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README.md

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 # regspy
 
+regspy is a regex pattern generator, you enter some data -> select what you want matched and or not matched -> ??? -> Pattern!
+
+![alt text](imgs/demo.gif)
+
+This project started as me trying to learn dspy, its vibe coded to shit and back but it works and has some accomplishments:
+ - Runs on small models with 3B parameter at a minimum, so it should run on anything.
+ - It outperforms grex ~~in metrics that were defined by me~~.
+ - Learns from what you feed it, it generated a pattern you liked? add it to the training set!
+ - No human written prompts or rules or "make sure to NOT explode" bs.
+ - Context aware generation, it learns from failed patterns and most importantly WHY it failed.
+ - Generates patterns based on a scoring system that ranks patterns by:
+	 - **matches_all**: Percentage of required items the pattern matches
+	 - **excludes_all**: Percentage of excluded items the pattern avoids
+		 - *If no excluded items are selected, this metrics weights are divided equally amongst the others.*
+	 - **coherence**: How similar extra matches are to target items
+	 - **generalization**: Use of character classes (\\d, \\w) vs literals
+	 - **simplicity**: How short patterns are and without the use of branching
+
+Is it perfect? hell no, the training set, scoring system, hint generation could be improved upon, so if you want have a go at it i included a CLAUDE.md for you.
+
+But if you're a everyday smooth brain like me that needs a simple pattern on the fly because for some reason your brain is physically impossible of remembering that lookaheads exist, regspy should be of some help. 
+
+## Features
+
+- **Visual Text Selection**: Highlight text to create match examples (cyan) or exclusions (red)
+- **LLM-Powered Generation**: Uses local Ollama with qwen2.5-coder:3b for intelligent pattern creation
+- **Training Dataset**: 227+ curated examples with ability to add your own
+- **Pre-compilation**: Optional rule extraction for faster runtime inference
+- **Session Config**: Adjust model, temperature, and scoring weights on the fly
+
+## Installation
+
+- **AutoHotkey v2.0** - [Download](https://www.autohotkey.com/)
+- **Python Libs**:
+  ```bash
+  pip install dspy grex ollama
+  ```
+- **Ollama**:
+  ```bash
+  ollama serve
+  ollama pull qwen2.5-coder:3b
+  ```
+- **Run**:
+  ```bash
+  AutoHotkey64.exe regspy.ahk # Or just double click regspy.ahk
+  ```
+
+### CLI flags
+
+```bash
+# Run test suite
+python regexgen.py --test
+
+# Pre-compile for faster runtime
+python regexgen.py --compile
+
+# Generate regex from JSON input
+python regexgen.py input.json output.json
+
+# With custom config
+python regexgen.py input.json output.json --config config.json
+
+# Dataset management
+python regexgen.py --list-dataset output.json
+python regexgen.py --add-example example.json
+python regexgen.py --delete-example <index>
+```
+
+## Architecture
+
+```
+┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
+│   AutoHotkey    │────▶│  Web Frontend   │────▶│     Python      │
+│   (Host)        │◀────│  (WebView2)     │◀────│   (DSPy/LLM)    │
+└─────────────────┘     └─────────────────┘     └─────────────────┘
+     Window              Text Selection          Regex Generation
+     Management          Highlighting            Multi-criteria
+     IPC Bridge          Results Display         Scoring
+```
+
+## Configuration
+
+The Config tab allows session-level adjustments:
+
+- **Model**: Ollama model name (default: `qwen2.5-coder:3b`)
+- **Temperature**: LLM creativity (default: 0.4)
+- **Max Attempts**: Refinement iterations (default: 10)
+- **Reward Threshold**: Stop early if score exceeds (default: 0.85)
+- **Scoring Weights**: Adjust the 5 criteria weights
+- **Context Window** (`num_ctx`): Ollama context size (default: 8192). Ollama defaults to 4096 which can truncate prompts with many training examples. If you see "truncating input prompt" warnings in Ollama logs, bump this up. Uses ~200MB extra VRAM per 4K increase on 3B models.