Release 0.10.3 (#1898)

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>

.github/workflows/lint_on_push_or_pull.yml

@@ -8,6 +8,11 @@ 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

@@ -1,6 +1,11 @@
 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

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

apps/next/CHANGELOG.md

@@ -1,5 +1,41 @@
 # @llamaindex/doc
 
+## 0.2.15
+
+### Patch Changes
+
+- Updated dependencies [3ee8c83]
+  - @llamaindex/core@0.6.3
+  - llamaindex@0.10.3
+  - @llamaindex/openai@0.3.5
+  - @llamaindex/cloud@4.0.4
+  - @llamaindex/node-parser@2.0.3
+  - @llamaindex/readers@3.1.1
+  - @llamaindex/workflow@1.0.4
+
+## 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

apps/next/fallback.js

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

apps/next/next.config.mjs

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

@@ -1,20 +1,21 @@
 {
   "name": "@llamaindex/doc",
-  "version": "0.2.11",
+  "version": "0.2.15",
   "private": true,
   "scripts": {
     "postinstall": "fumadocs-mdx",
     "prebuild": "pnpm run build:docs",
     "build": "next build",
-    "dev": "next dev",
+    "dev": "next dev --turbo",
     "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": "0.0.3",
+    "@llama-flow/docs": "0.0.5",
     "@llamaindex/chat-ui": "0.2.0",
     "@llamaindex/cloud": "workspace:*",
     "@llamaindex/core": "workspace:*",
@@ -23,6 +24,7 @@
     "@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",
@@ -40,9 +42,9 @@
     "fumadocs-core": "^15.2.7",
     "fumadocs-docgen": "^2.0.0",
     "fumadocs-mdx": "^11.6.0",
-    "fumadocs-openapi": "^6.3.0",
+    "fumadocs-openapi": "^8.0.1",
     "fumadocs-twoslash": "^3.1.1",
-    "fumadocs-typescript": "^3.1.0",
+    "fumadocs-typescript": "^4.0.2",
     "fumadocs-ui": "^15.2.7",
     "hast-util-to-jsx-runtime": "^2.3.2",
     "llamaindex": "workspace:*",
@@ -52,7 +54,6 @@
     "react": "^19.1.0",
     "react-dom": "^19.1.0",
     "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",
@@ -64,6 +65,8 @@
     "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"
@@ -79,7 +82,6 @@
     "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",
@@ -88,9 +90,9 @@
     "remark-stringify": "^11.0.0",
     "tailwindcss": "^4.0.9",
     "tsx": "^4.19.3",
-    "typedoc": "0.27.4",
-    "typedoc-plugin-markdown": "^4.3.1",
-    "typedoc-plugin-merge-modules": "^6.1.0",
+    "typedoc": "0.28.3",
+    "typedoc-plugin-markdown": "^4.6.2",
+    "typedoc-plugin-merge-modules": " ^7.0.0",
     "typescript": "^5.7.3"
   }
 }

apps/next/scripts/generate-docs.mts

@@ -1,27 +1,24 @@
-import { generateFiles as openapiGenerateFiles } from "fumadocs-openapi";
-import { generateFiles as typescriptGenerateFiles } from "fumadocs-typescript";
+import {
+  createGenerator,
+  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.mdx") && !v.endsWith("meta.json");
+    return !v.endsWith("index.md") && !v.endsWith("meta.json");
   },
 });
 
-void openapiGenerateFiles({
-  input: ["../../packages/cloud/openapi.json"],
-  output: "./src/content/docs/cloud/api",
-  groupBy: "tag",
-});
-
-void typescriptGenerateFiles({
-  input: ["./src/content/docs/api/**/*.mdx"],
+void typescriptGenerateFiles(generator, {
+  input: ["./src/content/docs/api/**/*.md"],
   output: (file) => path.resolve(path.dirname(file), path.basename(file)),
   transformOutput,
 });
@@ -30,19 +27,22 @@ 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, filePath)}`;
+  return `---\ntitle: ${title}\n---\n\n${transformAbsoluteUrl(
+    content.replace(/(?<!\\)\{([^}]+)(?<!\\)}/g, "\\{$1\\}"),
+    filePath,
+  )}`;
 }
 
 /**
- * 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)
+ * 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)
  */
 function transformAbsoluteUrl(content: string, filePath: string) {
   const group = path.dirname(filePath).split(path.sep).pop();
-  return content.replace(/\]\(([^)]+)\.mdx([^)]*)\)/g, (_, slug, anchor) => {
+  return content.replace(/\]\(([^)]+)\.md([^)]*)\)/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

