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course samples","2026-11-10 10:12:30",false,"---\nname: testing-course-samples\n---\n# 測試課程範例\n\n驗證課程筆記本和程式碼範例是否可在即時的\nMicrosoft Foundry \u002F Azure OpenAI 環境中執行。此倉庫包含一個執行器，位置為\n[`scripts\u002Fvalidate-notebooks.ps1`](..\u002F..\u002F..\u002F..\u002F..\u002Fscripts\u002Fvalidate-notebooks.ps1)，\n該執行器會以無頭模式執行所有 Python 筆記本並列印通過\u002F失敗矩陣。\n\n## 何時使用\n- 「驗證所有筆記本 \u002F 範例是否可在我的 Azure 訂閱中運行。」\n- 「在升級套件或更改模型後，對課程進行煙霧測試。」\n- 「哪些課程仍然可通過 \u002F 失敗於實際環境？」\n\n不要用於 AI 煙霧測試 GitHub Action（該 Action 驗證 \u003Cem>已部署\u003C\u002Fem> 的托管代理 — 請參閱 [`tests\u002FREADME.md`](..\u002F..\u002F..\u002Ftests\u002FREADME.md)）。此工具\n在本地端運行筆記本。\n\n\n## 先決條件（先檢查）\n1. **Python 3.12+** 及課程依賴：`python -m pip install -r requirements.txt`\n   外加執行環境：`python -m pip install nbconvert ipykernel`。\n2. **倉庫根目錄有 `.env`**（複製自 [`.env.example`](..\u002F..\u002F..\u002F..\u002F..\u002F.env.example)）並至少包含：\n   - `AZURE_AI_PROJECT_ENDPOINT` — Foundry 專案端點\n     (`https:\u002F\u002F\u003Caccount>.services.ai.azure.com\u002Fapi\u002Fprojects\u002F\u003Cproject>`)\n   - `AZURE_AI_MODEL_DEPLOYMENT_NAME` — 非棄用的部署名稱（例如 `gpt-5-mini`）\n   - `AZURE_OPENAI_ENDPOINT` (`https:\u002F\u002F\u003Caccount>.openai.azure.com`) 與 `AZURE_OPENAI_DEPLOYMENT`\n     用於直接呼叫 Azure OpenAI 的課程（第 06 課、02-azure-openai、14 handoff\u002Fhuman-loop）。\n3. 完成 **`az login`** — 範例使用 `AzureCliCredential` 認證（Entra ID、無需金鑰）。\n4. 確認模型部署存在：\n   `az cognitiveservices account deployment list -g \u003Crg> -n \u003Caccount> -o table`。\n\n## 執行驗證\n```powershell\n# 所有 Python 筆記本（跳過 .NET、.venv、site-packages、翻譯、技能資產）\npwsh scripts\u002Fvalidate-notebooks.ps1\n\n# 一個單一課程，每個儲存格較長的逾時時間\npwsh scripts\u002Fvalidate-notebooks.ps1 -Filter '08-*' -Timeout 600\n\n# 只列出會執行的內容（不執行）\npwsh scripts\u002Fvalidate-notebooks.ps1 -List\n\n# 明確指定解譯器（如果 `python` 不在 PATH，例如 Windows Store 別名）\npwsh scripts\u002Fvalidate-notebooks.ps1 -Python \"C:\u002Fpath\u002Fto\u002Fpython.exe\"\n```\n此腳本將執行結果的複本、筆記本日誌及 `results.json` 寫入\n`$env:TEMP\\aiab-nbval`，並以失敗數量作為退出碼結束。\n\n短暫失敗（共用訂閱 HTTP 429 限速、偶發的\n`AzureCliCredential` 權杖問題或逾時）會自動重試\n（`-Retries`，預設 2 次，並使用 `-RetryDelaySeconds` 延遲，預設 20 秒）。若\n模型部署經常出現 429，請檢查訂閱之 GlobalStandard\nTPM 配額（`az cognitiveservices usage list -l \u003Cregion>`）— 單一部署增量無助於當 \u003Cem>訂閱\u003C\u002Fem> 配額用盡時。