CADventory

CADventory is a 3D CAD file, categorical tagging, and associated metadata inventory management system.

It is tested on Mac, Linux, and Windows, includes code coverage testing, and code security testing.

Installation

1. Install: CMake 3.25+, SQLite3, BRLCAD, and Qt6
2. Clone/Install BRLCAD from https://github.com/BRL-CAD/brlcad.git
3. Clone/Download CADventory from source
4. Install Ollama and LLaMA 3 (required for AI tagging):
   • Download Ollama from: https://ollama.com
   • Once installed, run the following command to install the model:

     ollama pull llama3
     

5. Compile (see .gitlab-ci.yml for variations): mkdir .build cd .build cmake .. -DCMAKE_INSTALL_PREFIX=/path -DCMAKE_BUILD_TYPE=Release -DQt6_DIR=/path/to/qt6 -DBRLCAD_ROOT=/path/to/BRLCAD cmake --build . --config Release

Usage

Run (Mac and Linux): ./bin/cadventory Run (Windows): .\Release\bin\cadventory.exe

Click + in GUI and navigate to a folder to index.

⚠️ AI-based tagging requires Ollama to be running and the llama3 model to be available.

To clear all settings, run with --no-gui command-line option.

Testing

1. Create build directory from root of the project (mkdir build)
2. Navigate to build directory (cd build)
3. cmake -DCMAKE_BUILD_TYPE=Debug -DBRLCAD_DIR=/path/to/brlcad/build ..
4. make
5. ctest --output-on-failure
6. lcov --capture --directory . --output-file coverage.info
7. lcov --remove coverage.info '/usr/*' --output-file coverage_filtered.info
8. genhtml coverage_filtered.info --output-directory coverage_report
9. view coverage_report/index.html

Automated Test Suite via Python Script

1. Dependencies:

Install CMake: On macOS: brew install cmake On Ubuntu: sudo apt install cmake On Windows: Install from the CMake website. Install LCOV and GenHTML: On macOS: brew install lcov On Ubuntu: sudo apt install lcov On Windows: Install LCOV and GenHTML through Cygwin or equivalent. Ensure make or mingw32-make is available.

2. Environment Variable for BRLCAD_ROOT:

You can specify the BRLCAD_ROOT path interactively when the script runs.

3. Cross-Platform Commands:

make: Uses mingw32-make on Windows. File Paths: Adapts to Linux/macOS vs. Windows paths (/usr/* vs. C:/Program Files/*). Opening HTML Reports: Uses start, open, or xdg-open based on the OS.

4. Testing Workflow:

Compiles the application. Runs unit tests with CTest. Generates and filters a coverage report. Opens the report in the default browser.

5. Running the Script Ensure Python is installed (version 3.6+). Run it in your project directory (cadventory): python run_tests.py or python3 run_tests.py

Design

Here's an architecture diagram:

┌──────────────────────────────────────────────────────────────────────┐
│                               CADventory                             │
│                                                                      │
│    ┌──────────────────────────────────────────────────────────────┐  │
│    │                      User Interface (Qt)                     │  │
│    └─────────────┬───────────────────────┬─────────────────┬──────┘  │
│                  │                       │                 │         │
│    ┌─────────────▼───────────┐  ┌────────▼─────────┐  ┌────▼──────┐  │
│    │  Filesystem Processor   │  │ SQLite or JSON   │◄─│ Report    │  │
│    └─────────────┬───────────┘  │ Storage Manager  │  │ Generator │  │
│                  │              └──────────────────┘  └────┬──────┘  │
│                  │                                         │         │
│    ┌─────────────▼───────────┐                             │         │
│    │ Geometry/Image/Document │                             │         │
│    │ Handler                 │                             │         │
│    └─────────────┬───────────┘                             │         │
│                  │                                         │         │
│    ┌─────────────▼────────┐                                │         │
│    │     CAD Libraries    │◄───────────────────────────────┘         │
│    └──────────────────────┘                                          │
└──────────────────────────────────────────────────────────────────────┘

📦 Version 0.2.0 Documentation - What's new?

Features Added

• AI Powered Model Tagging (ModelTagging.cpp/.h)
• Tag based filtering/searching
• Logging System for AI
• Window compatible command execution
• Major UI/UX Improvements
• Major bug fixes (e.g report generation and image/thumbnail issues)

File Structure

/src
 ├── main.cpp                     # Entry point for GUI
 ├── ModelTagging.*              # AI tagging system using Ollama + LLaMA 3
 ├── ModelParser.*               # BRL-CAD model parser for title + object names
 ├── executeCommand.*            # Platform-specific command execution
 ├── UI files (.ui, etc)         # Qt user interface logic

/tests                           # Unit tests
/scripts                         # Automated testing tools

Core Components

ModelTagging.h/cpp

• Checks for Ollama + model
• Builds prompt and runs AI tagging
• Uses QProcess to write/read tag results
• Signals: tagsGenerated, tagGenerationCanceled

ModelParser.h/cpp

• Uses mged CLI to extract title and object paths
• Converts file paths to WSL-compatible if needed

executeCommand.h/cpp

• Windows-safe hidden execution of commands
• Also supports redirection for prompt generation

AI Tagging

• Powered by LLaMA 3 (via Ollama)
• Parses .g BRL-CAD files for object paths and titles
• Sends structured prompt to local LLaMA model via command-line interface
• Returns exactly 10 one-word tags for classification/search

How to Use

• Ensure Ollama is installed and added to your PATH
• Call ModelTagging::generateTags(filepath) to begin tag generation
• Automatically emits a tagsGenerated(tags) signal when complete

Roadmap

1. Implement automatic PDF report generation (1-page per library model).
2. Integrate metadata support into GUI for tagging primary models.
3. Graphically depict models and iconography during GUI browsing.
4. Implement support for creating & modifying collections manually.
5. Implement support for identifying between AI generated and manually added tags
6. Multi-threading support for tag generation
7. Refine accuracy of the AI Tagging

Credits

• AI tagging logic: Written in-house using prompts run via Ollama
• BRL-CAD Integration: Leveraging mged CLI from BRL-CAD
• AI backend: Powered by LLaMA 3 via Ollama
• Qt GUI: Built using Qt6

License

MIT