breviabook 0.1.0

Condense large technical ebooks (EPUB/PDF) preserving code and diagrams, optionally translating in the same pass.

BreviaBook

Condense large technical ebooks (EPUB/PDF) into a fast, filler-free version — preserving code, formulas, and the important diagrams — and optionally translate them in the same pass. Outputs EPUB, PDF, and Markdown.

Multi-provider LLM from day one: Ollama (local), OpenAI, Gemini, OpenRouter, and any OpenAI-compatible endpoint (vLLM, LM Studio, LocalAI). Runs fully local on a laptop with Ollama, or via a paid API when it makes sense.

Why

Technical books are long and padded. BreviaBook reads dense books fast without losing the parts that matter — code examples, tables, and meaningful figures survive; filler doesn't — and can deliver the result in your language (e.g. English → Spanish) in one go.

Features

• Structure-aware condensation — chapter-aware chunking; code blocks are never summarized or split.
• Hierarchical summarization — per-chunk condense + per-chapter synthesis with active length control toward a --target-ratio.
• Image preservation (the differentiator) — keeps images whose section survives (Strategy A); optional vision ranking (--rank-images) drops decorative images and improves captions.
• Integrated translation — translates the already-condensed book (much cheaper) with an optional glossary for consistent terminology; code stays untranslated. Runs in resilient batches: a malformed model response retries and falls back to the source text instead of crashing the run.
• Three outputs — EPUB (our own builder), PDF (weasyprint), Markdown.
• Live TUI — a banner plus per-phase progress bars (parse → condense → synthesize → translate → render) and a usage panel that ticks token/cost totals in real time. Degrades to plain text when output is piped.
• Cost control for reasoning models — --reasoning-effort disable turns off "thinking" on models like gemini-3-flash-preview, which otherwise spend most output tokens on discarded reasoning (~3.6× cheaper on a real run, same quality). See Cost & reasoning models.
• Compression report — every run prints how much smaller the result is and an approximate page count (e.g. ~479 → ~149 pages, 69% smaller).
• Resumable — --resume continues an interrupted job from a checkpoint (already-condensed chunks are reused, not re-billed).
• Dry-run + cost — --dry-run estimates tokens, pages, compression, and approximate cost without calling the LLM.
• Usage report — every run prints prompt/completion/cached tokens and estimated cost.

Install

Requires Python 3.11+ and uv. EPUB and Markdown output need no system libraries; PDF output adds the optional [pdf] extra (see PDF output requirements). Set credentials before running cloud providers — see Configuration.

Run with uvx (no install)

Run the latest straight from GitHub — no clone, no virtualenv:

# EPUB + Markdown (nothing else needed)
uvx --from "git+https://github.com/willywg/breviabook" breviabook condense book.epub --formats epub,md

# with PDF output
uvx --from "breviabook[pdf] @ git+https://github.com/willywg/breviabook" breviabook condense book.epub

Once published to PyPI this shortens to uvx breviabook condense book.epub (and uvx "breviabook[pdf]" … for PDF output).

Install the command

uv tool install "git+https://github.com/willywg/breviabook"   # append [pdf] for PDF output
breviabook condense book.epub --out ./out/                     # now on your PATH

(After PyPI: uv tool install breviabook.)

From source (development)

git clone https://github.com/willywg/breviabook.git
cd breviabook
uv sync                # EPUB + Markdown
uv sync --extra pdf    # add PDF output
uv run breviabook --help

PDF output requirements

PDF rendering uses weasyprint, which needs system libraries (EPUB and Markdown need nothing extra):

• macOS: brew install pango gdk-pixbuf libffi — on Apple Silicon also export DYLD_FALLBACK_LIBRARY_PATH=/opt/homebrew/lib.
• Debian/Ubuntu: libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf-2.0-0 libffi-dev.

