Create lucky-doors-lay.md

.changeset/lucky-doors-lay.md

@@ -0,0 +1,5 @@
+---
+"@llamaindex/server": patch
+---
+
+feat: support cn utils for server UI

.github/workflows/lint_on_push_or_pull.yml

@@ -8,11 +8,6 @@ on:
     branches:
       - main
 
-env:
-  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
-  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
-  TURBO_REMOTE_ONLY: true
-
 jobs:
   lint:
     runs-on: ubuntu-latest

.github/workflows/preview.yml

@@ -1,11 +1,6 @@
 name: Publish Preview
 on: [pull_request]
 
-env:
-  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
-  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
-  TURBO_REMOTE_ONLY: true
-
 jobs:
   pre_release:
     name: Pre Release

.prettierignore

@@ -7,4 +7,3 @@ dist/
 .source/
 # prttier doesn't support mdx3 we are using
 *.mdx
-packages/server/server/

apps/next/CHANGELOG.md

@@ -1,50 +1,5 @@
 # @llamaindex/doc
 
-## 0.2.14
-
-### Patch Changes
-
-- Updated dependencies [1e59695]
-  - @llamaindex/readers@3.1.0
-
-## 0.2.13
-
-### Patch Changes
-
-- Updated dependencies [e5c3f95]
-  - @llamaindex/openai@0.3.4
-  - llamaindex@0.10.2
-
-## 0.2.12
-
-### Patch Changes
-
-- Updated dependencies [96dd798]
-  - @llamaindex/openai@0.3.3
-  - llamaindex@0.10.1
-
-## 0.2.11
-
-### Patch Changes
-
-- 6cf928f: chore: use bunchee for llamaindex
-- Updated dependencies [6cf928f]
-  - llamaindex@0.10.0
-
-## 0.2.10
-
-### Patch Changes
-
-- 411dcea: Add Nova Premier to AWS Nova models. Add EU endpoints
-
-## 0.2.9
-
-### Patch Changes
-
-- Updated dependencies [d365eb2]
-  - @llamaindex/openai@0.3.2
-  - llamaindex@0.9.19
-
 ## 0.2.8
 
 ### Patch Changes

apps/next/fallback.js

@@ -1,2 +0,0 @@
-// fallback for `fs` usage in `web-tree-sitter`
-module.exports = {};

apps/next/next.config.mjs

@@ -1,4 +1,5 @@
 import { createMDX } from "fumadocs-mdx/next";
+import MonacoWebpackPlugin from "monaco-editor-webpack-plugin";
 const withMDX = createMDX();
 
 /** @type {import('next').NextConfig} */
@@ -15,12 +16,7 @@ const config = {
     "twoslash",
     "typescript",
   ],
-  turbopack: {
-    resolveAlias: {
-      fs: { browser: "./fallback.js" },
-    },
-  },
-  webpack: (config) => {
+  webpack: (config, { isServer }) => {
     if (Array.isArray(config.target) && config.target.includes("web")) {
       config.target = ["web", "es2020"];
     }
@@ -32,6 +28,14 @@ const config = {
     };
     config.resolve.fallback ??= {};
     config.resolve.fallback.fs = false;
+    if (!isServer) {
+      config.plugins.push(
+        new MonacoWebpackPlugin({
+          languages: ["typescript"],
+          filename: "static/[name].worker.js",
+        }),
+      );
+    }
     config.resolve.alias["replicate"] = false;
     return config;
   },

apps/next/package.json

@@ -1,21 +1,19 @@
 {
   "name": "@llamaindex/doc",
-  "version": "0.2.14",
+  "version": "0.2.8",
   "private": true,
   "scripts": {
     "postinstall": "fumadocs-mdx",
     "prebuild": "pnpm run build:docs",
     "build": "next build",
-    "dev": "next dev --turbo",
+    "dev": "next dev",
     "start": "next start",
     "postbuild": "tsx scripts/post-build.mts && tsx scripts/validate-links.mts",
     "build:docs": "cross-env NODE_OPTIONS=\"--max-old-space-size=8192\" typedoc && tsx scripts/generate-docs.mts",
     "validate-links": "tsx scripts/validate-links.mts"
   },
   "dependencies": {
-    "@huggingface/transformers": "^3.5.0",
     "@icons-pack/react-simple-icons": "^10.1.0",
-    "@llama-flow/docs": "0.0.5",
     "@llamaindex/chat-ui": "0.2.0",
     "@llamaindex/cloud": "workspace:*",
     "@llamaindex/core": "workspace:*",
@@ -24,7 +22,6 @@
     "@llamaindex/readers": "workspace:*",
     "@llamaindex/workflow": "workspace:*",
     "@mdx-js/mdx": "^3.1.0",
-    "@monaco-editor/react": "^4.7.0",
     "@number-flow/react": "^0.3.4",
     "@radix-ui/react-dialog": "^1.1.2",
     "@radix-ui/react-icons": "^1.3.2",
@@ -39,21 +36,22 @@
     "clsx": "2.1.1",
     "foxact": "^0.2.41",
     "framer-motion": "^11.11.17",
-    "fumadocs-core": "^15.2.7",
+    "fumadocs-core": "^15.0.15",
     "fumadocs-docgen": "^2.0.0",
-    "fumadocs-mdx": "^11.6.0",
-    "fumadocs-openapi": "^8.0.1",
-    "fumadocs-twoslash": "^3.1.1",
-    "fumadocs-typescript": "^4.0.2",
-    "fumadocs-ui": "^15.2.7",
+    "fumadocs-mdx": "^11.5.6",
+    "fumadocs-openapi": "^6.3.0",
+    "fumadocs-twoslash": "^3.1.0",
+    "fumadocs-typescript": "^3.1.0",
+    "fumadocs-ui": "^15.0.15",
     "hast-util-to-jsx-runtime": "^2.3.2",
     "llamaindex": "workspace:*",
     "lucide-react": "^0.460.0",
-    "next": "^15.3.0",
+    "next": "^15.2.4",
     "next-themes": "^0.4.3",
-    "react": "^19.1.0",
-    "react-dom": "^19.1.0",
+    "react": "^19.0.0",
+    "react-dom": "^19.0.0",
     "react-icons": "^5.3.0",
+    "react-monaco-editor": "^0.56.2",
     "react-use-measure": "^2.1.1",
     "rehype-katex": "^7.0.1",
     "remark-math": "^6.0.0",
@@ -65,14 +63,12 @@
     "tailwindcss-animate": "^1.0.7",
     "tree-sitter": "^0.22.1",
     "tree-sitter-typescript": "^0.23.2",
-    "ts-morph": "^25.0.1",
-    "twoslash": "^0.3.1",
     "use-stick-to-bottom": "^1.0.42",
     "web-tree-sitter": "^0.24.4",
     "zod": "^3.23.8"
   },
   "devDependencies": {
-    "@next/env": "^15.3.0",
+    "@next/env": "^15.2.1",
     "@tailwindcss/postcss": "^4.0.9",
     "@types/mdx": "^2.0.13",
     "@types/node": "22.9.0",
@@ -82,6 +78,7 @@
     "cross-env": "^7.0.3",
     "fast-glob": "^3.3.2",
     "gray-matter": "^4.0.3",
+    "monaco-editor-webpack-plugin": "^7.1.0",
     "postcss": "^8.5.3",
     "raw-loader": "^4.0.2",
     "remark": "^15.0.1",
@@ -90,9 +87,9 @@
     "remark-stringify": "^11.0.0",
     "tailwindcss": "^4.0.9",
     "tsx": "^4.19.3",
-    "typedoc": "0.28.3",
-    "typedoc-plugin-markdown": "^4.6.2",
-    "typedoc-plugin-merge-modules": " ^7.0.0",
+    "typedoc": "0.27.4",
+    "typedoc-plugin-markdown": "^4.3.1",
+    "typedoc-plugin-merge-modules": "^6.1.0",
     "typescript": "^5.7.3"
   }
 }

apps/next/scripts/generate-docs.mts

@@ -1,24 +1,27 @@
-import {
-  createGenerator,
-  generateFiles as typescriptGenerateFiles,
-} from "fumadocs-typescript";
+import { generateFiles as openapiGenerateFiles } from "fumadocs-openapi";
+import { generateFiles as typescriptGenerateFiles } from "fumadocs-typescript";
 import fs from "node:fs";
 import * as path from "node:path";
 import { rimrafSync } from "rimraf";
 
-const generator = createGenerator();
 const out = "./src/content/docs/cloud/api";
 const apiRefOut = "./src/content/docs/api";
 
 // clean generated files
 rimrafSync(out, {
   filter(v) {
-    return !v.endsWith("index.md") && !v.endsWith("meta.json");
+    return !v.endsWith("index.mdx") && !v.endsWith("meta.json");
   },
 });
 
-void typescriptGenerateFiles(generator, {
-  input: ["./src/content/docs/api/**/*.md"],
+void openapiGenerateFiles({
+  input: ["../../packages/cloud/openapi.json"],
+  output: "./src/content/docs/cloud/api",
+  groupBy: "tag",
+});
+
+void typescriptGenerateFiles({
+  input: ["./src/content/docs/api/**/*.mdx"],
   output: (file) => path.resolve(path.dirname(file), path.basename(file)),
   transformOutput,
 });
@@ -27,22 +30,19 @@ function transformOutput(filePath: string, content: string) {
   const fileName = path.basename(filePath);
   let title = fileName.split(".")[0];
   if (title === "index") title = "LlamaIndex API Reference";
-  return `---\ntitle: ${title}\n---\n\n${transformAbsoluteUrl(
-    content.replace(/(?<!\\)\{([^}]+)(?<!\\)}/g, "\\{$1\\}"),
-    filePath,
-  )}`;
+  return `---\ntitle: ${title}\n---\n\n${transformAbsoluteUrl(content, filePath)}`;
 }
 
 /**
- * Transforms the content by converting relative MD links to absolute docs API links
- * Example: [text](../type-aliases/TaskHandler.md) -> [text](/docs/api/type-aliases/TaskHandler)
- * [text](BaseChatEngine.md) -> [text](/docs/api/classes/BaseChatEngine)
- * [text](BaseVectorStore.md#constructors) -> [text](/docs/api/classes/BaseVectorStore#constructors)
- * [text](TaskStep.md) -> [text](/docs/api/type-aliases/TaskStep)
+ * Transforms the content by converting relative MDX links to absolute docs API links
+ * Example: [text](../type-aliases/TaskHandler.mdx) -> [text](/docs/api/type-aliases/TaskHandler)
+ * [text](BaseChatEngine.mdx) -> [text](/docs/api/classes/BaseChatEngine)
+ * [text](BaseVectorStore.mdx#constructors) -> [text](/docs/api/classes/BaseVectorStore#constructors)
+ * [text](TaskStep.mdx) -> [text](/docs/api/type-aliases/TaskStep)
  */
 function transformAbsoluteUrl(content: string, filePath: string) {
   const group = path.dirname(filePath).split(path.sep).pop();
-  return content.replace(/\]\(([^)]+)\.md([^)]*)\)/g, (_, slug, anchor) => {
+  return content.replace(/\]\(([^)]+)\.mdx([^)]*)\)/g, (_, slug, anchor) => {
     const slugParts = slug.split("/");
     const fileName = slugParts[slugParts.length - 1];
     const fileGroup = slugParts[slugParts.length - 2] ?? group;

apps/next/scripts/validate-links.mts

@@ -28,14 +28,14 @@ interface RelativeLinkResult {
  * Get all valid documentation routes from the content directory
  */
 async function getValidRoutes(): Promise<Set<string>> {
-  const mdxFiles = await glob("**/*.mdx?", { cwd: CONTENT_DIR });
+  const mdxFiles = await glob("**/*.mdx", { cwd: CONTENT_DIR });
 
   const routes = new Set<string>();
 
   // Add each MDX file as a valid route
   for (const file of mdxFiles) {
     // Remove .mdx extension and normalize to route format
-    let route = file.replace(/\.mdx?$/, "");
+    let route = file.replace(/\.mdx$/, "");
 
