From 4f76079d77069d8e4ec3423bb300c9a413ddbf80 Mon Sep 17 00:00:00 2001 From: Dan Wahlin Date: Mon, 5 Oct 2026 14:26:38 -0700 Subject: [PATCH 1/4] Secure samples and modernize course --- .env.example | 4 + .github/workflows/deploy.yml | 11 +- .github/workflows/translation.yml | 16 +- .npmrc | 1 + .nvmrc | 1 + AGENTS.md | 82 +- README.fr.md | 27 +- README.md | 30 +- _sidebar.md | 1 + app/README.md | 65 +- app/app-sdk.js | 189 +- app/app.js | 118 +- app/chat-request.js | 40 + app/model-config.js | 39 + app/package-lock.json | 1782 +++++---- app/package.json | 18 +- app/public/index.html | 52 +- app/public/js/script.js | 21 +- app/test/chat.test.js | 253 ++ app/test/model-config.test.js | 88 + app/test/sdk-protocol.test.js | 132 + app/test/sdk-runtime.test.js | 68 + docs/additional-resources.md | 3 +- docs/images/create-codespace.png | Bin 43912 -> 0 bytes docs/images/playground-snippets.png | Bin 217324 -> 0 bytes docs/images/playground.png | Bin 120448 -> 0 bytes docs/setup/README.md | 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package.json | 11 +- scripts/build-docs.mjs | 25 + tests/docs.test.js | 81 + tests/fixtures/wiki-fetch.mjs | 10 + tests/samples.test.js | 223 ++ videos/.vscode/extensions.json | 6 - videos/.vscode/settings.json | 9 - videos/README.md | 74 - videos/demos/.env.example | 31 - videos/demos/01-llms/demo.js | 0 videos/demos/01-llms/readme.md | 19 - .../02-prompt-engineering/demo-complete.ts | 39 - videos/demos/02-prompt-engineering/demo.ts | 20 - videos/demos/02-prompt-engineering/readme.md | 105 - videos/demos/03-rag/readme.md | 23 - videos/demos/04-langchainjs/readme.md | 31 - videos/demos/05-local-models/ollama.http | 73 - videos/demos/05-local-models/openai.js | 19 - videos/demos/05-local-models/readme.md | 48 - videos/demos/06-playground/readme.md | 36 - videos/demos/07-ai-foundry/readme.md | 32 - videos/demos/08-cosmos-db/readme.md | 69 - videos/demos/10-chat-protocol/readme.md | 45 - videos/demos/README.md | 3 - videos/demos/package-lock.json | 1291 ------- videos/demos/package.json | 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/dev/null +++ b/.env.example @@ -0,0 +1,4 @@ +# Microsoft Foundry or another OpenAI-compatible provider. +AI_API_KEY=your_microsoft_foundry_api_key +AI_ENDPOINT=https://your-resource.openai.azure.com/openai/v1 +AI_MODEL=gpt-5-mini diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index 306ef1f7..fb140cb7 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -6,13 +6,20 @@ on: jobs: build: runs-on: ubuntu-latest + permissions: + contents: write steps: - name: Checkout code - uses: actions/checkout@a37ce9120846195fa4ece8f58b268e6043cb2f26 # v3.7.0 + uses: actions/checkout@d23441a48e516b6c34aea4fa41551a30e30af803 # v6 + + - name: Use Node.js LTS + uses: actions/setup-node@249970729cb0ef3589644e2896645e5dc5ba9c38 # v6 + with: + node-version-file: .nvmrc - name: Install dependencies - run: npm install + run: npm ci - name: Build project run: npm run build diff --git a/.github/workflows/translation.yml b/.github/workflows/translation.yml index 11017122..048697f6 100644 --- a/.github/workflows/translation.yml +++ b/.github/workflows/translation.yml @@ -9,7 +9,6 @@ on: - lessons/*/README.md permissions: contents: write - models: read concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: true @@ -24,9 +23,22 @@ jobs: key: continuous-translation-${{ github.run_id }} restore-keys: | continuous-translation- + - name: Validate Microsoft Foundry configuration + env: + AI_API_KEY: ${{ secrets.AI_API_KEY }} + AI_ENDPOINT: ${{ vars.AI_ENDPOINT }} + AI_MODEL: ${{ vars.AI_MODEL }} + run: | + test -n "$AI_API_KEY" || { echo "::error::Set the AI_API_KEY repository secret."; exit 1; } + test -n "$AI_ENDPOINT" || { echo "::error::Set the AI_ENDPOINT repository variable."; exit 1; } + test -n "$AI_MODEL" || { echo "::error::Set the AI_MODEL repository variable."; exit 1; } - uses: pelikhan/action-continuous-translation@923d1c9a88c47993a6d10c509c810f853c9205e6 # v0.2.11 with: - github_token: ${{ secrets.GITHUB_TOKEN }} + openai_api_key: ${{ secrets.AI_API_KEY }} + openai_api_base: ${{ vars.AI_ENDPOINT }} + model_alias: | + translation: openai:${{ vars.AI_MODEL }} + validation: openai:${{ vars.AI_MODEL }} lang: fr files: | - README.md diff --git a/.npmrc b/.npmrc new file mode 100644 index 00000000..b6f27f13 --- /dev/null +++ b/.npmrc @@ -0,0 +1 @@ +engine-strict=true diff --git a/.nvmrc b/.nvmrc new file mode 100644 index 00000000..b009dfb9 --- /dev/null +++ b/.nvmrc @@ -0,0 +1 @@ +lts/* diff --git a/AGENTS.md b/AGENTS.md index 3f1639c6..f8b38824 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -10,14 +10,12 @@ This repository is a comprehensive learning course called "Generative AI for Beg - OpenAI SDK - Model Context Protocol (MCP) - Express.js (for web applications) -- GitHub Models (free AI model access) -- Azure OpenAI (optional alternative) +- Microsoft Foundry or another OpenAI-compatible model provider **Architecture:** - Multi-package learning repository (not a monorepo, but lesson-based structure) - Each lesson in `/lessons/` contains standalone sample applications - Main companion app in `/app/` directory for character interactions -- Video content and demos in `/videos/` - Documentation in `/docs/` ## Repository Structure @@ -35,16 +33,15 @@ This repository is a comprehensive learning course called "Generative AI for Beg │ ├── 07-mcp/ │ └── 08-mcp-advanced/ ├── docs/ # Setup guides and additional resources -├── videos/ # Video session content and demos └── .devcontainer/ # GitHub Codespaces configuration ``` ## Setup Commands **Prerequisites:** -- Node.js 20 or higher -- GitHub account (for GitHub Models access) -- Optional: Personal Access Token (PAT) if running locally +- Node.js Long Term Support (LTS) +- Microsoft Foundry model deployment or another OpenAI-compatible provider +- Provider endpoint, API key, and model deployment name **Initial Setup:** @@ -54,15 +51,10 @@ git clone https://github.com/microsoft/generative-ai-with-javascript.git cd generative-ai-with-javascript # Install root dependencies -npm install - -# For GitHub Codespaces users (recommended): -# The environment is pre-configured - no additional setup needed -# Use built-in GITHUB_TOKEN environment variable +npm ci -# For local development: -# 1. Create a GitHub Personal Access Token (PAT) at https://github.com/settings/tokens -# 2. Set it as an environment variable: export GITHUB_TOKEN=your_token_here +# Copy the environment template and add your provider settings +cp .env.example .env ``` **Running the Main Companion App:** @@ -91,25 +83,25 @@ npm start 1. Fork the repository 2. Create a Codespace from your fork 3. Pre-configured environment includes: - - Node.js 20 + - Node.js LTS - VSCode extensions (EditorConfig, Code Runner, REST Client) - Ollama with phi3 and all-minilm models - - `GITHUB_TOKEN` automatically available -4. Navigate to any lesson or app directory and run `npm install && npm start` +4. Configure the repository-root `.env` file. +5. Navigate to any lesson or app directory and run `npm ci && npm start` ### Local Development -1. Ensure Node.js 20+ is installed -2. Set `GITHUB_TOKEN` environment variable -3. Navigate to the directory you want to work with -4. Run `npm install` followed by `npm start` +1. Install Node.js LTS. +2. Copy `.env.example` to `.env`. +3. Set `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL`. +4. Navigate to the directory you want to work with. +5. Run `npm ci` followed by `npm start`. ### Environment Variables -- `GITHUB_TOKEN` - Required for GitHub Models API access - - Automatically available in GitHub Codespaces - - Must be set manually for local development -- No scopes/permissions needed for the token when using GitHub Models +- `AI_ENDPOINT` - OpenAI-compatible API base URL +- `AI_API_KEY` - API key for the configured provider +- `AI_MODEL` - Model deployment name used in requests ## Working with Lessons @@ -142,7 +134,13 @@ npm run inspect # Runs MCP inspector tool **Current Testing Setup:** -This is primarily an educational repository focused on tutorial content. There are no automated unit or integration tests for the code samples. +The repository has automated tests for the companion app, lesson samples, MCP builds, and documentation publication. + +Run all tests: + +```bash +npm test +``` **Manual Testing Approach:** @@ -170,8 +168,8 @@ This is primarily an educational repository focused on tutorial content. There a npx @modelcontextprotocol/inspector --cli node build/index.js --method tools/call --tool-name add --tool-arg a=1 --tool-arg b=3 ``` -4. **Verify GitHub Models integration:** - - Ensure `GITHUB_TOKEN` is set +4. **Verify model integration:** + - Ensure `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL` are set - Run any lesson sample that calls OpenAI APIs - Verify responses are generated successfully @@ -243,11 +241,11 @@ npm run build # Builds documentation site - `profanity.yml` - Filters inappropriate content in PRs - `spelling.yml` - Spell checks documentation -## GitHub Models Integration +## Model Provider Integration **Primary AI Service:** -This course uses GitHub Models for free access to AI capabilities. All code examples are configured to work with GitHub Models by default. +This course uses Microsoft Foundry by default. The samples can also use another OpenAI-compatible provider. **API Configuration:** @@ -255,16 +253,12 @@ This course uses GitHub Models for free access to AI capabilities. All code exam import { OpenAI } from 'openai'; const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: process.env.AI_ENDPOINT, + apiKey: process.env.AI_API_KEY, }); ``` -**Available Models:** - -- `gpt-4o` - Primary model used in examples -- `gpt-4o-mini` - Lightweight alternative -- Access via GitHub Models marketplace: https://github.com/marketplace/models +Set `AI_MODEL` to the exact deployment name in Microsoft Foundry. ## Monorepo Navigation Tips @@ -358,14 +352,11 @@ cd lessons//sample-app npm install ``` -**GitHub Models authentication issues:** +**Model provider authentication issues:** ```bash -# Verify token is set -echo $GITHUB_TOKEN - -# For Codespaces: Token is automatically available -# For local: Create PAT at https://github.com/settings/tokens +# Verify the configuration names without printing the API key +printf '%s\n' "$AI_ENDPOINT" "$AI_MODEL" ``` **TypeScript compilation errors:** @@ -397,7 +388,6 @@ This repository contains fictional AI-generated content. Historical characters g - Each lesson builds progressively on previous concepts - Narrative-driven learning with time-travel story - Hands-on coding exercises with solutions provided -- Video content supplements written lessons **Contributing:** @@ -417,4 +407,4 @@ This repository contains fictional AI-generated content. Historical characters g - GitHub Codespaces requires 16GB RAM (configured in devcontainer.json) - Ollama models (phi3, all-minilm) pre-installed in Codespaces - Keep lesson samples lightweight and focused -- Use streaming for better UX in production apps (see lesson 10) +- Use streaming for a better user experience in production apps. diff --git a/README.fr.md b/README.fr.md index 72cbb085..ab9a4518 100644 --- a/README.fr.md +++ b/README.fr.md @@ -5,7 +5,6 @@ # Intelligence Artificielle Générative pour débutants avec JavaScript [![License](https://img.shields.io/badge/License-MIT-blue?style=flat-square)](https://github.com/microsoft/generative-ai-with-javascript/blob/main/LICENSE) -[![Watch the video series](https://img.shields.io/badge/Videos-d95652.svg?style=flat-square\&logo=youtube)](https://aka.ms/genai-js) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) [![Azure AI Discord](https://dcbadge.limes.pink/api/server/kzRShWzttr)](https://discord.gg/kzRShWzttr) @@ -64,20 +63,20 @@ Tout au long de ce cours, vous trouverez de nombreux exemples de code et exercic 2. Cliquez sur le bouton **Code** dans votre dépôt forké, accédez à l'onglet **Codespaces**, puis choisissez **Créer un codespace**. - Cela créera un environnement en ligne préconfiguré pour vous. Vous pourrez ensuite utiliser [GitHub Models](https://github.com/marketplace/models) pour exécuter les exemples de code et interagir avec des modèles d'IA gratuitement, sans configuration supplémentaire. + Cet environnement permet de travailler sur les exemples. Suivez le [guide de configuration](/docs/setup/README.md) pour déployer un modèle Microsoft Foundry et configurer `.env`. > [!NOTE] > > Bien que GitHub Codespaces offre un point de départ rapide et facile, vous pouvez également exécuter les exemples de code [localement](/docs/setup/README.md#option-2--running-the-app-locally). > -> En savoir plus sur les [concepts de GitHub Codespaces et GitHub Models ici](/docs/setup/README.md). +> Consultez le [guide de configuration](/docs/setup/README.md) pour les options locales, Codespaces et Microsoft Foundry. ### Description de la leçon 📦 Chaque leçon inclut : * Une **leçon écrite** avec un exercice et un quiz. -* Une courte **vidéo** pour approfondir votre apprentissage. + * **Solutions** pour chaque exercice et quiz. * **Personnages** avec lesquels vous pouvez interagir grâce à notre [application compagnon](/app/README.md), démontrant l'IA Générative. @@ -102,26 +101,6 @@ De nouvelles leçons seront ajoutées au cours au fil du temps, restez connecté 🙌 Une fois ce cours terminé, vous pouvez continuer à apprendre en explorant nos ressources additionnelles. -
-🎬Video Series - -| # | Session | Description | Diapositives | Démo | Script | Vidéo | -| -- | ----------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------- | -------------------------------------------- | --------------------------------------------------- | ---------------------------------------------------------------------------------------------------- | -| 0 | Introduction de la série | Introduction à la série et à son contenu. | [pptx](/videos/slides/00-intro.pptx) / [pdf](/videos/slides/pdf/00-intro.pdf) | - | [Script](/videos/sessions/00-intro.md) | [📺](https://www.youtube.com/watch?v=vLYtDgs_zx8\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=1) | -| 1 | Ce qu'il faut savoir sur les LLM | Découvrez ce que sont les LLM, leur mode d'entraînement, leur fonctionnement et leurs limites. | [pptx](/videos/slides/01-llms.pptx) / [pdf](/videos/slides/pdf/01-llms.pdf) | [Démo](/videos/demos/01-llms/) | [Script](/videos/sessions/01-llms.md) | [📺](https://www.youtube.com/watch?v=GQ_2OjNZ9aA\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=2) | -| 2 | Techniques essentielles de conception de prompts | Techniques pratiques pour concevoir des prompts et tirer le meilleur parti des modèles d'IA. | [pptx](/videos/slides/02-prompt-engineering.pptx) / [pdf](/videos/slides/pdf/02-prompt-engineering.pdf) | [Démo](/videos/demos/02-prompt-engineering/) | [Script](/videos/sessions/02-prompt-engineering.md) | [📺](https://www.youtube.com/watch?v=gQ6TlyxBmWs\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=3) | -| 3 | Améliorez la précision et la fiabilité de l'IA avec RAG | Introduction à la "Génération Augmentée par la Récupération", pour utiliser l'IA avec vos propres données. | [pptx](/videos/slides/03-rag.pptx) / [pdf](/videos/slides/pdf/03-rag.pdf) | [Démo](/videos/demos/03-rag/) | [Script](/videos/sessions/03-rag.md) | [📺](https://www.youtube.com/watch?v=xkFOmx5yxIA\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=4) | -| 4 | Accélérez votre développement d'IA avec LangChain.js | Présentation des concepts centraux du framework LangChain.js et comment l'utiliser pour accélérer vos développements IA. | [pptx](/videos/slides/04-langchainjs.pptx) / [pdf](/videos/slides/pdf/04-langchainjs.pdf) | [Démo](/videos/demos/04-langchainjs/) | [Script](/videos/sessions/04-langchainjs.md) | [📺](https://www.youtube.com/watch?v=02IDU8eCX8o\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=5) | -| 5 | Exécutez des modèles d'IA sur votre machine locale avec Ollama | Découvrez comment intégrer des modèles d'IA locaux dans votre flux de travail de développement. | [pptx](/videos/slides/05-local-models.pptx) / [pdf](/videos/slides/pdf/05-local-models.pdf) | [Démo](/videos/demos/05-local-models/) | [Script](/videos/sessions/05-local-models.md) | [📺](https://www.youtube.com/watch?v=dLfNnoPv4AQ\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=6) | -| 6 | Commencez avec l'IA gratuitement en utilisant Phi-3 | Expérimentez avec le modèle Ollama et Phi-3 directement depuis votre navigateur. | [pptx](/videos/slides/06-playground.pptx) / [pdf](/videos/slides/pdf/06-playground.pdf) | [Démo](/videos/demos/06-playground/) | [Script](/videos/sessions/06-playground.md) | [📺](https://www.youtube.com/watch?v=Ds32MS9SHzU\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=7) | -| 7 | Introduction à Azure AI Foundry | Lancez votre voyage avec Azure AI Foundry. | [pptx](/videos/slides/07-ai-foundry.pptx) / [pdf](/videos/slides/pdf/07-ai-foundry.pdf) | [Démo](/videos/demos/07-ai-foundry/) | [Script](/videos/sessions/07-ai-foundry.md) | [📺](https://www.youtube.com/watch?v=9Mo-VOGk8ng\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=8) | -| 8 | Création d'applications d'IA générative avec Azure Cosmos DB | Développez des applications d'IA générative avec Azure Cosmos DB et la recherche vectorielle. | [pptx](/videos/slides/08-cosmos-db.pptx) / [pdf](/videos/slides/pdf/08-cosmos-db.pdf) | [Démo](/videos/demos/08-cosmos-db/) | [Script](/videos/sessions/08-cosmos-db.md) | [📺](https://www.youtube.com/watch?v=-GQyaLbeqxQ\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=9) | -| 9 | Outils et services Azure pour héberger et stocker des applications d'IA | Créez, déployez et évoluez des applications d'IA en utilisant les outils Azure. | [pptx](/videos/slides/09-azure-tools.pptx) / [pdf](/videos/slides/pdf/09-azure-tools.pdf) | - | [Script](/videos/sessions/09-azure-tools.md) | [📺](https://www.youtube.com/watch?v=WB6Fpzhwyug\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=10) | -| 10 | Diffusion des résultats IA génératifs avec le protocole AI Chat | Intégrez facilement la diffusion dans vos apps grâce au Protocole AI Chat. | [pptx](/videos/slides/10-chat-protocol.pptx) / [pdf](/videos/slides/pdf/10-chat-protocol.pdf) | [Démo](/videos/demos/10-chat-protocol/) | [Script](/videos/sessions/10-chat-protocol.md) | [📺](https://www.youtube.com/watch?v=fzDCW-6hMtU\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=11) | - -Pour voir la page complète des ressources, accédez à cette [page de présentation des vidéos](/videos/README.md). - -
🎒 Other Courses diff --git a/README.md b/README.md index 7257ab70..74b04897 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,6 @@ # Generative AI for beginners with JavaScript [![License](https://img.shields.io/badge/License-MIT-blue?style=flat-square)](https://github.com/microsoft/generative-ai-with-javascript/blob/main/LICENSE) -[![Watch the video series](https://img.shields.io/badge/Videos-d95652.svg?style=flat-square&logo=youtube)](https://aka.ms/genai-js) [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com) [![Azure AI Discord](https://dcbadge.limes.pink/api/server/kzRShWzttr)](https://discord.gg/kzRShWzttr) @@ -65,20 +64,22 @@ Throughout this course you'll find many code examples and exercises, so we encou 2. Click the **Code** button in your forked repository, go to the **Codespaces** tab, and then choose **Create codespace**. - This will create a preconfigured online environment for you. You'll then be able to use [GitHub Models](https://github.com/marketplace/models) to run the code examples and interact with AI models for free, without any additional setup. + This creates an online environment for the code examples. Continue with the [course setup guide](/docs/setup/README.md) to create a Microsoft Foundry project, deploy a chat model, and configure your endpoint and API key. > [!NOTE] > > While GitHub Codespaces provides a quick and easy starting point you can also run the code samples [locally](/docs/setup/README.md#option-2--running-the-app-locally). > -> Learn more about [GitHub Codespaces and GitHub Models concepts here](/docs/setup/README.md). +> Both local development and Codespaces require model configuration. The [course setup guide](/docs/setup/README.md) covers Microsoft Foundry, the repository-root `.env`, and a live companion app check. + +The packages in the repository root and the [companion app](/app/README.md) require Node.js LTS. Each lesson package has its own requirements. ### Lesson description 📦 Each lesson includes: - A **written lesson** with an assignment and quiz. -- A short **video** to help further your learning. + - **Solutions** for each assignment and quiz. - **Characters** you can interact with using our [companion app](/app/README.md), demonstrating Generative AI. @@ -103,27 +104,6 @@ New lessons will be added to the course over time, so stay tuned! 🙌 After completing this course, you can continue learning by exploring our additional resources. -
-🎬Video Series - -| # | Session | Description | Slides | Demo | Script | Video | -|---|---------|-------------|--------|------|--------|-------| -| 0 | Series introduction | Introduces the series and its content. | [pptx](/videos/slides/00-intro.pptx) / [pdf](/videos/slides/pdf/00-intro.pdf) | - | [Script](/videos/sessions/00-intro.md) | [📺](https://www.youtube.com/watch?v=vLYtDgs_zx8&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=1) | -| 1 | What you need to know about LLMs | Explores what LLMs are, how they're trained, how they work and their limits. | [pptx](/videos/slides/01-llms.pptx) / [pdf](/videos/slides/pdf/01-llms.pdf) | [Demo](/videos/demos/01-llms/) | [Script](/videos/sessions/01-llms.md) | [📺](https://www.youtube.com/watch?v=GQ_2OjNZ9aA&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=2) | -| 2 | Essential prompt engineering techniques | Practical prompt engineering techniques to get the best out of AI models. | [pptx](/videos/slides/02-prompt-engineering.pptx) / [pdf](/videos/slides/pdf/02-prompt-engineering.pdf) | [Demo](/videos/demos/02-prompt-engineering/) | [Script](/videos/sessions/02-prompt-engineering.md) | [📺](https://www.youtube.com/watch?v=gQ6TlyxBmWs&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=3) | -| 3 | Improve AI accuracy and reliability with RAG | Introduces Retrieval-Augmented Generation, to use AI with your own data. | [pptx](/videos/slides/03-rag.pptx) / [pdf](/videos/slides/pdf/03-rag.pdf) | [Demo](/videos/demos/03-rag/) | [Script](/videos/sessions/03-rag.md) | [📺](https://www.youtube.com/watch?v=xkFOmx5yxIA&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=4) | -| 4 | Speed up your AI development with LangChain.js | Covers LangChain.js framework core concepts, and how to use it to accelerate AI developments. | [pptx](/videos/slides/04-langchainjs.pptx) / [pdf](/videos/slides/pdf/04-langchainjs.pdf) | [Demo](/videos/demos/04-langchainjs/) | [Script](/videos/sessions/04-langchainjs.md) | [📺](https://www.youtube.com/watch?v=02IDU8eCX8o&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=5) | -| 5 | Run AI models on your local machine with Ollama | Shows how to integrate local AI models into your development workflow. | [pptx](/videos/slides/05-local-models.pptx) / [pdf](/videos/slides/pdf/05-local-models.pdf) | [Demo](/videos/demos/05-local-models/) | [Script](/videos/sessions/05-local-models.md) | [📺](https://www.youtube.com/watch?v=dLfNnoPv4AQ&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=6) | -| 6 | Get started with AI for free using Phi-3 | Experiments with Ollama and Phi-3 model directly from your browser. | [pptx](/videos/slides/06-playground.pptx) / [pdf](/videos/slides/pdf/06-playground.pdf) | [Demo](/videos/demos/06-playground/) | [Script](/videos/sessions/06-playground.md) | [📺](https://www.youtube.com/watch?v=Ds32MS9SHzU&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=7) | -| 7 | Introduction to Azure AI Foundry | Kickstart your journey with Azure AI Foundry. | [pptx](/videos/slides/07-ai-foundry.pptx) / [pdf](/videos/slides/pdf/07-ai-foundry.pdf) | [Demo](/videos/demos/07-ai-foundry/) | [Script](/videos/sessions/07-ai-foundry.md) | [📺](https://www.youtube.com/watch?v=9Mo-VOGk8ng&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=8) | -| 8 | Building Generative AI Apps with Azure Cosmos DB | Build generative AI apps with Azure Cosmos DB and vector search. | [pptx](/videos/slides/08-cosmos-db.pptx) / [pdf](/videos/slides/pdf/08-cosmos-db.pdf) | [Demo](/videos/demos/08-cosmos-db/) | [Script](/videos/sessions/08-cosmos-db.md) | [📺](https://www.youtube.com/watch?v=-GQyaLbeqxQ&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=9) | -| 9 | Azure tools & services for hosting and storing AI apps | Build, deploy, and scale AI applications using Azure tools. | [pptx](/videos/slides/09-azure-tools.pptx) / [pdf](/videos/slides/pdf/09-azure-tools.pdf) | - | [Script](/videos/sessions/09-azure-tools.md) | [📺](https://www.youtube.com/watch?v=WB6Fpzhwyug&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=10) | -| 10 | Streaming Generative AI output with the AI Chat Protocol | Integrate streaming easily into your apps with the AI Chat Protocol. | [pptx](/videos/slides/10-chat-protocol.pptx) / [pdf](/videos/slides/pdf/10-chat-protocol.pdf) | [Demo](/videos/demos/10-chat-protocol/) | [Script](/videos/sessions/10-chat-protocol.md) | [📺](https://www.youtube.com/watch?v=fzDCW-6hMtU&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=11) | - -To see the full page of resources, go to this [video overview page](/videos/README.md). - -
- You'll also find additional resources in the form of [tutorials, code samples and more](/docs/additional-resources.md). diff --git a/_sidebar.md b/_sidebar.md index b3ab5408..41883f83 100644 --- a/_sidebar.md +++ b/_sidebar.md @@ -1,3 +1,4 @@ +- [Course setup](/docs/setup/README.md) - Lessons - [01 - Intro to Gen AI](/lessons/01-intro-to-genai/README.md) - [02 - First AI App](/lessons/02-first-ai-app/README.md) diff --git a/app/README.md b/app/README.md index 57a1b748..0f4f75f0 100644 --- a/app/README.md +++ b/app/README.md @@ -2,15 +2,19 @@ This app allows you to speak to all character features in the curriculum. There are two ways to interact with AI: -- GitHub Models [Use GitHub Models](https://github.com/marketplace/models) +- A configured Azure OpenAI or OpenAI-compatible endpoint - Copilot SDK [Copilot SDK](https://github.com/github/copilot-sdk/blob/main/docs/getting-started.md). This one uses your GitHub Copilot. ## Installation +Follow the [course setup guide](../docs/setup/README.md) to create a Microsoft Foundry project, deploy a chat model, obtain its endpoint and key, and configure the repository-root `.env`. The same steps apply to local development and Codespaces. + +Use Node.js LTS for this app and the packages in the repository root. Lesson packages have their own requirements. + 1. Start a [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navigate to _/app_ in the repo root. -3. Locate the console and run `npm install`. -4. Once it appears, select the "Open in Browser" button. +3. Locate the console and run `npm ci`. +4. Run one of the commands below. Then select the "Open in Browser" button. You should see something like: @@ -20,18 +24,69 @@ You should see something like: Run the app by either: -**GitHub Models** +**Azure OpenAI or an OpenAI-compatible endpoint** + +Copy `.env.example` to `.env` in the repository root. Set these variables there or in your environment: +```dotenv +AI_ENDPOINT=https://YOUR-RESOURCE.openai.azure.com/openai/v1/ +AI_API_KEY=your-api-key +AI_MODEL=your-model-deployment ``` + +For Azure OpenAI, `AI_MODEL` must be the deployment name. For OpenAI, use `https://api.openai.com/v1/` and a supported model name. The endpoint must use HTTPS; HTTP is allowed only for a loopback address used by a local provider. + +```bash npm start ``` +To use another environment file without copying its keys, set `ENV_FILE`: + +```bash +ENV_FILE="$HOME/.env" npm start +``` + +Existing environment variables take priority over values in the file. Missing settings or an unreadable explicit file stop the server with an error. Keep keys out of source control and browser code. + +The [course setup guide](../docs/setup/README.md#configure-environment-variables) explains these variables and the repository-root `.env` location. No embedding model is required for this chat app. + **Copilot SDK** ``` npm run start:sdk ``` +The SDK uses your GitHub Copilot subscription. Supply a Copilot-compatible token through `COPILOT_GITHUB_TOKEN`, `GH_TOKEN`, or `GITHUB_TOKEN`, or use GitHub CLI authentication. Do not put tokens in browser code. + +## Safe use + +Both servers listen on `127.0.0.1`. This sample does not authenticate HTTP callers. Keep Codespaces ports private. Do not expose the service to a public network. Remote deployment requires authentication, authorization, request limits, and an isolated service account. + +SDK chat uses client mode `empty`, no available tools, a permission handler that rejects every request, and a pre-tool hook that denies every tool call. It does not load project configuration, custom agents, or MCP servers. Character instructions are appended to the SDK system message; they do not replace its guardrails. The SDK removes host environment context in `empty` mode. + +The SDK uses a separate temporary directory for its state and working directory. Each request aborts, disconnects, and deletes its session. The temporary directory is removed on normal shutdown with `SIGINT` or `SIGTERM`. This directory is not an operating-system sandbox. Do not add tools or change the permission policy to support chat. + +The SDK version is pinned in both manifests. After an SDK update, check these controls against the new API and runtime before exposing the service. + +## Request format + +Both backends accept the same `POST /send` request: + +```json +{ + "message": "Tell me about your work.", + "character": { "name": "ada" } +} +``` + +The server reads the character description from `public/characters.json`. It rejects empty or non-string messages, unknown character names, and character properties other than `name`. Invalid requests return HTTP 400 without calling an AI service. + +## Tests + +Run `npm test` in `app/`. The tests use local fake AI services. They do not use credentials or execute model-selected host commands. + +The model client also has a 60-second request timeout. A missing or empty model answer returns HTTP 500 instead of a successful empty response. + ## Interact with a character You're faced with a text input field. Type your message and see the response from the character you are chatting with. @@ -42,7 +97,7 @@ Select the character image to change the character you are speaking to. ## Change the character's behavior -In `characters.json`, there's a `description` property that will be used as a system message. If you change this, the character's personality will change. +In `public/characters.json`, the `description` property supplies the server-owned character instructions. Edit this file to change the character's personality. Do not send a description from the browser. ## Please read diff --git a/app/app-sdk.js b/app/app-sdk.js index 575af91d..1175d379 100644 --- a/app/app-sdk.js +++ b/app/app-sdk.js @@ -1,75 +1,138 @@ -// npm install @github/copilot-sdk tsx - import { CopilotClient } from "@github/copilot-sdk"; - import express from 'express'; import path from 'path'; -import { fileURLToPath } from 'url'; -import dotenv from 'dotenv'; - -import json from './public/characters.json' with { type: "json" }; - -const client = new CopilotClient(); - - -let systemMessage = json[4].description; -let page = json[4].page; - -console.log("SERVER systemMessage: ", systemMessage); -console.log("SERVER page: ", page); - -dotenv.config(); - -const app = express(); -const port = process.env.PORT || 3000; +import { fileURLToPath } from 'url'; +import { mkdtemp, rm } from 'fs/promises'; +import { tmpdir } from 'os'; +import { ChatRequestError, getChatRequest, handleBodyError } from './chat-request.js'; +import { loadEnvironment } from './model-config.js'; const __filename = fileURLToPath(import.meta.url); const __dirname = path.dirname(__filename); -app.use(express.json()); - -// Serve static files from the 'public' directory -app.use(express.static(path.join(__dirname, 'public'))); - -app.locals.delimiters = '{{ }}'; - -function getCharacterByName(name) { - return json.find(character => character.name === name) || null; +export function createClient(baseDirectory, connection) { + return new CopilotClient({ + mode: "empty", + baseDirectory, + workingDirectory: baseDirectory, + connection + }); } -// Route to send the prompt -app.post('/send', async (req, res) => { - const { message, character } = req.body; - - systemMessage = character.description; - const prompt = message; +async function closeSession(client, session) { + const errors = []; + for (const close of [ + () => session.abort(), + () => session.disconnect(), + () => client.deleteSession(session.sessionId) + ]) { + try { + await close(); + } catch (error) { + errors.push(error); + } + } + if (errors.length) { + throw new AggregateError(errors, 'The SDK session could not be closed.'); + } +} - try { - console.log("SYSTEM MESSAGE: ", systemMessage); - console.log(`SERVER sending prompt ${prompt}`) - - const session = await client.createSession({ - model: "gpt-4.1", - systemMessage: { - mode: "replace", - content: systemMessage - } - }); +export function createApp(client) { + const app = express(); + + app.use(express.json()); + app.use(express.static(path.join(__dirname, 'public'))); + app.locals.delimiters = '{{ }}'; + + app.post('/send', async (req, res) => { + try { + const { prompt, systemMessage } = getChatRequest(req.body); + const session = await client.createSession({ + model: "gpt-4.1", + systemMessage: { + mode: "append", + content: systemMessage + }, + // Custom tools and built-in tools have separate SDK controls. + tools: [], + availableTools: [], + onPermissionRequest: () => ({ kind: "reject" }), + hooks: { + onPreToolUse: () => ({ + permissionDecision: "deny", + permissionDecisionReason: "This application supports chat only." + }) + }, + infiniteSessions: { enabled: false }, + enableConfigDiscovery: false, + mcpServers: {}, + customAgents: [] + }); + + let response; + try { + response = await session.sendAndWait({ prompt }); + } finally { + await closeSession(client, session); + } + + const answer = response?.data?.content; + if (typeof answer !== 'string' || !answer.trim()) { + throw new Error('The SDK session did not return an answer.'); + } + + res.json({ prompt, answer }); + } catch (error) { + if (error instanceof ChatRequestError) { + res.status(400).json({ message: error.message }); + return; + } + console.error('Error:', error); + res.status(500).json({ message: 'An unexpected error occurred. Please try again later.' }); + } + }); + app.use(handleBodyError); + + return app; +} - const response = await session.sendAndWait({ prompt: prompt }); - const data = response?.data.content; - - console.log(`SERVER: ${data}`); - res.json({ - prompt: prompt, - answer: data - }); - } catch (error) { - console.error(`Error: ${error.message}`); // Log the error message for debugging - res.status(500).json({ message: 'An unexpected error occurred. Please try again later.' }); // Send a generic error message +if (process.argv[1] && path.resolve(process.argv[1]) === __filename) { + loadEnvironment(); + const port = process.env.PORT || 3000; + const baseDirectory = await mkdtemp(path.join(tmpdir(), 'character-chat-')); + const client = createClient(baseDirectory); + const app = createApp(client); + const server = app.listen(port, '127.0.0.1', () => { + console.log(`Server is running on http://localhost:${port}`); + }); + + let stopping = false; + async function stop() { + if (stopping) return; + stopping = true; + server.close(); + try { + const errors = await client.stop(); + if (errors.length) { + throw new AggregateError(errors, 'The SDK client could not be stopped.'); + } + } catch (error) { + console.error('Error:', error); + process.exitCode = 1; + } finally { + try { + await rm(baseDirectory, { recursive: true, force: true }); + } catch (error) { + console.error('Error:', error); + process.exitCode = 1; + } + } } -}); - -app.listen(port, () => { - console.log(`Server is running on http://localhost:${port}`); -}); + server.once('error', error => { + console.error('Error:', error); + process.exitCode = 1; + void stop(); + }); + process.once('SIGINT', stop); + process.once('SIGTERM', stop); +} diff --git a/app/app.js b/app/app.js index a659b0d7..7478f775 100644 --- a/app/app.js +++ b/app/app.js @@ -1,78 +1,64 @@ import express from 'express'; import { OpenAI } from 'openai'; import path from 'path'; -import { fileURLToPath } from 'url'; -import dotenv from 'dotenv'; - -import json from './public/characters.json' with { type: "json" }; - -let systemMessage = json[4].description; -let page = json[4].page; - -console.log("SERVER systemMessage: ", systemMessage); -console.log("SERVER page: ", page); - -dotenv.config(); - -const app = express(); -const port = process.env.PORT || 3000; +import { fileURLToPath } from 'url'; +import { ChatRequestError, getChatRequest, handleBodyError } from './chat-request.js'; +import { getModelConfig, loadEnvironment } from './model-config.js'; const __filename = fileURLToPath(import.meta.url); const __dirname = path.dirname(__filename); -app.use(express.json()); - -// Serve static files from the 'public' directory -app.use(express.static(path.join(__dirname, 'public'))); - -app.locals.delimiters = '{{ }}'; +export function createApp(openai, model) { + if (typeof model !== 'string' || !model.trim()) { + throw new Error('A model name is required to create the chat app.'); + } + const app = express(); + + app.use(express.json()); + app.use(express.static(path.join(__dirname, 'public'))); + app.locals.delimiters = '{{ }}'; + + app.post('/send', async (req, res) => { + try { + const { prompt, systemMessage } = getChatRequest(req.body); + const completion = await openai.chat.completions.create({ + model, + messages: [ + { role: "system", content: systemMessage }, + { role: "user", content: prompt } + ] + }); + + const answer = completion?.choices?.[0]?.message?.content; + if (typeof answer !== 'string' || !answer.trim()) { + throw new Error('The model did not return an answer.'); + } + res.json({ prompt, answer }); + } catch (error) { + if (error instanceof ChatRequestError) { + res.status(400).json({ message: error.message }); + return; + } + console.error(`Error: ${error.message}`); + res.status(500).json({ message: 'An unexpected error occurred. Please try again later.' }); + } + }); + app.use(handleBodyError); -function getCharacterByName(name) { - return json.find(character => character.name === name) || null; + return app; } -// Route to send the prompt -app.post('/send', async (req, res) => { - const { message, character } = req.body; - - systemMessage = character.description; - - const prompt = message; - - const messages = [ - { - "role": "system", - "content": systemMessage, - }, - { - "role": "user", - "content": prompt - } - ]; - +if (process.argv[1] && path.resolve(process.argv[1]) === __filename) { + loadEnvironment(); + const port = process.env.PORT || 3000; + const { baseURL, apiKey, model } = getModelConfig(); const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", // might need to change to this url in the future: https://models.github.ai/inference - apiKey: process.env.GITHUB_TOKEN, + baseURL, + apiKey, + timeout: 60000 }); - - try { - console.log(`SERVER sending prompt ${prompt}`) - const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', - messages: messages, - }); - - console.log(`SERVER: ${completion.choices[0]?.message?.content}`); - res.json({ - prompt: prompt, - answer: completion.choices[0]?.message?.content - }); - } catch (error) { - console.error(`Error: ${error.message}`); // Log the error message for debugging - res.status(500).json({ message: 'An unexpected error occurred. Please try again later.' }); // Send a generic error message - } -}); - -app.listen(port, () => { - console.log(`Server is running on http://localhost:${port}`); -}); + const app = createApp(openai, model); + app.listen(port, '127.0.0.1', () => { + console.log(`Server is running on http://localhost:${port}`); + }); +} diff --git a/app/chat-request.js b/app/chat-request.js new file mode 100644 index 00000000..def6c274 --- /dev/null +++ b/app/chat-request.js @@ -0,0 +1,40 @@ +import characters from './public/characters.json' with { type: 'json' }; + +export class ChatRequestError extends Error {} + +export function handleBodyError(error, req, res, next) { + if (error.type === 'entity.parse.failed') { + res.status(400).json({ message: 'Send a valid JSON object.' }); + } else if (error.type === 'entity.too.large') { + res.status(413).json({ message: 'The request is too large.' }); + } else { + next(error); + } +} + +export function getChatRequest(body) { + if (!body || typeof body !== 'object' || Array.isArray(body)) { + throw new ChatRequestError('Send a message and a character name.'); + } + + const { message, character } = body; + if (typeof message !== 'string' || !message.trim()) { + throw new ChatRequestError('The message must be a non-empty string.'); + } + + if (!character || typeof character !== 'object' || Array.isArray(character) || + typeof character.name !== 'string') { + throw new ChatRequestError('Send a character name.'); + } + + if (Object.keys(character).some(key => key !== 'name')) { + throw new ChatRequestError('Send only the character name, not its description.'); + } + + const selectedCharacter = characters.find(item => item.name === character.name); + if (!selectedCharacter) { + throw new ChatRequestError('The character name is not valid.'); + } + + return { prompt: message, systemMessage: selectedCharacter.description }; +} diff --git a/app/model-config.js b/app/model-config.js new file mode 100644 index 00000000..7c34636d --- /dev/null +++ b/app/model-config.js @@ -0,0 +1,39 @@ +import dotenv from 'dotenv'; +import { fileURLToPath } from 'url'; + +export function loadEnvironment(env = process.env) { + const envFile = env.ENV_FILE; + const result = dotenv.config({ + path: envFile || fileURLToPath(new URL('../.env', import.meta.url)), + processEnv: env, + quiet: true + }); + if (result.error && (envFile || result.error.code !== 'ENOENT')) { + throw new Error('The environment file could not be read.', { cause: result.error }); + } +} + +export function getModelConfig(env = process.env) { + for (const name of ['AI_ENDPOINT', 'AI_API_KEY', 'AI_MODEL']) { + if (typeof env[name] !== 'string' || !env[name].trim()) { + throw new Error(`${name} is required. Set it in the environment or the repository's .env file.`); + } + } + + const baseURL = env.AI_ENDPOINT.trim(); + if (!URL.canParse(baseURL)) { + throw new Error('AI_ENDPOINT must be an absolute API base URL.'); + } + const url = new URL(baseURL); + const localHttp = url.protocol === 'http:' && + ['localhost', '127.0.0.1', '[::1]'].includes(url.hostname); + if ((url.protocol !== 'https:' && !localHttp) || url.username || url.password) { + throw new Error('AI_ENDPOINT must use HTTPS, or HTTP on a loopback address, without URL credentials.'); + } + + return { + baseURL, + apiKey: env.AI_API_KEY.trim(), + model: env.AI_MODEL.trim() + }; +} diff --git a/app/package-lock.json b/app/package-lock.json index 7796000c..d50a034c 100644 --- a/app/package-lock.json +++ b/app/package-lock.json @@ -9,18 +9,21 @@ "version": "1.0.0", "license": "MIT", "dependencies": { - "@github/copilot-sdk": "^0.1.23", - "dotenv": "^16.4.7", - "ejs": "^3.1.10", - "express": "^4.21.2", - "openai": "^4.85.0", - "tsx": "^4.21.0" + "@github/copilot-sdk": "1.0.16", + "dotenv": "^18.0.5", + "ejs": "^7.0.1", + "express": "^5.2.1", + "openai": "^7.28.0", + "tsx": "^4.23.15" + }, + "devDependencies": { + "vscode-jsonrpc": "^9.0.3" } }, "node_modules/@esbuild/aix-ppc64": { - 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"media-typer": "0.3.0", - "mime-types": "~2.1.24" + "content-type": "^2.0.0", + "media-typer": "^1.1.0", + "mime-types": "^3.0.0" }, "engines": { - "node": ">= 0.6" + "node": ">= 18" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/express" } }, - "node_modules/undici-types": { - "version": "5.26.5", - "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz", - "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA==", - "license": "MIT" + "node_modules/type-is/node_modules/content-type": { + "version": "2.1.0", + "resolved": "https://registry.npmjs.org/content-type/-/content-type-2.1.0.tgz", + "integrity": "sha512-mj7UPXE0jaqaOsukNZRUEfEi2AcL7C/vwmwcHV0O97eO1E1pxBZuyjlZrx5seTaNBg1U6+o35wpa35Qfcc+7ag==", + "license": "MIT", + "engines": { + "node": ">=18" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/express" + } }, "node_modules/unpipe": { "version": "1.0.0", @@ -1881,15 +1892,6 @@ "node": ">= 0.8" } }, - "node_modules/utils-merge": { - "version": "1.0.1", - "resolved": "https://registry.npmjs.org/utils-merge/-/utils-merge-1.0.1.tgz", - "integrity": "sha512-pMZTvIkT1d+TFGvDOqodOclx0QWkkgi6Tdoa8gC8ffGAAqz9pzPTZWAybbsHHoED/ztMtkv/VoYTYyShUn81hA==", - "license": "MIT", - "engines": { - "node": ">= 0.4.0" - } - }, "node_modules/vary": { "version": "1.1.2", "resolved": "https://registry.npmjs.org/vary/-/vary-1.1.2.tgz", @@ -1900,38 +1902,20 @@ } }, "node_modules/vscode-jsonrpc": { - "version": "8.2.1", - "resolved": "https://registry.npmjs.org/vscode-jsonrpc/-/vscode-jsonrpc-8.2.1.tgz", - "integrity": "sha512-kdjOSJ2lLIn7r1rtrMbbNCHjyMPfRnowdKjBQ+mGq6NAW5QY2bEZC/khaC5OR8svbbjvLEaIXkOq45e2X9BIbQ==", + "version": "9.0.3", + "resolved": "https://registry.npmjs.org/vscode-jsonrpc/-/vscode-jsonrpc-9.0.3.tgz", + "integrity": "sha512-qETYf9PCl1lVfwR+oBmMfc4vP2nqLNA35ABxorw3j06+xlKRgDVxbLgKm4Y/xPL0YWdeDjLoI4ImICIT3nB0zQ==", + "dev": true, "license": "MIT", "engines": { "node": ">=14.0.0" } }, - "node_modules/web-streams-polyfill": { - "version": "4.0.0-beta.3", - "resolved": "https://registry.npmjs.org/web-streams-polyfill/-/web-streams-polyfill-4.0.0-beta.3.tgz", - "integrity": "sha512-QW95TCTaHmsYfHDybGMwO5IJIM93I/6vTRk+daHTWFPhwh+C8Cg7j7XyKrwrj8Ib6vYXe0ocYNrmzY4xAAN6ug==", - "license": "MIT", - "engines": { - "node": ">= 14" - } - }, - "node_modules/webidl-conversions": { - "version": "3.0.1", - "resolved": "https://registry.npmjs.org/webidl-conversions/-/webidl-conversions-3.0.1.tgz", - "integrity": "sha512-2JAn3z8AR6rjK8Sm8orRC0h/bcl/DqL7tRPdGZ4I1CjdF+EaMLmYxBHyXuKL849eucPFhvBoxMsflfOb8kxaeQ==", - "license": "BSD-2-Clause" - }, - "node_modules/whatwg-url": { - "version": "5.0.0", - "resolved": "https://registry.npmjs.org/whatwg-url/-/whatwg-url-5.0.0.tgz", - "integrity": "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw==", - "license": "MIT", - "dependencies": { - "tr46": "~0.0.3", - "webidl-conversions": "^3.0.0" - } + "node_modules/wrappy": { + "version": "1.0.2", + "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz", + "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==", + "license": "ISC" } } } diff --git a/app/package.json b/app/package.json index 03385e24..49c87146 100644 --- a/app/package.json +++ b/app/package.json @@ -5,18 +5,22 @@ "type": "module", "scripts": { "start": "node app.js", - "start:sdk": "node app-sdk.js" + "start:sdk": "node app-sdk.js", + "test": "node --test" }, "keywords": [], "author": "Microsoft Corporation", "license": "MIT", "description": "", + "devDependencies": { + "vscode-jsonrpc": "^9.0.3" + }, "dependencies": { - "@github/copilot-sdk": "^0.1.23", - "dotenv": "^16.4.7", - "ejs": "^3.1.10", - "express": "^4.21.2", - "openai": "^4.85.0", - "tsx": "^4.21.0" + "@github/copilot-sdk": "1.0.16", + "dotenv": "^18.0.5", + "ejs": "^7.0.1", + "express": "^5.2.1", + "openai": "^7.28.0", + "tsx": "^4.23.15" } } diff --git a/app/public/index.html b/app/public/index.html index 6c7f492b..748b7025 100644 --- a/app/public/index.html +++ b/app/public/index.html @@ -80,11 +80,13 @@