@@ -1,13 +1,18 @@
-import { rehypeCodeDefaultOptions } from "fumadocs-core/mdx-plugins";
+import {
+  rehypeCodeDefaultOptions,
+  remarkStructure,
+} 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,
+  },
 });
 
 export default defineConfig({
@@ -21,11 +26,7 @@ export default defineConfig({
       },
       transformers: [
         ...(rehypeCodeDefaultOptions.transformers ?? []),
-        transformerTwoslash({
-          typesCache: createFileSystemTypesCache({
-            dir: ".next/cache/twoslash",
-          }),
-        }),
+        transformerTwoslash(),
         {
           name: "transformers:remove-notation-escape",
           code(hast) {
@@ -46,6 +47,7 @@ export default defineConfig({
       ],
     },
     remarkPlugins: [
+      remarkStructure,
       remarkMath,
       [remarkInstall, { persist: { id: "package-manager" } }],
       [remarkDocGen, { generators: [fileGenerator()] }],

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

@@ -10,16 +10,55 @@ import { MagicMove } from "@/components/magic-move";
 import { NpmInstall } from "@/components/npm-install";
 import { Supports } from "@/components/supports";
 import { Button } from "@/components/ui/button";
-import { Skeleton } from "@/components/ui/skeleton";
 import { 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";
-import { Suspense } from "react";
+
+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}\`);`,
+];
 
 export default function HomePage() {
   return (
@@ -62,65 +101,10 @@ 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."
         >
-          <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>
+          <MagicMove
+            placeholder={<CodeBlock lang="ts" code={codes[0]} />}
+            code={codes}
+          />
         </Feature>
         <Feature
           icon={Bot}

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

@@ -1,4 +1,12 @@
 import { source } from "@/lib/source";
+import { structure } from "fumadocs-core/mdx-plugins";
 import { createFromSource } from "fumadocs-core/search/server";
 
-export const { GET } = createFromSource(source);
+// 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),
+}));

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

@@ -1,7 +1,13 @@
+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 { createTypeTable } from "fumadocs-typescript/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 defaultMdxComponents from "fumadocs-ui/mdx";
 import {
@@ -12,6 +18,8 @@ import {
 } from "fumadocs-ui/page";
 import { notFound } from "next/navigation";
 
+const generator = createGenerator();
+
 export const revalidate = false;
 
 export default async function Page(props: {
@@ -21,14 +29,13 @@ export default async function Page(props: {
   const page = source.getPage(params.slug);
   if (!page) notFound();
 
-  const { AutoTypeTable } = createTypeTable();
-  const MDX = page.data.body;
+  const { body: MDX, toc, lastModified } = await page.data.load();
 
   return (
     <DocsPage
-      toc={page.data.toc}
+      toc={toc}
       full={page.data.full}
-      lastUpdate={page.data.lastModified}
+      lastUpdate={lastModified}
       editOnGithub={{
         owner: "run-llama",
         repo: "LlamaIndexTS",
@@ -41,14 +48,21 @@ export default async function Page(props: {
       <DocsBody>
         <MDX
           components={{
+            ...Icons,
             ...defaultMdxComponents,
-            APIPage: openapi.APIPage,
+            ...demos,
+            ChatDemoRSC,
+            Accordion,
+            Accordions,
+            APIPage: (props) => <APIPage {...openapi.getAPIPageProps(props)} />,
             Tab,
             Tabs,
             Popup,
             PopupContent,
             PopupTrigger,
-            AutoTypeTable,
+            AutoTypeTable: (props) => (
+              <AutoTypeTable generator={generator} {...props} />
+            ),
           }}
         />
       </DocsBody>

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

@@ -1,11 +1,7 @@
 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 }) {
@@ -13,23 +9,9 @@ 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/layout.config.tsx

@@ -27,9 +27,19 @@ export const baseOptions: BaseLayoutProps = {
   githubUrl: "https://github.com/run-llama/LlamaIndexTS",
   links: [
     {
-      text: "Docs",
+      text: "TypeScript",
       url: 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

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

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

@@ -1,24 +1,26 @@
 "use client";
-import { createContextState } from "foxact/context-state";
-import { useIsClient } from "foxact/use-is-client";
-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") {
-  promise = Parser.init({
-    locateFile(scriptName: string) {
-      return "/" + scriptName;
-    },
-  }).then(async () => {
+  async function run() {
+    const { default: Parser } = await import("web-tree-sitter");
+    await Parser.init({
+      locateFile(scriptName: string) {
+        return "/" + scriptName;
+      },
+    });
+
     const parser = new Parser();
     const Lang = await Parser.Language.load("/tree-sitter-typescript.wasm");
     parser.setLanguage(Lang);
@@ -26,7 +28,9 @@ if (typeof window !== "undefined") {
       getParser: () => parser,
       maxChars: 100,
     });
-  });
+  }
+
+  promise = run();
 }
 
 const [SliderProvider, useSlider, useSetSlider] = createContextState(100);
@@ -48,8 +52,6 @@ const john: Person = {
 
 console.log(greet(john));`);
 