     // Handle index files
     if (route.endsWith("/index")) {
@@ -131,7 +131,7 @@ function findRelativeLinksInFile(
  * Find relative links in all MDX files
  */
 async function findRelativeLinks(): Promise<RelativeLinkResult[]> {
-  const mdxFiles = await glob("**/*.mdx?", { cwd: CONTENT_DIR });
+  const mdxFiles = await glob("**/*.mdx", { cwd: CONTENT_DIR });
   const results: RelativeLinkResult[] = [];
 
   for (const file of mdxFiles) {
@@ -150,7 +150,7 @@ async function findRelativeLinks(): Promise<RelativeLinkResult[]> {
 }
 
 async function validateLinks(): Promise<LinkValidationResult[]> {
-  const mdxFiles = await glob("**/*.mdx?", { cwd: CONTENT_DIR });
+  const mdxFiles = await glob("**/*.mdx", { cwd: CONTENT_DIR });
   const validRoutes = await getValidRoutes();
 
   const results: LinkValidationResult[] = [];

apps/next/source.config.ts

@@ -1,18 +1,13 @@
-import {
-  rehypeCodeDefaultOptions,
-  remarkStructure,
-} from "fumadocs-core/mdx-plugins";
+import { rehypeCodeDefaultOptions } from "fumadocs-core/mdx-plugins";
 import { fileGenerator, remarkDocGen, remarkInstall } from "fumadocs-docgen";
 import { defineConfig, defineDocs } from "fumadocs-mdx/config";
 import { transformerTwoslash } from "fumadocs-twoslash";
+import { createFileSystemTypesCache } from "fumadocs-twoslash/cache-fs";
 import rehypeKatex from "rehype-katex";
 import remarkMath from "remark-math";
 
 export const docs = defineDocs({
-  dir: ["./src/content/docs", "./node_modules/@llama-flow/docs"],
-  docs: {
-    async: true,
-  },
+  dir: "./src/content/docs",
 });
 
 export default defineConfig({
@@ -26,7 +21,11 @@ export default defineConfig({
       },
       transformers: [
         ...(rehypeCodeDefaultOptions.transformers ?? []),
-        transformerTwoslash(),
+        transformerTwoslash({
+          typesCache: createFileSystemTypesCache({
+            dir: ".next/cache/twoslash",
+          }),
+        }),
         {
           name: "transformers:remove-notation-escape",
           code(hast) {
@@ -47,7 +46,6 @@ export default defineConfig({
       ],
     },
     remarkPlugins: [
-      remarkStructure,
       remarkMath,
       [remarkInstall, { persist: { id: "package-manager" } }],
       [remarkDocGen, { generators: [fileGenerator()] }],

apps/next/src/app/(home)/page.tsx

@@ -10,55 +10,16 @@ import { MagicMove } from "@/components/magic-move";
 import { NpmInstall } from "@/components/npm-install";
 import { Supports } from "@/components/supports";
 import { Button } from "@/components/ui/button";
-import { DOCUMENT_URL } from "@/lib/const";
+import { Skeleton } from "@/components/ui/skeleton";
+import { LEGACY_DOCUMENT_URL } from "@/lib/const";
 import { SiStackblitz } from "@icons-pack/react-simple-icons";
+import {
+  CodeBlock as FumaCodeBlock,
+  Pre,
+} from "fumadocs-ui/components/codeblock";
 import { Blocks, Bot, Footprints, Terminal } from "lucide-react";
 import Link from "next/link";
-
-const codes = [
-  `import { openai } from "@llamaindex/openai";
-
-const llm = openai();
-const response = await llm.complete({ prompt: "How are you?" });`,
-  `import { openai } from "@llamaindex/openai";
-
-const llm = openai();
-const response = await llm.chat({
-  messages: [{ content: "Tell me a joke.", role: "user" }],
-});`,
-  `import { agent } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-
-const analyseAgent = agent({
-  llm: openai({ model: "gpt-4o" }),
-  tools: [analyseTools],
-  systemPrompt,
-});
-const response = await analyseAgent.run(\`Analyse the given data:
-\${data}\`);`,
-  `import { agent, multiAgent } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-
-const analyseAgent = agent({
-  name: "AnalyseAgent",
-  llm: openai({ model: "gpt-4o" }),
-  tools: [analyseTools],
-});
-const reporterAgent = agent({
-  name: "ReporterAgent",
-  llm: openai({ model: "gpt-4o" }),
-  tools: [reporterTools],
-  canHandoffTo: [analyseAgent],
-});
-
-const agents = multiAgent({
-  agents: [analyseAgent, reporterAgent],
-  rootAgent: reporterAgent,
-});
-
-const response = await agents.run(\`Analyse the given data:
-\${data}\`);`,
-];
+import { Suspense } from "react";
 
 export default function HomePage() {
   return (
@@ -78,7 +39,7 @@ export default function HomePage() {
       </div>
 
       <div className="flex flex-wrap justify-center gap-4">
-        <Link href={DOCUMENT_URL}>
+        <Link href={LEGACY_DOCUMENT_URL}>
           <Button variant="outline">Get Started</Button>
         </Link>
         <NpmInstall />
@@ -101,10 +62,65 @@ export default function HomePage() {
           heading="From the simplest to the most complex"
           description="LlamaIndex.TS is designed to be simple to get started, but powerful enough to build complex, agentic AI applications using multi-agents."
         >
-          <MagicMove
-            placeholder={<CodeBlock lang="ts" code={codes[0]} />}
-            code={codes}
-          />
+          <Suspense
+            fallback={
+              <FumaCodeBlock allowCopy={false}>
+                <Pre>
+                  <div className="space-y-2">
+                    <Skeleton className="h-4 w-[250px]" />
+                    <Skeleton className="h-4 w-[200px]" />
+                  </div>
+                </Pre>
+              </FumaCodeBlock>
+            }
+          >
+            <MagicMove
+              code={[
+                `import { openai } from "@llamaindex/openai";
+
+const llm = openai();
+const response = await llm.complete({ prompt: "How are you?" });`,
+                `import { openai } from "@llamaindex/openai";
+
+const llm = openai();
+const response = await llm.chat({
+  messages: [{ content: "Tell me a joke.", role: "user" }],
+});`,
+                `import { agent } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+
+const analyseAgent = agent({
+  llm: openai({ model: "gpt-4o" }),
+  tools: [analyseTools],
+  systemPrompt,
+});
+const response = await analyseAgent.run(\`Analyse the given data:
+\${data}\`);`,
+                `import { agent, multiAgent } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+
+const analyseAgent = agent({
+  name: "AnalyseAgent",
+  llm: openai({ model: "gpt-4o" }),
+  tools: [analyseTools],
+});
+const reporterAgent = agent({
+  name: "ReporterAgent",
+  llm: openai({ model: "gpt-4o" }),
+  tools: [reporterTools],
+  canHandoffTo: [analyseAgent],
+});
+
+const agents = multiAgent({
+  agents: [analyseAgent, reporterAgent],
+  rootAgent: reporterAgent,
+});
+
+const response = await agents.run(\`Analyse the given data:
+\${data}\`);`,
+              ]}
+            />
+          </Suspense>
         </Feature>
         <Feature
           icon={Bot}

apps/next/src/app/api/search/route.ts

@@ -1,12 +1,4 @@
 import { source } from "@/lib/source";
-import { structure } from "fumadocs-core/mdx-plugins";
 import { createFromSource } from "fumadocs-core/search/server";
 
-// TODO: migrate to another search service, I don't think Vercel can handle that many of documents.
-export const { GET } = createFromSource(source, (page) => ({
-  id: page.file.path,
-  title: page.data.title,
-  description: page.data.description,
-  url: page.url,
-  structuredData: structure(page.data.content),
-}));
+export const { GET } = createFromSource(source);

apps/next/src/app/docs/[[...slug]]/page.tsx

@@ -1,14 +1,7 @@
-import { ChatDemoRSC } from "@/components/demo/chat/rsc/demo";
-import * as demos from "@/components/demo/lazy";
 import { createMetadata, metadataImage } from "@/lib/metadata";
 import { openapi, source } from "@/lib/source";
-import * as Icons from "@icons-pack/react-simple-icons";
-import { APIPage } from "fumadocs-openapi/ui";
 import { Popup, PopupContent, PopupTrigger } from "fumadocs-twoslash/ui";
-import { createGenerator } from "fumadocs-typescript";
-import { AutoTypeTable } from "fumadocs-typescript/ui";
-import { Accordion, Accordions } from "fumadocs-ui/components/accordion";
-import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+import { createTypeTable } from "fumadocs-typescript/ui";
 import defaultMdxComponents from "fumadocs-ui/mdx";
 import {
   DocsBody,
@@ -18,8 +11,6 @@ import {
 } from "fumadocs-ui/page";
 import { notFound } from "next/navigation";
 
-const generator = createGenerator();
-
 export const revalidate = false;
 
 export default async function Page(props: {
@@ -29,17 +20,17 @@ export default async function Page(props: {
   const page = source.getPage(params.slug);
   if (!page) notFound();
 
-  const { body: MDX, toc, lastModified } = await page.data.load();
+  const { AutoTypeTable } = createTypeTable();
+  const MDX = page.data.body;
 
   return (
     <DocsPage
-      toc={toc}
+      toc={page.data.toc}
       full={page.data.full}
-      lastUpdate={lastModified}
+      lastUpdate={page.data.lastModified}
       editOnGithub={{
         owner: "run-llama",
         repo: "LlamaIndexTS",
-        sha: "main",
         path: `apps/next/src/content/docs/${page.file.path}`,
       }}
     >
@@ -48,21 +39,12 @@ export default async function Page(props: {
       <DocsBody>
         <MDX
           components={{
-            ...Icons,
             ...defaultMdxComponents,
-            ...demos,
-            ChatDemoRSC,
-            Accordion,
-            Accordions,
-            APIPage: (props) => <APIPage {...openapi.getAPIPageProps(props)} />,
-            Tab,
-            Tabs,
+            APIPage: openapi.APIPage,
             Popup,
             PopupContent,
             PopupTrigger,
-            AutoTypeTable: (props) => (
-              <AutoTypeTable generator={generator} {...props} />
-            ),
+            AutoTypeTable,
           }}
         />
       </DocsBody>

apps/next/src/app/docs/layout.tsx

@@ -1,7 +1,11 @@
 import { baseOptions } from "@/app/layout.config";
+import { AITrigger } from "@/components/ai-chat";
+import { buttonVariants } from "@/components/ui/button";
 import { source } from "@/lib/source";
+import { cn } from "@/lib/utils";
 import "fumadocs-twoslash/twoslash.css";
 import { DocsLayout } from "fumadocs-ui/layouts/docs";
+import { MessageCircle } from "lucide-react";
 import type { ReactNode } from "react";
 
 export default function Layout({ children }: { children: ReactNode }) {
@@ -9,9 +13,23 @@ export default function Layout({ children }: { children: ReactNode }) {
     <DocsLayout
       tree={source.pageTree}
       {...baseOptions}
-      links={[]}
       nav={{
         ...baseOptions.nav,
+        children: (
+          <AITrigger
+            className={cn(
+              buttonVariants({
+                variant: "secondary",
+                size: "xs",
+                className:
+                  "text-fd-muted-foreground ms-2 gap-1.5 rounded-full px-2 md:flex-1",
+              }),
+            )}
+          >
+            <MessageCircle className="size-3" />
+            Ask LlamaCloud
+          </AITrigger>
+        ),
       }}
     >
       {children}

apps/next/src/app/global.css

@@ -1,7 +1,7 @@
 @import "tailwindcss";
 @import "fumadocs-ui/css/neutral.css";
 @import "fumadocs-ui/css/preset.css";
-@import "../../node_modules/fumadocs-twoslash/styles/twoslash.css";
+@import "../../node_modules/fumadocs-twoslash/dist/twoslash.css";
 @plugin "tailwindcss-animate";
 @source '../../node_modules/fumadocs-ui/dist/**/*.js';
 @source "../../node_modules/fumadocs-openapi/dist/**/*.js",

apps/next/src/app/layout.config.tsx

@@ -1,4 +1,4 @@
-import { DOCUMENT_URL } from "@/lib/const";
+import { LEGACY_DOCUMENT_URL } from "@/lib/const";
 import type { BaseLayoutProps } from "fumadocs-ui/layouts/shared";
 import Image from "next/image";
 
@@ -27,19 +27,9 @@ export const baseOptions: BaseLayoutProps = {
   githubUrl: "https://github.com/run-llama/LlamaIndexTS",
   links: [
     {
-      text: "TypeScript",
-      url: DOCUMENT_URL,
+      text: "Docs",
+      url: LEGACY_DOCUMENT_URL,
       active: "nested-url",
     },
-    {
-      text: "Python",
-      url: "https://docs.llamaindex.ai",
-      active: "url",
-    },
-    {
-      text: "LlamaCloud",
-      url: "https://docs.cloud.llamaindex.ai/",
-      active: "url",
-    },
   ],
 };

apps/next/src/app/llms.txt/route.ts

@@ -13,7 +13,11 @@ import remarkStringify from "remark-stringify";
 export const revalidate = false;
 
 export async function GET() {
-  const files = await fg(["./src/content/docs/**/*.mdx"]);
+  const files = await fg([
+    "./src/content/docs/**/*.mdx",
+    // remove generated openapi files
+    "!./src/content/docs/cloud/api/**/*",
+  ]);
 
   const scan = files.map(async (file) => {
     const fileContent = await fs.readFile(file);

apps/next/src/components/demo/code-node-parser.tsx

@@ -1,26 +1,24 @@
 "use client";
+import { createContextState } from "foxact/context-state";
+import { useIsClient } from "foxact/use-is-client";
+import { useShiki } from "fumadocs-core/utils/use-shiki";
+import { CodeBlock, Pre } from "fumadocs-ui/components/codeblock";
+import { lazy, Suspense, use, useMemo } from "react";
+import { StickToBottom, useStickToBottomContext } from "use-stick-to-bottom";
+import Parser from "web-tree-sitter";
+
 import { Label } from "@/components/ui/label";
 import { Skeleton } from "@/components/ui/skeleton";
 import { Slider } from "@/components/ui/slider";
 import { CodeSplitter } from "@llamaindex/node-parser/code";
-import { Editor } from "@monaco-editor/react";
-import { createContextState } from "foxact/context-state";
-import { useIsClient } from "foxact/use-is-client";
-import { useShiki } from "fumadocs-core/highlight/client";
-import { CodeBlock, Pre } from "fumadocs-ui/components/codeblock";
-import { Suspense, use, useMemo } from "react";
-import { StickToBottom, useStickToBottomContext } from "use-stick-to-bottom";
 
 let promise: Promise<CodeSplitter>;
 if (typeof window !== "undefined") {
-  async function run() {
-    const { default: Parser } = await import("web-tree-sitter");
-    await Parser.init({
-      locateFile(scriptName: string) {
-        return "/" + scriptName;
-      },
-    });
-
+  promise = Parser.init({
+    locateFile(scriptName: string) {
+      return "/" + scriptName;
+    },
+  }).then(async () => {
     const parser = new Parser();
     const Lang = await Parser.Language.load("/tree-sitter-typescript.wasm");
     parser.setLanguage(Lang);
@@ -28,9 +26,7 @@ if (typeof window !== "undefined") {
       getParser: () => parser,
       maxChars: 100,
     });
-  }
-
-  promise = run();
+  });
 }
 
 const [SliderProvider, useSlider, useSetSlider] = createContextState(100);
@@ -52,6 +48,8 @@ const john: Person = {
 
 console.log(greet(john));`);
 
