An innovative tool 🚀 for real-time analysis and management of Issues' alerts. 🔍 It identifies alert causes, proposes actionable solutions, 💡and detailed reports. 📈 Designed for developers 👨💻, managers 📋, and IT teams .💻 Alert-menta enhances productivity and software quality. 🌟
We reduce the burden of system failure response using LLM.
You can receive support for failure handling that is completed within GitHub.
- Execute commands interactively in GitHub Issue comments:
describecommand to summarize the Issueanalysiscommand for root cause analysis of failures (in development)suggestcommand for proposing improvement measures for failuresaskcommand for asking additional questions
- Mechanism to improve response accuracy using RAG (in development)
- Selectable LLM models (OpenAI, VertexAI)
- Extensible prompt text
- Multilingual support
Alert-menta is intended to be run on GitHub Actions.
Prepare a GitHub PAT with the following permissions and register it in Secrets:
- repo
- workflow
Generate an API key and register it in Secrets.
Enable Vertex AI on Google Cloud. Alert-menta obtains access to VertexAI using Workload Identity Federation. Please see here for details.
Create the alert-menta configuration file in the root of the repository. For details, please see here.
There is a template available, so please use it.
For the method to bring monitoring alerts to Issues, please see this repository.
Execute commands on the Issue. Run commands with a backslash at the beginning (e.g., /describe). For the ask command, leave a space and enter the question (e.g., /ask What about the Next Action?). Alert-menta includes the text of the Issue in the prompt and sends it to the LLM, then posts the response as a comment on the Issue.
It contains information such as the LLM model to use, system prompt text for each command, etc. The .alert-menta.user.yaml in this repository is a template. The contents are as follows:
system:
debug:
log_level: debug
ai:
provider: "openai" # "openai" or "vertexai"
openai:
model: "gpt-3.5-turbo" # Check the list of available models by curl https://api.openai.com/v1/models -H "Authorization: Bearer $OPENAI_API_KEY"
vertexai:
project: "<YOUR_PROJECT_ID>"
location: "us-central1"
model: "gemini-1.5-flash-001"
commands:
- describe:
description: "Generate a detailed description of the Issue."
system_prompt: "The following is the GitHub Issue and comments on it. Please Generate a detailed description.\n"
- suggest:
description: "Provide suggestions for improvement based on the contents of the Issue."
system_prompt: "The following is the GitHub Issue and comments on it. Please identify the issues that need to be resolved based on the contents of the Issue and provide three suggestions for improvement.\n"
- ask:
description: "Answer free-text questions."
system_prompt: "The following is the GitHub Issue and comments on it. Based on the content, provide a detailed response to the following question:\n"
Specify the LLM to use with ai.provider.
You can change the system prompt with commands.{command}.system_prompt.
The .github/workflows/alert-menta.yaml in this repository is a template. The contents are as follows:
name: "Alert-Menta: Reacts to specific commands"
run-name: LLM responds to issues against the repository.🚀
on:
issue_comment:
types: [created]
jobs:
Alert-Menta:
if: (startsWith(github.event.comment.body, '/describe') || startsWith(github.event.comment.body, '/suggest') || startsWith(github.event.comment.body, '/ask')) && (github.event.comment.author_association == 'MEMBER' || github.event.comment.author_association == 'OWNER')
runs-on: ubuntu-22.04
permissions:
issues: write
contents: read
steps:
- name: Check out repository code
uses: actions/checkout@v4
- name: Download and Install alert-menta
run: |
curl -sLJO -H 'Accept: application/octet-stream' \
"https://${{ secrets.GH_TOKEN }}@api.github.com/repos/3-shake/alert-menta/releases/assets/$( \
curl -sL "https://${{ secrets.GH_TOKEN }}@api.github.com/repos/3-shake/alert-menta/releases/tags/v0.1.0" \
| jq '.assets[] | select(.name | contains("Linux_x86")) | .id')"
tar -zxvf alert-menta_Linux_x86_64.tar.gz
- name: Set Command
id: set_command
run: |
COMMENT_BODY="${{ github.event.comment.body }}"
if [[ "$COMMENT_BODY" == /ask* ]]; then
COMMAND=ask
INTENT=${COMMENT_BODY:5}
echo "INTENT=$INTENT" >> $GITHUB_ENV
elif [[ "$COMMENT_BODY" == /describe* ]]; then
COMMAND=describe
elif [[ "$COMMENT_BODY" == /suggest* ]]; then
COMMAND=suggest
fi
echo "COMMAND=$COMMAND" >> $GITHUB_ENV
- run: echo "REPOSITORY_NAME=${GITHUB_REPOSITORY#${GITHUB_REPOSITORY_OWNER}/}" >> $GITHUB_ENV
- name: Get user defined config file
id: user_config
if: hashFiles('.alert-menta.user.yaml') != ''
run: |
curl -H "Authorization: token ${{ secrets.GH_TOKEN }}" -L -o .alert-menta.user.yaml "https://raw.githubusercontent.com/${{ github.repository_owner }}/${{ env.REPOSITORY_NAME }}/main/.alert-menta.user.yaml" && echo "CONFIG_FILE=./.alert-menta.user.yaml" >> $GITHUB_ENV
- name: Add Comment
run: |
if [[ "$COMMAND" == "ask" ]]; then
./alert-menta -owner ${{ github.repository_owner }} -issue ${{ github.event.issue.number }} -repo ${{ env.REPOSITORY_NAME }} -github-token ${{ secrets.GH_TOKEN }} -api-key ${{ secrets.OPENAI_API_KEY }} -command $COMMAND -config $CONFIG_FILE -intent "$INTENT"
else
./alert-menta -owner ${{ github.repository_owner }} -issue ${{ github.event.issue.number }} -repo ${{ env.REPOSITORY_NAME }} -github-token ${{ secrets.GH_TOKEN }} -api-key ${{ secrets.OPENAI_API_KEY }} -command $COMMAND -config $CONFIG_FILE
fi
Configure Workload Identity Federation with reference to the documentation.
In an environment where Golang can be executed, clone the repository and run it as follows:
go run ./cmd/main.go -repo <repository> -owner <owner> -issue <issue-number> -github-token $GITHUB_TOKEN -api-key $OPENAI_API_KEY -command <describe, etc.> -config <User_defined_config_file>
We welcome you. Please submit pull requests to the develop branch. See Branch strategy for more information.