diff --git a/docs/downloads.md b/docs/downloads.md deleted file mode 100644 index 83983f90..00000000 --- a/docs/downloads.md +++ /dev/null @@ -1,238 +0,0 @@ ---- -title: Downloads -description: Download the OpenJarvis desktop app, browser app, CLI, or Python SDK ---- - -# Downloads - -OpenJarvis runs entirely on your hardware. Choose the interface that fits your workflow. - ---- - -## Desktop App - -The native desktop app bundles Ollama (the inference engine) and the OpenJarvis Python backend -into a single installer. Download, open, and start chatting — no terminal required. - -### Download - -| Platform | Download | Notes | -|----------|----------|-------| -| macOS (Apple Silicon) | [:material-download: **OpenJarvis.dmg**](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_aarch64.dmg) | M1/M2/M3/M4 Macs | -| macOS (Intel) | [:material-download: **OpenJarvis.dmg**](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_x64.dmg) | Intel Macs (2020 and earlier) | -| Windows (64-bit) | [:material-download: **OpenJarvis-setup.exe**](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_x64-setup.exe) | Windows 10+ | -| Linux (DEB) | [:material-download: **OpenJarvis.deb**](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_amd64.deb) | Ubuntu, Debian | -| Linux (RPM) | [:material-download: **OpenJarvis.rpm**](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_amd64.rpm) | Fedora, RHEL | - -!!! tip "All releases" - Browse all versions on the [GitHub Releases](https://github.com/HazyResearch/OpenJarvis/releases) page. - -### What's included - -The desktop app ships with: - -- **Ollama** sidecar — inference engine runs automatically in the background -- **OpenJarvis backend** — Python API server managed by the app -- **Full chat UI** — same interface as the browser app -- **Energy monitoring** — real-time power consumption tracking -- **Telemetry dashboard** — token throughput, latency, and cost comparison - -### Build from source - -```bash -git clone https://github.com/HazyResearch/OpenJarvis.git -cd OpenJarvis/desktop -npm install -npm run tauri build -``` - -The built installer will be in `desktop/src-tauri/target/release/bundle/`. - ---- - -## Browser App - -Run the full chat UI in your browser. Everything stays local — the backend runs on -your machine and the frontend connects via `localhost`. - -### One-command setup - -```bash -git clone https://github.com/HazyResearch/OpenJarvis.git -cd OpenJarvis -./scripts/quickstart.sh -``` - -The script handles everything: - -1. Checks for Python 3.10+ and Node.js 22+ -2. Installs Ollama if not present and pulls a starter model -3. Installs Python and frontend dependencies -4. Starts the backend API server and frontend dev server -5. Opens `http://localhost:5173` in your browser - -### Manual setup - -If you prefer to run each step yourself: - -=== "Step 1: Clone and install" - - ```bash - git clone https://github.com/HazyResearch/OpenJarvis.git - cd OpenJarvis - uv sync --extra server - cd frontend && npm install && cd .. - ``` - -=== "Step 2: Start Ollama" - - ```bash - # Install from https://ollama.com if not already installed - ollama serve & - ollama pull qwen3:0.6b - ``` - -=== "Step 3: Start backend" - - ```bash - uv run jarvis serve --port 8000 - ``` - -=== "Step 4: Start frontend" - - ```bash - cd frontend - npm run dev - ``` - -Then open [http://localhost:5173](http://localhost:5173). - -### What you get - -- **Chat interface** — markdown rendering, streaming responses, conversation history -- **Tool use** — calculator, web search, code interpreter, file I/O -- **System panel** — live telemetry, energy monitoring, cost comparison vs. cloud models -- **Dashboard** — energy graphs, trace debugging, cost breakdown -- **Settings** — model selection, agent configuration, theme toggle - ---- - -## CLI - -The command-line interface is the fastest way to interact with OpenJarvis -programmatically. Every feature is accessible from the