\n\n\n## 解讀結果\n- `PASS` — 筆記本自始至終無任何單元錯誤。\n- `FAIL` — 顯示第一條 `*Error` \u002F `*Exception` 訊息；完整錯誤追蹤請開啟輸出目錄中的\n  `log_*.txt` 檔案。\n- 單一筆記本的失敗受 `-Timeout`（每單元）限制，故掛起的人類互動單元將顯示為 `StdinNotImplementedError` 而非無限掛起。\n\n\n## 需要額外資源（缺少時預期失敗）的課程\n| 課程 | 額外需求 |\n|--------|-------------------|\n| 05 Agentic RAG | Azure AI Search (`AZURE_SEARCH_SERVICE_ENDPOINT`、金鑰) — 有記憶體內備援路徑 |\n| 11 MCP \u002F GitHub | GitHub MCP 伺服器 + PAT |\n| 13 memory (cognee) | 已配置的 `cognee` 模型提供者 |\n| 15 browser-use | 安裝 Playwright 瀏覽器（`playwright install`）+ `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME` |\n| 17 local agent | Foundry 本地執行環境 + 下載的 Qwen 模型（本機端，無雲端） |\n| `*-dotnet-*` 筆記本 | .NET Interactive 核心（預設排除，使用 `-IncludeDotnet` 引入） |\n\n## 回報結果\n以課程為群組匯總成通過\u002F失敗表。將真正的回歸問題\n（需修正的程式碼／設定錯誤）與環境缺口（缺少 Search\u002FFoundry Local\u002FPAT）\n分開，並對每個真實失敗附上失敗的 `log_*.txt` 檔案。\n\n---\n\n\u003C!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**免責聲明**：\n本文件由 AI 翻譯服務 [Co-op Translator](https:\u002F\u002Fgithub.com\u002FAzure\u002Fco-op-translator) 翻譯而成。雖然我們致力於確保準確性，但請注意，機器自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要資訊，建議進行專業人工翻譯。我們不對因使用本翻譯而產生的任何誤解或誤釋承擔責任。\n\u003C!-- CO-OP TRANSLATOR DISCLAIMER END -->","# 測試課程範例\n\n驗證課程筆記本和程式碼範例是否可在即時的\nMicrosoft Foundry \u002F Azure OpenAI 環境中執行。此倉庫包含一個執行器，位置為\n[`scripts\u002Fvalidate-notebooks.ps1`](..\u002F..\u002F..\u002F..\u002F..\u002Fscripts\u002Fvalidate-notebooks.ps1)，\n該執行器會以無頭模式執行所有 Python 筆記本並列印通過\u002F失敗矩陣。\n\n## 何時使用\n- 「驗證所有筆記本 \u002F 範例是否可在我的 Azure 訂閱中運行。」\n- 「在升級套件或更改模型後，對課程進行煙霧測試。」\n- 「哪些課程仍然可通過 \u002F 失敗於實際環境？」\n\n不要用於 AI 煙霧測試 GitHub Action（該 Action 驗證 \u003Cem>已部署\u003C\u002Fem> 的托管代理 — 請參閱 [`tests\u002FREADME.md`](..\u002F..\u002F..\u002Ftests\u002FREADME.md)）。此工具\n在本地端運行筆記本。\n\n\n## 先決條件（先檢查）\n1. **Python 3.12+** 及課程依賴：`python -m pip install -r requirements.txt`\n   外加執行環境：`python -m pip install nbconvert ipykernel`。\n2. **倉庫根目錄有 `.env`**（複製自 [`.env.example`](..\u002F..\u002F..\u002F..\u002F..\u002F.env.example)）並至少包含：\n   - `AZURE_AI_PROJECT_ENDPOINT` — Foundry 專案端點\n     (`https:\u002F\u002F\u003Caccount>.services.ai.azure.com\u002Fapi\u002Fprojects\u002F\u003Cproject>`)\n   - `AZURE_AI_MODEL_DEPLOYMENT_NAME` — 非棄用的部署名稱（例如 `gpt-5-mini`）\n   - `AZURE_OPENAI_ENDPOINT` (`https:\u002F\u002F\u003Caccount>.openai.azure.com`) 與 `AZURE_OPENAI_DEPLOYMENT`\n     用於直接呼叫 Azure OpenAI 的課程（第 06 課、02-azure-openai、14 handoff\u002Fhuman-loop）。