ai-agents-for-beginners

Koristite kada se zatraži validacija, testiranje, smoke-test ili pokretanje bilježnica i primjera koda tečaja protiv aktivne Microsoft Foundry / Azure OpenAI konfiguracije. Obuhvaća postavljanje okruženja (.env, az login, pakete), pokretač scripts/validate-notebooks.ps1, tumačenje PASS/FAIL rezultata i koje lekcije zahtijevaju dodatne resurse (Azure AI Search, GitHub MCP, Foundry Local, Playwright).

Tumia wakati unapoombwa kuthibitisha, kujaribu, kufanya mtihani wa haraka, au kuendesha daftari la kozi na mifano ya msimbo dhidi ya usanidi hai wa Microsoft Foundry / Azure OpenAI. Inajumuisha usanidi wa mazingira (.env, az login, vifurushi), mchezaji wa scripts/validate-notebooks.ps1, kutafsiri matokeo ya PASS/FAIL, na ni masomo yapi yanayohitaji rasilimali za ziada (Azure AI Search, GitHub MCP, Foundry Local, Playwright).

Použite, keď sa vyžaduje overenie, testovanie, rýchle testovanie alebo spustenie poznámkového bloku kurzu a ukážok kódu proti živej konfigurácii Microsoft Foundry / Azure OpenAI. Pokrýva nastavenie prostredia (.env, az login, balíky), skript runner scripts/validate-notebooks.ps1, interpretáciu výsledkov PASS/FAIL a ktoré lekcie potrebujú ďalšie zdroje (Azure AI Search, GitHub MCP, Foundry Local, Playwright).

Brukes når du blir bedt om å validere, teste, røykteste eller kjøre kursets notatbok og kodeeksempler mot en aktiv Microsoft Foundry / Azure OpenAI-konfigurasjon. Dekker miljøoppsett (.env, az login, pakker), skriptet/validate-notebooks.ps1-kjøreren, tolkning av PASS/FAIL-resultater, og hvilke leksjoner som trenger ekstra ressurser (Azure AI Search, GitHub MCP, Foundry Local, Playwright).

Gamitin kapag hiniling na i-validate, subukan, smoke-test, o patakbuhin ang notebook ng kurso at mga halimbawa ng code laban sa isang live na Microsoft Foundry / Azure OpenAI na configuration. Saklaw nito ang pag-setup ng kapaligiran (.env, az login, mga package), ang scripts/validate-notebooks.ps1 runner, ang pagpapakahulugan ng mga resulta na PASS/FAIL, at kung aling mga aralin ang nangangailangan ng karagdagang mga mapagkukunan (Azure AI Search, GitHub MCP, Foundry Local, Playwright).

Use wen dem ask you to validate, test, smoke-test, or run di course notebook and code samples against live Microsoft Foundry / Azure OpenAI configuration. E cover environment setup (.env, az login, packages), di scripts/validate-notebooks.ps1 runner, how to understand PASS/FAIL results, and which lessons need extra resources (Azure AI Search, GitHub MCP, Foundry Local, Playwright).

Use when asked to validate, test, smoke-test, or run the course's notebook and code samples against a live Microsoft Foundry / Azure OpenAI configuration. Covers environment setup (.env, az login, packages), the scripts/validate-notebooks.ps1 runner, interpreting PASS/FAIL results, and which lessons need extra resources (Azure AI Search, GitHub MCP, Foundry Local, Playwright).

ਜਦੋਂ ਮੰਗ ਕੀਤੀ ਜਾਵੇ ਤਦ ਵੈਲਿਡੇਟ ਕਰਨ ਲਈ, ਟੈਸਟ ਕਰਨ ਲਈ, ਸਮੋਕ-ਟੈਸਟ ਕਰਨ ਲਈ, ਜਾਂ ਕੋਰਸ ਦੇ ਨੋਟਬੁੱਕ ਅਤੇ ਕੋਡ ਸੈਂਪਲਾਂ ਨੂੰ live Microsoft Foundry / Azure OpenAI ਕਨਫਿਗਰੇਸ਼ਨ ਖਿਲਾਫ ਚਲਾਉਣ ਲਈ ਵਰਤੋਂ ਕਰੋ। ਇਹ ਮਾਹੌਲ ਸੈਟਅੱਪ (.env, az login, ਪੈਕੇਜਜ਼), ਸਕ੍ਰਿਪਟਸ/validate-notebooks.ps1 ਰੱਨਰ, PASS/FAIL ਨਤੀਜੇ ਬੁਝਣ ਅਤੇ ਕਿਹੜੇ ਪਾਠਾਂ ਨੂੰ ਵਾਧੂ ਸਾਧਨਾਂ ਦੀ ਲੋੜ ਹੈ (Azure AI Search, GitHub MCP, Foundry Local, Playwright) ਨੂੰ ਕਵਰ ਕਰਦਾ ਹੈ।