Quickstart

# Local, all three formats, with Ollama
uv run breviabook condense book.epub --formats epub,pdf,md --out ./out/

# Estimate tokens + cost first, without calling the LLM
uv run breviabook condense book.epub --dry-run

# Condense + translate to Spanish with a cloud model (Gemini thinking is off by default)
uv run breviabook condense book.epub \
  --provider gemini --model gemini-3-flash-preview \
  --translate-to Spanish --source-lang English --glossary glossary.json \
  --formats epub,md --out ./out/

# Drop decorative images with a vision model, and resume if interrupted
uv run breviabook condense book.pdf --provider gemini --model gemini-3-flash-preview \
  --rank-images --resume --out ./out/

CLI

breviabook condense INPUT.{epub,pdf} [options]

  --provider        ollama | openai | gemini | openrouter        (default: ollama)
  --model           model tag (default from .env)
  --api-endpoint    base URL for OpenAI-compatible servers (vLLM/LM Studio/LocalAI)
  --target-ratio    target size, e.g. 0.30 = ~30% of the original
  --formats         comma list of epub,pdf,md                     (default: epub,pdf,md)
  --translate-to    target language (omit = no translation)
  --source-lang     source language (optional hint)
  --glossary        glossary JSON {source_term: target_term}
  --rank-images     use a vision model to score/drop images
  --reasoning-effort  auto | disable | low | medium | high  (thinking budget; Gemini defaults
                      to "disable" to save cost — pass "auto" to keep native thinking)
  --manual-toc      manual TOC JSON for PDFs without an outline
  --out             output directory                              (default: ./output)
  --resume          resume from checkpoint
  --dry-run         estimate tokens/cost/pages only, no LLM call

Cost & reasoning models

Some cloud models "think" before answering. For condensation/translation — rewriting tasks, not reasoning tasks — that thinking is pure waste: on a real run ~94% of output tokens were discarded reasoning, billed as output. BreviaBook therefore disables thinking by default for providers that have it on (Gemini). To restore a model's native thinking, pass --reasoning-effort auto (or set low/medium/high explicitly).

Run (Introducing Go, EPUB → Spanish)	Cost	Output tokens	Quality
thinking on (--reasoning-effort auto)	$0.78	243k	excellent
default (thinking disabled)	$0.22	55k	excellent (identical)

Estimate first with --dry-run (no LLM call). Note the dry-run assumes no reasoning tokens — which matches the default; if you re-enable thinking with --reasoning-effort auto, the real cost can be several times the estimate. Pricing for gemini-3-flash-preview: ~$0.50 / 1M input, ~$3.00 / 1M output.

Configuration

BreviaBook reads settings from environment variables or a .env file in the directory you run from. Environment variables take precedence. Local Ollama needs no key, so the defaults work out of the box; cloud providers need an API key. CLI flags (--provider, --model, …) override both.

# Option A — environment variable (best for `uvx`; works from any directory)
export GEMINI_API_KEY="your-key"
uvx breviabook condense book.epub --provider gemini --model gemini-3-flash-preview --translate-to Spanish

# one-off, inline (no export)
GEMINI_API_KEY="your-key" uvx breviabook condense book.epub --provider gemini

# Option B — a .env file in the current directory (from source: `cp .env.example .env`)
printf 'GEMINI_API_KEY=your-key\n' > .env

All recognized variables (every one optional; names are case-insensitive):

LLM_PROVIDER=ollama                       # ollama | openai | gemini | openrouter
OLLAMA_ENDPOINT=http://localhost:11434
DEFAULT_MODEL=gemma4:e4b

OPENAI_API_KEY=         # comma-separated for key rotation
GEMINI_API_KEY=
OPENROUTER_API_KEY=

DEFAULT_TARGET_RATIO=0.30
DEFAULT_CHUNK_TOKENS=2000
IMAGE_STRATEGY=keep_referenced   # keep_referenced | vision_ranked

Keys are sent only to the selected provider's endpoint, never elsewhere. .env is gitignored — don't commit credentials.

How it works

Everything flows through a format-agnostic Intermediate Representation (IR):

parse → chunk → condense → synthesize → (translate) → image-select → render
        EPUB/PDF → IR        per-chunk    per-chapter    glossary      Strategy A/B   EPUB/PDF/MD

Parsers turn EPUB/PDF into the IR; the condenser and translator transform its text blocks (leaving code and images intact); renderers emit the final files. Adding an input or output format means writing one parser or one renderer — the condensation logic doesn't change.

See the full design and build plan in docs/ROADMAP.md.

Development

uv run ruff check . && uv run ruff format --check .   # lint + format
uv run mypy --strict breviabook                           # types
uv run pytest -q                                       # tests
uv run pip-licenses --fail-on "GPL"                    # license audit (blocks GPL/AGPL)

License

Apache-2.0. BreviaBook is inspired by open-source work but contains no copied code and depends on no copyleft (GPL/AGPL) libraries — see docs/ROADMAP.md §14 and NOTICE.