+const Editor = lazy(() => import("react-monaco-editor"));
+
 export const IDE = () => {
   const codeSplitter = use(promise);
   const code = useCode();
@@ -75,6 +73,21 @@ export const IDE = () => {
         />
       </div>
       <Editor
+        editorWillMount={() => {}}
+        editorDidMount={() => {
+          window.MonacoEnvironment!.getWorkerUrl = (
+            _moduleId: string,
+            label: string,
+          ) => {
+            if (label === "json") return "/_next/static/json.worker.js";
+            if (label === "css") return "/_next/static/css.worker.js";
+            if (label === "html") return "/_next/static/html.worker.js";
+            if (label === "typescript" || label === "javascript")
+              return "/_next/static/ts.worker.js";
+            return "/_next/static/editor.worker.js";
+          };
+        }}
+        editorWillUnmount={() => {}}
         options={{
           minimap: {
             enabled: false,
@@ -84,9 +97,7 @@ export const IDE = () => {
         height="100%"
         width="100%"
         language="typescript"
-        onChange={(v) => {
-          if (v) setCode(v);
-        }}
+        onChange={setCode}
         value={code}
       />
     </div>

apps/next/src/components/demo/lazy.ts

@@ -1,18 +0,0 @@
-"use client";
-import dynamic from "next/dynamic";
-
-// lazy load client components
-export const ChatDemo = dynamic(() =>
-  import("@/components/demo/chat/api/demo").then((mod) => mod.ChatDemo),
-);
-
-export const CodeNodeParserDemo = dynamic(() =>
-  import("@/components/demo/code-node-parser").then(
-    (mod) => mod.CodeNodeParserDemo,
-  ),
-);
-export const WorkflowStreamingDemo = dynamic(() =>
-  import("@/components/demo/workflow-streaming-ui").then(
-    (mod) => mod.WorkflowStreamingDemo,
-  ),
-);

apps/next/src/components/magic-move.tsx

@@ -1,27 +1,25 @@
 "use client";
 import { Button } from "@/components/ui/button";
 import { cn } from "@/lib/utils";
-import { CodeBlock } from "fumadocs-ui/components/codeblock";
+import { CodeBlock, Pre } from "fumadocs-ui/components/codeblock";
 import { RotateCcw } from "lucide-react";
 import { useTheme } from "next-themes";
-import { type ReactNode, use, useCallback, useEffect, useState } from "react";
-import { createJavaScriptRegexEngine, getSingletonHighlighter } from "shiki";
+import { use, useCallback, useEffect, useState } from "react";
+import { getSingletonHighlighter } from "shiki";
 import { ShikiMagicMove } from "shiki-magic-move/react";
+import { createOnigurumaEngine } from "shiki/engine/oniguruma";
 
-const engine = createJavaScriptRegexEngine();
 const highlighterPromise = getSingletonHighlighter({
-  engine,
+  engine: createOnigurumaEngine(() => import("shiki/wasm")),
   themes: ["vesper", "github-light"],
   langs: ["js", "ts", "tsx"],
 });
 
 export type MagicMoveProps = {
   code: string[];
-  placeholder: ReactNode;
 };
 
 export function MagicMove(props: MagicMoveProps) {
-  const [mounted, setMounted] = useState(false);
   const [move, setMove] = useState<number>(0);
   const currentCode = props.code[move];
   const highlighter = use(highlighterPromise);
@@ -40,27 +38,24 @@ export function MagicMove(props: MagicMoveProps) {
     }
   }, [animate, move, props.code]);
 
-  useEffect(() => {
-    setMounted(true);
-  }, []);
-
-  if (!mounted) return props.placeholder;
-
   return (
     <CodeBlock allowCopy={false}>
-      <ShikiMagicMove
-        className="shiki !block p-4 *:!inline"
-        lang="ts"
-        theme={resolvedTheme === "dark" ? "vesper" : "github-light"}
-        highlighter={highlighter}
-        code={currentCode}
-        options={{
-          duration: 800,
-          stagger: 0.3,
-          lineNumbers: false,
-          containerStyle: false,
-        }}
-      />
+      {highlighter && (
+        <Pre>
+          <ShikiMagicMove
+            lang="ts"
+            theme={resolvedTheme === "dark" ? "vesper" : "github-light"}
+            highlighter={highlighter}
+            code={currentCode}
+            options={{
+              duration: 800,
+              stagger: 0.3,
+              lineNumbers: false,
+              containerStyle: false,
+            }}
+          />
+        </Pre>
+      )}
       <Button
         className={cn(
           "absolute bottom-2 right-2",

apps/next/src/content/docs/cloud/index.mdx

@@ -0,0 +1,8 @@
+---
+title: LlamaCloud
+description: LlamaCloud is a new generation of managed parsing, ingestion, and retrieval services, designed to bring production-grade context-augmentation to your LLM and RAG applications.
+---
+
+This is TypeScript binding for LlamaCloud API. It provides a simple way to interact with LlamaCloud API.
+
+If you are looking for the official documentation, please visit the [Official Document](https://docs.cloud.llamaindex.ai/)

apps/next/src/content/docs/cloud/meta.json

@@ -0,0 +1,6 @@
+{
+  "title": "LlamaCloud",
+  "description": "The Cloud framework for LLM",
+  "root": true,
+  "pages": ["---Guide---", "index", "..."]
+}

apps/next/src/content/docs/llamaindex/getting_started/create_llama.mdx

@@ -18,4 +18,4 @@ npm run dev
 
 to start the development server. You can then visit [http://localhost:3000](http://localhost:3000) to see your app, which should look something like this:
 
-![create-llama interface](/images/create_llama.png)
+![create-llama interface](./images/create_llama.png)

apps/next/src/content/docs/llamaindex/getting_started/examples.mdx

@@ -11,7 +11,7 @@ It may be useful to check out all the examples at once so you can try them out l
 ```bash npm2yarn
 npx degit run-llama/LlamaIndexTS/examples my-new-project
 cd my-new-project
-npm i
+npm install
 ```
 
 Then you can run any example in the folder with `tsx`, e.g.:

apps/next/src/content/docs/llamaindex/getting_started/installation/cloudflare.mdx

@@ -3,6 +3,13 @@ title: With Cloudflare Worker
 description: In this guide, you'll learn how to use LlamaIndex with CloudFlare Worker
 ---
 
+import {
+  SiNodedotjs,
+  SiDeno,
+  SiBun,
+  SiCloudflareworkers,
+} from "@icons-pack/react-simple-icons";
+
 Before you start, make sure you have try LlamaIndex.TS in Node.js to make sure you understand the basics.
 
 <Card

apps/next/src/content/docs/llamaindex/getting_started/installation/index.mdx

@@ -3,17 +3,46 @@ title: Installation
 description: How to install llamaindex packages.
 ---
 
+import {
+	SiNodedotjs,
+	SiTypescript,
+	SiNextdotjs,
+	SiCloudflareworkers,
+	SiVite
+} from "@icons-pack/react-simple-icons";
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
 To install llamaindex, run the following command:
 
-```package-install
-npm i llamaindex
-```
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex
+	```
+</Tabs>
 
 In most cases, you'll also need an LLM package to use LlamaIndex. For example, to use the OpenAI LLM, you would install the following:
 
-```package-install
-npm i @llamaindex/openai
-```
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add @llamaindex/openai
+	```
+</Tabs>
 
 Go to [LLM APIs](/docs/llamaindex/modules/models/llms) to find out how to use other LLMs.
 

apps/next/src/content/docs/llamaindex/getting_started/installation/node.mdx

@@ -3,6 +3,8 @@ title: With Node.js/Bun/Deno
 description: In this guide, you'll learn how to use LlamaIndex with Node.js, Bun, and Deno.
 ---
 
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
 ## Adding environment variables
 
 By default, LlamaIndex uses OpenAI provider, which requires an API key. You can set the `OPENAI_API_KEY` environment variable to authenticate with OpenAI.
@@ -26,9 +28,19 @@ For more information, see the [How to read environment variables from Node.js](h
 
 By the default, we are using `js-tiktoken` for tokenization. You can install `gpt-tokenizer` which is then automatically used by LlamaIndex to get a 60x speedup for tokenization:
 
-```package-install
-npm i gpt-tokenizer
-```
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install gpt-tokenizer
+	```
+
+	```shell tab="yarn"
+	yarn add gpt-tokenizer
+	```
+
+	```shell tab="pnpm"
+	pnpm add gpt-tokenizer
+	```
+</Tabs>
 
 **Note**: This only works for Node.js
 

apps/next/src/content/docs/llamaindex/getting_started/installation/typescript.mdx

@@ -45,7 +45,7 @@ We recommend using `bundler` or `nodenext`, but due to popularity of `node`, we
 So you may encounter type errors when importing sub paths from the `llamaindex` package like:
 
 ```ts
-import { Settings } from "llamaindex";
+import { Settings } from "llamaindex/Settings";
 ```
 
 The simplest way to fix this without changing `moduleResolution` is to import directly from `llamaindex`:

apps/next/src/content/docs/llamaindex/index.mdx

@@ -3,6 +3,13 @@ title: What is LlamaIndex.TS
 description: LlamaIndex is the leading data framework for building LLM applications
 ---
 
+import {
+  SiNodedotjs,
+  SiDeno,
+  SiBun,
+  SiCloudflareworkers,
+} from "@icons-pack/react-simple-icons";
+
 LlamaIndex is a framework for building context-augmented generative AI applications with LLMs including agents and workflows.
 
 The TypeScript implementation is designed for JavaScript server side applications using <SiNodedotjs className="inline" color="#5FA04E" /> Node.js, <SiDeno className="inline" color="#70FFAF" /> Deno, <SiBun className="inline" /> Bun, <SiCloudflareworkers className="inline" color="#F38020" /> Cloudflare Workers, and more.

apps/next/src/content/docs/llamaindex/integration/lang-trace.mdx

@@ -2,6 +2,7 @@
 title: Langtrace
 description: Learn how to integrate LlamaIndex.TS with Langtrace.
 ---
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
 
 Enhance your observability with Langtrace, a robust open-source tool supports OpenTelemetry and is designed to trace, evaluate, and manage LLM applications seamlessly. Langtrace integrates directly with LlamaIndex, offering detailed, real-time insights into performance metrics such as accuracy, evaluations, and latency.
 
@@ -9,9 +10,19 @@ Enhance your observability with Langtrace, a robust open-source tool supports Op
 
 - Self-host or sign-up and generate an API key using [Langtrace](https://www.langtrace.ai) Cloud
 
-```package-install
-npm i @langtrase/typescript-sdk
-```
+<Tabs groupId="install-langtrase" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @langtrase/typescript-sdk
+	```
+
+	```shell tab="yarn"
+	yarn add @langtrase/typescript-sdk
+	```
+
+	```shell tab="pnpm"
+	pnpm add @langtrase/typescript-sdk
+	```
+</Tabs>
 
 ## Initialize
 

apps/next/src/content/docs/llamaindex/integration/open-llm-metry.mdx

@@ -2,15 +2,27 @@
 title: OpenLLMetry
 description: Learn how to integrate LlamaIndex.TS with OpenLLMetry.
 ---
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
 
 [OpenLLMetry](https://github.com/traceloop/openllmetry-js) is an open-source project based on OpenTelemetry for tracing and monitoring
 LLM applications. It connects to [all major observability platforms](https://www.traceloop.com/docs/openllmetry/integrations/introduction) and installs in minutes.
 
 ### Usage Pattern
 
-```package-install
-npm i @traceloop/node-server-sdk
-```
+
+<Tabs groupId="install-traceloop" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @traceloop/node-server-sdk
+	```
+
+	```shell tab="yarn"
+	yarn add @traceloop/node-server-sdk
+	```
+
+	```shell tab="pnpm"
+	pnpm add @traceloop/node-server-sdk
+	```
+</Tabs>
 
 ```js
 import * as traceloop from "@traceloop/node-server-sdk";

apps/next/src/content/docs/llamaindex/integration/vercel.mdx

@@ -11,8 +11,8 @@ LlamaIndex provides integration with Vercel's AI SDK, allowing you to create pow
 
 First, install the required dependencies:
 
-```package-install
-npm i @llamaindex/vercel ai
+```bash
+npm install @llamaindex/vercel ai
 ```
 
 ## Using Vercel AI's Model Providers 

apps/next/src/content/docs/llamaindex/migration/0.8-to-0.9.mdx

@@ -2,6 +2,8 @@
 title: Migrating from v0.8 to v0.9
 ---
 
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
 Version 0.9 of LlamaIndex.TS introduces significant architectural changes to improve package size and runtime compatibility. The main goals of this release are:
 
 1. Reduce the package size of the main `llamaindex` package by moving dependencies into provider packages, making it more suitable for serverless environments
@@ -31,9 +33,19 @@ import { OpenAI } from "@llamaindex/openai";
 
 > Note: This examples requires installing the `@llamaindex/openai` package: 
 
-```package-install
-npm i @llamaindex/openai
-```
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add @llamaindex/openai
+	```
+</Tabs>
 
 For more details on available AI model providers and their configuration, see the [LLMs documentation](/docs/llamaindex/modules/models/llms) and the [Embedding Models documentation](/docs/llamaindex/modules/models/embeddings).
 

apps/next/src/content/docs/llamaindex/modules/agents/tool/index.mdx

@@ -57,41 +57,6 @@ const researchAgent = agent({
 });
 ```
 
-## MCP tools
-
-If you have a MCP server running, you can fetch tools from the server and use them in your agents.
-
-```ts
-// 1. Import MCP tools adapter
-import { mcp } from "@llamaindex/tools";
-import { agent } from "llamaindex";
-
-// 2. Initialize a MCP client
-// by npx
-const server = mcp({
-  command: "npx",
-  args: ["-y", "@modelcontextprotocol/server-filesystem", "."],
-  verbose: true,
-});
-// or by SSE
-const server = mcp({
-  url: "http://localhost:8000/mcp",
-  verbose: true,
-});
-
-// 3. Get tools from MCP server
-const tools = await server.tools();
-
-// Now you can create an agent with the tools
-const agent = agent({
-  name: "My Agent",
-  systemPrompt: "You are a helpful assistant that can use the provided tools to answer questions.",
-  llm: openai({ model: "gpt-4o" }),
-  tools: tools,
-});
-```
-
-
 ## Function tool
 
 You can still use the `FunctionTool` class to define a tool.

apps/next/src/content/docs/llamaindex/modules/agents/workflows.mdx

@@ -2,260 +2,163 @@
 title: Workflows
 ---
 
-A `Workflow` in LlamaIndex is a lightweight, event-driven abstraction used to chain together several events. Workflows are made up of `handlers`, with each one responsible for processing specific event types and emitting new events.
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/workflow/joke.ts";
 
-Workflows are designed to be flexible and can be used to build agents, RAG flows, extraction flows, or anything else you want to implement.
+A `Workflow` in LlamaIndexTS is an event-driven abstraction used to chain together several events. Workflows are made up of `steps`, with each step responsible for handling certain event types and emitting new events.
 
-```package-install
-npm i @llama-flow/core @llamaindex/openai
-```
+Workflows in LlamaIndexTS work by defining step functions that handle specific event types and emit new events.
+
+When a step function is added to a workflow, you need to specify the input and optionally the output event types (used for validation). The specification of the input events ensures each step only runs when an accepted event is ready.
+
+You can create a `Workflow` to do anything! Build an agent, a RAG flow, an extraction flow, or anything else you want.
+
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @llamaindex/workflow
+	```
+
+	```shell tab="yarn"
+	yarn add @llamaindex/workflow
+	```
+
+	```shell tab="pnpm"
+	pnpm add @llamaindex/workflow
+	```
+</Tabs>
 
 ## Getting Started
 
-Let's explore a simple workflow example where a joke is generated and then critiqued and iterated on:
+As an illustrative example, let's consider a naive workflow where a joke is generated and then critiqued.
 
