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new CI sync with new eval func CI
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‎.github/ISSUE_TEMPLATE/release-request.yml‎

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name: "Release Request"
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description: "Request a production deployment by specifying the chat function, changes, target branch, and version bump."
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title: "Release Request"
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labels:
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- release
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- deployment
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assignees: []
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body:
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- type: textarea
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id: description_of_changes
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attributes:
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label: Description of changes
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description: "Summarize what is changing and why. Include links to PRs, issues, or changelogs."
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placeholder: |
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- What changed:
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- Why:
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- Related PRs/Issues: #123, #456
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render: markdown
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validations:
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required: true
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- type: input
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id: branch_to_deploy
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attributes:
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label: Branch to deploy
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description: |
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Specify the branch to deploy.
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placeholder: "e.g., main"
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validations:
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required: true
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- type: dropdown
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id: version-bump
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attributes:
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label: "🚀 What kind of update is this?"
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description: "Tell us how significant this change is. This helps us set the correct new version number."
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options:
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- "Patch: A small fix for a bug. It won't break anything for existing users. (e.g., 1.2.3 ➔ 1.2.4)"
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- "Minor: Adds a new feature, but doesn't change how existing ones work. A safe update. (e.g., 1.2.3 ➔ 1.3.0)"
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- "Major: A big change that alters existing features. Users may need to update their work to adapt. (e.g., 1.2.3 ➔ 2.0.0)"
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default: 0
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validations:
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required: true
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- type: markdown
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attributes:
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value: |
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---
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### ⚡ Click the Link Below to Run the Workflow
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Clicking the link will take you to the Actions page. You will need to click the **"Run workflow"** button there to start the process.
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## [➡️ Go to Workflow Run Page](../actions/workflows/production-deploy.yml)

‎.github/workflows/main.yml‎

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This file was deleted.

‎.github/workflows/production-deploy.yml‎

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name: Deploy to Production
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on:
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workflow_dispatch:
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inputs:
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version-bump:
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description: "Version bump type"
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required: true
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type: choice
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options:
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- patch
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- minor
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- major
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default: patch
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branch:
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description: "Branch to release from"
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required: true
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type: string
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default: "main"
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jobs:
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deploy:
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name: Deploy to Lambda Feedback
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permissions:
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contents: write
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packages: write
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id-token: write
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uses: lambda-feedback/chat-function-workflows/.github/workflows/deploy.yml@main
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with:
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template-repository-name: "lambda-feedback/chat-function-boilerplate"
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environment: "production"
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version-bump: ${{ inputs.version-bump }}
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branch: ${{ inputs.branch }}
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deployed-environment-variables: '[\"OPENAI_API_KEY\",\"OPENAI_MODEL\",\"GOOGLE_AI_API_KEY\",\"GOOGLE_AI_MODEL\"]'
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secrets:
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aws-key-id: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_ID }}
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aws-secret-key: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_SECRET }}
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function-admin-api-key: ${{ secrets.FUNCTION_ADMIN_API_KEY }}

.github/workflows/dev.yml renamed to .github/workflows/staging-deploy.yml

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name: Dev deployment of chatbot lambda function
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name: Deploy to Staging
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on:
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push:
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branches: [dev]
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branches: [main]
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workflow_dispatch:
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jobs:
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test:
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name: Dev deployment of chatbot lambda function
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name: Test
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runs-on: ubuntu-latest
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permissions:
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contents: read
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actions: read
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checks: write
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pull-requests: write
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strategy:
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fail-fast: false
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matrix:
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with:
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python-version: ${{ matrix.python-version }}
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# - name: Load cached venv
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# id: dependencies-cache
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# uses: actions/cache@v3
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# with:
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# path: .venv
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# key: venv-${{ runner.os }}-${{ steps.python-setup.outputs.python-version }}
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- name: Create Venv if Cache not found
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# if: steps.dependencies-cache.outputs.cache-hit != 'true'
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run: |
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python -m venv .venv
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- name: Create Venv
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run: python -m venv .venv
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- name: Install dependencies
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# if: steps.dependencies-cache.outputs.cache-hit != 'true'
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run: |
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pip install --upgrade pip
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pip install -r requirements.txt
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path: ./reports/pytest.xml
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if-no-files-found: warn
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build:
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name: Build Docker Image
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uses: lambda-feedback/chat-function-workflows/.github/workflows/gh_build.yml@main
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needs: test
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permissions:
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contents: read
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id-token: write
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packages: write
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deploy:
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name: Deploy to Lambda Feedback
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uses: lambda-feedback/chat-function-workflows/.github/workflows/dev_deploy.yml@main
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needs: test
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permissions:
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contents: write
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packages: write
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id-token: write
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uses: lambda-feedback/chat-function-workflows/.github/workflows/deploy.yml@main
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with:
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template-repository-name: "lambda-feedback/chat-function-boilerplate"
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# allow for developer to specify the environment variables that are used by the deployed AWS Lambda. Default to mock then admin can update.
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environment: "staging"
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deployed-environment-variables: '[\"OPENAI_API_KEY\",\"OPENAI_MODEL\",\"GOOGLE_AI_API_KEY\",\"GOOGLE_AI_MODEL\"]'
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permissions:
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contents: read
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id-token: write
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packages: write
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secrets:
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aws-access-key-id: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_ID }}
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aws-secret-key: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_SECRET}}
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aws-key-id: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_ID }}
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aws-secret-key: ${{ secrets.LAMBDA_CONTAINER_PIPELINE_AWS_SECRET }}
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function-admin-api-key: ${{ secrets.FUNCTION_ADMIN_API_KEY }}