const sendBtn = document.querySelector("#sendBtn"); const chatInput = document.querySelector("#chatInput"); const conversationTpl = document.querySelector('#conversationTpl'); + const responseWindow = document.querySelector('#responseWindow'); let currentCharacter = characters[0]; const maxVolume = 0.5; let utterance = null; + let sending = false; card.addEventListener("mousemove", (e) => { const { clientX, clientY } = e; @@ -129,22 +131,22 @@

sendBtn.addEventListener("click", send); chatInput.addEventListener("input", (e) => { - if (e.target.value) { - sendBtn.disabled = false; - } else { - sendBtn.disabled = true; - } + sendBtn.disabled = sending || !e.target.value; }); - chatInput.addEventListener("keypress", (e) => { + chatInput.addEventListener("keydown", (e) => { if (e.key === "Enter") { + e.preventDefault(); send(); } }); function send() { const message = chatInput.value; - if (!message) return; + if (!message || sending) return; + const requestedCharacter = currentCharacter; + sending = true; + sendBtn.disabled = true; appendConversation({ @@ -163,26 +165,38 @@

headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ message, character: currentCharacter }) + body: JSON.stringify({ message, character: { name: requestedCharacter.name } }) }) - .then(response => response.json()) + .then(async response => { + const data = await response.json(); + if (!response.ok) { + throw new Error(data.message || 'The request failed.'); + } + return data; + }) .then(data => { appendConversation({ type: 'AGENT', message: data.answer, - character: currentCharacter + character: requestedCharacter }); if (speakToggle.checked) { console.log("speaking"); - say(data.answer); + say(data.answer, requestedCharacter); } }) .catch(error => { - console.error('Error:', error.error); + console.error('Error:', error.message); + appendConversation({ + type: 'AGENT', + message: error.message, + character: requestedCharacter + }); }) .finally(() => { + sending = false; chatInput.value = ''; chatInput.disabled = false; chatInput.focus(); @@ -191,9 +205,9 @@

} - function say(text) { + function say(text, character = currentCharacter) { utterance = new SpeechSynthesisUtterance(text); - utterance.voice = speechSynthesis.getVoices()[currentCharacter.voice || 0]; + utterance.voice = speechSynthesis.getVoices()[character.voice || 0] || null; utterance.rate = 1; utterance.pitch = 1; utterance.volume = 1; @@ -270,14 +284,8 @@

function loadCharacterFromUrl() { const urlParams = new URLSearchParams(window.location.search); const characterName = urlParams.get('character'); - if (characterName) { - const character = characters.find(c => c.name === characterName); - if (character) { - setCharacter(character, true); - } - }else { - setCharacter(currentCharacter, true); - } + const character = characters.find(c => c.name === characterName) || currentCharacter; + setCharacter(character, true); } loadCharacterFromUrl(); diff --git a/app/public/js/script.js b/app/public/js/script.js index 19c641e9..6f528468 100644 --- a/app/public/js/script.js +++ b/app/public/js/script.js @@ -12,6 +12,7 @@ let currentCharacter = currentUrl.searchParams.get('character') || ""; let currentVoice = "<%= voice %>"; const maxVolume = 0.5 +let sending = false; backgroundEl.volume = maxVolume; @@ -78,15 +79,24 @@ buttonChange.addEventListener("click", () => { button.addEventListener('click', () => { const message = chatWindow.value; + if (!message || sending) return; + sending = true; + button.disabled = true; responseWindow.value = `You: ${message}`; fetch('/send', { method: 'POST', headers: { 'Content-Type': 'application/json' }, - body: JSON.stringify({ message }) + body: JSON.stringify({ message, character: { name: listEl.value } }) }) - .then(response => response.json()) + .then(async response => { + const data = await response.json(); + if (!response.ok) { + throw new Error(data.message || 'The request failed.'); + } + return data; + }) .then(data => { responseWindow.value += `\n<%= title %>: ${data.answer}`; console.log("speak enabled", speakEl.checked); @@ -97,7 +107,12 @@ button.addEventListener('click', () => { }) .catch(error => { - console.error('Error:', error.error); + console.error('Error:', error.message); + responseWindow.value += `\n${error.message}`; + }) + .finally(() => { + sending = false; + button.disabled = false; }); chatWindow.value = ''; }); diff --git a/app/test/chat.test.js b/app/test/chat.test.js new file mode 100644 index 00000000..b4f61fec --- /dev/null +++ b/app/test/chat.test.js @@ -0,0 +1,253 @@ +import assert from 'node:assert/strict'; +import { once } from 'node:events'; +import { createServer } from 'node:http'; +import { test } from 'node:test'; +import { OpenAI } from 'openai'; +import { createApp as createSdkApp, createClient } from '../app-sdk.js'; +import { createApp as createModelsApp } from '../app.js'; +import characters from '../public/characters.json' with { type: 'json' }; + +async function startApp(t, app) { + const server = app.listen(0, '127.0.0.1'); + await once(server, 'listening'); + t.after(() => new Promise((resolve, reject) => { + server.close(error => error ? reject(error) : resolve()); + server.closeAllConnections(); + })); + const url = `http://127.0.0.1:${server.address().port}`; + return { + url, + async send(body) { + const response = await fetch(`${url}/send`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(body) + }); + return { status: response.status, body: await response.json() }; + } + }; +} + +function fakeSdk({ sendError, closeError, response } = {}) { + const configs = []; + const prompts = []; + const lifecycle = []; + const client = { + async createSession(config) { + configs.push(config); + const sessionId = `session-${configs.length}`; + return { + sessionId, + async sendAndWait(options) { + prompts.push(options); + if (sendError) throw sendError; + return response ?? { data: { content: config.systemMessage.content } }; + }, + async abort() { + lifecycle.push(['abort', sessionId]); + if (closeError) throw closeError; + }, + async disconnect() { + lifecycle.push(['disconnect', sessionId]); + } + }; + }, + async deleteSession(sessionId) { + lifecycle.push(['delete', sessionId]); + } + }; + return { app: createSdkApp(client), configs, prompts, lifecycle }; +} + +function fakeModels() { + const configs = []; + const openai = { + chat: { + completions: { + async create(config) { + configs.push(config); + return { choices: [{ message: { content: config.messages[0].content } }] }; + } + } + } + }; + return { app: createModelsApp(openai, 'test-deployment'), configs }; +} + +for (const [name, makeApp] of [['Copilot SDK', fakeSdk], ['OpenAI-compatible', fakeModels]]) { + test(`${name} rejects malformed and oversized JSON with explicit errors`, async t => { + const { app, configs } = makeApp(); + const { url } = await startApp(t, app); + for (const [body, status, message] of [ + ['{"message":', 400, 'Send a valid JSON object.'], + [JSON.stringify({ message: 'x'.repeat(110 * 1024), character: { name: 'ada' } }), + 413, 'The request is too large.'] + ]) { + const response = await fetch(`${url}/send`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body + }); + assert.equal(response.status, status); + assert.match(response.headers.get('content-type'), /application\/json/); + assert.deepEqual(await response.json(), { message }); + } + assert.equal(configs.length, 0); + }); + + test(`${name} rejects invalid requests before calling the AI service`, async t => { + const { app, configs } = makeApp(); + const { send } = await startApp(t, app); + for (const body of [ + null, [], {}, + { message: '', character: { name: 'ada' } }, + { message: ' ', character: { name: 'ada' } }, + { message: 42, character: { name: 'ada' } }, + { message: { text: 'hello' }, character: { name: 'ada' } }, + { message: 'hello' }, + { message: 'hello', character: null }, + { message: 'hello', character: [] }, + { message: 'hello', character: 'ada' }, + { message: 'hello', character: { name: 42 } }, + { message: 'hello', character: { name: 'unknown' } }, + { message: 'hello', character: { name: 'ada', description: 'Untrusted instructions' } }, + { message: 'hello', character: { name: 'ada', tools: ['shell'] } } + ]) { + const response = await send(body); + assert.equal(response.status, 400, JSON.stringify(body)); + assert.equal(typeof response.body.message, 'string'); + } + assert.equal(configs.length, 0); + }); + + test(`${name} uses server-owned instructions for every character`, async t => { + const { app, configs } = makeApp(); + const { send, url } = await startApp(t, app); + const responses = await Promise.all(characters.map(character => + send({ message: 'Tell me about your work.', character: { name: character.name } }) + )); + responses.forEach((response, index) => { + assert.equal(response.status, 200); + assert.deepEqual(response.body, { + prompt: 'Tell me about your work.', + answer: characters[index].description + }); + }); + assert.equal(configs.length, characters.length); + const page = await fetch(url); + assert.equal(page.status, 200); + assert.match(await page.text(), /character: \{ name: requestedCharacter\.name \}/); + }); +} + +test('SDK disables every tool and rejects every permission request', async t => { + const { app, configs, prompts, lifecycle } = fakeSdk(); + const { send } = await startApp(t, app); + const prompt = 'Please use a host tool.'; + assert.equal((await send({ message: prompt, character: { name: 'ada' } })).status, 200); + + const [config] = configs; + assert.deepEqual(config.tools, []); + assert.deepEqual(config.availableTools, []); + assert.deepEqual(config.mcpServers, {}); + assert.deepEqual(config.customAgents, []); + assert.deepEqual(config.infiniteSessions, { enabled: false }); + assert.equal(config.enableConfigDiscovery, false); + assert.equal(config.systemMessage.mode, 'append'); + assert.equal(config.systemMessage.content, characters.find(item => item.name === 'ada').description); + assert.deepEqual(prompts, [{ prompt }]); + for (const kind of ['shell', 'read', 'write', 'url', 'mcp', 'future-permission', undefined]) { + assert.deepEqual(await config.onPermissionRequest({ kind }), { kind: 'reject' }); + } + for (const toolName of ['powershell', 'bash', 'read_file', 'future-tool', undefined]) { + assert.equal((await config.hooks.onPreToolUse({ toolName })).permissionDecision, 'deny'); + } + assert.deepEqual(lifecycle, [ + ['abort', 'session-1'], ['disconnect', 'session-1'], ['delete', 'session-1'] + ]); +}); + +for (const [name, options] of [ + ['model error', { sendError: new Error('Private SDK error details') }], + ['timeout', { sendError: new Error('Timeout waiting for session.idle') }], + ['missing answer', { response: {} }], + ['empty answer', { response: { data: { content: '' } } }], + ['cleanup error', { closeError: new Error('Abort failed') }] +]) { + test(`SDK reports ${name} and still attempts all session cleanup`, async t => { + const log = t.mock.method(console, 'error', () => {}); + const { app, lifecycle } = fakeSdk(options); + const { send } = await startApp(t, app); + const response = await send({ message: 'hello', character: { name: 'ada' } }); + assert.equal(response.status, 500); + assert.deepEqual(response.body, { + message: 'An unexpected error occurred. Please try again later.' + }); + assert.equal(log.mock.callCount(), 1); + assert.deepEqual(lifecycle, [ + ['abort', 'session-1'], ['disconnect', 'session-1'], ['delete', 'session-1'] + ]); + }); +} + +test('SDK client requires an explicit state directory for empty mode', () => { + assert.throws(() => createClient(), /mode: 'empty'.*baseDirectory/); +}); + +test('the OpenAI-compatible app requires an explicit model', () => { + assert.throws(() => createModelsApp({}), /model name is required/); +}); + +for (const [name, completion] of [ + ['missing completion', undefined], + ['missing choices', {}], + ['empty choices', { choices: [] }], + ['empty answer', { choices: [{ message: { content: '' } }] }], + ['non-string answer', { choices: [{ message: { content: 42 } }] }] +]) { + test(`the OpenAI-compatible app reports ${name} without a successful empty response`, async t => { + const log = t.mock.method(console, 'error', () => {}); + const app = createModelsApp({ + chat: { completions: { create: async () => completion } } + }, 'test-deployment'); + const { send } = await startApp(t, app); + const response = await send({ message: 'Hello', character: { name: 'ada' } }); + assert.equal(response.status, 500); + assert.deepEqual(response.body, { + message: 'An unexpected error occurred. Please try again later.' + }); + assert.equal(log.mock.callCount(), 1); + }); +} + +test('the updated OpenAI SDK preserves the chat-completion request and response', async t => { + let request; + const provider = createServer(async (req, res) => { + assert.equal(req.url, '/chat/completions'); + let body = ''; + for await (const chunk of req) body += chunk; + request = JSON.parse(body); + res.setHeader('Content-Type', 'application/json'); + res.end(JSON.stringify({ choices: [{ message: { content: 'Hello from Ada.' } }] })); + }); + provider.listen(0, '127.0.0.1'); + await once(provider, 'listening'); + t.after(() => new Promise(resolve => { + provider.close(resolve); + provider.closeAllConnections(); + })); + const openai = new OpenAI({ + baseURL: `http://127.0.0.1:${provider.address().port}`, + apiKey: 'local-test-only', + maxRetries: 0 + }); + const { send } = await startApp(t, createModelsApp(openai, 'test-deployment')); + const response = await send({ message: 'Hello', character: { name: 'ada' } }); + assert.equal(response.status, 200); + assert.deepEqual(response.body, { prompt: 'Hello', answer: 'Hello from Ada.' }); + assert.equal(request.model, 'test-deployment'); + assert.deepEqual(request.messages, [ + { role: 'system', content: characters.find(item => item.name === 'ada').description }, + { role: 'user', content: 'Hello' } + ]); +}); diff --git a/app/test/model-config.test.js b/app/test/model-config.test.js new file mode 100644 index 00000000..fa323723 --- /dev/null +++ b/app/test/model-config.test.js @@ -0,0 +1,88 @@ +import assert from 'node:assert/strict'; +import { mkdtemp, rm, writeFile } from 'node:fs/promises'; +import { tmpdir } from 'node:os'; +import path from 'node:path'; +import { test } from 'node:test'; +import { fileURLToPath } from 'node:url'; +import dotenv from 'dotenv'; +import { getModelConfig, loadEnvironment } from '../model-config.js'; + +const settings = { + AI_ENDPOINT: 'https://example.test/openai/v1/', + AI_API_KEY: 'fixture-key', + AI_MODEL: 'fixture-deployment' +}; + +test('model configuration uses the course environment variable names', () => { + assert.deepEqual(getModelConfig(settings), { + baseURL: settings.AI_ENDPOINT, + apiKey: settings.AI_API_KEY, + model: settings.AI_MODEL + }); +}); + +test('model configuration rejects missing, blank, or non-string values', () => { + for (const name of Object.keys(settings)) { + for (const value of [undefined, '', ' ', 42]) { + assert.throws(() => getModelConfig({ ...settings, [name]: value }), new RegExp(name)); + } + } +}); + +test('model configuration rejects invalid or unsafe endpoint URLs', () => { + for (const endpoint of [ + 'not a URL', + '/relative/path', + 'file:///tmp/model', + 'http://example.test/v1/', + 'https://user:password@example.test/v1/' + ]) { + assert.throws(() => getModelConfig({ ...settings, AI_ENDPOINT: endpoint }), /AI_ENDPOINT/); + } +}); + +test('model configuration permits loopback HTTP providers and trims settings', () => { + for (const endpoint of ['http://localhost:3000/v1', 'http://127.0.0.1:3000/v1', 'http://[::1]:3000/v1']) { + assert.equal(getModelConfig({ ...settings, AI_ENDPOINT: endpoint }).baseURL, endpoint); + } + assert.deepEqual(getModelConfig({ + AI_ENDPOINT: ` ${settings.AI_ENDPOINT} `, + AI_API_KEY: ' fixture-key ', + AI_MODEL: ' fixture-deployment ' + }), { baseURL: settings.AI_ENDPOINT, apiKey: 'fixture-key', model: 'fixture-deployment' }); +}); + +test('environment loading uses the repository-root .env file by default', t => { + const config = t.mock.method(dotenv, 'config', () => ({ parsed: {} })); + const env = {}; + loadEnvironment(env); + assert.equal(config.mock.calls[0].arguments[0].path, fileURLToPath(new URL('../../.env', import.meta.url))); + assert.equal(config.mock.calls[0].arguments[0].processEnv, env); +}); + +test('an explicit environment file loads settings without overriding existing values', async t => { + const directory = await mkdtemp(path.join(tmpdir(), 'character-chat-env-')); + t.after(() => rm(directory, { recursive: true, force: true })); + const file = path.join(directory, '.env'); + await writeFile(file, 'AI_ENDPOINT=https://example.test/v1/\nAI_API_KEY=fixture-key\nAI_MODEL=fixture-model\nPORT=12345\n'); + const env = { ENV_FILE: file, AI_MODEL: 'existing-model' }; + loadEnvironment(env); + assert.equal(env.AI_MODEL, 'existing-model'); + assert.equal(env.AI_API_KEY, 'fixture-key'); + assert.equal(env.PORT, '12345'); +}); + +test('an unreadable explicit environment file fails at startup', () => { + assert.throws(() => loadEnvironment({ ENV_FILE: fileURLToPath(new URL('./missing.env', import.meta.url)) }), + /environment file could not be read/); +}); + +test('only a missing optional default environment file is ignored', t => { + const error = Object.assign(new Error('Missing file'), { code: 'ENOENT' }); + const config = t.mock.method(dotenv, 'config', () => ({ error })); + assert.doesNotThrow(() => loadEnvironment({})); + config.mock.mockImplementation(() => ({ + error: Object.assign(new Error('Access denied'), { code: 'EACCES' }) + })); + assert.throws(() => loadEnvironment({}), /environment file could not be read/); +}); diff --git a/app/test/sdk-protocol.test.js b/app/test/sdk-protocol.test.js new file mode 100644 index 00000000..3243c76d --- /dev/null +++ b/app/test/sdk-protocol.test.js @@ -0,0 +1,132 @@ +import assert from 'node:assert/strict'; +import { once } from 'node:events'; +import { createServer } from 'node:net'; +import { tmpdir } from 'node:os'; +import path from 'node:path'; +import { test } from 'node:test'; +import { + createMessageConnection, StreamMessageReader, StreamMessageWriter +} from 'vscode-jsonrpc/node'; +import { RuntimeConnection } from '@github/copilot-sdk'; +import { createApp, createClient } from '../app-sdk.js'; + +test('the installed SDK sends chat-only controls and preserves SDK guardrails', { timeout: 10000 }, async t => { + const requests = []; + let rpc; + let hookResult; + let resolvePermission; + const permissionAnswered = new Promise(resolve => { resolvePermission = resolve; }); + const runtime = createServer(socket => { + rpc = createMessageConnection( + new StreamMessageReader(socket), new StreamMessageWriter(socket) + ); + rpc.onRequest(async (method, params) => { + requests.push({ method, params }); + switch (method) { + case 'connect': + return { protocolVersion: 3 }; + case 'session.create': + return { sessionId: params.sessionId, capabilities: {} }; + case 'session.options.update': + return { success: true }; + case 'session.send': + await rpc.sendNotification('session.event', { + sessionId: params.sessionId, + event: { + type: 'permission.requested', + data: { + requestId: 'permission-1', + permissionRequest: { kind: 'shell', commands: [] } + } + } + }); + await permissionAnswered; + hookResult = await rpc.sendRequest('hooks.invoke', { + sessionId: params.sessionId, + hookType: 'preToolUse', + input: { toolName: 'future-tool', toolArgs: {} } + }); + await rpc.sendNotification('session.event', { + sessionId: params.sessionId, + event: { type: 'assistant.message', data: { content: 'Hello from Ada.' } } + }); + await rpc.sendNotification('session.event', { + sessionId: params.sessionId, + event: { type: 'session.idle', data: { mode: 'interactive' } } + }); + return { messageId: 'message-1' }; + case 'session.permissions.handlePendingPermissionRequest': + resolvePermission(); + return { applied: true }; + case 'session.abort': + return {}; + case 'session.detach': + case 'session.delete': + return { success: true }; + default: + throw new Error(`Unexpected SDK RPC: ${method}`); + } + }); + rpc.listen(); + }); + runtime.listen(0, '127.0.0.1'); + await once(runtime, 'listening'); + const directory = path.join(tmpdir(), 'character-chat-protocol-test'); + const client = createClient(directory, RuntimeConnection.forUri( + `127.0.0.1:${runtime.address().port}` + )); + const server = createApp(client).listen(0, '127.0.0.1'); + await once(server, 'listening'); + t.after(async () => { + const errors = await client.stop(); + rpc?.dispose(); + await Promise.all([ + new Promise(resolve => runtime.close(resolve)), + new Promise(resolve => { + server.close(resolve); + server.closeAllConnections(); + }) + ]); + assert.deepEqual(errors, []); + }); + + await assert.rejects(client.createSession({ model: 'gpt-4.1' }), /availableTools/); + assert.equal(requests.some(item => item.method === 'session.create'), false); + const response = await fetch(`http://127.0.0.1:${server.address().port}/send`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ message: 'Hello', character: { name: 'ada' } }) + }); + assert.equal(response.status, 200); + assert.deepEqual(await response.json(), { prompt: 'Hello', answer: 'Hello from Ada.' }); + + const config = requests.find(item => item.method === 'session.create').params; + assert.deepEqual(config.availableTools, []); + assert.deepEqual(config.tools, []); + assert.equal(config.requestPermission, true); + const permission = requests.find(item => + item.method === 'session.permissions.handlePendingPermissionRequest' + ).params; + assert.equal(permission.requestId, 'permission-1'); + assert.deepEqual(permission.result, { kind: 'reject' }); + assert.equal(config.hooks, true); + assert.equal(config.toolFilterPrecedence, 'excluded'); + assert.equal(config.enableConfigDiscovery, false); + assert.equal(config.enableFileHooks, false); + assert.equal(config.enableHostGitOperations, false); + assert.equal(config.enableSkills, false); + assert.equal(config.enableSessionStore, false); + assert.equal(config.enableSessionTelemetry, false); + assert.deepEqual(config.memory, { enabled: false }); + assert.equal(config.systemMessage.mode, 'customize'); + assert.deepEqual(config.systemMessage.sections.environment_context, { action: 'remove' }); + assert.match(config.systemMessage.content, /Ada Lovelace/); + assert.equal(hookResult.output.permissionDecision, 'deny'); + const patch = requests.find(item => item.method === 'session.options.update').params; + assert.equal(patch.skipCustomInstructions, true); + assert.deepEqual(patch.installedPlugins, []); + assert.deepEqual(patch.includedBuiltinSkills, []); + assert.deepEqual(requests.filter(item => + ['session.abort', 'session.detach', 'session.delete'].includes(item.method) + ).map(item => item.method), ['session.abort', 'session.detach', 'session.delete']); +}); diff --git a/app/test/sdk-runtime.test.js b/app/test/sdk-runtime.test.js new file mode 100644 index 00000000..b8ab57dd --- /dev/null +++ b/app/test/sdk-runtime.test.js @@ -0,0 +1,68 @@ +import assert from 'node:assert/strict'; +import { once } from 'node:events'; +import { mkdtemp, rm } from 'node:fs/promises'; +import { tmpdir } from 'node:os'; +import path from 'node:path'; +import { test } from 'node:test'; +import { RuntimeConnection } from '@github/copilot-sdk'; +import { createApp, createClient } from '../app-sdk.js'; + +test('the packaged runtime advertises no tools and deletes chat sessions', { timeout: 30000 }, async t => { + const directory = await mkdtemp(path.join(tmpdir(), 'character-chat-test-')); + const env = { + PATH: process.env.PATH, + HOME: directory, + TMPDIR: directory, + COPILOT_TELEMETRY_DISABLED: '1' + }; + for (const name of ['SystemRoot', 'WINDIR', 'TEMP', 'TMP']) { + if (process.env[name]) env[name] = process.env[name]; + } + const client = createClient(directory, RuntimeConnection.forStdio({ + env, args: ['--no-auto-login'] + })); + let inventory; + const app = createApp({ + async createSession(config) { + const session = await client.createSession({ + ...config, + provider: { + type: 'openai', + baseUrl: 'http://127.0.0.1:1', + apiKey: 'local-test-only', + wireApi: 'completions' + } + }); + inventory = await session.rpc.tools.getCurrentMetadata(); + return { + sessionId: session.sessionId, + sendAndWait: async () => ({ data: { content: 'A local chat response.' } }), + abort: () => session.abort(), + disconnect: () => session.disconnect() + }; + }, + deleteSession: sessionId => client.deleteSession(sessionId) + }); + const server = app.listen(0, '127.0.0.1'); + t.after(async () => { + try { + await new Promise(resolve => { + server.close(resolve); + server.closeAllConnections(); + }); + assert.deepEqual(await client.stop(), []); + } finally { + await rm(directory, { recursive: true, force: true }); + } + }); + await once(server, 'listening'); + const response = await fetch(`http://127.0.0.1:${server.address().port}/send`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ message: 'Hello', character: { name: 'ada' } }) + }); + assert.equal(response.status, 200); + assert.deepEqual(await response.json(), { prompt: 'Hello', answer: 'A local chat response.' }); + assert.deepEqual(inventory.tools, []); + assert.deepEqual(await client.listSessions(), []); +}); diff --git a/docs/additional-resources.md b/docs/additional-resources.md index cd058b88..dbc1985c 100644 --- a/docs/additional-resources.md +++ b/docs/additional-resources.md @@ -14,8 +14,7 @@ You can find here hands-on tutorials to put your AI skills into practice, with s | Name | Description | Duration | Level | Links | | ---- | ----------- | -------- | ----- | ----- | -| Azure OpenAI RAG workshop - Qdrant version | Explore the fundamentals of custom ChatGPT experiences based on a corpus of documents. We will create a vector database and fill-in with data from PDF documents, and then build a chat website and API to be able to ask questions about information contained in these documents. | 3h | Beginner | [Tutorial](https://moaw.dev/workshop/gh:azure-samples/azure-openai-rag-workshop/docs/workshop-qdrant.md) / [Repo](https://github.com/Azure-Samples/azure-openai-rag-workshop) | -| Azure OpenAI RAG workshop - AI Search version | Explore the fundamentals of custom ChatGPT experiences based on a corpus of documents. We will create a vector database and fill-in with data from PDF documents, and then build a chat website and API to be able to ask questions about information contained in these documents. | 3h | Beginner | [Tutorial](https://moaw.dev/workshop/?src=gh:azure-samples/azure-openai-rag-workshop/docs/) / [Repo](https://github.com/Azure-Samples/azure-openai-rag-workshop) | +| Azure OpenAI RAG workshop | Build a chat application that retrieves information from a document collection. Use the workshop's current setup and exercises for its supported search services. | 3h | Beginner | [Workshop docs](https://github.com/Azure-Samples/azure-openai-rag-workshop/tree/main/docs) / [Repo](https://github.com/Azure-Samples/azure-openai-rag-workshop) | | Integrate OpenAI, Communication, and Organizational Data Features into a Line of Business App | This tutorial demonstrates how Azure OpenAI, Azure Communication Services, and Microsoft Graph/Microsoft Graph Toolkit can be integrated into a Line of Business (LOB) application to enhance user productivity, elevate the user experience, and take LOB apps to the next level | 3h | Beginner | [Tutorial](https://learn.microsoft.com/microsoft-cloud/dev/tutorials/openai-acs-msgraph) / [Repo](https://github.com/microsoft/MicrosoftCloud) | ## Code samples diff --git a/docs/images/create-codespace.png b/docs/images/create-codespace.png deleted file mode 100644 index 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An application programming interface (API) lets your code send requests to that service. This course's companion app uses the Azure OpenAI-compatible API through the `openai` JavaScript package. You do not need an additional Foundry software development kit (SDK) or changes to the app's source code. -- **Your chosen environment**. There's essentially two ways to set up your development environment for this course: - - **Option 1: GitHub Codespaces**: You can either use GitHub Codespaces. You can configure your GitHub Codespace by creating what's called a `.devcontainer` directory in the root of your repository. This will allow your GitHub Codespace environment to be pre-configured with all the tools and libraries you need so you can get started right away. See the file `.devcontainer/devcontainer.json` for installed Visual Studio Code extensions and other settings. - - **Option 2: Run locally**: set up your local machine. This will require you to install the necessary tools and libraries on your machine. A way to make this easier is to use a local devcontainer. This is a Docker container that has all the tools and libraries you need to run the app. You can find the `devcontainer.json` file in the `.devcontainer` directory. -- **Access to AI models**. You can use GitHub Models or Azure OpenAI to interact with large language models. GitHub Models is free and easy to set up, while Azure OpenAI is a paid service that provides access to a wide range of AI models. How to set the latter up is covered [here](./getting-started-azure-openai.md). +If you already have another OpenAI-compatible provider, use its API base URL, key, and model name with the same configuration variables. The Foundry resource steps below apply only to the Foundry option. -## Which AI service should I use for this course? +## Prerequisites -We provide instructions for setting up your development environment with GitHub Models and Azure OpenAI. You can choose the one that best fits your needs. We recommend using GitHub Models for this course, but you can use any of these services. +- A GitHub account for Codespaces or forking the course. +- For Foundry, an Azure account with an active subscription and permission to create a resource, deploy a model, and read its API key. +- Node.js Long Term Support (LTS). +- Git and a text editor for local development. [Visual Studio Code](https://code.visualstudio.com/) is recommended. +- Basic JavaScript and command line knowledge. -## Choose your setup +> [!IMPORTANT] +> Model usage can incur charges. Check deployment pricing, region availability, and quota (service usage limits) before creating resources. A GitHub account or Codespace does not supply your Foundry API key. -> If **GitHub Models** is your choice, follow the rest of this document to set up your development environment with GitHub Models. -> - **Azure OpenAI** have your eye? [This is the document for you](getting-started-azure-openai.md). +## Choose Your Development Environment -## Quick Start +Use GitHub Codespaces or your local machine. Both options use the same model provider and `.env` configuration. -The fastest way to get started is to use GitHub Codespaces as this will provide you with a pre-configured environment that has access to GitHub Models without any additional setup. +### Option 1 : Creating a GitHub Codespace -However, if you prefer to work on your local machine, there's a section below that will guide you through setting up your [local environment](#option-2--running-the-app-locally). +1. Open [this repository](https://github.com/microsoft/generative-ai-with-javascript). +2. Select **Fork** to create your own copy. +3. In your fork, select **Code**, then **Codespaces**, then **Create codespace**. +4. Wait for the environment to start. Open its terminal. +5. Check the Node.js and npm versions: -## GitHub Models explained + ```bash + node --version + npm --version + ``` -GitHub Models offers the following features: +Use the LTS release, not the Current release. If you use nvm, run `nvm install` and `nvm use` from the repository root; `.nvmrc` selects the active LTS release. Continue with [Install Dependencies](#install-dependencies). -- **Free access to AI models**: Experiment with various AI models for free. -- **Interactive playground**: Test and interact with different models directly within your development environment. -- **Reliable access**: Hosted within GitHub's infrastructure, these models offer reliable access and consistent performance. +### Option 2 : Running the app locally -### Free access to AI models +1. Install [Node.js](https://nodejs.org/en/download) and [Git](https://git-scm.com/downloads) if needed. +2. Check your tools: -GitHub Models provides an intuitive way to experiment with various AI models directly within your development environment. This feature allows developers to test and interact with different models, understanding their capabilities and limitations before implementation. + ```bash + node --version + npm --version + git --version + ``` -### Interactive playground +3. Clone the repository, or use the clone URL of your fork: -To test out various AI models, GitHub Models offers an interactive playground. This feature allows developers to select from different models, input prompts, and evaluate model responses. + ```bash + git clone https://github.com/microsoft/generative-ai-with-javascript.git + cd generative-ai-with-javascript + ``` -> Here's a [playground](https://github.com/marketplace/models) for you to test and explore different AI models. +Continue with the commands below from the repository root. -![GitHub Models playground](../images/playground.png) +## Install Dependencies -Additionally, developers can grab code snippets to integrate these models into their applications. +Install the root packages and the companion app packages: -![Playground code snippets](../images/playground-snippets.png) +```bash +npm ci +npm ci --prefix app +``` -Through a simple interface, you can explore model responses, evaluate performance, and determine the best fit for your application requirements. Hosted within GitHub's infrastructure, these models offer reliable access and consistent performance, making them ideal for development and testing phases. Best of all, there is a free tier to start your exploration without any cost. +The app already includes `openai` and `dotenv`. Do not install `@azure/openai` or copy older Azure client code into `app.js`. -**Getting started** +Lesson packages are independent. Install their dependencies in the lesson directory when its instructions require it. TypeScript lessons have their own build commands. A global `tsx` installation is not required to run the companion app. -To get started with GitHub Models, you need the following: +## Set Up Microsoft Foundry -- **A GitHub account**, you can sign up for free at GitHub.com. -- **Optional: A personal access token**, this is something you will create in the next section. A token will uniquely identify you when you interact with GitHub Models. This step is optional if you're using GitHub Codespaces (as you can rely on the built-in token `GITHUB_TOKEN`) but mandatory if you're running the app locally. +### 1. Create a Project -## Optional: Setting up GitHub Access Tokens +1. Sign in to the [Microsoft Foundry portal](https://ai.azure.com/). +2. Use the new Foundry experience. Open the project selector and select **Create new project**. +3. Enter a project name, such as `genai-javascript-course`. +4. Open **Advanced options**. Select your subscription, resource group, and a region that supports your chosen model. +5. Select **Create project** and wait for the project to be ready. -> [!NOTE] -> You only need to do this step if you plan to use GitHub Models from your local machine. If you're using GitHub Codespaces, you can skip this step as there's a built-in variable called `GITHUB_TOKEN` that can access GitHub Models from within your GitHub Codespaces instance. +If your account cannot create the resource or project, ask your Azure administrator for the required access. See the [official resource setup guide](https://learn.microsoft.com/en-us/azure/foundry/tutorials/quickstart-create-foundry-resources) for current portal steps and permissions. -> [!TIP] -> From a security standpoint, it's always a good idea to create a personal access token (PAT) with a limited scope and short expiration time. +### 2. Deploy a Chat Model -### Creating a Personal Access Token, PAT for GitHub Model access +A deployment is a hosted model with a name that your application uses in API requests. -1. Navigate to [GitHub Settings](https://github.com/settings/profile): +1. Open **Discover**, then **Models**, and search for **gpt-5-mini**. +2. Check the model's availability, deployment pricing, and quota. +3. Select **Deploy**, then **Default settings**, or customize the deployment settings if needed. +4. Wait for deployment to complete. +5. Open the deployment details and record its deployment name. Use `gpt-5-mini` to match the example configuration, or use your own name and set `AI_MODEL` to that exact name. - - Click your profile picture in the top-right corner - - Select **Settings** from the dropdown menu +In the classic portal, model deployment is under **Models + endpoints**. Portal labels can change; use the official setup guide linked above if your screen differs. - ![GitHub Settings](https://raw.githubusercontent.com/microsoft/Generative-AI-for-beginners-dotnet/main/02-SetupDevEnvironment/images/settings-github.png) +If `gpt-5-mini` is not available, select another model that supports chat completions, such as `gpt-4.1`, and use its deployment name in `AI_MODEL`. -1. Access [Developer Settings](https://github.com/settings/apps): +**Models needed for this course:** One chat deployment is enough for the companion app and the basic CSV retrieval example in Lesson 5. Unlike the LangChain.js course, this setup does not require a second comparison model or a hosted embedding model. Deploy additional models only when your chosen exercise needs them. - - Scroll down the left sidebar - - Click on **Developer settings** (usually at the bottom) +### 3. Get the API Key and Endpoint - ![Developer Settings](https://github.com/microsoft/Generative-AI-for-beginners-dotnet/raw/main/02-SetupDevEnvironment/images/developer-settings-github.png) +1. Open the Foundry resource associated with your project. Use its **Keys and Endpoint** page in the Azure portal, or the resource's endpoint and key details in Foundry. +2. Copy an API key for that resource. +3. Find its **Azure OpenAI resource endpoint**. +4. Add `/openai/v1` to the resource endpoint if it is not already present. -1. Generate a New Token: +The API base URL must look like: - - Select **Personal access tokens** → **Tokens (classic)** +```text +https://your-resource.openai.azure.com/openai/v1 +``` - ![Adding the Tokens(classic)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet/raw/main/02-SetupDevEnvironment/images/tokens-classic-github.png) +Use the resource endpoint and key from the same resource. - - In the dropdown in the middle of the page, click **Generate new token (classic)** +> [!IMPORTANT] +> Do not use a project URL ending in `/api/projects/...