-```typescript
-import { OpenAI } from "@llamaindex/openai";
-import { createWorkflow, workflowEvent } from "@llama-flow/core";
-import { withStore } from "@llama-flow/core/middleware/store";
+<DynamicCodeBlock lang="ts" code={CodeSource} />
 
-// Create LLM instance
-const llm = new OpenAI({ model: "gpt-4.1-mini", apiKey: "..."});
-
-// Define our workflow events
-const startEvent = workflowEvent<string>(); // Input topic for joke
-const jokeEvent = workflowEvent<{ joke: string }>(); // Intermediate joke
-const critiqueEvent = workflowEvent<{ joke: string, critique: string }>(); // Intermediate critique
-const resultEvent = workflowEvent<{ joke: string, critique: string }>(); // Final joke + critique
-
-// Create our workflow
-const jokeFlow = withStore(
-    () => ({
-        numIterations: 0,
-        maxIterations: 3,
-    }),
-    createWorkflow()
-);
-
-// Define handlers for each step
-jokeFlow.handle([startEvent], async (event) => {
-  // Prompt the LLM to write a joke
-  const prompt = `Write your best joke about ${event.data}. Write the joke between <joke> and </joke> tags.`;
-  const response = await llm.complete({ prompt });
-
-  // Parse the joke from the response
-  const joke = response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ?? response.text;
-  return jokeEvent.with({ joke: joke });
-});
-
-jokeFlow.handle([jokeEvent], async (event) => {
-  // Prompt the LLM to critique the joke
-  const prompt = `Give a thorough critique of the following joke. If the joke needs improvement, put "IMPROVE" somewhere in the critique: ${event.data.joke}`;
-  const response = await llm.complete({ prompt });
-
-  // If the critique includes "IMPROVE", keep iterating, else, return the result
-  if (response.text.includes("IMPROVE")) {
-    return critiqueEvent.with({ joke: event.data.joke, critique: response.text });
-  }
-
-  return resultEvent.with({ joke: event.data.joke, critique: response.text });
-});
-
-jokeFlow.handle([critiqueEvent], async (event) => {
-  // Keep track of the number of iterations
-  const store = jokeFlow.getStore();
-  store.numIterations++;
-
-  // Write a new joke based on the previous joke and critique
-  const prompt = `Write a new joke based on the following critique and the original joke. Write the joke between <joke> and </joke> tags.\n\nJoke: ${event.data.joke}\n\nCritique: ${event.data.critique}`;
-  const response = await llm.complete({ prompt });
-  
-  // Parse the joke from the response
-  const joke = response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ?? response.text;
-  
-  // If we've done less than the max number of iterations, keep iterating
-  // else, return the result
-  if (store.numIterations < store.maxIterations) {
-    return jokeEvent.with({ joke: joke });
-  }
-
-  return resultEvent.with({ joke: joke, critique: event.data.critique });
-});
-
-// Usage
-async function main() {
-  const { stream, sendEvent } = jokeFlow.createContext();
-  sendEvent(startEvent.with("pirates"));
-
-  let result: { joke: string, critique: string } | undefined;
-
-  for await (const event of stream) {
-    // console.log(event.data);  optionally log the event data
-    if (resultEvent.include(event)) {
-      result = event.data;
-      break; // Stop when we get the final result
-    }
-  }
-  
-  console.log(result);
-}
-
-main().catch(console.error);
-```
-
-There are a few moving pieces here, so let's go through this step by step.
+There's a few moving pieces here, so let's go through this piece by piece.
 
 ### Defining Workflow Events
 
 ```typescript
-const startEvent = workflowEvent<string>(); // Input topic for joke
-const jokeEvent = workflowEvent<{ joke: string }>(); // Intermediate joke
-const critiqueEvent = workflowEvent<{ joke: string, critique: string }>(); // Intermediate critique
-const resultEvent = workflowEvent<{ joke: string, critique: string }>(); // Final joke + critique
+export class JokeEvent extends WorkflowEvent<{ joke: string }> {}
 ```
 
-Events are defined using the `workflowEvent` function and contain arbitrary data provided as a generic type. In this example, we have four events:
-- `startEvent`: Takes a string input (the joke topic)
-- `jokeEvent`: Contains an object with a joke property
-- `critiqueEvent`: Contains both the joke and its critique, used for the feedback loop
-- `resultEvent`: Contains the final joke and critique after any iterations
+Events are user-defined classes that extend `WorkflowEvent` and contain arbitrary data provided as template argument. In this case, our workflow relies on a single user-defined event, the `JokeEvent` with a `joke` attribute of type `string`.
 
-### Setting up the Workflow with Store Middleware
+### Setting up the Workflow Class
 
 ```typescript
-const jokeFlow = withStore(
-    () => ({
-        numIterations: 0,
-        maxIterations: 3,
-    }),
-    createWorkflow()
+const llm = new OpenAI();
+...
+const jokeFlow = new Workflow<unknown, string, string>();
+```
+
+Our workflow is implemented by initiating the `Workflow` class with three generic types: the context type (unknown), input type (string), and output type (string). The context type is `unknown`, as we're not using a shared context in this example.
+
+For simplicity, we created an `OpenAI` llm instance that we're using for inference in our workflow.
+
+### Workflow Entry Points
+
+```typescript
+const generateJoke = async (_: unknown, ev: StartEvent<string>) => {
+  const prompt = `Write your best joke about ${ev.data}.`;
+  const response = await llm.complete({ prompt });
+  return new JokeEvent({ joke: response.text });
+};
+```
+
+Here, we come to the entry-point of our workflow. While events are user-defined, there are two special-case events, the `StartEvent` and the `StopEvent`. These events are predefined, but we can specify the payload type using generic types. We're using `StartEvent<string>` to indicate that we're going to send an input of type string.
+
+To add this step to the workflow, we use the `addStep` method with an object specifying the input and output event types:
+
+```typescript
+jokeFlow.addStep(
+  {
+    inputs: [StartEvent<string>],
+    outputs: [JokeEvent],
+  },
+  generateJoke
 );
 ```
 
-Our workflow is implemented using the `createWorkflow()` function, enhanced with the `withStore` middleware. The store provides shared state across all handlers, which in this case tracks:
-- `numIterations`: Counts how many iterations of joke improvement we've done
-- `maxIterations`: Sets a limit to prevent infinite loops
-
-This store will be accesible within workflows by using the `jokeFlow.getStore()` function.
-
-### Adding Handlers with Loops
-
-We have three key handlers in our workflow:
-
-1. The first handler processes the `startEvent`, generates an initial joke, and emits a `jokeEvent`:
+### Workflow Exit Points
 
 ```typescript
-jokeFlow.handle([startEvent], async (event) => {
-  // Prompt the LLM to write a joke
-  const prompt = `Write your best joke about ${event.data}. Write the joke between <joke> and </joke> tags.`;
+const critiqueJoke = async (_: unknown, ev: JokeEvent) => {
+  const prompt = `Give a thorough critique of the following joke: ${ev.data.joke}`;
   const response = await llm.complete({ prompt });
-
-  // Parse the joke from the response
-  const joke = response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ?? response.text;
-  return jokeEvent.with({ joke: joke });
-});
+  return new StopEvent(response.text);
+};
 ```
 
-2. The second handler handles the `jokeEvent`, critiques the joke, and either:
-   - Emits a `critiqueEvent` if the joke needs improvement
-   - Emits a `resultEvent` if the joke is good enough
+Here, we have our second and last step in the workflow. We know it's the last step because the special `StopEvent` is returned. When the workflow encounters a returned `StopEvent`, it immediately stops the workflow and returns the result. Note that we're using the generic type `StopEvent<string>` to indicate that we're returning a string.
+
+Add this step to the workflow:
 
 ```typescript
-jokeFlow.handle([jokeEvent], async (event) => {
-  // Prompt the LLM to critique the joke
-  const prompt = `Give a thorough critique of the following joke. If the joke needs improvement, put "IMPROVE" somewhere in the critique: ${event.data.joke}`;
-  const response = await llm.complete({ prompt });
-
-  // If the critique includes "IMPROVE", keep iterating, else, return the result
-  if (response.text.includes("IMPROVE")) {
-    return critiqueEvent.with({ joke: event.data.joke, critique: response.text });
-  }
-
-  return resultEvent.with({ joke: event.data.joke, critique: response.text });
-});
-```
-
-3. The third handler processes the `critiqueEvent`, generates an improved joke based on the critique, and either:
-   - Loops back to the joke evaluation (if under the iteration limit)
-   - Emits the final `resultEvent` (if iteration limit reached)
-
-```typescript
-jokeFlow.handle([critiqueEvent], async (event) => {
-  // Keep track of the number of iterations
-  const store = jokeFlow.getStore();
-  store.numIterations++;
-
-  // Write a new joke based on the previous joke and critique
-  const prompt = `Write a new joke based on the following critique and the original joke. Write the joke between <joke> and </joke> tags.\n\nJoke: ${event.data.joke}\n\nCritique: ${event.data.critique}`;
-  const response = await llm.complete({ prompt });
-  
-  // Parse the joke from the response
-  const joke = response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ?? response.text;
-  
-  // If we've done less than the max number of iterations, keep iterating
-  // else, return the result
-  if (store.numIterations < store.maxIterations) {
-    return jokeEvent.with({ joke: joke });
-  }
-
-  return resultEvent.with({ joke: joke, critique: event.data.critique });
-});
+jokeFlow.addStep(
+  {
+    inputs: [JokeEvent],
+    outputs: [StopEvent<string>],
+  },
+  critiqueJoke
+);
 ```
 
 ### Running the Workflow
 
 ```typescript
-async function main() {
-  const { stream, sendEvent } = jokeFlow.createContext();
-  sendEvent(startEvent.with("pirates"));
-
-  let result: { joke: string, critique: string } | undefined;
-
-  for await (const event of stream) {
-    // console.log(event.data);  optionally log the event data
-    if (resultEvent.include(event)) {
-      result = event.data;
-      break; // Stop when we get the final result
-    }
-  }
-  
-  console.log(result);
-}
+const result = await jokeFlow.run("pirates");
+console.log(result.data.result);
 ```
 
-To run the workflow, we:
-1. Create a workflow context with `createContext()`
-2. Trigger the initial event with `sendEvent()`
-3. Listen to the event stream and process events as they arrive
-4. Use `include()` to check if an event is of a specific type
-5. Break the loop when we receive our final result
+Lastly, we run the workflow. The `.run()` method is async, so we use await here to wait for the result.
 
-### Using Stream Utilities
+## Working with Shared Context/State
 
-Workflows provide utility functions to make working with event streams easier:
+Optionally, you can choose to use a shared context between steps by specifying a context type when creating the workflow. Here's an example where multiple steps access a shared state:
 
 ```typescript
-import { collect } from "@llama-flow/core/stream/consumer";
-import { until } from "@llama-flow/core/stream/until";
+import { HandlerContext } from "llamaindex";
 
-// Create a workflow context and send the initial event
-const { stream, sendEvent } = jokeFlow.createContext();
-sendEvent(startEvent.with("pirates"));
+type MyContextData = {
+  query: string;
+  intermediateResults: any[];
+}
 
-// Collect all events until we get a resultEvent
-const allEvents = await collect(until(stream, resultEvent));
+const query = async (context: HandlerContext<MyContextData>, ev: MyEvent) => {
+  // get the query from the context
+  const query = context.data.query;
+  // do something with context and event
+  const val = ...
+  // store in context
+  context.data.intermediateResults.push(val);
 
-// The last event will be the resultEvent
-const finalEvent = allEvents[allEvents.length - 1];
-console.log(finalEvent.data); // Output the joke and critique
+  return new StopEvent({ result });
+};
 ```
 
-The stream utilities make it easier to work with the asynchronous event flow. In this example, we use:
-- `collect`: Aggregates all events into an array
-- `until`: Creates a stream that emits events until a condition is met (in this case, until a resultEvent is received)
+## Waiting for Multiple Events
 
-You can combine these utilities with other stream operators like `filter` and `map` to create powerful processing pipelines.
+The context does more than just hold data, it also provides utilities to buffer and wait for multiple events.
 