\n3. 完成 **`az login`** — 範例使用 `AzureCliCredential` 認證（Entra ID、無需金鑰）。\n4. 確認模型部署存在：\n   `az cognitiveservices account deployment list -g \u003Crg> -n \u003Caccount> -o table`。\n\n## 執行驗證\n```powershell\n# 所有 Python 筆記本（跳過 .NET、.venv、site-packages、翻譯、技能資產）\npwsh scripts\u002Fvalidate-notebooks.ps1\n\n# 一個單一課程，每個儲存格較長的逾時時間\npwsh scripts\u002Fvalidate-notebooks.ps1 -Filter '08-*' -Timeout 600\n\n# 只列出會執行的內容（不執行）\npwsh scripts\u002Fvalidate-notebooks.ps1 -List\n\n# 明確指定解譯器（如果 `python` 不在 PATH，例如 Windows Store 別名）\npwsh scripts\u002Fvalidate-notebooks.ps1 -Python \"C:\u002Fpath\u002Fto\u002Fpython.exe\"\n```\n此腳本將執行結果的複本、筆記本日誌及 `results.json` 寫入\n`$env:TEMP\\aiab-nbval`，並以失敗數量作為退出碼結束。\n\n短暫失敗（共用訂閱 HTTP 429 限速、偶發的\n`AzureCliCredential` 權杖問題或逾時）會自動重試\n（`-Retries`，預設 2 次，並使用 `-RetryDelaySeconds` 延遲，預設 20 秒）。若\n模型部署經常出現 429，請檢查訂閱之 GlobalStandard\nTPM 配額（`az cognitiveservices usage list -l \u003Cregion>`）— 單一部署增量無助於當 \u003Cem>訂閱\u003C\u002Fem> 配額用盡時。\n\n\n## 解讀結果\n- `PASS` — 筆記本自始至終無任何單元錯誤。\n- `FAIL` — 顯示第一條 `*Error` \u002F `*Exception` 訊息；完整錯誤追蹤請開啟輸出目錄中的\n  `log_*.txt` 檔案。\n- 單一筆記本的失敗受 `-Timeout`（每單元）限制，故掛起的人類互動單元將顯示為 `StdinNotImplementedError` 而非無限掛起。\n\n\n## 需要額外資源（缺少時預期失敗）的課程\n| 課程 | 額外需求 |\n|--------|-------------------|\n| 05 Agentic RAG | Azure AI Search (`AZURE_SEARCH_SERVICE_ENDPOINT`、金鑰) — 有記憶體內備援路徑 |\n| 11 MCP \u002F GitHub | GitHub MCP 伺服器 + PAT |\n| 13 memory (cognee) | 已配置的 `cognee` 模型提供者 |\n| 15 browser-use | 安裝 Playwright 瀏覽器（`playwright install`）+ `AZURE_OPENAI_CHAT_DEPLOYMENT_NAME` |\n| 17 local agent | Foundry 本地執行環境 + 下載的 Qwen 模型（本機端，無雲端） |\n| `*-dotnet-*` 筆記本 | .NET Interactive 核心（預設排除，使用 `-IncludeDotnet` 引入） |\n\n## 回報結果\n以課程為群組匯總成通過\u002F失敗表。將真正的回歸問題\n（需修正的程式碼／設定錯誤）與環境缺口（缺少 Search\u002FFoundry Local\u002FPAT）\n分開，並對每個真實失敗附上失敗的 `log_*.txt` 檔案。\n\n---\n\n\u003C!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**免責聲明**：\n本文件由 AI 翻譯服務 [Co-op Translator](https:\u002F\u002Fgithub.com\u002FAzure\u002Fco-op-translator) 翻譯而成。雖然我們致力於確保準確性，但請注意，機器自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要資訊，建議進行專業人工翻譯。我們不對因使用本翻譯而產生的任何誤解或誤釋承擔責任。\n\u003C!-- CO-OP TRANSLATOR DISCLAIMER END 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