-const Editor = lazy(() => import("react-monaco-editor"));
-
 export const IDE = () => {
   const codeSplitter = use(promise);
   const code = useCode();
@@ -73,21 +75,6 @@ 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,
@@ -97,7 +84,9 @@ export const IDE = () => {
         height="100%"
         width="100%"
         language="typescript"
-        onChange={setCode}
+        onChange={(v) => {
+          if (v) setCode(v);
+        }}
         value={code}
       />
     </div>

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

@@ -0,0 +1,13 @@
+"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,
+  ),
+);

apps/next/src/components/demo/workflow-streaming-ui.tsx

@@ -1,152 +0,0 @@
-"use client";
-import FlowInput from "@/components/flow-input";
-import { Button } from "@/components/ui/button";
-import {
-  StartEvent,
-  StopEvent,
-  Workflow,
-  WorkflowEvent,
-} from "@llamaindex/workflow";
-import { ReactNode, startTransition, useState } from "react";
-import { StickToBottom, useStickToBottomContext } from "use-stick-to-bottom";
-
-class ComputeEvent extends WorkflowEvent<number> {
-  constructor(data: number) {
-    super(data);
-  }
-}
-
-class ComputeResultEvent extends WorkflowEvent<number> {
-  constructor(data: number) {
-    super(data);
-  }
-}
-
-type ContextData = {
-  sum: number;
-};
-
-const workflow = new Workflow<ContextData, number, number>();
-
-const max = 1000;
-const min = 100;
-
-workflow.addStep(
-  {
-    inputs: [StartEvent<number>],
-    outputs: [StopEvent<number>],
-  },
-  async (context, event) => {
-    const total = event.data;
-    for (let i = 0; i < total; i++) {
-      context.sendEvent(new ComputeEvent(i));
-    }
-    console.log("waiting");
-    const computeResults = await Promise.all(
-      Array.from({ length: total }).map(() =>
-        context.requireEvent(ComputeResultEvent),
-      ),
-    );
-    context.data.sum = computeResults.reduce(
-      (acc, result) => acc + result.data,
-      0,
-    );
-    console.log("stop");
-    return new StopEvent(context.data.sum);
-  },
-);
-
-workflow.addStep(
-  {
-    inputs: [ComputeEvent],
-    outputs: [ComputeResultEvent],
-  },
-  async (context, event) => {
-    await new Promise((resolve) =>
-      setTimeout(resolve, Math.floor(Math.random() * (max - min + 1) + min)),
-    );
-    return new ComputeResultEvent(event.data);
-  },
-);
-
-function ScrollToBottom() {
-  const { isAtBottom, scrollToBottom } = useStickToBottomContext();
-
-  return (
-    !isAtBottom && (
-      <button
-        className="i-ph-arrow-circle-down-fill absolute bottom-0 left-[50%] translate-x-[-50%] rounded-lg text-4xl"
-        onClick={() => scrollToBottom()}
-      />
-    )
-  );
-}
-
-export function WorkflowStreamingDemo() {
-  const [ui, setUI] = useState<ReactNode[]>([
-    <div key={0} className="bg-gray-100 dark:bg-gray-800">
-      Waiting for workflow to start
-    </div>,
-  ]);
-  const [total, setTotal] = useState<number>(10);
-
-  return (
-    <div className="flex w-full flex-col items-start gap-2">
-      <div className="flex flex-row items-center justify-center">
-        <div className="mr-2 text-lg">Compute total</div>{" "}
-        <FlowInput value={total} onChange={(value) => setTotal(value)} />
-      </div>
-      <Button
-        onClick={async () => {
-          startTransition(() => {
-            setUI([]);
-          });
-          const context = workflow.run(total, {
-            sum: 0,
-          });
-          let i = 0;
-          for await (const event of context) {
-            console.log(event);
-            if (event instanceof ComputeEvent) {
-              setUI((ui) => [
-                ...ui,
-                <div key={i++} className="bg-yellow-100 dark:bg-yellow-800">
-                  Computing task id: {event.data}
-                </div>,
-              ]);
-            } else if (event instanceof ComputeResultEvent) {
-              setUI((ui) => [
-                ...ui,
-                <div key={i++} className="bg-green-100 dark:bg-green-800">
-                  Computed task id: {event.data}
-                </div>,
-              ]);
-            } else if (event instanceof StartEvent) {
-              setUI((ui) => [
-                ...ui,
-                <div key={i++} className="bg-blue-100 dark:bg-blue-800">
-                  Started workflow with total {event.data}
-                </div>,
-              ]);
-            } else if (event instanceof StopEvent) {
-              setUI((ui) => [
-                ...ui,
-                <div key={i++} className="bg-red-100 dark:bg-red-800">
-                  Workflow stopped
-                </div>,
-              ]);
-            }
-          }
-        }}
-      >
-        Start Workflow
-      </Button>
-      <StickToBottom className="flex max-h-96 w-full flex-col gap-2 overflow-y-auto rounded-lg border border-gray-200 p-2">
-        <StickToBottom.Content className="flex flex-col gap-2">
-          {ui}
-        </StickToBottom.Content>
-        <ScrollToBottom />
-      </StickToBottom>
-    </div>
-  );
-}

