Artificial Intelligence · 21.08.2026, 22:16 UTC
Building Agentic Document Intelligence Pipelines: Creating Scientific Figures with AutoFigure
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | MarkTechPost ↗ |
| Veröffentlicht | 21.08.2026 UTC |
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In this tutorial, we explore AutoFigure as a practical toolkit for generating scientific figures directly from text descriptions, paper-like content, and structured methodological explanations. In this tutorial, we set up the complete AutoFigure environment, fix dependency issues such as Pillow compatibility, and prepare the required rendering tools for SVG and PNG outputs. We then build a custom reference figure, configure an API-backed generation workflow, and use AutoFigure to convert a detailed agentic document intelligence pipeline into a publication-style scientific diagram. Along the way, we also test offline SVG rendering, inspect the generated files, create a sample paper and PDF, and export the final outputs to a reusable gallery and a zip archive.
Copy CodeCopiedUse a different Browserimport 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 = …