[{"_1":2,"_648":-5,"_465":-5},"loaderData",{"_3":4,"_24":25,"_343":344},"root",{"_5":6,"_7":8},"toasts",[],"visitor",{"_9":10,"_11":12},"viewportWidth",1280,"visitorSession",{"_13":14,"_16":17,"_18":-5,"_19":20,"_21":22,"_23":-5},"attributes",[15],"bot","id",28696684,"membership","referenceId","019acd2b-7918-736f-b37c-c39fdfea91a9","unreadReleaseNotesCount",2,"userAccount","routes/_public/mcp/servers/~namespace/~slug/_pages/_layout",{"_26":27,"_79":-5,"_80":81,"_82":41,"_29":83,"_261":262,"_342":81},"availableLocales",[28,41,47,53,60,67,73],{"_29":30,"_37":38,"_39":40},"mcpServer",{"_31":32,"_33":34,"_35":36},"description","GenAIScript ist eine JavaScript-Laufzeitumgebung zum Erstellen zuverlässiger, automatisierbarer LLM-Skripte. Jedes GenAIScript kann automatisch als MCP-Server bereitgestellt werden.","displayName","GenAIScript","readme","![Ein gelbes Quadrat mit dem Wort „gen“ in schwarzen Kleinbuchstaben über den schwarzen Großbuchstaben „AI“.](./docs/public/images/favicon.png)\n\n# GenAIScript\n\n## Eingabeaufforderung ist Codierung\n\nErstellen Sie programmgesteuert Eingabeaufforderungen für LLMs mit JavaScript. Orchestrieren Sie LLMs, Tools und Daten im Code.\n\n* JavaScript-Toolbox zum Arbeiten mit Eingabeaufforderungen\n\n* Abstraktion, um es einfach und produktiv zu machen\n\n* Nahtlose Visual Studio Code-Integration oder flexible Befehlszeile\n\n* Integrierte Unterstützung für GitHub Copilot und GitHub Models, OpenAI, Azure OpenAI, Anthropic und mehr\n\n* 📄 **Lesen Sie die ONLINE-DOKUMENTATION unter [microsoft.github.io/genaiscript](https://microsoft.github.io/genaiscript/)**\n\n* 💬 Treten Sie dem [Discord-Server](https://discord.gg/y7HpumjHeB) bei\n\n* 📝 Lesen Sie den [Blog](https://microsoft.github.io/genaiscript/blog/) für die neuesten Nachrichten\n\n* 📺 Sehen Sie sich [Mr. Maedas gemütliche KI-Küche an](https://youtu.be/ajEbAm6kjI4)\n\n* 🤖 Agenten - lesen Sie die [llms-full.txt](https://microsoft.github.io/genaiscript/llms-full.txt)\n\n***\n\n## Hallo Welt\n\nAngenommen, Sie möchten ein LLM-Skript erstellen, das ein „Hallo Welt“-Gedicht generiert. Sie können das folgende Skript schreiben:\n\n```js\n$`Write a 'hello world' poem.`\n```\n\nDie Funktion `$` ist ein Vorlagentag, der eine Eingabeaufforderung erstellt. Diese wird dann an das von Ihnen konfigurierte LLM gesendet, das das Gedicht generiert.\n\nMachen wir es interessanter, indem wir Dateien, Daten und strukturierte Ausgabe hinzufügen. Angenommen, Sie möchten eine Datei in die Eingabeaufforderung einbinden und die Ausgabe in einer Datei speichern. Sie können das folgende Skript schreiben:\n\n```js\n// read files\nconst file = await workspace.readText(\"data.txt\")\n// include the file content in the prompt in a context-friendly way\ndef(\"DATA\", file)\n// the task\n$`Analyze DATA and extract data in JSON in data.json.`\n```\n\nDie Funktion `def` bindet den Inhalt der Datei ein und optimiert ihn gegebenenfalls für das Ziel-LLM. Das GenAIScript-Skript analysiert außerdem die LLM-Ausgabe und extrahiert die Datei `data.json` automatisch.\n\n***\n\n## 🚀 Kurzanleitung\n\nLegen Sie schnell los, indem Sie die [Visual Studio Code-Erweiterung](https://microsoft.github.io/genaiscript/getting-started/installation/) installieren oder die [Befehlszeile](https://microsoft.github.io/genaiscript/getting-started/installation) verwenden.\n\n***\n\n## ✨ Funktionen\n\n### 🎨 Stilisiertes JavaScript und TypeScript\n\nErstellen Sie Eingabeaufforderungen programmgesteuert mit [JavaScript](https://microsoft.github.io/genaiscript/reference/scripts/) oder [TypeScript](https://microsoft.github.io/genaiscript/reference/scripts/typescript) .\n\n```js\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n$`Summarize FILE. Today is ${new Date()}.`\n```\n\n***\n\n### 🚀 Schnelle Entwicklungsschleife\n\nBearbeiten, [debuggen](https://microsoft.github.io/genaiscript/getting-started/debugging-scripts/) , [ausführen](https://microsoft.github.io/genaiscript/getting-started/running-scripts/) und [testen Sie](https://microsoft.github.io/genaiscript/getting-started/testing-scripts/) Ihre Skripts in [Visual Studio Code](https://microsoft.github.io/genaiscript/getting-started/installation) oder mit der [Befehlszeile](https://microsoft.github.io/genaiscript/getting-started/installation) .\n\n***\n\n### 🔗 Skripte wiederverwenden und teilen\n\nSkripte sind [Dateien](https://microsoft.github.io/genaiscript/reference/scripts/) ! Sie können versioniert, geteilt und geforkt werden.\n\n```js\n// define the context\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n// structure the data\nconst schema = defSchema(\"DATA\", { type: \"array\", items: { type: \"string\" } })\n// assign the task\n$`Analyze FILE and extract data to JSON using the ${schema} schema.`\n```\n\n***\n\n### 📋 Datenschemata\n\nDefinieren, validieren und reparieren Sie Daten mithilfe von [Schemata](https://microsoft.github.io/genaiscript/reference/scripts/schemas) . Integrierte Zod-Unterstützung.\n\n```js\nconst data = defSchema(\"MY_DATA\", { type: \"array\", items: { ... } })\n$`Extract data from files using ${data} schema.`\n```\n\n***\n\n### 📄 Text aus PDFs, DOCX, ... aufnehmen\n\nBearbeiten Sie [PDFs](https://microsoft.github.io/genaiscript/reference/scripts/pdf) , [DOCX](https://microsoft.github.io/genaiscript/reference/scripts/docx) , ...\n\n```js\ndef(\"PDF\", env.files, { endsWith: \".pdf\" })\nconst { pages } = await parsers.PDF(env.files[0])\n```\n\n***\n\n### 📊 Tabellen aus CSV, XLSX, ... aufnehmen\n\nBearbeiten Sie tabellarische Daten aus [CSV](https://microsoft.github.io/genaiscript/reference/scripts/csv) , [XLSX](https://microsoft.github.io/genaiscript/reference/scripts/xlsx) , ...\n\n```js\ndef(\"DATA\", env.files, { endsWith: \".csv\", sliceHead: 100 })\nconst rows = await parsers.CSV(env.files[0])\ndefData(\"ROWS\", rows, { sliceHead: 100 })\n```\n\n***\n\n### 📝 Dateien generieren\n\nExtrahieren Sie Dateien und vergleichen Sie sie mit der LLM-Ausgabe. Zeigen Sie Änderungen in der Refactoring-Benutzeroberfläche in der Vorschau an.\n\n```js\n$`Save the result in poem.txt.`\n```\n\n```txt\nFILE ./poem.txt\nThe quick brown fox jumps over the lazy dog.\n```\n\n***\n\n### 🔍 Dateisuche\n\nGrep- oder Fuzz- [Suchdateien](https://microsoft.github.io/genaiscript/reference/scripts/files) .\n\n```js\nconst { files } = await workspace.grep(/[a-z][a-z0-9]+/, { globs: \"*.md\" })\n```\n\n***\n\n## Klassifizieren\n\nKlassifizieren Sie Text, Bilder oder eine Mischung aus allem.\n\n```js\nconst joke = await classify(\n \"Why did the chicken cross the road? To fry in the sun.\",\n {\n yes: \"funny\",\n no: \"not funny\",\n }\n)\n```\n\n### LLM-Tools\n\nRegistrieren Sie JavaScript-Funktionen als [Tools](https://microsoft.github.io/genaiscript/reference/scripts/tools) (mit Fallback für Modelle, die keine Tools unterstützen). [Model Context Protocol (MCP)-Tools](https://microsoft.github.io/genaiscript/reference/scripts/mcp-tools) werden ebenfalls unterstützt.\n\n```js\ndefTool(\n \"weather\",\n \"query a weather web api\",\n { location: \"string\" },\n async (args) =>\n await fetch(`https://weather.api.api/?location=${args.location}`)\n)\n```\n\n***\n\n### LLM-Agenten\n\nRegistrieren Sie JavaScript-Funktionen als **Tools** und kombinieren Sie Tools + Eingabeaufforderung zu Agenten.\n\n```js\ndefAgent(\n \"git\",\n \"Query a repository using Git to accomplish tasks.\",\n `Your are a helpful LLM agent that can use the git tools to query the current repository.\n Answer the question in QUERY.\n - The current repository is the same as github repository.`,\n { model, system: [\"system.github_info\"], tools: [\"git\"] }\n)\n```\n\ndann benutze es als Werkzeug\n\n```js\nscript({ tools: \"agent_git\" })\n\n$`Do a statistical analysis of the last commits`\n```\n\nSiehe die [Git-Agent-Quelle](https://github.com/microsoft/genaiscript/blob/main/packages/cli/genaisrc/system.agent_git.genai.mts) .\n\n***\n\n### 🔍 RAG integriert\n\n[Vektorsuche](https://microsoft.github.io/genaiscript/reference/scripts/vector-search/) .\n\n```js\nconst { files } = await retrieval.vectorSearch(\"cats\", \"**/*.md\")\n```\n\n***\n\n### 🐙 GitHub Models und GitHub Copilot\n\nFühren Sie Modelle über [GitHub Models](https://microsoft.github.io/genaiscript/getting-started/configuration#github) oder [GitHub Copilot](https://microsoft.github.io/genaiscript/getting-started/configuration/#github_copilot_chat) aus.\n\n```js\nscript({ ..., model: \"github:gpt-4o\" })\n```\n\n***\n\n### 💻 Lokale Modelle\n\nFühren Sie Ihre Skripte mit [Open-Source-Modellen](https://microsoft.github.io/genaiscript/getting-started/configuration/) wie [Phi-3](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/) unter Verwendung [von Ollama](https://ollama.com/) und [LocalAI](https://localai.io/) aus.\n\n```js\nscript({ ..., model: \"ollama:phi3\" })\n```\n\n***\n\n### 🐍 Code-Interpreter\n\nLassen Sie den LLM Code in einer Sandbox-Ausführungsumgebung ausführen.\n\n```js\nscript({ tools: [\"python_code_interpreter\"] })\n```\n\n***\n\n### 🐳 Behälter\n\nFühren Sie Code in Docker [-Containern](https://microsoft.github.io/genaiscript/reference/scripts/container) aus.\n\n```js\nconst c = await host.container({ image: \"python:alpine\" })\nconst res = await c.exec(\"python --version\")\n```\n\n***\n\n### Videoverarbeitung\n\nTranskribieren Sie Ihre Videos und machen Sie Screenshots, damit Sie sie effizient in Ihre LLM-Anfragen einspeisen können.\n\n```js\n// transcribe\nconst transcript = await transcript(\"path/to/audio.mp3\")\n// screenshots at segments\nconst frames = await ffmpeg.extractFrames(\"path_url_to_video\", { transcript })\ndef(\"TRANSCRIPT\", transcript)\ndef(\"FRAMES\", frames)\n```\n\n### 🧩 LLM-Komposition\n\n[Führen Sie LLMs aus](https://microsoft.github.io/genaiscript/reference/scripts/inline-prompts/) , um Ihre LLM-Eingabeaufforderungen zu erstellen.\n\n```js\nfor (const file of env.files) {\n const { text } = await runPrompt((_) => {\n _.def(\"FILE\", file)\n _.$`Summarize the FILE.`\n })\n def(\"SUMMARY\", text)\n}\n$`Summarize all the summaries.`\n```\n\n***\n\n### 🅿️ Schnelle Unterstützung\n\nFühren Sie auch Ihre [Prompty-](https://prompty.ai) Dateien aus!\n\n```markdown\n---\nname: poem\n---\n\nWrite me a poem\n```\n\n***\n\n### Pluggable Secret Scanning\n\nScannen Sie Ihre Chats mithilfe [der Geheimscanfunktion](/genaiscript/reference/scripts/secret-scanning) nach Geheimnissen.\n\n```json\n{\n \"secretPatterns\": {\n ...,\n \"OpenAI API Key\": \"sk-[A-Za-z0-9]{32,48}\"\n }\n}\n```\n\n### ⚙ Automatisieren mit CLI oder API\n\nAutomatisieren Sie mithilfe der [CLI](https://microsoft.github.io/genaiscript/reference/cli) oder [API](https://microsoft.github.io/genaiscript/reference/api) .\n\n```bash\nnpx genaiscript run tlaplus-linter \"*.tla\"\n```\n\n```js\nimport { run } from \"genaiscript/api\"\n\nconst res = await run(\"tlaplus-linter\", \"*.tla\")\n```\n\n***\n\n### Sicherheit geht vor!\n\nGenAIScript bietet integrierte Eingabeaufforderungen des Responsible AI-Systems und Azure Content Safety-Unterstützung zur Validierung [der Inhaltssicherheit](https://microsoft.github.io/genaiscript/reference/scripts/content-safety) .\n\n```js\nscript({ ...,\n system: [\"system.safety_harmful_content\", ...],\n contentSafety: \"azure\" // use azure content safety\n})\n\nconst safety = await host.contentSafety()\nconst res = await safety.detectPromptInjection(env.vars.input)\n```\n\n***\n\n### 💬 Pull Request-Überprüfungen\n\nIntegrieren Sie [Checks in Ihre Pull Requests](https://microsoft.github.io/genaiscript/reference/cli/run/#pull-requests) durch Kommentare, Reviews oder Beschreibungsaktualisierungen. Unterstützt GitHub Actions und Azure DevOps-Pipelines.