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

@@ -1,25 +1,27 @@
 "use client";
 import { Button } from "@/components/ui/button";
 import { cn } from "@/lib/utils";
-import { CodeBlock, Pre } from "fumadocs-ui/components/codeblock";
+import { CodeBlock } from "fumadocs-ui/components/codeblock";
 import { RotateCcw } from "lucide-react";
 import { useTheme } from "next-themes";
-import { use, useCallback, useEffect, useState } from "react";
-import { getSingletonHighlighter } from "shiki";
+import { type ReactNode, use, useCallback, useEffect, useState } from "react";
+import { createJavaScriptRegexEngine, getSingletonHighlighter } from "shiki";
 import { ShikiMagicMove } from "shiki-magic-move/react";
-import { createOnigurumaEngine } from "shiki/engine/oniguruma";
 
+const engine = createJavaScriptRegexEngine();
 const highlighterPromise = getSingletonHighlighter({
-  engine: createOnigurumaEngine(() => import("shiki/wasm")),
+  engine,
   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);
@@ -38,24 +40,27 @@ export function MagicMove(props: MagicMoveProps) {
     }
   }, [animate, move, props.code]);
 
+  useEffect(() => {
+    setMounted(true);
+  }, []);
+
+  if (!mounted) return props.placeholder;
+
   return (
     <CodeBlock allowCopy={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>
-      )}
+      <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,
+        }}
+      />
       <Button
         className={cn(
           "absolute bottom-2 right-2",

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

@@ -1,8 +0,0 @@
----
-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

@@ -1,6 +0,0 @@
-{
-  "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/installation/cloudflare.mdx

@@ -3,13 +3,6 @@ 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

@@ -3,14 +3,6 @@ title: Installation
 description: How to install llamaindex packages.
 ---
 
-import {
-	SiNodedotjs,
-	SiTypescript,
-	SiNextdotjs,
-	SiCloudflareworkers,
-	SiVite
-} from "@icons-pack/react-simple-icons";
-
 To install llamaindex, run the following command:
 
 ```package-install

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

@@ -3,13 +3,6 @@ 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/modules/agents/tool/index.mdx

@@ -57,6 +57,41 @@ 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

@@ -2,152 +2,260 @@
 title: Workflows
 ---
 
-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!@/examples/workflow/joke.ts";
-
-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.
-
-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.
+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.
 
+Workflows are designed to be flexible and can be used to build agents, RAG flows, extraction flows, or anything else you want to implement.
 
 ```package-install
-npm i @llamaindex/workflow
+npm i @llama-flow/core @llamaindex/openai
 ```
 
 ## Getting Started
 
-As an illustrative example, let's consider a naive workflow where a joke is generated and then critiqued.
+Let's explore a simple workflow example where a joke is generated and then critiqued and iterated on:
 
-<DynamicCodeBlock lang="ts" code={CodeSource} />
+```typescript
+import { OpenAI } from "@llamaindex/openai";
+import { createWorkflow, workflowEvent } from "@llama-flow/core";
+import { withStore } from "@llama-flow/core/middleware/store";
 
-There's a few moving pieces here, so let's go through this piece by piece.
+// 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.
 
 ### Defining Workflow Events
 
 ```typescript
-export class JokeEvent extends WorkflowEvent<{ joke: string }> {}
+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
 ```
 
-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`.
+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
 
-### Setting up the Workflow Class
+### Setting up the Workflow with Store Middleware
 
