<p align="center">
<img src="assets/logo.svg" alt="srz - Rust CLI for Summarizing Webpages, Books, and Streams" width="760">
</p>
# `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**
- 📚 **Books**
- 📰 **Live news digests & universal RSS/Atom feeds**
- 📺 **YouTube video transcripts**
- 📄 **PDF documents & local files**
- ⌨️ **Unix stdin streams**
## Highlights
> 💡 **What makes `srz` different?** Unlike cloud-dependent AI tools that require subscriptions and upload private reading data, `srz` runs 100% locally with zero telemetry—pairing instant streaming synthesis and unified multi-format ingestion with embedded offline DuckDB search.
```mermaid
%%{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.
- ⚡ **Zero-Worry Dynamic Sizing**: Automatically probes server context (`n_ctx`) and concurrency slots to size chunks and eliminate OOM crashes.
- 💬 **Interactive Q&A**: Chat and ask questions about any document or read with token-budgeted memory.
- 🖥️ **Terminal Dashboard**: Browse, search, filter, and read past summaries.
## 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](https://github.com/ggml-org/Llama-macOS) (recommended local default; connects out-of-the-box on port `9931`).
- **Windows**: [Llama-Windows](https://github.com/ggml-org/Llama-Windows) (recommended local default; connects out-of-the-box on port `9931`).
- **Linux**: [llama.cpp](https://github.com/ggml-org/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](#configuring-your-llm-backend) below for step-by-step setup instructions and profile templates.
## Installation
### Quick Install
**macOS & Linux**:
```bash
**Windows**:
```powershell
### Homebrew (macOS & Linux)
```bash
brew install NewGuy012/tap/srz
```
### Build from Source (Cargo)
If you prefer building with Rust from source:
```bash
cargo install --path .
```
## Usage
### Hardware Recommendation
Run `srz recommend` to inspect your system's RAM/CPU and get the optimal model and context window:
```bash
srz recommend
```
### Summarize
```bash
srz "https://en.wikipedia.org/wiki/Rust_(programming_language)" # Web article
srz "https://youtu.be/5C_HPTJg5ek" # YouTube video
srz assets/document.pdf # PDF document
srz news "Rust language updates" # Live news digest
```
### Ask & Chat
```bash
srz ask "What are the main findings?" # Ask latest read
srz ask assets/document.pdf "Summary" # Ask document or URL (YouTube, PDF, web)
srz ask -i # Interactive chat
```
### Library, Search & Telemetry
```bash
srz browse # Terminal dashboard (press 'a' for grounded chat)
srz find "keyword" # Search past summaries
srz query "SELECT model, count(*) FROM history GROUP BY model" # SQL analytics
srz export --format markdown ~/Notes/Vault/ # Export vault for Obsidian / Logseq
srz export --format json library.json # Export as structured JSON
```
### Cache & Storage Maintenance
```bash
srz cache clean # Clean cache files
srz doctor # Backend diagnostics
```
Run `srz --help` to see all available subcommands and flags.
## 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](https://github.com/ggml-org/Llama-macOS) (macOS) or [Llama-Windows](https://github.com/ggml-org/Llama-Windows) (Windows). Launch the app and load the model—`srz` connects automatically to `http://localhost:9931/v1` with zero setup (default model: `ggml-org/gemma-4-E4B-it-GGUF:Q8_0`).
- In Llama GUI, simply select the simplified model card shown by `srz recommend`:
- Model: `gemma-4`
- Badges: `E4B` `Q8_0` (or `26B-A4B` `Q8_0` for 64GB+ workstations)
- Context: `32k ctx` (or `64k ctx` / `128k ctx`)
### Cloud Providers (Optional)
`srz` works with any OpenAI-compatible provider (OpenAI, Gemini, Groq, Anthropic):
```bash
export OPENAI_BASE_URL="https://api.openai.com/v1" OPENAI_MODEL="gpt-4o-mini" OPENAI_API_KEY="sk-..."
```
### Hardware-Tailored Configuration (`srz config init`)
Run `srz config init` to auto-generate a `~/.config/srz/config.toml` tailored to your machine's hardware:
```toml
base_url = "http://localhost:9931/v1"
model = "ggml-org/gemma-4-E4B-it-GGUF:Q8_0"
context_window = 32768
[profiles.openai]
base_url = "https://api.openai.com/v1"
model = "gpt-4o-mini"
api_key = "sk-..."
```
Switch profiles on the fly with `--profile`:
```bash
srz "The Rust Programming Language" --profile openai
```
## Documentation
For full guides, tutorials, architecture specifications, and complete command references, visit the [**srz Documentation Site**](https://newguy012.github.io/srz/).