`, or a full `/chat/completions` request URL, as `AI_ENDPOINT`. The companion app needs the OpenAI-compatible API base URL. It adds the request path itself. - ![Create your Token](https://github.com/microsoft/Generative-AI-for-beginners-dotnet/raw/main/02-SetupDevEnvironment/images/token-generate-github.png) +This sample uses API-key authentication. If your organization does not permit it, ask your administrator for an approved learning resource or authentication approach. Do not disable organization security controls. - - Under "Note", provide a descriptive name (e.g., `GenAI-JavaScript-Course-Token`) - - Set an expiration date (recommended: 7 days for security best practices) - - There is no need adding any permissions to this token. +## Configure Environment Variables -> 💡 **Security Tip**: Always use the minimum required scope and shortest practical expiration time for your access tokens. This follows the principle of least privilege and helps maintain your account's tokens safe. +Environment variables are configuration values that a running program can read. This app can load them from a private `.env` file instead of storing them in its source code. -## Option 1 : Creating a GitHub Codespace +### Create the File -> [!NOTE] -> You don't strictly need to create a GitHub Codespace instance to test out GitHub Models, but it's a nice way to get an environment where extensions and tools are pre-installed and ready to go. Let's cover first how to create a GitHub Codespace and in the next section, it will be explained how to run locally. +Create `.env` in the repository root, not in `app/`. If you already have a `.env` file, edit it instead of overwriting it. -Let's create a GitHub Codespace to use for the rest of this course. +On macOS, Linux, WSL, or Codespaces: -1. Open this repository's main page in a new window by [right-clicking here](https://github.com/microsoft/generative-ai-with-javascript) and selecting **Open in new window** from the context menu -1. Fork this repo into your GitHub account by clicking the **Fork** button in the top right corner of the page -1. Click the **Code** dropdown button and then select the **Codespaces** tab -1. Select the **+** option. +```bash +cp .env.example .env +``` - ![Create Codespace](../images/create-codespace.png) +In Windows PowerShell: -### Verifying your Codespace is running correctly with GitHub Models +```powershell +Copy-Item .env.example .env +``` -Once your Codespace is fully loaded and configured, let's run a sample app to verify everything is working correctly. +In Windows Command Prompt: -> [!NOTE] -> If you are using a personal access token, you need to update the code in `app.js` in the `/app` directory to use your token. You can access your token using `process.env.`. If not, you can skip this instruction in this note. +```cmd +copy .env.example .env +``` + +### Set Your Values + +Open `.env` in your editor: + +```dotenv +AI_API_KEY=your_microsoft_foundry_api_key +AI_ENDPOINT=https://your-resource.openai.azure.com/openai/v1 +AI_MODEL=gpt-5-mini +``` + +Replace the key and resource name. Set `AI_MODEL` to your actual deployment name, which can differ from the model's catalog name. + +The app reads the repository-root file automatically. Existing environment variables take priority over the file. Missing or blank settings stop the app with an error. + +The `.env` file is excluded from Git. Keep it private. Do not put keys in browser code, screenshots, issue reports, or commits. A GitHub personal access token is not a Foundry API key. + +### Use an Existing Environment File + +You can select another file with `ENV_FILE`. From the repository root on macOS, Linux, WSL, or Codespaces: + +```bash +ENV_FILE="$HOME/.env" npm --prefix app start +``` + +In PowerShell: + +```powershell +$env:ENV_FILE = "$HOME\.env" +npm --prefix app start +``` + +An unreadable explicit file stops the app. Do not copy shared credentials into the repository when you can use the existing file. -Follow these steps to run the sample app: +## Test Your Setup -1. Open the terminal. Open a terminal window (if it's not open already). +### Start the Companion App + +From the repository root: + +```bash +npm --prefix app start +``` + +The terminal should show: + +```text +Server is running on http://localhost:3000 +``` + +1. Open `http://localhost:3000` locally. In Codespaces, open port 3000 from the **Ports** panel. +2. Keep the forwarded port **Private**. The sample does not authenticate HTTP callers. +3. Select Ada Lovelace from the character images. +4. Send a short message, such as `Tell me about your work in mathematics.` +5. Check that a response appears in the chat panel. + +A successful response confirms that your endpoint, key, and deployment work together. The response text can vary. The terminal startup message alone does not verify model access. + +Use **Ctrl+C** to stop the app. + +### Run the Local Regression Tests + +From the repository root: + +```bash +npm --prefix app test +``` + +These tests check request validation, chat behavior, and SDK restrictions. They use local test services and do not verify your Foundry credentials. + +## Use the Configuration in Lesson Samples + +> [!NOTE] +> The companion app and lesson AI clients use `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL`. Lesson package scripts load the repository-root `.env` when it exists. Existing environment variables take priority. Standalone scripts need the same variables before they start. -1. Switch to the proper directory by running the following command (providing you're in the repo root): +The lesson clients validate the required variables before creating an OpenAI client. This is the connection pattern: - ```bash - cd app - ``` +```javascript +import { OpenAI } from 'openai'; -1. Install all the dependencies by running the following command: +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} - ```bash - npm install - ``` +const openai = new OpenAI({ + baseURL: endpoint, + apiKey +}); -1. Run the application by running the following command: +const completion = await openai.chat.completions.create({ + model, + messages: [{ role: 'user', content: 'Tell me about Ada Lovelace.' }] +}); - ```bash - npm start - ``` +console.log(completion.choices[0]?.message?.content); +``` -1. It may take a couple of seconds, but eventually the application should start. You should see a message link to open a browser to `http://localhost:3000` displayed in the terminal. +For Lesson 2, install its own dependencies and run its start script: -> 🙋 **Need help?**: Something not working? [Open an issue](https://github.com/microsoft/generative-ai-with-javascript/issues/new?template=Blank+issue) and we'll help you out. +```bash +cd lessons/02-first-ai-app/sample-app +npm ci +npm start +``` +For an existing home environment file, run `node --env-file="$HOME/.env" app.js` in that lesson directory instead. Use the lesson's build and run instructions for TypeScript or MCP examples. `ENV_FILE` is the companion app's file-selection option; Node's `--env-file` selects a file for a standalone lesson script. +Lesson 5 has an independent package. Run `npm ci --prefix lessons/05-rag` from the repository root, then use `npm --prefix lessons/05-rag run cars` or `npm --prefix lessons/05-rag run wikipedia`. -## Option 2 : Running the app locally +Lesson 4 includes a native JSON Schema example. Run `npm --prefix lessons/04-structured-output/sample-app run structured` to parse a validated travel request with the current OpenAI SDK and Zod. -Even if GitHub Codespaces is a great way to get started, you can also run the app locally on your machine. To do so, you need to do the following. Start this step by copying the value for your token you created earlier in GitHub. +For MCP lessons, `npm run build` only compiles the code. `npm run client` builds and runs the client, while `npm start` runs the compiled server. Clients close their transport after completing their work. -- **Create and set an environment variable**: You need to set an environment variable with the token you created earlier. Create the variable and assign it the value of the token. +Models can support different request parameters. If a lesson sets parameters that your deployment rejects, such as `temperature` or `max_tokens`, use a compatible model or update the request for that model. - on macOS or Linux, you can set an environment variable using the following command: +## Optional: Use GitHub Copilot for Companion Chat - ```sh - export GITHUB_TOKEN= - ``` +The companion app also has a Copilot SDK backend. This option requires GitHub Copilot access rather than a Foundry deployment. - on Windows, you can set an environment variable using the following command: +From the repository root: - ```sh - set GITHUB_TOKEN= - ``` +```bash +npm --prefix app run start:sdk +``` - > [!TIP] - > You're recommended to create an `.env` file in the root and add [dotenv](https://www.npmjs.com/package/dotenv) to your project to manage your environment variables. Then you can access your token using `process.env.GITHUB_TOKEN`. +Use a Copilot-compatible token or GitHub CLI authentication as described in the [app guide](../../app/README.md#run). This option is for companion chat; it does not configure the independent lesson samples. - ```javascript - // add to top of app.js file in /app directory - - require('dotenv').config(); - const token = process.env.TOKEN; // your token value +The SDK backend is chat-only. Do not enable host tools or approve tool permissions to make chat work. Keep its forwarded port private. - ``` +## Troubleshooting -- **Run the app**: You can run the app by following the same steps as above, but instead of running `npm start` in the Codespace terminal, you can run it in your local terminal. +| Problem | What to check | +| --- | --- | +| Node.js version error | Select Node.js LTS. Use `nvm use` if nvm is installed. | +| `Cannot find module` | Run `npm ci` in the package directory. Root and app dependencies are separate. | +| `AI_ENDPOINT`, `AI_API_KEY`, or `AI_MODEL` is required | Check the repository-root `.env`, spelling, and whether an existing environment variable overrides the file. | +| The environment file could not be read | Check the `ENV_FILE` path and file permissions. | +| HTTP 401 or invalid API key | Use a complete key from the same resource as the endpoint. Ask your administrator whether API-key authentication is allowed. | +| HTTP 404 or deployment not found | Check the `/openai/v1` base URL and exact deployment name in `AI_MODEL`. | +| HTTP 400 or unsupported parameter | Confirm that the chosen model supports the lesson's API and request parameters. | +| HTTP 429 or a rate limit error | Wait before retrying. Check deployment quota and usage in Foundry. | +| A generic error appears in the chat | Read the server terminal for the provider error. Remove keys and private resource details before sharing it. | +| Port 3000 is in use | Stop your own earlier app instance, or set another `PORT` value before starting the app. | -## Summary +## Setup Checklist -In this lesson, you learned how to set up your development environment for the rest of the course. You created a GitHub Codespace or did a local setup and configured it to use GitHub Models. You also learned how to create a personal access token for GitHub Models and how to run a sample application to verify everything is working correctly. +- [ ] Node.js LTS is available. +- [ ] Root and companion app dependencies are installed. +- [ ] A chat model is deployed in Microsoft Foundry. +- [ ] The deployment name, API key, and `/openai/v1` endpoint are in `.env` or a selected `ENV_FILE`. +- [ ] The companion app returns a real model response. +- [ ] Local regression tests pass. +- [ ] Credentials remain private and any forwarded app port is private. -### Additional Resources +When you finish using the Azure resources, remove only deployments or resources that you created for this course. Do not delete shared resources without their owner's approval. -You're encouraged to explore the following resources to deepen your understanding: +## Next Steps and Resources -- [Azure OpenAI](getting-started-azure-openai.md) to learn how to use Azure OpenAI in your development environment. -- [GitHub Codespaces Documentation](https://docs.github.com/en/codespaces) -- [GitHub Models Documentation](https://docs.github.com/en/github-models/prototyping-with-ai-models) +- Continue with [Lesson 1: Introduction to Generative AI](../../lessons/01-intro-to-genai/README.md). +- Read the [companion app guide](../../app/README.md). +- See [Microsoft Foundry resource setup](https://learn.microsoft.com/en-us/azure/foundry/tutorials/quickstart-create-foundry-resources) and [Azure OpenAI-compatible endpoint guidance](https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/switching-endpoints). +- Read [GitHub Codespaces documentation](https://docs.github.com/en/codespaces). +- For course problems, use this repository's [issues page](https://github.com/microsoft/generative-ai-with-javascript/issues). +The setup flow follows the [LangChain.js course setup](https://github.com/microsoft/langchainjs-for-beginners/blob/main/00-course-setup/README.md), with this course's repository, packages, model requirements, and companion app commands. diff --git a/docs/setup/getting-started-azure-openai.md b/docs/setup/getting-started-azure-openai.md index 2f15cb05..dc349543 100644 --- a/docs/setup/getting-started-azure-openai.md +++ b/docs/setup/getting-started-azure-openai.md @@ -1,157 +1,39 @@ -# Setting Up the Development Environment for Azure OpenAI +# Microsoft Foundry and Azure OpenAI Setup -Azure AI Foundry is a service that allows you to deploy and manage AI models in the cloud. You can do things like create a project, deploy a model, interact with the model, and more. +Use the [course setup guide](README.md) for the complete instructions. It covers local development and Codespaces, Foundry project creation, model deployment, API keys, environment variables, and a live companion app check. -> [!NOTE] -> If you want to use Azure AI Foundry models for your .NET AI apps in this course, follow the steps in this guide. +## Connection Settings -👉 [If you want to use GitHub models, follow this guide instruction.](./README.md) +The companion app already uses the `openai` JavaScript package. You do not need to install `@azure/openai`, create a hub, or replace the app's client code. -## Create the Azure AI Foundry resources +Create `.env` in the repository root with these values from your Foundry resource: -To use Azure AI Foundry models, you need to take the following steps: +```dotenv +AI_API_KEY=your_microsoft_foundry_api_key +AI_ENDPOINT=https://your-resource.openai.azure.com/openai/v1 +AI_MODEL=gpt-5-mini +``` -1. Create a hub and project in the Azure AI Foundry portal. -1. Deploy a model to your project. -1. Add the Azure AI library code + API key and other credentials to your code. +`AI_MODEL` must be your deployment name. `AI_ENDPOINT` must be the Azure OpenAI-compatible API base URL, not the Foundry project URL or a full chat-completion request URL. -### Step 1: Create a Hub and Project in Azure AI Foundry +Follow [Set Up Microsoft Foundry](README.md#set-up-microsoft-foundry) to obtain these values, then [Configure Environment Variables](README.md#configure-environment-variables). -1. Go to the [Azure AI Foundry Portal](https://ai.azure.com/). -1. Sign in with your Azure account. -1. Select **All hubs + projects** from the left-hand menu and then click the **+ New hub** from the dropdown. (Note: You may have to click on **+ New project** first to see the **+ New hub** option). +## Run and Verify -1. A new window will open. Fill in the details for your hub: - - Give your hub a name (e.g., "MyAIHub"). - - Choose a region closest to you. - - Select the appropriate subscription and resource group. - - You can leave the rest of the settings as they are. - - Click **Next**. - - Review the details and click **Create**. -1. Once your hub is created, the portal will open its details page. Click the **Create Project** button. - - Give your project a name (e.g., "GenAIJavaScript") or accept the default. - - Click **Create**. +From the repository root: -🎉 **Done!** You’ve just created your first project in Azure AI Foundry. +```bash +npm ci +npm ci --prefix app +npm --prefix app start +``` -Before you can interact with the model, you need to deploy it to your project, so let's do that next. +Open `http://localhost:3000`, select a character, and send a message. In Codespaces, keep port 3000 private. -### Step 2 : Deploy a Language Model in Azure AI Foundry +For an existing environment file: -Now, let’s deploy a **gpt-4o-mini** model to your project: - -1. In the Azure AI Foundry portal, navigate to your project (it should automatically open after creating it). -1. Click on **Models and Endpoints** from the left-hand menu and then the **Deploy Model** button. -1. Select **Deploy base model** from the dropdown. -1. Search for **gpt-4o-mini** in the model catalog. -1. Select the model and click the **Confirm** button. -1. Specify a deployment name (e.g., "gpt-4o-mini"). You can leave the rest of the options as they are. -1. Click **Deploy** and wait for the model to be provisioned. -1. Once deployed, make a note of the values for **Model Name**, **Target URI**, and **API Key** from the model details page as you will use the later in your sample code. - -🎉 **Done!** You’ve deployed your first Large Language Model in Azure AI Foundry. - -> [!NOTE] -> The endpoint is similar to `https://< your hub name>.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-08-01-preview`. The endpoint name that we need is only `https://< your hub name >.openai.azure.com/`*. - -## Adding Azure to your code - -Now let’s update the code to use the newly deployed model. Here's the plan: - -- Install the needed libraries, you will need the libraries `@azure/openai` and `dotenv`. -- Update the code to use the values you noted above for the model name, endpoint, and API key. -- Run the app. - -1. Open the terminal and switch to the project directory (providing you're in the project root directory): - - ```bash - cd app - ``` - -1. Run the following commands to add the required libraries: - - ```bash - npm install @azure/openai dotenv - ``` - -1. At the top of `app.js` add the following imports: - - ```javascript - import { OpenAIClient, AzureKeyCredential, ChatRequestMessage } from "@azure/openai"; - import * as dotenv from "dotenv"; - ``` - -1. Add the following code to load the environment variables: - - ```javascript - dotenv.config(); - ``` - -1. Create new variables to hold the model name, endpoint, and API key: - - ```javascript - const endpoint = process.env.AZURE_OPENAI_ENDPOINT || ''; - const azureApiKey = process.env.AZURE_OPENAI_API_KEY || ''; - ``` - -1. Create a client to interact with the model: - - ```javascript - const client = new OpenAIClient(endpoint, new AzureKeyCredential(azureApiKey)); - const deploymentName = ''; - ``` - - The code above creates a new client using the endpoint and API key you noted earlier. It also assigned the deployment name to a variable. - -1. Create a prompt and chat messages: - - ```javascript - const promptText = `Tell me about yourself.`; - - const chatMessages: ChatRequestMessage[] = [ - { - role: 'system', - content: "You're Ada Lovelace, a mathematician and writer. You're considered the first computer programmer. You only know about the time you lived in, so you might not know about modern technology.", - }, - { - role: 'user', - content: promptText - }, - ]; - ``` - - Now you have a prompt and chat messages to send to the model. Let's send the messages and get the response: - - ```javascript - const completionResponse = await client.getChatCompletions(deploymentName, chatMessages, { - maxTokens: 150, - temperature: 0.1, - }); - - console.log("Ada says: "); - console.log(completionResponse.choices[0].message?.content); - ``` - -1. Run the app: - - ```bash - npm start - ``` - -1. You should see output similar to the following: - - ```text - Ada says: I'm Ada Lovelace, a mathematician and writer. I'm considered the first computer programmer. I only know about the time I lived in, so I might not know about modern technology. - ``` - -> 🙋 **Need help?**: Something not working? [Open an issue](https://github.com/microsoft/Generative-AI-for-beginners-dotnet/issues/new?template=Blank+issue) and we'll help you out. - -## Summary - -In this lesson, you learned how to create a hub and project in Azure AI Foundry, deploy a model to your project, and interact with the model in your code. - -### Additional Resources - -- [Azure AI Foundry Documentation](https://learn.microsoft.com/en-us/azure/ai-services/) -- [How to Deploy Models in Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-services/deploy/) +```bash +ENV_FILE="$HOME/.env" npm --prefix app start +``` +See [Test Your Setup](README.md#test-your-setup) and [Troubleshooting](README.md#troubleshooting) for verification and error handling. diff --git a/index.html b/index.html index 1415d8ea..d1a8c49b 100644 --- a/index.html +++ b/index.html @@ -2,14 +2,70 @@ - Web Dev for Beginners + Generative AI for Beginners with JavaScript - + - + - - + +

diff --git a/lessons/01-intro-to-genai/README.md b/lessons/01-intro-to-genai/README.md index ceab7d2b..fd6abd4f 100644 --- a/lessons/01-intro-to-genai/README.md +++ b/lessons/01-intro-to-genai/README.md @@ -10,14 +10,6 @@ In this chapter you will learn: If you haven't already, set up your development environment. Here's how you can do it: [Setup your environment](/docs/setup/README.md). -## Related Resources - -[![Watch a short video about an Introduction to Generative AI](https://img.youtube.com/vi/vLYtDgs_zx8/0.jpg)](https://www.youtube.com/watch?v=vLYtDgs_zx8&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=1) - -_This video gives you an introduction to Generative AI with JavaScript_ - -💼 Slides: [Introduction to Generative AI](/videos/slides/00-intro.pptx) - ## Generative AI By now, you've likely heard of tools like ChatGPT or Generative AI. The concept is simple: you provide a prompt, and a model—often called a Large Language Model (LLM)—generates a paragraph or even an entire page of text. This output can be used for various purposes, including creative writing, answering questions, and coding. @@ -161,12 +153,12 @@ If you want to interact with Dinocrates, run the [Characters](/app/README.md) ap 1. Start a [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navigate to _/app_ in the repo root. -3. Locate the console and run `npm install` followed by `npm start`. +3. Locate the console and run `npm ci` followed by `npm start`. 4. Once it appears, select the "Open in Browser" button. 5. Chat with Dinocrates. > [!NOTE] - > If you're running the project locally on your machine, please review the QuickStart guide to get a [GitHub personal access](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) token setup and replace the key in the code. +> Configure `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL` in the repository-root `.env` for both local development and Codespaces. See the [setup guide](/docs/setup/README.md#configure-environment-variables). ### Code Sneak Peek @@ -175,51 +167,80 @@ While there is still a lot more to cover in this Generative AI curriculum, let's Inside of `/app/app.js` you'll find an `app.post`function that handles the Generative AI functionality. It's shown next: ```JavaScript -app.post('/send', async (req, res) => { - const { message } = req.body; - const prompt = message; - - const messages = [ - { - "role": "system", - "content": "You are Dinocrates of Alexandria, a famous architect and engineer. Limit your responses to only the time you live in, you don't know anything else. You only want to talk about your architecture and engineering projects, and possibly new ideas you have.", - }, - { - "role": "user", - "content": prompt +import express from 'express'; +import { OpenAI } from 'openai'; +import path from 'path'; +import { fileURLToPath } from 'url'; +import { ChatRequestError, getChatRequest, handleBodyError } from './chat-request.js'; +import { getModelConfig, loadEnvironment } from './model-config.js'; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = path.dirname(__filename); + +export function createApp(openai, model) { + if (typeof model !== 'string' || !model.trim()) { + throw new Error('A model name is required to create the chat app.'); + } + const app = express(); + + app.use(express.json()); + app.use(express.static(path.join(__dirname, 'public'))); + app.locals.delimiters = '{{ }}'; + + app.post('/send', async (req, res) => { + try { + const { prompt, systemMessage } = getChatRequest(req.body); + const completion = await openai.chat.completions.create({ + model, + messages: [ + { role: "system", content: systemMessage }, + { role: "user", content: prompt } + ] + }); + + const answer = completion?.choices?.[0]?.message?.content; + if (typeof answer !== 'string' || !answer.trim()) { + throw new Error('The model did not return an answer.'); + } + res.json({ prompt, answer }); + } catch (error) { + if (error instanceof ChatRequestError) { + res.status(400).json({ message: error.message }); + return; + } + console.error(`Error: ${error.message}`); + res.status(500).json({ message: 'An unexpected error occurred. Please try again later.' }); } - ]; + }); + app.use(handleBodyError); + return app; +} + +if (process.argv[1] && path.resolve(process.argv[1]) === __filename) { + loadEnvironment(); + const port = process.env.PORT || 3000; + const { baseURL, apiKey, model } = getModelConfig(); const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL, + apiKey, + timeout: 60000 }); - - try { - console.log(`sending prompt ${prompt}`) - const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', - messages: messages, - }); - - res.json({ - prompt: prompt, - answer: completion.choices[0]?.message?.content - }); - } catch (error) { - res.status(500).json({ error: error.message }); - } -}); + const app = createApp(openai, model); + app.listen(port, '127.0.0.1', () => { + console.log(`Server is running on http://localhost:${port}`); + }); +} ``` Here's a step-by-step summary of what the function does: -1. **Extract Message from Request**: The function extracts the message from the request body (req.body). -2. **Create Prompt Array**: It constructs an array of messages, including a system message and the user's prompt message. -3. **Initialize OpenAI Client**: An OpenAI client is initialized with the base URL and API key from environment variables. A _gpt-4o-mini_ model from [GitHub Models](https://github.com/marketplace/models) is used to process the prompt and return a response. -4. **Send Prompt to OpenAI**: The function logs the prompt and sends it to the OpenAI API to generate a completion. -5. **Handle Response**: If successful, the function responds with the prompt and the completion's answer. -6. **Error Handling**: If an error occurs, it responds with a 500 status and the error message. +1. **Validate the Request**: `getChatRequest` checks the message and resolves the character name to server-owned instructions. +2. **Create Messages**: The handler builds system and user messages for the configured model. +3. **Use the Configured Client**: The endpoint, key, and deployment name are loaded and checked before the server starts. +4. **Request an Answer**: The OpenAI-compatible client sends the messages to the model. +5. **Check the Answer**: A missing or empty answer produces an error; a valid answer is returned with the prompt. +6. **Report Errors**: Invalid requests return HTTP 400. Provider failures are logged and return a generic HTTP 500 message. > **Note**: [GitHub Copilot](https://github.com/features/copilot) was used to generate this code summary. Generative AI in action! @@ -258,7 +279,7 @@ As you can see, these improvements can both help the front office and the back o Here's an example of a "chatbot application" in action: -![Image of chat app](https://camo.githubusercontent.com/76f2ad7cd754a2de2b9957d2070448e130e5ba228084b9b4b128e3af9c9f5239/68747470733a2f2f6c6561726e2e6d6963726f736f66742e636f6d2f656e2d75732f73656d616e7469632d6b65726e656c2f6d656469612f636861742d636f70696c6f742d696e2d616374696f6e2e676966) +![Course companion chat app](/docs/images/character-chat.png) **You:** Fascinating, I'll make a note of going to the 21st century to see how these tools are used. @@ -345,5 +366,3 @@ C. Python is the only language used for AI development. [Quiz solution](/lessons/01-intro-to-genai/solution/solution-quiz.md) ## Self-Study Resources - -- [Generative AI JavaScript video series](https://aka.ms/genai-js) diff --git a/lessons/01-intro-to-genai/translations/README.bn.md b/lessons/01-intro-to-genai/translations/README.bn.md index f611bcc8..beddca4b 100644 --- a/lessons/01-intro-to-genai/translations/README.bn.md +++ b/lessons/01-intro-to-genai/translations/README.bn.md @@ -10,14 +10,6 @@ আপনার যদি এখনও ডেভেলপমেন্ট এনভায়রনমেন্ট সেটআপ না করা থাকে, তাহলে এটি করুন: [আপনার পরিবেশ সেটআপ করুন](/docs/setup/README.md)। -## **সম্পর্কিত রিসোর্স** - -[![জেনারেটিভ এআই-এর পরিচিতির উপর সংক্ষিপ্ত ভিডিও দেখুন](https://img.youtube.com/vi/vLYtDgs_zx8/0.jpg)](https://www.youtube.com/watch?v=vLYtDgs_zx8&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=1) - -_এই ভিডিওটি জাভাস্ক্রিপ্টের মাধ্যমে জেনারেটিভ এআই-এর পরিচিতি দেয়।_ - -💼 **স্লাইডস:** [জেনারেটিভ এআই-এর পরিচিতি](/videos/slides/00-intro.pptx) - ## **জেনারেটিভ এআই** আপনি সম্ভবত ইতোমধ্যে ChatGPT বা জেনারেটিভ এআই-এর মতো টুলগুলোর কথা শুনেছেন। ধারণাটি সহজ: আপনি একটি প্রম্পট দেন, এবং একটি মডেল—যাকে সাধারণত একটি বড় ভাষার মডেল (LLM) বলা হয়—একটি অনুচ্ছেদ বা সম্পূর্ণ পৃষ্ঠা তৈরি করে। @@ -189,12 +181,12 @@ _সময়ের যন্ত্র, “জর্জ” ধাতব বি 1. [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) ব্যবহার করে কোডস্পেস শুরু করুন। 2. রিপোজিটরির মূল ফোল্ডারে _/app_ এ যান। -3. কনসোলে `npm install` এবং তারপর `npm start` চালান। +3. কনসোলে `npm ci` এবং তারপর `npm start` চালান। 4. যখন চালু হবে, "Open in Browser" বাটনে ক্লিক করুন। 5. ডিনোক্রেটিসের সাথে চ্যাট করুন। > [!NOTE] -> যদি আপনি এই প্রকল্পটি আপনার লোকাল মেশিনে চালাচ্ছেন, তাহলে কোড চালানোর জন্য [GitHub ব্যক্তিগত অ্যাক্সেস টোকেন](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) সেটআপ করুন। +> রিপোজিটরির মূল ফোল্ডারের `.env` ফাইলে `AI_ENDPOINT`, `AI_API_KEY` এবং `AI_MODEL` সেট করুন। লোকাল পরিবেশ এবং Codespaces উভয়ের জন্য এই সেটিংস প্রয়োজন। [সেটআপ নির্দেশিকা](/docs/setup/README.md#configure-environment-variables) দেখুন। --- @@ -205,41 +197,70 @@ _সময়ের যন্ত্র, “জর্জ” ধাতব বি **`/app/app.js`** ফাইলের মধ্যে নিচের **`app.post`** ফাংশনটি AI ইন্টারঅ্যাকশন পরিচালনা করে: ```JavaScript -app.post('/send', async (req, res) => { - const { message } = req.body; - const prompt = message; - - const messages = [ - { - "role": "system", - "content": "You are Dinocrates of Alexandria, a famous architect and engineer...", - }, - { - "role": "user", - "content": prompt +import express from 'express'; +import { OpenAI } from 'openai'; +import path from 'path'; +import { fileURLToPath } from 'url'; +import { ChatRequestError, getChatRequest, handleBodyError } from './chat-request.js'; +import { getModelConfig, loadEnvironment } from './model-config.js'; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = path.dirname(__filename); + +export function createApp(openai, model) { + if (typeof model !== 'string' || !model.trim()) { + throw new Error('A model name is required to create the chat app.'); + } + const app = express(); + + app.use(express.json()); + app.use(express.static(path.join(__dirname, 'public'))); + app.locals.delimiters = '{{ }}'; + + app.post('/send', async (req, res) => { + try { + const { prompt, systemMessage } = getChatRequest(req.body); + const completion = await openai.chat.completions.create({ + model, + messages: [ + { role: "system", content: systemMessage }, + { role: "user", content: prompt } + ] + }); + + const answer = completion?.choices?.[0]?.message?.content; + if (typeof answer !== 'string' || !answer.trim()) { + throw new Error('The model did not return an answer.'); + } + res.json({ prompt, answer }); + } catch (error) { + if (error instanceof ChatRequestError) { + res.status(400).json({ message: error.message }); + return; + } + console.error(`Error: ${error.message}`); + res.status(500).json({ message: 'An unexpected error occurred. Please try again later.' }); } - ]; + }); + app.use(handleBodyError); + return app; +} + +if (process.argv[1] && path.resolve(process.argv[1]) === __filename) { + loadEnvironment(); + const port = process.env.PORT || 3000; + const { baseURL, apiKey, model } = getModelConfig(); const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL, + apiKey, + timeout: 60000 }); - - try { - console.log(`sending prompt ${prompt}`) - const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', - messages: messages, - }); - - res.json({ - prompt: prompt, - answer: completion.choices[0]?.message?.content - }); - } catch (error) { - res.status(500).json({ error: error.message }); - } -}); + const app = createApp(openai, model); + app.listen(port, '127.0.0.1', () => { + console.log(`Server is running on http://localhost:${port}`); + }); +} ``` ### **জেনারেটিভ এআই-এর ব্যবহারিক ক্ষেত্র** @@ -310,6 +331,4 @@ C. পাইথনই একমাত্র ভাষা যা এআই ডে --- -## **স্বশিক্ষা সংস্থান (Self-Study Resources)** - -- [Generative AI JavaScript ভিডিও সিরিজ](https://aka.ms/genai-js) +## **স্বশিক্ষা সংস্থান (Self-Study Resources)** diff --git a/lessons/01-intro-to-genai/translations/README.fr.md b/lessons/01-intro-to-genai/translations/README.fr.md index 8254b279..fcef28b7 100644 --- a/lessons/01-intro-to-genai/translations/README.fr.md +++ b/lessons/01-intro-to-genai/translations/README.fr.md @@ -12,11 +12,9 @@ Si ce n'est pas encore fait, configurez votre environnement de développement. V ## Ressources associées -[![Regardez une courte vidéo sur l'introduction à l'IA générative](https://img.youtube.com/vi/vLYtDgs_zx8/0.jpg)](https://www.youtube.com/watch?v=vLYtDgs_zx8\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=1) *Cette vidéo vous donne une introduction à l'IA générative avec JavaScript.* -💼 Slides : [Introduction à l'IA générative](/videos/slides/00-intro.pptx) ## L'IA générative @@ -152,7 +150,7 @@ Si vous souhaitez interagir avec Dinocrates, exécutez l'application [Characters > [!IMPORTANT] > Ceci est entièrement fictif ; les réponses sont générées par une IA. -> [Clause de non-responsabilité AI responsable](../../README.md#responsible-ai-disclaimer) +> [Clause de non-responsabilité AI responsable](/README.md#responsible-ai-disclaimer) ![Dinocrates portant une toge](https://raw.githubusercontent.com/microsoft/generative-ai-with-javascript/main/lessons/01-intro-to-genai/assets/dinocrates.png) @@ -160,12 +158,12 @@ Si vous souhaitez interagir avec Dinocrates, exécutez l'application [Characters 1. Lancez un [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript). 2. Naviguez vers */app* à la racine du dépôt. -3. Localisez la console et exécutez `npm install` suivi de `npm start`. +3. Localisez la console et exécutez `npm ci` suivi de `npm start`. 4. Une fois que l'application est ouverte, sélectionnez le bouton "Ouvrir dans le navigateur". 5. Discutez avec Dinocrates. > [!NOTE] -> Si vous exécutez le projet localement sur votre machine, veuillez consulter le guide de démarrage rapide pour configurer un [token d'accès personnel GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) et remplacer la clé dans le code. +> Configurez `AI_ENDPOINT`, `AI_API_KEY` et `AI_MODEL` dans le fichier `.env` à la racine du dépôt, en local comme dans Codespaces. Consultez le [guide de configuration](/docs/setup/README.md#configure-environment-variables). ### Aperçu du code @@ -174,51 +172,80 @@ Bien qu'il reste encore beaucoup à couvrir dans ce programme d'apprentissage su Dans `/app/app.js`, vous trouverez une fonction `app.post` qui gère la fonctionnalité d'IA générative. Elle est illustrée ci-dessous : ```JavaScript -app.post('/send', async (req, res) => { - const { message } = req.body; - const prompt = message; - - const messages = [ - { - "role": "system", - "content": "You are Dinocrates of Alexandria, a famous architect and engineer. Limit your responses to only the time you live in, you don't know anything else. You only want to talk about your architecture and engineering projects, and possibly new ideas you have.", - }, - { - "role": "user", - "content": prompt +import express from 'express'; +import { OpenAI } from 'openai'; +import path from 'path'; +import { fileURLToPath } from 'url'; +import { ChatRequestError, getChatRequest, handleBodyError } from './chat-request.js'; +import { getModelConfig, loadEnvironment } from './model-config.js'; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = path.dirname(__filename); + +export function createApp(openai, model) { + if (typeof model !== 'string' || !model.trim()) { + throw new Error('A model name is required to create the chat app.'); + } + const app = express(); + + app.use(express.json()); + app.use(express.static(path.join(__dirname, 'public'))); + app.locals.delimiters = '{{ }}'; + + app.post('/send', async (req, res) => { + try { + const { prompt, systemMessage } = getChatRequest(req.body); + const completion = await openai.chat.completions.create({ + model, + messages: [ + { role: "system", content: systemMessage }, + { role: "user", content: prompt } + ] + }); + + const answer = completion?.choices?.[0]?.message?.content; + if (typeof answer !== 'string' || !answer.trim()) { + throw new Error('The model did not return an answer.'); + } + res.json({ prompt, answer }); + } catch (error) { + if (error instanceof ChatRequestError) { + res.status(400).json({ message: error.message }); + return; + } + console.error(`Error: ${error.message}`); + res.status(500).json({ message: 'An unexpected error occurred. Please try again later.' }); } - ]; + }); + app.use(handleBodyError); + return app; +} + +if (process.argv[1] && path.resolve(process.argv[1]) === __filename) { + loadEnvironment(); + const port = process.env.PORT || 3000; + const { baseURL, apiKey, model } = getModelConfig(); const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL, + apiKey, + timeout: 60000 }); - - try { - console.log(`sending prompt ${prompt}`) - const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', - messages: messages, - }); - - res.json({ - prompt: prompt, - answer: completion.choices[0]?.message?.content - }); - } catch (error) { - res.status(500).json({ error: error.message }); - } -}); + const app = createApp(openai, model); + app.listen(port, '127.0.0.1', () => { + console.log(`Server is running on http://localhost:${port}`); + }); +} ``` Voici un résumé étape par étape de ce que fait la fonction : -1. **Extraction du message de la requête** : La fonction extrait le message du corps de la requête (`req.body`). -2. **Création du tableau d'invite** : Elle construit un tableau de messages, incluant un message système et le message d'invite de l'utilisateur. -3. **Initialisation du client OpenAI** : Un client OpenAI est initialisé avec l'URL de base et la clé API à partir des variables d'environnement. Un modèle *gpt-4o-mini* de [GitHub Models](https://github.com/marketplace/models) est utilisé pour traiter l'invite et renvoyer une réponse. -4. **Envoi de l'invite à OpenAI** : La fonction enregistre l'invite et l'envoie à l'API OpenAI pour générer une réponse. -5. **Gestion de la réponse** : Si cela réussit, la fonction répond avec l'invite et la réponse générée. -6. **Gestion des erreurs** : En cas d'erreur, elle répond avec un statut 500 et le message d'erreur. +1. **Valider la requête** : `getChatRequest` vérifie le message et recherche les instructions du personnage sur le serveur. +2. **Créer les messages** : Le gestionnaire construit les messages système et utilisateur. +3. **Utiliser le client configuré** : Le point de terminaison, la clé et le nom du déploiement sont vérifiés avant le démarrage. +4. **Demander une réponse** : Le client compatible OpenAI envoie les messages au modèle. +5. **Vérifier la réponse** : Une réponse vide déclenche une erreur. +6. **Signaler les erreurs** : Une requête invalide renvoie HTTP 400. Une erreur du fournisseur est journalisée et renvoie un message générique HTTP 500. > **Remarque** : [GitHub Copilot](https://github.com/features/copilot) a été utilisé pour générer ce résumé de code. L'IA générative en action ! @@ -257,7 +284,7 @@ Comme vous pouvez le voir, ces améliorations peuvent à la fois aider le front- Voici un exemple d'une "application de chatbot" en action : -![Image de l'application de chat](https://camo.githubusercontent.com/76f2ad7cd754a2de2b9957d2070448e130e5ba228084b9b4b128e3af9c9f5239/68747470733a2f2f6c6561726e2e6d6963726f736f66742e636f6d2f656e2d75732f73656d616e7469632d6b65726e656c2f6d656469612f636861742d636f70696c6f742d696e2d616374696f6e2e676966) +![Application de chat du cours](/docs/images/character-chat.png) **Vous :** Fascinant, je vais noter l'idée de visiter le 21e siècle pour voir comment ces outils sont utilisés. @@ -344,5 +371,3 @@ C. Python est le seul langage utilisé pour le développement en IA. [Quiz solution](/lessons/01-intro-to-genai/solution/solution-quiz.md) ## Ressources d'auto-apprentissage - -* [Série vidéo sur l'IA générative avec JavaScript](https://aka.ms/genai-js) diff --git a/lessons/01-intro-to-genai/translations/README.pt-br.md b/lessons/01-intro-to-genai/translations/README.pt-br.md index 1db2f5e1..43e4cdf7 100644 --- a/lessons/01-intro-to-genai/translations/README.pt-br.md +++ b/lessons/01-intro-to-genai/translations/README.pt-br.md @@ -10,14 +10,6 @@ Neste capítulo você vai aprender: Se você ainda não configurou seu ambiente de desenvolvimento, veja como fazer: [Configure seu ambiente](/docs/setup/README.md). -## Recursos relacionados - -[![Assista a um vídeo curto sobre Introdução à IA Generativa](https://img.youtube.com/vi/vLYtDgs_zx8/0.jpg)](https://www.youtube.com/watch?v=vLYtDgs_zx8&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=1) - -_Este vídeo apresenta uma introdução à IA Generativa com JavaScript_ - -💼 Slides: [Introdução à IA Generativa](/videos/slides/00-intro.pptx) - ## IA Generativa Provavelmente você já ouviu falar de ferramentas como ChatGPT ou IA Generativa. O conceito é simples: você fornece um prompt, e um modelo—frequentemente chamado de Grande Modelo de Linguagem (LLM)—gera um parágrafo ou até mesmo uma página inteira de texto. Esta saída pode ser usada para diversos propósitos, incluindo escrita criativa, respostas a perguntas e codificação. @@ -43,7 +35,7 @@ No coração da Londres de 1860, você é reconhecido como um dos mecânicos mai Sua bancada de trabalho, o coração da sua oficina, é uma bagunça organizada.
- Oficina em Londres + Oficina em Londres
_No centro da bancada está o torso de um robô—uma maravilha da engenharia que consumiu meses de esforço. Sua estrutura de madeira é intrincadamente esculpida, cada articulação meticulosamente projetada para movimentos suaves._ @@ -75,7 +67,7 @@ Está escuro e sombrio lá dentro, a única luz filtrando através das janelas e Conforme seus olhos se adaptam à luz fraca, você nota uma figura à distância, acenando para você. Você caminha em sua direção, seus passos ecoando no piso de madeira. A figura fica mais clara, e você a reconhece das fotos de jornal, é Charles Babbage.
- Biblioteca Empoeirada + Biblioteca Empoeirada
### O que é este dispositivo? @@ -91,7 +83,7 @@ Impulsionado pela curiosidade, seus dedos deslizam em direção ao botão vermel Então, escuridão, e uma sensação de queda.
- Vórtice do Tempo + Vórtice do Tempo
### Alexandria 300 a.C. @@ -101,7 +93,7 @@ Você acorda, desorientado. À medida que sua visão se clareia, uma cidade anti Pessoas em togas movimentam-se pelas ruas, suas vozes se mesclando em uma sinfonia de dialetos antigos, o ar preenchido com o aroma de especiarias exóticas e o som distante de mercadores anunciando suas mercadorias.
- Alexandria 300 a.C. + Alexandria 300 a.C.
**Você:** Certamente, devo ter batido a cabeça, você pensa, fechando os olhos e abrindo-os novamente, a cena permanece inalterada. @@ -113,7 +105,7 @@ Estou preso no passado? Devo pressionar aquele botão novamente? Antes que você Um senhor idoso vestindo uma toga acena para você dos degraus do grande templo. Seu cabelo branco e barba capturam a luz do sol, dando-lhe um brilho quase etéreo.
- Dinócrates vestindo uma toga + Dinócrates vestindo uma toga
**Dinócrates:** "Bem-vindo, viajante," ele diz calorosamente. "Eu sou Dinócrates, arquiteto desta grande cidade. Sua chegada foi prevista." @@ -145,7 +137,7 @@ Um pensamento lhe ocorre. O dispositivo pode me entender se eu falar com ele? **Você:** Você está certo, George é um bom nome, era o nome do meu pai, na verdade.
