
width=3,
)
draw.text(
(x + bw / 2, y + 45),
title,
anchor="mm",
fill="black",
font=box_font,
)
draw.text(
(x + bw / 2, y + 82),
subtitle,
anchor="mm",
fill=(70, 70, 70),
font=small_font,
)
if i < len(boxes) - 1:
ax = x + bw + 20
ay = y + bh / 2
bx = boxes[i + 1][0] - 20
by = ay
draw.line([ax, ay, bx, by], fill="black", width=5)
draw.polygon(
[(bx, by), (bx - 18, by - 10), (bx - 18, by + 10)],
fill="black",
)
draw.rounded_rectangle(
[180, 500, 1150, 585],
radius=24,
fill=(252, 252, 252),
outline=(80, 80, 80),
width=2,
)
draw.text(
(665, 542),
"Design cue: aligned modules, sparse labels, strong flow direction, clean academic styling",
anchor="mm",
fill=(40, 40, 40),
font=small_font,
)
img.save(reference_path)
print(f"Custom reference saved: {reference_path}")
display_file_if_possible(reference_path, "Custom reference figure")
heading("5. Configuring API-backed AutoFigure")
API_KEY = collect_api_key(PROVIDER)
if not API_KEY:
print("No API key provided. Cloud generation sections will be skipped.")
else:
print(f"Provider: {PROVIDER}")
print(f"Generation model: {GENERATION_MODEL}")
print("API key received. The key is not printed.")
config = None
agent = None
if API_KEY:
config = Config(
generation_api_key=API_KEY,
generation_provider=PROVIDER,
generation_model=GENERATION_MODEL,
methodology_api_key=API_KEY,
methodology_provider=PROVIDER,
methodology_model=GENERATION_MODEL,
enhancement_api_key=API_KEY if RUN_IMAGE_ENHANCEMENT else None,
enhancement_provider=PROVIDER,
enhancement_model=os.environ.get(
"AUTOFIGURE_ENHANCEMENT_MODEL",
"google/gemini-3.1-flash-image-preview"
if PROVIDER == "openrouter"
else "gemini-3.1-flash-image-preview",
),
max_iterations=MAX_ITERATIONS,
quality_threshold=QUALITY_THRESHOLD,
output_dir=str(OUTPUT_ROOT / "02_text_to_figure"),
custom_references=[str(reference_path)],
art_style=ART_STYLE,
)
validation_errors = config.validate()
print(f"Config validation errors: {validation_errors if validation_errors else 'none'}")
print(f"References found by config: {len(config.get_references())}")
agent = AutoFigureAgent(config)
heading("6. Prompt template preview")
prompt_preview = get_initial_prompt_template(
topic="paper",
content=FIGURE_DESCRIPTION[:2500],
output_format="svg",
)
print(prompt_preview[:2500])
print("\n... prompt preview truncated ...")
if API_KEY and RUN_TEXT_TO_FIGURE:
heading("7. Running text-to-figure generation")
text_output_dir = OUTPUT_ROOT / "02_text_to_figure"
text_output_dir.mkdir(parents=True, exist_ok=True)
text_result = agent.generate(
description=FIGURE_DESCRIPTION,
max_iterations=MAX_ITERATIONS,
quality_threshold=QUALITY_THRESHOLD,
output_format=TEXT_OUTPUT_FORMAT,
enable_enhancement=RUN_IMAGE_ENHANCEMENT,
art_style=ART_STYLE,
enhancement_input_type="code2prompt",
enhancement_count=1,
custom_references=[str(reference_path)],
output_dir=str(text_output_dir),
topic="paper",
)
summarize_generation_result(text_result, "Text-to-Figure Result")
else:
print("Skipping text-to-figure generation.")
Създаваме персонализирано референтно изображение, което показва какъв изчистен, модулен научен дизайн искаме AutoFigure да следва. След това конфигурираме AutoFigure с избраните доставчик, модел, API ключ, директория за изход, референтно изображение, настройки за итерациите и визуален стил. Накрая преглеждаме вътрешния шаблон на подканата и стартираме основния работен процес за генериране на фигура от текст, за да създадем научна фигура от подробното описание на системата.
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heading("8. Paper methodology extraction dry check")
paper_dir = OUTPUT_ROOT / "03_paper_to_figure"
paper_dir.mkdir(parents=True, exist_ok=True)
paper_md_path = paper_dir / "mini_paper.md"
paper_md_path.write_text(MINI_PAPER_MARKDOWN, encoding="utf-8")
if API_KEY:
if RUN_PAPER_TO_FIGURE:
extractor = MethodologyExtractor(config)
extracted = extractor.extract_from_file(str(paper_md_path))
print("\nExtracted methodology preview:")
print((extracted or "")[:2500])
else:
print(f"Created demo paper markdown at: {paper_md_path}")
print("Set RUN_PAPER_TO_FIGURE = True to run LLM methodology extraction and figure generation.")