Migrasi aplikasi Python dari Azure OpenAI Chat Completions ke Responses API. Meliputi migrasi klien AzureOpenAI/AsyncAzureOpenAI ke endpoint v1, penstriman, alat, output berstruktur, multi-sesi, pengesahan EntraID, dan pemeriksaan keserasian model. Berfokus pada Python dan khusus untuk Azure OpenAI. GUNA UNTUK: migrasi ke responses API, bertukar dari chat completions, openai responses, peningkatan openai SDK, migrasi responses API, berpindah dari completions ke responses, migrasi gpt-5, migrasi python azure openai, chat completions ke responses, AzureOpenAI ke klien OpenAI, peningkatan python azure openai. JANGAN GUNA UNTUK: membina aplikasi baru dari awal (mulakan terus dengan responses), migrasi Node/TypeScript/C#/Java/Go (kemahiran ini hanya untuk Python), persediaan infrastruktur Azure (guna azure-prepare), penyebaran model (guna microsoft-foundry).

Gunakan apabila diminta untuk mengesahkan, menguji, melakukan ujian asap, atau menjalankan buku nota dan contoh kod kursus terhadap konfigurasi Microsoft Foundry / Azure OpenAI secara langsung. Meliputi penyediaan persekitaran (.env, az login, pakej), skrip/validate-notebooks.ps1 sebagai pelancar, mentafsir keputusan LULUS/GAGAL, dan pelajaran mana yang memerlukan sumber tambahan (Azure AI Search, GitHub MCP, Foundry Local, Playwright).

Ilipat ang mga Python app mula sa Azure OpenAI Chat Completions papuntang Responses API. Saklaw nito ang pag-migrate ng AzureOpenAI/AsyncAzureOpenAI client sa v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, at mga pagsusuri sa compatibility ng modelo. Nakatuon sa Python, para sa Azure OpenAI. GAMITIN PARA SA: pag-migrate sa responses API, paglipat mula sa chat completions, openai responses, pag-upgrade ng openai SDK, migration sa responses API, paglipat mula completions sa responses, gpt-5 migration, azure openai python migration, chat completions papuntang responses, AzureOpenAI papuntang OpenAI client, python azure openai upgrade. HUWAG GAMITIN PARA SA: paggawa ng mga bagong app mula sa simula (simulan direkta sa responses), Node/TypeScript/C#/Java/Go migrations (Python lang ang kasanayang ito), Azure infrastructure setup (gumamit ng azure-prepare), pag-deploy ng mga modelo (gumamit ng microsoft-foundry).

Dalhin ang isang gumaganang prototype ng agent sa isang scalable, observable na production deployment sa Microsoft Foundry. Saklaw nito ang mga deployment pattern (client-hosted, hosted agents, agent workflows), ang lifecycle ng agent, model routing, response caching, evaluation gates, human-in-the-loop approval, observability gamit ang OpenTelemetry, cost optimisation, at smoke-testing ng mga deployed na agent gamit ang AI Smoke Test action. Batay sa Lesson 16 ng AI Agents for Beginners. GAMITIN PARA SA: pag-deploy ng agent sa production, pag-scale ng agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test ng hosted agent, production customer support agent. HUWAG GAMITIN PARA SA: pagbuo ng iyong unang agent (simulan sa Lesson 01), pagpapatakbo ng mga agent nang lokal sa device (gamitin ang local-ai-agents / Lesson 17), Azure infrastructure prov