 ```typescript
-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
+const jokeFlow = withStore(
+    () => ({
+        numIterations: 0,
+        maxIterations: 3,
+    }),
+    createWorkflow()
 );
 ```
 
-### Workflow Exit Points
+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`:
 
 ```typescript
-const critiqueJoke = async (_: unknown, ev: JokeEvent) => {
-  const prompt = `Give a thorough critique of the following joke: ${ev.data.joke}`;
+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 });
-  return new StopEvent(response.text);
-};
+
+  // Parse the joke from the response
+  const joke = response.text.match(/<joke>([\s\S]*?)<\/joke>/)?.[1]?.trim() ?? response.text;
+  return jokeEvent.with({ joke: joke });
+});
 ```
 
-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:
+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
 
 ```typescript
-jokeFlow.addStep(
-  {
-    inputs: [JokeEvent],
-    outputs: [StopEvent<string>],
-  },
-  critiqueJoke
-);
+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 });
+});
 ```
 
 ### Running the Workflow
 
 ```typescript
-const result = await jokeFlow.run("pirates");
-console.log(result.data.result);
-```
+async function main() {
+  const { stream, sendEvent } = jokeFlow.createContext();
+  sendEvent(startEvent.with("pirates"));
 
-Lastly, we run the workflow. The `.run()` method is async, so we use await here to wait for the result.
+  let result: { joke: string, critique: string } | undefined;
 
-## Working with Shared Context/State
-
-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 { HandlerContext } from "llamaindex";
-
-type MyContextData = {
-  query: string;
-  intermediateResults: any[];
+  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 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);
-
-  return new StopEvent({ result });
-};
 ```
 
-## Waiting for Multiple Events
+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
 
-The context does more than just hold data, it also provides utilities to buffer and wait for multiple events.
+### Using Stream Utilities
 
-For example, you might have a step that waits for a query and retrieved nodes before synthesizing a response:
+Workflows provide utility functions to make working with event streams easier:
 
 ```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 });
-};
+import { collect } from "@llama-flow/core/stream/consumer";
+import { until } from "@llama-flow/core/stream/until";
+
+// Create a workflow context and send the initial event
+const { stream, sendEvent } = jokeFlow.createContext();
+sendEvent(startEvent.with("pirates"));
+
+// Collect all events until we get a resultEvent
+const allEvents = await collect(until(stream, resultEvent));
+
+// The last event will be the resultEvent
+const finalEvent = allEvents[allEvents.length - 1];
+console.log(finalEvent.data); // Output the joke and critique
 ```
 
-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.
+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)
 
-## Manually Triggering Events
+You can combine these utilities with other stream operators like `filter` and `map` to create powerful processing pipelines.
 
-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.
+## Next Steps
 
-## Examples
-
-You can find many useful examples of using workflows in the [examples folder](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/workflow).
+To learn more about workflows, check out [the documentation in the tutorial section](../../../llamaflow).

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

@@ -3,10 +3,6 @@ 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
@@ -22,13 +18,13 @@ 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:
 
-<DynamicCodeBlock lang="ts" code={CodeSource2} />
+<include cwd>../../examples/cloud/chat.ts</include>
 
 ## Use a Managed Index
 
 Here's an example of how to use a managed index together with a chat engine:
 
-<DynamicCodeBlock lang="ts" code={CodeSource} />
+<include cwd>../../examples/cloud/from-documents.ts</include>
 
 ## API Reference
 

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

@@ -2,7 +2,6 @@
 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';
 
 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).
 
@@ -150,8 +149,6 @@ 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

@@ -2,12 +2,15 @@
 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).
@@ -15,7 +18,7 @@ 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.
 
-<DynamicCodeBlock lang="ts" code={CodeSource} />
+<include cwd>../../examples/discord/reader.ts</include>
 
 ### Params
 

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

@@ -3,11 +3,6 @@ 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 { 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.
 
@@ -26,27 +21,18 @@ To install readers call:
 
 We offer readers for different file formats.
 
-```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
+```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';
 ```
 
-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)
@@ -55,7 +41,7 @@ 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.
 
