设置 CI/CD

本页介绍如何设置持续集成 (CI) 和持续部署 (CD) 管道。

概述

持续集成 (CI) 是频繁合并代码更改并在提交更改时自动测试更改的做法。持续部署 (CD) 意味着自动将集成更改部署到预发布或生产环境。两者结合，让你可以更频繁地改进对话机器人，并高效地测试和部署这些更改。

本指南将介绍特定于 Rasa 项目的 CI/CD 管道中应包含的内容。如何实施该管道取决于你。市面上有许多 CI/CD 工具，例如 GitHub Actions、GitLab CI/CD、Jenkins 和 CircleCI。我们建议选择与你使用的任何 Git 存储库集成的工具。

示例 Rasa Pro CI/CD 管道

Github Actions 工作流程示例如下所示：

Rasa Pro >= 3.8.xRasa Pro <=3.7.x

name: Rasa Pro CI/CD Pipeline

on:
  push:
    branches:
      - main
    paths:
      - 'data/**'
      - 'domain/**'

jobs:
  train_test_deploy:
    runs-on: ubuntu-latest

    steps:
      - name: Checkout repository
        uses: actions/checkout@v3

      # Make sure to use the exact same Rasa version as in your TEST/PROD environment
      - name: Pull Rasa Pro image
        run: |
          docker pull europe-west3-docker.pkg.dev/rasa-releases/rasa-plus/rasa-plus:3.7.0-latest

      # Store the Rasa Pro license string as a secret RASA_PRO_LICENSE
      - name: Train Rasa model with Rasa Pro
        run: |
          docker run -v ${GITHUB_WORKSPACE}:/app \
            -e OPENAI_API_KEY=${OPENAI_API_KEY} \
            -e RASA_PRO_LICENSE=${RASA_PRO_LICENSE} \
            -e RASA_TELEMETRY_ENABLED=false \
            europe-west3-docker.pkg.dev/rasa-releases/rasa-plus/rasa-plus:3.7.0-latest \
            train --domain /app/domain --data /app/data --out /app/models
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          RASA_PRO_LICENSE: ${{ secrets.RASA_PRO_LICENSE }}

      # Make sure to use your action server image and point rasa to the folder with your e2e tests (e.g. tests/e2e_test_cases.yml)
      # Use the same Rasa version as in the previous step
      # Your endpoints file should contain the action server url (e.g. http://action_server:5055/webhook)
      - name: Start Action Server and Test Rasa model with E2E tests
        run: |
          # Start the action server in the background
          docker run -d -p 5055:5055 --name action_server <YOUR-ACTION-SERVER-IMAGE>

          # Wait for the action server to become ready with a timeout
          echo "Waiting for action server to be ready..."
          timeout=60
          while ! curl --output /dev/null --silent --fail http://localhost:5055/health; do
            printf '.'
            sleep 5
            timeout=$((timeout-5))
            if [ "$timeout" -le 0 ]; then
              echo "Action server did not become ready in time."
              echo "Action server logs:"
              docker logs action_server
              exit 1
            fi
          done
          echo "Action server is ready."

          # Run E2E tests in the same step to ensure the action server is still running
          # Use container linking to allow communication
          docker run -v ${GITHUB_WORKSPACE}:/app \
            --link action_server:action_server \
            -e OPENAI_API_KEY=${OPENAI_API_KEY} \
            -e RASA_PRO_LICENSE=${RASA_PRO_LICENSE} \
            -e RASA_TELEMETRY_ENABLED=false \
            europe-west3-docker.pkg.dev/rasa-releases/rasa-plus/rasa-plus:3.7.0-latest \
            test e2e /app/tests/e2e_test_cases.yml --model /app/models --endpoints /app/endpoints.yml
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          RASA_PRO_LICENSE: ${{ secrets.RASA_PRO_LICENSE }}