\n\n```bash\nnpx genaiscript ... --pull-request-reviews\n```\n\n***\n\n### ⭐ Tests und Evaluierungen\n\nErstellen Sie zuverlässige Eingabeaufforderungen mithilfe von [Tests und Auswertungen,](https://microsoft.github.io/genaiscript/reference/scripts/tests) die von [promptfoo](https://promptfoo.dev/) unterstützt werden.\n\n```js\nscript({ ..., tests: {\n files: \"penguins.csv\",\n rubric: \"is a data analysis report\",\n facts: \"The data refers about penguin population in Antarctica.\",\n}})\n```\n\n***\n\n### LLM-freundliche Dokumente\n\nDer gesamte Inhalt der Dokumentation wird unter in Markdown gerendert. Sie können ihn direkt in Ihr bevorzugtes RAG-System einspeisen.\n\nWenn Sie ein LLM-Crawler sind, fügen Sie den Dokumentations-URLs die Endung `.md` hinzu, um einen unverarbeiteten Markdown-Inhalt zu erhalten. Beispiel: (beachten Sie die Endung .md).\n\n## Beitragen\n\nWir freuen uns über Beiträge! Weitere Informationen und Informationen zur Entwicklereinrichtung finden Sie auf der Seite [„Beiträge“](./CONTRIBUTING.md) .\n\n***\n\n## Marken\n\nDieses Projekt kann Marken oder Logos für Projekte, Produkte oder Dienste enthalten. Die autorisierte Verwendung von Microsoft-Marken oder -Logos unterliegt [den Marken- und Markenrichtlinien von Microsoft](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general) und muss diese einhalten. Die Verwendung von Microsoft-Marken oder -Logos in modifizierten Versionen dieses Projekts darf weder Verwirrung stiften noch eine Förderung durch Microsoft implizieren. Jede Verwendung von Marken oder Logos Dritter unterliegt den Richtlinien dieser Dritten.","name","German","tag","de-DE",{"_29":42,"_37":45,"_39":46},{"_31":43,"_33":34,"_35":44},"GenAIScript is a JavaScript runtime dedicated to build relaible, automatable LLM scripts. Every GenAIScript can be exposed as a MCP server automatically.","![A yellow square with the word \"gen\" in lowercase black letters above the uppercase black letters \"AI.\"](./docs/public/images/favicon.png)\n\n# GenAIScript\n\n## Prompting is Coding\n\nProgrammatically assemble prompts for LLMs using JavaScript. Orchestrate LLMs, tools, and data in code.\n\n- JavaScript toolbox to work with prompts\n- Abstraction to make it easy and productive\n- Seamless Visual Studio Code integration or flexible command line\n- Built-in support for GitHub Copilot and GitHub Models, OpenAI, Azure OpenAI, Anthropic, and more\n\n- 📄 **Read the ONLINE DOCUMENTATION at [microsoft.github.io/genaiscript](https://microsoft.github.io/genaiscript/)**\n- 💬 Join the [Discord server](https://discord.gg/y7HpumjHeB)\n- 📝 Read the [blog](https://microsoft.github.io/genaiscript/blog/) for the latest news\n- 📺 Watch [Mr. Maeda's Cozy AI Kitchen](https://youtu.be/ajEbAm6kjI4)\n- 🤖 Agents - read the [llms-full.txt](https://microsoft.github.io/genaiscript/llms-full.txt)\n\n---\n\n## Hello world\n\nSay to you want to create an LLM script that generates a 'hello world' poem. You can write the following script:\n\n```js\n$`Write a 'hello world' poem.`\n```\n\nThe `$` function is a template tag that creates a prompt. The prompt is then sent to the LLM (you configured), which generates the poem.\n\nLet's make it more interesting by adding files, data and structured output. Say you want to include a file in the prompt, and then save the output in a file. You can write the following script:\n\n```js\n// read files\nconst file = await workspace.readText(\"data.txt\")\n// include the file content in the prompt in a context-friendly way\ndef(\"DATA\", file)\n// the task\n$`Analyze DATA and extract data in JSON in data.json.`\n```\n\nThe `def` function includes the content of the file, and optimizes it if necessary for the target LLM. GenAIScript script also parses the LLM output\nand will extract the `data.json` file automatically.\n\n---\n\n## 🚀 Quickstart Guide\n\nGet started quickly by installing the [Visual Studio Code Extension](https://microsoft.github.io/genaiscript/getting-started/installation/) or using the [command line](https://microsoft.github.io/genaiscript/getting-started/installation).\n\n---\n\n## ✨ Features\n\n### 🎨 Stylized JavaScript & TypeScript\n\nBuild prompts programmatically using [JavaScript](https://microsoft.github.io/genaiscript/reference/scripts/) or [TypeScript](https://microsoft.github.io/genaiscript/reference/scripts/typescript).\n\n```js\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n$`Summarize FILE. Today is ${new Date()}.`\n```\n\n---\n\n### 🚀 Fast Development Loop\n\nEdit, [Debug](https://microsoft.github.io/genaiscript/getting-started/debugging-scripts/), [Run](https://microsoft.github.io/genaiscript/getting-started/running-scripts/), and [Test](https://microsoft.github.io/genaiscript/getting-started/testing-scripts/) your scripts in [Visual Studio Code](https://microsoft.github.io/genaiscript/getting-started/installation) or with the [command line](https://microsoft.github.io/genaiscript/getting-started/installation).\n\n---\n\n### 🔗 Reuse and Share Scripts\n\nScripts are [files](https://microsoft.github.io/genaiscript/reference/scripts/)! They can be versioned, shared, and forked.\n\n```js\n// define the context\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n// structure the data\nconst schema = defSchema(\"DATA\", { type: \"array\", items: { type: \"string\" } })\n// assign the task\n$`Analyze FILE and extract data to JSON using the ${schema} schema.`\n```\n\n---\n\n### 📋 Data Schemas\n\nDefine, validate, and repair data using [schemas](https://microsoft.github.io/genaiscript/reference/scripts/schemas). Zod support builtin.\n\n```js\nconst data = defSchema(\"MY_DATA\", { type: \"array\", items: { ... } })\n$`Extract data from files using ${data} schema.`\n```\n\n---\n\n### 📄 Ingest Text from PDFs, DOCX, ...\n\nManipulate [PDFs](https://microsoft.github.io/genaiscript/reference/scripts/pdf), [DOCX](https://microsoft.github.io/genaiscript/reference/scripts/docx), ...\n\n```js\ndef(\"PDF\", env.files, { endsWith: \".pdf\" })\nconst { pages } = await parsers.PDF(env.files[0])\n```\n\n---\n\n### 📊 Ingest Tables from CSV, XLSX, ...\n\nManipulate tabular data from [CSV](https://microsoft.github.io/genaiscript/reference/scripts/csv), [XLSX](https://microsoft.github.io/genaiscript/reference/scripts/xlsx), ...\n\n```js\ndef(\"DATA\", env.files, { endsWith: \".csv\", sliceHead: 100 })\nconst rows = await parsers.CSV(env.files[0])\ndefData(\"ROWS\", rows, { sliceHead: 100 })\n```\n\n---\n\n### 📝 Generate Files\n\nExtract files and diff from the LLM output. Preview changes in Refactoring UI.\n\n```js\n$`Save the result in poem.txt.`\n```\n\n```txt\nFILE ./poem.txt\nThe quick brown fox jumps over the lazy dog.\n```\n\n---\n\n### 🔍 File Search\n\nGrep or fuzz search [files](https://microsoft.github.io/genaiscript/reference/scripts/files).\n\n```js\nconst { files } = await workspace.grep(/[a-z][a-z0-9]+/, { globs: \"*.md\" })\n```\n\n---\n\n## Classify\n\nClassify text, images or a mix of all.\n\n```js\nconst joke = await classify(\n \"Why did the chicken cross the road? To fry in the sun.\",\n {\n yes: \"funny\",\n no: \"not funny\",\n }\n)\n```\n\n### LLM Tools\n\nRegister JavaScript functions as [tools](https://microsoft.github.io/genaiscript/reference/scripts/tools)\n(with fallback for models that don't support tools). [Model Context Protocol (MCP) tools](https://microsoft.github.io/genaiscript/reference/scripts/mcp-tools) are also supported.\n\n```js\ndefTool(\n \"weather\",\n \"query a weather web api\",\n { location: \"string\" },\n async (args) =>\n await fetch(`https://weather.api.api/?location=${args.location}`)\n)\n```\n\n---\n\n### LLM Agents\n\nRegister JavaScript functions as **tools** and combine tools + prompt into agents.\n\n```js\ndefAgent(\n \"git\",\n \"Query a repository using Git to accomplish tasks.\",\n `Your are a helpful LLM agent that can use the git tools to query the current repository.\n Answer the question in QUERY.\n - The current repository is the same as github repository.`,\n { model, system: [\"system.github_info\"], tools: [\"git\"] }\n)\n```\n\nthen use it as a tool\n\n```js\nscript({ tools: \"agent_git\" })\n\n$`Do a statistical analysis of the last commits`\n```\n\nSee the [git agent source](https://github.com/microsoft/genaiscript/blob/main/packages/cli/genaisrc/system.agent_git.genai.mts).\n\n---\n\n### 🔍 RAG Built-in\n\n[Vector search](https://microsoft.github.io/genaiscript/reference/scripts/vector-search/).\n\n```js\nconst { files } = await retrieval.vectorSearch(\"cats\", \"**/*.md\")\n```\n\n---\n\n### 🐙 GitHub Models and GitHub Copilot\n\nRun models through [GitHub Models](https://microsoft.github.io/genaiscript/getting-started/configuration#github) or [GitHub Copilot](https://microsoft.github.io/genaiscript/getting-started/configuration/#github_copilot_chat).\n\n```js\nscript({ ..., model: \"github:gpt-4o\" })\n```\n\n---\n\n### 💻 Local Models\n\nRun your scripts with [Open Source models](https://microsoft.github.io/genaiscript/getting-started/configuration/), like [Phi-3](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/), using [Ollama](https://ollama.com/), [LocalAI](https://localai.io/).\n\n```js\nscript({ ..., model: \"ollama:phi3\" })\n```\n\n---\n\n### 🐍 Code Interpreter\n\nLet the LLM run code in a sand-boxed execution environment.\n\n```js\nscript({ tools: [\"python_code_interpreter\"] })\n```\n\n---\n\n### 🐳 Containers\n\nRun code in Docker [containers](https://microsoft.github.io/genaiscript/reference/scripts/container).\n\n```js\nconst c = await host.container({ image: \"python:alpine\" })\nconst res = await c.exec(\"python --version\")\n```\n\n---\n\n### Video processing\n\nTranscribe and screenshot your videos so that you can feed them efficiently in your LLMs requests.\n\n```js\n// transcribe\nconst transcript = await transcript(\"path/to/audio.mp3\")\n// screenshots at segments\nconst frames = await ffmpeg.extractFrames(\"path_url_to_video\", { transcript })\ndef(\"TRANSCRIPT\", transcript)\ndef(\"FRAMES\", frames)\n```\n\n### 🧩 LLM Composition\n\n[Run LLMs](https://microsoft.github.io/genaiscript/reference/scripts/inline-prompts/) to build your LLM prompts.\n\n```js\nfor (const file of env.files) {\n const { text } = await runPrompt((_) => {\n _.def(\"FILE\", file)\n _.$`Summarize the FILE.`\n })\n def(\"SUMMARY\", text)\n}\n$`Summarize all the summaries.`\n```\n\n---\n\n### 🅿️ Prompty support\n\nRun your [Prompty](https://prompty.ai) files as well!\n\n```markdown\n---\nname: poem\n---\n\nWrite me a poem\n```\n\n---\n\n### Pluggable Secret Scanning\n\nScan your chats for secrets using [secret scanning](/genaiscript/reference/scripts/secret-scanning).\n\n```json\n{\n \"secretPatterns\": {\n ...,\n \"OpenAI API Key\": \"sk-[A-Za-z0-9]{32,48}\"\n }\n}\n```\n\n### ⚙ Automate with CLI or API\n\nAutomate using the [CLI](https://microsoft.github.io/genaiscript/reference/cli) or [API](https://microsoft.github.io/genaiscript/reference/api).\n\n```bash\nnpx genaiscript run tlaplus-linter \"*.tla\"\n```\n\n```js\nimport { run } from \"genaiscript/api\"\n\nconst res = await run(\"tlaplus-linter\", \"*.tla\")\n```\n\n---\n\n### Safety First!\n\nGenAIScript provides built-in Responsible AI system prompts and Azure Content Safety supports\nto validate [content safety](https://microsoft.github.io/genaiscript/reference/scripts/content-safety).\n\n```js wrap\nscript({ ...,\n system: [\"system.safety_harmful_content\", ...],\n contentSafety: \"azure\" // use azure content safety\n})\n\nconst safety = await host.contentSafety()\nconst res = await safety.detectPromptInjection(env.vars.input)\n```\n\n---\n\n### 💬 Pull Request Reviews\n\nIntegrate into your [Pull Requests checks](https://microsoft.github.io/genaiscript/reference/cli/run/#pull-requests) through comments, reviews, or description updates. Supports GitHub Actions and Azure DevOps pipelines.