-## Next Steps
+For example, you might have a step that waits for a query and retrieved nodes before synthesizing a response:
 
-To learn more about workflows, check out [the documentation in the tutorial section](../../../llamaflow).
+```typescript
+const synthesize = async (context: Context, ev1: QueryEvent, ev2: RetrieveEvent) => {
+  const subPrompts = [`Answer this query using the context provided: ${ev1.data.query}`, `Context: ${ev2.data.context}`];
+  const prompt = subPrompts.join("\n");
+  const response = await llm.complete({ prompt });
+  return new StopEvent({ result: response.text });
+};
+```
+
+Passing multiple events, we can buffer and wait for ALL expected events to arrive. The receiving step function will only be called once all events have arrived.
+
+## Manually Triggering Events
+
+Normally, events are triggered by returning another event during a step. However, events can also be manually dispatched using the `ctx.sendEvent(event)` method within a workflow.
+
+## Examples
+
+You can find many useful examples of using workflows in the [examples folder](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/workflow).

apps/next/src/content/docs/llamaindex/modules/data/data_index/managed.mdx

@@ -3,6 +3,10 @@ title: Managed Index
 description: Managed index using LlamaCloud
 ---
 
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/cloud/chat.ts";
+import CodeSource2 from "!raw-loader!@/examples/cloud/from-documents.ts";
+
 LlamaCloud is a new generation of managed parsing, ingestion, and retrieval services, designed to bring production-grade context-augmentation to your LLM and RAG applications.
 
 LlamaCloud supports
@@ -18,13 +22,13 @@ Visit [LlamaCloud](https://cloud.llamaindex.ai) to sign in and get an API key.
 
 Here's an example of how to create a managed index by ingesting a couple of documents:
 
-<include cwd>../../examples/cloud/chat.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource2} />
 
 ## Use a Managed Index
 
 Here's an example of how to use a managed index together with a chat engine:
 
-<include cwd>../../examples/cloud/from-documents.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource} />
 
 ## API Reference
 

apps/next/src/content/docs/llamaindex/modules/data/ingestion_pipeline/index.mdx

@@ -7,9 +7,21 @@ These `Transformations` are applied to your input data, and the resulting nodes
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/openai @llamaindex/qdrant
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai @llamaindex/qdrant
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai @llamaindex/qdrant
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai @llamaindex/qdrant
+	```
+</Tabs>
 
 ## Usage Pattern
 

apps/next/src/content/docs/llamaindex/modules/data/ingestion_pipeline/transformations/node-parser.mdx

@@ -2,6 +2,8 @@
 title: Node Parsers / Text Splitters
 description: Learn how to use Node Parsers and Text Splitters to extract data from documents.
 ---
+import { CodeNodeParserDemo } from '@/components/demo/code-node-parser.tsx';
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
 
 Node parsers are a simple abstraction that take a list of `Document` objects, and chunk them into `Node` objects, such that each node is a specific chunk of the parent document. When a document is broken into nodes, all of it's attributes are inherited to the children nodes (i.e. `metadata`, text and metadata templates, etc.). You can read more about `Node` and `Document` properties [here](/docs/llamaindex/modules/data).
 
@@ -149,6 +151,8 @@ Try it out ⬇️
 
 <CodeNodeParserDemo/>
 
+import { Accordion, Accordions } from 'fumadocs-ui/components/accordion';
+
 <Accordions>
 	<Accordion title="Use it in browser">
 		You might setup WASM files for `web-tree-sitter` and use it in the browser.

apps/next/src/content/docs/llamaindex/modules/data/readers/discord.mdx

@@ -2,15 +2,12 @@
 title: DiscordReader
 ---
 
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/readers/src/discord";
+
 DiscordReader is a simple data loader that reads all messages in a given Discord channel and returns them as Document objects.
 It uses the [@discordjs/rest](https://github.com/discordjs/discord.js/tree/main/packages/rest) library to fetch the messages.
 
-## Installation
-
-```package-install
-npm install @llamaindex/discord
-```
-
 ## Usage
 
 First step is to create a Discord Application and generating a bot token [here](https://discord.com/developers/applications).
@@ -18,7 +15,7 @@ In your Discord Application, go to the `OAuth2` tab and generate an invite URL b
 This will invite the bot with the necessary permissions to read messages.
 Copy the URL in your browser and select the server you want your bot to join.
 
-<include cwd>../../examples/discord/reader.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource} />
 
 ### Params
 

apps/next/src/content/docs/llamaindex/modules/data/readers/index.mdx

@@ -3,6 +3,12 @@ title: Loading Data
 description: Loading data using Readers into Documents
 ---
 
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/readers/src/simple-directory-reader";
+import CodeSource2 from "!raw-loader!@/examples/readers/src/custom-simple-directory-reader";
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+import { Accordion, Accordions } from 'fumadocs-ui/components/accordion';
+
 Before you can start indexing your documents, you need to load them into memory.
 A reader is a module that loads data from a file into a `Document` object.
 
@@ -13,26 +19,46 @@ To install readers call:
 
 		If you want to use the reader module, you need to install `@llamaindex/readers`
 
-    ```package-install
-    npm i @llamaindex/readers
-    ```
+		<Tabs groupId="install-llamaindex" items={["npm", "yarn", "pnpm"]} persist>
+			```shell tab="npm"
+			npm install @llamaindex/readers
+			```
+
+			```shell tab="yarn"
+			yarn add @llamaindex/readers
+			```
+
+			```shell tab="pnpm"
+			pnpm add @llamaindex/readers
+			```
+
+		</Tabs>
 	</Accordion>
 </Accordions>
 
 We offer readers for different file formats.
 
-```ts twoslash
-import { CSVReader } from '@llamaindex/readers/csv';
-import { DocxReader } from '@llamaindex/readers/docx';
-import { HTMLReader } from '@llamaindex/readers/html';
-import { ImageReader } from '@llamaindex/readers/image';
-import { JSONReader } from '@llamaindex/readers/json';
-import { MarkdownReader } from '@llamaindex/readers/markdown';
-import { ObsidianReader } from '@llamaindex/readers/obsidian';
-import { PDFReader } from '@llamaindex/readers/pdf';
-import { TextFileReader } from '@llamaindex/readers/text';
+```ts twoslash 
+import { CSVReader } from '@llamaindex/readers/csv'
+import { PDFReader } from '@llamaindex/readers/pdf'
+import { JSONReader } from '@llamaindex/readers/json'
+import { MarkdownReader } from '@llamaindex/readers/markdown'
+import { HTMLReader } from '@llamaindex/readers/html'
+// you can find more readers in the documentation
 ```
 
+Additionally the following loaders exist without separate documentation:
+
+- `AssemblyAIReader` transcribes audio using [AssemblyAI](https://www.assemblyai.com/).
+  - [AudioTranscriptReader](/docs/api/classes/AudioTranscriptReader): loads entire transcript as a single document.
+  - [AudioTranscriptParagraphsReader](/docs/api/classes/AudioTranscriptParagraphsReader): creates a document per paragraph.
+  - [AudioTranscriptSentencesReader](/docs/api/classes/AudioTranscriptSentencesReader): creates a document per sentence.
+  - [AudioSubtitlesReader](/docs/api/classes/AudioTranscriptParagraphsReader): creates a document containing the subtitles of a transcript.
+- [NotionReader](/docs/api/classes/NotionReader) loads [Notion](https://www.notion.so/) pages.
+- [SimpleMongoReader](/docs/api/classes/SimpleMongoReader) loads data from a [MongoDB](https://www.mongodb.com/).
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
 ## SimpleDirectoryReader
 
 [Open in StackBlitz](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples/readers?file=src/simple-directory-reader.ts&title=Simple%20Directory%20Reader)
@@ -41,7 +67,7 @@ LlamaIndex.TS supports easy loading of files from folders using the `SimpleDirec
 
 It is a simple reader that reads all files from a directory and its subdirectories and delegates the actual reading to the reader specified in the `fileExtToReader` map.
 
-<include cwd>../../examples/readers/src/simple-directory-reader.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource} />
 
 Currently, the following readers are mapped to specific file types:
 
@@ -63,7 +89,7 @@ SimpleDirectoryReader supports up to 9 concurrent requests. Use the `numWorkers`
 
 ### Example
 
-<include cwd>../../examples/readers/src/custom-simple-directory-reader.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource2} />
 
 ## Tips when using in non-Node.js environments
 

apps/next/src/content/docs/llamaindex/modules/data/readers/json.mdx

@@ -8,9 +8,21 @@ Supports streaming of large JSON data using [@discoveryjs/json-ext](https://gith
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/readers
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/readers
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/readers
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/readers
+	```
+</Tabs>
 
 ## Usage
 

apps/next/src/content/docs/llamaindex/modules/data/readers/llama_parse/images.mdx

@@ -6,9 +6,21 @@ LlamaParse `json` mode supports extracting any images found in a page object by
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/cloud @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/cloud @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/cloud @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/cloud @llamaindex/openai
+	```
+</Tabs>
 
 ## Usage
 
@@ -112,3 +124,6 @@ The returned `imageDocs` have the alt text assigned as text and the image path a
 
 You can see the full example file [here](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/readers/src/llamaparse-json.ts).
 
+## API Reference
+
+- [LlamaParseReader](/docs/api/classes/LlamaParseReader)

apps/next/src/content/docs/llamaindex/modules/data/readers/llama_parse/index.mdx

@@ -2,6 +2,10 @@
 title: LlamaParse
 ---
 
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/readers/src/llamaparse";
+import CodeSource2 from "!raw-loader!@/examples/readers/src/simple-directory-reader-with-llamaparse.ts";
+
 LlamaParse is an API created by LlamaIndex to efficiently parse files, e.g. it's great at converting PDF tables into markdown.
 
 To use it, first login and get an API key from https://cloud.llamaindex.ai. Make sure to store the key as `apiKey` parameter or in the environment variable `LLAMA_CLOUD_API_KEY`.
@@ -13,7 +17,7 @@ Official documentation for LlamaParse can be found [here](https://docs.cloud.lla
 You can then use the `LlamaParseReader` class to load local files and convert them into a parsed document that can be used by LlamaIndex.
 See [reader.ts](https://github.com/run-llama/LlamaIndexTS/blob/main/packages/cloud/src/reader.ts) for a list of supported file types:
 
-<include cwd>../../examples/readers/src/llamaparse.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource} />
 
 ### Params
 
@@ -32,7 +36,7 @@ They can be divided into two groups.
 #### Advanced params:
 
 - `resultType` can be set to `markdown`, `text` or `json`. Defaults to `text`. More information about `json` mode on the next pages.
-- `language` primarily helps with OCR recognition. Defaults to `en`.
+- `language` primarily helps with OCR recognition. Defaults to `en`. Click [here](/docs/api/type-aliases/Language) for a list of supported languages.
 - `parsingInstructions?` Optional. Can help with complicated document structures. See this [LlamaIndex Blog Post](https://www.llamaindex.ai/blog/launching-the-first-genai-native-document-parsing-platform) for an example.
 - `skipDiagonalText?` Optional. Set to true to ignore diagonal text. (Text that is not rotated 0, 90, 180 or 270 degrees)
 - `invalidateCache?` Optional. Set to true to ignore the LlamaCloud cache. All document are kept in cache for 48hours after the job was completed to avoid processing the same document twice. Can be useful for testing when trying to re-parse the same document with, e.g. different `parsingInstructions`.
@@ -56,8 +60,9 @@ They can be divided into two groups.
 
 Below a full example of `LlamaParse` integrated in `SimpleDirectoryReader` with additional options.
 
