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`srz` (Summarize)

srz (Summarize) is a high-performance, modular Rust CLI tool for extracting and synthesizing structured summaries using local OpenAI-compliant LLMs (such as llama.cpp, Ollama, LM Studio, or vLLM) from:

🌐 Webpages & articles (Mozilla Readability extraction)
📺 YouTube video transcripts
📄 PDF documents & local files
⌨️ Unix stdin streams
📚 Books
📰 Live news digests & universal RSS/Atom feeds

📖 Documentation: Visit the srz Documentation Site for full guides, tutorials, and command references.

Highlights

%%{init: {"flowchart": {"minNodeWidth": 210, "minNodeHeight": 54}}}%%
flowchart LR
    subgraph Sources["     Input Sources"]
        S1["🌐 Web & YouTube URLs"]
        S2["📄 PDFs & Documents"]
        S3["📚 Book Synopses"]
        S4["📰 Live News Feeds"]
    end

    subgraph Core["    srz Core (100% Private)"]
        C["⚡ Ingest & Chunking Pipeline\n🔒 100% Local LLM Inference\n📝 Summary Presets & Synthesis"]
    end

    subgraph Highlights["     Key Highlights"]
        H1["⚡ Rich Terminal Paging"]
        H2["💬 Grounded Q&A Chat"]
        H3["🖥️ TUI Dashboard View"]
        H4["📖 Clean Reader Views"]
    end

    S1 --> C
    S2 --> C
    S3 --> C
    S4 --> C

    C --> H1
    C --> H2
    C --> H3
    C --> H4

🔒 100% Local & Private: Runs on your device with zero fees and no API keys.
🎯 3 Summary Presets: Choose concise, bulleted, or detailed summaries.
📰 Google News Feeds: Summarize top stories and search topics by default.
⚡ Terminal Paging: Clean CLI output with auto-paging for long summaries.
💬 Interactive Q&A: Chat and ask questions about any document or read.
🖥️ Terminal Dashboard: Browse, search, filter, and read past summaries.
🔍 Fast Offline Search: Find past summaries instantly without internet.
🗄️ Local Smart Cache: Saves past reads locally for instant offline review.
📖 Clean Reader Views: Distraction-free HTML and Markdown with 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/scripts/install.sh | sh

Windows:

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

Homebrew (macOS & Linux)

brew install NewGuy012/tap/srz

Build from Source (Cargo)

If you prefer building with Rust from source:

cargo install --path .

Usage

Summarize

srz "https://en.wikipedia.org/wiki/Rust_(programming_language)" # Article
srz "https://youtu.be/5C_HPTJg5ek"          # YouTube video
srz assets/document.pdf                     # PDF document
srz "The Rust Programming Language" --open  # Book synopsis
srz news "Rust language updates" -t week    # News digest
git diff | srz -                            # Stdin stream

Ask & Chat

srz ask "What are the main findings?"  # Ask latest read
srz ask assets/document.pdf "Summary"  # Ask document
srz ask -i                             # Interactive chat
srz ask --chats                        # List saved chats
srz ask --resume 1                     # Resume chat session
srz ask --show-chat 1                  # Show transcript
srz ask --delete-chat 1                # Delete chat session

Library, Search & Telemetry

srz browse         # Terminal dashboard
srz find "keyword" # Search past summaries
srz history        # View recent reads
srz stats          # Reading statistics

Cache & Storage Maintenance

srz cache size                   # Check disk usage
srz cache prune --older-than 14d # Prune older cache
srz cache prune --max-size 500MB # Enforce size limit
srz cache clean                  # Clean cache files
srz cache clean --reset-db       # Full database reset
srz doctor                       # Backend diagnostics

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

Features

For full guides and tutorials, visit the Documentation Website. For architectural diagrams, lifecycle execution sequences, and in-depth subsystem specs, see OVERVIEW.md.

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:

# Default local model
base_url = "http://localhost:9931/v1"
model = "ggml-org/gemma-4-E4B-it-GGUF:Q8_0"

# Storage limits and retention policies
[cache]
max_size_mb = 500         # Disk budget (LRU eviction past threshold)
pages_ttl_days = 7        # Scraped web content & news TTL
search_ttl_days = 14      # Book metadata & search candidates TTL
summaries_ttl_days = 90   # Generated LLM outputs TTL
auto_prune = true         # Opportunistic 24h background pruning

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

Switch profiles on the fly with --profile:

srz "The Rust Programming Language"                  # Default model
srz "The Rust Programming Language" --profile openai # Cloud profile

Documentation & Architecture

🌐 Documentation Website: Comprehensive mdBook site covering installation, quick start, command reference, and configuration.
🏗️ Architecture Overview (OVERVIEW.md): Detailed Mermaid architecture diagrams, execution sequence flows, storage schemas, and subsystem specifications.
🦀 Rust Learning Guide (RUST_LEARNING_GUIDE.md): Hands-on case study exploring Rust systems programming concepts and patterns using real srz code.

Running Tests & Static Analysis

# Run full automated test suite (119 tests)
cargo test

# Strict static analysis (warnings denied)
cargo clippy --all-targets -- -D warnings

srz 0.6.2

srz (Summarize)