Create Coderabbit.yml #213
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Yes! Below is the updated BrainRabbit.yml file, now configured to use GitHub.com/bearycool11/pmll_knowledge_block.git as the primary repository. This ensures the workflow pulls directly from your PMLL knowledge block for integration into the Persistent Memory Logic Loop (PMLL) and CodeRabbitAI Azure setup.
Updated BrainRabbit.yml
name: BrainRabbit-CI/CD
on:
push:
branches:
- main
pull_request:
branches:
- main
jobs:
rabbit-build:
name: Build Rabbit Protocol and Modular Brain Components
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ubuntu-latest, windows-2025, macos-15]
steps:
- name: Checkout Code
uses: actions/checkout@v3
with:
repository: bearycool11/pmll_knowledge_block
- name: Setup Environment
run: |
if [[ $RUNNER_OS == 'Linux' ]]; then
sudo apt-get update && sudo apt-get install -y docker.io clang
elif [[ $RUNNER_OS == 'macOS' ]]; then
brew install golang docker clang
else
choco install golang docker-desktop
./install_docker_ce.ps1
fi
- name: Install Dependencies
run: |
go mod tidy
go mod vendor
- name: Build Modular Brain Components
run: |
gcc brainOrgan.c -o modular_brain_executable
gcc ears.c -o inner_ear_module
gcc pml_logic_loop.c -o logic_module
- name: Build Rabbit Protocol Components
run: |
go build -o rabbit_protocol_clang cmd/main.go
- name: Save Build Artifacts
uses: actions/upload-artifact@v3
with:
name: build-artifacts
path: |
modular_brain_executable
inner_ear_module
logic_module
rabbit_protocol_clang
rabbit-run:
name: Test Modular Brain and Rabbit Protocol
runs-on: ${{ matrix.os }}
needs: rabbit-build
strategy:
matrix:
os: [ubuntu-latest, windows-2025, macos-15]
steps:
- name: Checkout Code
uses: actions/checkout@v3
with:
repository: bearycool11/pmll_knowledge_block
- name: Run Tests
run: |
./modular_brain_executable --test
./logic_module --run-tests
./inner_ear_module --validate
if [[ $RUNNER_OS == 'Linux' || $RUNNER_OS == 'macOS' ]]; then
docker run --rm rabbit_protocol_clang
else
docker run --rm rabbit_protocol_clang.exe
persistent-memory-loop:
name: Persistent Memory Logic Loop (PMLL) Integration
runs-on: ubuntu-latest
needs: rabbit-run
steps:
- name: Checkout Code
uses: actions/checkout@v3
with:
repository: bearycool11/pmll_knowledge_block
- name: Set Up Persistent Memory Logic Loop
run: |
./pmll_setup.sh --init
./pmll_train.sh --dataset cognitive_graphs.json
./pmll_test.sh --validate-memory
- name: Log Memory Loop Activity
run: ./pmll_logger.sh --log
bugzap-pesterbot:
name: Scan and Fix Rogue Code
runs-on: ubuntu-latest
needs: persistent-memory-loop
steps:
- name: Scan for Rogue Code
run: grep -r "pesterbot" ./cmd || echo "No rogue code found"
- name: Remove Rogue Code
run: sed -i '/pesterbot/d' ./cmd/main.go
azure-coderabbitai:
name: Set Up and Sync Azure for CodeRabbitAI
runs-on: ubuntu-latest
needs: bugzap-pesterbot
steps:
- name: Checkout Code
uses: actions/checkout@v3
with:
repository: bearycool11/pmll_knowledge_block
- name: Login to Azure
run: |
az login --service-principal --username $AZURE_USER --password $AZURE_PASSWORD --tenant $AZURE_TENANT
- name: Create CodeRabbitAI Database
run: |
az storage account create --name coderabbitaiStorage --resource-group CodeRabbitAI --location EastUS --sku Standard_LRS
az storage container create --account-name coderabbitaiStorage --name neural-mem-cache
az storage container create --account-name coderabbitaiStorage --name task-execution-data
- name: Deploy CodeRabbitAI AI Model
run: |
az ml model deploy --name rabbitai-pmll --resource-group CodeRabbitAI --workspace-name AIWorkspace \
--model-id rabbitai-pmll:v1 --compute-target AzureMLCompute
- name: Validate CodeRabbitAI Deployment
run: ./validate_coderabbitai.sh
package-toolbelt:
name: Package and Publish Toolbelt
runs-on: ubuntu-latest
needs: azure-coderabbitai
steps:
- name: Download Build Artifacts
uses: actions/download-artifact@v3
with:
name: build-artifacts
- name: Build Docker Image
run: docker build -t modular_brain_toolbelt:latest .
- name: Push Docker Image to Registry
run: |
docker tag modular_brain_toolbelt:latest ghcr.io/bearycool11/modular_brain_toolbelt:latest
docker push ghcr.io/bearycool11/modular_brain_toolbelt:latest
Key Enhancements with bearycool11/pmll_knowledge_block
1. Repository Integration:
• All jobs now use bearycool11/pmll_knowledge_block.git as the primary repo.
• Ensures direct PMLL access for CodeRabbitAI tasks.
2. PMLL Memory Loop Integration:
• persistent-memory-loop job ensures continuous memory updates.
• Automated PMLL validation for CodeRabbitAI.
3. Seamless Azure AI Model Deployment:
• Deploys rabbitai-pmll AI model within CodeRabbitAI Azure workspace.
• Includes task-execution-data storage.
4. Optimized CI/CD Flow:
• Ensures memory loop is trained, validated, and deployed before final packaging.
• Final Docker image is pushed to ghcr.io/bearycool11/modular_brain_toolbelt:latest.
Expected Benefits
✅ Direct PMLL integration from your GitHub repository
✅ Real-time memory training for CodeRabbitAI’s AI model
✅ Seamless Azure setup with memory-cached task execution
✅ Optimized for long-term AI memory retention and scalability
Would you like email notifications or Slack alerts on failures for better monitoring?
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bearycool11
commented
Feb 2, 2025
There was a problem hiding this comment.
For more information on brainOrgan.c/.h please go to the repo GitHub.com/bearycool11/pmll_knowledge_block it’s basically just a Short Term Memory logic loop that runs memory data through an AI’s brain/GPU to process so that it uses less GPU energy and tokens when it goes into the LTM gradient.
minor typo
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Below is the updated BrainRabbit.yml file, now configured to use GitHub.com/bearycool11/pmll_knowledge_block.git as the primary repository. This ensures the workflow pulls directly from your PMLL knowledge block for integration into the Persistent Memory Logic Loop (PMLL) and CodeRabbitAI Azure setup.
Updated BrainRabbit.yml to coderabbit.yml. This can be easily renamed back to Brain rabbit.yml if that provides cleaner documentation for you @coderabbitai
Expected Benefits
✅ Direct PMLL integration from your GitHub repository ✅ Real-time memory training for CodeRabbitAI’s AI model ✅ Seamless Azure setup with memory-cached task execution ✅ Optimized for long-term AI memory retention and scalability
Would you like email notifications or Slack alerts on failures for better monitoring?