-<DynamicCodeBlock lang="ts" code={CodeSource} />
+<include cwd>../../examples/readers/src/simple-directory-reader.ts</include>
 
 Currently, the following readers are mapped to specific file types:
 
@@ -77,7 +63,7 @@ SimpleDirectoryReader supports up to 9 concurrent requests. Use the `numWorkers`
 
 ### Example
 
-<DynamicCodeBlock lang="ts" code={CodeSource2} />
+<include cwd>../../examples/readers/src/custom-simple-directory-reader.ts</include>
 
 ## Tips when using in non-Node.js environments
 

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

@@ -112,6 +112,3 @@ 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

@@ -2,10 +2,6 @@
 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`.
@@ -17,7 +13,7 @@ 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:
 
-<DynamicCodeBlock lang="ts" code={CodeSource} />
+<include cwd>../../examples/readers/src/llamaparse.ts</include>
 
 ### Params
 
@@ -36,7 +32,7 @@ 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`. Click [here](/docs/api/type-aliases/Language) for a list of supported languages.
+- `language` primarily helps with OCR recognition. Defaults to `en`.
 - `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`.
@@ -60,9 +56,8 @@ They can be divided into two groups.
 
 Below a full example of `LlamaParse` integrated in `SimpleDirectoryReader` with additional options.
 
-<DynamicCodeBlock lang="ts" code={CodeSource2} />
+<include cwd>../../examples/readers/src/simple-directory-reader-with-llamaparse.ts</include>
 
 ## 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

@@ -98,5 +98,4 @@ 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/models/llms/groq.mdx

@@ -2,9 +2,6 @@
 title: Groq
 ---
 
-import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!@/examples/groq.ts";
-
 ## Installation
 
 ```package-install
@@ -58,7 +55,7 @@ const results = await queryEngine.query({
 
 ## Full Example
 
-<DynamicCodeBlock lang="ts" code={CodeSource} />
+<include cwd>../../examples/groq.ts</include>
 
 ## API Reference
 

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

@@ -2,7 +2,6 @@
 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/llamaindex-server.mdx

@@ -22,7 +22,7 @@ npm i @llamaindex/server
 
 ## Quick Start
 
-Create index.ts file and add the following code:
+Create an `index.ts` file and add the following code:
 
 ```ts
 import { LlamaIndexServer } from "@llamaindex/server";
@@ -43,20 +43,20 @@ 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
+The `LlamaIndexServer` accepts the following configuration options:
 
 - `workflow`: A callable function that creates a workflow instance for each request
 - `uiConfig`: An object to configure the chat UI containing the following properties:
@@ -68,6 +68,72 @@ The LlamaIndexServer accepts the following configuration
 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
@@ -85,69 +151,19 @@ 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. 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.
+- 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.
 
 
-## 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 fresh workflow instances
-2. Use environment variables for sensitive configuration
-3. Use starter questions to guide users in the chat UI
+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.
 
 ## 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.
+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)

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

@@ -2,7 +2,6 @@
 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

@@ -3,13 +3,6 @@ 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

@@ -8,7 +8,7 @@ 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
 

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

@@ -58,25 +58,9 @@ 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 = [
@@ -85,9 +69,17 @@ 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

@@ -12,6 +12,7 @@ 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

@@ -2,9 +2,6 @@
 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
@@ -27,7 +24,7 @@ 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
 
-<DynamicCodeBlock lang="ts" code={CodeSource} />
+<include cwd>../../examples/agent/openai.ts</include>
 
 To run the code:
 

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

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

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

@@ -2,10 +2,6 @@
 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
@@ -30,11 +26,11 @@ 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
 
-<DynamicCodeBlock lang="ts" code={CodeSource} />
+<include cwd>../../examples/vectorIndex.ts</include>
 
 Create a `tsconfig.json` file in the same folder:
 
-<DynamicCodeBlock lang="json" code={TSConfigSource} />
+<include cwd>../../examples/tsconfig.json</include>
 
 Now you can run the code with
 

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

@@ -2,9 +2,6 @@
 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).
@@ -26,7 +23,7 @@ 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
 
-<DynamicCodeBlock lang="ts" code={CodeSource} />
+<include cwd>../../examples/jsonExtract.ts</include>
 
 To run the code:
 

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

@@ -1,226 +0,0 @@
----
-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.

