NVEIL Toolkit

Describe your data. Get production charts. Your data stays local.

Quickstart • API Reference • Examples • Changelog

--- NVEIL is an AI-powered data visualization toolkit. Write one line of natural language, and NVEIL processes your data and generates publication-ready visualizations — no chart code, no hallucinations, no data leaving your machine. ```python import nveil nveil.configure(api_key="nveil_...") # Pass a file path directly — no DataFrame loading required. spec = nveil.generate_spec("Revenue by region, colored by quarter", "sales.csv") fig = spec.render("sales.csv") # 100% local — no API call nveil.show(fig) # opens in browser ``` ### From your shell After `pip install nveil` the `nveil` command is on your `$PATH`: ```bash export NVEIL_API_KEY=nveil_... # Ground yourself on the dataset (shape / dtypes / head preview) nveil describe sales.csv # Generate HTML + PNG + a reusable .nveil spec, print the explanation nveil generate "Revenue by region, colored by quarter" \ --data sales.csv --format all --explain # Re-render an existing spec on fresh data — no API call nveil render chart.nveil --data new_sales.csv ``` ### For AI agents (Claude Code / Claude Desktop / Cursor / Codex / …) NVEIL ships first-class integrations: ```bash # Claude Code / Claude Desktop — install the bundled skill nveil install-skill # Claude Desktop, Cursor, any MCP client — add an MCP server: # {"mcpServers": {"nveil": {"command": "nveil", "args": ["mcp"]}}} nveil mcp # stdio server; launched by the MCP client ```

NVEIL multi-panel dashboard with charts, heatmaps, and flow diagrams

## Why NVEIL? | Capability | **NVEIL** | Chatbot data analysis¹ | LLM-to-viz libraries² | Traditional plotting³ | |---|:-:|:-:|:-:|:-:| | Natural-language input | ✓ | ✓ | ✓ | ✗ | | Raw data stays on your machine | ✓ | ✗ | ✗ | ✓ | | Only schema + stats sent to server | ✓ | ✗ | ✗ | N/A | | Deterministic, reproducible output | ✓ | ✗ | ✗ | ✓ | | Offline re-rendering, zero API calls | ✓ | ✗ | ✗ | ✓ | | Portable saved specs (`.nveil` files) | ✓ | ✗ | ✗ | ✗ | | 2D + 3D + geospatial + scientific | ✓ | 2D | 2D | varies | | Multi-backend (Plotly, VTK, DeckGL) | ✓ | ✗ | ✗ | ✗ | | Data processing engine | ✓ | ✓ | partial | ✗ | _{¹ ChatGPT Advanced Data Analysis, Claude Analysis tool, Gemini Data Agent · ² PandasAI, LIDA, Julius, Vanna · ³ Plotly, Matplotlib, Seaborn} ## How It Works ``` Your Data ──> Toolkit ──metadata only──> NVEIL AI ──> Processing Plan ──> Local Execution ──> Result ^ ^ raw data stays here raw data stays here ``` 1. **You describe** what you want in plain language 2. **NVEIL AI plans** the data processing and visualization (only metadata is sent — column names, types, statistics) 3. **The Toolkit executes locally** — joins, aggregations, pivots, rendering — all on your machine 4. **You get a figure** — Plotly, VTK, or DeckGL, auto-selected for your data ## Key Features

### 🧠 Two Engines in One Data processing (joins, pivots, aggregations, geocoding, time series) **AND** visualization generation from a single prompt. ### 🔒 Data Privacy by Design Raw data never leaves your machine. Only column names, types, and aggregate statistics are sent. ### 📈 Multi-Backend Rendering Auto-detects the best engine: **Plotly** (2D charts), **VTK** (3D/medical), **DeckGL** (geospatial).

### 🧪 Auditable Results Powered by constraint solving, not random generation. Same input = same output, every time. ### ⚡ Offline Rendering `spec.render()` runs 100% locally with zero API calls. ### 💾 Reusable Specs Save to `.nveil` files, reload later, render on new data — no server needed.

## Beyond Simple Charts

NVEIL AI chat — conversational data exploration with geospatial heatmaps

NVEIL handles geospatial heatmaps, 3D volumes, scientific visualizations, medical imaging (DICOM), biosignal data (EDF/EDF+), network graphs, and 50+ other visualization types — all from natural language. ## Save Once, Render Forever ```python # Generate once (API call) spec = nveil.generate_spec("Monthly trend by category", df) spec.save("trend.nveil") # Reload anywhere — no API call, no server, no cost spec = nveil.load_spec("trend.nveil") fig = spec.render(fresh_data) nveil.save_image(fig, "report.png") ``` ## Installation ```bash pip install nveil ``` **Requirements:** Python 3.10+ ## Getting Started 1. Create an account at [app.nveil.com](https://app.nveil.com) 2. Generate an API key in **Settings** 3. Start visualizing ```python import os import nveil nveil.configure(api_key=os.environ["NVEIL_API_KEY"]) spec = nveil.generate_spec("scatter plot of price vs area", df) fig = spec.render(df) nveil.show(fig) ``` See the [examples/](examples/) directory for more usage patterns. ## Documentation Full documentation is available at **[docs.nveil.com](https://docs.nveil.com)**: - [Quickstart Guide](https://docs.nveil.com/getting-started/quickstart/) - [Core Concepts](https://docs.nveil.com/concepts/) — sessions, specs, and the two-stage flow - [API Reference](https://docs.nveil.com/api-reference/) — full reference for all public functions - [Privacy Model](https://docs.nveil.com/concepts/privacy-model/) — what data is sent, what stays local - [Examples](https://docs.nveil.com/examples/) — bar charts, multi-dataset, offline rendering ## Contributing Contributions are welcome under the project's [Contributor License Agreement](licensing/CLA.md). Bug reports and feature requests are welcome via [GitHub Issues](https://github.com/nveil-ai/nveil-toolkit/issues). ## License GNU AGPL v3 or later. See [LICENSE](LICENSE). Commercial dual-licensing is available — contact `pierre.jacquet@nveil.com`. ---

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