-<include cwd>../../examples/readers/src/simple-directory-reader-with-llamaparse.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource2} />
 
 ## API Reference
 
 - [SimpleDirectoryReader](/docs/api/classes/SimpleDirectoryReader)
+- [LlamaParseReader](/docs/api/classes/LlamaParseReader)

apps/next/src/content/docs/llamaindex/modules/data/readers/llama_parse/json_mode.mdx

@@ -6,9 +6,21 @@ In JSON mode, LlamaParse will return a data structure representing the parsed ob
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/cloud
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/cloud
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/cloud
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/cloud
+	```
+</Tabs>
 
 ## Usage
 
@@ -98,4 +110,5 @@ You can assign any other values of the JSON response to the Document as needed.
 
 ## API Reference
 
+- [LlamaParseReader](/docs/api/classes/LlamaParseReader)
 - [SimpleDirectoryReader](/docs/api/classes/SimpleDirectoryReader)

apps/next/src/content/docs/llamaindex/modules/data/stores/doc_stores/index.mdx

@@ -13,9 +13,21 @@ Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for t
 
 ## Using PostgreSQL as Document Store
 
-```package-install
-npm i llamaindex @llamaindex/postgres
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/postgres
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/postgres
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/postgres
+	```
+</Tabs>
 
 You can configure the `schemaName`, `tableName`, `namespace`, and
 `connectionString`. If a `connectionString` is not

apps/next/src/content/docs/llamaindex/modules/data/stores/index_stores/index.mdx

@@ -13,9 +13,21 @@ Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for t
 
 ## Using PostgreSQL as Index Store
 
-```package-install
-npm i llamaindex @llamaindex/postgres
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/postgres
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/postgres
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/postgres
+	```
+</Tabs>
 
 You can configure the `schemaName`, `tableName`, `namespace`, and
 `connectionString`. If a `connectionString` is not

apps/next/src/content/docs/llamaindex/modules/data/stores/vector_stores/qdrant.mdx

@@ -13,9 +13,21 @@ docker run -p 6333:6333 qdrant/qdrant
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/qdrant
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/qdrant
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/qdrant
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/qdrant
+	```
+</Tabs>
 
 ## Importing the modules
 

apps/next/src/content/docs/llamaindex/modules/data/stores/vector_stores/supabase.mdx

@@ -8,9 +8,21 @@ To use this vector store, you need a Supabase project. You can create one at [su
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/supabase
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+  ```shell tab="npm"
+  npm install llamaindex @llamaindex/supabase
+  ```
+
+  ```shell tab="yarn"
+  yarn add llamaindex @llamaindex/supabase
+  ```
+
+  ```shell tab="pnpm"
+  pnpm add llamaindex @llamaindex/supabase
+  ```
+</Tabs>
 
 ## Database Setup
 

apps/next/src/content/docs/llamaindex/modules/evaluation/correctness.mdx

@@ -10,9 +10,21 @@ This is useful for measuring if the response was correct. The evaluator returns
 
 Firstly, you need to install the package:
 
-```package-install
-npm i llamaindex @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
 
 Set the OpenAI API key:
 

apps/next/src/content/docs/llamaindex/modules/evaluation/faithfulness.mdx

@@ -12,9 +12,22 @@ This is useful for measuring if the response was hallucinated. The evaluator ret
 
 Firstly, you need to install the package:
 
-```package-install
-npm i llamaindex @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
+
 
 Set the OpenAI API key:
 

apps/next/src/content/docs/llamaindex/modules/evaluation/relevancy.mdx

@@ -10,9 +10,22 @@ It is useful for measuring if the response was relevant to the query. The evalua
 
 Firstly, you need to install the package:
 
-```package-install
-npm i llamaindex @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
+
 
 Set the OpenAI API key:
 

apps/next/src/content/docs/llamaindex/modules/models/embeddings/deepinfra.mdx

@@ -7,9 +7,21 @@ Check out available embedding models [here](https://deepinfra.com/models/embeddi
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/deepinfra
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/deepinfra
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/deepinfra
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/deepinfra
+	```
+</Tabs>
 
 ```ts
 import { Document, Settings, VectorStoreIndex } from "llamaindex";

apps/next/src/content/docs/llamaindex/modules/models/embeddings/gemini.mdx

@@ -6,9 +6,21 @@ To use Gemini embeddings, you need to import `GeminiEmbedding` from `@llamaindex
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/google
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/google
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/google
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/google
+	```
+</Tabs>
 
 ```ts
 import { Document, Settings, VectorStoreIndex } from "llamaindex";

apps/next/src/content/docs/llamaindex/modules/models/embeddings/huggingface.mdx

@@ -6,9 +6,21 @@ To use HuggingFace embeddings, you need to import `HuggingFaceEmbedding` from `@
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/huggingface
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/huggingface
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/huggingface
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/huggingface
+	```
+</Tabs>
 
 ```ts
 import { Document, Settings, VectorStoreIndex } from "llamaindex";

apps/next/src/content/docs/llamaindex/modules/models/embeddings/index.mdx

@@ -8,9 +8,21 @@ This can be explicitly updated through `Settings.embedModel`.
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
 
 ```typescript
 import { OpenAIEmbedding } from "@llamaindex/openai";

apps/next/src/content/docs/llamaindex/modules/models/embeddings/mistral.mdx

@@ -6,9 +6,21 @@ To use MistralAI embeddings, you need to import `MistralAIEmbedding` from `@llam
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/mistral
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/mistral
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/mistral
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/mistral
+	```
+</Tabs>
 
 ```ts
 import { Document, Settings, VectorStoreIndex } from "llamaindex";

apps/next/src/content/docs/llamaindex/modules/models/embeddings/mixedbreadai.mdx

@@ -14,9 +14,22 @@ To find out more about the latest features, updates, and available models, visit
 
 ## Setup
 
-```package-install
-npm i llamaindex @llamaindex/mixedbread
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/mixedbread
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/mixedbread
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/mixedbread
+	```
+</Tabs>
+
 
 Next, sign up for an API key at [mixedbread.ai](https://mixedbread.ai/). Once you have your API key, you can import the necessary modules and create a new instance of the `MixedbreadAIEmbeddings` class.
 

apps/next/src/content/docs/llamaindex/modules/models/embeddings/ollama.mdx

@@ -14,9 +14,21 @@ ollama pull nomic-embed-text
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/ollama
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/ollama
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/ollama
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/ollama
+	```
+</Tabs>
 
 ```ts
 import { OllamaEmbedding } from "@llamaindex/ollama";

apps/next/src/content/docs/llamaindex/modules/models/embeddings/openai.mdx

@@ -6,9 +6,21 @@ To use OpenAI embeddings, you need to import `OpenAIEmbedding` from `@llamaindex
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
 
 ```ts
 import { OpenAIEmbedding } from "@llamaindex/openai";

apps/next/src/content/docs/llamaindex/modules/models/embeddings/voyageai.mdx

@@ -6,9 +6,21 @@ To use VoyageAI embeddings, you need to import `VoyageAIEmbedding` from `@llamai
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/voyage-ai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/voyage-ai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/voyage-ai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/voyage-ai
+	```
+</Tabs>
 
 ```ts
 import { VoyageAIEmbedding } from "@llamaindex/voyage-ai";

apps/next/src/content/docs/llamaindex/modules/models/llms/anthropic.mdx

@@ -4,9 +4,21 @@ title: Anthropic
 
 ## Installation
 
-```shell tab="npm"
-npm i llamaindex @llamaindex/anthropic
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/anthropic
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/anthropic
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/anthropic
+	```
+</Tabs>
 
 ## Usage
 

apps/next/src/content/docs/llamaindex/modules/models/llms/azure.mdx

@@ -16,9 +16,21 @@ export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
 
 ## Usage
 

apps/next/src/content/docs/llamaindex/modules/models/llms/bedrock.mdx

@@ -4,9 +4,21 @@ title: Bedrock
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/community
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/community
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/community
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/community
+	```
+</Tabs>
 
 ## Usage
 
@@ -45,7 +57,6 @@ META_LLAMA3_2_1B_INSTRUCT = "meta.llama3-2-1b-instruct-v1:0"; // only available
 META_LLAMA3_2_3B_INSTRUCT = "meta.llama3-2-3b-instruct-v1:0"; // only available via inference endpoints (see below)
 META_LLAMA3_2_11B_INSTRUCT = "meta.llama3-2-11b-instruct-v1:0"; // only available via inference endpoints (see below), multimodal and function call supported
 META_LLAMA3_2_90B_INSTRUCT = "meta.llama3-2-90b-instruct-v1:0"; // only available via inference endpoints (see below), multimodal and function call supported
-AMAZON_NOVA_PREMIER_1 = "amazon.nova-premier-v1:0";
 AMAZON_NOVA_PRO_1 = "amazon.nova-pro-v1:0";
 AMAZON_NOVA_LITE_1 = "amazon.nova-lite-v1:0";
 AMAZON_NOVA_MICRO_1 = "amazon.nova-micro-v1:0";
@@ -65,7 +76,6 @@ US_META_LLAMA_3_2_1B_INSTRUCT = "us.meta.llama3-2-1b-instruct-v1:0";
 US_META_LLAMA_3_2_3B_INSTRUCT = "us.meta.llama3-2-3b-instruct-v1:0";
 US_META_LLAMA_3_2_11B_INSTRUCT = "us.meta.llama3-2-11b-instruct-v1:0";
 US_META_LLAMA_3_2_90B_INSTRUCT = "us.meta.llama3-2-90b-instruct-v1:0";
-US_AMAZON_NOVA_PRO_1 = "us.amazon.nova-premier-v1:0";
 US_AMAZON_NOVA_PRO_1 = "us.amazon.nova-pro-v1:0";
 US_AMAZON_NOVA_LITE_1 = "us.amazon.nova-lite-v1:0";
 US_AMAZON_NOVA_MICRO_1 = "us.amazon.nova-micro-v1:0";
@@ -76,10 +86,6 @@ EU_ANTHROPIC_CLAUDE_3_SONNET = "eu.anthropic.claude-3-sonnet-20240229-v1:0";
 EU_ANTHROPIC_CLAUDE_3_5_SONNET = "eu.anthropic.claude-3-5-sonnet-20240620-v1:0";
 EU_META_LLAMA_3_2_1B_INSTRUCT = "eu.meta.llama3-2-1b-instruct-v1:0";
 EU_META_LLAMA_3_2_3B_INSTRUCT = "eu.meta.llama3-2-3b-instruct-v1:0";
-EU_AMAZON_NOVA_PRO_1 = "eu.amazon.nova-premier-v1:0";
-EU_AMAZON_NOVA_PRO_1 = "eu.amazon.nova-pro-v1:0";
-EU_AMAZON_NOVA_LITE_1 = "eu.amazon.nova-lite-v1:0";
-EU_AMAZON_NOVA_MICRO_1 = "eu.amazon.nova-micro-v1:0";
 ```
 
 Sonnet, Haiku and Opus are multimodal, image_url only supports base64 data url format, e.g. `data:image/jpeg;base64,SGVsbG8sIFdvcmxkIQ==`

apps/next/src/content/docs/llamaindex/modules/models/llms/deepinfra.mdx

@@ -6,9 +6,21 @@ Check out available LLMs [here](https://deepinfra.com/models/text-generation).
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/deepinfra
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/deepinfra
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/deepinfra
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/deepinfra
+	```
+</Tabs>
 
 ```ts
 import { DeepInfra } from "@llamaindex/deepinfra";

apps/next/src/content/docs/llamaindex/modules/models/llms/gemini.mdx

@@ -4,9 +4,21 @@ title: Gemini
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/google
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/google
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/google
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/google
+	```
+</Tabs>
 
 ## Usage
 
@@ -57,7 +69,7 @@ const gemini = new Gemini({
 To authenticate for local development:
 
 ```bash
-npm i @google-cloud/vertexai
+npm install @google-cloud/vertexai
 gcloud auth application-default login
 ```
 

apps/next/src/content/docs/llamaindex/modules/models/llms/groq.mdx

@@ -2,11 +2,26 @@
 title: Groq
 ---
 
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/groq.ts";
+
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/groq
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/groq
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/groq
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/groq
+	```
+</Tabs>
 
 ## Usage
 
@@ -55,7 +70,7 @@ const results = await queryEngine.query({
 
 ## Full Example
 
-<include cwd>../../examples/groq.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource} />
 
 ## API Reference
 

apps/next/src/content/docs/llamaindex/modules/models/llms/index.mdx

@@ -8,9 +8,21 @@ The LLM can be explicitly updated through `Settings`.
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
 
 ```typescript
 import { OpenAI } from "@llamaindex/openai";

apps/next/src/content/docs/llamaindex/modules/models/llms/llama2.mdx

@@ -4,9 +4,21 @@ title: LLama2
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/replicate
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/replicate
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/replicate
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/replicate
+	```
+</Tabs>
 
 ## Usage
 

apps/next/src/content/docs/llamaindex/modules/models/llms/mistral.mdx

@@ -4,9 +4,21 @@ title: Mistral
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/mistral
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/mistral
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/mistral
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/mistral
+	```
+</Tabs>
 
 ## Usage
 

apps/next/src/content/docs/llamaindex/modules/models/llms/ollama.mdx

@@ -4,9 +4,22 @@ title: Ollama
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/ollama
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/ollama
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/ollama
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/ollama
+	```
+</Tabs>
+
 
 ## Usage
 

apps/next/src/content/docs/llamaindex/modules/models/llms/openai.mdx

@@ -4,9 +4,22 @@ title: OpenAI
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
+
 
 ```ts
 import { OpenAI } from "@llamaindex/openai";

apps/next/src/content/docs/llamaindex/modules/models/llms/perplexity.mdx

@@ -4,9 +4,21 @@ title: Perplexity LLM
 
 ## Installation
 
-```package-install
-npm i @llamaindex/perplexity
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @llamaindex/perplexity
+	```
+
+	```shell tab="yarn"
+	yarn add @llamaindex/perplexity
+	```
+
+	```shell tab="pnpm"
+	pnpm add @llamaindex/perplexity
+	```
+</Tabs>
 
 ## Usage
 

apps/next/src/content/docs/llamaindex/modules/models/llms/portkey.mdx

@@ -4,9 +4,22 @@ title: Portkey LLM
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/portkey-ai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/portkey-ai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/portkey-ai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/portkey-ai
+	```
+</Tabs>
+
 
 ## Usage
 

apps/next/src/content/docs/llamaindex/modules/models/llms/together.mdx

@@ -4,9 +4,21 @@ title: Together LLM
 
 ## Installation
 
-```package-install
-npm i @llamaindex/together
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @llamaindex/together
+	```
+
+	```shell tab="yarn"
+	yarn add @llamaindex/together
+	```
+
+	```shell tab="pnpm"
+	pnpm add @llamaindex/together
+	```
+</Tabs>
 
 ## Usage
 

apps/next/src/content/docs/llamaindex/modules/rag/node_postprocessors/cohere_reranker.mdx

@@ -8,9 +8,21 @@ The Cohere Reranker is a postprocessor that uses the Cohere API to rerank the re
 
 Firstly, you will need to install the `llamaindex` package.
 
-```package-install
-npm i llamaindex @llamaindex/cohere @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/cohere @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/cohere @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/cohere @llamaindex/openai
+	```
+</Tabs>
 
 Now, you will need to sign up for an API key at [Cohere](https://cohere.ai/). Once you have your API key you can import the necessary modules and create a new instance of the `CohereRerank` class.
 