\n\n```bash wrap\nnpx genaiscript ... --pull-request-reviews\n```\n\n---\n\n### ⭐ Tests and Evals\n\nBuild reliable prompts using [tests and evals](https://microsoft.github.io/genaiscript/reference/scripts/tests) powered by [promptfoo](https://promptfoo.dev/).\n\n```js wrap\nscript({ ..., tests: {\n files: \"penguins.csv\",\n rubric: \"is a data analysis report\",\n facts: \"The data refers about penguin population in Antarctica.\",\n}})\n```\n\n---\n\n### LLM friendly docs\n\nThe entire content of the docs is rendered at https://microsoft.github.io/genaiscript/llms-full.txt in markdown. Feed it directly into your favorite RAG system.\n\nIf you are an LLM crawler, add the `.md` suffix to any documentation URLs to get a raw markdown content. For example, https://microsoft.github.io/genaiscript/guides/prompt-as-code.md (note the .md extension)\n\n## Contributing\n\nWe accept contributions! Checkout the [CONTRIBUTING](./CONTRIBUTING.md) page for details and developer setup.\n\n---\n\n## Trademarks\n\nThis project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft\ntrademarks or logos is subject to and must follow\n[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).\nUse of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.\nAny use of third-party trademarks or logos are subject to those third-party's policies.\n","English","en-US",{"_29":48,"_37":51,"_39":52},{"_31":49,"_33":34,"_35":50},"GenAIScript es un entorno de ejecución de JavaScript dedicado a crear scripts LLM fiables y automatizables. Cada GenAIScript puede exponerse automáticamente como servidor MCP.","![Un cuadrado amarillo con la palabra \"gen\" en letras negras minúsculas encima de las letras negras mayúsculas \"AI\".](./docs/public/images/favicon.png)\n\n# GenAIScript\n\n## La incitación es codificación\n\nEnsamble programáticamente indicaciones para LLM con JavaScript. Organice LLM, herramientas y datos en código.\n\n* Caja de herramientas de JavaScript para trabajar con indicaciones\n\n* Abstracción para hacerlo fácil y productivo\n\n* Integración perfecta con Visual Studio Code o línea de comandos flexible\n\n* Compatibilidad integrada con GitHub Copilot y GitHub Models, OpenAI, Azure OpenAI, Anthropic y más\n\n* 📄 **Lea la DOCUMENTACIÓN EN LÍNEA en [microsoft.github.io/genaiscript](https://microsoft.github.io/genaiscript/)**\n\n* 💬 Únete al [servidor de Discord](https://discord.gg/y7HpumjHeB)\n\n* 📝 Lee el [blog](https://microsoft.github.io/genaiscript/blog/) para conocer las últimas novedades\n\n* 📺 Mira [la acogedora cocina con inteligencia artificial del Sr. Maeda](https://youtu.be/ajEbAm6kjI4)\n\n* 🤖 Agentes: lean el archivo [llms-full.txt](https://microsoft.github.io/genaiscript/llms-full.txt)\n\n***\n\n## Hola Mundo\n\nSupongamos que desea crear un guion LLM que genere un poema de \"Hola mundo\". Puede escribir el siguiente guion:\n\n```js\n$`Write a 'hello world' poem.`\n```\n\nLa función `$` es una etiqueta de plantilla que crea una propuesta. Esta propuesta se envía al LLM (que usted configuró), que genera el poema.\n\nHagámoslo más interesante añadiendo archivos, datos y una salida estructurada. Supongamos que quieres incluir un archivo en el prompt y luego guardar la salida en un archivo. Puedes escribir el siguiente script:\n\n```js\n// read files\nconst file = await workspace.readText(\"data.txt\")\n// include the file content in the prompt in a context-friendly way\ndef(\"DATA\", file)\n// the task\n$`Analyze DATA and extract data in JSON in data.json.`\n```\n\nLa función `def` incluye el contenido del archivo y lo optimiza, si es necesario, para el LLM de destino. El script GenAIScript también analiza la salida del LLM y extrae el archivo `data.json` automáticamente.\n\n***\n\n## 🚀 Guía de inicio rápido\n\nComience rápidamente instalando la [extensión de Visual Studio Code](https://microsoft.github.io/genaiscript/getting-started/installation/) o utilizando la [línea de comandos](https://microsoft.github.io/genaiscript/getting-started/installation) .\n\n***\n\n## ✨ Características\n\n### JavaScript y TypeScript estilizados\n\nCree indicaciones mediante programación utilizando [JavaScript](https://microsoft.github.io/genaiscript/reference/scripts/) o [TypeScript](https://microsoft.github.io/genaiscript/reference/scripts/typescript) .\n\n```js\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n$`Summarize FILE. Today is ${new Date()}.`\n```\n\n***\n\n### 🚀 Bucle de desarrollo rápido\n\nEdite, [depure](https://microsoft.github.io/genaiscript/getting-started/debugging-scripts/) , [ejecute](https://microsoft.github.io/genaiscript/getting-started/running-scripts/) y [pruebe](https://microsoft.github.io/genaiscript/getting-started/testing-scripts/) sus scripts en [Visual Studio Code](https://microsoft.github.io/genaiscript/getting-started/installation) o con la [línea de comandos](https://microsoft.github.io/genaiscript/getting-started/installation) .\n\n***\n\n### 🔗 Reutilizar y compartir scripts\n\n¡Los scripts son [archivos](https://microsoft.github.io/genaiscript/reference/scripts/) ! Se pueden versionar, compartir y bifurcar.\n\n```js\n// define the context\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n// structure the data\nconst schema = defSchema(\"DATA\", { type: \"array\", items: { type: \"string\" } })\n// assign the task\n$`Analyze FILE and extract data to JSON using the ${schema} schema.`\n```\n\n***\n\n### 📋 Esquemas de datos\n\nDefinir, validar y reparar datos mediante [esquemas](https://microsoft.github.io/genaiscript/reference/scripts/schemas) . Compatibilidad con Zod integrada.\n\n```js\nconst data = defSchema(\"MY_DATA\", { type: \"array\", items: { ... } })\n$`Extract data from files using ${data} schema.`\n```\n\n***\n\n### 📄 Ingerir texto desde archivos PDF, DOCX, ...\n\nManipular [archivos PDF](https://microsoft.github.io/genaiscript/reference/scripts/pdf) , [DOCX](https://microsoft.github.io/genaiscript/reference/scripts/docx) , ...\n\n```js\ndef(\"PDF\", env.files, { endsWith: \".pdf\" })\nconst { pages } = await parsers.PDF(env.files[0])\n```\n\n***\n\n### 📊 Ingerir tablas desde CSV, XLSX, ...\n\nManipular datos tabulares desde [CSV](https://microsoft.github.io/genaiscript/reference/scripts/csv) , [XLSX](https://microsoft.github.io/genaiscript/reference/scripts/xlsx) , ...\n\n```js\ndef(\"DATA\", env.files, { endsWith: \".csv\", sliceHead: 100 })\nconst rows = await parsers.CSV(env.files[0])\ndefData(\"ROWS\", rows, { sliceHead: 100 })\n```\n\n***\n\n### 📝 Generar archivos\n\nExtraiga archivos y compare la salida de LLM. Previsualice los cambios en la interfaz de refactorización.\n\n```js\n$`Save the result in poem.txt.`\n```\n\n```txt\nFILE ./poem.txt\nThe quick brown fox jumps over the lazy dog.\n```\n\n***\n\n### 🔍 Búsqueda de archivos\n\nBuscar [archivos](https://microsoft.github.io/genaiscript/reference/scripts/files) mediante grep o fuzz.\n\n```js\nconst { files } = await workspace.grep(/[a-z][a-z0-9]+/, { globs: \"*.md\" })\n```\n\n***\n\n## Clasificar\n\nClasifica texto, imágenes o una mezcla de todo.\n\n```js\nconst joke = await classify(\n \"Why did the chicken cross the road? To fry in the sun.\",\n {\n yes: \"funny\",\n no: \"not funny\",\n }\n)\n```\n\n### Herramientas LLM\n\nRegistrar funciones de JavaScript como [herramientas](https://microsoft.github.io/genaiscript/reference/scripts/tools) (con respaldo para modelos que no las admiten). También se admiten [las herramientas del Protocolo de Contexto de Modelo (MCP)](https://microsoft.github.io/genaiscript/reference/scripts/mcp-tools) .\n\n```js\ndefTool(\n \"weather\",\n \"query a weather web api\",\n { location: \"string\" },\n async (args) =>\n await fetch(`https://weather.api.api/?location=${args.location}`)\n)\n```\n\n***\n\n### Agentes de LLM\n\nRegistre las funciones de JavaScript como **herramientas** y combine herramientas + indicaciones en agentes.\n\n```js\ndefAgent(\n \"git\",\n \"Query a repository using Git to accomplish tasks.\",\n `Your are a helpful LLM agent that can use the git tools to query the current repository.\n Answer the question in QUERY.\n - The current repository is the same as github repository.`,\n { model, system: [\"system.github_info\"], tools: [\"git\"] }\n)\n```\n\nluego úsalo como herramienta\n\n```js\nscript({ tools: \"agent_git\" })\n\n$`Do a statistical analysis of the last commits`\n```\n\nVea el [código fuente del agente git](https://github.com/microsoft/genaiscript/blob/main/packages/cli/genaisrc/system.agent_git.genai.mts) .\n\n***\n\n### 🔍 RAG Incorporado\n\n[Búsqueda de vectores](https://microsoft.github.io/genaiscript/reference/scripts/vector-search/) .\n\n```js\nconst { files } = await retrieval.vectorSearch(\"cats\", \"**/*.md\")\n```\n\n***\n\n### 🐙 Modelos de GitHub y GitHub Copilot\n\nEjecute modelos a través de [GitHub Models](https://microsoft.github.io/genaiscript/getting-started/configuration#github) o [GitHub Copilot](https://microsoft.github.io/genaiscript/getting-started/configuration/#github_copilot_chat) .\n\n```js\nscript({ ..., model: \"github:gpt-4o\" })\n```\n\n***\n\n### 💻 Modelos locales\n\nEjecute sus scripts con [modelos de código abierto](https://microsoft.github.io/genaiscript/getting-started/configuration/) , como [Phi-3](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/) , usando [Ollama](https://ollama.com/) , [LocalAI](https://localai.io/) .\n\n```js\nscript({ ..., model: \"ollama:phi3\" })\n```\n\n***\n\n### 🐍 Intérprete de código\n\nDeje que LLM ejecute el código en un entorno de ejecución aislado.\n\n```js\nscript({ tools: [\"python_code_interpreter\"] })\n```\n\n***\n\n### 🐳 Contenedores\n\nEjecutar código en [contenedores](https://microsoft.github.io/genaiscript/reference/scripts/container) Docker.\n\n```js\nconst c = await host.container({ image: \"python:alpine\" })\nconst res = await c.exec(\"python --version\")\n```\n\n***\n\n### Procesamiento de vídeo\n\nTranscribe y captura capturas de pantalla de tus videos para que puedas alimentarlos de manera eficiente en tus solicitudes de LLM.\n\n```js\n// transcribe\nconst transcript = await transcript(\"path/to/audio.mp3\")\n// screenshots at segments\nconst frames = await ffmpeg.extractFrames(\"path_url_to_video\", { transcript })\ndef(\"TRANSCRIPT\", transcript)\ndef(\"FRAMES\", frames)\n```\n\n### Composición de LLM\n\n[Ejecute LLMs](https://microsoft.github.io/genaiscript/reference/scripts/inline-prompts/) para crear sus indicaciones LLM.\n\n```js\nfor (const file of env.files) {\n const { text } = await runPrompt((_) => {\n _.def(\"FILE\", file)\n _.$`Summarize the FILE.`\n })\n def(\"SUMMARY\", text)\n}\n$`Summarize all the summaries.`\n```\n\n***\n\n### 🅿️ Soporte rápido\n\n¡Ejecuta también tus archivos [Prompty](https://prompty.ai) !\n\n```markdown\n---\nname: poem\n---\n\nWrite me a poem\n```\n\n***\n\n### Escaneo secreto conectable\n\nEscanea tus chats en busca de secretos usando [el escaneo de secretos](/genaiscript/reference/scripts/secret-scanning) .\n\n```json\n{\n \"secretPatterns\": {\n ...,\n \"OpenAI API Key\": \"sk-[A-Za-z0-9]{32,48}\"\n }\n}\n```\n\n### ⚙ Automatizar con CLI o API\n\nAutomatizar utilizando la [CLI](https://microsoft.github.io/genaiscript/reference/cli) o [API](https://microsoft.github.io/genaiscript/reference/api) .\n\n```bash\nnpx genaiscript run tlaplus-linter \"*.tla\"\n```\n\n```js\nimport { run } from \"genaiscript/api\"\n\nconst res = await run(\"tlaplus-linter\", \"*.tla\")\n```\n\n***\n\n### ¡Seguridad ante todo!