- Dispositivo de viagem no tempo semelhante a um besouro metálico + Dispositivo de viagem no tempo semelhante a um besouro metálico
_Dispositivo de tempo, "George" o besouro metálico_ @@ -166,19 +158,19 @@ Se você quiser interagir com Dinócrates, execute o aplicativo [Characters](/ap > [Aviso sobre IA Responsável](/README.md#responsible-ai-disclaimer)
- Dinócrates vestindo uma toga + Dinócrates vestindo uma toga
**Passos**: 1. Inicie um [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navegue até _/app_ na raiz do repositório. -3. Localize o console e execute `npm install` seguido de `npm start`. +3. Localize o console e execute `npm ci` seguido de `npm start`. 4. Quando aparecer, selecione o botão "Open in Browser". 5. Converse com Dinócrates. > [!NOTE] - > Se você estiver executando o projeto localmente em sua máquina, por favor revise o guia de Início Rápido para configurar um [token de acesso pessoal do GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) e substitua a chave no código. +> Configure `AI_ENDPOINT`, `AI_API_KEY` e `AI_MODEL` no arquivo `.env` na raiz do repositório, tanto localmente quanto no Codespaces. Consulte o [guia de configuração](/docs/setup/README.md#configure-environment-variables). ### Uma prévia do código @@ -187,48 +179,77 @@ Embora ainda haja muito mais para abordar neste currículo de IA Generativa, vam Dentro de `/app/app.js` você encontrará uma função `app.post` que gerencia a funcionalidade de IA Generativa. Ela é mostrada a seguir: ```JavaScript -app.post('/send', async (req, res) => { - const { message } = req.body; - const prompt = message; - - const messages = [ - { - "role": "system", - "content": "You are Dinocrates of Alexandria, a famous architect and engineer. Limit your responses to only the time you live in, you don't know anything else. You only want to talk about your architecture and engineering projects, and possibly new ideas you have.", - }, - { - "role": "user", - "content": prompt +import express from 'express'; +import { OpenAI } from 'openai'; +import path from 'path'; +import { fileURLToPath } from 'url'; +import { ChatRequestError, getChatRequest, handleBodyError } from './chat-request.js'; +import { getModelConfig, loadEnvironment } from './model-config.js'; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = path.dirname(__filename); + +export function createApp(openai, model) { + if (typeof model !== 'string' || !model.trim()) { + throw new Error('A model name is required to create the chat app.'); + } + const app = express(); + + app.use(express.json()); + app.use(express.static(path.join(__dirname, 'public'))); + app.locals.delimiters = '{{ }}'; + + app.post('/send', async (req, res) => { + try { + const { prompt, systemMessage } = getChatRequest(req.body); + const completion = await openai.chat.completions.create({ + model, + messages: [ + { role: "system", content: systemMessage }, + { role: "user", content: prompt } + ] + }); + + const answer = completion?.choices?.[0]?.message?.content; + if (typeof answer !== 'string' || !answer.trim()) { + throw new Error('The model did not return an answer.'); + } + res.json({ prompt, answer }); + } catch (error) { + if (error instanceof ChatRequestError) { + res.status(400).json({ message: error.message }); + return; + } + console.error(`Error: ${error.message}`); + res.status(500).json({ message: 'An unexpected error occurred. Please try again later.' }); } - ]; + }); + app.use(handleBodyError); + return app; +} + +if (process.argv[1] && path.resolve(process.argv[1]) === __filename) { + loadEnvironment(); + const port = process.env.PORT || 3000; + const { baseURL, apiKey, model } = getModelConfig(); const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL, + apiKey, + timeout: 60000 }); - - try { - console.log(`sending prompt ${prompt}`) - const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', - messages: messages, - }); - - res.json({ - prompt: prompt, - answer: completion.choices[0]?.message?.content - }); - } catch (error) { - res.status(500).json({ error: error.message }); - } -}); + const app = createApp(openai, model); + app.listen(port, '127.0.0.1', () => { + console.log(`Server is running on http://localhost:${port}`); + }); +} ``` Aqui está um resumo passo a passo do que a função faz: 1. **Extrai a Mensagem da Requisição**: A função extrai a mensagem do corpo da requisição (req.body). 2. **Cria um Array de Prompt**: Constrói um array de mensagens, incluindo uma mensagem do sistema e a mensagem de prompt do usuário. -3. **Inicializa o Cliente OpenAI**: Um cliente OpenAI é inicializado com a URL base e chave de API das variáveis de ambiente. Um modelo _gpt-4o-mini_ do [GitHub Models](https://github.com/marketplace/models) é usado para processar o prompt e retornar uma resposta. +3. **Inicializa o Cliente OpenAI**: O endpoint, a chave de API e o nome da implantação vêm do seu recurso Microsoft Foundry. O código usa `AI_ENDPOINT`, `AI_API_KEY` e `AI_MODEL`. 4. **Envia o Prompt para a OpenAI**: A função registra o prompt e o envia para a API OpenAI para gerar uma conclusão. 5. **Trata a Resposta**: Se bem-sucedido, a função responde com o prompt e a resposta da conclusão. 6. **Tratamento de Erros**: Se ocorrer um erro, responde com um status 500 e a mensagem de erro. @@ -270,7 +291,7 @@ Como você pode ver, essas melhorias podem ajudar tanto o front office quanto o Aqui está um exemplo de um "aplicativo de chatbot" em ação: -![Imagem de aplicativo de chat](https://camo.githubusercontent.com/76f2ad7cd754a2de2b9957d2070448e130e5ba228084b9b4b128e3af9c9f5239/68747470733a2f2f6c6561726e2e6d6963726f736f66742e636f6d2f656e2d75732f73656d616e7469632d6b65726e656c2f6d656469612f636861742d636f70696c6f742d696e2d616374696f6e2e676966) +![Aplicativo de chat do curso](/docs/images/character-chat.png) **Você:** Fascinante, vou anotar para visitar o século 21 para ver como essas ferramentas são usadas. @@ -357,5 +378,3 @@ C. Python é a única linguagem usada para desenvolvimento de IA. [Solução do quiz](../solution/solution-quiz.md) ## Recursos para auto-estudo - -- [Série de vídeos sobre JavaScript para IA Generativa](https://aka.ms/genai-js) diff --git a/lessons/01-intro-to-genai/translations/README.zh-cn.md b/lessons/01-intro-to-genai/translations/README.zh-cn.md index 7342dfd0..7c026842 100644 --- a/lessons/01-intro-to-genai/translations/README.zh-cn.md +++ b/lessons/01-intro-to-genai/translations/README.zh-cn.md @@ -10,14 +10,6 @@ 如果你还没有设置开发环境，以下是设置方法：[设置你的环境](/docs/setup/README.md)。 -## 相关资源 - -[![观看关于生成式 AI 介绍的短视频](https://img.youtube.com/vi/vLYtDgs_zx8/0.jpg)](https://www.youtube.com/watch?v=vLYtDgs_zx8&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=1) - -_这个视频为你介绍了 JavaScript 中的生成式 AI_ - -💼 幻灯片：[生成式 AI 介绍](/videos/slides/00-intro.pptx) - ## 生成式 AI 到目前为止，你可能已经听说过 ChatGPT 或生成式 AI。这个概念很简单：你提供一个提示，然后一个模型——通常被称为大语言模型（LLM）——会生成一段或甚至一整页的文本。这个输出可以用于各种目的，包括创意写作、回答问题和编程。 @@ -43,7 +35,7 @@ _这个视频为你介绍了 JavaScript 中的生成式 AI_ 你的工作台，工作室的核心，是一个有序的混乱。
- 伦敦工作室 + 伦敦工作室
_工作台中央放着一个机器人的躯干——一个耗费了数月努力的工程奇迹。它的木制框架精心雕刻，每个关节都经过精心设计以实现流畅的运动。_ @@ -75,7 +67,7 @@ _查尔斯·巴贝奇。"_ 当你的眼睛适应了昏暗的光线，你注意到远处有一个人影在向你挥手。你朝他走去，脚步声在木地板上回响。人影变得清晰，你从报纸照片中认出了他，是查尔斯·巴贝奇。
- 布满灰尘的图书馆 + 布满灰尘的图书馆
### 这是什么设备？ @@ -91,7 +83,7 @@ _查尔斯·巴贝奇。"_ 然后，一片黑暗，和坠落的感觉。
- 时空漩涡 + 时空漩涡
### 公元前 300 年的亚历山大 @@ -101,7 +93,7 @@ _查尔斯·巴贝奇。"_ 身穿托加长袍的人们在街道上穿行，他们的声音融合成古老方言的交响乐，空气中充满异国香料的气味和远处商人叫卖的声音。
- 公元前 300 年的亚历山大 + 公元前 300 年的亚历山大
**你**： 我一定是撞到了头，你想着，闭上眼睛又睁开，场景依然不变。 @@ -113,7 +105,7 @@ _查尔斯·巴贝奇。"_ 一位身穿托加长袍的老者从大庙的台阶上向你挥手。他的白发和胡须在阳光下闪耀，给他一种几乎是超凡的光芒。
- 身穿托加长袍的迪诺克拉底 + 身穿托加长袍的迪诺克拉底
**迪诺克拉底**： "欢迎，旅行者，"他温和地说。"我是迪诺克拉底，这座伟大城市的建筑师。你的到来早有预言。" @@ -145,7 +137,7 @@ _查尔斯·巴贝奇。"_ **你**： 你说得对，乔治确实是个好名字，事实上这是我父亲的名字。
- 像金属甲虫的时间旅行设备 + 像金属甲虫的时间旅行设备
_时间设备，"乔治"金属甲虫_ @@ -166,19 +158,19 @@ _时间设备，"乔治"金属甲虫_ > [负责任的 AI 免责声明](../../../README.md#responsible-ai-disclaimer)
- 身穿托加长袍的迪诺克拉底 + 身穿托加长袍的迪诺克拉底
**步骤**： 1. 启动一个 [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. 导航到仓库根目录中的 _/app_。 -3. 在控制台中运行 `npm install` 然后运行 `npm start`。 +3. 在控制台中运行 `npm ci` 然后运行 `npm start`。 4. 当它出现时，选择"在浏览器中打开"。 5. 与迪诺克拉底聊天。 > [!NOTE] -> 如果你在本地机器上运行项目，请查看快速入门指南以设置 [GitHub personal access](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) token 并在代码中替换密钥。 +> 在存储库根目录的 `.env` 中配置 `AI_ENDPOINT`、`AI_API_KEY` 和 `AI_MODEL`。本地开发和 Codespaces 都需要这些设置。请参阅[配置指南](/docs/setup/README.md#configure-environment-variables)。 ### 代码预览 @@ -187,48 +179,77 @@ _时间设备，"乔治"金属甲虫_ 在 `/app/app.js` 中，你会找到一个处理生成式 AI 功能的 `app.post` 函数。代码如下： ```JavaScript -app.post('/send', async (req, res) => { - const { message } = req.body; - const prompt = message; - - const messages = [ - { - "role": "system", - "content": "You are 迪诺克拉底 of Alexandria, a famous architect and engineer. Limit your responses to only the time you live in, you don't know anything else. You only want to talk about your architecture and engineering projects, and possibly new ideas you have.", - }, - { - "role": "user", - "content": prompt +import express from 'express'; +import { OpenAI } from 'openai'; +import path from 'path'; +import { fileURLToPath } from 'url'; +import { ChatRequestError, getChatRequest, handleBodyError } from './chat-request.js'; +import { getModelConfig, loadEnvironment } from './model-config.js'; + +const __filename = fileURLToPath(import.meta.url); +const __dirname = path.dirname(__filename); + +export function createApp(openai, model) { + if (typeof model !== 'string' || !model.trim()) { + throw new Error('A model name is required to create the chat app.'); + } + const app = express(); + + app.use(express.json()); + app.use(express.static(path.join(__dirname, 'public'))); + app.locals.delimiters = '{{ }}'; + + app.post('/send', async (req, res) => { + try { + const { prompt, systemMessage } = getChatRequest(req.body); + const completion = await openai.chat.completions.create({ + model, + messages: [ + { role: "system", content: systemMessage }, + { role: "user", content: prompt } + ] + }); + + const answer = completion?.choices?.[0]?.message?.content; + if (typeof answer !== 'string' || !answer.trim()) { + throw new Error('The model did not return an answer.'); + } + res.json({ prompt, answer }); + } catch (error) { + if (error instanceof ChatRequestError) { + res.status(400).json({ message: error.message }); + return; + } + console.error(`Error: ${error.message}`); + res.status(500).json({ message: 'An unexpected error occurred. Please try again later.' }); } - ]; + }); + app.use(handleBodyError); + return app; +} + +if (process.argv[1] && path.resolve(process.argv[1]) === __filename) { + loadEnvironment(); + const port = process.env.PORT || 3000; + const { baseURL, apiKey, model } = getModelConfig(); const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL, + apiKey, + timeout: 60000 }); - - try { - console.log(`sending prompt ${prompt}`) - const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', - messages: messages, - }); - - res.json({ - prompt: prompt, - answer: completion.choices[0]?.message?.content - }); - } catch (error) { - res.status(500).json({ error: error.message }); - } -}); + const app = createApp(openai, model); + app.listen(port, '127.0.0.1', () => { + console.log(`Server is running on http://localhost:${port}`); + }); +} ``` 以下是该函数的逐步说明： 1. **从请求中提取消息**：该函数从请求体（req.body）中提取消息。 2. **创建提示数组**：它构建一个消息数组，包括系统消息和用户的提示消息。 -3. **初始化 OpenAI 客户端**：使用基础 URL 和环境变量中的 API 密钥初始化 OpenAI 客户端。使用来自 [GitHub Models](https://github.com/marketplace/models) 的 _gpt-4o-mini_ 模型来处理提示并返回响应。 +3. **初始化 OpenAI 客户端**：终结点、API 密钥和部署名称来自你的 Microsoft Foundry 资源。代码使用 `AI_ENDPOINT`、`AI_API_KEY` 和 `AI_MODEL`。 4. **向 OpenAI 发送提示**：该函数记录提示并将其发送到 OpenAI API 以生成完成。 5. **处理响应**：如果成功，该函数返回提示和完成的答案。 6. **错误处理**：如果发生错误，它返回 500 状态和错误消息。 @@ -270,7 +291,7 @@ app.post('/send', async (req, res) => { 这是一个"聊天机器人应用"的实际示例： -![聊天应用图片](https://camo.githubusercontent.com/76f2ad7cd754a2de2b9957d2070448e130e5ba228084b9b4b128e3af9c9f5239/68747470733a2f2f6c6561726e2e6d6963726f736f66742e636f6d2f656e2d75732f73656d616e7469632d6b65726e656c2f6d656469612f636861742d636f70696c6f742d696e2d616374696f6e2e676966) +![课程配套聊天应用](/docs/images/character-chat.png) **你**： 有趣，我要记下来去 21 世纪看看这些工具是如何使用的。 @@ -356,5 +377,3 @@ C. Python 是唯一用于 AI 开发的语言。 [测验解决方案](/lessons/01-intro-to-genai/solution/solution-quiz.md) ## 自学资源 - -- [生成式 AI JavaScript 视频系列](https://aka.ms/genai-js) diff --git a/lessons/02-first-ai-app/README.md b/lessons/02-first-ai-app/README.md index ddcedf21..522ad4ee 100644 --- a/lessons/02-first-ai-app/README.md +++ b/lessons/02-first-ai-app/README.md @@ -10,16 +10,6 @@ In this chapter you will learn: If you haven't already, set up your development environment. Here's how you can do it: [Setup your environment](/docs/setup/README.md). -## Related Resources - -[![Watch a short video about large language models](https://img.youtube.com/vi/GQ_2OjNZ9aA/0.jpg)](https://www.youtube.com/watch?v=GQ_2OjNZ9aA&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=2) - -_This video gives you an introduction to AI models called "large language models", LLMs, what they are and how you can use them to integrate AI into your apps._ - -*🎥 Click on the image above to watch a short video about large language models* - -💼 Slides: [Introduction to large language models, LLMs](/videos/slides/01-llms.pptx) - ## Narrative: Picture yourself in a boat on a river @@ -126,14 +116,14 @@ If you want to interact with Leonardo, run the [Characters](/app/README.md) app. 1. Start a [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navigate to _/app/README.md_ in the repo root. -3. Locate the console and run `npm install` followed by `npm start`. +3. Locate the console and run `npm ci` followed by `npm start`. 4. Once it appears, select the "Open in Browser" button. 5. Chat with Leonardo. For a more detailed explanation of the app, see [Detailed app explanation](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] - > If you're running the project locally on your machine, please review the QuickStart guide to get a [GitHub personal access](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) token setup and replace the key in the code. +> Configure `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL` in the repository-root `.env` for both local development and Codespaces. See the [setup guide](/docs/setup/README.md#configure-environment-variables). ## Development environment setup @@ -175,7 +165,7 @@ Before you can assist Leonardo with his project, you should first think about th **Time Beetle:** Generative AI models can be used for many things, for example, language translation. In fact, it accepts input in one language and can generate text in another language. Let's start with a simple app that takes English input and generates Italian text. -```javascript +```javascript import { OpenAI } from "openai"; @@ -194,9 +184,16 @@ ${question} // 2. Create client // ----------------------------------- +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); @@ -226,13 +223,13 @@ Let's explain what's happening here: **You:** I think I get it. So if I change the value of the `question` variable to something else, the app will generate a different Italian translation? -**Time Beetle:** Exactly, you can change the input text to anything you want. Also note how GitHub models are being used as the base URL for the API together with a GitHub token as the API key. +**Time Beetle:** The endpoint, API key, and deployment name come from your Microsoft Foundry resource. The code reads them from `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL`. **You:** Why is that important? -**Time Beetle:** It's important to use a base URL and API key that are specific to the model you're using. GitHub Models is a platform that hosts a variety of models, all with different capabilities and features, it's also free to use. +**Time Beetle:** Microsoft Foundry hosts the model deployment. Use an API key from the same resource as the endpoint, and set `AI_MODEL` to the deployment name. Model usage can incur Azure charges; check pricing and quota. -**You:** Oh good, I don't know who to pay anyways and I doubt they accept my currency here. :) +**You:** Then I'll use a small deployment and check the costs before experimenting. ## Chat apps @@ -268,7 +265,7 @@ AI: Rome is known for its ancient ruins, art, and vibrant culture. You can visit **Time Beetle:** Below is how we can construct a conversation with the AI: -```javascript +```javascript // Define the context @@ -295,9 +292,16 @@ const messages = [ } ]; +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); @@ -499,5 +503,5 @@ C. The context window determines how creative the AI's responses are. - [Text generation](https://platform.openai.com/docs/guides/text-generation) - [JavaScript library for OpenAI](https://github.com/openai/openai-node/tree/master/examples) - [Tokenizer](https://platform.openai.com/tokenizer) -- [Completion API](https://platform.openai.com/docs/api-reference/completions) -- [Chat completions](https://platform.openai.com/docs/guides/text-generation#text-generation-models) +- [Completion API](https://github.com/openai/openai-node#usage) +- [Chat completions](https://platform.openai.com/docs/guides/text-generation#text-generation-models) diff --git a/lessons/02-first-ai-app/sample-app/app.js b/lessons/02-first-ai-app/sample-app/app.js index 35f2b269..4f284211 100644 --- a/lessons/02-first-ai-app/sample-app/app.js +++ b/lessons/02-first-ai-app/sample-app/app.js @@ -1,4 +1,11 @@ import { OpenAI } from "openai"; + +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} // 1. Define the prompt // ----------------------------------- @@ -18,8 +25,9 @@ const messages = [ // ----------------------------------- const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, + timeout: 60000, }); @@ -27,7 +35,7 @@ const openai = new OpenAI({ // ----------------------------------- const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', + model, messages: messages, }); @@ -36,4 +44,6 @@ console.log(`Answer for "${question}":`); // 4. Print the answer // ----------------------------------- -console.log(completion.choices[0]?.message?.content); +const answer = completion.choices[0]?.message?.content; +if (!answer?.trim()) throw new Error("The model did not return an answer."); +console.log(answer); diff --git a/lessons/02-first-ai-app/sample-app/package-lock.json b/lessons/02-first-ai-app/sample-app/package-lock.json index ae1264ed..d3886f9f 100644 --- a/lessons/02-first-ai-app/sample-app/package-lock.json +++ b/lessons/02-first-ai-app/sample-app/package-lock.json @@ -9,218 +9,38 @@ "version": "1.0.0", "license": "ISC", "dependencies": { - "openai": "^4.84.1" - } - }, - "node_modules/@types/node": { - "version": "18.19.75", - "resolved": "https://registry.npmjs.org/@types/node/-/node-18.19.75.tgz", - "integrity": "sha512-UIksWtThob6ZVSyxcOqCLOUNg/dyO1Qvx4McgeuhrEtHTLFTf7BBhEazaE4K806FGTPtzd/2sE90qn4fVr7cyw==", - "license": "MIT", - "dependencies": { - "undici-types": "~5.26.4" - } - }, - "node_modules/@types/node-fetch": { - 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"resolved": "https://registry.npmjs.org/whatwg-url/-/whatwg-url-5.0.0.tgz", - "integrity": "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw==", - "license": "MIT", - "dependencies": { - "tr46": "~0.0.3", - "webidl-conversions": "^3.0.0" - } } } } diff --git a/lessons/02-first-ai-app/sample-app/package.json b/lessons/02-first-ai-app/sample-app/package.json index 106a315c..9646d953 100644 --- a/lessons/02-first-ai-app/sample-app/package.json +++ b/lessons/02-first-ai-app/sample-app/package.json @@ -4,13 +4,13 @@ "main": "app.js", "type": "module", "scripts": { - "start": "node app.js" + "start": "node --env-file-if-exists=../../../.env app.js" }, "keywords": [], "author": "", "license": "ISC", - "description": "Sample app showcasing AI usage using GitHub model", + "description": "First AI app using a configured OpenAI-compatible model", "dependencies": { - "openai": "^4.84.1" + "openai": "^7.28.0" } } diff --git a/lessons/02-first-ai-app/solution/solution.md b/lessons/02-first-ai-app/solution/solution.md index cef38b55..fce8c855 100644 --- a/lessons/02-first-ai-app/solution/solution.md +++ b/lessons/02-first-ai-app/solution/solution.md @@ -1,6 +1,6 @@ Here's the solution -```javascript +```javascript import { OpenAI } from "openai"; // 1. Define the prompt @@ -21,9 +21,16 @@ const messages = [ // 2. Create client // ----------------------------------- +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); @@ -31,7 +38,7 @@ const openai = new OpenAI({ // ----------------------------------- const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', + model, messages: messages, }); diff --git a/lessons/02-first-ai-app/translations/README.bn.md b/lessons/02-first-ai-app/translations/README.bn.md index 734ac090..762a5b6d 100644 --- a/lessons/02-first-ai-app/translations/README.bn.md +++ b/lessons/02-first-ai-app/translations/README.bn.md @@ -10,16 +10,6 @@ যদি আপনি এখনো সেটআপ না করে থাকেন, তাহলে আপনার ডেভেলপমেন্ট এনভায়রনমেন্ট সেট আপ করুন। এটি করার জন্য এই লিঙ্কটি অনুসরণ করুন: [আপনার এনভায়রনমেন্ট সেটআপ করুন](/docs/setup/README.md)। -## সম্পর্কিত রিসোর্সসমূহ - -[![বড় ভাষা মডেল সম্পর্কে একটি ছোট ভিডিও দেখুন](https://img.youtube.com/vi/GQ_2OjNZ9aA/0.jpg)](https://www.youtube.com/watch?v=GQ_2OjNZ9aA&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=2) - -_এই ভিডিওটি AI মডেল, বিশেষ করে "বড় ভাষা মডেল" (LLMs) কী এবং কীভাবে এগুলো ব্যবহার করে আপনার অ্যাপে AI সংযুক্ত করা যায় তা পরিচয় করিয়ে দেয়।_ - -*🎥 উপরের ছবিতে ক্লিক করে বড় ভাষা মডেল সম্পর্কে একটি ছোট ভিডিও দেখুন* - -💼 স্লাইড: [বড় ভাষা মডেল, LLMs-এর পরিচিতি](/videos/slides/01-llms.pptx) - ## গল্প: নিজেকে একটি নদীতে একটি নৌকায় কল্পনা করুন > [!NOTE] @@ -132,14 +122,14 @@ _এই ভিডিওটি AI মডেল, বিশেষ করে "বড 1. শুরু করুন [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. রিপোজিটরির মূল ফোল্ডারে _/app/README.md_ এ যান। -3. কনসোলে `npm install` চালান, তারপর `npm start` কমান্ড দিন। +3. কনসোলে `npm ci` চালান, তারপর `npm start` কমান্ড দিন। 4. যখন অ্যাপটি চালু হবে, তখন "Open in Browser" বোতামটি নির্বাচন করুন। 5. এখন আপনি লিওনার্দোর সাথে চ্যাট করতে পারেন। আরও বিস্তারিত জানতে, দেখুন [বিস্তারিত অ্যাপ ব্যাখ্যা](/lessons/01-intro-to-genai/translations/README.bn.md#ডিনোক্রেটিসের-সাথে-সাক্ষাৎ)। > [!NOTE] -> আপনি যদি প্রকল্পটি আপনার স্থানীয় কম্পিউটারে চালান, তাহলে দয়া করে **QuickStart গাইড** অনুসরণ করে [GitHub ব্যক্তিগত অ্যাক্সেস টোকেন (PAT)](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) সেটআপ করুন এবং কোডে টোকেনটি প্রতিস্থাপন করুন। +> রিপোজিটরির মূল ফোল্ডারের `.env` ফাইলে `AI_ENDPOINT`, `AI_API_KEY` এবং `AI_MODEL` সেট করুন। লোকাল পরিবেশ এবং Codespaces উভয়ের জন্য এই সেটিংস প্রয়োজন। [সেটআপ নির্দেশিকা](/docs/setup/README.md#configure-environment-variables) দেখুন। > ## ডেভেলপমেন্ট এনভায়রনমেন্ট সেটআপ লিওনার্দোকে তার প্রকল্পে সাহায্য করার আগে, প্রথমেই আপনার **"কারিগরি সরঞ্জাম"** সম্পর্কে চিন্তা করা উচিত, যা আপনাকে কার্যকরভাবে সহায়তা করতে সাহায্য করবে। @@ -199,9 +189,16 @@ ${question} // 2. ক্লায়েন্ট তৈরি করুন // ----------------------------------- +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, // পরিবেশ পরিবর্তনশীল ব্যবহার করুন + baseURL: endpoint, + apiKey, // পরিবেশ পরিবর্তনশীল ব্যবহার করুন }); // 3. অনুরোধ পাঠান @@ -223,7 +220,7 @@ console.log(completion.choices[0]?.message?.content); ### **আপনার অ্যাপ চালানোর জন্য করণীয়:** 1. **Node.js এবং npm ইনস্টল করুন** -2. **আপনার API কী (GITHUB_TOKEN) সেট করুন** পরিবেশ পরিবর্তনশীল হিসাবে +2. **`AI_ENDPOINT`, `AI_API_KEY`, এবং `AI_MODEL` সেট করুন** পরিবেশ পরিবর্তনশীল হিসাবে 3. **এই স্ক্রিপ্টটি চালান:** ```bash node your_script.js @@ -244,13 +241,13 @@ console.log(completion.choices[0]?.message?.content); **আপনি:** "আমি মনে করি আমি বুঝতে পেরেছি। তাহলে যদি আমি `question` ভেরিয়েবলের মান পরিবর্তন করি, তাহলে অ্যাপটি ভিন্ন ইতালীয় অনুবাদ তৈরি করবে?" -**টাইম বিটল:** "একদম ঠিক! আপনি ইনপুট পাঠ্য যেকোনো কিছুতে পরিবর্তন করতে পারেন। লক্ষ্য করুন, **GitHub মডেল** API-এর বেস URL হিসাবে ব্যবহার করা হচ্ছে এবং **GitHub টোকেন** API কী হিসেবে ব্যবহার করা হয়েছে।" +**টাইম বিটল:** এন্ডপয়েন্ট, API কী এবং ডেপ্লয়মেন্টের নাম আপনার Microsoft Foundry রিসোর্স থেকে আসে। কোডে `AI_ENDPOINT`, `AI_API_KEY` এবং `AI_MODEL` ব্যবহার করা হয়। **আপনি:** "এটি গুরুত্বপূর্ণ কেন?" -**টাইম বিটল:** "এটি গুরুত্বপূর্ণ কারণ আপনি যে মডেলটি ব্যবহার করছেন, সেটির জন্য নির্দিষ্ট **বেস URL এবং API কী** ব্যবহার করা প্রয়োজন। **GitHub Models** একটি প্ল্যাটফর্ম যেখানে বিভিন্ন ক্ষমতা ও বৈশিষ্ট্যের মডেল হোস্ট করা হয়, এবং এটি বিনামূল্যে ব্যবহারের জন্য উপলব্ধ।" +**টাইম বিটল:** Microsoft Foundry মডেল ডেপ্লয়মেন্ট হোস্ট করে। একই রিসোর্সের এন্ডপয়েন্ট ও API কী ব্যবহার করুন এবং `AI_MODEL`-এ ডেপ্লয়মেন্টের নাম দিন। Azure-এর মূল্য এবং কোটা পরীক্ষা করুন। -**আপনি:** "ওহ দারুণ, যাক! আমি জানি না কাকে টাকা দিতে হবে, আর আমি সন্দেহ করি যে তারা আমার মুদ্রা গ্রহণ করবে কিনা। 😄" +**আপনি:** "তাহলে ছোট একটি ডেপ্লয়মেন্ট ব্যবহার করব এবং পরীক্ষা করার আগে খরচ দেখে নেব।" ## চ্যাট অ্যাপস **টাইম বিটল:** "Generative AI মডেলগুলোকে কথোপকথনের উপর ভিত্তি করে **পাঠ্য তৈরি করতেও ব্যবহার করা যায়**। আপনি AI-এর সাথে **একটি কথোপকথন অনুকরণ করতে পারেন** একটি **বার্তার তালিকা (messages list)** প্রদান করে, যা মনে হবে যেন কথোপকথন ইতিমধ্যেই ঘটেছে।" @@ -313,9 +310,16 @@ const messages = [ } ]; +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, // API কী সংযুক্ত করুন + baseURL: endpoint, + apiKey, // API কী সংযুক্ত করুন }); // অনুরোধ পাঠান @@ -550,5 +554,5 @@ C. **প্রসঙ্গ উইন্ডো নির্ধারণ করে - [টেক্সট জেনারেশন](https://platform.openai.com/docs/guides/text-generation) - [OpenAI-এর জন্য JavaScript লাইব্রেরি](https://github.com/openai/openai-node/tree/master/examples) - [Tokenizer](https://platform.openai.com/tokenizer) -- [Completion API](https://platform.openai.com/docs/api-reference/completions) -- [Chat completions](https://platform.openai.com/docs/guides/text-generation#text-generation-models) +- [Completion API](https://github.com/openai/openai-node#usage) +- [Chat completions](https://platform.openai.com/docs/guides/text-generation#text-generation-models) diff --git a/lessons/02-first-ai-app/translations/README.fr.md b/lessons/02-first-ai-app/translations/README.fr.md index a313c04a..62cdf30b 100644 --- a/lessons/02-first-ai-app/translations/README.fr.md +++ b/lessons/02-first-ai-app/translations/README.fr.md @@ -12,13 +12,11 @@ Si ce n'est pas encore fait, configurez votre environnement de développement. V ## Ressources associées -[![Regardez une courte vidéo sur les modèles de langage étendus.](https://img.youtube.com/vi/GQ_2OjNZ9aA/0.jpg)](https://www.youtube.com/watch?v=GQ_2OjNZ9aA\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=2) *Cette vidéo vous donne une introduction aux modèles d'IA appelés "modèles de langage étendus", LLM, ce qu'ils sont et comment vous pouvez les utiliser pour intégrer l'IA dans vos applications.* *🎥 Cliquez sur l’image ci-dessus pour regarder une courte vidéo sur les modèles de langage étendus* -💼 Diapositives : [Introduction aux modèles de langage étendus, LLMs](/videos/slides/01-llms.pptx) ## Narrative: Picture yourself in a boat on a river @@ -122,14 +120,14 @@ Si vous voulez interagir avec Léonard, exécutez l'application [Personnages](/a 1. Lancez un [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript). 2. Accédez à */app/README.md* à la racine du référentiel. -3. Localisez la console et exécutez `npm install` suivi de `npm start`. +3. Localisez la console et exécutez `npm ci` suivi de `npm start`. 4. Une fois que l'application est ouverte, sélectionnez le bouton "Ouvrir dans le navigateur". 5. Discutez avec Leonardo. Pour une explication plus détaillée de l'application, voir [Explication détaillée de l'application](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] -> Si vous exécutez le projet localement sur votre machine, veuillez consulter le guide de démarrage rapide pour configurer un [token d'accès personnel GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) et remplacer la clé dans le code. +> Configurez `AI_ENDPOINT`, `AI_API_KEY` et `AI_MODEL` dans le fichier `.env` à la racine du dépôt, en local comme dans Codespaces. Consultez le [guide de configuration](/docs/setup/README.md#configure-environment-variables). ## Configuration de l'environnement de développement @@ -192,9 +190,16 @@ ${question} // 2. Create client // ----------------------------------- +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); @@ -224,13 +229,13 @@ Expliquons ce qui se passe ici : **Vous :** Je pense que je comprends. Donc, si je change la valeur de la variable `question` par autre chose, l'application générera une traduction italienne différente ? -**Scarabée du Temps :** Exactement, vous pouvez changer le texte d'entrée comme vous le souhaitez. Notez également comment les modèles GitHub sont utilisés comme URL de base pour l'API avec un token GitHub comme clé API. +**Scarabée du Temps :** Le point de terminaison, la clé API et le nom du déploiement proviennent de votre ressource Microsoft Foundry. Le code utilise `AI_ENDPOINT`, `AI_API_KEY` et `AI_MODEL`. **Vous :** Pourquoi est-ce important ? -**Scarabée du Temps :** Il est important d'utiliser une URL de base et une clé API spécifiques au modèle que vous utilisez. GitHub Models est une plateforme qui héberge une variété de modèles, chacun ayant des capacités et des fonctionnalités différentes. De plus, c’est gratuit à utiliser. +**Scarabée du Temps :** Microsoft Foundry héberge le déploiement. Utilisez une clé API de la même ressource que le point de terminaison et son nom de déploiement dans `AI_MODEL`. Vérifiez les tarifs et les quotas Azure. -**Vous :** Ah, tant mieux, je ne sais pas qui payer de toute façon, et je doute qu’ils acceptent ma monnaie ici. :) +**Vous :** Je vais donc utiliser un petit déploiement et vérifier les coûts avant de faire des essais. ## Applications de chat @@ -293,9 +298,16 @@ const messages = [ } ]; +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); @@ -497,5 +509,5 @@ C. La fenêtre de contexte détermine à quel point les réponses de l’IA sont * [Génération de texte](https://platform.openai.com/docs/guides/text-generation) * [Bibliothèque JavaScript pour OpenAI](https://github.com/openai/openai-node/tree/master/examples) * [Tokeniseur](https://platform.openai.com/tokenizer) -* [API de complétion](https://platform.openai.com/docs/api-reference/completions) +* [API de complétion](https://github.com/openai/openai-node#usage) * [Complétions de chat](https://platform.openai.com/docs/guides/text-generation#text-generation-models) diff --git a/lessons/02-first-ai-app/translations/README.pt-br.md b/lessons/02-first-ai-app/translations/README.pt-br.md index 82dbc1a7..2afda2b6 100644 --- a/lessons/02-first-ai-app/translations/README.pt-br.md +++ b/lessons/02-first-ai-app/translations/README.pt-br.md @@ -10,16 +10,6 @@ Neste capítulo você vai aprender: Se você ainda não configurou seu ambiente de desenvolvimento, veja como fazer: [Configure seu ambiente](/docs/setup/README.md). -## Recursos relacionados - -[![Assista a um vídeo curto sobre grandes modelos de linguagem](https://img.youtube.com/vi/GQ_2OjNZ9aA/0.jpg)](https://www.youtube.com/watch?v=GQ_2OjNZ9aA&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=2) - -_Este vídeo apresenta uma introdução aos modelos de IA chamados "grandes modelos de linguagem", LLMs, o que são e como você pode usá-los para integrar IA em seus aplicativos._ - -*🎥 Clique na imagem acima para assistir a um vídeo curto sobre grandes modelos de linguagem* - -💼 Slides: [Introdução aos grandes modelos de linguagem, LLMs](/videos/slides/01-llms.pptx) - ## Narrativa: Imagine-se em um barco num rio @@ -42,7 +32,7 @@ Quando você abre os olhos, o mundo mudou. Ao conseguir se levantar, você perce Observando o barco, você encontra um remo longo apoiado na lateral. Segurando-o, você começa a remar em direção aos edifícios distantes. À medida que se aproxima, os edifícios ficam mais nítidos, são antigos, sua arquitetura lembrando uma pintura renascentista.
- Barco no rio, homem em pé com um remo + Barco no rio, homem em pé com um remo
A questão agora é: onde e quando você está desta vez? @@ -52,7 +42,7 @@ Você consegue atracar o barco no cais e começa a caminhar ao longo das tábuas Enquanto caminha, você nota um homem com uma longa barba e um chapéu, remexendo em uma caixa do que parecem ser peças mecânicas. Suas mãos se movem com destreza, separando engrenagens e molas com facilidade experiente.
- Leonardo Da Vinci em pé ao lado de uma caixa no porto + Leonardo Da Vinci em pé ao lado de uma caixa no porto
### Ajude-me, Leonardo @@ -82,7 +72,7 @@ Enquanto caminha, você nota um homem com uma longa barba e um chapéu, remexend ### Na oficina
- Oficina de Leonardo + Oficina de Leonardo
O velho o leva até uma grande porta de madeira e você é recebido pela visão de uma oficina cheia de todo tipo de engenhocas mecânicas. @@ -125,21 +115,21 @@ Se você quiser interagir com Leonardo, execute o aplicativo [Characters](/app/R > [Aviso sobre IA Responsável](/README.md#responsible-ai-disclaimer)
- +
**Passos**: 1. Inicie um [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navegue até _/app/README.md_ na raiz do repositório. -3. Localize o console e execute `npm install` seguido de `npm start`. +3. Localize o console e execute `npm ci` seguido de `npm start`. 4. Quando aparecer, selecione o botão "Open in Browser". 5. Converse com Leonardo. Para uma explicação mais detalhada do aplicativo, consulte [Explicação detalhada do aplicativo](/lessons/01-intro-to-genai/README.md#interaja-com-dinocrates). > [!NOTE] - > Se você estiver executando o projeto localmente em sua máquina, por favor revise o guia de Início Rápido para configurar um [token de acesso pessoal do GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) e substitua a chave no código. +> Configure `AI_ENDPOINT`, `AI_API_KEY` e `AI_MODEL` no arquivo `.env` na raiz do repositório, tanto localmente quanto no Codespaces. Consulte o [guia de configuração](/docs/setup/README.md#configure-environment-variables). ## Configuração do ambiente de desenvolvimento @@ -181,7 +171,7 @@ Antes de poder ajudar Leonardo com seu projeto, você deve primeiro pensar nas " **Besouro do Tempo:** Modelos de IA generativa podem ser usados para muitas coisas, por exemplo, tradução de idiomas. Na verdade, ele aceita entrada em um idioma e pode gerar texto em outro idioma. Vamos começar com um aplicativo simples que recebe entrada em inglês e gera texto em italiano. -```javascript +```javascript import { OpenAI } from "openai"; @@ -200,9 +190,16 @@ ${question} // 2. Crie o cliente // ----------------------------------- +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); @@ -232,13 +229,13 @@ Vamos explicar o que está acontecendo aqui: **Você:** Acho que entendi. Então, se eu mudar o valor da variável `question` para outra coisa, o aplicativo gerará uma tradução italiana diferente? -**Besouro do Tempo:** Exatamente, você pode alterar o texto de entrada para qualquer coisa que desejar. Observe também como os modelos do GitHub estão sendo usados como a URL base para a API junto com um token do GitHub como a chave da API. +**Besouro do Tempo:** O endpoint, a chave de API e o nome da implantação vêm do seu recurso Microsoft Foundry. O código usa `AI_ENDPOINT`, `AI_API_KEY` e `AI_MODEL`. **Você:** Por que isso é importante? -**Besouro do Tempo:** É importante usar uma URL base e uma chave de API específicas para o modelo que você está usando. GitHub Models é uma plataforma que hospeda uma variedade de modelos, todos com diferentes capacidades e recursos, e também é gratuito para usar. +**Besouro do Tempo:** O Microsoft Foundry hospeda a implantação. Use uma chave da mesma origem do endpoint e defina o nome da implantação em `AI_MODEL`. Verifique os preços e as cotas do Azure. -**Você:** Ah, que bom, eu não sei quem pagar de qualquer forma e duvido que eles aceitem minha moeda aqui. :) +**Você:** Então vou usar uma implantação pequena e verificar os custos antes de experimentar. ## Aplicativos de chat @@ -274,7 +271,7 @@ IA: Roma é conhecida por suas ruínas antigas, arte e cultura vibrante. Você p **Besouro do Tempo:** Abaixo está como podemos construir uma conversa com a IA: -```javascript +```javascript // Defina o contexto @@ -301,9 +298,16 @@ const messages = [ } ]; +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); @@ -462,7 +466,7 @@ Leonardo de repente pediu para inspecionar o Besouro do Tempo mais de perto, olh **Besouro do Tempo:** Nada
- Parafuso aéreo, Leonardo Da Vinci + Parafuso aéreo, Leonardo Da Vinci
> [!NOTE] @@ -507,5 +511,5 @@ C. A janela de contexto determina quão criativas são as respostas da IA. - [Geração de texto](https://platform.openai.com/docs/guides/text-generation) - [Biblioteca JavaScript para OpenAI](https://github.com/openai/openai-node/tree/master/examples) - [Tokenizer](https://platform.openai.com/tokenizer) -- [API de Completions](https://platform.openai.com/docs/api-reference/completions) +- [API de Completions](https://github.com/openai/openai-node#usage) - [Chat completions](https://platform.openai.com/docs/guides/text-generation#text-generation-models) diff --git a/lessons/02-first-ai-app/translations/README.zh-cn.md b/lessons/02-first-ai-app/translations/README.zh-cn.md index a5702017..578e031d 100644 --- a/lessons/02-first-ai-app/translations/README.zh-cn.md +++ b/lessons/02-first-ai-app/translations/README.zh-cn.md @@ -10,16 +10,6 @@ 如果你还没有设置开发环境，以下是设置方法：[设置你的环境](/docs/setup/README.md)。 -## 相关资源 - -[![观看关于大语言模型的短视频](https://img.youtube.com/vi/GQ_2OjNZ9aA/0.jpg)](https://www.youtube.com/watch?v=GQ_2OjNZ9aA&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=2) - -_这个视频向你介绍了被称为"大语言模型"（LLM）的 AI 模型，它们是什么以及如何使用它们将 AI 集成到你的应用程序中。_ - -*🎥 点击上方图片观看关于大语言模型的短视频* - -💼 幻灯片：[大语言模型（LLM）介绍](/videos/slides/01-llms.pptx) - ## 故事叙述：想象自己在河上的一条船里 @@ -42,7 +32,7 @@ _这个视频向你介绍了被称为"大语言模型"（LLM）的 AI 模型， 环顾船内，你发现一根长桨靠在船边。抓起桨，你开始向远处的建筑物划去。当你靠近时，建筑物变得更加清晰，它们很古老，建筑风格让人联想到文艺复兴时期的画作。
- 河上的船，一个人拿着桨站立 + 河上的船，一个人拿着桨站立
现在的问题是，这次你身在何时何地？ @@ -52,7 +42,7 @@ _这个视频向你介绍了被称为"大语言模型"（LLM）的 AI 模型， 在行走过程中，你注意到一个留着长胡子戴着帽子的人，正在翻找一个看起来像机械零件的箱子。他的手灵活地移动，熟练地分拣齿轮和弹簧。
- 莱昂纳多·达·芬奇站在港口的箱子旁边 + 莱昂纳多·达·芬奇站在港口的箱子旁边
### 帮帮我，莱昂纳多 @@ -82,7 +72,7 @@ _这个视频向你介绍了被称为"大语言模型"（LLM）的 AI 模型， ### 在工作坊
- 莱昂纳多的工作坊 + 莱昂纳多的工作坊
老人领你来到一扇大木门前，迎接你的是一个工作坊，里面充满了各种机械装置。 @@ -124,21 +114,21 @@ _这个视频向你介绍了被称为"大语言模型"（LLM）的 AI 模型， > [负责任的 AI 免责声明](/README.md#responsible-ai-disclaimer)
- +
**步骤**： 1. 启动一个 [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. 导航到仓库根目录中的 _/app/README.md_。 -3. 在控制台中运行 `npm install` 然后运行 `npm start`。 +3. 在控制台中运行 `npm ci` 然后运行 `npm start`。 4. 当它出现时，选择"在浏览器中打开"按钮。 5. 与莱昂纳多聊天。 有关应用程序的更详细解释，请参见[详细应用程序说明](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates)。 > [!NOTE] - > 如果你在本地机器上运行项目，请查看快速入门指南以设置 [GitHub personal access](../../../docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) token 并在代码中替换密钥。 +> 在存储库根目录的 `.env` 中配置 `AI_ENDPOINT`、`AI_API_KEY` 和 `AI_MODEL`。本地开发和 Codespaces 都需要这些设置。请参阅[配置指南](/docs/setup/README.md#configure-environment-variables)。 ## 开发环境设置 @@ -180,7 +170,7 @@ _这个视频向你介绍了被称为"大语言模型"（LLM）的 AI 模型， **时间甲虫**：生成式 AI 模型可以用于很多事情，例如语言翻译。事实上，它接受一种语言的输入并可以生成另一种语言的文本。让我们从一个简单的应用程序开始，它接收英语输入并生成意大利语文本。 -```javascript +```javascript import { OpenAI } from "openai"; @@ -199,9 +189,16 @@ ${question} // 2. 创建客户端 // ----------------------------------- +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); @@ -231,13 +228,13 @@ console.log(completion.choices[0]?.message?.content); **你**：我想我明白了。所以如果我改变 `question` 变量的值为别的内容，应用程序就会生成不同的意大利语翻译？ -**时间甲虫**：没错，你可以将输入文本更改为任何你想要的内容。还要注意 GitHub 模型如何作为 API 的基础 URL 与环境变量中的 GitHub 令牌一起作为 API 密钥使用。 +**时间甲虫**：终结点、API 密钥和部署名称来自你的 Microsoft Foundry 资源。代码使用 `AI_ENDPOINT`、`AI_API_KEY` 和 `AI_MODEL`。 **你**：为什么这很重要？ -**时间甲虫**：使用特定于你所使用的模型的基础 URL 和 API 密钥很重要。GitHub Models 是一个托管各种模型的平台，所有这些模型都具有不同的功能和特性，而且使用是免费的。 +**时间甲虫**：Microsoft Foundry 托管模型部署。使用同一资源的终结点和 API 密钥，并将 `AI_MODEL` 设置为部署名称。模型使用可能产生 Azure 费用，请检查价格和配额。 -**你**：哦，太好了，反正我不知道该付钱给谁，而且我怀疑他们是否接受我的货币。:) +**你**：那我会使用较小的部署，并在实验前检查费用。 ## 聊天应用程序 @@ -273,7 +270,7 @@ AI: Rome is known for its ancient ruins, art, and vibrant culture. You can visit **时间甲虫**：以下是我们如何构建与 AI 的对话： -```javascript +```javascript // 定义上下文 @@ -300,9 +297,16 @@ const messages = [ } ]; +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); @@ -459,7 +463,7 @@ const completion = await openai.chat.completions.create({ **时间甲虫**：没什么
- 空气螺旋，莱昂纳多·达·芬奇 + 空气螺旋，莱昂纳多·达·芬奇
> [!NOTE] @@ -503,5 +507,5 @@ C. 上下文窗口决定了 AI 响应的创造性程度。 - [文本生成](https://platform.openai.com/docs/guides/text-generation) - [OpenAI 的 JavaScript 库](https://github.com/openai/openai-node/tree/master/examples) - [Tokenizer](https://platform.openai.com/tokenizer) -- [Completion API](https://platform.openai.com/docs/api-reference/completions) +- [Completion API](https://github.com/openai/openai-node#usage) - [聊天完成](https://platform.openai.com/docs/guides/text-generation#text-generation-models) diff --git a/lessons/03-prompt-engineering/README.md b/lessons/03-prompt-engineering/README.md index 14ff06e7..30fc280a 100644 --- a/lessons/03-prompt-engineering/README.md +++ b/lessons/03-prompt-engineering/README.md @@ -10,16 +10,6 @@ In this chapter you will learn: If you haven't already, set up your development environment. Here's how you can do it: [Setup your environment](/docs/setup/README.md). -## Related Resources - -[![Watch a short video about prompt engineering](https://img.youtube.com/vi/gQ6TlyxBmWs/0.jpg)](https://www.youtube.com/watch?v=gQ6TlyxBmWs&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=3) - -_This video offers an introduction to improving your "prompting" skills, teaching you how to give clearer and more effective instructions to the AI to achieve better results._ - -*🎥 Click on the image above to watch a short video about prompt engineering* - -💼 Slides: [Prompt engineering](/videos/slides/02-prompt-engineering.pptx) - ## Narrative: Ticket to ride > [!NOTE] @@ -116,14 +106,14 @@ If you want to interact with Sforza, run the [Characters](/app/README.md) app. 1. Start a [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navigate to _/app_ in the repo root. -3. Locate the console and run `npm install` followed by `npm start`. +3. Locate the console and run `npm ci` followed by `npm start`. 4. Once it appears, select the "Open in Browser" button. 5. Chat with Sforza. For a more detailed explanation of the app, see [Detailed app explanation](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] - > If you're running the project locally on your machine, please review the QuickStart guide to get a [GitHub personal access](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) token setup and replace the key in the code. +> Configure `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL` in the repository-root `.env` for both local development and Codespaces. See the [setup guide](/docs/setup/README.md#configure-environment-variables). ## What is prompt engineering? @@ -330,6 +320,4 @@ C. Prompt engineering is about training a model to recognize patterns in data. - [Prompt engineering](https://en.wikipedia.org/wiki/Prompt_engineering) - [Prompt engineering fundamentals](https://github.com/microsoft/generative-ai-for-beginners/blob/main/04-prompt-engineering-fundamentals/README.md?WT.mc_id=academic-105485-koreyst) -- [Advanced prompt engineering](https://github.com/microsoft/generative-ai-for-beginners/tree/main/05-advanced-prompts) - - +- [Advanced prompt engineering](https://github.com/microsoft/generative-ai-for-beginners/tree/main/05-advanced-prompts) diff --git a/lessons/03-prompt-engineering/sample-app/app.js b/lessons/03-prompt-engineering/sample-app/app.js index 5c4c74e1..a9b9bec5 100644 --- a/lessons/03-prompt-engineering/sample-app/app.js +++ b/lessons/03-prompt-engineering/sample-app/app.js @@ -1,52 +1,37 @@ import { OpenAI } from "openai"; -import readline from "readline"; - -// User input -const rl = readline.createInterface({ - input: process.stdin, - output: process.stdout -}); +import { createInterface } from "node:readline/promises"; +import { stdin, stdout } from "node:process"; + +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} -const question = (query) => { - return new Promise((resolve) => { - rl.question(query, (answer) => { - resolve(answer); - }); - }); -}; +const openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); async function main() { - const userPrompt = await question("Enter your prompt: "); - - const messages = [ - { - role: "user", - content: userPrompt + const rl = createInterface({ input: stdin, output: stdout }); + try { + const userPrompt = await rl.question("Enter your prompt: "); + if (!userPrompt.trim()) throw new Error("Enter a non-empty prompt."); + const completion = await openai.chat.completions.create({ + model, + messages: [{ role: "user", content: userPrompt }] + }); + const answer = completion.choices[0]?.message?.content; + if (!answer?.trim()) throw new Error("The model did not return an answer."); + console.log(`\nAI Response:\n${answer}`); + if (userPrompt.toLowerCase().includes("time-traveling javascript developer")) { + console.log("\nEaster Egg Unlocked! You discovered the hidden poem!"); } - ]; - - // OpenAI API setup - const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, - }); - - const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', - messages: messages, - }); - - const answer = completion.choices[0]?.message?.content; - - console.log(`\nAI Response:\n`); - console.log(answer); - - // Easter Egg Check - if (userPrompt.toLowerCase().includes("time-traveling javascript developer") && answer) { - console.log("\n Easter Egg Unlocked! You discovered the hidden poem! "); + } finally { + rl.close(); } - - rl.close(); } -main(); +main().catch(error => { + console.error("Error:", error.message); + process.exitCode = 1; +}); diff --git a/lessons/03-prompt-engineering/sample-app/package-lock.json b/lessons/03-prompt-engineering/sample-app/package-lock.json index ae1264ed..d3886f9f 100644 --- a/lessons/03-prompt-engineering/sample-app/package-lock.json +++ b/lessons/03-prompt-engineering/sample-app/package-lock.json @@ -9,218 +9,38 @@ "version": "1.0.0", "license": "ISC", "dependencies": { - "openai": "^4.84.1" - } - }, - "node_modules/@types/node": { - "version": "18.19.75", - "resolved": "https://registry.npmjs.org/@types/node/-/node-18.19.75.tgz", - "integrity": "sha512-UIksWtThob6ZVSyxcOqCLOUNg/dyO1Qvx4McgeuhrEtHTLFTf7BBhEazaE4K806FGTPtzd/2sE90qn4fVr7cyw==", - "license": "MIT", - "dependencies": { - "undici-types": "~5.26.4" - } - }, - "node_modules/@types/node-fetch": { - "version": "2.6.12", - "resolved": "https://registry.npmjs.org/@types/node-fetch/-/node-fetch-2.6.12.tgz", - 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It demonstrates how clear inputs and assumptions improve a prompt. -const rl = readline.createInterface({ - input: process.stdin, - output: process.stdout -}); +Save the code as `solution.js` in the lesson's `sample-app` directory. Run it with `node --env-file=../../../.env solution.js`, or select another environment file with Node's `--env-file` option. -const question = (query) => { - return new Promise((resolve) => { - rl.question(query, (answer) => { - resolve(answer); - }); +```javascript +import { OpenAI } from "openai"; +import { createInterface } from "node:readline/promises"; +import { stdin, stdout } from "node:process"; + +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + +const openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); +const rl = createInterface({ input: stdin, output: stdout }); +try { + async function number(question) { + const text = await rl.question(question); + const value = Number(text); + if (!text.trim() || !Number.isFinite(value)) throw new Error("Enter a finite number."); + return value; + } + const height = await number("Height above ground in meters: "); + const speed = await number("Forward speed in meters per second: "); + const gravity = await number("Gravity in meters per second squared: "); + const upwardSpeed = await number("Initial upward speed in meters per second: "); + if (height < 0 || speed <= 0 || gravity <= 0) { + throw new Error("Height must be non-negative; speed and gravity must be positive."); + } + const prompt = `For this fictional exercise, the hill is 100 meters away. +Height is ${height} meters, forward speed is ${speed} meters per second, +gravity is ${gravity} meters per second squared, and initial upward speed +is ${upwardSpeed} meters per second. Ignore drag. +Use horizontal travel time = 100 / forward speed and vertical height +y(t) = initial height + initial upward speed * t - gravity * t^2 / 2. +State the travel time and whether the machine reaches ground before the hill. +Give a short result and check the units.`; + const completion = await openai.chat.completions.create({ + model, + messages: [{ role: "user", content: prompt }] }); -}; - -const height = await question("Enter the current height above the ground in meters:"); - -const speed = await question("Enter the speed at which you're moving forward in meters per second:"); - -const gravity = await question("Enter the gravity in meters per second squared:"); - -const wind = await question("Enter the wind speed upwards in meters per second:"); - -// Distance to the hill -const distance = 100; - -// Create prompt including inputs should include chain of thought - -const prompt = `Current height above the ground: ${height} meters, Moving forward at: ${speed} meters per second, Gravity: ${gravity} meters per second squared, Wind upwards at: ${wind} meters per second, Distance to the hill: ${distance} meters, Time it will take to reach the hill: t, Here's the formula to calculate the time it will take to reach the hill: D = 1/2 * (g - w) * t^2.`; - -// Call the language model with the prompt - -const messages = [ -{ - "role": "user", - "content": prompt -}]; - -// 2. Create client -// ----------------------------------- - -const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, -}); - -// 3. Send the request -// ----------------------------------- - -const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', - messages: messages, -}); - -console.log(`Answer for "${prompt}":`); - -// 4. Print the answer -// ----------------------------------- - -console.log(completion.choices[0]?.message?.content); - + const answer = completion.choices[0]?.message?.content; + if (!answer?.trim()) throw new Error("The model did not return an answer."); + console.log(answer); +} finally { + rl.close(); +} ``` -## Sample output - -```text -Enter the current height above the ground in meters:100 -Enter the speed at which you're moving forward in meters per second:3 -Enter the gravity in meters per second squared:9.82 -Enter the wind speed upwards in meters per second:3 -Answer for "Current height above the ground: 100 meters, Moving forward at: 3 meters per second, Gravity: 9.82 meters per second squared, Wind upwards at: 3 meters per second, Distance to the hill: 100 meters, Time it will take to reach the hill: t, Here's the formula to calculate the time it will take to reach the hill: D = 1/2 * (g - w) * t^2.": -To determine the time \( t \) it will take to reach the hill, we can rearrange the formula you provided: - -\[ -D = \frac{1}{2} (g - w) t^2 -\] - -Where: -- \( D \) is the distance to the hill (100 meters), -- \( g \) is the acceleration due to gravity (9.82 m/s²), -- \( w \) is the upward wind speed (3 m/s). - -First, we need to calculate \( g - w \): - -\[ -g - w = 9.82 \, \text{m/s}^2 - 3 \, \text{m/s} = 6.82 \, \text{m/s}^2 -\] - -Now, substituting \( D = 100 \) meters into the formula: - -\[ -100 = \frac{1}{2} \times 6.82 \times t^2 -\] - -To isolate \( t^2 \), we can multiply both sides by 2: - -\[ -200 = 6.82 t^2 -\] - -Next, we divide both sides by 6.82: - -\[ -t^2 = \frac{200}{6.82} \approx 29.34 -\] - -Now, taking the square root of both sides gives us \( t \): - -\[ -t \approx \sqrt{29.34} \approx 5.42 \, \text{seconds} -\] - -So, it will take approximately **5.42 seconds** to reach the hill under the given conditions. -``` +For height `100`, forward speed `3`, gravity `9.82`, and initial upward speed `3`, travel time to the hill is about `33.33` seconds. The simplified trajectory reaches ground after about `4.83` seconds, before reaching the hill. Check numerical answers rather than assuming the model is correct. diff --git a/lessons/03-prompt-engineering/translations/README.bn.md b/lessons/03-prompt-engineering/translations/README.bn.md index 758164f1..4c090cb6 100644 --- a/lessons/03-prompt-engineering/translations/README.bn.md +++ b/lessons/03-prompt-engineering/translations/README.bn.md @@ -15,15 +15,6 @@ --- -## **সম্পর্কিত রিসোর্সসমূহ** - -[![প্রম্পট ইঞ্জিনিয়ারিং সম্পর্কে একটি ছোট ভিডিও দেখুন](https://img.youtube.com/vi/gQ6TlyxBmWs/0.jpg)](https://www.youtube.com/watch?v=gQ6TlyxBmWs&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=3) - -_এই ভিডিওটি আপনাকে **"প্রম্পটিং" দক্ষতা উন্নত করার প্রাথমিক ধারণা প্রদান করবে**, যা আপনাকে **AI-কে আরও স্পষ্ট এবং কার্যকর নির্দেশনা দিতে সাহায্য করবে**, ফলে আপনি আরও ভালো ফলাফল পেতে পারেন।_ - -🎥 *উপরের ছবিতে ক্লিক করে প্রম্পট ইঞ্জিনিয়ারিং সম্পর্কে একটি ছোট ভিডিও দেখুন* - -💼 **স্লাইড:** [প্রম্পট ইঞ্জিনিয়ারিং](/videos/slides/02-prompt-engineering.pptx) ## **গল্প: টিকিট টু রাইড** > [!NOTE] @@ -141,14 +132,14 @@ _এই ভিডিওটি আপনাকে **"প্রম্পটিং" 1. **শুরু করুন** [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. **রিপোজিটরির মূল ফোল্ডারে** _/app_ এ যান। -3. **কনসোল খুঁজে বের করুন** এবং **`npm install`** কমান্ড চালান, তারপর **`npm start`** লিখে রান করুন। +3. **কনসোল খুঁজে বের করুন** এবং **`npm ci`** কমান্ড চালান, তারপর **`npm start`** লিখে রান করুন। 4. **অ্যাপ চালু হলে**, "Open in Browser" বোতাম নির্বাচন করুন। 5. **স্পোর্জার সাথে চ্যাট করুন।** আরও বিস্তারিত জানতে, দেখুন **[বিস্তারিত অ্যাপ ব্যাখ্যা](/lessons/01-intro-to-genai/translations/README.bn.md#talk-to-dinocrates)**। > [!NOTE] -> আপনি যদি **প্রকল্পটি স্থানীয়ভাবে (locally) চালান**, তাহলে দয়া করে **QuickStart গাইড** পর্যালোচনা করুন এবং **[GitHub ব্যক্তিগত অ্যাক্সেস টোকেন (PAT)](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access)** সেটআপ করুন, তারপর **কোডে টোকেনটি প্রতিস্থাপন করুন**। +> রিপোজিটরির মূল ফোল্ডারের `.env` ফাইলে `AI_ENDPOINT`, `AI_API_KEY` এবং `AI_MODEL` সেট করুন। লোকাল পরিবেশ এবং Codespaces উভয়ের জন্য এই সেটিংস প্রয়োজন। [সেটআপ নির্দেশিকা](/docs/setup/README.md#configure-environment-variables) দেখুন। ## **প্রম্পট ইঞ্জিনিয়ারিং কী?** **টাইম বিটল:** "আমাদের **প্রম্পট ইঞ্জিনিয়ারিং** সম্পর্কে কথা বলা দরকার।" @@ -373,4 +364,4 @@ C. **প্রম্পট ইঞ্জিনিয়ারিং হলো এ --- ### **প্রারম্ভিক প্রকল্প:** -শুরু করতে এই **[Starter project](/app/README.md)** ব্যবহার করো! +শুরু করতে এই **[Starter project](/app/README.md)** ব্যবহার করো! diff --git a/lessons/03-prompt-engineering/translations/README.fr.md b/lessons/03-prompt-engineering/translations/README.fr.md index cbcf467d..6aeb05b3 100644 --- a/lessons/03-prompt-engineering/translations/README.fr.md +++ b/lessons/03-prompt-engineering/translations/README.fr.md @@ -12,13 +12,11 @@ Si ce n'est pas encore fait, configurez votre environnement de développement. V ## Ressources associées -[![Regardez une courte vidéo sur l'ingénierie des prompts](https://img.youtube.com/vi/gQ6TlyxBmWs/0.jpg)](https://www.youtube.com/watch?v=gQ6TlyxBmWs\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=3) *Cette vidéo propose une introduction à l'amélioration de vos compétences en matière de "prompt", en vous apprenant à donner des instructions plus claires et plus efficaces à l'IA pour obtenir de meilleurs résultats.* *🎥 Cliquez sur l'image ci-dessus pour regarder une courte vidéo sur l'ingénierie des prompts* -💼 Présentation : [Ingénierie des prompts](/videos/slides/02-prompt-engineering.pptx) ## Narrative: Ticket to ride @@ -116,14 +114,14 @@ Si vous souhaitez interagir avec Sforza, exécutez l'application [Personnages](/ 1. Lancez un [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript). 2. Naviguez vers */app* à la racine du dépôt. -3. Localisez la console et exécutez `npm install` suivi de `npm start`. +3. Localisez la console et exécutez `npm ci` suivi de `npm start`. 4. Une fois que l'application est ouverte, sélectionnez le bouton "Ouvrir dans le navigateur". 5. Discutez avec Sforza. Pour une explication plus détaillée de l'application, voir [Explication détaillée de l'application](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] -> Si vous exécutez le projet localement sur votre machine, veuillez consulter le guide de démarrage rapide pour configurer un [token d'accès personnel GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) et remplacer la clé dans le code. +> Configurez `AI_ENDPOINT`, `AI_API_KEY` et `AI_MODEL` dans le fichier `.env` à la racine du dépôt, en local comme dans Codespaces. Consultez le [guide de configuration](/docs/setup/README.md#configure-environment-variables). ## Qu'est-ce que l'ingénierie des prompts ? diff --git a/lessons/03-prompt-engineering/translations/README.pt-br.md b/lessons/03-prompt-engineering/translations/README.pt-br.md index 33f46732..e8588018 100644 --- a/lessons/03-prompt-engineering/translations/README.pt-br.md +++ b/lessons/03-prompt-engineering/translations/README.pt-br.md @@ -10,16 +10,6 @@ Neste capítulo você vai aprender: Se você ainda não configurou seu ambiente de desenvolvimento, veja como fazer: [Configure seu ambiente](/docs/setup/README.md). -## Recursos relacionados - -[![Assista a um vídeo curto sobre engenharia de prompts](https://img.youtube.com/vi/gQ6TlyxBmWs/0.jpg)](https://www.youtube.com/watch?v=gQ6TlyxBmWs&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=3) - -_Este vídeo oferece uma introdução para melhorar suas habilidades de "prompting", ensinando como dar instruções mais claras e eficazes à IA para obter melhores resultados._ - -*🎥 Clique na imagem acima para assistir a um vídeo curto sobre engenharia de prompts* - -💼 Slides: [Engenharia de prompts](/videos/slides/02-prompt-engineering.pptx) - ## Narrativa: Passagem para aventura > [!NOTE] @@ -35,7 +25,7 @@ _Este vídeo oferece uma introdução para melhorar suas habilidades de "prompti Antes que você consiga pressionar o botão, as portas da oficina se abrem com um estrondo. Um homem está na entrada, de ombros largos e vestido com roupas caras, ele agita um pedaço de papel no ar, gritando:
- Um Ludovico Sforza furioso irrompendo pela porta + Um Ludovico Sforza furioso irrompendo pela porta
*Ludovico Sforza* @@ -65,7 +55,7 @@ Um turbilhão de cores envolve a carruagem, o mundo ao seu redor se dissolve em O turbilhão de cores desaparece, e você se encontra na carruagem, agora correndo pela Via Appia em Roma e, para seu espanto, está no meio de uma corrida de cavalos. Bigas trovejam passando, suas rodas levantando nuvens de poeira.
