dredge-cli 0.1.5

DREDGE — A small Python utility (scaffold).

DREDGE

DREDGE — small Python package scaffold with String Theory integration.

📖 Documentation

• BUILD.md - Comprehensive build, test, and development guide
• CONTRIBUTING.md - Contribution guidelines
• SWIFT_PACKAGE_GUIDE.md - Swift development guide
• docs/VSCODE_SETUP.md - VS Code setup instructions
• docs/CONTAINER_ARCHITECTURE.md - Container architecture and deployment guide
• docs/CONTAINER_QUICKSTART.md - Quick start for container deployment
• docs/GITHUB_ACTIONS_CONTAINERS.md - GitHub Actions workflows for containers

🚀 Quick Start

Local Development (with Makefile)

# Clone and enter repository
git clone https://github.com/QueenFi703/DREDGE-Cli.git
cd DREDGE-Cli

# Install all dependencies (Python + Swift)
make install-all

# Run DREDGE server (port 3001)
make serve

# Or run MCP server (port 3002)
make mcp

# Run tests
make test-all

Container Development

Pre-built images are available on GitHub Container Registry:

# Pull and run latest CPU image
docker pull ghcr.io/queenfi703/dredge-cli:latest-cpu
docker run -p 3001:3001 ghcr.io/queenfi703/dredge-cli:latest-cpu

# Pull and run latest GPU image
docker pull ghcr.io/queenfi703/dredge-cli:latest-gpu
docker run -p 3002:3002 --gpus all ghcr.io/queenfi703/dredge-cli:latest-gpu

Or build and run locally:

# CPU-only Flask server
make docker-up-cpu

# GPU-enabled MCP server
make docker-up-gpu

# Full stack with monitoring
make docker-profile-full

See docs/CONTAINER_QUICKSTART.md for more container deployment options.

See BUILD.md for complete build instructions, CI triggers, and troubleshooting.

🚀 Quick Start with VS Code

Clone this repository directly into VS Code!

See the detailed VS Code Setup Guide for complete instructions on cloning, setting up, and developing in VS Code.

Quick clone command:

git clone https://github.com/QueenFi703/DREDGE-Cli.git
code DREDGE-Cli

Repository Structure

• src/dredge/ - Python package source code
• tests/ - Test files
• docs/ - Documentation files (see docs/mobile-optimization.md for mobile guidance)
• benchmarks/ - Benchmark scripts and results
• swift/ - Swift implementation files
• DREDGE-Cli.xcworkspace - Xcode workspace for Swift development
• archives/ - Archived files (excluded from version control)

Install

Create a virtual environment and install:

python -m venv .venv source .venv/bin/activate # or .venv\Scripts\activate on Windows pip install -e .

Server Usage

DREDGE includes two web servers:

1. DREDGE x Dolly Server (Port 3001)

A web server for API-based interaction with Dolly integration.

Starting the Server

python -m dredge serve
# or
dredge-cli serve --host 0.0.0.0 --port 3001 --debug

API Endpoints

• GET / - API information and available endpoints
• GET /health - Health check endpoint
• POST /lift - Lift an insight with Dolly integration
• GET /quasimoto-gpu - Quasimoto GPU visualization (repository language statistics)

Example Usage

# Get API info
curl http://localhost:3001/

# Check health
curl http://localhost:3001/health

# Lift an insight
curl -X POST http://localhost:3001/lift \
  -H "Content-Type: application/json" \
  -d '{"insight_text": "Digital memory must be human-reachable."}'

# View Quasimoto GPU visualization
open http://localhost:3001/quasimoto-gpu

2. MCP Server (Port 3002) - Quasimoto Integration

A Model Context Protocol server for serving Quasimoto neural wave function models with String Theory integration.

Starting the MCP Server

python -m dredge mcp
# or
dredge-cli mcp --host 0.0.0.0 --port 3002 --debug

MCP Protocol Endpoint

• GET / - MCP server capabilities and model information
• POST /mcp - MCP protocol endpoint for model operations

Available Operations

1. list_capabilities - List available models and operations
2. load_model - Load Quasimoto models (1D, 4D, 6D, ensemble) or String Theory models
3. inference - Run inference on loaded models
4. get_parameters - Retrieve model parameters
5. benchmark - Run performance benchmarks
6. string_spectrum - Compute string theory vibrational spectrum
7. string_parameters - Calculate fundamental string theory parameters
8. unified_inference - Run unified DREDGE + Quasimoto + String Theory inference
9. get_dependabot_alerts - Retrieve Dependabot security alerts for a repository
10. explain_dependabot_alert - Get detailed explanation of a specific Dependabot alert
11. update_dependabot_alert - Update Dependabot alert status (dismiss or reopen)