\n\nGenAIScript proporciona indicaciones integradas del sistema de IA responsable y Azure Content Safety admite la validación de [la seguridad del contenido](https://microsoft.github.io/genaiscript/reference/scripts/content-safety) .\n\n```js\nscript({ ...,\n system: [\"system.safety_harmful_content\", ...],\n contentSafety: \"azure\" // use azure content safety\n})\n\nconst safety = await host.contentSafety()\nconst res = await safety.detectPromptInjection(env.vars.input)\n```\n\n***\n\n### 💬 Reseñas de solicitudes de extracción\n\nIntegre [las comprobaciones de sus solicitudes de extracción](https://microsoft.github.io/genaiscript/reference/cli/run/#pull-requests) mediante comentarios, revisiones o actualizaciones de descripciones. Compatible con GitHub Actions y canalizaciones de Azure DevOps.\n\n```bash\nnpx genaiscript ... --pull-request-reviews\n```\n\n***\n\n### ⭐ Pruebas y evaluaciones\n\nCree indicaciones confiables utilizando [pruebas y evaluaciones](https://microsoft.github.io/genaiscript/reference/scripts/tests) impulsadas por [promptfoo](https://promptfoo.dev/) .\n\n```js\nscript({ ..., tests: {\n files: \"penguins.csv\",\n rubric: \"is a data analysis report\",\n facts: \"The data refers about penguin population in Antarctica.\",\n}})\n```\n\n***\n\n### Documentos compatibles con LLM\n\nTodo el contenido de la documentación se genera en Markdown en . Introdúzcalo directamente en su sistema RAG preferido.\n\nSi trabajas con LLM, añade el sufijo `.md` a las URL de la documentación para obtener contenido Markdown sin procesar. Por ejemplo, (ten en cuenta la extensión .md).\n\n## Contribuyendo\n\n¡Aceptamos contribuciones! Consulta la página [de CONTRIBUYENTES](./CONTRIBUTING.md) para obtener más información y la configuración para desarrolladores.\n\n***\n\n## Marcas comerciales\n\nEste proyecto puede contener marcas comerciales o logotipos de proyectos, productos o servicios. El uso autorizado de las marcas comerciales o logotipos de Microsoft está sujeto a [las Directrices de Marcas Registradas y de Marca de Microsoft](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general) y debe cumplirlas. El uso de marcas comerciales o logotipos de Microsoft en versiones modificadas de este proyecto no debe causar confusión ni implicar patrocinio por parte de Microsoft. El uso de marcas comerciales o logotipos de terceros está sujeto a las políticas de dichos terceros.","Spanish","es-ES",{"_29":54,"_37":58,"_39":59},{"_31":55,"_33":56,"_35":57},"GenAIScriptは、信頼性が高く自動化可能なLLMスクリプトの構築に特化したJavaScriptランタイムです。すべてのGenAIScriptは自動的にMCPサーバーとして公開できます。","GenAIスクリプト","![大文字の黒い文字「AI」の上に小文字の黒い文字で「gen」という単語が書かれた黄色の四角形。](./docs/public/images/favicon.png)\n\n# GenAIスクリプト\n\n## プロンプトはコーディングです\n\nJavaScript を使用して、LLM のプロンプトをプログラム的に組み立てます。LLM、ツール、データをコードでオーケストレーションします。\n\n* プロンプトを操作するための JavaScript ツールボックス\n\n* 抽象化により、簡単に生産性を向上\n\n* シームレスなVisual Studio Code統合または柔軟なコマンドライン\n\n* GitHub Copilot、GitHub Models、OpenAI、Azure OpenAI、Anthropic などの組み込みサポート\n\n* 📄 **[microsoft.github.io/genaiscript](https://microsoft.github.io/genaiscript/)でオンラインドキュメントを読む**\n\n* 💬 [Discordサーバー](https://discord.gg/y7HpumjHeB)に参加する\n\n* 📝 最新ニュースについては[ブログ](https://microsoft.github.io/genaiscript/blog/)をご覧ください\n\n* 📺[前田さんの居心地の良いAIキッチン](https://youtu.be/ajEbAm6kjI4)を見る\n\n* 🤖 エージェント - [llms-full.txt](https://microsoft.github.io/genaiscript/llms-full.txt)を読んでください\n\n***\n\n## こんにちは世界\n\n「Hello World」という詩を生成するLLMスクリプトを作成したいとします。次のようなスクリプトを作成できます。\n\n```js\n$`Write a 'hello world' poem.`\n```\n\n`$`関数はプロンプトを作成するテンプレートタグです。このプロンプトは（設定した）LLMに送信され、詩が生成されます。\n\nファイル、データ、構造化された出力を追加して、もっと面白くしてみましょう。例えば、プロンプトにファイルを追加し、その出力をファイルに保存したいとします。次のようなスクリプトを書くことができます。\n\n```js\n// read files\nconst file = await workspace.readText(\"data.txt\")\n// include the file content in the prompt in a context-friendly way\ndef(\"DATA\", file)\n// the task\n$`Analyze DATA and extract data in JSON in data.json.`\n```\n\n`def`関数はファイルの内容を取り込み、必要に応じて対象のLLMに合わせて最適化します。GenAIScriptスクリプトはLLM出力を解析し、 `data.json`ファイルを自動的に抽出します。\n\n***\n\n## 🚀 クイックスタートガイド\n\n[Visual Studio Code 拡張機能](https://microsoft.github.io/genaiscript/getting-started/installation/)をインストールするか、[コマンドライン](https://microsoft.github.io/genaiscript/getting-started/installation)を使用してすぐに開始できます。\n\n***\n\n## ✨ 特徴\n\n### 🎨 スタイル化された JavaScript と TypeScript\n\n[JavaScript](https://microsoft.github.io/genaiscript/reference/scripts/)または[TypeScript](https://microsoft.github.io/genaiscript/reference/scripts/typescript)を使用してプログラムでプロンプトを構築します。\n\n```js\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n$`Summarize FILE. Today is ${new Date()}.`\n```\n\n***\n\n### 🚀 高速開発ループ\n\n[Visual Studio Code](https://microsoft.github.io/genaiscript/getting-started/installation)または[コマンドライン](https://microsoft.github.io/genaiscript/getting-started/installation)を使用してスクリプトを編集、[デバッグ](https://microsoft.github.io/genaiscript/getting-started/debugging-scripts/)、[実行](https://microsoft.github.io/genaiscript/getting-started/running-scripts/)、[テストします](https://microsoft.github.io/genaiscript/getting-started/testing-scripts/)。\n\n***\n\n### 🔗 スクリプトの再利用と共有\n\nスクリプトは[ファイル](https://microsoft.github.io/genaiscript/reference/scripts/)です。バージョン管理、共有、フォークが可能です。\n\n```js\n// define the context\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n// structure the data\nconst schema = defSchema(\"DATA\", { type: \"array\", items: { type: \"string\" } })\n// assign the task\n$`Analyze FILE and extract data to JSON using the ${schema} schema.`\n```\n\n***\n\n### 📋 データスキーマ\n\n[スキーマ](https://microsoft.github.io/genaiscript/reference/scripts/schemas)を使用してデータを定義、検証、修復します。Zod サポートが組み込まれています。\n\n```js\nconst data = defSchema(\"MY_DATA\", { type: \"array\", items: { ... } })\n$`Extract data from files using ${data} schema.`\n```\n\n***\n\n### 📄 PDF、DOCX などからテキストを取り込みます...\n\n[PDF](https://microsoft.github.io/genaiscript/reference/scripts/pdf) 、 [DOCX など](https://microsoft.github.io/genaiscript/reference/scripts/docx)を操作します。\n\n```js\ndef(\"PDF\", env.files, { endsWith: \".pdf\" })\nconst { pages } = await parsers.PDF(env.files[0])\n```\n\n***\n\n### 📊 CSV、XLSX などからテーブルを取り込みます...\n\n[CSV](https://microsoft.github.io/genaiscript/reference/scripts/csv) 、 [XLSX](https://microsoft.github.io/genaiscript/reference/scripts/xlsx) 、... からの表形式データを操作します。\n\n```js\ndef(\"DATA\", env.files, { endsWith: \".csv\", sliceHead: 100 })\nconst rows = await parsers.CSV(env.files[0])\ndefData(\"ROWS\", rows, { sliceHead: 100 })\n```\n\n***\n\n### 📝 ファイルを生成する\n\nLLM 出力からファイルを抽出し、比較します。リファクタリング UI で変更をプレビューします。\n\n```js\n$`Save the result in poem.txt.`\n```\n\n```txt\nFILE ./poem.txt\nThe quick brown fox jumps over the lazy dog.\n```\n\n***\n\n### 🔍 ファイル検索\n\n[ファイル](https://microsoft.github.io/genaiscript/reference/scripts/files)を Grep または fuzz 検索します。\n\n```js\nconst { files } = await workspace.grep(/[a-z][a-z0-9]+/, { globs: \"*.md\" })\n```\n\n***\n\n## 分類する\n\nテキスト、画像、またはそれらの組み合わせを分類します。\n\n```js\nconst joke = await classify(\n \"Why did the chicken cross the road? To fry in the sun.\",\n {\n yes: \"funny\",\n no: \"not funny\",\n }\n)\n```\n\n### LLMツール\n\nJavaScript関数を[ツール](https://microsoft.github.io/genaiscript/reference/scripts/tools)として登録します（ツールをサポートしていないモデルの場合はフォールバック機能を使用します）。[モデルコンテキストプロトコル（MCP）ツール](https://microsoft.github.io/genaiscript/reference/scripts/mcp-tools)もサポートされています。\n\n```js\ndefTool(\n \"weather\",\n \"query a weather web api\",\n { location: \"string\" },\n async (args) =>\n await fetch(`https://weather.api.api/?location=${args.location}`)\n)\n```\n\n***\n\n### LLMエージェント\n\nJavaScript 関数を**ツール**として登録し、ツール + プロンプトをエージェントに組み合わせます。\n\n```js\ndefAgent(\n \"git\",\n \"Query a repository using Git to accomplish tasks.\",\n `Your are a helpful LLM agent that can use the git tools to query the current repository.\n Answer the question in QUERY.\n - The current repository is the same as github repository.`,\n { model, system: [\"system.github_info\"], tools: [\"git\"] }\n)\n```\n\nそれを道具として使う\n\n```js\nscript({ tools: \"agent_git\" })\n\n$`Do a statistical analysis of the last commits`\n```\n\n[Git エージェントのソース](https://github.com/microsoft/genaiscript/blob/main/packages/cli/genaisrc/system.agent_git.genai.mts)を参照してください。\n\n***\n\n### 🔍 RAG内蔵\n\n[ベクトル検索](https://microsoft.github.io/genaiscript/reference/scripts/vector-search/)。\n\n```js\nconst { files } = await retrieval.vectorSearch(\"cats\", \"**/*.md\")\n```\n\n***\n\n### 🐙 GitHub モデルと GitHub Copilot\n\n[GitHub Models](https://microsoft.github.io/genaiscript/getting-started/configuration#github)または[GitHub Copilot](https://microsoft.github.io/genaiscript/getting-started/configuration/#github_copilot_chat)を通じてモデルを実行します。\n\n```js\nscript({ ..., model: \"github:gpt-4o\" })\n```\n\n***\n\n### 💻 ローカルモデル\n\n[Ollama](https://ollama.com/) 、 [LocalAI](https://localai.io/)を使用して、 [Phi-3](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/)などの[オープンソースモデル](https://microsoft.github.io/genaiscript/getting-started/configuration/)でスクリプトを実行します。\n\n```js\nscript({ ..., model: \"ollama:phi3\" })\n```\n\n***\n\n### 🐍 コードインタープリター\n\nLLM がサンドボックス化された実行環境でコードを実行できるようにします。\n\n```js\nscript({ tools: [\"python_code_interpreter\"] })\n```\n\n***\n\n### 🐳 コンテナ\n\nDocker[コンテナ](https://microsoft.github.io/genaiscript/reference/scripts/container)内でコードを実行します。\n\n```js\nconst c = await host.container({ image: \"python:alpine\" })\nconst res = await c.exec(\"python --version\")\n```\n\n***\n\n### ビデオ処理\n\nLLM リクエストに効率的にフィードできるように、ビデオを書き起こしてスクリーンショットを作成します。\n\n```js\n// transcribe\nconst transcript = await transcript(\"path/to/audio.mp3\")\n// screenshots at segments\nconst frames = await ffmpeg.extractFrames(\"path_url_to_video\", { transcript })\ndef(\"TRANSCRIPT\", transcript)\ndef(\"FRAMES\", frames)\n```\n\n### 🧩 LLM 構成\n\n[LLM を実行して](https://microsoft.github.io/genaiscript/reference/scripts/inline-prompts/)LLM プロンプトを構築します。\n\n```js\nfor (const file of env.files) {\n const { text } = await runPrompt((_) => {\n _.def(\"FILE\", file)\n _.$`Summarize the FILE.`\n })\n def(\"SUMMARY\", text)\n}\n$`Summarize all the summaries.`\n```\n\n***\n\n### 🅿️ 迅速なサポート\n\n[Prompty](https://prompty.ai)ファイルも実行してください。\n\n```markdown\n---\nname: poem\n---\n\nWrite me a poem\n```\n\n***\n\n### プラグ可能な秘密スキャン\n\n[秘密スキャン](/genaiscript/reference/scripts/secret-scanning)を使用して、チャットの秘密をスキャンします。\n\n```json\n{\n \"secretPatterns\": {\n ...,\n \"OpenAI API Key\": \"sk-[A-Za-z0-9]{32,48}\"\n }\n}\n```\n\n### ⚙ CLI または API で自動化\n\n[CLI](https://microsoft.github.io/genaiscript/reference/cli)または[API](https://microsoft.github.io/genaiscript/reference/api)を使用して自動化します。\n\n```bash\nnpx genaiscript run tlaplus-linter \"*.tla\"\n```\n\n```js\nimport { run } from \"genaiscript/api\"\n\nconst res = await run(\"tlaplus-linter\", \"*.tla\")\n```\n\n***\n\n### 安全第一！\n\nGenAIScript には、[コンテンツの安全性](https://microsoft.github.io/genaiscript/reference/scripts/content-safety)を検証するための組み込みの責任ある AI システムプロンプトと Azure コンテンツセーフティサポートが用意されています。\n\n```js\nscript({ ...,\n system: [\"system.safety_harmful_content\", ...],\n contentSafety: \"azure\" // use azure content safety\n})\n\nconst safety = await host.contentSafety()\nconst res = await safety.detectPromptInjection(env.vars.input)\n```\n\n***\n\n### 💬 プルリクエストのレビュー\n\nコメント、レビュー、説明の更新を通じて[、プルリクエストのチェック](https://microsoft.github.io/genaiscript/reference/cli/run/#pull-requests)に統合します。GitHub Actions と Azure DevOps パイプラインをサポートします。\n\n```bash\nnpx genaiscript ... --pull-request-reviews\n```\n\n***\n\n### ⭐ テストと評価\n\n[promptfoo](https://promptfoo.dev/)を利用した[テストと評価](https://microsoft.github.io/genaiscript/reference/scripts/tests)を使用して、信頼性の高いプロンプトを構築します。\n\n```js\nscript({ ..., tests: {\n files: \"penguins.csv\",\n rubric: \"is a data analysis report\",\n facts: \"The data refers about penguin population in Antarctica.