terminal. - -### Install - -=== "uv (recommended)" - - ```bash - uv pip install openjarvis - ``` - -=== "pip" - - ```bash - pip install openjarvis - ``` - -=== "From source" - - ```bash - git clone https://github.com/HazyResearch/OpenJarvis.git - cd OpenJarvis - uv sync - ``` - -### Verify - -```bash -jarvis --version -# jarvis, version 1.0.0 -``` - -### First commands - -```bash -# Ask a question -jarvis ask "What is the capital of France?" - -# Use an agent with tools -jarvis ask --agent orchestrator --tools calculator "What is 137 * 42?" - -# Start the API server -jarvis serve --port 8000 - -# Run diagnostics -jarvis doctor - -# List available models -jarvis model list - -# Interactive chat -jarvis chat -``` - -!!! info "Inference backend required" - The CLI requires a running inference backend (e.g., Ollama). See the - [Installation guide](getting-started/installation.md#setting-up-an-inference-backend) - for setup instructions. - ---- - -## Python SDK - -For programmatic access, the `Jarvis` class provides a high-level sync API. - -### Install - -```bash -pip install openjarvis -``` - -### Quick example - -```python -from openjarvis import Jarvis - -j = Jarvis() -print(j.ask("Explain quicksort in two sentences.")) -j.close() -``` - -### With agents and tools - -```python -result = j.ask_full( - "What is the square root of 144?", - agent="orchestrator", - tools=["calculator", "think"], -) -print(result["content"]) # "12" -print(result["tool_results"]) # tool invocations -print(result["turns"]) # number of agent turns -``` - -### Composition layer - -For full control, use the `SystemBuilder`: - -```python -from openjarvis import SystemBuilder - -system = ( - SystemBuilder() - .engine("ollama") - .model("qwen3:8b") - .agent("orchestrator") - .tools(["calculator", "web_search", "file_read"]) - .enable_telemetry() - .enable_traces() - .build() -) - -result = system.ask("Summarize the latest AI news.") -system.close() -``` - -See the [Python SDK guide](user-guide/python-sdk.md) for the full API reference. diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md index 793e960a..f2166982 100644 --- a/docs/getting-started/installation.md +++ b/docs/getting-started/installation.md @@ -1,15 +1,20 @@ --- title: Installation -description: Install OpenJarvis and set up an inference backend +description: Get OpenJarvis running — browser app, desktop app, CLI, or Python SDK --- # Installation -This guide covers installing OpenJarvis, its optional extras, and setting up an inference backend. +OpenJarvis runs entirely on your hardware. Choose the interface that fits your workflow. -## Quickstart (Recommended) +--- -The fastest way to get everything running — browser UI, backend, and inference engine — with a single command: +## Browser App + +Run the full chat UI in your browser. Everything stays local — the backend runs on +your machine and the frontend connects via `localhost`. + +### One-command setup ```bash git clone https://github.com/HazyResearch/OpenJarvis.git @@ -17,32 +22,101 @@ cd OpenJarvis ./scripts/quickstart.sh ``` -This script checks for Python 3.10+, Node.js, and Ollama (installing what's missing), pulls a starter model, installs all dependencies, starts the backend and frontend servers, and opens the chat UI in your browser. +The script handles everything: -!!! tip "Desktop app" - Prefer a native app? Download the [Desktop App](../downloads.md#desktop-app) instead — it bundles everything into a single installer. +1. Checks for Python 3.10+ and Node.js 18+ +2. Installs Ollama if not present and pulls a starter model +3. Installs Python and frontend dependencies +4. Starts the backend API server and frontend dev server +5. Opens `http://localhost:5173` in your browser ---- +### Manual setup -## Requirements +If you prefer to run each step yourself: -| Requirement | Version | Notes | -|-------------|---------|-------| -| Python | 3.10+ | Required | -| Inference backend | Any | At least