Bumuo ng mga local-first AI agents na tumatakbo nang buong-buo sa isang developer workstation gamit ang Microsoft Foundry Local at Qwen function-calling models. Saklaw nito ang Small Language Models (SLMs), ang OpenAI-compatible na lokal na endpoint, sandboxed local tools, lokal na RAG gamit ang Chroma, lokal na MCP servers, hybrid cloud/local routing, at ang privacy/cost/offline trade-offs. Batay sa Lesson 17 ng AI Agents for Beginners. GAMITIN PARA SA: pagpapatakbo ng agent nang lokal, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, lokal na RAG, Chroma vector database, lokal na MCP server, privacy-preserving agent, hybrid local at cloud agent, small language model agent, engineering assistant sa aking makina. HUWAG GAMITIN PARA SA: pag-deploy ng mga agents sa cloud nang malakihan (gamitin ang deploying-scalable-agents / Lesson 16), paggawa ng iyong unang agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.

השתמש כאשר מתבקשים לבדוק תוקף, לבצע בדיקת פונקציונליות בסיסית, או להריץ את המחברת ודוגמאות הקוד של הקורס נגד תצורת Microsoft Foundry / Azure OpenAI חיה. כולל הגדרת סביבה (.env, az login, חבילות), הרצת הסקריפט scripts/validate-notebooks.ps1, פירוש תוצאות PASS/FAIL, ואילו שיעורים דורשים משאבים נוספים (Azure AI Search, GitHub MCP, Foundry Local, Playwright).

Shift Python apps dem from Azure OpenAI Chat Completions go Responses API. E cover AzureOpenAI/AsyncAzureOpenAI client shift go v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, plus model compatibility checks. Na Python-focused, Azure OpenAI-specific. USE FOR: shift go responses API, change from chat completions, openai responses, upgrade openai SDK, responses API migration, move from completions go responses, gpt-5 migration, azure openai python migration, chat completions go responses, AzureOpenAI go OpenAI client, python azure openai upgrade. DO NOT USE FOR: build new apps from scratch (start with responses directly), Node/TypeScript/C#/Java/Go migrations (dis skill na Python-only), Azure infrastructure setup (use azure-prepare), deploy models (use microsoft-foundry).

Take one working agent prototype go scalable, observable production deployment for Microsoft Foundry. E cover deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluation gates, human-in-the-loop approval, observability with OpenTelemetry, cost optimisation, and smoke-testing deployed agents with the AI Smoke Test action. Based on Lesson 16 of AI Agents for Beginners. USE FOR: deploy one agent go production, scale one agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test one hosted agent, production customer support agent. DO NOT USE FOR: building your first agent (start with Lesson 01), running agents locally on-device (use local-ai-agents / Lesson 17), Azure infrastructure provisioning we no relate to agents, non-Foundry deployment targets.

Build local-first AI agents wey dey run fully for developer workstation wit Microsoft Foundry Local and Qwen function-calling models. E cover Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG wit Chroma, local MCP servers, hybrid cloud/local routing, and di privacy/cost/offline trade-offs. E based on Lesson 17 of AI Agents for Beginners. USE FOR: run agent locally, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, local RAG, Chroma vector database, local MCP server, privacy-preserving agent, hybrid local and cloud agent, small language model agent, engineering assistant for my machine. DO NOT USE FOR: deploying agents to di cloud at scale (use deploying-scalable-agents / Lesson 16), building your first agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.

Use when asked to validate, test, smoke-test, or run the course's notebook and code samples against a live Microsoft Foundry / Azure OpenAI configuration. Covers environment setup (.env, az login, packages), the scripts/validate-notebooks.ps1 runner, interpreting PASS/FAIL results, and which lessons need extra resources (Azure AI Search, GitHub MCP, Foundry Local, Playwright).

Migrate Python apps from Azure OpenAI Chat Completions to the Responses API. Covers AzureOpenAI/AsyncAzureOpenAI client migration to the v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, and model compatibility checks. Python-focused, Azure OpenAI-specific. USE FOR: migrate to responses API, switch from chat completions, openai responses, upgrade openai SDK, responses API migration, move from completions to responses, gpt-5 migration, azure openai python migration, chat completions to responses, AzureOpenAI to OpenAI client, python azure openai upgrade. DO NOT USE FOR: building new apps from scratch (start with responses directly), Node/TypeScript/C#/Java/Go migrations (this skill is Python-only), Azure infrastructure setup (use azure-prepare), deploying models (use microsoft-foundry).