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

@@ -1,196 +0,0 @@
----
-title: Basic Usage
-description: Learn how to use the LlamaIndex workflow.
----
-
-A `Workflow` in LlamaIndex.TS 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.
-
-Workflows are designed for any cases that benefit from event-driven programming, not only for LLM and AI tasks.
-
-```package-install
-npm i @llamaindex/workflow
-```
-
-## Start from scratch
-
-Let's start from a Hello World workflow.
-
-```ts twoslash
-import { Workflow } from '@llamaindex/workflow';
-
-type ContextData = {
-	counter: number;
-}
-// ---cut---
-const contextData: ContextData = { counter: 0 };
-
-const workflow = new Workflow<ContextData, string, string>();
-//     ^?
-
-
-
-```
-
-First, we define a workflow with 3 generic types: `ContextData`, `Input`, and `Output`.
-
-In general, `ContextData` is used to store the shared data between steps, `Input` is the type of the input event, and `Output` is the type of the output event.
-
-In you code logic, you should **share state between steps via `ContextData`**.
-
-```ts twoslash
-import { Workflow, StartEvent, StopEvent } from '@llamaindex/workflow';
-
-type ContextData = {
-	counter: number;
-}
-
-const contextData: ContextData = { counter: 0 };
-
-const workflow = new Workflow<ContextData, string, string>();
-// ---cut---
-workflow.addStep({
-	inputs: [StartEvent<string>],
-	outputs: [StopEvent<string>]
-}, async (context, startEvent) => {
-	const input = startEvent.data;
-	context.data.counter++;
-	return new StopEvent(`Hello, ${input}!`);
-});
-```
-
-In the workflow, we add a step that listens to `StartEvent<string>` and emits `StopEvent<string>`.
-
-The step is an async function that takes two arguments: `context` and `event`.
-
-### `context` type
-
-<AutoTypeTable path="./src/deps/type.ts" name="HandlerContext" />
-
-There are two more properties in `HandlerContext`:
-
-- `sendEvent`: invoke another event in the workflow, other than `StartEvent`, `StopEvent`, or the current event. (Or there will have circular reference)
-- `requireEvent`: wait for a specific event to be emitted.
-
-You can use `sendEvent` and `requireEvent` to build complex workflows.
-
-```ts twoslash
-import { Workflow, StartEvent, StopEvent, WorkflowEvent } from '@llamaindex/workflow';
-
-type ContextData = {
-	counter: number;
-}
-
-const contextData: ContextData = { counter: 0 };
-
-const workflow = new Workflow<ContextData, string, string>();
-
-// ---cut---
-class AnalysisStartEvent extends WorkflowEvent<string> {}
-class AnalysisStopEvent extends WorkflowEvent<boolean> {}
-workflow.addStep({
-	inputs: [AnalysisStartEvent],
-	outputs: [AnalysisStopEvent]
-}, async (...args) => {
-	// do some analysis
-	return new AnalysisStopEvent(true);
-})
-workflow.addStep({
-	inputs: [StartEvent<string>],
-	outputs: [StopEvent<string>]
-}, async (context, startEvent) => {
-	const input = startEvent.data;
-	context.sendEvent(new AnalysisStartEvent('start'));
-	context.data.counter++;
-	const { data } = await context.requireEvent(AnalysisStopEvent);
-	return new StopEvent(`Hello, ${input}! Analysis result: ${data ? 'success' : 'fail'}`);
-});
-```
-
-For example, you can compile `requireEvent` with `waitUntil` in [Vercel Functions](https://vercel.com/docs/functions/functions-api-reference#waituntil) or [Cloudflare Worker](https://developers.cloudflare.com/workers/runtime-apis/context/#waituntil)
-
-```ts twoslash
-import { waitUntil } from '@vercel/functions';
-import { Workflow, StartEvent, StopEvent, WorkflowEvent } from '@llamaindex/workflow';
-
-type ContextData = {
-	counter: number;
-}
-
-const contextData: ContextData = { counter: 0 };
-
-const workflow = new Workflow<ContextData, string, string>();
-
-class AnalysisStartEvent extends WorkflowEvent<string> {}
-class AnalysisStopEvent extends WorkflowEvent<boolean> {}
-
-// ---cut---
-workflow.addStep({
-	inputs: [StartEvent<string>],
-	outputs: [StopEvent<string>]
-}, async (context, startEvent) => {
-	const input = startEvent.data;
-	context.sendEvent(new AnalysisStartEvent('start'));
-	context.data.counter++;
-	waitUntil(context.requireEvent(AnalysisStopEvent));
-	// note that `waitUntil` is not a promise, it will extend the lifetime of the workflow
-	// you can wait for some background tasks to finish
-	return new StopEvent(`Hello, ${input}!`);
-});
-```
-
-## Multiple runs
-
-You can run the same workflow multiple times with different inputs.
-
-```ts twoslash
-import { Workflow, StartEvent, StopEvent } from '@llamaindex/workflow';
-
-type ContextData = {
-	counter: number;
-}
-
-const contextData: ContextData = { counter: 0 };
-
-const workflow = new Workflow<ContextData, string, string>();
-
-workflow.addStep({
-	inputs: [StartEvent<string>],
-	outputs: [StopEvent<string>]
-}, async (context, startEvent) => {
-	const input = startEvent.data;
-	context.data.counter++;
-	return new StopEvent(`Hello, ${input}!`);
-});
-
-// ---cut---
-{
-	const ret = await workflow.run('Alex', contextData);
-	console.log(ret.data); // Hello, Alex!
-}
-
-{
-	const ret = await workflow.run('World', contextData);
-	console.log(ret.data); // Hello, World!
-}
-```
-
-Context is shared between runs, so the counter will be increased.
-
-Ideally, it should be serializable to make sure it can be recovered from HTTP requests or other storage.
-
-### Full example
-
-<iframe
-	className="w-full h-[440px]"
-	aria-label="Workflow example"
-	src="https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples?file=node/workflow/basic.ts"
-/>
-
-## `Workflow` type
-
-<AutoTypeTable path="./src/deps/type.ts" name="Workflow" />
-
-## `WorkflowContext` type
-
-<AutoTypeTable path="./src/deps/type.ts" name="WorkflowContext" />

