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ADR-007: Heavy extraction runtimes are opt-in, installed at container startup

  • Status: Accepted
  • Date: 2026-07
  • Related: #1122 (this decision), #432/#1118 (Crawl4AI engine), #1104/#1120 (OCR toggle), #975 (pre-flight file support), #374 (offline install), ADR-002 (external libraries), Content Processing Engines

Context

The Content Core 2.x upgrade (#939) exposed two selectable extraction runtimes that are far heavier than the rest of the stack:

  • Docling (content-core[docling]) — layout-aware document parsing and OCR. Pulls a large machine-learning stack (torch/transformers and models), from several hundred MB to multiple GB.
  • Crawl4AI local (content-core[crawl4ai]) — JavaScript rendering via a bundled Chromium browser (~300 MB plus system libraries).

The initial 2.x work bundled Crawl4AI into every image (#1118) and shipped an OCR toggle (#1120) that depended on Docling — which was never actually installed, making the toggle a silent no-op and image sources unsupported. So the default image was simultaneously too heavy (Chromium for everyone) and missing a runtime its own UI advertised.

Baking both into the default image would push it into the multi-GB range and contradict Open Notebook's lean, privacy-first, self-hostable posture (ADR-002 keeps this repo focused on the knowledge layer, delegating extraction to Content Core). Most users need neither runtime.

Decision

Keep a single lean default image. Make Docling and local Crawl4AI opt-in via environment variables; when enabled, install them at container startup and cache the downloads on the data volume.

  • Two booleans gate them: OPEN_NOTEBOOK_ENABLE_DOCLING and OPEN_NOTEBOOK_ENABLE_CRAWL4AI. A remote Crawl4AI server (CRAWL4AI_API_URL) needs no local install.
  • The container entrypoint (scripts/docker-entrypoint.sh) runs the install before api/worker/frontend start (blocking), pinning each extra to the base image's content-core version for dependency compatibility.
  • Degrade, don't die: a failed install (e.g. no network) logs loudly and boot continues; the runtime is reported unavailable rather than crashing the app.
  • Persist caches, not the venv. UV_CACHE_DIR, PLAYWRIGHT_BROWSERS_PATH and HF_HOME live under /app/data (the user's volume), so a second boot reinstalls from cache without re-downloading. The virtualenv stays in the image layer and is repopulated from the cache each boot — immune to Python-version bumps in the base image. Chromium's apt system libraries live in the container filesystem and are reinstalled once per container.
  • A backend capability probe (GET /api/capabilities) reports actual importability, and the Settings UI disables unavailable engines/OCR with an env-var hint — so the UI never advertises a runtime that isn't there (including mid-install).

Alternatives considered

  • Bundle both into the default image — rejected: multi-GB image for a feature most users don't use; wrong for a self-hostable, privacy-first product.
  • Publish a separate "full" image tag — rejected for now: doubles the release/registry matrix and forces users to pick an image up front. Revisit if startup-install friction proves too high.
  • Persist the whole virtualenv on the volume (the ComfyUI/Automatic1111 pattern) — rejected: couples the venv to the image's Python version and breaks on upgrades. Persisting only the download caches gets the "no re-download" benefit without that fragility.
  • Keep Crawl4AI bundled, add only Docling opt-in — rejected: leaves ~300 MB of Chromium in every image for an engine that is itself optional. Treating both the same way is simpler and leaner.

Consequences

  • The default image is small again; base extraction (text PDFs, Office, EPUB, URLs via bs4/Jina/Firecrawl, audio, YouTube) is unchanged.
  • First boot with a flag set is slow (minutes for Docling's ML stack); this is logged. Later boots are fast thanks to the volume cache.
  • Offline / air-gapped: first-boot install needs network. Air-gapped operators leave the flags off. A build-arg to pre-bake the extras is possible future work (#374).
  • Reproducibility depends on pinning the extra to the locked content-core version, done in the entrypoint.
  • This validates ADR-002 in practice: because all extraction sits behind Content Core's single extract_content() boundary, adding an opt-in install path touched infra + one probe endpoint, not the extraction code.