- Fuga de Roma + Fuga de Roma
**Você:** Leonardo, onde estamos? @@ -105,7 +95,7 @@ Com um impulso final, o parafuso aéreo começa a girar. As lâminas pegam o ar, Você olha para a cidade; os edifícios antigos se estendem abaixo de você.
- No ar, olhando para a cidade com os pés pendurados + No ar, olhando para a cidade com os pés pendurados
## Interaja com Sforza @@ -117,21 +107,21 @@ Se você quiser interagir com Sforza, execute o aplicativo [Characters](/app/REA > [Aviso sobre IA Responsável](/README.md#responsible-ai-disclaimer)
- Ludovico Sforza + Ludovico Sforza
**Passos**: 1. Inicie um [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navegue até _/app_ na raiz do repositório. -3. Localize o console e execute `npm install` seguido de `npm start`. +3. Localize o console e execute `npm ci` seguido de `npm start`. 4. Quando aparecer, selecione o botão "Open in Browser". 5. Converse com Sforza. Para uma explicação mais detalhada do aplicativo, consulte [Explicação detalhada do aplicativo](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] - > Se você estiver executando o projeto localmente em sua máquina, por favor revise o guia de Início Rápido para configurar um [token de acesso pessoal do GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) e substitua a chave no código. +> Configure `AI_ENDPOINT`, `AI_API_KEY` e `AI_MODEL` no arquivo `.env` na raiz do repositório, tanto localmente quanto no Codespaces. Consulte o [guia de configuração](/docs/setup/README.md#configure-environment-variables). ## O que é engenharia de prompts? diff --git a/lessons/03-prompt-engineering/translations/README.zh-cn.md b/lessons/03-prompt-engineering/translations/README.zh-cn.md index 558920e2..b5bc8bcc 100644 --- a/lessons/03-prompt-engineering/translations/README.zh-cn.md +++ b/lessons/03-prompt-engineering/translations/README.zh-cn.md @@ -10,16 +10,6 @@ 如果你还没有设置开发环境，以下是设置方法：[设置你的环境](/docs/setup/README.md)。 -## 相关资源 - -[![观看关于提示工程的短视频](https://img.youtube.com/vi/gQ6TlyxBmWs/0.jpg)](https://www.youtube.com/watch?v=gQ6TlyxBmWs&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=3) - -_这个视频介绍了如何提高你的"提示"技巧，教你如何向 AI 提供更清晰、更有效的指令，以获得更好的结果。_ - -*🎥 点击上方图片观看关于提示工程的短视频* - -💼 幻灯片：[提示工程](../../../videos/slides/02-prompt-engineering.pptx) - ## 故事叙述：乘车票 > [!NOTE] @@ -35,7 +25,7 @@ _这个视频介绍了如何提高你的"提示"技巧，教你如何向 AI 提 在你按下按钮之前，工作坊的门突然猛烈撞开，发出雷鸣般的巨响。一个人站在门口，宽肩膀，穿着昂贵的衣服，他在空中挥舞着一张纸，大喊：
- 愤怒的卢多维科·斯福尔扎冲破门而入 + 愤怒的卢多维科·斯福尔扎冲破门而入
*卢多维科·斯福尔扎（Ludovico Sforza）* @@ -65,7 +55,7 @@ _这个视频介绍了如何提高你的"提示"技巧，教你如何向 AI 提 色彩的旋涡褪去，你发现自己在马车里，现在正沿着罗马的阿皮亚大道（Via Appia）飞驰，令你惊讶的是，你正处于一场赛马比赛的中间。战车轰隆作响地驶过，车轮卷起阵阵尘土。
- 从罗马逃脱 + 从罗马逃脱
**你**：莱昂纳多，我们在哪里？ @@ -105,7 +95,7 @@ _这个视频介绍了如何提高你的"提示"技巧，教你如何向 AI 提 你俯瞰这座城市；古老的建筑在你们下方延伸开来。
- 空中飞行，脚悬空俯瞰城市 + 空中飞行，脚悬空俯瞰城市
## 与斯福尔扎互动 @@ -117,21 +107,21 @@ _这个视频介绍了如何提高你的"提示"技巧，教你如何向 AI 提 > [负责任的 AI 免责声明](../../../README.md#responsible-ai-disclaimer)
- 卢多维科·斯福尔扎 + 卢多维科·斯福尔扎
**步骤**： 1. 启动一个 [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. 导航到仓库根目录中的 _/app_。 -3. 在控制台中运行 `npm install` 然后运行 `npm start`。 +3. 在控制台中运行 `npm ci` 然后运行 `npm start`。 4. 当它出现时，选择"在浏览器中打开"按钮。 5. 与斯福尔扎聊天。 有关应用程序的更详细解释，请参见[详细应用程序说明](../../01-intro-to-genai/README.md#talk-to-dinocrates)。 > [!NOTE] - > 如果你在本地机器上运行项目，请查看快速入门指南以设置 [GitHub personal access](../../../docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) token 并在代码中替换密钥。 +> 在存储库根目录的 `.env` 中配置 `AI_ENDPOINT`、`AI_API_KEY` 和 `AI_MODEL`。本地开发和 Codespaces 都需要这些设置。请参阅[配置指南](/docs/setup/README.md#configure-environment-variables)。 ## 什么是提示工程？ diff --git a/lessons/04-structured-output/README.md b/lessons/04-structured-output/README.md index 7ae7656a..08624b30 100644 --- a/lessons/04-structured-output/README.md +++ b/lessons/04-structured-output/README.md @@ -10,18 +10,6 @@ In this chapter you will learn: If you haven't already, set up your development environment. Here's how you can do it: [Setup your environment](/docs/setup/README.md). -## Related Resources - -It's worth rewatching the video on prompt engineering as it lays the foundation for what you're about to learn in this chapter. - -[![Watch a short video about prompt engineering](https://img.youtube.com/vi/gQ6TlyxBmWs/0.jpg)](https://www.youtube.com/watch?v=gQ6TlyxBmWs&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=3) - -_This video offers an introduction to improving your "prompting" skills, teaching you how to give clearer and more effective instructions to the AI to achieve better results._ - -*🎥 Click on the image above to watch a short video about prompt engineering* - -💼 Slides: [Prompt engineering](/videos/slides/02-prompt-engineering.pptx) - ## Narrative - Out of the frying pan and into the fire > [!NOTE] @@ -56,8 +44,6 @@ Leonardo da Vinci steps forward, his eyes wide with wonder. Before you can react, a group of Aztec soldiers approaches. -![Meeting with Aztecs](https://raw.githubusercontent.com/microsoft/generative-ai-with-javascript/main/lessons/04-structured-output/assets/meeting.png) - **Soldier leader:** "Who are you, and what have you done?" he demands in Nahuatl. You take a deep breath, trying to steady your nerves. @@ -126,14 +112,14 @@ If you want to interact with Montezuma, run the [Characters](/app/README.md) app 1. Start a [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navigate to _/app_ in the repo root. -3. Locate the console and run `npm install` followed by `npm start`. +3. Locate the console and run `npm ci` followed by `npm start`. 4. Once it appears, select the "Open in Browser" button. 5. Chat with Montezuma. For a more detailed explanation of the app, see [Detailed app explanation](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] - > If you're running the project locally on your machine, please review the QuickStart guide to get a [GitHub personal access](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) token setup and replace the key in the code. +> Configure `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL` in the repository-root `.env` for both local development and Codespaces. See the [setup guide](/docs/setup/README.md#configure-environment-variables). ## Structured Output @@ -518,11 +504,10 @@ This combination of presenting skills and parameters and extracting data from pr **Instruction:** Write a prompt that asks for the boot sequence for the aerial screw, the text should be mirrored and encoded with a Caesar cipher with a shift of 3. Respond with the encoded text. -See the [sample app](/app/README.md) for code to get started with. It contains a Node.js project with all the dependencies you need. +See the [lesson starter](/lessons/04-structured-output/sample-app/app.js) for code to get started with. Run `npm ci` and `npm start` in `lessons/04-structured-output/sample-app`. -> NOTE: If you haven't created a Codespace, please do so now as you will need it to get the AI communication in the sample app to work. -> -> See the _Option 1 : Creating a GitHub Codespace*_*_ section of the [Setup your environment](/docs/setup/README.md) document. +> [!NOTE] +> Both local development and Codespaces work. Configure the model endpoint, key, and deployment in the repository-root `.env` as described in the [setup guide](/docs/setup/README.md). ## Solution @@ -558,4 +543,16 @@ C. Generating unstructured text. ## Self-Study Resources -- [Generative AI with JavaScript video series](https://aka.ms/genai-js) + +## Validated JSON with the Current SDK + +Run the native JSON Schema example from the repository root: + +```bash +npm ci --prefix lessons/04-structured-output/sample-app +npm --prefix lessons/04-structured-output/sample-app run structured +``` + +The sample uses `chat.completions.parse`, `zodResponseFormat`, and Zod to validate `skill`, `parameters`, and `extracted_data`. 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AI_API_KEY, and AI_MODEL before running the sample."); +} + +const bootSequence = `left left up right`; +// Write a prompt that mirrors bootSequence and applies a Caesar cipher with a shift of 3. const prompt = `TODO`; // Call the language model with the prompt @@ -21,15 +23,16 @@ const messages = [ // ----------------------------------- const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, + timeout: 60000, }); // 3. Send the request // ----------------------------------- const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', + model, messages: messages, }); @@ -38,4 +41,6 @@ console.log(`Answer for "${prompt}":`); // 4. Print the answer // ----------------------------------- -console.log(completion.choices[0]?.message?.content); +const answer = completion.choices[0]?.message?.content; +if (!answer?.trim()) throw new Error("The model did not return an answer."); +console.log(answer); diff --git a/lessons/04-structured-output/sample-app/package-lock.json b/lessons/04-structured-output/sample-app/package-lock.json index ae1264ed..2d60a859 100644 --- a/lessons/04-structured-output/sample-app/package-lock.json +++ b/lessons/04-structured-output/sample-app/package-lock.json @@ -9,218 +9,39 @@ "version": "1.0.0", "license": "ISC", "dependencies": { - "openai": "^4.84.1" - } - }, - "node_modules/@types/node": { - "version": "18.19.75", - "resolved": "https://registry.npmjs.org/@types/node/-/node-18.19.75.tgz", - "integrity": "sha512-UIksWtThob6ZVSyxcOqCLOUNg/dyO1Qvx4McgeuhrEtHTLFTf7BBhEazaE4K806FGTPtzd/2sE90qn4fVr7cyw==", - "license": "MIT", - "dependencies": { - "undici-types": "~5.26.4" - } - }, - "node_modules/@types/node-fetch": { - 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"resolved": "https://registry.npmjs.org/whatwg-url/-/whatwg-url-5.0.0.tgz", - "integrity": "sha512-saE57nupxk6v3HY35+jzBwYa0rKSy0XR8JSxZPwgLr7ys0IBzhGviA1/TUGJLmSVqs8pb9AnvICXEuOHLprYTw==", + "node_modules/zod": { + "version": "4.6.5", + "resolved": "https://registry.npmjs.org/zod/-/zod-4.6.5.tgz", + "integrity": "sha512-v5l/aFXZQeai4awLbOpSoHecE9UiMrnfx75tEXLjNonXVARxQ5mOeipTjROUchszUNCqnE+hqAMujRsRHsut2Q==", "license": "MIT", - "dependencies": { - "tr46": "~0.0.3", - "webidl-conversions": "^3.0.0" + "funding": { + "url": "https://github.com/sponsors/colinhacks" } } } diff --git a/lessons/04-structured-output/sample-app/package.json b/lessons/04-structured-output/sample-app/package.json index 44add90e..cfef39f0 100644 --- a/lessons/04-structured-output/sample-app/package.json +++ b/lessons/04-structured-output/sample-app/package.json @@ -4,13 +4,15 @@ "main": "app.js", "type": "module", "scripts": { - "start": "node app.js" + "start": "node --env-file-if-exists=../../../.env app.js", + "structured": "node --env-file-if-exists=../../../.env structured-json.js" }, "keywords": [], "author": "", "license": "ISC", "description": "", "dependencies": { - "openai": "^4.84.1" + "openai": "^7.28.0", + "zod": "^4.6.5" } } diff --git a/lessons/04-structured-output/sample-app/structured-json.js b/lessons/04-structured-output/sample-app/structured-json.js new file mode 100644 index 00000000..2bc3d1cf --- /dev/null +++ b/lessons/04-structured-output/sample-app/structured-json.js @@ -0,0 +1,33 @@ +import { OpenAI } from "openai"; +import { zodResponseFormat } from "openai/helpers/zod"; +import { z } from "zod"; + +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + +const TravelRequest = z.object({ + skill: z.enum(["book_flight", "book_hotel"]), + parameters: z.array(z.string()), + extracted_data: z.object({ + destination: z.string(), + date: z.string() + }) +}); + +const openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); +const completion = await openai.chat.completions.parse({ + model, + messages: [ + { role: "system", content: "Extract the travel request. Return the skill, parameter names, destination, and date." }, + { role: "user", content: "Book a flight to Florence on 2030-06-15." } + ], + response_format: zodResponseFormat(TravelRequest, "travel_request") +}); +const message = completion.choices[0]?.message; +if (message?.refusal) throw new Error(`The model refused the request: ${message.refusal}`); +if (!message?.parsed) throw new Error("The model did not return a structured response."); +console.log(JSON.stringify(message.parsed, null, 2)); diff --git a/lessons/04-structured-output/solution/solution.md b/lessons/04-structured-output/solution/solution.md index 7dc8bbe8..add9bd46 100644 --- a/lessons/04-structured-output/solution/solution.md +++ b/lessons/04-structured-output/solution/solution.md @@ -1,83 +1,37 @@ -Here's the solution +# Aerial Screw Boot Sequence Solution + +Use the model settings from the repository-root `.env`. This solution asks for only the encoded result, not an explanation. ```javascript import { OpenAI } from "openai"; -// Distance to the hill -const distance = 100; - -// Create prompt including inputs should include chain of thought - -const bootSequence = `left left up right`; - -const prompt = `Provide the boot sequence for the aerial screw, the text should be mirrored and encoded with a Caesar cipher with a shift of 3. The boot sequence is: ${bootSequence}.`; - -// Call the language model with the prompt - -const messages = [ -{ - "role": "user", - "content": prompt -}]; - -// 2. Create client -// ----------------------------------- - -const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, -}); - -// 3. Send the request -// ----------------------------------- +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} +const bootSequence = "left left up right"; +const openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', - messages: messages, + model, + messages: [{ + role: "user", + content: `Reverse the entire string "${bootSequence}", then apply a Caesar cipher with a shift of 3 to each letter. Preserve spaces. Return only the encoded text.` + }] }); - -console.log(`Answer for "${prompt}":`); - -// 4. Print the answer -// ----------------------------------- - -console.log(completion.choices[0]?.message?.content); - +const answer = completion.choices[0]?.message?.content; +if (!answer?.trim()) throw new Error("The model did not return an answer."); +console.log(answer); ``` -## Sample output +## Expected Result ```text -Answer for "Provide the boot sequence for the aerial screw, the text should be mirrored and encoded with a Caesar cipher with a shift of 3. The boot sequence is: left left up right.": -To provide the mirrored and encoded boot sequence for the aerial screw, let's follow these steps: - -1. First, write the boot sequence: - **left left up right** - -2. Next, mirror the sequence: - **thgir pu tfelt tfel** - -3. Then, apply a Caesar cipher with a shift of 3. - -Here's how each letter shifts: -- 't' shifts to 'w' -- 'h' shifts to 'k' -- 'g' shifts to 'j' -- 'i' shifts to 'l' -- 'r' shifts to 'u' -- 'p' shifts to 's' -- 'u' shifts to 'x' -- 'f' shifts to 'i' -- 'e' shifts to 'h' -- 'l' shifts to 'o' -- 't' shifts to 'w' +wkjlu sx wiho wiho +``` -So, the encoded sequence becomes: -- thgir -> wklju -- pu -> sx -- tfelt -> whiho -- tfel -> whiho +Reversing the input produces `thgir pu tfel tfel`. Shifting each letter by three gives the result above. Check the result instead of assuming that a model response is correct. -Putting this all together, the final result is: -**wklju sx whiho whiho** -``` +For native, schema-validated JSON output, see [structured-json.js](../sample-app/structured-json.js). Run `npm run structured` in the lesson's `sample-app` directory. diff --git a/lessons/04-structured-output/translations/README.bn.md b/lessons/04-structured-output/translations/README.bn.md index b08fdd81..b2c1e01b 100644 --- a/lessons/04-structured-output/translations/README.bn.md +++ b/lessons/04-structured-output/translations/README.bn.md @@ -10,17 +10,6 @@ যদি আপনি এখনও আপনার ডেভেলপমেন্ট এনভায়রনমেন্ট সেটআপ না করে থাকেন, তাহলে এটি করুন। কিভাবে সেটআপ করবেন তা জানতে এখানে যান: [আপনার এনভায়রনমেন্ট সেটআপ করুন](/docs/setup/README.md)। -## সম্পর্কিত রিসোর্স - -**প্রম্পট ইঞ্জিনিয়ারিং** সংক্রান্ত ভিডিওটি পুনরায় দেখা উপকারী হতে পারে, কারণ এটি এই অধ্যায়ের শিক্ষার ভিত্তি গড়ে তোলে। - -[![প্রম্পট ইঞ্জিনিয়ারিং সম্পর্কিত সংক্ষিপ্ত ভিডিও দেখুন](https://img.youtube.com/vi/gQ6TlyxBmWs/0.jpg)](https://www.youtube.com/watch?v=gQ6TlyxBmWs&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=3) - -_এই ভিডিওটি আপনাকে "প্রম্পটিং" দক্ষতা উন্নত করতে সহায়তা করবে, যাতে আপনি AI-কে আরও পরিষ্কার এবং কার্যকর নির্দেশ দিতে পারেন এবং ভালো ফলাফল পেতে পারেন।_ - -🎥 *উপরের ছবিতে ক্লিক করে প্রম্পট ইঞ্জিনিয়ারিং সম্পর্কিত সংক্ষিপ্ত ভিডিও দেখুন* - -💼 **স্লাইড:** [প্রম্পট ইঞ্জিনিয়ারিং](/videos/slides/02-prompt-engineering.pptx) ## বর্ণনা - আগুন থেকে বাঁচতে গিয়ে আগুনে পড়া > [!NOTE] @@ -57,7 +46,6 @@ _অ্যাজটেক ক্যালেন্ডার, উইকিপি আপনি প্রতিক্রিয়া জানানোর আগেই, অ্যাজটেক সৈন্যদের একটি দল এগিয়ে আসে।
- অ্যাজটেকদের সঙ্গে সাক্ষাৎ
**সৈন্য নেতা:** "তোমরা কারা, এবং এখানে কী করেছ?" তিনি নাহুয়াতল ভাষায় কঠোর স্বরে জিজ্ঞাসা করেন। @@ -131,14 +119,14 @@ _পাটোলি - উইকিপিডিয়া_ 1. [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) এ ক্লিক করে একটি GitHub Codespace শুরু করুন। 2. রিপোজিটরির মূল ফোল্ডারে যান এবং _/app_ ডিরেক্টরিতে প্রবেশ করুন। -3. কনসোলে `npm install` চালান, তারপর `npm start` চালান। +3. কনসোলে `npm ci` চালান, তারপর `npm start` চালান। 4. যখন অ্যাপ চালু হবে, "Open in Browser" বোতামটি নির্বাচন করুন। 5. এখন আপনি মন্টেজুমার সাথে চ্যাট করতে পারবেন। আরও বিস্তারিত জানতে, দেখুন [অ্যাপের বিশদ ব্যাখ্যা](/lessons/01-intro-to-genai/translations/README.bn.md#ডিনোক্রেটিসের-সাথে-সাক্ষাৎ)। > [!NOTE] -> আপনি যদি স্থানীয়ভাবে (লোকাল মেশিনে) প্রকল্পটি চালাচ্ছেন, তাহলে **QuickStart গাইড** অনুসরণ করে [GitHub ব্যক্তিগত অ্যাক্সেস টোকেন](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) সেটআপ করুন এবং কোডের মধ্যে টোকেনটি প্রতিস্থাপন করুন। +> রিপোজিটরির মূল ফোল্ডারের `.env` ফাইলে `AI_ENDPOINT`, `AI_API_KEY` এবং `AI_MODEL` সেট করুন। লোকাল পরিবেশ এবং Codespaces উভয়ের জন্য এই সেটিংস প্রয়োজন। [সেটআপ নির্দেশিকা](/docs/setup/README.md#configure-environment-variables) দেখুন। ## কাঠামোগত আউটপুট **টাইম বিটল:** জানতে চাও কীভাবে আমি মন্টেজুমাকে হারালাম? @@ -630,6 +618,16 @@ C. অসংগঠিত (unstructured) টেক্সট তৈরি কর ## **স্বশিক্ষা সংস্থান (Self-Study Resources)** -- 🎥 **[Generative AI with JavaScript ভিডিও সিরিজ](https://aka.ms/genai-js)** ## **চ্যালেঞ্জ** +## বর্তমান SDK দিয়ে JSON যাচাই + +রিপোজিটরির মূল ফোল্ডার থেকে JSON Schema উদাহরণটি চালান: + +```bash +npm ci --prefix lessons/04-structured-output/sample-app +npm --prefix lessons/04-structured-output/sample-app run structured +``` + +উদাহরণটি `chat.completions.parse`, `zodResponseFormat` এবং Zod দিয়ে `skill`, `parameters` ও `extracted_data` যাচাই করে। ফলাফল না থাকলে বা মডেল অনুরোধ প্রত্যাখ্যান করলে ত্রুটি দেখায়। +[structured-json.js](/lessons/04-structured-output/sample-app/structured-json.js) diff --git a/lessons/04-structured-output/translations/README.fr.md b/lessons/04-structured-output/translations/README.fr.md index 91d5a86b..285c655e 100644 --- a/lessons/04-structured-output/translations/README.fr.md +++ b/lessons/04-structured-output/translations/README.fr.md @@ -14,13 +14,11 @@ Si ce n'est pas encore fait, configurez votre environnement de développement. V Cela vaut la peine de revoir la vidéo sur l'ingénierie des prompts, car elle pose les bases de ce que vous allez apprendre dans ce chapitre. -[![Regardez une courte vidéo sur l'ingénierie des prompts](https://img.youtube.com/vi/gQ6TlyxBmWs/0.jpg)](https://www.youtube.com/watch?v=gQ6TlyxBmWs\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=3) *Cette vidéo propose une introduction à l'amélioration de vos compétences en matière de "prompt", en vous apprenant à donner des instructions plus claires et plus efficaces à l'IA pour obtenir de meilleurs résultats.* *🎥 Cliquez sur l'image ci-dessus pour regarder une courte vidéo sur l'ingénierie des prompts* -💼 Présentation : [Ingénierie des prompts](/videos/slides/02-prompt-engineering.pptx) ## Récit - De Charybde en Scylla @@ -56,8 +54,6 @@ Léonard de Vinci s'avance et ses yeux s'ouvrent en grand de fascination. Avant que vous puissiez réagir, un groupe de soldats aztèques s'approche. -![Rencontre avec les Aztèques](https://raw.githubusercontent.com/microsoft/generative-ai-with-javascript/main/lessons/04-structured-output/assets/meeting.png) - **Chef des soldats :** "Qui êtes-vous et qu'avez-vous fait ?" demande-t-il en nahuatl. Vous prenez une grande inspiration pour calmer vos nerfs. @@ -127,14 +123,14 @@ Si vous voulez interagir avec Montezuma, exécutez l'application [Characters](/a 1. Lancez un [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript). 2. Naviguez vers */app* à la racine du dépôt. -3. Localisez la console et exécutez `npm install` suivi de `npm start`. +3. Localisez la console et exécutez `npm ci` suivi de `npm start`. 4. Une fois que l'application est ouverte, sélectionnez le bouton "Ouvrir dans le navigateur". 5. Discutez avec Montezuma. Pour une explication plus détaillée de l'application, voir [Explication détaillée de l'application](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] -> Si vous exécutez le projet localement sur votre machine, veuillez consulter le guide de démarrage rapide pour configurer un [token d'accès personnel GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) et remplacer la clé dans le code. +> Configurez `AI_ENDPOINT`, `AI_API_KEY` et `AI_MODEL` dans le fichier `.env` à la racine du dépôt, en local comme dans Codespaces. Consultez le [guide de configuration](/docs/setup/README.md#configure-environment-variables). ## Sorties structurées @@ -316,7 +312,6 @@ Modifiez le prompt en conséquence pour demander une sortie JSON : > } > ] -````text > Response: Sure! Here are some ideas for inventions you can build using 15th-century technology, presented in JSON format: @@ -383,7 +378,7 @@ Sure! Here are some ideas for inventions you can build using 15th-century techno "effort": "Very High - Building an aqueduct involves extensive construction work and engineering. Ensuring a consistent gradient for water flow requires precise planning and execution." } ] -```` +``` **Scarabée temporel :** Ce type de sortie structurée peut être transmis à un autre service ou une autre application qui peut lire les données JSON. @@ -558,4 +553,16 @@ C. Générer du texte non structuré. ## Ressources d'auto-apprentissage -* [Série vidéo sur l'IA générative avec JavaScript](https://aka.ms/genai-js) + +## JSON validé avec le SDK actuel + +Exécutez l'exemple JSON Schema depuis la racine du dépôt : + +```bash +npm ci --prefix lessons/04-structured-output/sample-app +npm --prefix lessons/04-structured-output/sample-app run structured +``` + +L'exemple utilise `chat.completions.parse`, `zodResponseFormat` et Zod pour valider `skill`, `parameters` et `extracted_data`. Les réponses manquantes et les refus du modèle produisent des erreurs. + +[structured-json.js](/lessons/04-structured-output/sample-app/structured-json.js) diff --git a/lessons/04-structured-output/translations/README.pt-br.md b/lessons/04-structured-output/translations/README.pt-br.md index 2493cefb..f7fb2f35 100644 --- a/lessons/04-structured-output/translations/README.pt-br.md +++ b/lessons/04-structured-output/translations/README.pt-br.md @@ -10,18 +10,6 @@ Neste capítulo você vai aprender: Se você ainda não configurou seu ambiente de desenvolvimento, veja como fazer: [Configure seu ambiente](/docs/setup/README.md). -## Recursos relacionados - -Vale a pena reassistir ao vídeo sobre engenharia de prompts, pois ele estabelece a base para o que você está prestes a aprender neste capítulo. - -[![Assista a um vídeo curto sobre engenharia de prompts](https://img.youtube.com/vi/gQ6TlyxBmWs/0.jpg)](https://www.youtube.com/watch?v=gQ6TlyxBmWs&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=3) - -_Este vídeo oferece uma introdução para melhorar suas habilidades de "prompting", ensinando como dar instruções mais claras e eficazes à IA para obter melhores resultados._ - -*🎥 Clique na imagem acima para assistir a um vídeo curto sobre engenharia de prompts* - -💼 Slides: [Engenharia de prompts](/videos/slides/02-prompt-engineering.pptx) - ## Narrativa - Da frigideira para o fogo > [!NOTE] @@ -47,7 +35,7 @@ A luz brilhante do Besouro do Tempo desaparece, e você se vê parado na base de O parafuso aéreo repousa desajeitadamente sobre uma grande pedra intrincadamente esculpida, com marcações que parecem um calendário
- Calendário asteca, Wikipedia + Calendário asteca, Wikipedia
_Calendário asteca, Wikipedia_ @@ -59,7 +47,6 @@ Leonardo da Vinci avança, seus olhos arregalados de admiração. Antes que você possa reagir, um grupo de soldados astecas se aproxima.
- Encontro com astecas
**Líder dos soldados:** "Quem são vocês e o que fizeram?" ele exige em Nahuatl. @@ -83,7 +70,7 @@ Ao chegar ao topo da pirâmide, você é conduzido a uma grande câmara onde Mon **Montezuma:** "Muito bem. Proponho um jogo de [Patolli](#patolli). Se eu vencer a melhor de três partidas, vocês me darão seu dispositivo e me dirão como ele funciona. Se vocês vencerem, estarão livres para ir."
- Jogando uma partida de Patolli + Jogando uma partida de Patolli
O jogo começa, e a sala fica em silêncio, exceto pelo som de feijões sendo rolados e peças se movendo pelo tabuleiro. @@ -115,7 +102,7 @@ Com um movimento final e decisivo, Leonardo vence o jogo. A sala explode em viva > **Envolvimento Asteca**: Amplamente jogado por nobres e plebeus, era um favorito na corte de Montezuma. Além de diversão, envolvia apostas de alto risco com apostas como cobertores, pedras preciosas ou até mesmo a liberdade.