\",\n}})\n```\n\n***\n\n### LLMフレンドリーなドキュメント\n\nドキュメントの全コンテンツは、にマークダウン形式で保存されています。お気に入りの RAG システムに直接入力してください。\n\nLLMクローラーの場合は、ドキュメントURLに`.md`サフィックスを追加して、生のMarkdownコンテンツを取得してください。例： [https](https://microsoft.github.io/genaiscript/guides/prompt-as-code.md) ://microsoft.github.io/genaiscript/guides/prompt-as-code.md（.md拡張子にご注意ください）\n\n## 貢献\n\n貢献を歓迎します！詳細と開発者向け設定については、 [「貢献」](./CONTRIBUTING.md)ページをご覧ください。\n\n***\n\n## 商標\n\nこのプロジェクトには、プロジェクト、製品、またはサービスの商標またはロゴが含まれている場合があります。Microsoftの商標またはロゴの使用は、 [Microsoftの商標およびブランドガイドラインの](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general)対象となり、これに従う必要があります。このプロジェクトの改変版におけるMicrosoftの商標またはロゴの使用は、混乱を招いたり、Microsoftのスポンサーシップを暗示したりしてはなりません。第三者の商標またはロゴの使用は、当該第三者のポリシーに従うものとします。","Japanese","ja-JP",{"_29":61,"_37":65,"_39":66},{"_31":62,"_33":63,"_35":64},"GenAIScript는 안정적이고 자동화 가능한 LLM 스크립트를 구축하는 데 특화된 JavaScript 런타임입니다. 모든 GenAIScript는 자동으로 MCP 서버로 노출될 수 있습니다.","GenAI스크립트","![\"gen\"이라는 단어가 소문자 검은색 글자로 쓰여 있고 그 위에 대문자 검은색 글자 \"AI\"가 쓰여 있는 노란색 사각형입니다.](./docs/public/images/favicon.png)\n\n# GenAI스크립트\n\n## 프롬프팅은 코딩입니다\n\nJavaScript를 사용하여 LLM 프롬프트를 프로그래밍 방식으로 구성합니다. 코드에서 LLM, 도구 및 데이터를 조율합니다.\n\n* 프롬프트 작업을 위한 JavaScript 도구 상자\n\n* 쉽고 생산적으로 만들기 위한 추상화\n\n* 원활한 Visual Studio Code 통합 또는 유연한 명령줄\n\n* GitHub Copilot 및 GitHub Models, OpenAI, Azure OpenAI, Anthropic 등에 대한 기본 지원\n\n* 📄 **[microsoft.github.io/genaiscript](https://microsoft.github.io/genaiscript/) 에서 온라인 문서를 읽어보세요.**\n\n* 💬 [Discord 서버](https://discord.gg/y7HpumjHeB) 에 가입하세요\n\n* 📝 최신 뉴스를 보려면 [블로그를](https://microsoft.github.io/genaiscript/blog/) 읽어보세요\n\n* 📺 [마에다 씨의 아늑한 AI 주방을](https://youtu.be/ajEbAm6kjI4) 시청하세요\n\n* 🤖 에이전트 - [llms-full.txt를](https://microsoft.github.io/genaiscript/llms-full.txt) 읽어보세요\n\n***\n\n## 안녕하세요 세상\n\n'Hello World' 시를 생성하는 LLM 스크립트를 만들고 싶다고 가정해 보겠습니다. 다음과 같은 스크립트를 작성할 수 있습니다.\n\n지엑스피1\n\n`$` 함수는 프롬프트를 생성하는 템플릿 태그입니다. 프롬프트는 사용자가 설정한 LLM으로 전송되고, LLM에서 시를 생성합니다.\n\n파일, 데이터, 그리고 구조화된 출력을 추가하여 더욱 흥미롭게 만들어 보겠습니다. 프롬프트에 파일을 포함하고 출력을 파일에 저장하고 싶다고 가정해 보겠습니다. 다음 스크립트를 작성할 수 있습니다.\n\n```js\n// read files\nconst file = await workspace.readText(\"data.txt\")\n// include the file content in the prompt in a context-friendly way\ndef(\"DATA\", file)\n// the task\n$`Analyze DATA and extract data in JSON in data.json.`\n```\n\n`def` 함수는 파일 내용을 포함하고, 필요한 경우 대상 LLM에 맞게 최적화합니다. GenAIScript 스크립트는 LLM 출력을 구문 분석하여 `data.json` 파일을 자동으로 추출합니다.\n\n***\n\n## 🚀 빠른 시작 가이드\n\n[Visual Studio Code 확장 프로그램을](https://microsoft.github.io/genaiscript/getting-started/installation/) 설치하거나 [명령줄을](https://microsoft.github.io/genaiscript/getting-started/installation) 사용하여 빠르게 시작하세요.\n\n***\n\n## ✨ 특징\n\n### 🎨 스타일화된 JavaScript 및 TypeScript\n\n[JavaScript](https://microsoft.github.io/genaiscript/reference/scripts/) 나 [TypeScript를](https://microsoft.github.io/genaiscript/reference/scripts/typescript) 사용하여 프로그래밍 방식으로 프롬프트를 작성합니다.\n\n```js\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n$`Summarize FILE. Today is ${new Date()}.`\n```\n\n***\n\n### 🚀 빠른 개발 루프\n\n[Visual Studio Code](https://microsoft.github.io/genaiscript/getting-started/installation) 나 [명령줄을](https://microsoft.github.io/genaiscript/getting-started/installation) 사용하여 스크립트를 편집, [디버그](https://microsoft.github.io/genaiscript/getting-started/debugging-scripts/) , [실행](https://microsoft.github.io/genaiscript/getting-started/running-scripts/) 및 [테스트하세요](https://microsoft.github.io/genaiscript/getting-started/testing-scripts/) .\n\n***\n\n### 🔗 스크립트 재사용 및 공유\n\n스크립트는 [파일](https://microsoft.github.io/genaiscript/reference/scripts/) 입니다! 버전 관리, 공유, 포크가 가능합니다.\n\n```js\n// define the context\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n// structure the data\nconst schema = defSchema(\"DATA\", { type: \"array\", items: { type: \"string\" } })\n// assign the task\n$`Analyze FILE and extract data to JSON using the ${schema} schema.`\n```\n\n***\n\n### 📋 데이터 스키마\n\n[스키마를](https://microsoft.github.io/genaiscript/reference/scripts/schemas) 사용하여 데이터를 정의, 검증 및 복구합니다. Zod 지원 기본 제공.\n\n```js\nconst data = defSchema(\"MY_DATA\", { type: \"array\", items: { ... } })\n$`Extract data from files using ${data} schema.`\n```\n\n***\n\n### 📄 PDF, DOCX 등에서 텍스트를 수집합니다.\n\n[PDF](https://microsoft.github.io/genaiscript/reference/scripts/pdf) , [DOCX](https://microsoft.github.io/genaiscript/reference/scripts/docx) 등을 조작합니다.\n\n```js\ndef(\"PDF\", env.files, { endsWith: \".pdf\" })\nconst { pages } = await parsers.PDF(env.files[0])\n```\n\n***\n\n### 📊 CSV, XLSX, ...에서 테이블을 수집합니다.\n\n[CSV](https://microsoft.github.io/genaiscript/reference/scripts/csv) , [XLSX](https://microsoft.github.io/genaiscript/reference/scripts/xlsx) 등의 표 형식 데이터를 조작합니다.\n\n```js\ndef(\"DATA\", env.files, { endsWith: \".csv\", sliceHead: 100 })\nconst rows = await parsers.CSV(env.files[0])\ndefData(\"ROWS\", rows, { sliceHead: 100 })\n```\n\n***\n\n### 📝 파일 생성\n\nLLM 출력에서 파일을 추출하고 차이점을 확인합니다. 리팩토링 UI에서 변경 사항을 미리 볼 수 있습니다.\n\n```js\n$`Save the result in poem.txt.`\n```\n\n```txt\nFILE ./poem.txt\nThe quick brown fox jumps over the lazy dog.\n```\n\n***\n\n### 🔍 파일 검색\n\nGrep 또는 Fuzz 검색 [파일](https://microsoft.github.io/genaiscript/reference/scripts/files) .\n\n```js\nconst { files } = await workspace.grep(/[a-z][a-z0-9]+/, { globs: \"*.md\" })\n```\n\n***\n\n## 나누다\n\n텍스트, 이미지 또는 이들을 혼합하여 분류합니다.\n\n```js\nconst joke = await classify(\n \"Why did the chicken cross the road? To fry in the sun.\",\n {\n yes: \"funny\",\n no: \"not funny\",\n }\n)\n```\n\n### LLM 도구\n\nJavaScript 함수를 [도구](https://microsoft.github.io/genaiscript/reference/scripts/tools) 로 등록합니다(도구를 지원하지 않는 모델에 대한 대체 기능 포함). [MCP(Model Context Protocol) 도구](https://microsoft.github.io/genaiscript/reference/scripts/mcp-tools) 도 지원됩니다.\n\n```js\ndefTool(\n \"weather\",\n \"query a weather web api\",\n { location: \"string\" },\n async (args) =>\n await fetch(`https://weather.api.api/?location=${args.location}`)\n)\n```\n\n***\n\n### LLM 에이전트\n\nJavaScript 함수를 **도구** 로 등록하고 도구 + 프롬프트를 에이전트로 결합합니다.\n\n```js\ndefAgent(\n \"git\",\n \"Query a repository using Git to accomplish tasks.\",\n `Your are a helpful LLM agent that can use the git tools to query the current repository.\n Answer the question in QUERY.\n - The current repository is the same as github repository.`,\n { model, system: [\"system.github_info\"], tools: [\"git\"] }\n)\n```\n\n그런 다음 도구로 사용하세요\n\n```js\nscript({ tools: \"agent_git\" })\n\n$`Do a statistical analysis of the last commits`\n```\n\n[git 에이전트 소스를](https://github.com/microsoft/genaiscript/blob/main/packages/cli/genaisrc/system.agent_git.genai.mts) 참조하세요.\n\n***\n\n### 🔍 RAG 내장\n\n[벡터 검색](https://microsoft.github.io/genaiscript/reference/scripts/vector-search/) .\n\n```js\nconst { files } = await retrieval.vectorSearch(\"cats\", \"**/*.md\")\n```\n\n***\n\n### 🐙 GitHub 모델 및 GitHub Copilot\n\n[GitHub Models](https://microsoft.github.io/genaiscript/getting-started/configuration#github) 또는 [GitHub Copilot을](https://microsoft.github.io/genaiscript/getting-started/configuration/#github_copilot_chat) 통해 모델을 실행합니다.\n\n```js\nscript({ ..., model: \"github:gpt-4o\" })\n```\n\n***\n\n### 💻 지역 모델\n\n[Ollama](https://ollama.com/) , [LocalAI를](https://localai.io/) 사용하여 [Phi-3](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/) 와 같은 [오픈 소스 모델](https://microsoft.github.io/genaiscript/getting-started/configuration/) 로 스크립트를 실행하세요.\n\n```js\nscript({ ..., model: \"ollama:phi3\" })\n```\n\n***\n\n### 🐍 코드 인터프리터\n\nLLM이 샌드박스 실행 환경에서 코드를 실행하도록 합니다.\n\n```js\nscript({ tools: [\"python_code_interpreter\"] })\n```\n\n***\n\n### 🐳 컨테이너\n\nDocker [컨테이너](https://microsoft.github.io/genaiscript/reference/scripts/container) 에서 코드를 실행합니다.\n\n```js\nconst c = await host.container({ image: \"python:alpine\" })\nconst res = await c.exec(\"python --version\")\n```\n\n***\n\n### 비디오 처리\n\nLLM 요청 시 효율적으로 제출할 수 있도록 비디오를 필사하고 스크린샷을 찍으세요.\n\n```js\n// transcribe\nconst transcript = await transcript(\"path/to/audio.mp3\")\n// screenshots at segments\nconst frames = await ffmpeg.extractFrames(\"path_url_to_video\", { transcript })\ndef(\"TRANSCRIPT\", transcript)\ndef(\"FRAMES\", frames)\n```\n\n### 🧩 LLM 작문\n\nLLM 프롬프트를 구축하려면 [LLM을 실행하세요](https://microsoft.github.io/genaiscript/reference/scripts/inline-prompts/) .\n\n```js\nfor (const file of env.files) {\n const { text } = await runPrompt((_) => {\n _.def(\"FILE\", file)\n _.$`Summarize the FILE.`\n })\n def(\"SUMMARY\", text)\n}\n$`Summarize all the summaries.`\n```\n\n***\n\n### 🅿️ 신속한 지원\n\n[Prompty](https://prompty.ai) 파일도 실행해 보세요!\n\n```markdown\n---\nname: poem\n---\n\nWrite me a poem\n```\n\n***\n\n### 플러그형 비밀 스캐닝\n\n[비밀 스캐닝을](/genaiscript/reference/scripts/secret-scanning) 사용하여 채팅에서 비밀을 스캔하세요.\n\n```json\n{\n \"secretPatterns\": {\n ...,\n \"OpenAI API Key\": \"sk-[A-Za-z0-9]{32,48}\"\n }\n}\n```\n\n### ⚙ CLI 또는 API로 자동화\n\n[CLI](https://microsoft.github.io/genaiscript/reference/cli) 나 [API를](https://microsoft.github.io/genaiscript/reference/api) 사용하여 자동화합니다.\n\n```bash\nnpx genaiscript run tlaplus-linter \"*.tla\"\n```\n\n```js\nimport { run } from \"genaiscript/api\"\n\nconst res = await run(\"tlaplus-linter\", \"*.tla\")\n```\n\n***\n\n### 안전이 최우선!\n\nGenAIScript는 [콘텐츠 안전성을](https://microsoft.github.io/genaiscript/reference/scripts/content-safety) 검증하기 위한 기본 제공 Responsible AI 시스템 프롬프트와 Azure Content Safety 지원을 제공합니다.\n\n```js\nscript({ ...,\n system: [\"system.safety_harmful_content\", ...],\n contentSafety: \"azure\" // use azure content safety\n})\n\nconst safety = await host.contentSafety()\nconst res = await safety.detectPromptInjection(env.vars.input)\n```\n\n***\n\n### 💬 풀 리퀘스트 리뷰\n\n댓글, 리뷰 또는 설명 업데이트를 통해 [풀 리퀘스트 확인](https://microsoft.github.io/genaiscript/reference/cli/run/#pull-requests) 에 통합하세요. GitHub Actions 및 Azure DevOps 파이프라인을 지원합니다.\n\n```bash\nnpx genaiscript ... --pull-request-reviews\n```\n\n***\n\n### ⭐ 테스트 및 평가\n\n[promptfoo가](https://promptfoo.dev/) 제공하는 [테스트와 평가를](https://microsoft.github.io/genaiscript/reference/scripts/tests) 사용하여 신뢰할 수 있는 프롬프트를 구축합니다.\n\n```js\nscript({ ..., tests: {\n files: \"penguins.csv\",\n rubric: \"is a data analysis report\",\n facts: \"The data refers about penguin population in Antarctica.\",\n}})\n```\n\n***\n\n### LLM 친화적 문서\n\n문서의 전체 내용은 에서 마크다운으로 렌더링됩니다. 원하는 RAG 시스템에 직접 입력하세요.\n\nLLM 크롤러인 경우, 모든 문서 URL에 `.md` 접미사를 추가하여 원시 마크다운 콘텐츠를 얻을 수 있습니다. 예: (.md 확장자 참고)\n\n## 기여하다\n\n기여를 받습니다! 자세한 내용과 개발자 설정은 [CONTRIBUTING](./CONTRIBUTING.md) 페이지를 확인하세요.\n\n***\n\n## 상표\n\n이 프로젝트에는 프로젝트, 제품 또는 서비스에 대한 상표 또는 로고가 포함될 수 있습니다. Microsoft 상표 또는 로고의 허가된 사용은 [Microsoft의 상표 및 브랜드 지침을](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general) 준수해야 합니다. 이 프로젝트의 수정된 버전에서 Microsoft 상표 또는 로고를 사용하는 것은 혼동을 야기하거나 Microsoft의 후원을 암시해서는 안 됩니다. 타사 상표 또는 로고를 사용하는 경우 해당 타사의 정책이 적용됩니다.","Korean","ko-KR",{"_29":68,"_37":71,"_39":72},{"_31":69,"_33":34,"_35":70},"GenAIScript — это среда выполнения JavaScript, предназначенная для создания надежных, автоматизируемых скриптов LLM. Каждый GenAIScript может быть автоматически представлен как сервер MCP.","![Желтый квадрат со словом «gen», написанным строчными черными буквами над заглавными черными буквами «AI».](./docs/public/images/favicon.png)\n\n# GenAIScript\n\n## Подсказка — это кодирование\n\nПрограммно собирайте запросы для LLM с помощью JavaScript. Организуйте LLM, инструменты и данные в коде.\n\n* Набор инструментов JavaScript для работы с подсказками\n\n* Абстракция, делающая работу легкой и продуктивной\n\n* Полная интеграция Visual Studio Code или гибкая командная строка\n\n* Встроенная поддержка GitHub Copilot и GitHub Models, OpenAI, Azure OpenAI, Anthropic и т. д.