apps/next/src/content/docs/llamaindex/modules/rag/node_postprocessors/index.mdx

@@ -4,9 +4,21 @@ title: Node Postprocessors
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/cohere @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/cohere @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/cohere @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/cohere @llamaindex/openai
+	```
+</Tabs>
 
 ## Concept
 

apps/next/src/content/docs/llamaindex/modules/rag/node_postprocessors/jinaai_reranker.mdx

@@ -8,9 +8,22 @@ The Jina AI Reranker is a postprocessor that uses the Jina AI Reranker API to re
 
 Firstly, you will need to install the `llamaindex` package.
 
-```package-install
-npm i llamaindex @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
+
 
 Now, you will need to sign up for an API key at [Jina AI](https://jina.ai/reranker). Once you have your API key you can import the necessary modules and create a new instance of the `JinaAIReranker` class.
 

apps/next/src/content/docs/llamaindex/modules/rag/node_postprocessors/mixedbreadiai_reranker.mdx

@@ -17,9 +17,22 @@ To find out more about the latest features and updates, visit the [mixedbread.ai
 
 First, you will need to install the `llamaindex` package.
 
-```package-install
-npm i llamaindex @llamaindex/openai @llamaindex/mixedbread
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai @llamaindex/mixedbread
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai @llamaindex/mixedbread
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai @llamaindex/mixedbread
+	```
+</Tabs>
+
 
 Next, sign up for an API key at [mixedbread.ai](https://mixedbread.ai/). Once you have your API key, you can import the necessary modules and create a new instance of the `MixedbreadAIReranker` class.
 

apps/next/src/content/docs/llamaindex/modules/rag/query_engines/metadata_filtering.mdx

@@ -10,9 +10,21 @@ You can also check our multi-tenancy blog post to see how metadata filtering can
 
 Firstly if you haven't already, you need to install the `llamaindex` package:
 
-```package-install
-npm i llamaindex @llamaindex/openai @llamaindex/chroma
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai @llamaindex/chroma
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai @llamaindex/chroma
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai @llamaindex/chroma
+	```
+</Tabs>
 
 Then you can import the necessary modules from `llamaindex`:
 

apps/next/src/content/docs/llamaindex/modules/rag/query_engines/router_query_engine.mdx

@@ -8,9 +8,21 @@ In this tutorial, we define a custom router query engine that selects one out of
 
 First, we need to install import the necessary modules from `llamaindex`:
 
-```package-install
-npm i llamaindex @llamaindex/openai @llamaindex/readers
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai @llamaindex/readers
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai @llamaindex/readers
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai @llamaindex/readers
+	```
+</Tabs>
 
 ```ts
 import {

apps/next/src/content/docs/llamaindex/modules/ui/chat.mdx

@@ -2,6 +2,7 @@
 title: Using API Route
 description: Chat interface for your LlamaIndexTS application using API Route
 ---
+import { ChatDemo } from '../../../../../components/demo/chat/api/demo';
 
 Using [chat-ui](https://github.com/run-llama/chat-ui), it's easy to add a chat interface to your LlamaIndexTS application.
 You just need to create an API route that provides an `api/chat` endpoint and a chat component to consume the API.

apps/next/src/content/docs/llamaindex/modules/ui/install.mdx

@@ -16,7 +16,7 @@ npx shadcn@latest add https://ui.llamaindex.ai/r/chat.json
 To install the package, run the following command in your project directory:
 
 ```sh
-npm i @llamaindex/chat-ui
+npm install @llamaindex/chat-ui
 ```
 
 For more information, check out the [github.comrun-llama/chat-ui](https://github.com/run-llama/chat-ui)

apps/next/src/content/docs/llamaindex/modules/ui/llamaindex-server.mdx

@@ -3,6 +3,8 @@ title: Using LlamaIndex Server
 description: Running LlamaIndex workflows with both API endpoints and a user interface for interaction
 ---
 
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
 # LlamaIndex Server
 
 LlamaIndexServer is a Next.js-based application that allows you to quickly launch your [LlamaIndex Workflows](https://ts.llamaindex.ai/docs/llamaindex/modules/agents/workflows) and [Agent Workflows](https://ts.llamaindex.ai/docs/llamaindex/modules/agents/agent_workflow) as an API server with an optional chat UI. It provides a complete environment for running LlamaIndex workflows with both API endpoints and a user interface for interaction.
@@ -16,13 +18,23 @@ LlamaIndexServer is a Next.js-based application that allows you to quickly launc
 
 ## Installation
 
-```package-install
-npm i @llamaindex/server
-```
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @llamaindex/server
+	```
+
+	```shell tab="yarn"
+	yarn add @llamaindex/server
+	```
+
+	```shell tab="pnpm"
+	pnpm add @llamaindex/server
+	```
+</Tabs>
 
 ## Quick Start
 
-Create an `index.ts` file and add the following code:
+Create index.ts file and add the following code:
 
 ```ts
 import { LlamaIndexServer } from "@llamaindex/server";
@@ -43,20 +55,20 @@ new LlamaIndexServer({
 
 In the same directory as `index.ts`, run the following command to start the server:
 
-```bash
-tsx index.ts
-```
+  ```bash
+  tsx index.ts
+  ```
 The server will start at `http://localhost:3000`
 
 You can also make a request to the server:
 
-```bash
-curl -X POST "http://localhost:3000/api/chat" -H "Content-Type: application/json" -d '{"message": "Who is the first president of the United States?"}'
-```
+  ```bash
+  curl -X POST "http://localhost:3000/api/chat" -H "Content-Type: application/json" -d '{"message": "Who is the first president of the United States?"}'
+  ```
 
 ## Configuration Options
 
-The `LlamaIndexServer` accepts the following configuration options:
+The LlamaIndexServer accepts the following configuration
 
 - `workflow`: A callable function that creates a workflow instance for each request
 - `uiConfig`: An object to configure the chat UI containing the following properties:
@@ -68,72 +80,6 @@ The `LlamaIndexServer` accepts the following configuration options:
 LlamaIndexServer accepts all the configuration options from Nextjs Custom Server such as `port`, `hostname`, `dev`, etc.
 See all Nextjs Custom Server options [here](https://nextjs.org/docs/app/building-your-application/configuring/custom-server).
 
-## AI-generated UI Components
-
-The LlamaIndex server provides support for rendering workflow events using custom UI components, allowing you to extend and customize the chat interface. 
-These components can be auto-generated using an LLM by providing a JSON schema of the workflow event. 
-
-### UI Event Schema 
-
-To display custom UI components, your workflow needs to emit UI events that have an event type for identification and a data object:
-
-```typescript
-class UIEvent extends WorkflowEvent<{
-  type: "ui_event";
-  data: UIEventData;
-}> {}
-```
-
-The `data` object can be any JSON object. To enable AI generation of the UI component, you need to provide a schema for that data (here we're using Zod):
-
-```typescript
-const MyEventDataSchema = z.object({
-  stage: z.enum(["retrieve", "analyze", "answer"]).describe("The current stage the workflow process is in."),
-  progress: z.number().min(0).max(1).describe("The progress in percent of the current stage"),
-}).describe("WorkflowStageProgress");
-
-type UIEventData = z.infer<typeof MyEventDataSchema>;
-```
-
-### Generate UI Components
-
-The `generateEventComponent` function uses an LLM to generate a custom UI component based on the JSON schema of a workflow event. The schema should contain accurate descriptions of each field so that the LLM can generate matching components for your use case. We've done this for you in the example above using the `describe` function from Zod:
-
-```typescript
-import { OpenAI } from "llamaindex";
-import { generateEventComponent } from "@llamaindex/server";
-import { MyEventDataSchema } from "./your-workflow";
-
-// Also works well with Claude 3.5 Sonnet and Google Gemini 2.5 Pro
-const llm = new OpenAI({ model: "gpt-4.1" });
-const code = generateEventComponent(MyEventDataSchema, llm);
-```
-
-After generating the code, we need to save it to a file. The file name must match the event type from your workflow (e.g., `ui_event.jsx` for handling events with `ui_event` type):
-
-```ts
-fs.writeFileSync("components/ui_event.jsx", code);
-```
-
-Feel free to modify the generated code to match your needs. If you're not satisfied with the generated code, we suggest improving the provided JSON schema first or trying another LLM.
-
-> Note that `generateEventComponent` is generating JSX code, but you can also provide a TSX file.
-
-
-### Server Setup
-
-To use the generated UI components, you need to initialize the LlamaIndex server with the `componentsDir` that contains your custom UI components:
-
-```ts
-new LlamaIndexServer({
-  workflow: createWorkflow,
-  uiConfig: {
-    appTitle: "LlamaIndex App",
-    componentsDir: "components",
-  },
-}).start();
-```
-
 ## Default Endpoints and Features
 
 ### Chat Endpoint
@@ -151,19 +97,69 @@ The server always provides a chat interface at the root path (`/`) with:
 ### Static File Serving
 
 - The server automatically mounts the `data` and `output` folders at `{server_url}{api_prefix}/files/data` (default: `/api/files/data`) and `{server_url}{api_prefix}/files/output` (default: `/api/files/output`) respectively.
-- Your workflows can use both folders to store and access files. By convention, the `data` folder is used for documents that are ingested, and the `output` folder is used for documents generated by the workflow.
+- Your workflows can use both folders to store and access files. As a convention, the `data` folder is used for documents that are ingested and the `output` folder is used for documents that are generated by the workflow.
 
 
+## Custom UI Components
+
+The LlamaIndex server provides support for rendering workflow events using custom UI components, allowing you to extend and customize the chat interface.
+
+### Overview
+
+Custom UI components are a powerful feature that enables you to:
+
+- Add custom interface elements to the chat UI using React JSX or TSX files
+- Extend the default chat interface functionality
+- Create specialized visualizations or interactions
+
+### Configuration
+
+Your workflow must emit events that fit this structure, allowing the LlamaIndex server to display the right UI components based on the event type.
+
+```json
+{
+    "type": "<event_name>",
+    "data": <data model>
+}
+```
+
+### Server Setup
+
+1. Initialize the LlamaIndex server with a component directory:
+
+```ts
+new LlamaIndexServer({
+  workflow: createWorkflow,
+  uiConfig: {
+    appTitle: "LlamaIndex App",
+    componentsDir: "components",
+  },
+}).start();
+```
+
+2. Add the custom component code to the directory following the naming pattern:
+
+   - File Extension: `.jsx` and `.tsx` for React components
+   - File Name: Should match the event type from your workflow (e.g., `deep_research_event.jsx` for handling `deep_research_event` type that you defined in your workflow). If there are TSX and JSX files with the same name, the TSX file will be used.
+   - Component Name: Export a default React component named `Component` that receives props from the event data
+
+   Example component structure:
+
+   ```jsx
+   function Component({ events }) {
+       // Your component logic here
+       return (
+           // Your UI code here
+       );
+   }
+   ```
+
 ## Best Practices
 
-1. Always provide a workflow factory that creates a fresh workflow instance for each request.
-2. Use environment variables for sensitive configuration (e.g., API keys).
-3. Use starter questions to guide users in the chat UI.
+1. Always provide a workflow factory that creates fresh workflow instances
+2. Use environment variables for sensitive configuration
+3. Use starter questions to guide users in the chat UI
 
 ## Getting Started with a New Project
 
-Want to start a new project with LlamaIndexServer? Check out our [create-llama](https://github.com/run-llama/create-llama) tool to quickly generate a new project with LlamaIndexServer.
-
-## API Reference
-
-- [LlamaIndexServer](/docs/api/classes/LlamaIndexServer)
+Want to start a new project with LlamaIndexServer? Check out our [create-llama](https://github.com/run-llama/create-llama) tool to quickly generate a new project with LlamaIndexServer.

apps/next/src/content/docs/llamaindex/modules/ui/rsc.mdx

@@ -2,6 +2,7 @@
 title: Using Next.js RSC
 description: Chat interface for your LlamaIndexTS application using Next.js RSC
 ---
+import { ChatDemoRSC } from '../../../../../components/demo/chat/rsc/demo';
 
 Using [chat-ui](https://github.com/run-llama/chat-ui), it's easy to add a chat interface to your LlamaIndexTS application using [Next.js RSC](https://nextjs.org/docs/app/building-your-application/rendering/server-components) and [Vercel AI RSC](https://sdk.vercel.ai/docs/ai-sdk-rsc/overview).
 

apps/next/src/content/docs/llamaindex/more.mdx

@@ -3,6 +3,13 @@ title: More
 description: More
 ---
 
+import {
+  SiGithub,
+  SiNpm,
+  SiX,
+  SiDiscord,
+} from "@icons-pack/react-simple-icons";
+
 ## 🗺️ Ecosystem
 
 To download or contribute, find LlamaIndex on:

apps/next/src/content/docs/llamaindex/tutorials/agents/1_setup.mdx

@@ -8,14 +8,14 @@ In this guide we'll walk you through the process of building an Agent in JavaScr
 
 In LlamaIndex, an agent is a semi-autonomous piece of software powered by an LLM that is given a task and executes a series of steps towards solving that task. It is given a set of tools, which can be anything from arbitrary functions up to full LlamaIndex query engines, and it selects the best available tool to complete each step. When each step is completed, the agent judges whether the task is now complete, in which case it returns a result to the user, or whether it needs to take another step, in which case it loops back to the start.
 
-![agent flow](/images/agent_flow.png)
+![agent flow](./images/agent_flow.png)
 
 ## Install LlamaIndex.TS
 
 You'll need to have a recent version of [Node.js](https://nodejs.org/en) installed. Then you can install LlamaIndex.TS by running
 
-```package-install
-npm i llamaindex @llamaindex/openai @llamaindex/readers @llamaindex/huggingface
+```bash
+npm install llamaindex @llamaindex/openai @llamaindex/readers @llamaindex/huggingface
 ```
 
 ## Choose your model
@@ -33,7 +33,7 @@ OPENAI_API_KEY=sk-XXXXXXXXXXXXXXXXXXXXXXXX
 We'll use `dotenv` to pull the API key out of that .env file, so also run:
 
 ```bash
-npm i dotenv
+npm install dotenv
 ```
 
 Now you're ready to [create your agent](2_create_agent).