Take a working agent prototype to a scalable, observable production deployment on Microsoft Foundry. Covers deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluation gates, human-in-the-loop approval, observability with OpenTelemetry, cost optimisation, and smoke-testing deployed agents with the AI Smoke Test action. Based on Lesson 16 of AI Agents for Beginners. USE FOR: deploy an agent to production, scale an agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test a hosted agent, production customer support agent. DO NOT USE FOR: building your first agent (start with Lesson 01), running agents locally on-device (use local-ai-agents / Lesson 17), Azure infrastructure provisioning unrelated to agents, non-Foundry deployment targets.

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the privacy/cost/offline trade-offs. Based on Lesson 17 of AI Agents for Beginners. USE FOR: run an agent locally, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, local RAG, Chroma vector database, local MCP server, privacy-preserving agent, hybrid local and cloud agent, small language model agent, engineering assistant on my machine. DO NOT USE FOR: deploying agents to the cloud at scale (use deploying-scalable-agents / Lesson 16), building your first agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.

Migrate Python apps from Azure OpenAI Chat Completions to the Responses API. Covers AzureOpenAI/AsyncAzureOpenAI client migration to the v1 endpoint, streaming, tools, structured output, multi-turn, EntraID auth, and model compatibility checks. Python-focused, Azure OpenAI-specific. USE FOR: migrate to responses API, switch from chat completions, openai responses, upgrade openai SDK, responses API migration, move from completions to responses, gpt-5 migration, azure openai python migration, chat completions to responses, AzureOpenAI to OpenAI client, python azure openai upgrade. DO NOT USE FOR: building new apps from scratch (start with responses directly), Node/TypeScript/C#/Java/Go migrations (this skill is Python-only), Azure infrastructure setup (use azure-prepare), deploying models (use microsoft-foundry).

Take a working agent prototype to a scalable, observable production deployment on Microsoft Foundry. Covers deployment patterns (client-hosted, hosted agents, agent workflows), the agent lifecycle, model routing, response caching, evaluation gates, human-in-the-loop approval, observability with OpenTelemetry, cost optimisation, and smoke-testing deployed agents with the AI Smoke Test action. Based on Lesson 16 of AI Agents for Beginners. USE FOR: deploy an agent to production, scale an agent, Microsoft Foundry hosted agent, Foundry Agent Service, model routing, response caching, evaluation gate, release gate, human approval workflow, agent observability, agent tracing, agent cost optimisation, smoke test a hosted agent, production customer support agent. DO NOT USE FOR: building your first agent (start with Lesson 01), running agents locally on-device (use local-ai-agents / Lesson 17), Azure infrastructure provisioning unrelated to agents, non-Foundry deployment targets.

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the privacy/cost/offline trade-offs. Based on Lesson 17 of AI Agents for Beginners. USE FOR: run an agent locally, offline agent, on-device agent, Foundry Local, Qwen function calling, local tool calling, local RAG, Chroma vector database, local MCP server, privacy-preserving agent, hybrid local and cloud agent, small language model agent, engineering assistant on my machine. DO NOT USE FOR: deploying agents to the cloud at scale (use deploying-scalable-agents / Lesson 16), building your first agent concept (Lesson 01), Foundry (cloud) hosted agents, GPU cluster / server-side inference provisioning.

ユーザーが実験、探索、またはチュートリアル用の Jupyter Notebook（`.ipynb`）を作成、スキャフォールド、または編集するよう依頼したときに使用します。バンドルされたテンプレートを優先し、ヘルパースクリプト `new_notebook.py` を実行してクリーンな開始ノートブックを生成してください。

사용자가 실험, 탐색 또는 튜토리얼을 위한 Jupyter 노트북(`.ipynb`)을 생성, 스캐폴딩하거나 편집해 달라고 요청할 때 사용합니다; 번들로 제공되는 템플릿을 우선 사용하고 깨끗한 시작 노트북을 생성하기 위해 헬퍼 스크립트 `new_notebook.py`를 실행하는 것을 권장합니다.