‎.github/workflows/test-lint.yml‎

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name: Test and Lint
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on:
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pull_request:
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jobs:
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test:
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name: Test
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runs-on: ubuntu-latest
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permissions:
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contents: read
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actions: read
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checks: write
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pull-requests: write
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.13"]
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env:
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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OPENAI_MODEL: ${{ vars.OPENAI_MODEL }}
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GOOGLE_AI_API_KEY: ${{ secrets.GOOGLE_AI_API_KEY }}
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GOOGLE_AI_MODEL: ${{ vars.GOOGLE_AI_MODEL }}
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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id: python-setup
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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- name: Create Venv
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run: python -m venv .venv
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- name: Install dependencies
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run: |
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pip install --upgrade pip
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pip install -r requirements.txt
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pip install flake8
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- name: Lint with flake8
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run: |
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# Fail on syntax errors / undefined names
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flake8 ./src ./index.py --count --select=E9,F63,F7,F82 --show-source --statistics
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# Style report (non-blocking)
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flake8 ./src ./index.py --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
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- name: Run tests
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if: always()
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run: |
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source .venv/bin/activate
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export PYTHONPATH=$PYTHONPATH:.
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pytest --junit-xml=./reports/pytest.xml --tb=auto -v
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- name: Upload test results
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uses: actions/upload-artifact@v4
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if: always()
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with:
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name: test-results-${{ matrix.python-version }}
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path: ./reports/pytest.xml
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if-no-files-found: warn

‎.github/workflows/test-report.yml‎

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on:
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workflow_run:
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workflows: ["Build, Test and Deploy"]
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workflows: ["Deploy to Staging", "Deploy to Production", "Test and Lint"]
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types:
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- completed
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‎AGENTS.md‎

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## Project Overview
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This is a boilerplate for creating AI educational chatbots that integrate with the **Lambda-Feedback** educational platform. It deploys as an AWS Lambda function (containerized via Docker) that receives student chat messages with educational context and returns LLM-powered chatbot responses.
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This is a boilerplate for creating AI educational chatbots that integrate with the **Lambda-Feedback** educational platform. It deploys as an AWS Lambda function (containerized via Docker) that receives student chat messages with educational context and returns LLM-powered chatbot responses. Incoming requests follow the [muEd API](https://mued.org/) schema (`context`, `user`, `messages`).
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## Commands
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```bash
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curl -X POST http://localhost:8080/2015-03-31/functions/function/invocations \
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-H 'Content-Type: application/json' \
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-d '{"body":"{\"conversationId\": \"12345Test\", \"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}], \"user\": {\"type\": \"LEARNER\"}}"}'
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-d '{"body":"{\"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}]}"}'
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```
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**Run a single test:**
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## Deployment
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- Pushing to `dev` branch triggers the dev deployment GitHub Actions workflow
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- Pushing to `main` triggers staging deployment, with manual approval required for production
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- Pull requests: `.github/workflows/test-lint.yml` runs pytest only
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- Pushing to `main`: `.github/workflows/staging-deploy.yml` runs tests then deploys to AWS staging via the shared `lambda-feedback/chat-function-workflows` reusable workflows
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- Production: `.github/workflows/production-deploy.yml` is `workflow_dispatch`-only with a `version-bump` input; redeploys staging, pauses on the `production-override` GitHub Environment for manual approval, then creates a `vX.Y.Z` tag + GitHub Release and deploys to prod
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- All environment variables (API keys, model names) are injected via GitHub Actions secrets/variables — do not hardcode them

‎CLAUDE.md‎

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## Project Overview
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This is a boilerplate for creating AI educational chatbots that integrate with the **Lambda-Feedback** educational platform. It deploys as an AWS Lambda function (containerized via Docker) that receives student chat messages with educational context and returns LLM-powered chatbot responses.
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This is a boilerplate for creating AI educational chatbots that integrate with the **Lambda-Feedback** educational platform. It deploys as an AWS Lambda function (containerized via Docker) that receives student chat messages with educational context and returns LLM-powered chatbot responses. Incoming requests follow the [muEd API](https://mued.org/) schema (`context`, `user`, `messages`).
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## Commands
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```bash
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curl -X POST http://localhost:8080/2015-03-31/functions/function/invocations \
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-H 'Content-Type: application/json' \
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-d '{"body":"{\"conversationId\": \"12345Test\", \"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}], \"user\": {\"type\": \"LEARNER\"}}"}'
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-d '{"body":"{\"messages\": [{\"role\": \"USER\", \"content\": \"hi\"}]}"}'
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```
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**Run a single test:**
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## Deployment
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- Pushing to `dev` branch triggers the dev deployment GitHub Actions workflow
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- Pushing to `main` triggers staging deployment, with manual approval required for production
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- Pull requests: `.github/workflows/test-lint.yml` runs pytest only
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- Pushing to `main`: `.github/workflows/staging-deploy.yml` runs tests then deploys to AWS staging via the shared `lambda-feedback/chat-function-workflows` reusable workflows
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- Production: `.github/workflows/production-deploy.yml` is `workflow_dispatch`-only with a `version-bump` input; redeploys staging, pauses on the `production-override` GitHub Environment for manual approval, then creates a `vX.Y.Z` tag + GitHub Release and deploys to prod
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- All environment variables (API keys, model names) are injected via GitHub Actions secrets/variables — do not hardcode them

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