apps/next/src/content/docs/llamaindex/tutorials/agents/4_agentic_rag.mdx

@@ -15,9 +15,22 @@ We're going to start with the same agent we [built in step 1](https://github.com
 
 ## Installation
 
-```package-install
-npm i llamaindex @llamaindex/openai @llamaindex/huggingface
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai @llamaindex/huggingface 
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai @llamaindex/huggingface 
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai @llamaindex/huggingface 
+	```
+</Tabs>
+
 
 ### New dependencies
 
@@ -58,9 +71,25 @@ We will convert our text into embeddings using the `VectorStoreIndex` class thro
 const index = await VectorStoreIndex.fromDocuments(documents);
 ```
 
+### Configure a retriever
+
+Before LlamaIndex can send a query to the LLM, it needs to find the most relevant chunks to send. That's the purpose of a `Retriever`. We're going to get `VectorStoreIndex` to act as a retriever for us
+
+```javascript
+const retriever = await index.asRetriever();
+```
+
+### Configure how many documents to retrieve
+
+By default LlamaIndex will retrieve just the 2 most relevant chunks of text. This document is complex though, so we'll ask for more context.
+
+```javascript
+retriever.similarityTopK = 10;
+```
+
 ### Use index.queryTool
 
-`index.queryTool` creates a `QueryEngineTool` that can be used be an agent to query data from the index:
+`index.queryTool` creates a `QueryEngineTool` that can be used be an agent to query data from the index. 
 
 ```javascript
 const tools = [
@@ -69,17 +98,9 @@ const tools = [
       name: "san_francisco_budget_tool",
       description: `This tool can answer detailed questions about the individual components of the budget of San Francisco in 2023-2024.`,
     },
-    options: { similarityTopK: 10 },
   }),
 ];
-```
 
-The `metadata` that we're setting helps the agent to decide when to use the tool.
-Note that by default LlamaIndex will retrieve just the 2 most relevant chunks of text. This document is complex though, so we'll ask for more context by setting `similarityTopK` to 10.
-
-Now, we can create an agent using the `QueryEngineTool`:
-
-```javascript
 // Create an agent using the tools array
 const ragAgent = agent({ tools });
 

apps/next/src/content/docs/llamaindex/tutorials/agents/5_rag_and_tools.mdx

@@ -12,7 +12,6 @@ const tools = [
       name: "san_francisco_budget_tool",
       description: `This tool can answer detailed questions about the individual components of the budget of San Francisco in 2023-2024.`,
     },
-    options: { similarityTopK: 10 },
   }),
   tool({
     name: "sumNumbers",

apps/next/src/content/docs/llamaindex/tutorials/basic_agent.mdx

@@ -2,6 +2,9 @@
 title: Basic Agent 
 ---
 
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/agent/openai";
+
 We have a comprehensive, step-by-step [guide to building agents in LlamaIndex.TS](/docs/llamaindex/tutorials/agents/1_setup) that we recommend to learn what agents are and how to build them for production. But building a basic agent is simple:
 
 ## Set up
@@ -10,7 +13,7 @@ In a new folder:
 
 ```bash npm2yarn
 npm init
-npm i -D typescript @types/node
+npm install -D typescript @types/node
 ```
 
 ## Run agent
@@ -24,7 +27,7 @@ Create the file `example.ts`. This code will:
 - Give an example of the data structure we wish to generate
 - Prompt the LLM with instructions and the example, plus a sample transcript
 
-<include cwd>../../examples/agent/openai.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource} />
 
 To run the code:
 

apps/next/src/content/docs/llamaindex/tutorials/local_llm.mdx

@@ -2,6 +2,8 @@
 title: Local LLMs
 ---
 
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
 LlamaIndex.TS supports OpenAI and [other remote LLM APIs](/docs/llamaindex/modules/models/llms). You can also run a local LLM on your machine!
 
 ## Using a local model via Ollama
@@ -26,9 +28,19 @@ The first time you run it will also automatically download and install the model
 
 To switch the LLM in your code, you first need to make sure to install the package for the Ollama model provider:
 
-```package-install
-npm i @llamaindex/ollama
-```
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @llamaindex/ollama
+	```
+
+	```shell tab="yarn"
+	yarn add @llamaindex/ollama
+	```
+
+	```shell tab="pnpm"
+	pnpm add @llamaindex/ollama
+	```
+</Tabs>
 
 Then, to tell LlamaIndex to use a local LLM, use the `Settings` object:
 
@@ -47,9 +59,19 @@ If you're doing retrieval-augmented generation, LlamaIndex.TS will also call out
 
 First install the Huggingface model provider package: 
 
-```package-install
-npm i @llamaindex/huggingface
-```
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @llamaindex/huggingface
+	```
+
+	```shell tab="yarn"
+	yarn add @llamaindex/huggingface
+	```
+
+	```shell tab="pnpm"
+	pnpm add @llamaindex/huggingface
+	```
+</Tabs>
 
 And then set the embedding model in your code:
 

apps/next/src/content/docs/llamaindex/tutorials/meta.json

@@ -4,7 +4,7 @@
     "basic_agent",
     "rag",
     "agents",
-    "../../llamaflow",
+    "workflow",
     "local_llm",
     "chatbot",
     "structured_data_extraction"

apps/next/src/content/docs/llamaindex/tutorials/rag/index.mdx

@@ -2,6 +2,10 @@
 title: Retrieval Augmented Generation (RAG) 
 ---
 
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/vectorIndex";
+import TSConfigSource from "!!raw-loader!@/examples/tsconfig.json";
+
 One of the most common use-cases for LlamaIndex is Retrieval-Augmented Generation or RAG, in which your data is indexed and selectively retrieved to be given to an LLM as source material for responding to a query. You can learn more about the [concepts behind RAG](/docs/llamaindex/tutorials/rag/concepts).
 
 ## Set up the project
@@ -10,7 +14,7 @@ In a new folder, run:
 
 ```bash npm2yarn
 npm init
-npm i -D typescript @types/node
+npm install -D typescript @types/node
 ```
 
 Then, check out the [installation](/docs/llamaindex/getting_started/installation) steps to install LlamaIndex.TS and prepare an OpenAI key.
@@ -26,11 +30,11 @@ Create the file `example.ts`. This code will
 - index it (which creates embeddings using OpenAI)
 - create a query engine to answer questions about the data
 
-<include cwd>../../examples/vectorIndex.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource} />
 
 Create a `tsconfig.json` file in the same folder:
 
-<include cwd>../../examples/tsconfig.json</include>
+<DynamicCodeBlock lang="json" code={TSConfigSource} />
 
 Now you can run the code with
 

apps/next/src/content/docs/llamaindex/tutorials/structured_data_extraction.mdx

@@ -2,6 +2,9 @@
 title: Structured data extraction 
 ---
 
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/jsonExtract";
+
 Make sure you have installed LlamaIndex.TS and have an OpenAI key. If you haven't, check out the [installation](/docs/llamaindex/getting_started/installation) guide.
 
 You can use [other LLMs](/docs/llamaindex/modules/models/llms) via their APIs; if you would prefer to use local models check out our [local LLM example](/docs/llamaindex/tutorials/local_llm).
@@ -12,7 +15,7 @@ In a new folder:
 
 ```bash npm2yarn
 npm init
-npm i -D typescript @types/node
+npm install -D typescript @types/node
 ```
 
 ## Extract data
@@ -23,7 +26,7 @@ Create the file `example.ts`. This code will:
 - Give an example of the data structure we wish to generate
 - Prompt the LLM with instructions and the example, plus a sample transcript
 
-<include cwd>../../examples/jsonExtract.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource} />
 
 To run the code:
 

apps/next/src/content/docs/llamaindex/tutorials/workflow/different-inputs-outputs.mdx

@@ -0,0 +1,226 @@
+---
+title: Inputs / Outputs
+description: Learn how to use different inputs and outputs in your workflows.
+---
+
+Inputs and outputs are the way to communicate between steps in a workflow. In the previous example,
+we used `StartEvent` and `StopEvent` to communicate between steps. However, you can use any type of event to communicate between steps.
+
+## Multiple inputs
+
+You can define multiple inputs for a step.
+
+In the following example, we define a complex workflow with multiple inputs and outputs.
+
+```ts twoslash
+import { Workflow, StartEvent, StopEvent, WorkflowEvent } from '@llamaindex/workflow';
+
+class AEvent extends WorkflowEvent<string> {
+	constructor(data: string) {
+		super(data);
+	}
+}
+
+class BEvent extends WorkflowEvent<number> {
+	constructor(data: number) {
+		super(data);
+	}
+}
+
+class ResultEvent extends WorkflowEvent<string> {
+	constructor(data: string) {
+		super(data);
+	}
+}
+```
+
+First, let's define the events that we will use in the workflow.
+
+```ts twoslash
+import { Workflow, StartEvent, StopEvent, WorkflowEvent } from '@llamaindex/workflow';
+
+class AEvent extends WorkflowEvent<string> {
+	constructor(data: string) {
+		super(data);
+	}
+}
+
+class BEvent extends WorkflowEvent<number> {
+	constructor(data: number) {
+		super(data);
+	}
+}
+
+class ResultEvent extends WorkflowEvent<string> {
+	constructor(data: string) {
+		super(data);
+	}
+}
+
+const workflow = new Workflow<never, string, string>();
+
+workflow.addStep({
+	inputs: [StartEvent<string>],
+	outputs: [StopEvent<string>]
+}, async (
+	context,
+	startEvent
+) => {
+	const input = startEvent.data;
+	const aEvent = await context.requireEvent(AEvent);
+	const bEvent = await context.requireEvent(BEvent);
+	const a = aEvent.data;
+	const b = bEvent.data;
+	return new StopEvent(`Hello, ${input}! A: ${a}, B: ${b}`);
+});
+
+// ---cut---
+workflow.addStep({
+	inputs: [AEvent, BEvent],
+	outputs: [ResultEvent]
+}, async (
+	context,
+	aEvent,
+	bEvent
+) => {
+	const a = aEvent.data;
+	const b = bEvent.data;
+	return new ResultEvent(`A: ${a}, B: ${b}`);
+});
+```
+
+This step means that it requires two events: `AEvent` and `BEvent`. It will return a `ResultEvent` with the data `A: ${a}, B: ${b}`.
+
+## A or B input
+
+If we want to have a step that can accept either `AEvent` or `BEvent`, we can define the step like this:
+
+```ts twoslash
+import { Workflow, StartEvent, StopEvent, WorkflowEvent } from '@llamaindex/workflow';
+
+class AEvent extends WorkflowEvent<string> {
+	constructor(data: string) {
+		super(data);
+	}
+}
+
+class BEvent extends WorkflowEvent<number> {
+	constructor(data: number) {
+		super(data);
+	}
+}
+
+class ResultEvent extends WorkflowEvent<string> {
+	constructor(data: string) {
+		super(data);
+	}
+}
+
+const workflow = new Workflow<never, string, string>();
+
+workflow.addStep({
+	inputs: [StartEvent<string>],
+	outputs: [StopEvent<string>]
+}, async (
+	context,
+	startEvent
+) => {
+	const input = startEvent.data;
+	const aEvent = await context.requireEvent(AEvent);
+	const bEvent = await context.requireEvent(BEvent);
+	const a = aEvent.data;
+	const b = bEvent.data;
+	return new StopEvent(`Hello, ${input}! A: ${a}, B: ${b}`);
+});
+
+// ---cut---
+workflow.addStep({
+	inputs: [WorkflowEvent.or(AEvent, BEvent)],
+	outputs: [ResultEvent]
+}, async (
+	context,
+	aOrBEvent
+) => {
+	if (aOrBEvent instanceof AEvent) {
+		// ^?
+
+
+		const a = aOrBEvent.data;
+		//        ^?
+
+
+		return new ResultEvent(`A: ${a}`);
+	} else {
+		const b = aOrBEvent.data;
+		//        ^?
+
+
+		return new ResultEvent(`B: ${b}`);
+	}
+});
+```
+
+This step means that it requires either `AEvent` or `BEvent`. It will return a `ResultEvent` with the data `A: ${a}` or `B: ${b}`.
+
+You can still combine the logic with `context.requireEvent` to get the data from the event.
+
+import { Accordion, Accordions } from 'fumadocs-ui/components/accordion';
+
+<Accordions>
+	<Accordion title="Under the hood">
+		We use JavaScript Inheritance and the prototype chain to implement the `or` logic.
+		The `or` method creates a new class that extends the two classes that you pass to it.
+
+		<a
+			target="_blank"
+			href="https://developer.mozilla.org/en-US/docs/Web/JavaScript/Inheritance_and_the_prototype_chain"
+		>
+			MDN - Inheritance and the prototype chain
+		</a>
+	</Accordion>
+</Accordions>
+
+## Multiple outputs
+
+You can define multiple outputs for a step.
+
+```ts twoslash
+import { Workflow, StartEvent, StopEvent, WorkflowEvent } from '@llamaindex/workflow';
+
+class AEvent extends WorkflowEvent<string> {
+	constructor(data: string) {
+		super(data);
+	}
+}
+
+class BEvent extends WorkflowEvent<number> {
+	constructor(data: number) {
+		super(data);
+	}
+}
+
+class ResultEvent extends WorkflowEvent<string> {
+	constructor(data: string) {
+		super(data);
+	}
+}
+
+const workflow = new Workflow<never, string, string>();
+// ---cut---
+workflow.addStep({
+	inputs: [StartEvent<string>],
+	outputs: [AEvent, BEvent]
+}, async (
+	context,
+	startEvent
+) => {
+	const input = startEvent.data;
+	if (Math.random() > 0.5) {
+		return new AEvent(`Hello, ${input}!`);
+	} else {
+		return new BEvent(42);
+	}
+});
+```
+
+This step will return either an `AEvent` or a `BEvent` based on a random number.