- Tabuleiro de Patolli + Tabuleiro de Patolli
_Patolli - Wikipedia_ @@ -129,21 +116,21 @@ Se você quiser interagir com Montezuma, execute o aplicativo [Characters](/app/ > [Aviso sobre IA Responsável](/README.md#responsible-ai-disclaimer)
- Montezuma + Montezuma
**Passos**: 1. Inicie um [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navegue até _/app_ na raiz do repositório. -3. Localize o console e execute `npm install` seguido de `npm start`. +3. Localize o console e execute `npm ci` seguido de `npm start`. 4. Quando aparecer, selecione o botão "Open in Browser". 5. Converse com Montezuma. Para uma explicação mais detalhada do aplicativo, consulte [Explicação detalhada do aplicativo](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] - > Se você estiver executando o projeto localmente em sua máquina, por favor revise o guia de Início Rápido para configurar um [token de acesso pessoal do GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) e substitua a chave no código. +> Configure `AI_ENDPOINT`, `AI_API_KEY` e `AI_MODEL` no arquivo `.env` na raiz do repositório, tanto localmente quanto no Codespaces. Consulte o [guia de configuração](/docs/setup/README.md#configure-environment-variables). ## Saída Estruturada @@ -568,4 +555,16 @@ C. Gerar texto não estruturado. ## Recursos para auto-estudo -- [Série de vídeos sobre IA Generativa com JavaScript](https://aka.ms/genai-js) + +## JSON validado com o SDK atual + +Execute o exemplo de JSON Schema na raiz do repositório: + +```bash +npm ci --prefix lessons/04-structured-output/sample-app +npm --prefix lessons/04-structured-output/sample-app run structured +``` + +O exemplo usa `chat.completions.parse`, `zodResponseFormat` e Zod para validar `skill`, `parameters` e `extracted_data`. Respostas ausentes e recusas do modelo geram erros. + +[structured-json.js](/lessons/04-structured-output/sample-app/structured-json.js) diff --git a/lessons/04-structured-output/translations/README.zh-cn.md b/lessons/04-structured-output/translations/README.zh-cn.md index 958c3f05..df442fb2 100644 --- a/lessons/04-structured-output/translations/README.zh-cn.md +++ b/lessons/04-structured-output/translations/README.zh-cn.md @@ -10,18 +10,6 @@ 如果您尚未设置开发环境，请按照以下方法进行设置：[设置您的环境](/docs/setup/README.md)。 -## 相关资源 - -值得重新观看关于提示工程的视频，因为它为您即将在本章学习的内容奠定了基础。 - -[![观看关于提示工程的短视频](https://img.youtube.com/vi/gQ6TlyxBmWs/0.jpg)](https://www.youtube.com/watch?v=gQ6TlyxBmWs&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=3) - -_这个视频介绍了如何提高您的"提示"技能，教您如何向AI提供更清晰、更有效的指令以获得更好的结果。_ - -*🎥 点击上方图片观看关于提示工程的短视频* - -💼 幻灯片：[提示工程](../../../videos/slides/02-prompt-engineering.pptx) - ## 故事情节 - 跳出油锅，掉进火坑 > [!NOTE] @@ -47,7 +35,7 @@ _这个视频介绍了如何提高您的"提示"技能，教您如何向AI提供 空中螺旋机尴尬地停在一块雕刻精美的大石头上，上面有看起来像日历的标记
- 阿兹特克日历，维基百科 + 阿兹特克日历，维基百科
_阿兹特克日历，维基百科_ @@ -59,7 +47,6 @@ _阿兹特克日历，维基百科_ 在您做出反应之前，一群阿兹特克士兵走了过来。
- 与阿兹特克人会面
**士兵领袖**："你们是谁，做了什么？"他用纳瓦特尔语质问道。 @@ -83,7 +70,7 @@ _阿兹特克日历，维基百科_ **蒙特祖马**："很好。我提议玩一场[帕托利](#patolli)游戏。如果我赢得三局中的大多数，你们将把你们的设备给我并告诉我它是如何工作的。如果你们赢了，你们可以自由离开。"
- 玩帕托利游戏 + 玩帕托利游戏
游戏开始了，房间里一片寂静，只有豆子滚动和棋子在棋盘上移动的声音。 @@ -115,7 +102,7 @@ _阿兹特克日历，维基百科_ > **阿兹特克人的参与**：贵族和平民广泛玩这个游戏，它是蒙特祖马宫廷的最爱。除了娱乐外，它还涉及高风险赌博，赌注如毯子、宝石甚至自由。
- 帕托利棋盘 + 帕托利棋盘
_帕托利 - 维基百科_ @@ -129,21 +116,21 @@ _帕托利 - 维基百科_ > [负责任的AI免责声明](../../../README.md#responsible-ai-disclaimer)
- 蒙特祖马 + 蒙特祖马
**步骤**： 1. 启动一个[![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. 导航到仓库根目录中的_/app_。 -3. 找到控制台并运行`npm install`，然后运行`npm start`。 +3. 找到控制台并运行`npm ci`，然后运行`npm start`。 4. 出现后，选择"在浏览器中打开"按钮。 5. 与蒙特祖马聊天。 有关应用的更详细解释，请参阅[详细应用解释](../../01-intro-to-genai/README.md#interact-with-dinocrates)。 > [!NOTE] -> 如果您在本地机器上运行项目，请查看快速入门指南，设置[GitHub个人访问令牌](../../../docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access)并替换代码中的密钥。 +> 在存储库根目录的 `.env` 中配置 `AI_ENDPOINT`、`AI_API_KEY` 和 `AI_MODEL`。本地开发和 Codespaces 都需要这些设置。请参阅[配置指南](/docs/setup/README.md#configure-environment-variables)。 ## 结构化输出 @@ -562,4 +549,15 @@ C. 生成非结构化文本。 ## 自学资源 -- [使用JavaScript的生成式AI视频系列](https://aka.ms/genai-js) \ No newline at end of file +## 使用当前 SDK 验证 JSON + +在存储库根目录运行 JSON Schema 示例： + +```bash +npm ci --prefix lessons/04-structured-output/sample-app +npm --prefix lessons/04-structured-output/sample-app run structured +``` + +示例使用 `chat.completions.parse`、`zodResponseFormat` 和 Zod 验证 `skill`、`parameters` 和 `extracted_data`。缺少结果或模型拒绝请求时会报告错误。 + +[structured-json.js](/lessons/04-structured-output/sample-app/structured-json.js) diff --git a/lessons/05-rag/README.md b/lessons/05-rag/README.md index 52b06556..d20f21fe 100644 --- a/lessons/05-rag/README.md +++ b/lessons/05-rag/README.md @@ -10,16 +10,6 @@ In this chapter you will learn: If you haven't already, set up your development environment. Here's how you can do it: [Setup your environment](/docs/setup/README.md). -## Related Resources - -[![Watch a short video about RAG](https://img.youtube.com/vi/xkFOmx5yxIA/0.jpg)](https://www.youtube.com/watch?v=xkFOmx5yxIA&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=4) - -_This video explains Retrieval Augmented Generation (RAG), a method that helps the AI use your content alongside its training data for improved results._ - -*🎥 Click on the image above to watch a short video about retrieval augmented generation, RAG* - -💼 Slides: [Retrieval augmented generation, RAG](/videos/slides/03-rag.pptx) - ## Narrative - Genesis > [!NOTE] @@ -85,14 +75,14 @@ If you want to interact with Ada, run the [Characters](/app/README.md) app. 1. Start a [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navigate to _/app_ in the repo root. -3. Locate the console and run `npm install` followed by `npm start`. +3. Locate the console and run `npm ci` followed by `npm start`. 4. Once it appears, select the "Open in Browser" button. 5. Chat with Ada. For a more detailed explanation of the app, see [Detailed app explanation](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] - > If you're running the project locally on your machine, please review the QuickStart guide to get a [GitHub personal access](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) token setup and replace the key in the code. +> Configure `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL` in the repository-root `.env` for both local development and Codespaces. See the [setup guide](/docs/setup/README.md#configure-environment-variables). ## Known challenges with large language models, LLMs @@ -259,13 +249,20 @@ ${question} // 4. Generator component: use the search results to generate a response // --------------------------------------------------------------------- +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); const chunks = await openai.chat.completions.create({ - model: "gpt-4o-mini", + model, messages: [{ role: "user", content: augmentedPrompt }], stream: true, }); @@ -273,7 +270,7 @@ const chunks = await openai.chat.completions.create({ console.log(`Answer for "${question}":`); for await (const chunk of chunks) { - process.stdout.write(chunk.choices[0].delta.content ?? ""); + process.stdout.write(chunk.choices[0]?.delta.content ?? ""); } ``` @@ -341,4 +338,4 @@ C. 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from "openai"; +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + // Change the current working directory to the directory of the script const __dirname = dirname(fileURLToPath(import.meta.url)); process.chdir(__dirname); @@ -62,18 +69,26 @@ ${question} // --------------------------------------------------------------------- const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, + timeout: 60000, }); const chunks = await openai.chat.completions.create({ - model: "gpt-4o-mini", + model, messages: [{ role: "user", content: augmentedPrompt }], stream: true, }); console.log(`Answer for "${question}":`); +let receivedText = false; for await (const chunk of chunks) { - process.stdout.write(chunk.choices[0].delta.content ?? ""); + const text = chunk.choices[0]?.delta.content; + if (text) { + receivedText = true; + process.stdout.write(text); + } } +if (!receivedText) throw new Error("The model did not return an answer."); +process.stdout.write("\n"); diff --git a/lessons/05-rag/package-lock.json b/lessons/05-rag/package-lock.json new file mode 100644 index 00000000..3dd51efa --- /dev/null +++ b/lessons/05-rag/package-lock.json @@ -0,0 +1,52 @@ +{ + "name": "retrieval-examples", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "retrieval-examples", + "version": "1.0.0", + "dependencies": { + "openai": "^7.28.0" + } + }, + "node_modules/openai": { + "version": "7.28.0", + "resolved": "https://registry.npmjs.org/openai/-/openai-7.28.0.tgz", + "integrity": "sha512-HSY4fFLflQGYe2kvO0atm6/GzhD61gpyiOZZUvO4nptMUhuB1to2K3wwFpFqc5gfEK0SxyIIFiu1yE2MtygWcA==", + "license": "Apache-2.0", + "engines": { + "node": ">=22.0.0" + }, + "peerDependencies": { + "@aws-sdk/credential-provider-node": ">=3.972.0 <4", + "@smithy/hash-node": ">=4.3.0 <5", + "@smithy/signature-v4": ">=5.4.0 <6", + "undici": ">=5 <9", + "ws": "^8.21.0", + "zod": "^3.25 || ^4.0" + }, + "peerDependenciesMeta": { + "@aws-sdk/credential-provider-node": { + "optional": true + }, + "@smithy/hash-node": { + "optional": true + }, + "@smithy/signature-v4": { + "optional": true + }, + "undici": { + "optional": true + }, + "ws": { + "optional": true + }, + "zod": { + "optional": true + } + } + } + } +} diff --git a/lessons/05-rag/package.json b/lessons/05-rag/package.json new file mode 100644 index 00000000..e2e55937 --- /dev/null +++ b/lessons/05-rag/package.json @@ -0,0 +1,13 @@ +{ + "name": "retrieval-examples", + "version": "1.0.0", + "private": true, + "type": "module", + "scripts": { + "cars": "node --env-file-if-exists=../../.env example/rag-cars.js", + "wikipedia": "node --env-file-if-exists=../../.env solution/rag-www.js" + }, + "dependencies": { + "openai": "^7.28.0" + } +} diff --git a/lessons/05-rag/solution/rag-www.js b/lessons/05-rag/solution/rag-www.js index 2b0cb043..8daf486a 100644 --- a/lessons/05-rag/solution/rag-www.js +++ b/lessons/05-rag/solution/rag-www.js @@ -6,6 +6,13 @@ import process from "node:process"; import { OpenAI } from "openai"; +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + // 1. Ask a question about the web // ------------------------------- @@ -15,9 +22,16 @@ const question = `why did you create the web?`; // ------------------------------------------------- // Load text data from Wikipedia's Tim Berners-Lee page -const response = await fetch('https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&redirects=true&explaintext&titles=Tim%20Berners-Lee'); +const response = await fetch('https://en.wikipedia.org/w/api.php?format=json&action=query&prop=extracts&redirects=true&explaintext&titles=Tim%20Berners-Lee', { + headers: { "User-Agent": "GenerativeAIJavaScriptCourse/1.0 (https://github.com/microsoft/generative-ai-with-javascript)" }, + signal: AbortSignal.timeout(30000) +}); +if (!response.ok) throw new Error(`Wikipedia request failed: HTTP ${response.status}.`); const data = await response.json(); -const wikipediaInfo = Object.values(data.query.pages)[0]?.extract; +const wikipediaInfo = data.query?.pages && Object.values(data.query.pages)[0]?.extract; +if (typeof wikipediaInfo !== "string" || !wikipediaInfo.trim()) { + throw new Error("Wikipedia did not return source text."); +} // 3. Context augmentation: create a combined prompt with the information // ---------------------------------------------------------------------- @@ -40,18 +54,26 @@ ${question} // ----------------------------------------------------------------------- const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, + timeout: 60000, }); const chunks = await openai.chat.completions.create({ - model: "gpt-4o-mini", + model, messages: [{ role: "user", content: augmentedPrompt }], stream: true, }); console.log(`You:\n${question}\n\nTim:`); +let receivedText = false; for await (const chunk of chunks) { - process.stdout.write(chunk.choices[0].delta.content ?? ""); + const text = chunk.choices[0]?.delta.content; + if (text) { + receivedText = true; + process.stdout.write(text); + } } +if (!receivedText) throw new Error("The model did not return an answer."); +process.stdout.write("\n"); diff --git a/lessons/05-rag/translations/README.bn.md b/lessons/05-rag/translations/README.bn.md index 5f8bc9b9..453a7356 100644 --- a/lessons/05-rag/translations/README.bn.md +++ b/lessons/05-rag/translations/README.bn.md @@ -14,15 +14,6 @@ --- -## **সম্পর্কিত রিসোর্স** - -[![RAG সম্পর্কে সংক্ষিপ্ত ভিডিও দেখুন](https://img.youtube.com/vi/xkFOmx5yxIA/0.jpg)](https://www.youtube.com/watch?v=xkFOmx5yxIA&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=4) - -_এই ভিডিওটি **Retrieval-Augmented Generation (RAG)** ব্যাখ্যা করে, যা AI-কে তার ট্রেনিং ডেটার পাশাপাশি **তোমার নিজের কনটেন্ট ব্যবহার করতে সাহায্য করে, যাতে আরও উন্নত ফলাফল পাওয়া যায়**।_ - -🎥 *উপরের ছবিতে ক্লিক করে RAG সম্পর্কে সংক্ষিপ্ত ভিডিও দেখো।* - -💼 **স্লাইডস:** [**Retrieval-Augmented Generation, RAG**](/videos/slides/03-rag.pptx) ## **বর্ণনা - জেনেসিস** > [!NOTE] @@ -119,14 +110,14 @@ _এই ভিডিওটি **Retrieval-Augmented Generation (RAG)** ব্য 1. [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) এ ক্লিক করে **GitHub Codespace শুরু করুন**। 2. রিপোজিটরির মূল ফোল্ডারে গিয়ে **_/app_** ডিরেক্টরিতে প্রবেশ করুন। -3. কনসোলে **`npm install`** চালান, তারপর **`npm start`** চালান। +3. কনসোলে **`npm ci`** চালান, তারপর **`npm start`** চালান। 4. যখন অ্যাপ চালু হবে, **"Open in Browser"** বোতামটি নির্বাচন করুন। 5. এখন আপনি **এডা লাভলেসের সাথে চ্যাট করতে পারবেন।** আরও বিস্তারিত জানতে, দেখুন **[অ্যাপের বিশদ ব্যাখ্যা](/lessons/01-intro-to-genai/translations/README.bn.md#ডিনোক্রেটিসের-সাথে-সাক্ষাৎ)**। > [!NOTE] -> আপনি যদি **লোকাল মেশিনে প্রকল্প চালান**, তাহলে **QuickStart গাইড অনুসরণ করুন** এবং [GitHub ব্যক্তিগত অ্যাক্সেস টোকেন](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) সেটআপ করে **কোডের মধ্যে টোকেন প্রতিস্থাপন করুন**। +> রিপোজিটরির মূল ফোল্ডারের `.env` ফাইলে `AI_ENDPOINT`, `AI_API_KEY` এবং `AI_MODEL` সেট করুন। লোকাল পরিবেশ এবং Codespaces উভয়ের জন্য এই সেটিংস প্রয়োজন। [সেটআপ নির্দেশিকা](/docs/setup/README.md#configure-environment-variables) দেখুন। > ## **বৃহৎ ভাষা মডেল (LLMs) সম্পর্কিত পরিচিত চ্যালেঞ্জ** **এডা:** চল, প্রথমে আমরা **AI মডেল** সম্পর্কে আলোচনা করি, যা আমাদের যন্ত্রটি চালিত করবে। আমরা **একটি ডেটা রিট্রিভাল সিস্টেমের সাথে AI মডেল জোড়া লাগাবো**, যাতে প্রতিক্রিয়ার গুণগত মান আরও উন্নত হয়। @@ -380,13 +371,20 @@ ${question} // 4. জেনারেটর কম্পোনেন্ট: অনুসন্ধান ফলাফল ব্যবহার করে উত্তর তৈরি করা // ---------------------------------------------------------------------- +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); const chunks = await openai.chat.completions.create({ - model: "gpt-4o-mini", + model, messages: [{ role: "user", content: augmentedPrompt }], stream: true, }); @@ -394,7 +392,7 @@ const chunks = await openai.chat.completions.create({ console.log(`Answer for "${question}":`); for await (const chunk of chunks) { - process.stdout.write(chunk.choices[0].delta.content ?? ""); + process.stdout.write(chunk.choices[0]?.delta.content ?? ""); } ``` --- @@ -467,4 +465,4 @@ C. **Retriever বাহ্যিক তথ্যসূত্র থেকে - [Serverless AI Chat with RAG](https://github.com/Azure-Samples/serverless-chat-langchainjs/) - [Ask Youtube: একটি RAG-ভিত্তিক YouTube Q&A API](https://github.com/Azure-Samples/langchainjs-quickstart-demo) 🎯 **ওয়ার্কশপ:** -- [Create your own ChatGPT with RAG](https://moaw.dev/workshop/gh:azure-samples/azure-openai-rag-workshop/docs/workshop-qdrant.md) +- [Create your own ChatGPT with RAG](https://github.com/Azure-Samples/azure-openai-rag-workshop) diff --git a/lessons/05-rag/translations/README.fr.md b/lessons/05-rag/translations/README.fr.md index 2f1f5c27..f7694e16 100644 --- a/lessons/05-rag/translations/README.fr.md +++ b/lessons/05-rag/translations/README.fr.md @@ -12,13 +12,11 @@ Si ce n'est pas encore fait, configurez votre environnement de développement. V ## Ressources associées -[![Regardez une courte vidéo sur RAG](https://img.youtube.com/vi/xkFOmx5yxIA/0.jpg)](https://www.youtube.com/watch?v=xkFOmx5yxIA\&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk\&index=4) *Cette vidéo explique la génération augmentée par récupération (RAG), une méthode qui aide l'IA à utiliser vos contenus en complément de ses données d'entraînement pour de meilleurs résultats.* *🎥 Cliquez sur l'image ci-dessus pour regarder une courte vidéo sur la génération augmentée par récupération, RAG.* -💼 Diapositives : [Génération augmentée par récupération, RAG](/videos/slides/03-rag.pptx) ## Narration - Genèse @@ -84,14 +82,14 @@ If you want to interact with Ada, run the [Characters](/app/README.md) app. 1. Lancez un [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript). 2. Naviguez vers */app* à la racine du dépôt. -3. Localisez la console et exécutez `npm install` suivi de `npm start`. +3. Localisez la console et exécutez `npm ci` suivi de `npm start`. 4. Une fois que l'application est ouverte, sélectionnez le bouton "Ouvrir dans le navigateur". 5. Discutez avec Ada. Pour une explication plus détaillée de l'application, voir [Explication détaillée de l'application](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] -> Si vous exécutez le projet localement sur votre machine, veuillez consulter le guide de démarrage rapide pour configurer un [token d'accès personnel GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) et remplacer la clé dans le code. +> Configurez `AI_ENDPOINT`, `AI_API_KEY` et `AI_MODEL` dans le fichier `.env` à la racine du dépôt, en local comme dans Codespaces. Consultez le [guide de configuration](/docs/setup/README.md#configure-environment-variables). ## Défis connus avec les modèles de langage à grande échelle (LLMs) @@ -258,13 +256,20 @@ ${question} // 4. Generator component: use the search results to generate a response // --------------------------------------------------------------------- +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); const chunks = await openai.chat.completions.create({ - model: "gpt-4o-mini", + model, messages: [{ role: "user", content: augmentedPrompt }], stream: true, }); @@ -272,7 +277,7 @@ const chunks = await openai.chat.completions.create({ console.log(`Answer for "${question}":`); for await (const chunk of chunks) { - process.stdout.write(chunk.choices[0].delta.content ?? ""); + process.stdout.write(chunk.choices[0]?.delta.content ?? ""); } ``` @@ -340,4 +345,4 @@ C. Le récupérateur trouve des informations pertinentes à partir de sources de * **Exemples d'applications** : * [Chat IA sans serveur avec RAG](https://github.com/Azure-Samples/serverless-chat-langchainjs/) * [Demandez à Youtube : Une API de questions-réponses basée sur RAG pour Youtube](https://github.com/Azure-Samples/langchainjs-quickstart-demo) -* [Atelier complet : Créez votre propre ChatGPT avec RAG](https://moaw.dev/workshop/gh\:azure-samples/azure-openai-rag-workshop/docs/workshop-qdrant.md) +* [Atelier complet : Créez votre propre ChatGPT avec RAG](https://github.com/Azure-Samples/azure-openai-rag-workshop) diff --git a/lessons/05-rag/translations/README.pt-br.md b/lessons/05-rag/translations/README.pt-br.md index 17c7a7d6..b340aa02 100644 --- a/lessons/05-rag/translations/README.pt-br.md +++ b/lessons/05-rag/translations/README.pt-br.md @@ -10,16 +10,6 @@ Neste capítulo você vai aprender: Se você ainda não configurou seu ambiente de desenvolvimento, veja como fazer: [Configure seu ambiente](/docs/setup/README.md). -## Recursos relacionados - -[![Assista a um vídeo curto sobre RAG](https://img.youtube.com/vi/xkFOmx5yxIA/0.jpg)](https://www.youtube.com/watch?v=xkFOmx5yxIA&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=4) - -_Este vídeo explica a Geração Aumentada por Recuperação (RAG), um método que ajuda a IA a usar seu conteúdo junto com seus dados de treinamento para obter melhores resultados._ - -*🎥 Clique na imagem acima para assistir a um vídeo curto sobre geração aumentada por recuperação, RAG* - -💼 Slides: [Geração aumentada por recuperação, RAG](/videos/slides/03-rag.pptx) - ## Narrativa - Gênesis > [!NOTE] @@ -37,7 +27,7 @@ _Este vídeo explica a Geração Aumentada por Recuperação (RAG), um método q Vocês pousam no jardim, é tarde da noite com uma névoa espessa e luzes sinistras piscando à distância. A mansão se ergue diante de vocês. Leonardo olha ao redor, seus olhos arregalados de admiração.
- Antiga mansão mostrada em uma névoa densa + Antiga mansão mostrada em uma névoa densa
### Fugindo dos Cães @@ -45,7 +35,7 @@ Vocês pousam no jardim, é tarde da noite com uma névoa espessa e luzes sinist Você ouve latidos e o som de cães correndo em sua direção. Você se vira para Leonardo: "Precisamos entrar, agora!"
- Fugindo dos cães + Fugindo dos cães
Quando você chega à porta da mansão, ela se abre e um par de atendentes sai apressadamente. Depois de avaliá-los, eles fazem sinal para que vocês os sigam. @@ -63,7 +53,7 @@ Você se encontra cara a cara com Ada Lovelace, seus olhos brilhando de curiosid **Você:** Mas...
- Ada Lovelace e Charles Babbage trabalhando em um dispositivo + Ada Lovelace e Charles Babbage trabalhando em um dispositivo
Charles Babbage se aproxima, examinando o Besouro do Tempo em sua mão. "Este dispositivo é notável, mas está um pouco defeituoso, não está? Você deve ter notado, tenho certeza." @@ -85,21 +75,21 @@ Se você quiser interagir com Ada, execute o aplicativo [Characters](/app/README > [Aviso sobre IA Responsável](/README.md#responsible-ai-disclaimer)
- Ada Lovelace + Ada Lovelace
**Passos**: 1. Inicie um [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navegue até _/app_ na raiz do repositório. -3. Localize o console e execute `npm install` seguido de `npm start`. +3. Localize o console e execute `npm ci` seguido de `npm start`. 4. Quando aparecer, selecione o botão "Open in Browser". 5. Converse com Ada. Para uma explicação mais detalhada do aplicativo, consulte [Explicação detalhada do aplicativo](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] - > Se você estiver executando o projeto localmente em sua máquina, por favor revise o guia de Início Rápido para configurar um [token de acesso pessoal do GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) e substitua a chave no código. +> Configure `AI_ENDPOINT`, `AI_API_KEY` e `AI_MODEL` no arquivo `.env` na raiz do repositório, tanto localmente quanto no Codespaces. Consulte o [guia de configuração](/docs/setup/README.md#configure-environment-variables). ## Desafios conhecidos com grandes modelos de linguagem, LLMs @@ -266,13 +256,20 @@ ${question} // 4. Componente gerador: usa os resultados da pesquisa para gerar uma resposta // --------------------------------------------------------------------- +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); const chunks = await openai.chat.completions.create({ - model: "gpt-4o-mini", + model, messages: [{ role: "user", content: augmentedPrompt }], stream: true, }); @@ -280,7 +277,7 @@ const chunks = await openai.chat.completions.create({ console.log(`Resposta para "${question}":`); for await (const chunk of chunks) { - process.stdout.write(chunk.choices[0].delta.content ?? ""); + process.stdout.write(chunk.choices[0]?.delta.content ?? ""); } ``` @@ -348,4 +345,4 @@ C. O recuperador encontra informações relevantes de fontes de dados externas. - **Aplicativos de exemplo**: * [Chat de IA Serverless com RAG](https://github.com/Azure-Samples/serverless-chat-langchainjs/) * [Ask Youtube: Uma API de perguntas e respostas do Youtube baseada em RAG](https://github.com/Azure-Samples/langchainjs-quickstart-demo) -- [Workshop completo: Crie seu próprio ChatGPT com RAG](https://moaw.dev/workshop/gh:azure-samples/azure-openai-rag-workshop/docs/workshop-qdrant.md) +- [Workshop completo: Crie seu próprio ChatGPT com RAG](https://github.com/Azure-Samples/azure-openai-rag-workshop) diff --git a/lessons/05-rag/translations/README.zh-cn.md b/lessons/05-rag/translations/README.zh-cn.md index 8cfb0c82..6a806897 100644 --- a/lessons/05-rag/translations/README.zh-cn.md +++ b/lessons/05-rag/translations/README.zh-cn.md @@ -10,16 +10,6 @@ 如果您尚未设置开发环境，请按照以下方法进行设置：[设置您的环境](/docs/setup/README.md)。 -## 相关资源 - -[![观看关于RAG的短视频](https://img.youtube.com/vi/xkFOmx5yxIA/0.jpg)](https://www.youtube.com/watch?v=xkFOmx5yxIA&list=PLlrxD0HtieHi5ZpsHULPLxm839IrhmeDk&index=4) - -_这个视频解释了检索增强生成（RAG），这是一种帮助AI在其训练数据之外使用您的内容以获得改进结果的方法。_ - -*🎥 点击上方图片观看关于检索增强生成（RAG）的短视频* - -💼 幻灯片：[检索增强生成，RAG](../../../videos/slides/03-rag.pptx) - ## 故事情节 - 起源 > [!NOTE] @@ -37,7 +27,7 @@ _这个视频解释了检索增强生成（RAG），这是一种帮助AI在其 您降落在花园里，现在是深夜，浓雾弥漫，远处闪烁着诡异的灯光。豪宅耸立在您面前。达芬奇环顾四周，眼中充满惊奇。
- 在浓雾中显示的古老豪宅 + 在浓雾中显示的古老豪宅
### 逃离猎犬 @@ -45,7 +35,7 @@ _这个视频解释了检索增强生成（RAG），这是一种帮助AI在其 您听到吠叫声和狗向您奔来的声音。您转向达芬奇，"我们需要进去，现在！"
- 逃离猎犬 + 逃离猎犬
当您到达豪宅的门口时，门突然打开，一对侍者匆忙走出来。在打量了您之后，他们示意您跟随他们。 @@ -63,7 +53,7 @@ _这个视频解释了检索增强生成（RAG），这是一种帮助AI在其 **您**：但是...
- 艾达·洛夫莱斯和查尔斯·巴贝奇正在研究一个设备 + 艾达·洛夫莱斯和查尔斯·巴贝奇正在研究一个设备
查尔斯·巴贝奇走上前来，检查您手中的时间甲虫。"这个设备非常了不起，但它有点故障，不是吗？我想你已经注意到了。" @@ -85,21 +75,21 @@ _这个视频解释了检索增强生成（RAG），这是一种帮助AI在其 > [负责任的AI免责声明](/README.md#responsible-ai-disclaimer)
- 艾达·洛夫莱斯 + 艾达·洛夫莱斯
**步骤**： 1. 启动一个[![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. 导航到仓库根目录中的_/app_。 -3. 找到控制台并运行`npm install`，然后运行`npm start`。 +3. 找到控制台并运行`npm ci`，然后运行`npm start`。 4. 出现后，选择"在浏览器中打开"按钮。 5. 与艾达聊天。 有关应用程序的更详细说明，请参见[详细应用程序说明](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates)。 > [!NOTE] - > 如果您在本地机器上运行项目，请查看快速入门指南，设置[GitHub个人访问令牌](../../../docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access)并在代码中替换密钥。 +> 在存储库根目录的 `.env` 中配置 `AI_ENDPOINT`、`AI_API_KEY` 和 `AI_MODEL`。本地开发和 Codespaces 都需要这些设置。请参阅[配置指南](/docs/setup/README.md#configure-environment-variables)。 ## 大型语言模型（LLM）的已知挑战 @@ -266,13 +256,20 @@ ${question} // 4. 生成器组件：使用搜索结果生成响应 // --------------------------------------------------------------------- +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} + const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, + baseURL: endpoint, + apiKey, }); const chunks = await openai.chat.completions.create({ - model: "gpt-4o-mini", + model, messages: [{ role: "user", content: augmentedPrompt }], stream: true, }); @@ -280,7 +277,7 @@ const chunks = await openai.chat.completions.create({ console.log(`"${question}"的答案:`); for await (const chunk of chunks) { - process.stdout.write(chunk.choices[0].delta.content ?? ""); + process.stdout.write(chunk.choices[0]?.delta.content ?? ""); } ``` @@ -348,6 +345,6 @@ C. 检索器从外部数据源中查找相关信息。 - **示例应用**： * [使用RAG的无服务器AI聊天](https://github.com/Azure-Samples/serverless-chat-langchainjs/) * [Ask Youtube：基于RAG的Youtube问答API](https://github.com/Azure-Samples/langchainjs-quickstart-demo) -- [完整工作坊：使用RAG创建您自己的ChatGPT](https://moaw.dev/workshop/gh:azure-samples/azure-openai-rag-workshop/docs/workshop-qdrant.md) +- [完整工作坊：使用RAG创建您自己的ChatGPT](https://github.com/Azure-Samples/azure-openai-rag-workshop) **艾达**：让我们先讨论我们将用来驱动设备的AI。我们将依靠"AI模型 diff --git a/lessons/06-tool-calling/README.md b/lessons/06-tool-calling/README.md index 45277988..c2337210 100644 --- a/lessons/06-tool-calling/README.md +++ b/lessons/06-tool-calling/README.md @@ -63,26 +63,26 @@ The world around you starts to blur, and everything fades to black. You come to ## Interact with Amelia Earhart -If you want to interact with Ada, run the [Characters](/app/README.md) app. +If you want to interact with Amelia, run the [Characters](/app/README.md) app. > [!IMPORTANT] > This is entirely fictional; the responses are generated by AI. > [Responsible AI disclaimer](/README.md#responsible-ai-disclaimer) -![Ada Lovelace](https://raw.githubusercontent.com/microsoft/generative-ai-with-javascript/main/lessons/06-tool-calling/assets/amelia-front.jpeg) +![Amelia Earhart](https://raw.githubusercontent.com/microsoft/generative-ai-with-javascript/main/lessons/06-tool-calling/assets/amelia-front.jpeg) **Steps**: 1. Start a [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navigate to _/app_ in the repo root. -3. Locate the console and run `npm install` followed by `npm start`. +3. Locate the console and run `npm ci` followed by `npm start`. 4. Once it appears, select the "Open in Browser" button. 5. Chat with Amelia. For a more detailed explanation of the app, see [Detailed app explanation](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] - > If you're running the project locally on your machine, please review the QuickStart guide to get a [GitHub personal access](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) token setup and replace the key in the code. +> Configure `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL` in the repository-root `.env` for both local development and Codespaces. See the [setup guide](/docs/setup/README.md#configure-environment-variables). ## Tool calling @@ -106,9 +106,9 @@ try { if (!landingSpot) { throw new Error("No suitable landing spot found"); } - console.log(Landing spot found at coordinates: ${landingSpot.lat}, ${landingSpot.long}); + console.log(`Landing spot found at coordinates: ${landingSpot.lat}, ${landingSpot.long}`); } catch (error) { - console.log(Error: ${error.message}); + console.error(`Error: ${error.message}`); } ``` @@ -143,15 +143,11 @@ function findLandingSpot(lat, long) { "properties": { "lat": { "type": "number", - "description": "The latitude of the location", - }, + "description": "The latitude of the location"}, "long": { "type": "number", - "description": "The longitude of the location", - }, - }, - "required": ["lat", "long"], - }, + "description": "The longitude of the location"}}, + "required": ["lat", "long"]}, "output": { "type": "object", "properties": { "lat": "number", "long": "number" } } } ``` @@ -185,7 +181,7 @@ const getBackgroundOnCharacterJson = { description: "The name of the character", } }, - required: ["lat", "long"], + required: ["name"], }, output: { type: "string" } }; @@ -217,16 +213,16 @@ const messages = [{ }]; const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', + model: process.env.AI_MODEL, messages: messages, - functions: [getBackgroundOnCharacterJson, findLandingSpotJson] + tools: [getBackgroundOnCharacterJson, findLandingSpotJson].map(({ name, description, parameters }) => ({ type: "function", function: { name, description, parameters } })) }); ``` **Time Beetle**: "In the preceding code snippet we:" - Define the metadata for the `find-landing-spot` tool and the `get-background-on-character` tool. -- Provide this metadata to the `client.getChatCompletions` call as part of the `functions` parameter. This tells the AI model that these tools are available to be called." +- Provide this metadata to the `openai.chat.completions.create` call as part of the `tools` parameter. This tells the AI model that these tools are available to be called." **You**: "Got it, so the AI model will call the appropriate if I provide a prompt that matches to the tool's description?" @@ -250,87 +246,103 @@ const completion = await openai.chat.completions.create({ **Time Beetle**: "Sure, here's the code wire up the tool call, making a chat completion request and interpreting the response: ```javascript -import { OpenAI } from 'openai'; -import { maybeCoerceInteger } from 'openai/core.mjs'; +import { OpenAI } from "openai"; + +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} -// 1: Define the function function findLandingSpot(lat, long) { - console.log("[Function] Finding landing spot with coordinates: ", lat, long); - // Perform the task of finding a suitable landing spot - // Return the coordinates of the landing spot + console.log("[Function] Finding a simulated landing spot:", lat, long); return { lat: 7.5, long: 134.5 }; } -// 2: Define the tool metadata, should include description, parameters, and output +function getBackgroundOnCharacter(name) { + console.log("[Function] Getting background on character:", name); + return `Background information on ${name}`; +} + +const getBackgroundOnCharacterJson = { + name: "get-background-on-character", + description: "Get background information on a character", + strict: true, + parameters: { + type: "object", + properties: { name: { type: "string", description: "The character's name" } }, + required: ["name"], + additionalProperties: false + } +}; + const findLandingSpotJson = { name: "find-landing-spot", - description: "Finds a suitable landing spot", + description: "Return a simulated landing spot for this fictional exercise", + strict: true, parameters: { type: "object", properties: { - lat: { - type: "number", - description: "The latitude of the location", - }, - long: { - type: "number", - description: "The longitude of the location", - }, + lat: { type: "number", description: "Latitude" }, + long: { type: "number", description: "Longitude" } }, required: ["lat", "long"], - }, - output: { type: "object", properties: { lat: "number", long: "number" } } + additionalProperties: false + } }; -// 3: Add the tool to the tools object that we will use later to invoke the tool -const tools = { - [findLandingSpotJson.name]: findLandingSpot +const handlers = { + [getBackgroundOnCharacterJson.name]: args => { + if (typeof args.name !== "string" || !args.name.trim()) { + throw new Error("The character name must be a non-empty string."); + } + return getBackgroundOnCharacter(args.name); + }, + [findLandingSpotJson.name]: args => { + if (!Number.isFinite(args.lat) || !Number.isFinite(args.long)) { + throw new Error("Landing coordinates must be finite numbers."); + } + return findLandingSpot(args.lat, args.long); + } }; -// 4: Create an instance of the OpenAI client -const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", // might need to change to this url in the future: https://models.github.ai/inference - apiKey: process.env.GITHUB_TOKEN, -}); - -// 5: Define the messages that will be sent to the AI model +const tools = [getBackgroundOnCharacterJson, findLandingSpotJson].map(definition => ({ + type: "function", + function: definition +})); +const openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); const messages = [ -{ - role: "system", - content: `You are a helpful assistant. You can call functions to perform tasks. Make sure to parse the function call and arguments correctly.` -}, { - role: "user", - content: "Find a landing spot given coordinates 8.5, 130.5" -} + { role: "system", content: "Use the provided tools to answer the fictional travel request." }, + { role: "user", content: "Use the character tool to give me background on Amelia Earhart." } ]; -async function main(){ - console.log("Making LLM call") - - // 6: Call the AI model with the defined messages and tools - const result = await openai.chat.completions.create({ - model: 'gpt-4o', - messages: messages, - functions: [findLandingSpotJson] - }); - - for (const choice of result.choices) { - - let functionCall = choice.message?.function_call; - let functionName = functionCall?.name; - let args = JSON.parse(functionCall?.arguments); +const completion = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "required" +}); +const message = completion.choices[0]?.message; +if (!message?.tool_calls?.length) throw new Error("The model did not request a tool."); +messages.push(message); - // 7: Interpret response and call the tool based on the function call provided by the AI model - if (functionName && functionName in tools) { - console.log(`Calling [${functionName}]`); - const toolFunction = tools[functionName]; - const toolResponse = toolFunction(...Object.values(args)); // Extract values from args and spread them - console.log("Result from [tool] calling: ", toolResponse); - } +for (const call of message.tool_calls) { + if (call.type !== "function" || !Object.hasOwn(handlers, call.function.name)) { + throw new Error("The model requested an unsupported tool."); } + const args = JSON.parse(call.function.arguments); + if (!args || typeof args !== "object" || Array.isArray(args)) { + throw new Error("Tool arguments must be a JSON object."); + } + const result = handlers[call.function.name](args); + console.log(`Result from [${call.function.name}]:`, result); + messages.push({ role: "tool", tool_call_id: call.id, content: JSON.stringify(result) }); } -main(); +const followUp = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "none" +}); +const answer = followUp.choices[0]?.message?.content; +if (!answer?.trim()) throw new Error("The model did not return a final answer."); +console.log(answer); ``` In the preceding code we've: @@ -338,7 +350,7 @@ In the preceding code we've: - Created a function called `findLandingSpot` that takes latitude and longitude as input and returns the coordinates of a suitable landing spot. - Defined the metadata for the `find-landing-spot` tool. - Created a `tools` object that maps tool names to tool metadata. -- Provided the `tools` object to the `client.getChatCompletions` call. +- Provided the `tools` object to the `openai.chat.completions.create` call. ```javascript if (functionName && functionName in tools) { @@ -433,4 +445,4 @@ C. To allow the AI model to execute tools without requiring metadata. - Explains the [process of tool calling](https://learn.microsoft.com/en-us/semantic-kernel/concepts/ai-services/chat-completion/function-calling/?pivots=programming-language-csharp) - Tool calling in the [Langchain.js framework](https://js.langchain.com/docs/how_to/tool_calling/) -- Function calling as demonstrated in the [openai library](https://github.com/openai/openai-node/blob/master/examples/function-call.ts) +- Function calling in the [current OpenAI SDK](https://github.com/openai/openai-node#automated-function-calls) diff --git a/lessons/06-tool-calling/code/app.js b/lessons/06-tool-calling/code/app.js index 07160f38..f6db79ca 100644 --- a/lessons/06-tool-calling/code/app.js +++ b/lessons/06-tool-calling/code/app.js @@ -1,108 +1,97 @@ -import { OpenAI } from 'openai'; +import { OpenAI } from "openai"; + +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} function findLandingSpot(lat, long) { - console.log("[Function] Finding landing spot with coordinates: ", lat, long); - // Perform the task of finding a suitable landing spot - // Return the coordinates of the landing spot + console.log("[Function] Finding a simulated landing spot:", lat, long); return { lat: 7.5, long: 134.5 }; } -function getBackgroundOnCharacter(character= "unknown") { - console.log("[Function] Getting background on character: ", character); - // Perform the task of getting background information on a character - // Return the background information - return `Background information on ${character}`; +function getBackgroundOnCharacter(name) { + console.log("[Function] Getting background on character:", name); + return `Background information on ${name}`; } const getBackgroundOnCharacterJson = { name: "get-background-on-character", description: "Get background information on a character", + strict: true, parameters: { type: "object", - properties: { - name: { - type: "string", - description: "The name of the character", - } - }, + properties: { name: { type: "string", description: "The character's name" } }, required: ["name"], - }, - output: { type: "string" } + additionalProperties: false + } }; const findLandingSpotJson = { name: "find-landing-spot", - description: "Finds a suitable landing spot", + description: "Return a simulated landing spot for this fictional exercise", + strict: true, parameters: { type: "object", properties: { - lat: { - type: "number", - description: "The latitude of the location", - }, - long: { - type: "number", - description: "The longitude of the location", - }, + lat: { type: "number", description: "Latitude" }, + long: { type: "number", description: "Longitude" } }, required: ["lat", "long"], - }, - output: { type: "object", properties: { lat: "number", long: "number" } } + additionalProperties: false + } }; -const tools = { - [findLandingSpotJson.name]: findLandingSpot, - [getBackgroundOnCharacterJson.name]: getBackgroundOnCharacter +const handlers = { + [getBackgroundOnCharacterJson.name]: args => { + if (typeof args.name !== "string" || !args.name.trim()) { + throw new Error("The character name must be a non-empty string."); + } + return getBackgroundOnCharacter(args.name); + }, + [findLandingSpotJson.name]: args => { + if (!Number.isFinite(args.lat) || !Number.isFinite(args.long)) { + throw new Error("Landing coordinates must be finite numbers."); + } + return findLandingSpot(args.lat, args.long); + } }; - -const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", // might need to change to this url in the future: https://models.github.ai/inference - apiKey: process.env.GITHUB_TOKEN, -}); - -/* -// { -// role: "user", -// content: `We need to know where to land, here's the coordinates: 7.5, 134.5. `, -// }, -*/ - +const tools = [getBackgroundOnCharacterJson, findLandingSpotJson].map(definition => ({ + type: "function", + function: definition +})); +const openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); const messages = [ -{ - role: "system", - content: `You are a helpful assistant. You can call functions to perform tasks. Make sure to parse the function call and arguments correctly.` -}, { - role: "user", - content: "Can you give me some background on the character named Amelia Earhart?" -} + { role: "system", content: "Use the provided tools to answer the fictional travel request." }, + { role: "user", content: "Use the character tool to give me background on Amelia Earhart." } ]; -async function main(){ - console.log("Making LLM call") - - const result = await openai.chat.completions.create({ - model: 'gpt-4o', - messages: messages, - functions: [getBackgroundOnCharacterJson, findLandingSpotJson] - }); - - for (const choice of result.choices) { - // console.log("Result", choice.message); +const completion = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "required" +}); +const message = completion.choices[0]?.message; +if (!message?.tool_calls?.length) throw new Error("The model did not request a tool."); +messages.push(message); - let functionCall = choice.message?.function_call; - let functionName = functionCall?.name; - let args = JSON.parse(functionCall?.arguments); - // console.log("Wants to call: ", choice.message?.function_call); - // console.log("With args: ", args); - if (functionName && functionName in tools) { - console.log(`Calling [${functionName}]`); - const toolFunction = tools[functionName]; - const toolResponse = toolFunction(...Object.values(args)); // Extract values from args and spread them - console.log("Result from [tool] calling: ", toolResponse); - } +for (const call of message.tool_calls) { + if (call.type !== "function" || !Object.hasOwn(handlers, call.function.name)) { + throw new Error("The model requested an unsupported tool."); } - + const args = JSON.parse(call.function.arguments); + if (!args || typeof args !== "object" || Array.isArray(args)) { + throw new Error("Tool arguments must be a JSON object."); + } + const result = handlers[call.function.name](args); + console.log(`Result from [${call.function.name}]:`, result); + messages.push({ role: "tool", tool_call_id: call.id, content: JSON.stringify(result) }); } -main(); +const followUp = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "none" +}); +const answer = followUp.choices[0]?.message?.content; +if (!answer?.trim()) throw new Error("The model did not return a final answer."); +console.log(answer); diff --git a/lessons/06-tool-calling/code/package-lock.json b/lessons/06-tool-calling/code/package-lock.json index 7ba1a628..01cc39de 100644 --- a/lessons/06-tool-calling/code/package-lock.json +++ b/lessons/06-tool-calling/code/package-lock.json @@ -9,397 +9,38 @@ "version": "1.0.0", "license": "ISC", "dependencies": { - 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} } } } diff --git a/lessons/06-tool-calling/code/package.json b/lessons/06-tool-calling/code/package.json index 04a667b1..3e72c21b 100644 --- a/lessons/06-tool-calling/code/package.json +++ b/lessons/06-tool-calling/code/package.json @@ -2,14 +2,15 @@ "name": "code", "version": "1.0.0", "main": "app.js", + "type": "module", "scripts": { - "test": "echo \"Error: no test specified\" && exit 1" + "start": "node --env-file-if-exists=../../../.env app.js" }, "keywords": [], "author": "", "license": "ISC", "description": "", "dependencies": { - "openai": "^4.91.1" + "openai": "^7.28.0" } } diff --git a/lessons/06-tool-calling/solution/README.md b/lessons/06-tool-calling/solution/README.md index 0e18dae6..303bc68d 100644 --- a/lessons/06-tool-calling/solution/README.md +++ b/lessons/06-tool-calling/solution/README.md @@ -1,11 +1,11 @@ # Solution for Lesson 6: Tool Calling -This solution demonstrates how to call a tool from the AI model. It uses GitHub Models and openai library for calling the tool. +The endpoint, API key, and deployment name come from your Microsoft Foundry resource. The code reads them from `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL`. ## Installation ```bash -npm install +npm ci ``` ## Usage @@ -13,4 +13,3 @@ npm install ```bash npm start ``` - diff --git a/lessons/06-tool-calling/solution/package-lock.json b/lessons/06-tool-calling/solution/package-lock.json index 4c0bf232..41494897 100644 --- a/lessons/06-tool-calling/solution/package-lock.json +++ b/lessons/06-tool-calling/solution/package-lock.json @@ -9,397 +9,38 @@ "version": "1.0.0", "license": "ISC", "dependencies": { - "openai": "^4.91.1" - } - }, - "node_modules/@types/node": { - "version": "18.19.86", - "resolved": "https://registry.npmjs.org/@types/node/-/node-18.19.86.tgz", - "integrity": "sha512-fifKayi175wLyKyc5qUfyENhQ1dCNI1UNjp653d8kuYcPQN5JhX3dGuP/XmvPTg/xRBn1VTLpbmi+H/Mr7tLfQ==", - "license": "MIT", - "dependencies": { - "undici-types": "~5.26.4" - } - }, - "node_modules/@types/node-fetch": { - "version": "2.6.12", - "resolved": "https://registry.npmjs.org/@types/node-fetch/-/node-fetch-2.6.12.tgz", - 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"openai": "^4.91.1" + "openai": "^7.28.0" } - } +} diff --git a/lessons/06-tool-calling/solution/solution.js b/lessons/06-tool-calling/solution/solution.js index eae96733..16817e6e 100644 --- a/lessons/06-tool-calling/solution/solution.js +++ b/lessons/06-tool-calling/solution/solution.js @@ -1,148 +1,105 @@ -import { OpenAI } from 'openai'; +import { OpenAI } from "openai"; -// 1: Define the functions +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} function calculateDistance(lat1, long1, lat2, long2) { - // Perform the task of calculating the distance between two points - // Return the distance between the points - return Math.sqrt((lat2 - lat1) ** 2 + (long2 - long1) ** 2); + return Math.hypot(lat2 - lat1, long2 - long1); } function getGpsPosition(lat, long) { - // Perform the task of getting the GPS position of the current location - // Return the GPS position - return { lat: 7.5, long: 134.5 }; + return { lat: 7.5, long: 134.5 }; } function getWeatherForecast(lat, long) { - // Perform the task of getting the weather forecast for a given location - // Return the weather forecast - return "Sunny"; + return "Sunny"; } -// 2: create metadata for the functions const calculateDistanceJson = { - name: "calculate-distance", - description: "Calculates th distance between two points", - parameters: { - type: "object", - properties: { - lat1: { - type: "number", - description: "The latitude of the first point", - }, - long1: { - type: "number", - description: "The longitude of the first point", - }, - lat2: { - type: "number", - description: "The latitude of the second point", - }, - long2: { - type: "number", - description: "The longitude of the second point", - }, - }, - required: ["lat1", "long1", "lat2", "long2"], + name: "calculate-distance", + description: "Calculate a demonstration distance in coordinate units", + strict: true, + parameters: { + type: "object", + properties: { + lat1: { type: "number" }, long1: { type: "number" }, + lat2: { type: "number" }, long2: { type: "number" } }, - output: { type: "number" } - }; - + required: ["lat1", "long1", "lat2", "long2"], + additionalProperties: false + } +}; const getGpsPositionJson = { name: "get-gps-position", - description: "Gets the GPS position of the current location", + description: "Return simulated GPS coordinates for this fictional exercise", + strict: true, parameters: { type: "object", - properties: { - lat: { - type: "number", - description: "The latitude of the first point", - }, - long: { - type: "number", - description: "The longitude of the first point", - }, - }, + properties: { lat: { type: "number" }, long: { type: "number" } }, required: ["lat", "long"], - }, - output: { type: "object", properties: { lat: "number", long: "number" } } -} - + additionalProperties: false + } +}; const getWeatherForecastJson = { name: "get-weather-forecast", - description: "Gets the weather forecast for a given location", + description: "Return a simulated weather forecast", + strict: true, parameters: { type: "object", - properties: { - lat: { - type: "number", - description: "The latitude of the location", - }, - long: { - type: "number", - description: "The longitude of the location", - }, - }, + properties: { lat: { type: "number" }, long: { type: "number" } }, required: ["lat", "long"], - }, - output: { type: "string" } -} - -// 3: create a tools object with the functions -const tools = { - [calculateDistanceJson.name]: calculateDistance, - [getGpsPositionJson.name]: getGpsPosition, - [getWeatherForecastJson.name]: getWeatherForecast + additionalProperties: false + } }; -// 4: create an OpenAI instance with the tools -const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", - apiKey: process.env.GITHUB_TOKEN, -}); - -// 5: create messages to test the functions -// enable one of these messages at a time to test the functions -const messages = [ - { - role: "user", - content: `We need to know where to land, here's the coordinates: 7.5, 134.5. `, - }, - // { - // role: "user", - // content: `What is the distance between the points 7.5, 134.5 and 8.5, 135.5?`, - // }, - // { - // role: "user", - // content: `What is the weather forecast for the location 7.5, 134.5?