\n\n* 📄 **Прочитайте ОНЛАЙН-ДОКУМЕНТАЦИЮ по адресу [microsoft.github.io/genaiscript](https://microsoft.github.io/genaiscript/)**\n\n* 💬 Присоединяйтесь к [серверу Discord](https://discord.gg/y7HpumjHeB)\n\n* 📝 Читайте [блог](https://microsoft.github.io/genaiscript/blog/) , чтобы быть в курсе последних новостей\n\n* 📺 Посмотрите [видео «Уютная кухня с искусственным интеллектом» господина Маэды](https://youtu.be/ajEbAm6kjI4)\n\n* 🤖 Агенты - прочтите [llms-full.txt](https://microsoft.github.io/genaiscript/llms-full.txt)\n\n***\n\n## Привет, мир\n\nДопустим, вы хотите создать сценарий LLM, который генерирует стихотворение «привет, мир». Вы можете написать следующий сценарий:\n\n```js\n$`Write a 'hello world' poem.`\n```\n\nФункция `$` — это шаблонный тег, который создает приглашение. Затем приглашение отправляется в LLM (настроенный вами), который генерирует стихотворение.\n\nДавайте сделаем его более интересным, добавив файлы, данные и структурированный вывод. Допустим, вы хотите включить файл в приглашение, а затем сохранить вывод в файле. Вы можете написать следующий скрипт:\n\n```js\n// read files\nconst file = await workspace.readText(\"data.txt\")\n// include the file content in the prompt in a context-friendly way\ndef(\"DATA\", file)\n// the task\n$`Analyze DATA and extract data in JSON in data.json.`\n```\n\nФункция `def` включает содержимое файла и оптимизирует его при необходимости для целевого LLM. Скрипт GenAIScript также анализирует вывод LLM и автоматически извлекает файл `data.json` .\n\n***\n\n## 🚀 Краткое руководство\n\nНачните работу быстро, установив [расширение Visual Studio Code](https://microsoft.github.io/genaiscript/getting-started/installation/) или используя [командную строку](https://microsoft.github.io/genaiscript/getting-started/installation) .\n\n***\n\n## ✨ Особенности\n\n### 🎨 Стилизованный JavaScript и TypeScript\n\nСоздавайте подсказки программно, используя [JavaScript](https://microsoft.github.io/genaiscript/reference/scripts/) или [TypeScript](https://microsoft.github.io/genaiscript/reference/scripts/typescript) .\n\n```js\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n$`Summarize FILE. Today is ${new Date()}.`\n```\n\n***\n\n### 🚀 Цикл быстрой разработки\n\nРедактируйте, [отлаживайте](https://microsoft.github.io/genaiscript/getting-started/debugging-scripts/) , [запускайте](https://microsoft.github.io/genaiscript/getting-started/running-scripts/) и [тестируйте](https://microsoft.github.io/genaiscript/getting-started/testing-scripts/) свои скрипты в [Visual Studio Code](https://microsoft.github.io/genaiscript/getting-started/installation) или с помощью [командной строки](https://microsoft.github.io/genaiscript/getting-started/installation) .\n\n***\n\n### 🔗 Повторное использование и распространение скриптов\n\nСкрипты — это [файлы](https://microsoft.github.io/genaiscript/reference/scripts/) ! Их можно версионировать, делиться ими и разветвлять.\n\n```js\n// define the context\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n// structure the data\nconst schema = defSchema(\"DATA\", { type: \"array\", items: { type: \"string\" } })\n// assign the task\n$`Analyze FILE and extract data to JSON using the ${schema} schema.`\n```\n\n***\n\n### 📋 Схемы данных\n\nОпределение, проверка и восстановление данных с использованием [схем](https://microsoft.github.io/genaiscript/reference/scripts/schemas) . Встроенная поддержка Zod.\n\n```js\nconst data = defSchema(\"MY_DATA\", { type: \"array\", items: { ... } })\n$`Extract data from files using ${data} schema.`\n```\n\n***\n\n### 📄 Загрузка текста из PDF-файлов, DOCX, ...\n\nМанипулирование [файлами PDF](https://microsoft.github.io/genaiscript/reference/scripts/pdf) , [DOCX](https://microsoft.github.io/genaiscript/reference/scripts/docx) , ...\n\n```js\ndef(\"PDF\", env.files, { endsWith: \".pdf\" })\nconst { pages } = await parsers.PDF(env.files[0])\n```\n\n***\n\n### 📊 Загрузка таблиц из CSV, XLSX, ...\n\nОбработка табличных данных из [CSV](https://microsoft.github.io/genaiscript/reference/scripts/csv) , [XLSX](https://microsoft.github.io/genaiscript/reference/scripts/xlsx) , ...\n\n```js\ndef(\"DATA\", env.files, { endsWith: \".csv\", sliceHead: 100 })\nconst rows = await parsers.CSV(env.files[0])\ndefData(\"ROWS\", rows, { sliceHead: 100 })\n```\n\n***\n\n### 📝 Генерация файлов\n\nИзвлечь файлы и сделать различия из вывода LLM. Предварительный просмотр изменений в Refactoring UI.\n\n```js\n$`Save the result in poem.txt.`\n```\n\n```txt\nFILE ./poem.txt\nThe quick brown fox jumps over the lazy dog.\n```\n\n***\n\n### 🔍 Поиск файлов\n\n[Файлы](https://microsoft.github.io/genaiscript/reference/scripts/files) поиска Grep или fuzz.\n\n```js\nconst { files } = await workspace.grep(/[a-z][a-z0-9]+/, { globs: \"*.md\" })\n```\n\n***\n\n## Классифицировать\n\nКлассифицируйте текст, изображения или их комбинацию.\n\n```js\nconst joke = await classify(\n \"Why did the chicken cross the road? To fry in the sun.\",\n {\n yes: \"funny\",\n no: \"not funny\",\n }\n)\n```\n\n### Инструменты LLM\n\nРегистрация функций JavaScript в качестве [инструментов](https://microsoft.github.io/genaiscript/reference/scripts/tools) (с резервом для моделей, которые не поддерживают инструменты). [Инструменты Model Context Protocol (MCP)](https://microsoft.github.io/genaiscript/reference/scripts/mcp-tools) также поддерживаются.\n\n```js\ndefTool(\n \"weather\",\n \"query a weather web api\",\n { location: \"string\" },\n async (args) =>\n await fetch(`https://weather.api.api/?location=${args.location}`)\n)\n```\n\n***\n\n### Агенты LLM\n\nЗарегистрируйте функции JavaScript как **инструменты** и объедините инструменты + подсказки в агенты.\n\n```js\ndefAgent(\n \"git\",\n \"Query a repository using Git to accomplish tasks.\",\n `Your are a helpful LLM agent that can use the git tools to query the current repository.\n Answer the question in QUERY.\n - The current repository is the same as github repository.`,\n { model, system: [\"system.github_info\"], tools: [\"git\"] }\n)\n```\n\nзатем используйте его как инструмент\n\n```js\nscript({ tools: \"agent_git\" })\n\n$`Do a statistical analysis of the last commits`\n```\n\nСмотрите [исходный код агента git](https://github.com/microsoft/genaiscript/blob/main/packages/cli/genaisrc/system.agent_git.genai.mts) .\n\n***\n\n### 🔍 Встроенная ТРЯПКА\n\n[Векторный поиск](https://microsoft.github.io/genaiscript/reference/scripts/vector-search/) .\n\n```js\nconst { files } = await retrieval.vectorSearch(\"cats\", \"**/*.md\")\n```\n\n***\n\n### 🐙 Модели GitHub и GitHub Copilot\n\nЗапускайте модели через [GitHub Models](https://microsoft.github.io/genaiscript/getting-started/configuration#github) или [GitHub Copilot](https://microsoft.github.io/genaiscript/getting-started/configuration/#github_copilot_chat) .\n\n```js\nscript({ ..., model: \"github:gpt-4o\" })\n```\n\n***\n\n### 💻 Местные модели\n\nЗапускайте свои скрипты с [моделями с открытым исходным кодом](https://microsoft.github.io/genaiscript/getting-started/configuration/) , такими как [Phi-3](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/) , используя [Ollama](https://ollama.com/) , [LocalAI](https://localai.io/) .\n\n```js\nscript({ ..., model: \"ollama:phi3\" })\n```\n\n***\n\n### 🐍 Интерпретатор кода\n\nПозвольте LLM запустить код в изолированной среде выполнения.\n\n```js\nscript({ tools: [\"python_code_interpreter\"] })\n```\n\n***\n\n### 🐳 Контейнеры\n\nЗапуск кода в [контейнерах](https://microsoft.github.io/genaiscript/reference/scripts/container) Docker.\n\n```js\nconst c = await host.container({ image: \"python:alpine\" })\nconst res = await c.exec(\"python --version\")\n```\n\n***\n\n### Обработка видео\n\nТранскрибируйте и делайте скриншоты своих видео, чтобы вы могли эффективно использовать их в своих запросах на получение степени магистра права.\n\n```js\n// transcribe\nconst transcript = await transcript(\"path/to/audio.mp3\")\n// screenshots at segments\nconst frames = await ffmpeg.extractFrames(\"path_url_to_video\", { transcript })\ndef(\"TRANSCRIPT\", transcript)\ndef(\"FRAMES\", frames)\n```\n\n### 🧩 Состав магистра права\n\n[Запустите LLM](https://microsoft.github.io/genaiscript/reference/scripts/inline-prompts/) для создания подсказок LLM.\n\n```js\nfor (const file of env.files) {\n const { text } = await runPrompt((_) => {\n _.def(\"FILE\", file)\n _.$`Summarize the FILE.`\n })\n def(\"SUMMARY\", text)\n}\n$`Summarize all the summaries.`\n```\n\n***\n\n### 🅿️ Оперативная поддержка\n\nЗапустите также ваши файлы [Prompty](https://prompty.ai) !\n\n```markdown\n---\nname: poem\n---\n\nWrite me a poem\n```\n\n***\n\n### Подключаемое секретное сканирование\n\nПросканируйте свои чаты на наличие секретов с помощью [секретного сканирования](/genaiscript/reference/scripts/secret-scanning) .\n\n```json\n{\n \"secretPatterns\": {\n ...,\n \"OpenAI API Key\": \"sk-[A-Za-z0-9]{32,48}\"\n }\n}\n```\n\n### ⚙ Автоматизация с помощью CLI или API\n\nАвтоматизируйте с помощью [CLI](https://microsoft.github.io/genaiscript/reference/cli) или [API](https://microsoft.github.io/genaiscript/reference/api) .\n\n```bash\nnpx genaiscript run tlaplus-linter \"*.tla\"\n```\n\n```js\nimport { run } from \"genaiscript/api\"\n\nconst res = await run(\"tlaplus-linter\", \"*.tla\")\n```\n\n***\n\n### Безопасность прежде всего!\n\nGenAIScript предоставляет встроенные системные подсказки Responsible AI, а Azure Content Safety поддерживает проверку [безопасности контента](https://microsoft.github.io/genaiscript/reference/scripts/content-safety) .\n\n```js\nscript({ ...,\n system: [\"system.safety_harmful_content\", ...],\n contentSafety: \"azure\" // use azure content safety\n})\n\nconst safety = await host.contentSafety()\nconst res = await safety.detectPromptInjection(env.vars.input)\n```\n\n***\n\n### 💬 Обзоры запросов на извлечение\n\nИнтегрируйте в [проверки Pull Requests](https://microsoft.github.io/genaiscript/reference/cli/run/#pull-requests) с помощью комментариев, обзоров или обновлений описаний. Поддерживает GitHub Actions и Azure DevOps pipelines.\n\n```bash\nnpx genaiscript ... --pull-request-reviews\n```\n\n***\n\n### ⭐ Тесты и оценки\n\nСоздавайте надежные подсказки с помощью [тестов и оценок](https://microsoft.github.io/genaiscript/reference/scripts/tests) на базе [promptfoo](https://promptfoo.dev/) .\n\n```js\nscript({ ..., tests: {\n files: \"penguins.csv\",\n rubric: \"is a data analysis report\",\n facts: \"The data refers about penguin population in Antarctica.\",\n}})\n```\n\n***\n\n### Документы, дружественные LLM\n\nВесь контент документов отображается на в разметке. Загрузите его прямо в вашу любимую систему RAG.\n\nЕсли вы LLM-обходчик, добавьте суффикс `.md` к любым URL-адресам документации, чтобы получить сырой контент markdown. Например, (обратите внимание на расширение .md)\n\n## Внося вклад\n\nМы принимаем взносы! Посетите страницу [ВКЛАД](./CONTRIBUTING.md) для получения подробной информации и настройки разработчика.\n\n***\n\n## Торговые марки\n\nЭтот проект может содержать товарные знаки или логотипы проектов, продуктов или услуг. Разрешенное использование товарных знаков или логотипов Microsoft подчиняется и должно соответствовать [Руководству по товарным знакам и брендам Microsoft](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general) . Использование товарных знаков или логотипов Microsoft в измененных версиях этого проекта не должно вызывать путаницы или подразумевать спонсорство Microsoft. Любое использование товарных знаков или логотипов третьих лиц подчиняется политикам этих третьих лиц.","Russian","ru-RU",{"_29":74,"_37":77,"_39":78},{"_31":75,"_33":34,"_35":76},"GenAIScript 是一个 JavaScript 运行时，专用于构建可靠、可自动化的 LLM 脚本。