      # if tests fail, next steps won't be executed and model won't be uploaded to the bucket
      # Authenticate to Google Cloud again to avoid permission issues and use a service account with Storage Object Creator role
      - name: Authenticate to Google Cloud
        uses: google-github-actions/[email protected]
        with:
          credentials_json: '${{ secrets.GCP_SA_KEY }}'
      - name: Configure Google Cloud project
        run: |
          echo $GCP_SA_KEY | gcloud auth activate-service-account --key-file=-
        env:
          GCP_SA_KEY: ${{ secrets.GCP_SA_KEY }}
      # Upload the trained model to the Model Storage bucket associated with     your deployment
      - name: Upload model to Google Cloud Storage
        run: |
          gsutil cp -r models/* gs://my-model-storage/

name: Rasa Pro CI/CD Pipeline

on:
  push:
    branches:
      - main
    paths:
      - 'data/**'
      - 'domain/**'

jobs:
  train_test_deploy:
    runs-on: ubuntu-latest

    steps:
      - name: Checkout repository
        uses: actions/checkout@v3

      # Make sure to use the exact same Rasa version as in your TEST/PROD environment
      - name: Pull Rasa Pro image
        run: |
          docker pull europe-west3-docker.pkg.dev/rasa-releases/rasa-pro/rasa-pro:3.8.0-latest

      # Store the Rasa Pro license string as a secret RASA_PRO_LICENSE
      - name: Train Rasa model with Rasa Pro
        run: |
          docker run -v ${GITHUB_WORKSPACE}:/app \
            -e OPENAI_API_KEY=${OPENAI_API_KEY} \
            -e RASA_PRO_LICENSE=${RASA_PRO_LICENSE} \
            -e RASA_TELEMETRY_ENABLED=false \
            europe-west3-docker.pkg.dev/rasa-releases/rasa-pro/rasa-pro:3.8.0-latest \
            train --domain /app/domain --data /app/data --out /app/models
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          RASA_PRO_LICENSE: ${{ secrets.RASA_PRO_LICENSE }}

      # Make sure to use your action server image and point rasa to the folder with your e2e tests (e.g. tests/e2e_test_cases.yml)
      # Use the same Rasa version as in the previous step
      # Your endpoints file should contain the action server url (e.g. http://action_server:5055/webhook)
      - name: Start Action Server and Test Rasa model with E2E tests
        run: |
          # Start the action server in the background
          docker run -d -p 5055:5055 --name action_server <YOUR-ACTION-SERVER-IMAGE>

          # Wait for the action server to become ready with a timeout
          echo "Waiting for action server to be ready..."
          timeout=60
          while ! curl --output /dev/null --silent --fail http://localhost:5055/health; do
            printf '.'
            sleep 5
            timeout=$((timeout-5))
            if [ "$timeout" -le 0 ]; then
              echo "Action server did not become ready in time."
              echo "Action server logs:"
              docker logs action_server
              exit 1
            fi
          done
          echo "Action server is ready."

          # Run E2E tests in the same step to ensure the action server is still running
          # Use container linking to allow communication
          docker run -v ${GITHUB_WORKSPACE}:/app \
            --link action_server:action_server \
            -e OPENAI_API_KEY=${OPENAI_API_KEY} \
            -e RASA_PRO_LICENSE=${RASA_PRO_LICENSE} \
            -e RASA_TELEMETRY_ENABLED=false \
            europe-west3-docker.pkg.dev/rasa-releases/rasa-pro/rasa-pro:3.8.0-latest \
            test e2e /app/tests/e2e_test_cases.yml --model /app/models --endpoints /app/endpoints.yml
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
          RASA_PRO_LICENSE: ${{ secrets.RASA_PRO_LICENSE }}

      # if tests fail, next steps won't be executed and model won't be uploaded to the bucket
      # Authenticate to Google Cloud again to avoid permission issues and use a service account with Storage Object Creator role
      - name: Authenticate to Google Cloud
        uses: google-github-actions/[email protected]
        with:
          credentials_json: '${{ secrets.GCP_SA_KEY }}'
      - name: Configure Google Cloud project
        run: |
          echo $GCP_SA_KEY | gcloud auth activate-service-account --key-file=-
        env:
          GCP_SA_KEY: ${{ secrets.GCP_SA_KEY }}
      # Upload the trained model to the Model Storage bucket associated with your deployment
      - name: Upload model to Google Cloud Storage
        run: |
          gsutil cp -r models/* gs://my-model-storage/