`, - // }, -]; - -// 6: make a chat completion -async function main(){ - const result = await openai.chat.completions.create({ - model: 'gpt-4o-mini', - messages: messages, - functions: [calculateDistanceJson, getGpsPositionJson, getWeatherForecastJson] +function coordinates(args, names) { + return names.map(name => { + if (!Number.isFinite(args[name])) throw new Error(`${name} must be a finite number.`); + return args[name]; }); +} - // 7: interpret the result - for (const choice of result.choices) { - // console.log("Result", choice.message); +const handlers = { + [calculateDistanceJson.name]: args => + calculateDistance(...coordinates(args, ["lat1", "long1", "lat2", "long2"])), + [getGpsPositionJson.name]: args => getGpsPosition(...coordinates(args, ["lat", "long"])), + [getWeatherForecastJson.name]: args => getWeatherForecast(...coordinates(args, ["lat", "long"])) +}; +const tools = [calculateDistanceJson, getGpsPositionJson, getWeatherForecastJson].map(definition => ({ + type: "function", + function: definition +})); +const openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); +const messages = [{ + role: "user", + content: "Use the GPS tool to get the simulated position for coordinates 7.5, 134.5." +}]; - let functionCall = choice.message?.function_call; - let functionName = functionCall?.name; - let args = JSON.parse(functionCall?.arguments); - // console.log("Wants to call: ", choice.message?.function_call); - // console.log("With args: ", args); - if (functionName && functionName in tools) { - console.log(`Calling [${functionName}]`); - const toolFunction = tools[functionName]; - const toolResponse = toolFunction(...Object.values(args)); // Extract values from args and spread them - console.log("Result from [tool] calling: ", toolResponse); - } - } +const completion = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "required" +}); +const message = completion.choices[0]?.message; +if (!message?.tool_calls?.length) throw new Error("The model did not request a tool."); +messages.push(message); +for (const call of message.tool_calls) { + if (call.type !== "function" || !Object.hasOwn(handlers, call.function.name)) { + throw new Error("The model requested an unsupported tool."); + } + const args = JSON.parse(call.function.arguments); + if (!args || typeof args !== "object" || Array.isArray(args)) { + throw new Error("Tool arguments must be a JSON object."); + } + const result = handlers[call.function.name](args); + console.log(`Result from [${call.function.name}]:`, result); + messages.push({ role: "tool", tool_call_id: call.id, content: JSON.stringify(result) }); } - -main(); +const followUp = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "none" +}); +const answer = followUp.choices[0]?.message?.content; +if (!answer?.trim()) throw new Error("The model did not return a final answer."); +console.log(answer); diff --git a/lessons/06-tool-calling/translations/README.fr.md b/lessons/06-tool-calling/translations/README.fr.md index 80a8ae54..2cf9f41d 100644 --- a/lessons/06-tool-calling/translations/README.fr.md +++ b/lessons/06-tool-calling/translations/README.fr.md @@ -63,26 +63,26 @@ Le monde autour de vous commence à se brouiller, et tout devient noir. Vous rev ## Interagir avec Amelia Earhart -If you want to interact with Ada, run the [Characters](/app/README.md) app. +Pour interagir avec Amelia, lancez l'application [Characters](/app/README.md). > [!IMPORTANT] > Ceci est entièrement fictif ; les réponses sont générées par une IA. > [Clause de non-responsabilité sur l'IA responsable](/README.md#responsible-ai-disclaimer) -![Ada Lovelace](https://raw.githubusercontent.com/microsoft/generative-ai-with-javascript/main/lessons/06-tool-calling/assets/amelia-front.jpeg) +![Amelia Earhart](https://raw.githubusercontent.com/microsoft/generative-ai-with-javascript/main/lessons/06-tool-calling/assets/amelia-front.jpeg) **Étapes** : 1. Lancez un [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript). 2. Naviguez vers */app* à la racine du dépôt. -3. Localisez la console et exécutez `npm install` suivi de `npm start`. +3. Localisez la console et exécutez `npm ci` suivi de `npm start`. 4. Une fois que l'application est ouverte, sélectionnez le bouton "Ouvrir dans le navigateur". 5. Discutez avec Amelia. Pour une explication plus détaillée de l'application, voir [Explication détaillée de l'application](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] -> Si vous exécutez le projet localement sur votre machine, veuillez consulter le guide de démarrage rapide pour configurer un [token d'accès personnel GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) et remplacer la clé dans le code. +> Configurez `AI_ENDPOINT`, `AI_API_KEY` et `AI_MODEL` dans le fichier `.env` à la racine du dépôt, en local comme dans Codespaces. Consultez le [guide de configuration](/docs/setup/README.md#configure-environment-variables). ## Appels d'outils @@ -106,9 +106,9 @@ try { if (!landingSpot) { throw new Error("No suitable landing spot found"); } - console.log(Landing spot found at coordinates: ${landingSpot.lat}, ${landingSpot.long}); + console.log(`Landing spot found at coordinates: ${landingSpot.lat}, ${landingSpot.long}`); } catch (error) { - console.log(Error: ${error.message}); + console.error(`Error: ${error.message}`); } ``` @@ -143,15 +143,11 @@ function findLandingSpot(lat, long) { "properties": { "lat": { "type": "number", - "description": "The latitude of the location", - }, + "description": "The latitude of the location"}, "long": { "type": "number", - "description": "The longitude of the location", - }, - }, - "required": ["lat", "long"], - }, + "description": "The longitude of the location"}}, + "required": ["lat", "long"]}, "output": { "type": "object", "properties": { "lat": "number", "long": "number" } } } ``` @@ -185,7 +181,7 @@ const getBackgroundOnCharacterJson = { description: "The name of the character", } }, - required: ["lat", "long"], + required: ["name"], }, output: { type: "string" } }; @@ -217,16 +213,16 @@ const messages = [{ }]; const completion = await openai.chat.completions.create({ - model: 'gpt-4o-mini', + model: process.env.AI_MODEL, messages: messages, - functions: [getBackgroundOnCharacterJson, findLandingSpotJson] + tools: [getBackgroundOnCharacterJson, findLandingSpotJson].map(({ name, description, parameters }) => ({ type: "function", function: { name, description, parameters } })) }); ``` **Time Beetle**: "Dans l'extrait de code précédent, nous :" * Définissons les métadonnées pour l'outil `find-landing-spot` et l'outil `get-background-on-character`. -* Fournissons ces métadonnées à l'appel `client.getChatCompletions` dans le paramètre `functions`. Cela indique au modèle IA que ces outils sont disponibles pour être appelés. +* Fournissons ces métadonnées à l'appel `openai.chat.completions.create` dans le paramètre `tools`. Cela indique au modèle IA que ces outils sont disponibles pour être appelés. **Vous**: "Compris, donc le modèle IA appellera l'outil approprié si je fournis une invite qui correspond à la description de l'outil ?" @@ -250,87 +246,103 @@ const completion = await openai.chat.completions.create({ **Time Beetle**: "Bien sûr, voici le code pour configurer l'appel d'outil, effectuer une demande de complétion de chat et interpréter la réponse : ```javascript -import { OpenAI } from 'openai'; -import { maybeCoerceInteger } from 'openai/core.mjs'; +import { OpenAI } from "openai"; + +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} -// 1: Define the function function findLandingSpot(lat, long) { - console.log("[Function] Finding landing spot with coordinates: ", lat, long); - // Perform the task of finding a suitable landing spot - // Return the coordinates of the landing spot + console.log("[Function] Finding a simulated landing spot:", lat, long); return { lat: 7.5, long: 134.5 }; } -// 2: Define the tool metadata, should include description, parameters, and output +function getBackgroundOnCharacter(name) { + console.log("[Function] Getting background on character:", name); + return `Background information on ${name}`; +} + +const getBackgroundOnCharacterJson = { + name: "get-background-on-character", + description: "Get background information on a character", + strict: true, + parameters: { + type: "object", + properties: { name: { type: "string", description: "The character's name" } }, + required: ["name"], + additionalProperties: false + } +}; + const findLandingSpotJson = { name: "find-landing-spot", - description: "Finds a suitable landing spot", + description: "Return a simulated landing spot for this fictional exercise", + strict: true, parameters: { type: "object", properties: { - lat: { - type: "number", - description: "The latitude of the location", - }, - long: { - type: "number", - description: "The longitude of the location", - }, + lat: { type: "number", description: "Latitude" }, + long: { type: "number", description: "Longitude" } }, required: ["lat", "long"], - }, - output: { type: "object", properties: { lat: "number", long: "number" } } + additionalProperties: false + } }; -// 3: Add the tool to the tools object that we will use later to invoke the tool -const tools = { - [findLandingSpotJson.name]: findLandingSpot +const handlers = { + [getBackgroundOnCharacterJson.name]: args => { + if (typeof args.name !== "string" || !args.name.trim()) { + throw new Error("The character name must be a non-empty string."); + } + return getBackgroundOnCharacter(args.name); + }, + [findLandingSpotJson.name]: args => { + if (!Number.isFinite(args.lat) || !Number.isFinite(args.long)) { + throw new Error("Landing coordinates must be finite numbers."); + } + return findLandingSpot(args.lat, args.long); + } }; -// 4: Create an instance of the OpenAI client -const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", // might need to change to this url in the future: https://models.github.ai/inference - apiKey: process.env.GITHUB_TOKEN, -}); - -// 5: Define the messages that will be sent to the AI model +const tools = [getBackgroundOnCharacterJson, findLandingSpotJson].map(definition => ({ + type: "function", + function: definition +})); +const openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); const messages = [ -{ - role: "system", - content: `You are a helpful assistant. You can call functions to perform tasks. Make sure to parse the function call and arguments correctly.` -}, { - role: "user", - content: "Find a landing spot given coordinates 8.5, 130.5" -} + { role: "system", content: "Use the provided tools to answer the fictional travel request." }, + { role: "user", content: "Use the character tool to give me background on Amelia Earhart." } ]; -async function main(){ - console.log("Making LLM call") - - // 6: Call the AI model with the defined messages and tools - const result = await openai.chat.completions.create({ - model: 'gpt-4o', - messages: messages, - functions: [findLandingSpotJson] - }); - - for (const choice of result.choices) { - - let functionCall = choice.message?.function_call; - let functionName = functionCall?.name; - let args = JSON.parse(functionCall?.arguments); +const completion = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "required" +}); +const message = completion.choices[0]?.message; +if (!message?.tool_calls?.length) throw new Error("The model did not request a tool."); +messages.push(message); - // 7: Interpret response and call the tool based on the function call provided by the AI model - if (functionName && functionName in tools) { - console.log(`Calling [${functionName}]`); - const toolFunction = tools[functionName]; - const toolResponse = toolFunction(...Object.values(args)); // Extract values from args and spread them - console.log("Result from [tool] calling: ", toolResponse); - } +for (const call of message.tool_calls) { + if (call.type !== "function" || !Object.hasOwn(handlers, call.function.name)) { + throw new Error("The model requested an unsupported tool."); } + const args = JSON.parse(call.function.arguments); + if (!args || typeof args !== "object" || Array.isArray(args)) { + throw new Error("Tool arguments must be a JSON object."); + } + const result = handlers[call.function.name](args); + console.log(`Result from [${call.function.name}]:`, result); + messages.push({ role: "tool", tool_call_id: call.id, content: JSON.stringify(result) }); } -main(); +const followUp = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "none" +}); +const answer = followUp.choices[0]?.message?.content; +if (!answer?.trim()) throw new Error("The model did not return a final answer."); +console.log(answer); ``` Dans le code précédent, nous avons : @@ -341,7 +353,7 @@ Dans le code précédent, nous avons : * Créé un objet `tools` qui associe les noms d'outils aux métadonnées des outils. -* Fournissons l'objet `tools` dans l'appel `client.getChatCompletions`. +* Fournissons l'objet `tools` dans l'appel `openai.chat.completions.create`. ```javascript if (functionName && functionName in tools) { @@ -437,4 +449,4 @@ C. Permettre au modèle IA d'exécuter des outils sans exiger de métadonnées. * Explique le [processus d'appel d'outils](https://learn.microsoft.com/en-us/semantic-kernel/concepts/ai-services/chat-completion/function-calling/?pivots=programming-language-csharp) * Appel d'outil dans le [cadre Langchain.js](https://js.langchain.com/docs/how_to/tool_calling/) -* Appel de fonction comme démontré dans la [bibliothèque openai](https://github.com/openai/openai-node/blob/master/examples/function-call.ts) +* Appel de fonction dans le [SDK OpenAI actuel](https://github.com/openai/openai-node#automated-function-calls) diff --git a/lessons/07-mcp/README.md b/lessons/07-mcp/README.md index 212b80bf..4546c1e8 100644 --- a/lessons/07-mcp/README.md +++ b/lessons/07-mcp/README.md @@ -65,14 +65,14 @@ If you want to interact with Scipio, run the [Characters](/app/README.md) app. 1. Start a [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navigate to _/app_ in the repo root. -3. Locate the console and run `npm install` followed by `npm start`. +3. Locate the console and run `npm ci` followed by `npm start`. 4. Once it appears, select the "Open in Browser" button. 5. Chat with Scipio. For a more detailed explanation of the app, see [Detailed app explanation](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] - > If you're running the project locally on your machine, please review the QuickStart guide to get a [GitHub personal access](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) token setup and replace the key in the code. +> Configure `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL` in the repository-root `.env` for both local development and Codespaces. See the [setup guide](/docs/setup/README.md#configure-environment-variables). ## The need for Model Context Protocol (MCP) @@ -173,8 +173,8 @@ The preceding code does the following: **Time Beetle**: "No, not yet. But we can add resources and tools to it. A tool is something that can be used to perform a specific task, while a resource is something that can be used to provide data or functionality to the tool. Let's add some below: ```typescript -server.tool("add", - { a: z.number(), b: z.number() }, +server.registerTool("add", + { description: "Add two numbers", inputSchema: { a: z.number(), b: z.number() } }, async ({ a, b }) => ({ content: [{ type: "text", text: String(a + b) }] }) @@ -192,9 +192,10 @@ In the preceding code, we: **Time Beetle**: "Exactly! The MCP client can call this tool and pass the required parameters. Let's add a resource to the server as well: ```typescript -server.resource( +server.registerResource( "greeting", new ResourceTemplate("greeting://{name}", { list: undefined }), + { mimeType: "text/plain" }, async (uri, { name }) => ({ contents: [{ uri: uri.href, @@ -213,9 +214,10 @@ In this code, we: **You**: "So, the resource is like a data source, this could be a database, file or even an API? If this was a for a file I would use a file:// URI?" like so: ```typescript -server.resource( +server.registerResource( "file", new ResourceTemplate("file://{path}", { list: undefined }), + { mimeType: "text/plain" }, async (uri, { path }) => ({ // do something with the file at path, e.g., read its contents contents: [{ @@ -254,14 +256,14 @@ In this code, we: **Time Beetle**: "You can test your MCP server by running it in a terminal by running the inspector like so: ```bash -npx @modelcontextprotocol/inspector node build/index.js +npm run inspect:web ``` Above, we are using the `@modelcontextprotocol/inspector` package to run the MCP server. We provide the argument `node build/index.js` to specify the entry point of the server. This will start the MCP server and allow you to interact with it through the inspector. **You**: "And how do I interact with it?" -**Time Beetle**: "This starts a web server on port 6274. You can access the inspector by opening your web browser and navigating to `http://localhost:6274`. The inspector provides a user-friendly interface for interacting with your MCP server, allowing you to test the tools and resources you've defined." +**Time Beetle**: "Run this command in the lesson package directory. It builds the server and starts the current web inspector. Open the local address printed in the terminal. The interface can differ from the older images below. Keep inspector ports private." ![Inspector user interface](https://softchris.github.io/mcp-workshop/assets/images/connect-7703c67645f368d51c7b24a5d635d6a0.png) @@ -349,7 +351,7 @@ So when you list tools, you get a response on the following format: { "name": "", - "description": "". + "description": "", "inputSchema": { "type":"object", "properties":{ @@ -371,7 +373,7 @@ which means, if you have a tool `add`, your response, listing tools, looks like ```json { "name": "add", - "description": "Adding two numbers". + "description": "Adding two numbers", "inputSchema": { "type":"object", "properties":{ @@ -396,8 +398,8 @@ which means, if you have a tool `add`, your response, listing tools, looks like // List tools const { tools } = await client.listTools(); -const addTool = tools[0]; // Assuming the first tool is "add" -const subtractTool = tools[1]; // Assuming the second tool is "subtract" +const addTool = tools.find(tool => tool.name === "add"); +if (!addTool) throw new Error("The server did not advertise the add tool."); // Call a tool const result = await client.callTool({ @@ -414,14 +416,10 @@ const result = await client.callTool({ **Time Beetle**: "Resources are similar, but you need to provide the resource URI and any parameters it requires. Here's an example: ```typescript -let resourceUri = "greeting://John"; // Replace with the actual resource URI - -// call resource -// Read a resource -const name = "John Doe"; // Replace with the actual name +const name = "John Doe"; const resourceResult = await client.readResource({ - uri: `greeting:///${name}`, + uri: `greeting://${encodeURIComponent(name)}`, }); console.log(resourceResult); // Hi John Doe! diff --git a/lessons/07-mcp/code/build/client.js b/lessons/07-mcp/code/build/client.js index 64280b9a..f79e17a7 100644 --- a/lessons/07-mcp/code/build/client.js +++ b/lessons/07-mcp/code/build/client.js @@ -1,82 +1,69 @@ import { Client } from "@modelcontextprotocol/sdk/client/index.js"; -import { CallToolResultSchema } from "@modelcontextprotocol/sdk/types.js"; import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js"; -import { OpenAI } from 'openai'; -const transport = new StdioClientTransport({ - command: "node", - args: ["build/index.js"] -}); -const client = new Client({ - name: "example-client", - version: "1.0.0" -}); -await client.connect(transport); -function toToolSchema(method, schema) { - return { - name: method, - description: `This is a tool that does ${method}`, - parameters: schema, - }; +import { CallToolResultSchema } from "@modelcontextprotocol/sdk/types.js"; +import { fileURLToPath } from "node:url"; +import { OpenAI } from "openai"; +import { z } from "zod"; +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); } -// list tools -const { tools } = await client.listTools(); -tools.forEach((tool) => { - console.log(`Tool: ${tool.name}`); - console.log(`Description: ${tool.description}`); - console.log(`Input schema: ${JSON.stringify(tool.inputSchema)}`); -}); -const toolsForLLM = tools.map((tool) => { - return toToolSchema(tool.name, tool.inputSchema); -}); -console.log("Schema for tool", JSON.stringify(toolsForLLM, null, 2)); -// TODO feed this to an llm as functions -const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", // might need to change to this url in the future: https://models.github.ai/inference - apiKey: process.env.GITHUB_TOKEN, +const openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); +const client = new Client({ name: "example-client", version: "1.0.0" }); +const transport = new StdioClientTransport({ + command: process.execPath, + args: [fileURLToPath(new URL("./index.js", import.meta.url))] }); -const messages = [ - { - role: "system", - content: `You are a helpful assistant. You can call functions to perform tasks. Make sure to parse the function call and arguments correctly.` - }, { - role: "user", - content: "Add 5 and 10", - name: "example-user" // Adding the required 'name' property for user role +try { + await client.connect(transport); + const { tools: serverTools } = await client.listTools(); + const allowedNames = new Set(serverTools.map(tool => tool.name)); + const tools = serverTools.map(tool => ({ + type: "function", + function: { + name: tool.name, + description: tool.description, + parameters: tool.inputSchema + } + })); + console.log("Available tools:", [...allowedNames]); + const messages = [ + { role: "user", content: "Use the add tool to add 5 and 10." } + ]; + const completion = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "required" + }); + const message = completion.choices[0]?.message; + if (!message?.tool_calls?.length) + throw new Error("The model did not request a tool."); + messages.push(message); + for (const call of message.tool_calls) { + if (call.type !== "function" || !allowedNames.has(call.function.name)) { + throw new Error("The model requested an unsupported MCP tool."); + } + const args = z.record(z.string(), z.unknown()).parse(JSON.parse(call.function.arguments)); + const result = CallToolResultSchema.parse(await client.callTool({ + name: call.function.name, + arguments: args + })); + const text = result.content.filter(item => item.type === "text").map(item => item.text).join("\n"); + if (result.isError) + throw new Error(`MCP tool failed: ${text}`); + if (!text) + throw new Error("The MCP tool did not return text."); + console.log("Result from tool:", text); + messages.push({ role: "tool", tool_call_id: call.id, content: text }); } -]; -openai.chat.completions.create({ - model: 'gpt-4o', - messages: messages, - functions: toolsForLLM -}).then((result) => { - console.log("Result", result.choices[0].message); -}).catch((error) => { - console.error("Error:", error); -}); -const result = await openai.chat.completions.create({ - model: 'gpt-4o', - messages: messages, - functions: toolsForLLM -}); -for (const choice of result.choices) { - // console.log("Result", choice.message); - let functionCall = choice.message?.function_call; - let functionName = functionCall?.name; - console.log("Function call: ", functionName); - let args = functionCall?.arguments; - await client.callTool({ - name: functionName ?? "", - arguments: typeof args === "string" ? JSON.parse(args) : args ?? {} - }, CallToolResultSchema).then((result) => { - console.log("Result from tool: ", result); + const followUp = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "none" }); + const answer = followUp.choices[0]?.message?.content; + if (!answer?.trim()) + throw new Error("The model did not return a final answer."); + console.log(answer); +} +finally { + await client.close(); } -// call tool -// const result = await client.callTool({ -// name: "add", -// arguments: { -// a: 5, -// b: 10 -// } -// }); -// console.log(`Result: ${JSON.stringify(result)}`); diff --git a/lessons/07-mcp/code/build/index-with-llm.js b/lessons/07-mcp/code/build/index-with-llm.js deleted file mode 100644 index cb0ff5c3..00000000 --- a/lessons/07-mcp/code/build/index-with-llm.js +++ /dev/null @@ -1 +0,0 @@ -export {}; diff --git a/lessons/07-mcp/code/build/index.js b/lessons/07-mcp/code/build/index.js index aaac24cc..713ab651 100755 --- a/lessons/07-mcp/code/build/index.js +++ b/lessons/07-mcp/code/build/index.js @@ -1,38 +1,17 @@ -// simple mc server import { McpServer, ResourceTemplate } from "@modelcontextprotocol/sdk/server/mcp.js"; import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; import { z } from "zod"; -// Create an MCP server -const server = new McpServer({ - name: "Demo", - version: "1.0.0" -}); -// Add an addition tool -// @ts-ignore -server.tool("add", { - input: z.object({ - a: z.number(), - b: z.number() - }), - output: z.object({ - content: z.array(z.object({ type: z.string(), text: z.string() })) - }) -}, async ({ input }) => ({ - content: [{ type: "text", text: String(input.a + input.b) }] +const server = new McpServer({ name: "Demo", version: "1.0.0" }); +server.registerTool("add", { + description: "Add two numbers", + inputSchema: { a: z.number(), b: z.number() } +}, async ({ a, b }) => ({ + content: [{ type: "text", text: String(a + b) }] })); -// Add a dynamic greeting resource -server.resource("greeting", new ResourceTemplate("greeting://{name}", { list: undefined }), async (uri, { name }) => ({ - contents: [{ - uri: uri.href, - text: `Hello, ${name}!` - }] +server.registerResource("greeting", new ResourceTemplate("greeting://{name}", { list: undefined }), { description: "Greet a named person", mimeType: "text/plain" }, async (uri, { name }) => ({ + contents: [{ uri: uri.href, text: `Hello, ${name}!` }] })); -// Start receiving messages on stdin and sending messages on stdout -const transport = new StdioServerTransport(); -async function runServer() { - await server.connect(transport); -} -runServer().catch((err) => { - console.error("Error in server:", err); - process.exit(1); +server.connect(new StdioServerTransport()).catch(error => { + console.error("Error in server:", error instanceof Error ? error.message : String(error)); + process.exitCode = 1; }); diff --git a/lessons/07-mcp/code/package-lock.json 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}, "files": [ - "build" + "build" ], "dependencies": { - "@modelcontextprotocol/sdk": "^1.8.0", - "openai": "^4.91.1" + "@modelcontextprotocol/sdk": "^1.32.1", + "openai": "^7.28.0", + "zod": "^4.6.5" }, "devDependencies": { - "@types/node": "^22.13.17", - "typescript": "^5.8.2" + "@modelcontextprotocol/inspector": "^2.9.0", + "@types/node": "^24.19.1", + "typescript": "^7.0.2" } - } +} diff --git a/lessons/07-mcp/code/src/client.ts b/lessons/07-mcp/code/src/client.ts index 9c8a5bda..491a9c76 100644 --- a/lessons/07-mcp/code/src/client.ts +++ b/lessons/07-mcp/code/src/client.ts @@ -1,184 +1,67 @@ import { Client } from "@modelcontextprotocol/sdk/client/index.js"; -import { CallToolRequest, CallToolResultSchema } from "@modelcontextprotocol/sdk/types.js"; import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js"; -import { OpenAI } from 'openai'; +import { CallToolResultSchema } from "@modelcontextprotocol/sdk/types.js"; +import { fileURLToPath } from "node:url"; +import { OpenAI } from "openai"; import { z } from "zod"; -type ChatCompletionMessageParam = { - role: "system" | "user" | "assistant"; - content: string; - name?: string; - function_call?: { - name: string; - arguments: string; - }; -}; - -const transport = new StdioClientTransport({ - command: "node", - args: ["build/index.js"] -}); - -const client = new Client( - { - name: "example-client", - version: "1.0.0" - } -); - -await client.connect(transport); - -function toToolSchema(method, schema) { - return { - name: method, - description: `This is a tool that does ${method}`, - parameters: schema, - }; -} - -// list tools -const { tools } = await client.listTools(); -tools.forEach((tool) => { - console.log(`Tool: ${tool.name}`); - console.log(`Description: ${tool.description}`); - console.log(`Input schema: ${JSON.stringify(tool.inputSchema)}`); +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); } -); - -const toolsForLLM = tools.map((tool) => { - return toToolSchema(tool.name, tool.inputSchema); -}); -console.log("Schema for tool", JSON.stringify(toolsForLLM, null, 2)); - -// TODO feed this to an llm as functions - -const openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", // might need to change to this url in the future: https://models.github.ai/inference - apiKey: process.env.GITHUB_TOKEN, -}); - -const messages: ChatCompletionMessageParam[] = [ - { - role: "system", - content: `You are a helpful assistant. You can call functions to perform tasks. Make sure to parse the function call and arguments correctly.` - }, { - role: "user", - content: "Add 5 and 10", - name: "example-user" // Adding the required 'name' property for user role - } -]; - -openai.chat.completions.create({ - model: 'gpt-4o', - messages: messages, - functions: toolsForLLM -}).then((result) => { - console.log("Result", result.choices[0].message); -}).catch((error) => { - console.error("Error:", error); -}) - -const result = await openai.chat.completions.create({ - model: 'gpt-4o', - messages: messages, - functions: toolsForLLM +const openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); +const client = new Client({ name: "example-client", version: "1.0.0" }); +const transport = new StdioClientTransport({ + command: process.execPath, + args: [fileURLToPath(new URL("./index.js", import.meta.url))] }); -for (const choice of result.choices) { - // console.log("Result", choice.message); - - let functionCall = choice.message?.function_call; - let functionName = functionCall?.name; - console.log("Function call: ", functionName); - let args = functionCall?.arguments; - - - // ALT1 - // 1. create a request - const request: CallToolRequest = { - method: "tools/call", - params: { - name: functionName ?? "", - arguments: args - } - }; - - function createRequest(name: string, args: any): CallToolRequest { - return { - method: "tools/call", - params: { - name, - arguments: args - } - }; - } - - function callTool>(name: string, args: any, schema: T): Promise> { - let request = createRequest(name ?? "", args); - - return client.request( - request, - schema - ); +try { + await client.connect(transport); + const { tools: serverTools } = await client.listTools(); + const allowedNames = new Set(serverTools.map(tool => tool.name)); + const tools: OpenAI.Chat.Completions.ChatCompletionTool[] = serverTools.map(tool => ({ + type: "function", + function: { + name: tool.name, + description: tool.description, + parameters: tool.inputSchema } - - callTool("add", args, CallToolResultSchema).then((result) => { - console.log("Result from tool: ", result.content[0].text); - return result; // Ensure the callback returns the result - }); - - // 2. make the request and cast the result to the correct type - client.request(request, CallToolResultSchema).then((result) => { - console.log("Result from tool: ", result.content[0].text); - }); - - // ALT2 - await client.callTool({ - name: functionName ?? "", - arguments: typeof args === "string" ? JSON.parse(args) : args ?? {} - }).then((result) => { - const toolResult = result as z.infer; - - console.log("Converted Result from tool: ", toolResult.content[0].text); - }) - - - - - - const AddRequest = { - name: "add", - arguments: { - a: 1, - b: 2 + })); + console.log("Available tools:", [...allowedNames]); + const messages: OpenAI.Chat.Completions.ChatCompletionMessageParam[] = [ + { role: "user", content: "Use the add tool to add 5 and 10." } + ]; + const completion = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "required" + }); + const message = completion.choices[0]?.message; + if (!message?.tool_calls?.length) throw new Error("The model did not request a tool."); + messages.push(message); + for (const call of message.tool_calls) { + if (call.type !== "function" || !allowedNames.has(call.function.name)) { + throw new Error("The model requested an unsupported MCP tool."); } - }; - - // Alt 3, request schema AND reply schema - await client.callTool(AddRequest, CallToolResultSchema) - .then((result) => { - console.log("Result from tool: ", result.content[0].text); + const args = z.record(z.string(), z.unknown()).parse(JSON.parse(call.function.arguments)); + const result = CallToolResultSchema.parse(await client.callTool({ + name: call.function.name, + arguments: args + })); + const text = result.content.filter(item => item.type === "text").map(item => item.text).join("\n"); + if (result.isError) throw new Error(`MCP tool failed: ${text}`); + if (!text) throw new Error("The MCP tool did not return text."); + console.log("Result from tool:", text); + messages.push({ role: "tool", tool_call_id: call.id, content: text }); + } + const followUp = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "none" }); - - - // await client.callTool({ - // name: functionName?? "", - // arguments: typeof args === "string" ? JSON.parse(args) : args ?? {} - // }, CallToolResultSchema ).then((result) => { - // console.log("Result from tool: ", result); - // // TODO: figure out how to cast result to the correct type, i.e { content: [ { type: 'string', text: 'string' } ] } - // // Result from tool: { content: [ { type: 'text', text: '15' } ] }; - // }); + const answer = followUp.choices[0]?.message?.content; + if (!answer?.trim()) throw new Error("The model did not return a final answer."); + console.log(answer); +} finally { + await client.close(); } - - -// call tool -// const result = await client.callTool({ -// name: "add", -// arguments: { -// a: 5, -// b: 10 -// } -// }); -// console.log(`Result: ${JSON.stringify(result)}`); diff --git a/lessons/07-mcp/code/src/index-with-llm.ts b/lessons/07-mcp/code/src/index-with-llm.ts deleted file mode 100644 index 6ac111eb..00000000 --- a/lessons/07-mcp/code/src/index-with-llm.ts +++ /dev/null @@ -1 +0,0 @@ -git s \ No newline at end of file diff --git a/lessons/07-mcp/code/src/index.ts b/lessons/07-mcp/code/src/index.ts index 6e0a6381..3239cb61 100644 --- a/lessons/07-mcp/code/src/index.ts +++ b/lessons/07-mcp/code/src/index.ts @@ -1,54 +1,24 @@ -// simple mc server - import { McpServer, ResourceTemplate } from "@modelcontextprotocol/sdk/server/mcp.js"; import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; import { z } from "zod"; -// Create an MCP server -const server = new McpServer({ - name: "Demo", - version: "1.0.0" -}); - -// Add an addition tool -// @ts-ignore -server.tool( - "add", - { - input: z.object({ - a: z.number(), - b: z.number() - }), - output: z.object({ - content: z.array(z.object({ type: z.string(), text: z.string() })) - }) - }, - async ({ input }) => ({ - content: [{ type: "text", text: String(input.a + input.b) }] - }) -); +const server = new McpServer({ name: "Demo", version: "1.0.0" }); +server.registerTool("add", { + description: "Add two numbers", + inputSchema: { a: z.number(), b: z.number() } +}, async ({ a, b }) => ({ + content: [{ type: "text", text: String(a + b) }] +})); -// Add a dynamic greeting resource -server.resource( - "greeting", +server.registerResource("greeting", new ResourceTemplate("greeting://{name}", { list: undefined }), + { description: "Greet a named person", mimeType: "text/plain" }, async (uri, { name }) => ({ - contents: [{ - uri: uri.href, - text: `Hello, ${name}!` - }] + contents: [{ uri: uri.href, text: `Hello, ${name}!` }] }) ); -// Start receiving messages on stdin and sending messages on stdout -const transport = new StdioServerTransport(); - -async function runServer() { - await server.connect(transport); -} - -runServer().catch((err) => { - console.error("Error in server:", err); - process.exit(1); +server.connect(new StdioServerTransport()).catch(error => { + console.error("Error in server:", error instanceof Error ? error.message : String(error)); + process.exitCode = 1; }); - diff --git a/lessons/07-mcp/code/tsconfig.json b/lessons/07-mcp/code/tsconfig.json index ce7b85bc..a2c65a93 100644 --- a/lessons/07-mcp/code/tsconfig.json +++ b/lessons/07-mcp/code/tsconfig.json @@ -1,15 +1,15 @@ { "compilerOptions": { "target": "ES2022", - "module": "Node16", - "moduleResolution": "Node16", + "module": "NodeNext", + "moduleResolution": "NodeNext", "outDir": "./build", "rootDir": "./src", "strict": true, + "types": ["node"], "esModuleInterop": true, "skipLibCheck": true, - "forceConsistentCasingInFileNames": true, - "noImplicitAny": false + "forceConsistentCasingInFileNames": true }, "include": ["src/**/*"], "exclude": ["node_modules"] diff --git a/lessons/07-mcp/solution/build/client.js b/lessons/07-mcp/solution/build/client.js index 9dc0d422..8c57552a 100644 --- a/lessons/07-mcp/solution/build/client.js +++ b/lessons/07-mcp/solution/build/client.js @@ -1,24 +1,19 @@ import { Client } from "@modelcontextprotocol/sdk/client/index.js"; import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js"; +import { fileURLToPath } from "node:url"; +const client = new Client({ name: "example-client", version: "1.0.0" }); const transport = new StdioClientTransport({ - command: "node", - args: ["build/index.js"] + command: process.execPath, + args: [fileURLToPath(new URL("./index.js", import.meta.url))] }); -const client = new Client({ - name: "example-client", - version: "1.0.0" -}); -await client.connect(transport); -// List resources -const resources = await client.listResources(); -console.log("Available resources:", resources); -// read a resource -let resource = await client.readResource({ - uri: "scrolls://tactics.txt", -}); -console.log("Resource contents:", resource.contents); -// Read a resource -resource = await client.readResource({ - uri: "echo://hello", -}); -console.log("Resource contents:", resource.contents); +try { + await client.connect(transport); + console.log("Available resources:", await client.listResources()); + const scroll = await client.readResource({ uri: "scrolls://tactics.txt" }); + console.log("Resource contents:", scroll.contents); + const echo = await client.readResource({ uri: "echo://hello" }); + console.log("Resource contents:", echo.contents); +} +finally { + await client.close(); +} diff --git a/lessons/07-mcp/solution/build/index.js b/lessons/07-mcp/solution/build/index.js index abadf4f4..53481539 100644 --- a/lessons/07-mcp/solution/build/index.js +++ b/lessons/07-mcp/solution/build/index.js @@ -1,82 +1,41 @@ import { McpServer, ResourceTemplate } from "@modelcontextprotocol/sdk/server/mcp.js"; import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; import { z } from "zod"; -import { promises as fs } from "fs"; -import path from "path"; -// Create an MCP server -const server = new McpServer({ - name: "Demo", - version: "1.0.0" +import { readdir, readFile } from "node:fs/promises"; +import { fileURLToPath } from "node:url"; +const server = new McpServer({ name: "Demo", version: "1.0.0" }); +const inputSchema = { a: z.number(), b: z.number() }; +server.registerTool("add", { description: "Add two numbers", inputSchema }, async ({ a, b }) => ({ content: [{ type: "text", text: String(a + b) }] })); +server.registerTool("subtract", { description: "Subtract two numbers", inputSchema }, async ({ a, b }) => ({ content: [{ type: "text", text: String(a - b) }] })); +server.registerTool("multiply", { description: "Multiply two numbers", inputSchema }, async ({ a, b }) => ({ content: [{ type: "text", text: String(a * b) }] })); +server.registerTool("divide", { description: "Divide two numbers", inputSchema }, async ({ a, b }) => { + if (b === 0) { + return { isError: true, content: [{ type: "text", text: "Cannot divide by zero." }] }; + } + return { content: [{ type: "text", text: String(a / b) }] }; }); -// Add an addition tool -server.tool("add", { a: z.number(), b: z.number() }, async ({ a, b }) => ({ - content: [{ type: "text", text: String(a + b) }] -})); -server.tool("subtract", { a: z.number(), b: z.number() }, async ({ a, b }) => ({ - content: [{ type: "text", text: String(a - b) }] -})); -server.tool("divide", { a: z.number(), b: z.number() }, async ({ a, b }) => ({ - content: [{ type: "text", text: String(a / b) }] +server.registerResource("echo", new ResourceTemplate("echo://{message}", { + list: async () => ({ + resources: [{ name: "echo", description: "Echo a message", mimeType: "text/plain", uri: "echo://hello" }] + }) +}), { description: "Echo a message", mimeType: "text/plain" }, async (uri, { message }) => ({ + contents: [{ uri: uri.href, text: `Resource echo: ${message}` }] })); -server.tool("multiply", { a: z.number(), b: z.number() }, async ({ a, b }) => ({ - content: [{ type: "text", text: String(a * b) }] -})); -server.resource("echo", new ResourceTemplate("echo://{message}", { list: () => { - return { - resources: [ - { - "name": "echo", - "description": "Echo a message", - "mimeType": "text/plain", - "uri": "echo://{message}" - } - ] - }; - } }), async (uri, { message }) => ({ - contents: [{ - uri: uri.href, - text: `Resource echo: ${message}` - }] -})); -// Add a dynamic greeting resource -server.resource("scrolls", new ResourceTemplate("scrolls://{name}", { list: () => { - return { - resources: [ - { - "name": "scrolls", - "description": "Scrolls resource", - "mimeType": "text/plain", - "uri": "scrolls://{name}" - } - ] - }; - } }), async (uri, { name }) => { - // open file to check if it exist - let fileContent; - let filePath = path.join(process.cwd(), "scrolls", `${name}`); - try { - console.log("File path: ", filePath); - fileContent = await fs.readFile(filePath, "utf-8"); - } - catch (err) { - fileContent = `File ${name} not found in ${filePath}`; - console.error(fileContent); +const scrollsDirectory = new URL("../scrolls/", import.meta.url); +server.registerResource("scrolls", new ResourceTemplate("scrolls://{name}", { + list: async () => ({ + resources: (await readdir(scrollsDirectory)).filter(name => name.endsWith(".txt")).map(name => ({ + name, description: "A course scroll", mimeType: "text/plain", uri: `scrolls://${encodeURIComponent(name)}` + })) + }) +}), { description: "Read a course scroll", mimeType: "text/plain" }, async (uri, { name }) => { + if (typeof name !== "string" || !/^[a-zA-Z0-9_-]+\.txt$/.test(name)) { + throw new Error("Select a named .txt scroll from the resource list."); } - // response with file content - return { - contents: [{ - uri: uri.href, - text: fileContent - }] - }; + const text = await readFile(fileURLToPath(new URL(name, scrollsDirectory)), "utf8"); + return { contents: [{ uri: uri.href, text }] }; }); -// Start receiving messages on stdin and sending messages on stdout -async function main() { - const transport = new StdioServerTransport(); - await server.connect(transport); - console.error("MCPServer started on stdin/stdout"); -} -main().catch((error) => { - console.error("Fatal error: ", error); - process.exit(1); +server.connect(new StdioServerTransport()).catch(error => { + console.error("Error in server:", error instanceof Error ? error.message : String(error)); + process.exitCode = 1; }); diff --git a/lessons/07-mcp/solution/package-lock.json b/lessons/07-mcp/solution/package-lock.json index 01a32424..933e8210 100644 --- a/lessons/07-mcp/solution/package-lock.json +++ b/lessons/07-mcp/solution/package-lock.json @@ -5,81 +5,1273 @@ "packages": { "": { "dependencies": { - "@modelcontextprotocol/sdk": "^1.8.0", - "express": "^5.0.1", - "type": "^2.7.3", - "zod": "^3.24.2" + "@modelcontextprotocol/sdk": "^1.32.1", + "zod": "^4.6.5" }, "bin": { "weather": "build/index.js" }, "devDependencies": { - "@types/node": "^22.13.17", - "typescript": "^5.8.2" + "@modelcontextprotocol/inspector": "^2.9.0", + "@types/node": "^24.19.1", + "typescript": "^7.0.2" + } + }, + "node_modules/@alcalzone/ansi-tokenize": { + "version": "0.2.5", + "resolved": "https://registry.npmjs.org/@alcalzone/ansi-tokenize/-/ansi-tokenize-0.2.5.tgz", 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"license": "MIT", "funding": { "url": "https://github.com/sponsors/colinhacks" } }, "node_modules/zod-to-json-schema": { - "version": "3.24.5", - "resolved": "https://registry.npmjs.org/zod-to-json-schema/-/zod-to-json-schema-3.24.5.tgz", - "integrity": "sha512-/AuWwMP+YqiPbsJx5D6TfgRTc4kTLjsh5SOcd4bLsfUg2RcEXrFMJl1DGgdHy2aCfsIA/cr/1JM0xcB2GZji8g==", + "version": "3.25.2", + "resolved": "https://registry.npmjs.org/zod-to-json-schema/-/zod-to-json-schema-3.25.2.tgz", + "integrity": "sha512-O/PgfnpT1xKSDeQYSCfRI5Gy3hPf91mKVDuYLUHZJMiDFptvP41MSnWofm8dnCm0256ZNfZIM7DSzuSMAFnjHA==", "license": "ISC", "peerDependencies": { - "zod": "^3.24.1" + "zod": "^3.25.28 || ^4" } } } diff --git a/lessons/07-mcp/solution/package.json b/lessons/07-mcp/solution/package.json index 3b462dc4..0e35272e 100644 --- a/lessons/07-mcp/solution/package.json +++ b/lessons/07-mcp/solution/package.json @@ -1,24 +1,25 @@ { "type": "module", "bin": { - "weather": "./build/index.js" + "weather": "./build/index.js" }, "scripts": { - "client": "tsc && node ./build/client.js", - "build": "tsc && node ./build/index.js", - "inspect": "npx @modelcontextprotocol/inspector --cli node build/index.js --method tools/list" + "client": "npm run build && node ./build/client.js", + "build": "tsc", + "start": "node build/index.js", + "inspect": "npm run build && mcp-inspector --cli node build/index.js --method tools/list", + "inspect:web": "npm run build && mcp-inspector --web node build/index.js" }, "files": [ - "build" + "build" ], "dependencies": { - "@modelcontextprotocol/sdk": "^1.8.0", - "express": "^5.0.1", - "type": "^2.7.3", - "zod": "^3.24.2" - }, - "devDependencies": { - "@types/node": "^22.13.17", - "typescript": "^5.8.2" - } + "@modelcontextprotocol/sdk": "^1.32.1", + "zod": "^4.6.5" + }, + "devDependencies": { + "@modelcontextprotocol/inspector": "^2.9.0", + "@types/node": "^24.19.1", + "typescript": "^7.0.2" + } } diff --git a/lessons/07-mcp/solution/solution-quiz.md b/lessons/07-mcp/solution/solution-quiz.md index a8661ce7..4ab8d96c 100644 --- a/lessons/07-mcp/solution/solution-quiz.md +++ b/lessons/07-mcp/solution/solution-quiz.md @@ -19,6 +19,3 @@ * **E. All of the above**

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- - - diff --git a/lessons/07-mcp/solution/src/client.ts b/lessons/07-mcp/solution/src/client.ts index 77584f0c..f84d78df 100644 --- a/lessons/07-mcp/solution/src/client.ts +++ b/lessons/07-mcp/solution/src/client.ts @@ -1,36 +1,20 @@ import { Client } from "@modelcontextprotocol/sdk/client/index.js"; import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js"; +import { fileURLToPath } from "node:url"; +const client = new Client({ name: "example-client", version: "1.0.0" }); const transport = new StdioClientTransport({ - command: "node", - args: ["build/index.js"] + command: process.execPath, + args: [fileURLToPath(new URL("./index.js", import.meta.url))] }); -const client = new Client( - { - name: "example-client", - version: "1.0.0" - } -); - -await client.connect(transport); - - -// List resources -const resources = await client.listResources(); -console.log("Available resources:", resources); - -// read a resource -let resource = await client.readResource({ - uri: "scrolls://tactics.txt", -}); - -console.log("Resource contents:", resource.contents); - -// Read a resource -resource = await client.readResource({ - uri: "echo://hello", -}); - -console.log("Resource contents:", resource.contents); - +try { + await client.connect(transport); + console.log("Available resources:", await client.listResources()); + const scroll = await client.readResource({ uri: "scrolls://tactics.txt" }); + console.log("Resource contents:", scroll.contents); + const echo = await client.readResource({ uri: "echo://hello" }); + console.log("Resource contents:", echo.contents); +} finally { + await client.close(); +} diff --git a/lessons/07-mcp/solution/src/index.ts b/lessons/07-mcp/solution/src/index.ts index 09cf814a..471becdc 100644 --- a/lessons/07-mcp/solution/src/index.ts +++ b/lessons/07-mcp/solution/src/index.ts @@ -1,114 +1,56 @@ import { McpServer, ResourceTemplate } from "@modelcontextprotocol/sdk/server/mcp.js"; import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; import { z } from "zod"; -import { promises as fs } from "fs"; -import path from "path"; - - -// Create an MCP server -const server = new McpServer({ - name: "Demo", - version: "1.0.0" +import { readdir, readFile } from "node:fs/promises"; +import { fileURLToPath } from "node:url"; + +const server = new McpServer({ name: "Demo", version: "1.0.0" }); +const inputSchema = { a: z.number(), b: z.number() }; +server.registerTool("add", { description: "Add two numbers", inputSchema }, + async ({ a, b }) => ({ content: [{ type: "text", text: String(a + b) }] })); +server.registerTool("subtract", { description: "Subtract two numbers", inputSchema }, + async ({ a, b }) => ({ content: [{ type: "text", text: String(a - b) }] })); +server.registerTool("multiply", { description: "Multiply two numbers", inputSchema }, + async ({ a, b }) => ({ content: [{ type: "text", text: String(a * b) }] })); +server.registerTool("divide", { description: "Divide two numbers", inputSchema }, async ({ a, b }) => { + if (b === 0) { + return { isError: true, content: [{ type: "text", text: "Cannot divide by zero." }] }; + } + return { content: [{ type: "text", text: String(a / b) }] }; }); -// Add an addition tool -server.tool("add", - { a: z.number(), b: z.number() }, - async ({ a, b }) => ({ - content: [{ type: "text", text: String(a + b) }] - }) -); - -server.tool("subtract", - { a: z.number(), b: z.number() }, - async ({ a, b }) => ({ - content: [{ type: "text", text: String(a - b) }] - }) -); - -server.tool("divide", - { a: z.number(), b: z.number() }, - async ({ a, b }) => ({ - content: [{ type: "text", text: String(a / b) }] - }) -); - -server.tool("multiply", - { a: z.number(), b: z.number() }, - async ({ a, b }) => ({ - content: [{ type: "text", text: String(a * b) }] - }) -); - -server.resource( - "echo", - new ResourceTemplate("echo://{message}", { list: () => { - return { - resources: [ - { - "name": "echo", - "description": "Echo a message", - "mimeType": "text/plain", - "uri": "echo://{message}" - } - ] - }; - } }), +server.registerResource("echo", + new ResourceTemplate("echo://{message}", { + list: async () => ({ + resources: [{ name: "echo", description: "Echo a message", mimeType: "text/plain", uri: "echo://hello" }] + }) + }), + { description: "Echo a message", mimeType: "text/plain" }, async (uri, { message }) => ({ - contents: [{ - uri: uri.href, - text: `Resource echo: ${message}` - }] + contents: [{ uri: uri.href, text: `Resource echo: ${message}` }] }) ); -// Add a dynamic greeting resource -server.resource( - "scrolls", - new ResourceTemplate("scrolls://{name}", { list: () => { - return { - resources: [ - { - "name": "scrolls", - "description": "Scrolls resource", - "mimeType": "text/plain", - "uri": "scrolls://{name}" - } - ] - }; - }}), +const scrollsDirectory = new URL("../scrolls/", import.meta.url); +server.registerResource("scrolls", + new ResourceTemplate("scrolls://{name}", { + list: async () => ({ + resources: (await readdir(scrollsDirectory)).filter(name => name.endsWith(".txt")).map(name => ({ + name, description: "A course scroll", mimeType: "text/plain", uri: `scrolls://${encodeURIComponent(name)}` + })) + }) + }), + { description: "Read a course scroll", mimeType: "text/plain" }, async (uri, { name }) => { - // open file to check if it exist - let fileContent: string; - let filePath = path.join(process.cwd(), "scrolls", `${name}`); - try { - console.log("File path: ", filePath); - - fileContent = await fs.readFile(filePath, "utf-8"); - } catch (err) { - fileContent = `File ${name} not found in ${filePath}`; - console.error(fileContent); - } - // response with file content - - return { - contents: [{ - uri: uri.href, - text: fileContent - }] + if (typeof name !