ប្រើនៅពេលដែលអ្នកប្រើស្នើឲ្យបង្កើត រៀបចំ (scaffold) ឬកែសម្រួល Jupyter notebooks (`.ipynb`) សម្រាប់សាកល្បង ស្វែងរក ឬមេរៀន; អនុសាសន៍ឲ្យប្រើទំរង់គំរូដែលភ្ជាប់មកជាមួយ និងរត់ស្គ្រីបជំនួយ `new_notebook.py` ដើម្បីបង្កើតសៀវភៅចាប់ផ្តើមដែលស្អាត។

ស្វែងរកឯកសារផ្លូវការរបស់ Microsoft ដើម្បីរកមើល គំនិត មេរៀន និងឧទាហរណ៍កូដ នៅលើ Azure, .NET, Agent Framework, Aspire, VS Code, GitHub និងផ្សេងទៀត។ ប្រើ Microsoft Learn MCP ជាលំនាំដើម ហើយប្រើ Context7 និង Aspire MCP សម្រាប់មាតិកាដែលស្ថិតនៅខាងក្រៅ learn.microsoft.com។

ಬಳಕೆದಾರರು ಪ್ರಯೋಗಗಳು, ಅನ್ವೇಷಣೆಗಳು ಅಥವಾ ಟ್ಯುಟೋರಿಯಲ್‌ಗಳಿಗಾಗಿ Jupyter ನೋಟ್ಬುಕ್‌ಗಳು (`.ipynb`) ರಚಿಸಲು, ಮೂಲ ರಚನೆ ಸಿದ್ಧಪಡಿಸಲು ಅಥವಾ ಸಂಪಾದಿಸಲು ಕೇಳಿದಾಗ ಬಳಸಿರಿ; ಸಂಯೋಜಿತ ಟೆಂಪ್ಲೇಟ್‌ಗಳಿಗೆ ಪ್ರಾಧಾನ್ಯ ನೀಡಿ ಮತ್ತು ಪ್ರಾರಂಭಿಕ ಸ್ವಚ್ಛ ನೋಟ್ಬುಕ್ ರಚಿಸಲು ಸಹಾಯಕ ಸ್ಕ್ರಿಪ್ಟ್ `new_notebook.py` ಅನ್ನು ರನ್ ಮಾಡಿ.

ಅಧಿಕೃತ Microsoft ಡಾಕ್ಯುಮೆಂಟೇಶನ್ ಅನ್ನು ಪ್ರಶ್ನೆ ಮಾಡಿ Azure, .NET, Agent Framework, Aspire, VS Code, GitHub ಮತ್ತು ಇತರೆ ಸಂಬಂಧಿತ ವಿಷಯಗಳ ಮೇಲೆ ತತ್ವಗಳು, ಟ್ಯುಟೋರಿಯಲ್ಗಳು ಮತ್ತು ಕೋಡ್ ಉದಾಹರಣೆಗಳನ್ನು ಕಂಡುಹಿಡಿಯಿರಿ. ಡೀಫಾಲ್ಟ್‌ವಾಗಿ Microsoft Learn MCP ಅನ್ನು ಬಳಸುತ್ತದೆ; learn.microsoft.com ಹೊರಗಿನ ವಿಷಯಗಳಿಗೆ Context7 ಮತ್ತು Aspire MCP ಅನ್ನು ಬಳಸಲಾಗುತ್ತದೆ.

ഉപയോക്താവ് പരീക്ഷണങ്ങൾ, അന്വേഷണങ്ങൾ, അല്ലെങ്കിൽ ട്യൂട്ടോറിയലുകൾക്കുള്ള Jupyter നോട്ട്ബുക്കുകൾ (`.ipynb`) സൃഷ്ടിക്കാൻ, സ്‌കാഫോൾഡ് ചെയ്യാൻ, അല്ലെങ്കിൽ തിരുത്താൻ ആവശ്യപ്പെടുമ്പോൾ ഉപയോഗിക്കുക; ബണ്ടിൽ ചെയ്ത ടെംപ്ലേറ്റുകൾ പ്രാഥമ്യം നൽകുക, കൂടാതെ ശുദ്ധമായ ആരംഭ നോട്ട്ബുക്ക് സൃഷ്ടിക്കാൻ സഹായി സ്ക്രിപ്റ്റ് `new_notebook.py` ഓടിക്കുക.