one of Ollama, vLLM, llama.cpp, SGLang, or a cloud API | -| Node.js | 18+ | Required for the browser UI; 22+ for OpenClaw agent | - -## Installing OpenJarvis - -=== "Quickstart script" +=== "Step 1: Clone and install" ```bash git clone https://github.com/HazyResearch/OpenJarvis.git cd OpenJarvis - ./scripts/quickstart.sh + uv sync --extra server + cd frontend && npm install && cd .. ``` - Handles everything: deps, Ollama, model pull, backend, frontend, browser open. +=== "Step 2: Start Ollama" + + ```bash + # Install from https://ollama.com if not already installed + ollama serve & + ollama pull qwen3:0.6b + ``` + +=== "Step 3: Start backend" + + ```bash + uv run jarvis serve --port 8000 + ``` + +=== "Step 4: Start frontend" + + ```bash + cd frontend + npm run dev + ``` + +Then open [http://localhost:5173](http://localhost:5173). + +--- + +## Desktop App + +The desktop app is a native window for the OpenJarvis chat UI. All inference and backend +processing happens on your local machine — the app connects to the backend you start locally. + +### Setup + +**Step 1.** Start the backend (same as Browser App): + +```bash +git clone https://github.com/HazyResearch/OpenJarvis.git +cd OpenJarvis +./scripts/quickstart.sh +``` + +**Step 2.** Download and open the desktop app: + +| Platform | Download | +|----------|----------| +| macOS (Apple Silicon) | [:material-download: **OpenJarvis.dmg**](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis_1.0.0_aarch64.dmg) | +| Windows (64-bit) | [:material-download: **OpenJarvis-setup.exe**](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis_1.0.0_x64-setup.exe) | +| Linux (DEB) | [:material-download: **OpenJarvis.deb**](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis_1.0.0_amd64.deb) | +| Linux (RPM) | [:material-download: **OpenJarvis.rpm**](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis-1.0.0-1.x86_64.rpm) | +| Linux (AppImage) | [:material-download: **OpenJarvis.AppImage**](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis_1.0.0_amd64.AppImage) | + +The app connects to `http://localhost:8000` automatically. + +!!! tip "All releases" + Browse all versions on the [GitHub Releases](https://github.com/HazyResearch/OpenJarvis/releases) page. + +### Build from source + +```bash +git clone https://github.com/HazyResearch/OpenJarvis.git +cd OpenJarvis/desktop +npm install +npm run tauri build +``` + +The built installer will be in `desktop/src-tauri/target/release/bundle/`. + +--- + +## CLI + +The command-line interface is the fastest way to interact with OpenJarvis +programmatically. Every feature is accessible from the terminal. + +### Install === "uv (recommended)" @@ -64,64 +138,134 @@ This script checks for Python 3.10+, Node.js, and Ollama (installing what's miss uv sync ``` - For development with all dev tools: +### Verify - ```bash - uv sync --extra dev - ``` +```bash +jarvis --version +# jarvis, version 1.0.0 +``` + +### First commands + +```bash +jarvis ask "What is the capital of France?" + +jarvis ask --agent orchestrator --tools calculator "What is 137 * 42?" + +jarvis serve --port 8000 + +jarvis doctor + +jarvis model list + +jarvis chat +``` + +!!! info "Inference backend required" + The CLI requires a running inference backend (e.g., Ollama). See + [Setting up an inference backend](#setting-up-an-inference-backend) below. + +--- + +## Python SDK + +For programmatic access, the `Jarvis` class provides a high-level sync API. + +### Install + +```bash +pip install openjarvis +``` + +### Quick example + +```python +from openjarvis import Jarvis + +j = Jarvis() +print(j.ask("Explain quicksort in two sentences.")) +j.close() +``` + +### With agents and tools + +```python +result = j.ask_full( + "What is the square root of 144?", + agent="orchestrator", + tools=["calculator", "think"], +) +print(result["content"]) # "12" +print(result["tool_results"]) # tool invocations +print(result["turns"]) # number of agent turns +``` + +### Composition layer + +For full control, use the `SystemBuilder`: + +```python +from openjarvis import SystemBuilder + +system = ( + SystemBuilder() + .engine("ollama") + .model("qwen3:8b") + .agent("orchestrator") + .tools(["calculator", "web_search", "file_read"]) + .enable_telemetry() + .enable_traces() + .build() +) + +result = system.ask("Summarize the latest AI news.") +system.close() +``` + +See the [Python SDK guide](../user-guide/python-sdk.md) for the full API reference. + +--- + +## Requirements + +| Requirement | Version | Notes | +|-------------|---------|-------| +| Python | 3.10+ | Required | +| Inference backend | Any | At least one of Ollama, vLLM, llama.cpp, SGLang, or a cloud API | +| Node.js | 18+ | Required for the browser UI; 22+ for OpenClaw agent | ## Optional Extras -OpenJarvis uses optional extras to keep the base installation lightweight. Install only what you need. +OpenJarvis uses optional extras to keep the base installation lightweight. ### Inference Backends -| Extra | Install Command | Dependencies | Description | -|-------|----------------|--------------|-------------| -| `inference-ollama` | `pip install 'openjarvis[inference-ollama]'` | None (HTTP-based) | Ollama backend. Communicates via HTTP API. | -| `inference-vllm` | `pip install 'openjarvis[inference-vllm]'` | None (HTTP-based) | vLLM backend. Communicates via OpenAI-compatible API. | -| `inference-llamacpp` | `pip install 'openjarvis[inference-llamacpp]'` | None (HTTP-based) | llama.cpp server backend. | -| `inference-cloud` | `pip install 'openjarvis[inference-cloud]'` | `openai>=1.30`, `anthropic>=0.30` | Cloud inference via OpenAI and Anthropic APIs. | -| `inference-google` | `pip install 'openjarvis[inference-google]'` | `google-genai>=1.0` | Google Gemini API backend. | +| Extra | Install Command | Description | +|-------|----------------|-------------| +| `inference-cloud` | `pip install 'openjarvis[inference-cloud]'` | OpenAI and Anthropic APIs | +| `inference-google` | `pip install 'openjarvis[inference-google]'` | Google Gemini API | !!! note "Ollama, vLLM, and llama.cpp are HTTP-based" - The `inference-ollama`, `inference-vllm`, and `inference-llamacpp` extras have no additional Python dependencies. OpenJarvis communicates with these engines over HTTP using the `httpx` library that is already a core dependency. You still need the actual engine software running on your machine or network. + These engines have no additional Python dependencies — OpenJarvis communicates over HTTP. You still need the engine software running on your machine. ### Memory Backends -| Extra | Install Command | Dependencies | Description | -|-------|----------------|--------------|-------------| -| `memory-faiss` | `pip install 'openjarvis[memory-faiss]'` | `faiss-cpu>=1.7`, `sentence-transformers>=2.2`, `numpy>=1.24` | FAISS vector store with sentence-transformer embeddings. | -| `memory-colbert` | `pip install 'openjarvis[memory-colbert]'` | `colbert-ai>=0.2`, `torch>=2.0` | ColBERTv2 late-interaction retrieval. | -| `memory-bm25` | `pip install 'openjarvis[memory-bm25]'` | `rank-bm25>=0.2.2` | BM25 sparse retrieval backend. | -| `memory-pdf` | `pip install 'openjarvis[memory-pdf]'` | `pdfplumber>=0.10` | PDF document ingestion support. | +| Extra | Install Command | Description | +|-------|----------------|-------------| +| `memory-faiss` | `pip install 'openjarvis[memory-faiss]'` | FAISS vector store | +| `memory-colbert` | `pip install 'openjarvis[memory-colbert]'` | ColBERTv2 late-interaction retrieval | +| `memory-bm25` | `pip install 'openjarvis[memory-bm25]'` | BM25 sparse retrieval | !!! tip "SQLite memory is always available" - The default SQLite/FTS5 memory backend requires no additional dependencies. It is always available and suitable for most use cases. + The default SQLite/FTS5 memory backend requires no additional dependencies. -### Tools +### Server & Other -| Extra | Install Command | Dependencies | Description | -|-------|----------------|--------------|-------------| -| `tools-search` | `pip install 'openjarvis[tools-search]'` | `tavily-python>=0.3` | Web search tool via the Tavily API. | - -### Server - -| Extra | Install Command | Dependencies | Description | -|-------|----------------|--------------|-------------| -| `server` | `pip install 'openjarvis[server]'` | `fastapi>=0.110`, `uvicorn>=0.30`, `pydantic>=2.0` | OpenAI-compatible API server (`jarvis serve`). | - -### Other Extras - -| Extra | Install Command | Dependencies | Description | -|-------|----------------|--------------|-------------| -| `agents` | `pip install 'openjarvis[agents]'` | None | Agent infrastructure (included in base). | -| `learning` | `pip install 'openjarvis[learning]'` | None | Learning/router policy system (included in base). | -| `openclaw` | `pip install 'openjarvis[openclaw]'` | None | OpenClaw agent transport layer. Requires Node.js 22+ at runtime. | -| `docs` | `pip install 'openjarvis[docs]'` | `mkdocs>=1.6`, `mkdocs-material>=9.5`, `mkdocstrings[python]>=0.25` | Documentation build tools. | -| `dev` | `pip install 'openjarvis[dev]'` | `pytest>=8`, `pytest-asyncio>=0.24`, `pytest-cov>=5`, `respx>=0.22`, `ruff>=0.4` | Development and testing tools. | - -### Installing Multiple Extras +| Extra | Install Command | Description | +|-------|----------------|-------------| +| `server` | `pip install 'openjarvis[server]'` | OpenAI-compatible API server (`jarvis serve`) | +| `dev` | `pip install 'openjarvis[dev]'` | Development and testing tools | +| `docs` | `pip install 'openjarvis[docs]'` | Documentation build tools | Combine extras with commas: @@ -129,149 +273,52 @@ Combine extras with commas: pip install 'openjarvis[server,memory-faiss,inference-cloud]' ``` -Or with `uv`: - -```bash -uv pip install 'openjarvis[server,memory-faiss,inference-cloud]' -``` - -## Verifying Installation - -After installation, verify that the CLI is available: - -```bash -jarvis --version -``` - -Expected output: - -``` -jarvis, version 1.0.0 -``` - -View all available commands: - -```bash -jarvis --help -``` - -Expected output: - -``` -Usage: jarvis [OPTIONS] COMMAND [ARGS]... - - OpenJarvis -- modular AI assistant backend - -Options: - --version Show the version and exit. - --help Show this message and exit. - -Commands: - ask Ask Jarvis a question. - bench Run inference benchmarks. - init Detect hardware and generate ~/.openjarvis/config.toml. - memory Manage the memory store. - model Manage language models. - serve Start the OpenAI-compatible API server. - telemetry Query and manage inference telemetry data. -``` - ## Setting Up an Inference Backend -OpenJarvis requires at least one inference backend to generate responses. Choose the backend that best matches your hardware. +OpenJarvis requires at least one inference backend. Choose the one that matches your hardware. -### Ollama (Recommended for most users) +### Ollama (Recommended) -Ollama is the easiest way to get started. It handles model downloading and serving automatically. +The easiest way to get started. Handles model downloading and serving automatically. -1. Install Ollama from [ollama.com](https://ollama.com) -2. Start the server: +1. Install from [ollama.com](https://ollama.com) +2. Start the server and pull a model: ```bash ollama serve + ollama pull qwen3:0.6b ``` -3. Pull a model: - - ```bash - ollama pull qwen3:8b - ``` - - Or pull directly via the Jarvis CLI: - - ```bash - jarvis model pull qwen3:8b - ``` - -4. Verify the engine is detected: - - ```bash - jarvis model list - ``` +3. Verify: `jarvis model list` !!! tip "Best for: Apple Silicon Macs, consumer NVIDIA GPUs, CPU-only systems" -### vLLM (High-throughput serving) +### vLLM -vLLM provides high-throughput serving optimized for datacenter GPUs. +High-throughput serving optimized for datacenter GPUs. -1. Install vLLM following the [official guide](https://docs.vllm.ai) -2. Start