docs

README.md

@@ -1,4 +1,66 @@
-# Star-Mapper
+# Star-Mapper — Cortex Graph Explorer
+
+A high-performance Neo4j graph visualization app with Python-precomputed layouts and a Canvas 2D frontend.
+
+## Quick Start
+
+```bash
+# 1. Clone & enter the repo
+git clone https://github.com/Askill/Star-Mapper.git
+cd Star-Mapper
+
+# 2. Create a virtual environment & install dependencies
+python3 -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
+
+# 3. Run the app
+python app.py
+```
+
+Open **http://localhost:5555** in your browser.
+
+### Environment Variables (optional)
+
+| Variable | Default | Description |
+|---|---|---|
+| `NEO4J_HTTP_URL` | `https://neo4j.develop.cortex.cloud.otto.de` | Neo4j HTTP endpoint |
+| `NEO4J_USER` | `neo4j` | Username |
+| `NEO4J_PASSWORD` | _(empty)_ | Password |
+| `NEO4J_DATABASE` | `neo4j` | Database name |
+
+```bash
+# Example: connect to a different instance
+NEO4J_HTTP_URL=https://my-neo4j.example.com NEO4J_USER=admin NEO4J_PASSWORD=secret python app.py
+```
+
+You can also change the connection at runtime via the **Connection Settings** panel in the sidebar.
+
+## Features
+
+- **Neo4j connectivity** via HTTP Transactional API (works behind ALB/HTTP proxies)
+- **Python-precomputed layouts** using igraph (C-based) — auto-selects algorithm by graph size
+- **Canvas 2D rendering** with D3 zoom/pan, quadtree hit testing, viewport frustum culling
+- **Curved edges** with configurable curvature, multi-edge spreading
+- **Recursive highlight diffusion** — click a node to BFS-highlight neighbors with decaying opacity
+- **Visual settings sliders** — curvature, edge opacity/width/color, node size, label size/zoom, spacing, iterations
+- **Schema browser**, sample queries, node search, minimap, dark glass-morphism theme
+
+## Performance
+
+| Nodes | Layout Time |
+|-------|------------|
+| 300   | ~10 ms     |
+| 3,000 | ~77 ms     |
+| 5,000 | ~313 ms    |
+
+---
+
+_Original Star-Mapper description below._
+
+---
+
+## Original: Website Mapper
 
 Calls every link on a given website and produces an explorable graph visualization.