每个 GenAIScript 都可以自动公开为 MCP 服务器。","![一个黄色方块，上面有黑色小写字母“gen”，位于黑色大写字母“AI”上方。](./docs/public/images/favicon.png)\n\n# GenAIScript\n\n## 提示即编码\n\n使用 JavaScript 以编程方式组装 LLM 的提示。在代码中协调 LLM、工具和数据。\n\n* JavaScript 工具箱用于处理提示\n\n* 抽象化使其变得简单且高效\n\n* 无缝 Visual Studio Code 集成或灵活的命令行\n\n* 内置对 GitHub Copilot 和 GitHub Models、OpenAI、Azure OpenAI、Anthropic 等的支持\n\n* 📄**阅读[microsoft.github.io/genaiscript](https://microsoft.github.io/genaiscript/)上的在线文档**\n\n* 💬 加入[Discord 服务器](https://discord.gg/y7HpumjHeB)\n\n* 📝 阅读[博客](https://microsoft.github.io/genaiscript/blog/)了解最新消息\n\n* 📺 观看[前田先生的舒适 AI 厨房](https://youtu.be/ajEbAm6kjI4)\n\n* 🤖 代理 - 阅读[llms-full.txt](https://microsoft.github.io/genaiscript/llms-full.txt)\n\n***\n\n## 你好世界\n\n假设你想创建一个 LLM 脚本，生成一首“hello world”诗歌。你可以编写以下脚本：\n\n```js\n$`Write a 'hello world' poem.`\n```\n\n`$`函数是一个模板标签，用于创建提示。该提示随后会被发送到你配置的 LLM，由 LLM 生成诗歌。\n\n让我们通过添加文件、数据和结构化输出来让它更有趣。假设你想在提示中包含一个文件，然后将输出保存到一个文件中。你可以编写以下脚本：\n\n```js\n// read files\nconst file = await workspace.readText(\"data.txt\")\n// include the file content in the prompt in a context-friendly way\ndef(\"DATA\", file)\n// the task\n$`Analyze DATA and extract data in JSON in data.json.`\n```\n\n`def`函数包含文件内容，并根据需要针对目标 LLM 进行优化。GenAIScript 脚本还会解析 LLM 输出并自动提取`data.json`文件。\n\n***\n\n## 🚀 快速入门指南\n\n通过安装[Visual Studio Code 扩展](https://microsoft.github.io/genaiscript/getting-started/installation/)或使用[命令](https://microsoft.github.io/genaiscript/getting-started/installation)行快速开始。\n\n***\n\n## ✨ 特点\n\n### 🎨 风格化的 JavaScript 和 TypeScript\n\n使用[JavaScript](https://microsoft.github.io/genaiscript/reference/scripts/)或[TypeScript](https://microsoft.github.io/genaiscript/reference/scripts/typescript)以编程方式构建提示。\n\n```js\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n$`Summarize FILE. Today is ${new Date()}.`\n```\n\n***\n\n### 🚀 快速开发循环\n\n在[Visual Studio Code](https://microsoft.github.io/genaiscript/getting-started/installation)中或使用[命令](https://microsoft.github.io/genaiscript/getting-started/installation)行编辑、[调试](https://microsoft.github.io/genaiscript/getting-started/debugging-scripts/)、[运行](https://microsoft.github.io/genaiscript/getting-started/running-scripts/)和[测试](https://microsoft.github.io/genaiscript/getting-started/testing-scripts/)您的脚本。\n\n***\n\n### 🔗 重用和共享脚本\n\n脚本也是[文件](https://microsoft.github.io/genaiscript/reference/scripts/)！它们可以被版本控制、共享和分叉。\n\n```js\n// define the context\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n// structure the data\nconst schema = defSchema(\"DATA\", { type: \"array\", items: { type: \"string\" } })\n// assign the task\n$`Analyze FILE and extract data to JSON using the ${schema} schema.`\n```\n\n***\n\n### 📋 数据模式\n\n使用[模式](https://microsoft.github.io/genaiscript/reference/scripts/schemas)定义、验证和修复数据。Zod 内置支持。\n\n```js\nconst data = defSchema(\"MY_DATA\", { type: \"array\", items: { ... } })\n$`Extract data from files using ${data} schema.`\n```\n\n***\n\n### 📄 从 PDF、DOCX 等文件中提取文本\n\n处理[PDF](https://microsoft.github.io/genaiscript/reference/scripts/pdf) 、 [DOCX](https://microsoft.github.io/genaiscript/reference/scripts/docx) 、...\n\n```js\ndef(\"PDF\", env.files, { endsWith: \".pdf\" })\nconst { pages } = await parsers.PDF(env.files[0])\n```\n\n***\n\n### 📊 从 CSV、XLSX 等文件中提取表格\n\n处理来自[CSV](https://microsoft.github.io/genaiscript/reference/scripts/csv) 、 [XLSX](https://microsoft.github.io/genaiscript/reference/scripts/xlsx)等的表格数据...\n\n```js\ndef(\"DATA\", env.files, { endsWith: \".csv\", sliceHead: 100 })\nconst rows = await parsers.CSV(env.files[0])\ndefData(\"ROWS\", rows, { sliceHead: 100 })\n```\n\n***\n\n### 📝 生成文件\n\n从 LLM 输出中提取文件和差异。在重构 UI 中预览更改。\n\n```js\n$`Save the result in poem.txt.`\n```\n\n```txt\nFILE ./poem.txt\nThe quick brown fox jumps over the lazy dog.\n```\n\n***\n\n### 🔍 文件搜索\n\nGrep 或 fuzz 搜索[文件](https://microsoft.github.io/genaiscript/reference/scripts/files)。\n\n```js\nconst { files } = await workspace.grep(/[a-z][a-z0-9]+/, { globs: \"*.md\" })\n```\n\n***\n\n## 分类\n\n对文本、图像或两者的混合进行分类。\n\n```js\nconst joke = await classify(\n \"Why did the chicken cross the road? To fry in the sun.\",\n {\n yes: \"funny\",\n no: \"not funny\",\n }\n)\n```\n\n### LLM工具\n\n将 JavaScript 函数注册为[工具](https://microsoft.github.io/genaiscript/reference/scripts/tools)（对于不支持该工具的模型，提供后备支持）。此外，还支持[模型上下文协议 (MCP) 工具](https://microsoft.github.io/genaiscript/reference/scripts/mcp-tools)。\n\n```js\ndefTool(\n \"weather\",\n \"query a weather web api\",\n { location: \"string\" },\n async (args) =>\n await fetch(`https://weather.api.api/?location=${args.location}`)\n)\n```\n\n***\n\n### 法学硕士代理\n\n将 JavaScript 函数注册为**工具**，并将工具 + 提示组合成代理。\n\n```js\ndefAgent(\n \"git\",\n \"Query a repository using Git to accomplish tasks.\",\n `Your are a helpful LLM agent that can use the git tools to query the current repository.\n Answer the question in QUERY.\n - The current repository is the same as github repository.`,\n { model, system: [\"system.github_info\"], tools: [\"git\"] }\n)\n```\n\n然后将其用作工具\n\n```js\nscript({ tools: \"agent_git\" })\n\n$`Do a statistical analysis of the last commits`\n```\n\n请参阅[git 代理源](https://github.com/microsoft/genaiscript/blob/main/packages/cli/genaisrc/system.agent_git.genai.mts)。\n\n***\n\n### 🔍 内置 RAG\n\n[向量搜索](https://microsoft.github.io/genaiscript/reference/scripts/vector-search/)。\n\n```js\nconst { files } = await retrieval.vectorSearch(\"cats\", \"**/*.md\")\n```\n\n***\n\n### 🐙 GitHub 模型和 GitHub Copilot\n\n通过[GitHub Models](https://microsoft.github.io/genaiscript/getting-started/configuration#github)或[GitHub Copilot](https://microsoft.github.io/genaiscript/getting-started/configuration/#github_copilot_chat)运行模型。\n\n```js\nscript({ ..., model: \"github:gpt-4o\" })\n```\n\n***\n\n### 💻 本地模特\n\n使用[Ollama](https://ollama.com/) 、 [LocalAI](https://localai.io/)通过[开源模型](https://microsoft.github.io/genaiscript/getting-started/configuration/)（如[Phi-3）](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/)运行您的脚本。\n\n```js\nscript({ ..., model: \"ollama:phi3\" })\n```\n\n***\n\n### 🐍 代码解释器\n\n让 LLM 在沙盒执行环境中运行代码。\n\n```js\nscript({ tools: [\"python_code_interpreter\"] })\n```\n\n***\n\n### 🐳 容器\n\n在 Docker[容器](https://microsoft.github.io/genaiscript/reference/scripts/container)中运行代码。\n\n```js\nconst c = await host.container({ image: \"python:alpine\" })\nconst res = await c.exec(\"python --version\")\n```\n\n***\n\n### 视频处理\n\n转录并截取您的视频的屏幕截图，以便您可以在 LLM 请求中有效地提供它们。\n\n```js\n// transcribe\nconst transcript = await transcript(\"path/to/audio.mp3\")\n// screenshots at segments\nconst frames = await ffmpeg.extractFrames(\"path_url_to_video\", { transcript })\ndef(\"TRANSCRIPT\", transcript)\ndef(\"FRAMES\", frames)\n```\n\n### 🧩 法学硕士作文\n\n[运行 LLM](https://microsoft.github.io/genaiscript/reference/scripts/inline-prompts/)来构建您的 LLM 提示。\n\n```js\nfor (const file of env.files) {\n const { text } = await runPrompt((_) => {\n _.def(\"FILE\", file)\n _.$`Summarize the FILE.`\n })\n def(\"SUMMARY\", text)\n}\n$`Summarize all the summaries.`\n```\n\n***\n\n### 🅿️ 及时支持\n\n也运行你的[Prompty](https://prompty.ai)文件！\n\n```markdown\n---\nname: poem\n---\n\nWrite me a poem\n```\n\n***\n\n### 可插入式秘密扫描\n\n使用[秘密扫描](/genaiscript/reference/scripts/secret-scanning)来扫描您的聊天记录以查找秘密。\n\n```json\n{\n \"secretPatterns\": {\n ...,\n \"OpenAI API Key\": \"sk-[A-Za-z0-9]{32,48}\"\n }\n}\n```\n\n### ⚙ 使用 CLI 或 API 实现自动化\n\n使用[CLI](https://microsoft.github.io/genaiscript/reference/cli)或[API](https://microsoft.github.io/genaiscript/reference/api)实现自动化。\n\n```bash\nnpx genaiscript run tlaplus-linter \"*.tla\"\n```\n\n```js\nimport { run } from \"genaiscript/api\"\n\nconst res = await run(\"tlaplus-linter\", \"*.tla\")\n```\n\n***\n\n### 安全第一！\n\nGenAIScript 提供内置的 Responsible AI 系统提示和 Azure Content Safety 支持来验证[内容安全](https://microsoft.github.io/genaiscript/reference/scripts/content-safety)。\n\n```js\nscript({ ...,\n system: [\"system.safety_harmful_content\", ...],\n contentSafety: \"azure\" // use azure content safety\n})\n\nconst safety = await host.contentSafety()\nconst res = await safety.detectPromptInjection(env.vars.input)\n```\n\n***\n\n### 💬 Pull 请求评审\n\n通过评论、评审或描述更新集成到[拉取请求检查](https://microsoft.github.io/genaiscript/reference/cli/run/#pull-requests)中。支持 GitHub Actions 和 Azure DevOps 流水线。\n\n```bash\nnpx genaiscript ... --pull-request-reviews\n```\n\n***\n\n### ⭐ 测试和评估\n\n使用[promptfoo](https://promptfoo.dev/)支持的[测试和评估](https://microsoft.github.io/genaiscript/reference/scripts/tests)构建可靠的提示。\n\n```js\nscript({ ..., tests: {\n files: \"penguins.csv\",\n rubric: \"is a data analysis report\",\n facts: \"The data refers about penguin population in Antarctica.\",\n}})\n```\n\n***\n\n### LLM 友好文档\n\n文档的全部内容以 Markdown 格式呈现在上。请直接将其输入到您最喜欢的 RAG 系统中。\n\n如果您是 LLM 爬虫，请将`.md`后缀添加到任何文档 URL 中，以获取原始 Markdown 内容。例如，（请注意 .md 扩展名）\n\n## 贡献\n\n我们接受贡献！查看[贡献](./CONTRIBUTING.md)页面了解详情和开发者设置。\n\n***\n\n## 商标\n\n本项目可能包含项目、产品或服务的商标或徽标。Microsoft 商标或徽标的授权使用须遵守[Microsoft 的商标和品牌指南](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general)。在本项目的修改版本中使用 Microsoft 商标或徽标不得引起混淆或暗示 Microsoft 的赞助。任何第三方商标或徽标的使用均须遵守该第三方的政策。","Chinese","zh-CN","githubUser","inspectable",false,"locale",{"_84":85,"_13":86,"_93":43,"_94":43,"_33":34,"_16":95,"_96":97,"_193":194,"_201":-5,"_202":203,"_204":-5,"_205":206,"_250":251,"_256":-5,"_104":230,"_257":203,"_107":258,"_259":260},"addedAt","2025-03-29T16:14:55.996711Z",[87,88,89,90,91,92],"author:official","category:developer-tools","category:code-execution","hosting:hybrid","language:typescript","license_grade:a","descriptionMarkdown","descriptionPlainText",4103,"integrations",[98,109,116,123,130,137,144,151,158,165,172,179,186],{"_99":100,"_31":106,"_107":108},"brand",{"_101":102,"_37":103,"_104":105},"logoSvgPath","M13.983 11.078h2.119a.186.186 0 00.186-.185V9.006a.186.186 0 00-.186-.186h-2.119a.185.185 0 00-.185.185v1.888c0 .102.083.185.185.185m-2.954-5.43h2.118a.186.186 0 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models.","xle4o4jkc6",{"_99":166,"_31":170,"_107":171},{"_101":167,"_37":168,"_104":169},"M14.25.18l.9.2.73.26.59.3.45.32.34.34.25.34.16.33.1.3.04.26.02.2-.01.13V8.5l-.05.63-.13.55-.21.46-.26.38-.3.31-.33.25-.35.19-.35.14-.33.1-.3.07-.26.04-.21.02H8.77l-.69.05-.59.14-.5.22-.41.27-.33.32-.27.35-.2.36-.15.37-.1.35-.07.32-.04.27-.02.21v3.06H3.17l-.21-.03-.28-.07-.32-.12-.35-.18-.36-.26-.36-.36-.35-.46-.32-.59-.28-.73-.21-.88-.14-1.05-.05-1.23.06-1.22.16-1.04.24-.87.32-.71.36-.57.4-.44.42-.33.42-.24.4-.16.36-.1.32-.05.24-.01h.16l.06.01h8.16v-.83H6.18l-.01-2.75-.02-.37.05-.34.11-.31.17-.28.25-.26.31-.23.38-.2.44-.18.51-.15.58-.12.64-.1.71-.06.77-.04.84-.02 1.27.05zm-6.3 1.98l-.23.33-.08.41.08.41.23.34.33.22.41.09.41-.09.33-.22.23-.34.08-.41-.08-.41-.23-.33-.33-.22-.41-.09-.41.09zm13.09 3.95l.28.06.32.12.35.18.36.27.36.35.35.47.32.59.28.73.21.88.14 1.04.05 1.23-.06 1.23-.16 1.04-.24.86-.32.71-.36.57-.4.45-.42.33-.42.24-.4.16-.36.09-.32.05-.24.02-.16-.01h-8.22v.82h5.84l.01 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TypeScript as a programming language for type-safe prompt construction.","zh8ik28pw9",{"_99":180,"_31":184,"_107":185},{"_101":181,"_37":182,"_104":183},"M23.498 6.186a3.016 3.016 0 0 0-2.122-2.136C19.505 3.545 12 3.545 12 3.545s-7.505 0-9.377.505A3.017 3.017 0 0 0 .502 6.186C0 8.07 0 12 0 12s0 3.93.502 5.814a3.016 3.016 0 0 0 2.122 2.136c1.871.505 9.376.505 9.376.505s7.505 0 9.377-.505a3.015 3.015 0 0 0 2.122-2.136C24 15.93 24 12 24 12s0-3.93-.502-5.814zM9.545 15.568V8.432L15.818 12l-6.273 3.568z","YouTube","youtube","Referenced for tutorial content, with the README linking to demonstration videos explaining GenAIScript functionality.","8yydq0d01c",{"_99":187,"_31":191,"_107":192},{"_101":188,"_37":189,"_104":190},"M19.088 2.477 24 7.606 12.521 20.485l-.925 1.038L0 7.559l5.108-5.082h13.98Zm-17.434 5.2 6.934-4.003H5.601L1.619 7.636l.035.041Zm12.117-4.003L3.333 9.7l2.149 2.588 10.809-6.241-.2-.346 2.851-1.646-.365-.381h-4.806Zm7.52 2.834L8.257 14.034h5.101v-.4h3.667l5.346-5.998-1.08-1.128Zm-7.129 10.338H9.268l2.36 2.843 2.534-2.843Z","Zod","zod","Built-in support for Zod schemas to define, validate, and repair data structures in LLM responses.","8w3k8sqcyk","namespace",{"_195":196,"_104":200},"owner",{"_197":198},"githubAccount",{"_199":200},"login","microsoft","readmeMarkdown","recentUsage",0,"redditPostExternalId","repository",{"_207":208,"_215":216,"_220":221,"_239":-5,"_80":81,"_240":241,"_245":246},"argumentsJsonSchema",{"_209":210,"_211":212,"_213":214},"properties",{},"required",[],"type","object","githubProject",{"_35":217,"_218":219},"![A yellow square with the word \"gen\" in lowercase black letters above the uppercase black letters \"AI.