the server: +1. Install following the [official guide](https://docs.vllm.ai) +2. Start: `vllm serve Qwen/Qwen2.5-7B-Instruct` +3. Auto-detected at `http://localhost:8000` - ```bash - vllm serve Qwen/Qwen2.5-7B-Instruct - ``` +!!! tip "Best for: NVIDIA datacenter GPUs (A100, H100), AMD GPUs" -3. OpenJarvis will auto-detect it at `http://localhost:8000` +### llama.cpp -!!! tip "Best for: NVIDIA datacenter GPUs (A100, H100, L40), AMD GPUs" +Efficient CPU and GPU inference with GGUF quantized models. -### llama.cpp (Lightweight, CPU-friendly) +1. Build from [github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp) +2. Start: `llama-server -m /path/to/model.gguf --port 8080` +3. Auto-detected at `http://localhost:8080` -llama.cpp provides efficient CPU and GPU inference with GGUF quantized models. - -1. Build llama.cpp from [github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp) -2. Start the server: - - ```bash - llama-server -m /path/to/model.gguf --port 8080 - ``` - -3. OpenJarvis will auto-detect it at `http://localhost:8080` - -!!! tip "Best for: CPU-only machines, constrained environments, GGUF models" - -### SGLang - -SGLang provides structured generation and high-performance serving. - -1. Install SGLang following the [official guide](https://github.com/sgl-project/sglang) -2. Start the server: - - ```bash - python -m sglang.launch_server --model Qwen/Qwen2.5-7B-Instruct --port 30000 - ``` - -3. OpenJarvis will auto-detect it at `http://localhost:30000` - -### Cloud APIs (OpenAI, Anthropic, Google) - -For cloud-based inference, install the cloud extras and set your API keys: +### Cloud APIs ```bash pip install 'openjarvis[inference-cloud,inference-google]' -``` - -Set environment variables: - -```bash export OPENAI_API_KEY="sk-..." export ANTHROPIC_API_KEY="sk-ant-..." -export GOOGLE_API_KEY="..." ``` -OpenJarvis will automatically detect available cloud providers. - ## Next Steps - [Quick Start](quickstart.md) — Run your first query diff --git a/docs/index.md b/docs/index.md index 43296c5e..c622361d 100644 --- a/docs/index.md +++ b/docs/index.md @@ -34,12 +34,16 @@ Everything runs on your hardware. Cloud APIs are optional. === "Desktop App" - Download the native desktop app — it bundles Ollama and the Python backend - so everything works out of the box. + The desktop app is a native window for the chat UI. Start the backend first, + then open the app. - [Download for macOS (Apple Silicon)](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_aarch64.dmg){ .md-button .md-button--primary } + **1.** Start backend: `git clone ... && cd OpenJarvis && ./scripts/quickstart.sh` - Also available for [macOS (Intel)](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_x64.dmg), [Windows](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_x64-setup.exe), [Linux (DEB)](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_amd64.deb), and [Linux (RPM)](https://github.com/HazyResearch/OpenJarvis/releases/latest/download/OpenJarvis_amd64.rpm). See the [Downloads](downloads.md) page for details. + **2.** Download the app: + + [Download for macOS (Apple Silicon)](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis_1.0.0_aarch64.dmg){ .md-button .md-button--primary } + + Also available for [Windows](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis_1.0.0_x64-setup.exe), [Linux (DEB)](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis_1.0.0_amd64.deb), and [Linux (RPM)](https://github.com/HazyResearch/OpenJarvis/releases/download/desktop-latest/OpenJarvis-1.0.0-1.x86_64.rpm). See [Installation](getting-started/installation.md#desktop-app) for details. === "Python SDK" diff --git a/mkdocs.yml b/mkdocs.yml index 073f0049..2beb20d0 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -124,7 +124,6 @@ extra: nav: - Home: index.md - - Downloads: downloads.md - Getting Started: - Installation: getting-started/installation.md - Quick Start: getting-started/quickstart.md