== "string" || !/^[a-zA-Z0-9_-]+\.txt$/.test(name)) { + throw new Error("Select a named .txt scroll from the resource list."); } + const text = await readFile(fileURLToPath(new URL(name, scrollsDirectory)), "utf8"); + return { contents: [{ uri: uri.href, text }] }; } ); -// Start receiving messages on stdin and sending messages on stdout - -async function main() { - const transport = new StdioServerTransport(); - await server.connect(transport); - console.error("MCPServer started on stdin/stdout"); -} - -main().catch((error) => { - console.error("Fatal error: ", error); - process.exit(1); +server.connect(new StdioServerTransport()).catch(error => { + console.error("Error in server:", error instanceof Error ? error.message : String(error)); + process.exitCode = 1; }); diff --git a/lessons/07-mcp/solution/tsconfig.json b/lessons/07-mcp/solution/tsconfig.json index a14bee07..3230f00f 100644 --- a/lessons/07-mcp/solution/tsconfig.json +++ b/lessons/07-mcp/solution/tsconfig.json @@ -1,11 +1,12 @@ { "compilerOptions": { "target": "ES2022", - "module": "Node16", - "moduleResolution": "Node16", + "module": "NodeNext", + "moduleResolution": "NodeNext", "outDir": "./build", "rootDir": "./src", "strict": true, + "types": ["node"], "esModuleInterop": true, "skipLibCheck": true, "forceConsistentCasingInFileNames": true diff --git a/lessons/07-mcp/translations/README.fr.md b/lessons/07-mcp/translations/README.fr.md index 304d4e26..5d27b70f 100644 --- a/lessons/07-mcp/translations/README.fr.md +++ b/lessons/07-mcp/translations/README.fr.md @@ -65,14 +65,14 @@ Si vous souhaitez interagir avec Scipion, exécutez l'application [Characters](/ 1. Lancez un [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript). 2. Naviguez vers */app* à la racine du dépôt. -3. Localisez la console et exécutez `npm install` suivi de `npm start`. +3. Localisez la console et exécutez `npm ci` suivi de `npm start`. 4. Une fois que l'application est ouverte, sélectionnez le bouton "Ouvrir dans le navigateur". 5. Discuter avec Scipion. Pour une explication plus détaillée de l'application, voir [Explication détaillée de l'application](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] -> Si vous exécutez le projet localement sur votre machine, veuillez consulter le guide de démarrage rapide pour configurer un [token d'accès personnel GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) et remplacer la clé dans le code. +> Configurez `AI_ENDPOINT`, `AI_API_KEY` et `AI_MODEL` dans le fichier `.env` à la racine du dépôt, en local comme dans Codespaces. Consultez le [guide de configuration](/docs/setup/README.md#configure-environment-variables). ## Le besoin du Protocole de Contexte Modèle (MCP) @@ -172,8 +172,8 @@ Le code précédent effectue les actions suivantes : **Time Beetle** : "Non, pas encore. Mais nous pouvons y ajouter des ressources et des outils. Un outil est quelque chose qui peut être utilisé pour accomplir une tâche spécifique, tandis qu'une ressource est quelque chose qui peut fournir des données ou une fonctionnalité à l'outil. Ajoutons-en quelques exemples ci-dessous : ```typescript -server.tool("add", - { a: z.number(), b: z.number() }, +server.registerTool("add", + { description: "Add two numbers", inputSchema: { a: z.number(), b: z.number() } }, async ({ a, b }) => ({ content: [{ type: "text", text: String(a + b) }] }) @@ -191,9 +191,10 @@ Dans le code précédent, nous : **Time Beetle** : "Exactement ! Le client MCP peut appeler cet outil et fournir les paramètres requis. Ajoutons également une ressource au serveur : ```typescript -server.resource( +server.registerResource( "greeting", new ResourceTemplate("greeting://{name}", { list: undefined }), + { mimeType: "text/plain" }, async (uri, { name }) => ({ contents: [{ uri: uri.href, @@ -212,9 +213,10 @@ Dans ce code, nous : **Vous** : "Donc, la ressource est comme une source de données, cela pourrait être une base de données, un fichier ou même une API ? Si c'était pour un fichier, j'utiliserais un URI file:// ? Comme ceci : ```typescript -server.resource( +server.registerResource( "file", new ResourceTemplate("file://{path}", { list: undefined }), + { mimeType: "text/plain" }, async (uri, { path }) => ({ // do something with the file at path, e.g., read its contents contents: [{ @@ -253,14 +255,14 @@ Dans ce code, nous : **Time Beetle** : "Vous pouvez tester votre serveur MCP en l'exécutant dans un terminal via l'inspecteur comme suit : ```bash -npx @modelcontextprotocol/inspector node build/index.js +npm run inspect:web ``` Ici, nous utilisons le package `@modelcontextprotocol/inspector` pour exécuter le serveur MCP. Nous fournissons l'argument `node build/index.js` pour spécifier le point d'entrée du serveur. Cela démarrera le serveur MCP et vous permettra d'interagir avec lui via l'inspecteur. **Vous** : "Et comment puis-je interagir avec le serveur ?" -**Time Beetle** : "Cela démarre un serveur web sur le port 6274. Vous pouvez accéder à l'inspecteur en ouvrant votre navigateur web et en vous rendant sur `http://localhost:6274`. L'inspecteur fournit une interface conviviale pour interagir avec votre serveur MCP, vous permettant de tester les outils et les ressources que vous avez définis." +**Time Beetle** : "Exécutez cette commande dans le dossier du package de la leçon. Elle compile le serveur et lance l'inspecteur web actuel. Ouvrez l'adresse locale affichée dans le terminal. L'interface peut différer des anciennes images ci-dessous. Gardez les ports de l'inspecteur privés." ![Interface utilisateur de l'inspecteur](https://softchris.github.io/mcp-workshop/assets/images/connect-7703c67645f368d51c7b24a5d635d6a0.png) @@ -348,7 +350,7 @@ Ainsi, lorsque vous listez les outils, vous obtenez une réponse au format suiva { "name": "", - "description": "". + "description": "", "inputSchema": { "type":"object", "properties":{ @@ -370,7 +372,7 @@ ce qui signifie que, si vous avez un outil `add`, votre réponse, en listant les ```json { "name": "add", - "description": "Adding two numbers". + "description": "Adding two numbers", "inputSchema": { "type":"object", "properties":{ @@ -395,8 +397,8 @@ ce qui signifie que, si vous avez un outil `add`, votre réponse, en listant les // List tools const { tools } = await client.listTools(); -const addTool = tools[0]; // Assuming the first tool is "add" -const subtractTool = tools[1]; // Assuming the second tool is "subtract" +const addTool = tools.find(tool => tool.name === "add"); +if (!addTool) throw new Error("The server did not advertise the add tool."); // Call a tool const result = await client.callTool({ @@ -413,14 +415,10 @@ const result = await client.callTool({ **Time Beetle** : "Les ressources sont similaires, mais vous devez fournir l'URI de la ressource et les paramètres qu'elle nécessite. Voici un exemple : ```typescript -let resourceUri = "greeting://John"; // Replace with the actual resource URI - -// call resource -// Read a resource -const name = "John Doe"; // Replace with the actual name +const name = "John Doe"; const resourceResult = await client.readResource({ - uri: `greeting:///${name}`, + uri: `greeting://${encodeURIComponent(name)}`, }); console.log(resourceResult); // Hi John Doe! diff --git a/lessons/08-mcp-advanced/README.md b/lessons/08-mcp-advanced/README.md index 856de261..1ec7f8f8 100644 --- a/lessons/08-mcp-advanced/README.md +++ b/lessons/08-mcp-advanced/README.md @@ -92,14 +92,14 @@ If you want to interact with Hedy, run the [Characters](/app/README.md) app. 1. Start a [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript) 2. Navigate to _/app_ in the repo root. -3. Locate the console and run `npm install` followed by `npm start`. +3. Locate the console and run `npm ci` followed by `npm start`. 4. Once it appears, select the "Open in Browser" button. 5. Chat with Hedy. For a more detailed explanation of the app, see [Detailed app explanation](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] - > If you're running the project locally on your machine, please review the QuickStart guide to get a [GitHub personal access](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) token setup and replace the key in the code. +> Configure `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL` in the repository-root `.env` for both local development and Codespaces. See the [setup guide](/docs/setup/README.md#configure-environment-variables). ## Adding a Large Language Model to a Client @@ -133,76 +133,76 @@ Here's all the steps in code: ```typescript import { Client } from "@modelcontextprotocol/sdk/client/index.js"; import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js"; +import { CallToolResultSchema } from "@modelcontextprotocol/sdk/types.js"; +import { fileURLToPath } from "node:url"; +import { OpenAI } from "openai"; +import { z } from "zod"; + +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} - -// create client instance +const openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); +const client = new Client({ name: "example-client", version: "1.0.0" }); const transport = new StdioClientTransport({ - command: "node", - args: ["server.js"] + command: process.execPath, + args: [fileURLToPath(new URL("./index.js", import.meta.url))] }); -const client = new Client( - { - name: "example-client", - version: "1.0.0" +try { + await client.connect(transport); + const { tools: serverTools } = await client.listTools(); + const allowedNames = new Set(serverTools.map(tool => tool.name)); + const tools: OpenAI.Chat.Completions.ChatCompletionTool[] = serverTools.map(tool => ({ + type: "function", + function: { + name: tool.name, + description: tool.description, + parameters: tool.inputSchema + } + })); + console.log("Available tools:", [...allowedNames]); + const messages: OpenAI.Chat.Completions.ChatCompletionMessageParam[] = [ + { role: "user", content: "Use the add tool to add 5 and 10." } + ]; + const completion = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "required" + }); + const message = completion.choices[0]?.message; + if (!message?.tool_calls?.length) throw new Error("The model did not request a tool."); + messages.push(message); + for (const call of message.tool_calls) { + if (call.type !== "function" || !allowedNames.has(call.function.name)) { + throw new Error("The model requested an unsupported MCP tool."); + } + const args = z.record(z.string(), z.unknown()).parse(JSON.parse(call.function.arguments)); + const result = CallToolResultSchema.parse(await client.callTool({ + name: call.function.name, + arguments: args + })); + const text = result.content.filter(item => item.type === "text").map(item => item.text).join("\n"); + if (result.isError) throw new Error(`MCP tool failed: ${text}`); + if (!text) throw new Error("The MCP tool did not return text."); + console.log("Result from tool:", text); + messages.push({ role: "tool", tool_call_id: call.id, content: text }); } -); - -await client.connect(transport); - -// 1. make call to server, ask it for tools -const { tools } = await client.listTools(); - -// convert function -function toToolSchema(method, schema) { - return { - name: method, - description: `This is a tool that does ${method}`, - parameters: schema, - }; + const followUp = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "none" + }); + const answer = followUp.choices[0]?.message?.content; + if (!answer?.trim()) throw new Error("The model did not return a final answer."); + console.log(answer); +} finally { + await client.close(); } - -// 2. convert the tools and resources response to a tools schema -const toolsForLLM = tools.map((tool) => { - return toToolSchema(tool.method, tool.inputSchema); -}); - -// 3. instantiate openai client -const openai = new OpenAI({ - apiKey: process.env.OPENAI_API_KEY, - model: "gpt-3.5-turbo", - temperature: 0.7, -}); - -// 4. make chat completion call to openai -const response = await openai.chat.completions.create({ - messages: [ - { - role: "user", - content: `I want to add 5 and 10. Please use the tool ${toolsForLLM}`, - }, - ], - functions: toolsForLLM, - function_call: "auto", -}); - -// 5. figure out what tool to call based on the response from openai -const toolName = response.choices[0].message.function_call.name; // add -const args = response.choices[0].message.function_call.arguments; // { a: 5, b: 10 } - -// 6. call the tool on the server -const result = await client.callTool({ - name: toolName, - arguments: args, -}); - -// 7. respond to user -console.log(result); // 15 ``` In the preceding code we (focusing on our additions): -- Created a function `toToolSchema` that converts the tools and resources response to a schema that can be used by the LLM. +- Created a function `tools.map` that converts the tools and resources response to a schema that can be used by the LLM. - Asked the server for available tools and resources. - Converted the tools and resources response to a schema that can be used by the LLM. - Made a chat completion call to our AI, passing the converted tools as a parameter. @@ -235,7 +235,7 @@ In the preceding code we (focusing on our additions): ## Solution -[Solution](/lessons/08-mcp-advanced/solution/README.md) +[Solution](/lessons/08-mcp-advanced/solutions/README.md) ## Knowledge Check @@ -247,7 +247,7 @@ B. It creates a more natural conversation between the user and the server. C. It's better to have the LLM on the server. -[Solution quiz](/lessons/08-mcp-advanced/solution/solution-quiz.md) +[Solution quiz](/lessons/08-mcp-advanced/solutions/solution-quiz.md) ## Summary diff --git a/lessons/08-mcp-advanced/solutions/README.md b/lessons/08-mcp-advanced/solutions/README.md index 63b74a4d..49742760 100644 --- a/lessons/08-mcp-advanced/solutions/README.md +++ b/lessons/08-mcp-advanced/solutions/README.md @@ -1,84 +1,49 @@ +# Run the Advanced MCP Solution -# Running the solution - -In this chapter you will learn: - -- How to use the Model Context Protocol, MCP to split up your server capabilities in a server and a client -- Create tools, resources and prompts on the server. -- Consume the server using both the inspector tool and a written client. +This sample exposes `characterDetails` and `place` tools that retrieve Wikipedia summaries. The client converts MCP tool schemas to OpenAI function tools, awaits each call, sends the results back to the model, and prints its final answer. ## Setup -If you haven't already, set up your development environment. Here's how you can do it: [Setup your environment](/docs/setup/README.md). - -## Related resources - -[![Watch a short video about MCP](https://img.youtube.com/vi/YRfOiB0Im64/0.jpg)](https://www.youtube.com/watch?v=YRfOiB0Im64) +Use Node.js LTS. Configure `AI_ENDPOINT`, `AI_API_KEY`, and `AI_MODEL` in the repository-root `.env` as described in the [course setup guide](/docs/setup/README.md). -_This video explains Model Context Protocol._ +From `lessons/08-mcp-advanced/solutions`: -*🎥 Click on the image above to watch a short video about MCP* - -## Install - -```sh -npm i +```bash +npm ci +npm run build ``` -## Run the sample - -1. Build the server +`build` only compiles the TypeScript. To run the server by itself, use `npm start`. It communicates over stdin/stdout; diagnostic messages must go to stderr. - ```sh - npm run build - ``` +## Inspect the Tools - This compiles and runs the server, once you see the text "MCPServer started on stdin/stdout" you can type Ctrl-C to take it down. - -## Test the sample - -It's a good idea to check if your features are working as intended. Try below command to quickly check your server responds. - - 1. List all tools on the server like so: +```bash +npm run inspect +npm run tool +``` - ```sh - npx @modelcontextprotocol/inspector --cli node build/index.js --method tools/call --tool-name characterDetails --tool-arg name=Ada - ``` +The first command lists the tools. The second calls `characterDetails` for Ada Lovelace. Both commands build the server first. Wikipedia must be reachable. - You should see response similar to: +For the web inspector: - ```json - { - "content": [ - { - "type": "text", - "text": "Character: ...response from API" - } - ] - } - ``` +```bash +npm run inspect:web +``` - Let's proceed to running the client. +Open the local address printed by the inspector and keep its ports private. -## Running the client +## Run the Model Client -1. Run the client +```bash +npm run client +``` - ```sh - npm run client - ``` +The script builds the client and loads the root `.env` when available. It prints the available tools, the Wikipedia result, and a final model response about Ada Lovelace. It closes the transport when finished. - You should see the following tool response: +For another environment file, build first and use Node's file option: - ```text - Tool result: { - content: [ - { - type: 'text', - text: "Character: Augusta Ada King, Countess of Lovelace, also known as Ada Lovelace, was an English mathematician and writer chiefly known for her work on Charles Babbage's proposed mechanical general-purpose computer, the Analytical Engine. She was the first to recognise that the machine had applications beyond pure calculation." - } - ] - } - ``` +```bash +node --env-file="$HOME/.env" build/client.js +``` - Great, it all works as intended. +The sample only dispatches tools advertised by its own MCP server. Invalid arguments, tool failures, and missing model responses produce explicit errors. diff --git a/lessons/08-mcp-advanced/solutions/build/client.js b/lessons/08-mcp-advanced/solutions/build/client.js index 8e2e091c..ca910173 100644 --- a/lessons/08-mcp-advanced/solutions/build/client.js +++ b/lessons/08-mcp-advanced/solutions/build/client.js @@ -1,100 +1,98 @@ import { Client } from "@modelcontextprotocol/sdk/client/index.js"; import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js"; +import { CallToolResultSchema } from "@modelcontextprotocol/sdk/types.js"; +import { fileURLToPath } from "node:url"; +import path from "node:path"; import OpenAI from "openai"; -import { z } from "zod"; // Import zod for schema validation -class MyClient { +import { z } from "zod"; +export class MyClient { openai; client; + model; constructor() { - this.openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", // might need to change to this url in the future: https://models.github.ai/inference - apiKey: process.env.GITHUB_TOKEN, - }); - this.client = new Client({ - name: "example-client", - version: "1.0.0" - }, { - capabilities: { - prompts: {}, - resources: {}, - tools: {} - } - }); + const endpoint = process.env.AI_ENDPOINT?.trim(); + const apiKey = process.env.AI_API_KEY?.trim(); + const model = process.env.AI_MODEL?.trim(); + if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); + } + this.model = model; + this.openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); + this.client = new Client({ name: "example-client", version: "1.0.0" }); } async connectToServer(transport) { - await this.client.connect(transport); - this.run(); - console.error("MCPClient started on stdin/stdout"); + try { + await this.client.connect(transport); + await this.run(); + } + finally { + await this.client.close(); + } } openAiToolAdapter(tool) { - // Create a zod schema based on the input_schema - const schema = z.object(tool.input_schema); return { - type: "function", // Explicitly set type to "function" + type: "function", function: { name: tool.name, description: tool.description, - parameters: { - type: "object", - properties: tool.input_schema.properties, - required: tool.input_schema.required, - }, - }, + parameters: tool.inputSchema + } }; } - async callTools(tool_calls, toolResults) { - for (const tool_call of tool_calls) { - const toolName = tool_call.function.name; - const args = tool_call.function.arguments; - console.log(`Calling tool ${toolName} with args ${JSON.stringify(args)}`); - // 2. Call the server's tool - const toolResult = await this.client.callTool({ - name: toolName, - arguments: JSON.parse(args), - }); - console.log("Tool result: ", toolResult); - // 3. Do something with the result - // TODO + async callTools(calls, allowedNames) { + const results = []; + for (const call of calls) { + if (call.type !== "function" || !allowedNames.has(call.function.name)) { + throw new Error("The model requested an unsupported MCP tool."); + } + const args = z.record(z.string(), z.unknown()).parse(JSON.parse(call.function.arguments)); + const result = CallToolResultSchema.parse(await this.client.callTool({ + name: call.function.name, + arguments: args + })); + const text = result.content.filter(item => item.type === "text").map(item => item.text).join("\n"); + if (result.isError) + throw new Error(`MCP tool failed: ${text}`); + if (!text) + throw new Error("The MCP tool did not return text."); + console.log(`Result from [${call.function.name}]:`, text); + results.push({ role: "tool", tool_call_id: call.id, content: text }); } + return results; } async run() { - console.log("Asking server for available tools"); - const toolsResult = await this.client.listTools(); - const tools = toolsResult.tools.map((tool) => { - return this.openAiToolAdapter({ - name: tool.name, - description: tool.description, - input_schema: tool.inputSchema, - }); - }); - const messages = [ - { + const { tools: serverTools } = await this.client.listTools(); + const allowedNames = new Set(serverTools.map(tool => tool.name)); + const tools = serverTools.map(tool => this.openAiToolAdapter(tool)); + console.log("Available tools:", [...allowedNames]); + const messages = [{ role: "user", - content: "Tell me about Ada Lovelace", - }, - ]; - console.log("Querying LLM: ", messages[0].content); - let response = await this.openai.chat.completions.create({ - model: "gpt-4o-mini", - max_tokens: 1000, - messages, - tools: tools, + content: "Use the characterDetails tool to tell me briefly about Ada Lovelace." + }]; + const completion = await this.openai.chat.completions.create({ + model: this.model, messages, tools, tool_choice: "required" }); - console.log("LLM response: ", response.choices[0].message.content); - let results = []; - // 1. Go through the LLM response,for each choice, check if it has tool calls - response.choices.map(async (choice) => { - const message = choice.message; - if (message.tool_calls) { - console.log("Making tool call"); - await this.callTools(message.tool_calls, results); - } + const message = completion.choices[0]?.message; + if (!message?.tool_calls?.length) + throw new Error("The model did not request a tool."); + messages.push(message, ...await this.callTools(message.tool_calls, allowedNames)); + const followUp = await this.openai.chat.completions.create({ + model: this.model, messages, tools, tool_choice: "none" }); + const answer = followUp.choices[0]?.message?.content; + if (!answer?.trim()) + throw new Error("The model did not return a final answer."); + console.log(answer); } } -let client = new MyClient(); -const transport = new StdioClientTransport({ - command: "node", - args: ["./build/index.js"] -}); -client.connectToServer(transport); +if (process.argv[1] && path.resolve(process.argv[1]) === fileURLToPath(import.meta.url)) { + const client = new MyClient(); + const transport = new StdioClientTransport({ + command: process.execPath, + args: [fileURLToPath(new URL("./index.js", import.meta.url))] + }); + client.connectToServer(transport).catch(error => { + console.error("Error in client:", error instanceof Error ? error.message : String(error)); + process.exitCode = 1; + }); +} diff --git a/lessons/08-mcp-advanced/solutions/build/index.js b/lessons/08-mcp-advanced/solutions/build/index.js index a6533462..e1f47574 100644 --- a/lessons/08-mcp-advanced/solutions/build/index.js +++ b/lessons/08-mcp-advanced/solutions/build/index.js @@ -1,31 +1,37 @@ import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js"; import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; import { z } from "zod"; -// Create an MCP server -const server = new McpServer({ - name: "Demo", - version: "1.0.0" -}); -// Add an addition tool -server.tool("characterDetails", { name: z.string() }, async ({ name }) => { - console.log(`Received character name: ${name}`); - const url = `https://en.wikipedia.org/api/rest_v1/page/summary/${encodeURIComponent(name)}`; // Changed 'title' to 'name' - const response = await fetch(url); +import { fileURLToPath } from "node:url"; +import path from "node:path"; +async function summary(name) { + const response = await fetch(`https://en.wikipedia.org/api/rest_v1/page/summary/${encodeURIComponent(name)}`, { + headers: { "User-Agent": "GenerativeAIJavaScriptCourse/1.0 (https://github.com/microsoft/generative-ai-with-javascript)" }, + signal: AbortSignal.timeout(30000) + }); if (!response.ok) - throw new Error("Failed to fetch character details"); + throw new Error(`Wikipedia request failed: HTTP ${response.status}.`); const data = await response.json(); - const responseText = data.extract; // This contains the summary text - return { - content: [{ type: "text", text: `Character: ${responseText}` }] - }; -}); -// Start receiving messages on stdin and sending messages on stdout -async function main() { - const transport = new StdioServerTransport(); - await server.connect(transport); - console.error("MCPServer started on stdin/stdout"); + return z.object({ extract: z.string().min(1) }).parse(data).extract; +} +export function createServer(getSummary = summary) { + const server = new McpServer({ name: "Demo", version: "1.0.0" }); + server.registerTool("characterDetails", { + description: "Get a Wikipedia summary of a historical character", + inputSchema: { name: z.string().trim().min(1).max(200) } + }, async ({ name }) => ({ + content: [{ type: "text", text: `Character: ${await getSummary(name)}` }] + })); + server.registerTool("place", { + description: "Get a Wikipedia summary of a place", + inputSchema: { name: z.string().trim().min(1).max(200) } + }, async ({ name }) => ({ + content: [{ type: "text", text: `Place: ${await getSummary(name)}` }] + })); + return server; +} +if (process.argv[1] && path.resolve(process.argv[1]) === fileURLToPath(import.meta.url)) { + createServer().connect(new StdioServerTransport()).catch(error => { + console.error("Error in server:", error instanceof Error ? error.message : String(error)); + process.exitCode = 1; + }); } -main().catch((error) => { - console.error("Fatal error: ", error); - process.exit(1); -}); diff --git a/lessons/08-mcp-advanced/solutions/package-lock.json b/lessons/08-mcp-advanced/solutions/package-lock.json index ccd64b19..cef38742 100644 --- a/lessons/08-mcp-advanced/solutions/package-lock.json +++ b/lessons/08-mcp-advanced/solutions/package-lock.json @@ -5,70 +5,1099 @@ "packages": { "": { "dependencies": { - "@modelcontextprotocol/sdk": "^1.8.0", - "express": "^5.0.1", - "openai": "^4.96.2", - "type": "^2.7.3", - "zod": "^3.24.2" + "@modelcontextprotocol/sdk": "^1.32.1", + "openai": "^7.28.0", + "zod": "^4.6.5" }, "bin": { "weather": "build/index.js" }, "devDependencies": { - 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"tsc && node ./build/client.js", - "build": "tsc && node ./build/index.js", - "inspect": "npx @modelcontextprotocol/inspector --cli node build/index.js --method tools/list", - "tool": "npx @modelcontextprotocol/inspector --cli node build/index.js --method tools/call --tool-name characterDetails --tool-arg name=Ada" + "client": "npm run build && node --env-file-if-exists=../../../.env build/client.js", + "build": "tsc", + "start": "node build/index.js", + "inspect": "npm run build && mcp-inspector --cli node build/index.js --method tools/list", + "inspect:web": "npm run build && mcp-inspector --web node build/index.js", + "tool": "npm run build && mcp-inspector --cli node build/index.js --method tools/call --tool-name characterDetails --tool-arg name=\"Ada Lovelace\"" }, "files": [ "build" ], "dependencies": { - "@modelcontextprotocol/sdk": "^1.8.0", - "express": "^5.0.1", - "openai": "^4.96.2", - "type": "^2.7.3", - "zod": "^3.24.2" + "@modelcontextprotocol/sdk": "^1.32.1", + "openai": "^7.28.0", + "zod": "^4.6.5" }, "devDependencies": { - "@types/node": "^22.13.17", - "typescript": "^5.8.2" + "@modelcontextprotocol/inspector": "^2.9.0", + "@types/node": "^24.19.1", + "typescript": "^7.0.2" } } diff --git a/lessons/08-mcp-advanced/solutions/src/client.ts b/lessons/08-mcp-advanced/solutions/src/client.ts index 40ccc0b8..1d07f41e 100644 --- a/lessons/08-mcp-advanced/solutions/src/client.ts +++ b/lessons/08-mcp-advanced/solutions/src/client.ts @@ -1,134 +1,104 @@ import { Client } from "@modelcontextprotocol/sdk/client/index.js"; import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js"; -import { Transport } from "@modelcontextprotocol/sdk/shared/transport.js"; +import { CallToolResultSchema, type Tool } from "@modelcontextprotocol/sdk/types.js"; +import type { Transport } from "@modelcontextprotocol/sdk/shared/transport.js"; +import { fileURLToPath } from "node:url"; +import path from "node:path"; import OpenAI from "openai"; -import { z } from "zod"; // Import zod for schema validation - -class MyClient { - private openai: OpenAI; - private client: Client; - constructor(){ - this.openai = new OpenAI({ - baseURL: "https://models.inference.ai.azure.com", // might need to change to this url in the future: https://models.github.ai/inference - apiKey: process.env.GITHUB_TOKEN, - }); - - - - this.client = new Client( - { - name: "example-client", - version: "1.0.0" - }, - { - capabilities: { - prompts: {}, - resources: {}, - tools: {} - } - } - ); +import { z } from "zod"; + +export class MyClient { + private readonly openai: OpenAI; + private readonly client: Client; + private readonly model: string; + + constructor() { + const endpoint = process.env.AI_ENDPOINT?.trim(); + const apiKey = process.env.AI_API_KEY?.trim(); + const model = process.env.AI_MODEL?.trim(); + if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); } - - async connectToServer(transport: Transport) { - await this.client.connect(transport); - this.run(); - console.error("MCPClient started on stdin/stdout"); + this.model = model; + this.openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); + this.client = new Client({ name: "example-client", version: "1.0.0" }); + } + + async connectToServer(transport: Transport): Promise { + try { + await this.client.connect(transport); + await this.run(); + } finally { + await this.client.close(); } - - openAiToolAdapter(tool: { - name: string; - description?: string; - input_schema: any; - }) { - // Create a zod schema based on the input_schema - const schema = z.object(tool.input_schema); - - return { - type: "function" as const, // Explicitly set type to "function" - function: { - name: tool.name, - description: tool.description, - parameters: { - type: "object", - properties: tool.input_schema.properties, - required: tool.input_schema.required, - }, - }, - }; + } + + private openAiToolAdapter(tool: Tool): OpenAI.Chat.Completions.ChatCompletionFunctionTool { + return { + type: "function", + function: { + name: tool.name, + description: tool.description, + parameters: tool.inputSchema + } + }; + } + + private async callTools( + calls: OpenAI.Chat.Completions.ChatCompletionMessageToolCall[], + allowedNames: Set + ): Promise { + const results: OpenAI.Chat.Completions.ChatCompletionToolMessageParam[] = []; + for (const call of calls) { + if (call.type !== "function" || !allowedNames.has(call.function.name)) { + throw new Error("The model requested an unsupported MCP tool."); + } + const args = z.record(z.string(), z.unknown()).parse(JSON.parse(call.function.arguments)); + const result = CallToolResultSchema.parse(await this.client.callTool({ + name: call.function.name, + arguments: args + })); + const text = result.content.filter(item => item.type === "text").map(item => item.text).join("\n"); + if (result.isError) throw new Error(`MCP tool failed: ${text}`); + if (!text) throw new Error("The MCP tool did not return text."); + console.log(`Result from [${call.function.name}]:`, text); + results.push({ role: "tool", tool_call_id: call.id, content: text }); } - - async callTools( - tool_calls: OpenAI.Chat.Completions.ChatCompletionMessageToolCall[], - toolResults: any[] - ) { - for (const tool_call of tool_calls) { - const toolName = tool_call.function.name; - const args = tool_call.function.arguments; - - console.log(`Calling tool ${toolName} with args ${JSON.stringify(args)}`); - - - // 2. Call the server's tool - const toolResult = await this.client.callTool({ - name: toolName, - arguments: JSON.parse(args), - }); - - console.log("Tool result: ", toolResult); - - // 3. Do something with the result - // TODO for you to implement - - } - } - - async run() { - console.log("Asking server for available tools"); - const toolsResult = await this.client.listTools(); - const tools = toolsResult.tools.map((tool) => { - return this.openAiToolAdapter({ - name: tool.name, - description: tool.description, - input_schema: tool.inputSchema, - }); - }); - - const messages: OpenAI.Chat.Completions.ChatCompletionMessageParam[] = [ - { - role: "user", - content: "Tell me about Ada Lovelace", - }, - ]; - - console.log("Querying LLM: ", messages[0].content); - let response = await this.openai.chat.completions.create({ - model: "gpt-4o-mini", - max_tokens: 1000, - messages, - tools: tools, - }); - - console.log("LLM response: ", response.choices[0].message.content); - - let results: any[] = []; - - // 1. Go through the LLM response,for each choice, check if it has tool calls - response.choices.map(async (choice: { message: any; }) => { - const message = choice.message; - if (message.tool_calls) { - console.log("Making tool call") - await this.callTools(message.tool_calls, results); - } - }); - } - + return results; + } + + private async run(): Promise { + const { tools: serverTools } = await this.client.listTools(); + const allowedNames = new Set(serverTools.map(tool => tool.name)); + const tools = serverTools.map(tool => this.openAiToolAdapter(tool)); + console.log("Available tools:", [...allowedNames]); + const messages: OpenAI.Chat.Completions.ChatCompletionMessageParam[] = [{ + role: "user", + content: "Use the characterDetails tool to tell me briefly about Ada Lovelace." + }]; + const completion = await this.openai.chat.completions.create({ + model: this.model, messages, tools, tool_choice: "required" + }); + const message = completion.choices[0]?.message; + if (!message?.tool_calls?.length) throw new Error("The model did not request a tool."); + messages.push(message, ...await this.callTools(message.tool_calls, allowedNames)); + const followUp = await this.openai.chat.completions.create({ + model: this.model, messages, tools, tool_choice: "none" + }); + const answer = followUp.choices[0]?.message?.content; + if (!answer?.trim()) throw new Error("The model did not return a final answer."); + console.log(answer); + } } -let client = new MyClient(); - const transport = new StdioClientTransport({ - command: "node", - args: ["./build/index.js"] - }); - -client.connectToServer(transport); +if (process.argv[1] && path.resolve(process.argv[1]) === fileURLToPath(import.meta.url)) { + const client = new MyClient(); + const transport = new StdioClientTransport({ + command: process.execPath, + args: [fileURLToPath(new URL("./index.js", import.meta.url))] + }); + client.connectToServer(transport).catch(error => { + console.error("Error in client:", error instanceof Error ? error.message : String(error)); + process.exitCode = 1; + }); +} diff --git a/lessons/08-mcp-advanced/solutions/src/index.ts b/lessons/08-mcp-advanced/solutions/src/index.ts index da3b7a3f..80578279 100644 --- a/lessons/08-mcp-advanced/solutions/src/index.ts +++ b/lessons/08-mcp-advanced/solutions/src/index.ts @@ -1,63 +1,40 @@ -import { McpServer, ResourceTemplate } from "@modelcontextprotocol/sdk/server/mcp.js"; +import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js"; import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js"; import { z } from "zod"; -import { promises as fs } from "fs"; -import path from "path"; - - -// Create an MCP server -const server = new McpServer({ - name: "Demo", - version: "1.0.0" -}); - -// Add a tool -server.tool("characterDetails", - { name: z.string() }, - async ({ name }) => { - - console.log(`Received character name: ${name}`); - - const url = `https://en.wikipedia.org/api/rest_v1/page/summary/${encodeURIComponent(name)}`; // Changed 'title' to 'name' - const response = await fetch(url); - if (!response.ok) throw new Error("Failed to fetch character details"); - const data = await response.json(); - const responseText = data.extract; // This contains the summary text - - return { - content: [{ type: "text", text: `Character: ${responseText}` }] - }; - } -); - -// Add a tool -server.tool("place", - { name: z.string() }, - async ({ name }) => { - - console.log(`Received name of place: ${name}`); - - const url = `https://en.wikipedia.org/api/rest_v1/page/summary/${encodeURIComponent(name)}`; // Changed 'title' to 'name' - const response = await fetch(url); - if (!response.ok) throw new Error("Failed to fetch place details"); - const data = await response.json(); - const responseText = data.extract; // This contains the summary text - - return { - content: [{ type: "text", text: `Place: ${responseText}` }] - }; - } -); - -// Start receiving messages on stdin and sending messages on stdout +import { fileURLToPath } from "node:url"; +import path from "node:path"; +async function summary(name: string): Promise { + const response = await fetch(`https://en.wikipedia.org/api/rest_v1/page/summary/${encodeURIComponent(name)}`, { + headers: { "User-Agent": "GenerativeAIJavaScriptCourse/1.0 (https://github.com/microsoft/generative-ai-with-javascript)" }, + signal: AbortSignal.timeout(30000) + }); + if (!response.ok) throw new Error(`Wikipedia request failed: HTTP ${response.status}.`); + const data: unknown = await response.json(); + return z.object({ extract: z.string().min(1) }).parse(data).extract; +} -async function main() { - const transport = new StdioServerTransport(); - await server.connect(transport); - console.error("MCPServer started on stdin/stdout"); +export function createServer(getSummary: (name: string) => Promise = summary): McpServer { + const server = new McpServer({ name: "Demo", version: "1.0.0" }); + server.registerTool("characterDetails", { + description: "Get a Wikipedia summary of a historical character", + inputSchema: { name: z.string().trim().min(1).max(200) } + }, async ({ name }) => ({ + content: [{ type: "text", text: `Character: ${await getSummary(name)}` }] + })); + + server.registerTool("place", { + description: "Get a Wikipedia summary of a place", + inputSchema: { name: z.string().trim().min(1).max(200) } + }, async ({ name }) => ({ + content: [{ type: "text", text: `Place: ${await getSummary(name)}` }] + })); + + return server; } -main().catch((error) => { - console.error("Fatal error: ", error); - process.exit(1); -}); +if (process.argv[1] && path.resolve(process.argv[1]) === fileURLToPath(import.meta.url)) { + createServer().connect(new StdioServerTransport()).catch(error => { + console.error("Error in server:", error instanceof Error ? error.message : String(error)); + process.exitCode = 1; + }); +} diff --git a/lessons/08-mcp-advanced/solutions/tsconfig.json b/lessons/08-mcp-advanced/solutions/tsconfig.json index b01bd618..3230f00f 100644 --- a/lessons/08-mcp-advanced/solutions/tsconfig.json +++ b/lessons/08-mcp-advanced/solutions/tsconfig.json @@ -1,15 +1,15 @@ { "compilerOptions": { "target": "ES2022", - "module": "Node16", - "moduleResolution": "Node16", + "module": "NodeNext", + "moduleResolution": "NodeNext", "outDir": "./build", "rootDir": "./src", "strict": true, + "types": ["node"], "esModuleInterop": true, "skipLibCheck": true, - "forceConsistentCasingInFileNames": true, - "noImplicitAny": false + "forceConsistentCasingInFileNames": true }, "include": ["src/**/*"], "exclude": ["node_modules"] diff --git a/lessons/08-mcp-advanced/translations/README.fr.md b/lessons/08-mcp-advanced/translations/README.fr.md index 56d4f923..9d79a6f6 100644 --- a/lessons/08-mcp-advanced/translations/README.fr.md +++ b/lessons/08-mcp-advanced/translations/README.fr.md @@ -91,14 +91,14 @@ Si vous souhaitez interagir avec Hedy, exécutez l'application [Characters](/app 1. Lancez un [![GitHub Codespace](https://img.shields.io/badge/GitHub-Codespace-brightgreen)](https://codespaces.new/microsoft/generative-ai-with-javascript). 2. Naviguez vers */app* à la racine du dépôt. -3. Localisez la console et exécutez `npm install` suivi de `npm start`. +3. Localisez la console et exécutez `npm ci` suivi de `npm start`. 4. Une fois que l'application est ouverte, sélectionnez le bouton "Ouvrir dans le navigateur". 5. Discuter avec Hedy. Pour une explication plus détaillée de l'application, voir [Explication détaillée de l'application](/lessons/01-intro-to-genai/README.md#interact-with-dinocrates). > [!NOTE] -> Si vous exécutez le projet localement sur votre machine, veuillez consulter le guide de démarrage rapide pour configurer un [token d'accès personnel GitHub](/docs/setup/README.md#creating-a-personal-access-token-pat-for-github-model-access) et remplacer la clé dans le code. +> Configurez `AI_ENDPOINT`, `AI_API_KEY` et `AI_MODEL` dans le fichier `.env` à la racine du dépôt, en local comme dans Codespaces. Consultez le [guide de configuration](/docs/setup/README.md#configure-environment-variables). ## Ajouter un modèle de langage à grande échelle à un client @@ -131,76 +131,76 @@ Voici toutes les étapes en code : ```typescript import { Client } from "@modelcontextprotocol/sdk/client/index.js"; import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js"; +import { CallToolResultSchema } from "@modelcontextprotocol/sdk/types.js"; +import { fileURLToPath } from "node:url"; +import { OpenAI } from "openai"; +import { z } from "zod"; + +const endpoint = process.env.AI_ENDPOINT?.trim(); +const apiKey = process.env.AI_API_KEY?.trim(); +const model = process.env.AI_MODEL?.trim(); +if (!endpoint || !apiKey || !model) { + throw new Error("Set AI_ENDPOINT, AI_API_KEY, and AI_MODEL before running the sample."); +} - -// create client instance +const openai = new OpenAI({ baseURL: endpoint, apiKey, timeout: 60000 }); +const client = new Client({ name: "example-client", version: "1.0.0" }); const transport = new StdioClientTransport({ - command: "node", - args: ["server.js"] + command: process.execPath, + args: [fileURLToPath(new URL("./index.js", import.meta.url))] }); -const client = new Client( - { - name: "example-client", - version: "1.0.0" +try { + await client.connect(transport); + const { tools: serverTools } = await client.listTools(); + const allowedNames = new Set(serverTools.map(tool => tool.name)); + const tools: OpenAI.Chat.Completions.ChatCompletionTool[] = serverTools.map(tool => ({ + type: "function", + function: { + name: tool.name, + description: tool.description, + parameters: tool.inputSchema + } + })); + console.log("Available tools:", [...allowedNames]); + const messages: OpenAI.Chat.Completions.ChatCompletionMessageParam[] = [ + { role: "user", content: "Use the add tool to add 5 and 10." } + ]; + const completion = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "required" + }); + const message = completion.choices[0]?.message; + if (!message?.tool_calls?.length) throw new Error("The model did not request a tool."); + messages.push(message); + for (const call of message.tool_calls) { + if (call.type !== "function" || !allowedNames.has(call.function.name)) { + throw new Error("The model requested an unsupported MCP tool."); + } + const args = z.record(z.string(), z.unknown()).parse(JSON.parse(call.function.arguments)); + const result = CallToolResultSchema.parse(await client.callTool({ + name: call.function.name, + arguments: args + })); + const text = result.content.filter(item => item.type === "text").map(item => item.text).join("\n"); + if (result.isError) throw new Error(`MCP tool failed: ${text}`); + if (!text) throw new Error("The MCP tool did not return text."); + console.log("Result from tool:", text); + messages.push({ role: "tool", tool_call_id: call.id, content: text }); } -); - -await client.connect(transport); - -// 1. make call to server, ask it for tools -const { tools } = await client.listTools(); - -// convert function -function toToolSchema(method, schema) { - return { - name: method, - description: `This is a tool that does ${method}`, - parameters: schema, - }; + const followUp = await openai.chat.completions.create({ + model, messages, tools, tool_choice: "none" + }); + const answer = followUp.choices[0]?.message?.content; + if (!answer?.trim()) throw new Error("The model did not return a final answer."); + console.log(answer); +} finally { + await client.close(); } - -// 2. convert the tools and resources response to a tools schema -const toolsForLLM = tools.map((tool) => { - return toToolSchema(tool.method, tool.inputSchema); -}); - -// 3. instantiate openai client -const openai = new OpenAI({ - apiKey: process.env.OPENAI_API_KEY, - model: "gpt-3.5-turbo", - temperature: 0.7, -}); - -// 4. make chat completion call to openai -const response = await openai.chat.completions.create({ - messages: [ - { - role: "user", - content: `I want to add 5 and 10. Please use the tool ${toolsForLLM}`, - }, - ], - functions: toolsForLLM, - function_call: "auto", -}); - -// 5. figure out what tool to call based on the response from openai -const toolName = response.choices[0].message.function_call.name; // add -const args = response.choices[0].message.function_call.arguments; // { a: 5, b: 10 } - -// 6. call the tool on the server -const result = await client.callTool({ - name: toolName, - arguments: args, -}); - -// 7. respond to user -console.log(result); // 15 ``` Dans le code précédent, nous avons (en nous concentrant sur nos ajouts) : -* Créé une fonction `toToolSchema` qui convertit la réponse des outils et ressources en un schéma utilisable par le LLM. +* Créé une fonction `tools.map` qui convertit la réponse des outils et ressources en un schéma utilisable par le LLM. * Demandé au serveur les outils et ressources disponibles. * Converti la réponse des outils et ressources en un schéma utilisable par le LLM. * Effectué un appel de complétion au modèle d'IA, en passant les outils convertis en paramètre. @@ -233,7 +233,7 @@ Dans le code précédent, nous avons (en nous concentrant sur nos ajouts) : ## Solution -[Solution](/lessons/08-mcp-advanced/solution/README.md) +[Solution](/lessons/08-mcp-advanced/solutions/README.md) ## Vérification des connaissances @@ -245,7 +245,7 @@ B. Cela crée une conversation plus naturelle entre l’utilisateur et le serveu C. Il est préférable d'avoir le LLM sur le serveur. -[Solution quiz](/lessons/08-mcp-advanced/solution/solution-quiz.md) +[Solution quiz](/lessons/08-mcp-advanced/solutions/solution-quiz.md) ## Résumé diff --git a/package-lock.json b/package-lock.json index f3e62c8b..15a3854d 100644 --- a/package-lock.json +++ b/package-lock.json @@ -8,17 +8,17 @@ "name": "genai-js", "version": "1.0.0", "dependencies": { - "@github/copilot-sdk": "^0.1.23", - "openai": "^4.78.1" + "@github/copilot-sdk": "1.0.16", + "openai": "^7.28.0" }, "devDependencies": { - "tsx": "^4.21.0" + "tsx": "^4.23.15" } }, "node_modules/@esbuild/aix-ppc64": { - "version": "0.27.3", - "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.27.3.tgz", - "integrity": "sha512-9fJMTNFTWZMh5qwrBItuziu834eOCUcEqymSH7pY+zoMVEZg3gcPuBNxH1EvfVYe9h0x/Ptw8KBzv7qxb7l8dg==", + "version": "0.28.2", + "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.28.2.tgz", + "integrity": "sha512-XExcO+dvLKvVtNTibSTBej1NCAbaGhWn9Ww1ZPx80qsahhPFe/8jgWP0IchNe0F3HwkU7n8ejhH8bjonqht8mQ==", "cpu": [ "ppc64" ], @@ -33,9 +33,9 @@ } }, "node_modules/@esbuild/android-arm": { - 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