srz (Summarize)

A fast, modular Rust CLI for distilling webpages, local files, stdin streams, books, and live news digests into structured summaries using any local OpenAI-compliant LLM (such as llama.cpp, Ollama, LM Studio, or vLLM).

Highlights

• Summarize anything: works with web links, book titles, local files, piped text, and news topics.
• Multi-source news briefings: aggregates top articles from Google News RSS for any topic, decodes destination publisher URLs, and synthesizes executive briefings.
• Fact-checked book summaries: pulls synopses from Wikipedia and Open Library—no ebook files or API keys needed.
• 100% local & private: runs on your own machine using llama.cpp, Ollama, LM Studio, or vLLM.
• Searchable personal library: saves every summary locally so you can search past reads offline.
• Clean reader views: generates one-click HTML and Markdown pages with automatic light and dark mode.

Prerequisites: LLM Backend Server

srz requires an OpenAI API-compliant LLM server. While cloud provider APIs (OpenAI, Anthropic, Google, Groq, etc.) are supported, running a local LLM is strongly preferred for complete privacy, zero subscription fees, and offline capability:

• macOS: Llama-macOS (recommended local default; connects out-of-the-box on port 9931).
• Windows: Llama-Windows (recommended local default; connects out-of-the-box on port 9931).
• Linux: llama.cpp (recommended local default; run llama-server).
• Other Backends: Compatible with Ollama, LM Studio, vLLM, or cloud providers (OpenAI, Anthropic, Google Gemini, Groq, etc.).

See Configuring Your LLM Backend below for step-by-step setup instructions and profile templates.

Installation

Quick Install

macOS & Linux:

curl -LsSf https://raw.githubusercontent.com/NewGuy012/srz/master/install.sh | sh

Windows:

powershell -ExecutionPolicy ByPass -c "irm https://raw.githubusercontent.com/NewGuy012/srz/master/install.ps1 | iex"

Build from Source (Cargo)

If you prefer building with Rust from source:

cargo install --path .

Usage

Pass any target directly—srz auto-detects URLs, books, files, news, or stdin.

Summarizing Content

srz "https://example.com/article"       # Web article
srz "The Hobbit"                        # Book title (Wikipedia + Open Library)
srz "paper.pdf"                         # Local document (PDF, EPUB, MD, TXT)
git diff | srz "-"                      # Standard input

Live News Briefings

srz news "AI updates"                   # Today's news digest
srz "current AI news" -t "week"         # Auto-detected news query (day/week/month)

Styles & Reader Views

srz "The Hobbit" --style "tldr"         # Presets: concise, detailed, bullets, tldr...
srz "The Hobbit" --open                 # Open distraction-free reader in browser

Offline Library & Search

srz find "ecology"                      # Search past summaries offline
srz index --open                        # Interactive HTML library dashboard
srz history                             # View past summaries & token metrics

Self-Update & Maintenance

srz upgrade                             # Upgrade srz in-place to latest release
srz upgrade --check                     # Check if an update is available
srz upgrade --force                     # Force reinstall or overwrite existing binary

Run srz --help to see all available subcommands and flags.

Features

Content Sources & Ingestion

• Web Articles & Documents: Summarizes webpages, PDFs, ebooks, text files, and piped terminal text.
• Books by Title: Generates verified overviews from Wikipedia and Open Library without needing an ebook file.
• Live News Briefings: Curates and summarizes top stories on any topic or timeframe from Google News.

LLM Engine & Synthesis

• 8 Summary Styles: Tailor output with presets like concise, detailed, bullet points, executive, or quick takeaways.
• Parallel Processing for Long Texts: Automatically splits and processes long documents in parallel.
• Visual Feedback & Live Streaming: Clean progress indicators with optional real-time streaming.

Offline Library & Storage

• Offline Full-Text Search: Search through your entire history of past summaries completely offline.
• Visual Library Dashboard: Browse, filter, and review all your saved summaries in an interactive dashboard.
• Instant Reader Views: Generates clean, distraction-free reading pages with automatic light and dark mode.

Configuration & CLI Experience

• Config File & Profiles: Save default preferences and switch between different models with ease.
• One-Command Upgrades: Safe in-place binary upgrades with SHA256 integrity verification (srz upgrade).
• Health Checks & Diagnostics: Verify backend connectivity and inspect available models with built-in tools.

Configuring Your LLM Backend

srz connects to any OpenAI-compatible API. Local inference is preferred for privacy and zero cost, but cloud providers are fully supported.

Local LLMs (Preferred)

• macOS & Windows (Default): Install Llama-macOS (macOS) or Llama-Windows (Windows). Launch the app and load a model—srz connects automatically to http://localhost:9931/v1 with zero setup.
• Linux: Build or install llama.cpp and launch llama-server:

  # Start llama-server on port 9931 for zero configuration:
  llama-server -m model.gguf --port 9931
  
  # Or on default port 8080:
  llama-server -m model.gguf --port 8080
  export OPENAI_BASE_URL="http://localhost:8080/v1"
  

• Diagnostics: Run srz doctor to test server latency and srz models to list loaded models.

Cloud Providers (Optional)

srz works with any OpenAI-compatible provider (OpenAI, Anthropic, Google, Groq, OpenRouter):

export OPENAI_BASE_URL="https://api.openai.com/v1" OPENAI_MODEL="gpt-4o-mini" OPENAI_API_KEY="sk-..."

# Or on the fly via CLI flag:
srz "1984" --base-url "https://api.openai.com/v1" --model "gpt-4o-mini" --api-key "sk-..."

Profile Switching (~/.config/srz/config.toml)

Run srz config init to generate a starter config, then configure named profiles for your favorite local or cloud models:

# Default local backend (runs out-of-the-box with Llama-macOS / Llama-Windows / llama-server)
base_url = "http://localhost:9931/v1"
model = "ggml-org/gemma-4-E4B-it-GGUF:Q8_0"

# Cloud profile: OpenAI
[profiles.openai]
base_url = "https://api.openai.com/v1"
model = "gpt-4o-mini"
api_key = "sk-..."

# Cloud profile: Anthropic Claude
[profiles.anthropic]
base_url = "https://api.anthropic.com/v1"
model = "claude-3-5-sonnet-20241022"
api_key = "sk-ant-..."

# Cloud profile: Google Gemini
[profiles.google]
base_url = "https://generativelanguage.googleapis.com/v1beta/openai"
model = "gemini-2.0-flash"
api_key = "..."

Run with your default local model, or switch to any cloud profile seamlessly:

srz "The Hobbit"                      # Runs 100% locally
srz "The Hobbit" --profile "openai"    # Runs via OpenAI
srz "The Hobbit" --profile "anthropic" # Runs via Anthropic Claude
srz "The Hobbit" --profile "google"    # Runs via Google Gemini

Architecture & Modules

See OVERVIEW.md for detailed Mermaid architecture and sequence diagrams.

Running Tests

cargo test