Example MCP Request

# List capabilities
curl http://localhost:3002/

# Load a model
curl -X POST http://localhost:3002/mcp \
  -H "Content-Type: application/json" \
  -d '{"operation": "load_model", "params": {"model_type": "quasimoto_1d"}}'

# Run inference
curl -X POST http://localhost:3002/mcp \
  -H "Content-Type: application/json" \
  -d '{"operation": "inference", "params": {"model_id": "quasimoto_1d_0", "inputs": {"x": [0.5], "t": [0.0]}}}'

Available Models

• quasimoto_1d - 1D wave function (8 parameters)
• quasimoto_4d - 4D spatiotemporal wave function (13 parameters)
• quasimoto_6d - 6D high-dimensional wave function (17 parameters)
• quasimoto_ensemble - Configurable ensemble models
• string_theory - String theory neural network (configurable dimensions)

String Theory Integration

The MCP server now includes string theory models that integrate with Quasimoto wave functions:

# Compute string vibrational spectrum
curl -X POST http://localhost:3002/mcp \
  -H "Content-Type: application/json" \
  -d '{"operation": "string_spectrum", "params": {"max_modes": 10, "dimensions": 10}}'

# Calculate fundamental string parameters
curl -X POST http://localhost:3002/mcp \
  -H "Content-Type: application/json" \
  -d '{"operation": "string_parameters", "params": {"energy_scale": 1.0, "coupling_constant": 0.1}}'

# Run unified inference (DREDGE + Quasimoto + String Theory)
curl -X POST http://localhost:3002/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "operation": "unified_inference",
    "params": {
      "dredge_insight": "Digital memory must be human-reachable",
      "quasimoto_coords": [0.5, 0.5, 0.5],
      "string_modes": [1, 2, 3]
    }
  }'

# Load a string theory model
curl -X POST http://localhost:3002/mcp \
  -H "Content-Type: application/json" \
  -d '{"operation": "load_model", "params": {"model_type": "string_theory", "config": {"dimensions": 10, "hidden_size": 64}}}'

Dependabot Alert Management

The MCP server now includes Dependabot alert integration for conversational dependency management:

# Get all Dependabot alerts for a repository
curl -X POST http://localhost:3002/mcp \
  -H "Content-Type: application/json" \
  -d '{"operation": "get_dependabot_alerts", "params": {"repo_owner": "QueenFi703", "repo_name": "DREDGE-Cli"}}'

# Explain a specific alert with AI-powered recommendations
curl -X POST http://localhost:3002/mcp \
  -H "Content-Type: application/json" \
  -d '{"operation": "explain_dependabot_alert", "params": {"alert_id": 1, "repo_owner": "QueenFi703", "repo_name": "DREDGE-Cli"}}'

# Update an alert status (dismiss or reopen)
curl -X POST http://localhost:3002/mcp \
  -H "Content-Type: application/json" \
  -d '{
    "operation": "update_dependabot_alert",
    "params": {
      "alert_id": 1,
      "state": "dismissed",
      "dismissed_reason": "not_used",
      "dismissed_comment": "This dependency is not used in production"
    }
  }'

Note: Dependabot operations require a GITHUB_TOKEN environment variable with the security_events scope.

Available dismissed reasons:

• fix_started - A fix has already been started
• inaccurate - This alert is inaccurate or incorrect
• no_bandwidth - No bandwidth to fix this
• not_used - Dependency is not used
• tolerable_risk - Risk is tolerable

GitHub Codespaces

The repository includes .devcontainer/devcontainer.json configured to:

• Automatically forward ports 3001 and 3002 when running in GitHub Codespaces
• Fetch the full git repository history (unshallow the repository) for complete commit access

Swift Development

DREDGE includes a Swift CLI implementation with MCP client and String Theory support. You can develop using:

Xcode Workspace

open DREDGE-Cli.xcworkspace

Swift Package Manager

# Build from root
swift build
swift run dredge-cli

# Or build from swift/ directory
cd swift
swift build
swift run dredge-cli

Swift Features

The Swift implementation includes:

• String Theory Models - 10D superstring vibrational modes and energy calculations
• MCP Client - Connect to MCP server for model operations
• Unified Integration - Combine DREDGE insights, Quasimoto coordinates, and String Theory modes

See SWIFT_PACKAGE_GUIDE.md for detailed Swift development information.

Test

Run tests with pytest:

pip install -U pytest pytest

Development

• Edit code in src/dredge
• Update version in pyproject.toml
• Tag releases with v and push tags