\"](./docs/public/images/favicon.png)\n\n# GenAIScript\n\n## Prompting is Coding\n\nProgrammatically assemble prompts for LLMs using JavaScript. Orchestrate LLMs, tools, and data in code.\n\n- JavaScript toolbox to work with prompts\n- Abstraction to make it easy and productive\n- Seamless Visual Studio Code integration or flexible command line\n- Built-in support for GitHub Copilot and GitHub Models, OpenAI, Azure OpenAI, Anthropic, and more\n\n- 📄 **Read the ONLINE DOCUMENTATION at [microsoft.github.io/genaiscript](https://microsoft.github.io/genaiscript/)**\n- 💬 Join the [Discord server](https://discord.gg/y7HpumjHeB)\n- 📝 Read the [blog](https://microsoft.github.io/genaiscript/blog/) for the latest news\n- 📺 Watch [Mr. Maeda's Cozy AI Kitchen](https://youtu.be/ajEbAm6kjI4)\n- 🤖 Agents - read the [llms-full.txt](https://microsoft.github.io/genaiscript/llms-full.txt)\n\n---\n\n## Hello world\n\nSay to you want to create an LLM script that generates a 'hello world' poem. You can write the following script:\n\n```js\n$`Write a 'hello world' poem.`\n```\n\nThe `$` function is a template tag that creates a prompt. The prompt is then sent to the LLM (you configured), which generates the poem.\n\nLet's make it more interesting by adding files, data and structured output. Say you want to include a file in the prompt, and then save the output in a file. You can write the following script:\n\n```js\n// read files\nconst file = await workspace.readText(\"data.txt\")\n// include the file content in the prompt in a context-friendly way\ndef(\"DATA\", file)\n// the task\n$`Analyze DATA and extract data in JSON in data.json.`\n```\n\nThe `def` function includes the content of the file, and optimizes it if necessary for the target LLM. GenAIScript script also parses the LLM output\nand will extract the `data.json` file automatically.\n\n---\n\n## 🚀 Quickstart Guide\n\nGet started quickly by installing the [Visual Studio Code Extension](https://microsoft.github.io/genaiscript/getting-started/installation/) or using the [command line](https://microsoft.github.io/genaiscript/getting-started/installation).\n\n---\n\n## ✨ Features\n\n### 🎨 Stylized JavaScript & TypeScript\n\nBuild prompts programmatically using [JavaScript](https://microsoft.github.io/genaiscript/reference/scripts/) or [TypeScript](https://microsoft.github.io/genaiscript/reference/scripts/typescript).\n\n```js\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n$`Summarize FILE. Today is ${new Date()}.`\n```\n\n---\n\n### 🚀 Fast Development Loop\n\nEdit, [Debug](https://microsoft.github.io/genaiscript/getting-started/debugging-scripts/), [Run](https://microsoft.github.io/genaiscript/getting-started/running-scripts/), and [Test](https://microsoft.github.io/genaiscript/getting-started/testing-scripts/) your scripts in [Visual Studio Code](https://microsoft.github.io/genaiscript/getting-started/installation) or with the [command line](https://microsoft.github.io/genaiscript/getting-started/installation).\n\n---\n\n### 🔗 Reuse and Share Scripts\n\nScripts are [files](https://microsoft.github.io/genaiscript/reference/scripts/)! They can be versioned, shared, and forked.\n\n```js\n// define the context\ndef(\"FILE\", env.files, { endsWith: \".pdf\" })\n// structure the data\nconst schema = defSchema(\"DATA\", { type: \"array\", items: { type: \"string\" } })\n// assign the task\n$`Analyze FILE and extract data to JSON using the ${schema} schema.`\n```\n\n---\n\n### 📋 Data Schemas\n\nDefine, validate, and repair data using [schemas](https://microsoft.github.io/genaiscript/reference/scripts/schemas). Zod support builtin.\n\n```js\nconst data = defSchema(\"MY_DATA\", { type: \"array\", items: { ... } })\n$`Extract data from files using ${data} schema.`\n```\n\n---\n\n### 📄 Ingest Text from PDFs, DOCX, ...\n\nManipulate [PDFs](https://microsoft.github.io/genaiscript/reference/scripts/pdf), [DOCX](https://microsoft.github.io/genaiscript/reference/scripts/docx), ...\n\n```js\ndef(\"PDF\", env.files, { endsWith: \".pdf\" })\nconst { pages } = await parsers.PDF(env.files[0])\n```\n\n---\n\n### 📊 Ingest Tables from CSV, XLSX, ...\n\nManipulate tabular data from [CSV](https://microsoft.github.io/genaiscript/reference/scripts/csv), [XLSX](https://microsoft.github.io/genaiscript/reference/scripts/xlsx), ...\n\n```js\ndef(\"DATA\", env.files, { endsWith: \".csv\", sliceHead: 100 })\nconst rows = await parsers.CSV(env.files[0])\ndefData(\"ROWS\", rows, { sliceHead: 100 })\n```\n\n---\n\n### 📝 Generate Files\n\nExtract files and diff from the LLM output. Preview changes in Refactoring UI.\n\n```js\n$`Save the result in poem.txt.`\n```\n\n```txt\nFILE ./poem.txt\nThe quick brown fox jumps over the lazy dog.\n```\n\n---\n\n### 🔍 File Search\n\nGrep or fuzz search [files](https://microsoft.github.io/genaiscript/reference/scripts/files).\n\n```js\nconst { files } = await workspace.grep(/[a-z][a-z0-9]+/, { globs: \"*.md\" })\n```\n\n---\n\n## Classify\n\nClassify text, images or a mix of all.\n\n```js\nconst joke = await classify(\n \"Why did the chicken cross the road? To fry in the sun.\",\n {\n yes: \"funny\",\n no: \"not funny\",\n }\n)\n```\n\n### LLM Tools\n\nRegister JavaScript functions as [tools](https://microsoft.github.io/genaiscript/reference/scripts/tools)\n(with fallback for models that don't support tools). [Model Context Protocol (MCP) tools](https://microsoft.github.io/genaiscript/reference/scripts/mcp-tools) are also supported.\n\n```js\ndefTool(\n \"weather\",\n \"query a weather web api\",\n { location: \"string\" },\n async (args) =>\n await fetch(`https://weather.api.api/?location=${args.location}`)\n)\n```\n\n---\n\n### LLM Agents\n\nRegister JavaScript functions as **tools** and combine tools + prompt into agents.\n\n```js\ndefAgent(\n \"git\",\n \"Query a repository using Git to accomplish tasks.\",\n `Your are a helpful LLM agent that can use the git tools to query the current repository.\n Answer the question in QUERY.\n - The current repository is the same as github repository.`,\n { model, system: [\"system.github_info\"], tools: [\"git\"] }\n)\n```\n\nthen use it as a tool\n\n```js\nscript({ tools: \"agent_git\" })\n\n$`Do a statistical analysis of the last commits`\n```\n\nSee the [git agent source](https://github.com/microsoft/genaiscript/blob/main/packages/cli/genaisrc/system.agent_git.genai.mts).\n\n---\n\n### 🔍 RAG Built-in\n\n[Vector search](https://microsoft.github.io/genaiscript/reference/scripts/vector-search/).\n\n```js\nconst { files } = await retrieval.vectorSearch(\"cats\", \"**/*.md\")\n```\n\n---\n\n### 🐙 GitHub Models and GitHub Copilot\n\nRun models through [GitHub Models](https://microsoft.github.io/genaiscript/configuration/github) or [GitHub Copilot](https://microsoft.github.io/genaiscript/configuration/github-copilot-chat).\n\n```js\nscript({ ..., model: \"github:gpt-4o\" })\n```\n\n---\n\n### 💻 Local Models\n\nRun your scripts with [Open Source models](https://microsoft.github.io/genaiscript/getting-started/configuration/), like [Phi-3](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/), using [Ollama](https://ollama.com/), [LocalAI](https://localai.io/).\n\n```js\nscript({ ..., model: \"ollama:phi3\" })\n```\n\n---\n\n### 🐍 Code Interpreter\n\nLet the LLM run code in a sand-boxed execution environment.\n\n```js\nscript({ tools: [\"python_code_interpreter\"] })\n```\n\n---\n\n### 🐳 Containers\n\nRun code in Docker [containers](https://microsoft.github.io/genaiscript/reference/scripts/container).\n\n```js\nconst c = await host.container({ image: \"python:alpine\" })\nconst res = await c.exec(\"python --version\")\n```\n\n---\n\n### Video processing\n\nTranscribe and screenshot your videos so that you can feed them efficiently in your LLMs requests.\n\n```js\n// transcribe\nconst transcript = await transcript(\"path/to/audio.mp3\")\n// screenshots at segments\nconst frames = await ffmpeg.extractFrames(\"path_url_to_video\", { transcript })\ndef(\"TRANSCRIPT\", transcript)\ndef(\"FRAMES\", frames)\n```\n\n### 🧩 LLM Composition\n\n[Run LLMs](https://microsoft.github.io/genaiscript/reference/scripts/inline-prompts/) to build your LLM prompts.\n\n```js\nfor (const file of env.files) {\n const { text } = await runPrompt((_) => {\n _.def(\"FILE\", file)\n _.$`Summarize the FILE.`\n })\n def(\"SUMMARY\", text)\n}\n$`Summarize all the summaries.`\n```\n\n---\n\n### 🅿️ Prompty support\n\nRun your [Prompty](https://prompty.ai) files as well!\n\n```markdown\n---\nname: poem\n---\n\nWrite me a poem\n```\n\n---\n\n### Pluggable Secret Scanning\n\nScan your chats for secrets using [secret scanning](/genaiscript/reference/scripts/secret-scanning).\n\n```json\n{\n \"secretPatterns\": {\n ...,\n \"OpenAI API Key\": \"sk-[A-Za-z0-9]{32,48}\"\n }\n}\n```\n\n### ⚙ Automate with CLI or API\n\nAutomate using the [CLI](https://microsoft.github.io/genaiscript/reference/cli) or [API](https://microsoft.github.io/genaiscript/reference/api).\n\n```bash\nnpx genaiscript run tlaplus-linter \"*.tla\"\n```\n\n```js\nimport { run } from \"genaiscript/api\"\n\nconst res = await run(\"tlaplus-linter\", \"*.tla\")\n```\n\n---\n\n### Safety First!\n\nGenAIScript provides built-in Responsible AI system prompts and Azure Content Safety supports\nto validate [content safety](https://microsoft.github.io/genaiscript/reference/scripts/content-safety).\n\n```js wrap\nscript({ ...,\n system: [\"system.safety_harmful_content\", ...],\n contentSafety: \"azure\" // use azure content safety\n})\n\nconst safety = await host.contentSafety()\nconst res = await safety.detectPromptInjection(env.vars.input)\n```\n\n---\n\n### 💬 Pull Request Reviews\n\nIntegrate into your [Pull Requests checks](https://microsoft.github.io/genaiscript/reference/cli/run/#pull-requests) through comments, reviews, or description updates. Supports GitHub Actions and Azure DevOps pipelines.\n\n```bash wrap\nnpx genaiscript ... --pull-request-reviews\n```\n\n---\n\n### ⭐ Tests and Evals\n\nBuild reliable prompts using [tests and evals](https://microsoft.github.io/genaiscript/reference/scripts/tests) powered by [promptfoo](https://promptfoo.dev/).\n\n```js wrap\nscript({ ..., tests: {\n files: \"penguins.csv\",\n rubric: \"is a data analysis report\",\n facts: \"The data refers about penguin population in Antarctica.\",\n}})\n```\n\n---\n\n### LLM friendly docs\n\nThe entire content of the docs is rendered at https://microsoft.github.io/genaiscript/llms-full.txt in markdown. Feed it directly into your favorite RAG system.\n\nIf you are an LLM crawler, add the `.md` suffix to any documentation URLs to get a raw markdown content. For example, https://microsoft.github.io/genaiscript/guides/prompt-as-code.md (note the .md extension)\n\n## Contributing\n\nWe accept contributions! 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MCP directory API