else:
print(f"Created demo paper markdown at: {paper_md_path}")
print("No API key available, so LLM methodology extraction is skipped.")
if API_KEY and RUN_PAPER_TO_FIGURE:
heading("9. Running paper-to-figure generation")
paper_result = agent.generate_from_paper(
paper_path=str(paper_md_path),
max_iterations=MAX_ITERATIONS,
output_format="svg",
enable_enhancement=RUN_IMAGE_ENHANCEMENT,
art_style=ART_STYLE,
enhancement_input_type="code2prompt",
enhancement_count=1,
custom_references=[str(reference_path)],
output_dir=str(paper_dir),
)
summarize_generation_result(paper_result, "Paper-to-Figure Result")
heading("10. Creating a tiny PDF and testing PDF text reading")
pdf_path = paper_dir / "mini_paper.pdf"
try:
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
c = canvas.Canvas(str(pdf_path), pagesize=letter)
width, height = letter
y = height - 50
for line in MINI_PAPER_MARKDOWN.splitlines():
line = line.strip()
if not line:
y -= 12
continue
for wrapped in textwrap.wrap(line, width=95):
c.drawString(50, y, wrapped)
y -= 14
if y < 60:
c.showPage()
y = height - 50
c.save()
print(f"Created demo PDF: {pdf_path}")
if API_KEY:
pdf_text = MethodologyExtractor(config)._read_pdf(pdf_path)
print("PDF text extraction preview:")
print((pdf_text or "")[:1500])
else:
print("PDF created. LLM-based paper-to-figure generation still requires an API key.")
except Exception as exc:
print(f"PDF creation or read test failed: {exc}")
if API_KEY and RUN_MXGRAPH_DEMO:
heading("11. Running editable mxGraph XML generation")
mxgraph_dir = OUTPUT_ROOT / "04_mxgraph_drawio"
mxgraph_dir.mkdir(parents=True, exist_ok=True)
mx_result = agent.generate(
description=FIGURE_DESCRIPTION,
max_iterations=MAX_ITERATIONS,
quality_threshold=QUALITY_THRESHOLD,
output_format=MXGRAPH_OUTPUT_FORMAT,
enable_enhancement=False,
custom_references=[str(reference_path)],
output_dir=str(mxgraph_dir),
topic="paper",
)
summarize_generation_result(mx_result, "mxGraph / draw.io Result")
else:
heading("11. mxGraph XML generation skipped")
print("Set RUN_MXGRAPH_DEMO = True to generate editable draw.io mxGraph XML.")
print("This path installs Chromium through Playwright and may be slower than SVG generation.")
heading("12. Output inventory and export")
all_files = []
for path in sorted(OUTPUT_ROOT.rglob("*")):
if path.is_file():
all_files.append(path)
print(f"Total files under {OUTPUT_ROOT}: {len(all_files)}")
for path in all_files:
rel = path.relative_to(OUTPUT_ROOT)
size_kb = path.stat().st_size / 1024
print(f"{rel} ({size_kb:.1f} KB)")
gallery_path = make_output_gallery(OUTPUT_ROOT)
print(f"\nGallery HTML: {gallery_path}")
zip_base = "/content/autofigure_colab_outputs"
zip_path = shutil.make_archive(zip_base, "zip", root_dir=str(OUTPUT_ROOT))
print(f"Zip archive: {zip_path}")
try:
from IPython.display import display, HTML
display(
HTML(
f"""
<h3>AutoFigure tutorial complete</h3>
<p><b>Output root:</b> {OUTPUT_ROOT}</p>
<p><b>Gallery:</b> {gallery_path}</p>
<p><b>Zip:</b> {zip_path}</p>
"""
)
)
except Exception:
pass
print("\nDone.")
print("If the model is unavailable or access is denied, change PROVIDER and GENERATION_MODEL near the top of the cell.")
Създаваме малък Markdown файл в стил научна статия и по желание използваме екстрактора на методологията на AutoFigure, за да генерираме фигура от съдържанието на статията. Създаваме и проста PDF версия на статията и проверяваме дали pipeline-ът за извличане на текст от PDF работи правилно. Завършваме с незадължително изпълнение на работния процес с mxGraph и draw.io, изброяване на всички генерирани файлове, създаване на HTML галерия и експортиране на цялата изходна папка като zip архив.
В заключение, завършихме този урок, като изградихме цялостен работен процес с AutoFigure, който преминава от настройване на средата до генериране, валидиране, визуализация и експортиране на фигури. Видяхме как AutoFigure ни помага да преобразуваме сложни изследователски или системни описания в структурирани научни визуализации, като същевременно ни предоставя контрол върху референциите, стила, изходния формат, итерациите и незадължителното извличане от статии. В края разполагаме с pipeline, готов за Colab, който може да генерира SVG фигури и при необходимост да подготвя редактируеми изходи в стил draw.io, да тества извличането на текст от PDF и да пакетира всички генерирани ресурси за по-късна употреба.
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