Microsoft-ന്റെ ഔദ്യോഗിക ഡോക്യുമെന്റേഷൻ അന്വേഷിച്ച് Azure, .NET, Agent Framework, Aspire, VS Code, GitHub എന്നിവ ഉൾപ്പെടെയുള്ള മേഖലയിലെ ആശയങ്ങൾ, ട്യൂട്ടോറിയലുകൾ, കോഡ് ഉദാഹരണങ്ങൾ എന്നിവ കണ്ടെത്തുക. ഡിഫോൾട്ട് ആയി Microsoft Learn MCP ഉപയോഗിക്കുന്നു; learn.microsoft.com-ന്റെ പുറത്തുള്ള ഉള്ളടക്കങ്ങൾക്ക് Context7യും Aspire MCPയും ഉപയോഗിക്കുന്നു.

వినియోగదారు ప్రయోగాలు, అన్వేషణలు లేదా పాఠ్యాల కోసం Jupyter నోట్‌బుక్స్ (`.ipynb`) సృష్టించమని, ఆధార నిర్మాణం (scaffold) చేయమని లేదా సవరిచమని అడిగినప్పుడు ఉపయోగించండి; బండిల్ చేయబడ్డ టెంప్లేట్లను ప్రాధాన్యంగా ఉపయోగించి సహాయక స్క్రిప్ట్ `new_notebook.py` ని నడపండి ఒక శుభ్రమైన ప్రారంభ నోట్‌బుక్ రూపొందించడానికి.

Microsoft యొక్క అధికారిక డాక్యుమెంటేషన్‌ను పరిశీలించి Azure, .NET, Agent Framework, Aspire, VS Code, GitHub మరియు మరెన్నో అంశాలకు సంబంధించిన కాన్సెప్ట్స్, ట్యుటోరియల్స్ మరియు కోడ్ ఉదాహరణలను కనుగొనండి. డిఫాల్ట్‌గా Microsoft Learn MCP ను ఉపయోగిస్తుంది, learn.microsoft.com వెలుపల ఉన్న కంటెంట్ కోసం Context7 మరియు Aspire MCP ను ఉపయోగిస్తుంది.

उपयोग तब करें जब उपयोगकर्ता प्रयोगों, खोजों, या ट्यूटोरियल्स के लिए Jupyter नोटबुक (`.ipynb`) बनाने, स्कैफोल्ड करने, या संपादित करने के लिए कहे; पैकेज किए गए टेम्पलेट्स को प्राथमिकता दें और एक साफ शुरुआत वाला नोटबुक बनाने के लिए सहायक स्क्रिप्ट `new_notebook.py` चलाएँ।

आधिकारिक Microsoft दस्तावेज़ों में क्वेरी करें ताकि Azure, .NET, Agent Framework, Aspire, VS Code, GitHub, और अन्य के बारे में अवधारणाएँ, ट्यूटोरियल और कोड उदाहरण मिल सकें। डिफ़ॉल्ट रूप से Microsoft Learn MCP का उपयोग करता है, और उन सामग्रियों के लिए जो learn.microsoft.com के बाहर रहती हैं Context7 और Aspire MCP का उपयोग करता है।

公式の Microsoft ドキュメントを検索して、Azure、.NET、Agent Framework、Aspire、VS Code、GitHub などの概念、チュートリアル、コード例を見つけます。既定では Microsoft Learn MCP を使用し、learn.microsoft.com の外にあるコンテンツには Context7 と Aspire MCP を使用します。

공식 Microsoft 문서를 조회하여 Azure, .NET, Agent Framework, Aspire, VS Code, GitHub 등 다양한 분야의 개념, 튜토리얼 및 코드 예제를 찾습니다. 기본적으로 Microsoft Learn MCP를 사용하며, learn.microsoft.com 외부에 있는 콘텐츠에 대해서는 Context7 및 Aspire MCP를 사용합니다.

Bruges når brugeren beder om at oprette, opsætte eller redigere Jupyter-notebooks (`.ipynb`) til eksperimenter, udforskninger eller vejledninger; foretræk de medfølgende skabeloner og kør hjælpeskriptet `new_notebook.py` for at generere en ren start-notebook.

Søg i den officielle Microsoft-dokumentation for at finde koncepter, vejledninger og kodeeksempler på tværs af Azure, .NET, Agent Framework, Aspire, VS Code, GitHub og mere. Bruger Microsoft Learn MCP som standard, samt Context7 og Aspire MCP til indhold, der findes uden for learn.microsoft.com.