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

@@ -1,6 +0,0 @@
-{
-  "title": "Workflow",
-  "description": "See how to use @llamaindex/workflow",
-  "defaultOpen": false,
-  "pages": ["index", "different-inputs-outputs", "streaming"]
-}

apps/next/src/content/docs/llamaindex/tutorials/workflow/streaming.mdx

@@ -1,199 +0,0 @@
----
-title: Streaming
-description: Learn how to use the LlamaIndex workflow with streaming.
----
-import { WorkflowStreamingDemo } from '../../../../../components/demo/workflow-streaming-ui';
-
-`Workflow` API by default is designed for streaming data. In this guide, we will show you how to use the `Workflow` API with streaming data.
-
-Each `workflow.run` call returns `WorkflowContext`, which implements `AsyncIterable` interface. You can use it to stream data.
-
-```ts twoslash
-import { Workflow, WorkflowEvent, StartEvent, StopEvent } from '@llamaindex/workflow';
-class ComputeEvent extends WorkflowEvent<number> {
-	constructor(data: number) {
-		super(data);
-	}
-}
-class ComputeResultEvent extends WorkflowEvent<number> {
-	constructor(data: number) {
-		super(data);
-	}
-}
-
-type ContextData = {
-	sum: number;
-}
-
-const workflow = new Workflow<ContextData, number, number>();
-workflow.addStep({
-	inputs: [StartEvent<number>],
-	outputs: [StopEvent<number>]
-}, async (context, startEvent) => {
-	const total = startEvent.data;
-	for (let i = 0; i < total; i++) {
-		context.sendEvent(new ComputeEvent(i));
-	}
-	const computeResults = await Promise.all(Array.from({ length: total }).map(() => context.requireEvent(ComputeResultEvent)));
-	// Workflow API allows you to start events in parallel and wait for all of them to finish
-	context.data.sum = computeResults.reduce((acc, curr) => acc + curr.data, 0);
-	return new StopEvent(context.data.sum);
-});
-```
-
-We define a parallel computation workflow that computes the sum of numbers from 0 to `total`.
-
-The workflow sends `ComputeEvent` events for each number and waits for `ComputeResultEvent` events. After receiving all `ComputeResultEvent` events, the workflow returns the sum as a `StopEvent`.
-
-What if we want cutoff if the sum exceeds a certain value?
-
-## Streaming
-
-```ts twoslash
-import { Workflow, WorkflowEvent, StartEvent, StopEvent } from '@llamaindex/workflow';
-import { StopCircle } from 'lucide-react';
-class ComputeEvent extends WorkflowEvent<number> {
-	constructor(data: number) {
-		super(data);
-	}
-}
-class ComputeResultEvent extends WorkflowEvent<number> {
-	constructor(data: number) {
-		super(data);
-	}
-}
-
-
-type ContextData = {
-	sum: number;
-}
-
-const workflow = new Workflow<ContextData, number, number>();
-// ---cut---
-const context = workflow.run(1000, {
-	sum: 0
-});
-
-for await (const event of context) {
-	if (event instanceof ComputeEvent) {
-		if (context.data.sum > 100) {
-			throw new Error('Sum exceeds 100');
-		}
-	}
-	if (event instanceof StopEvent) {
-		console.log('result', event.data);
-	}
-}
-```
-
-You can define more custom logic using `AsyncIterable` interface.
-
-For example. I just want to stop the workflow if I get a `ComputeResultEvent`
-
-
-```ts twoslash
-import { Workflow, WorkflowEvent, StartEvent, StopEvent } from '@llamaindex/workflow';
-import { StopCircle } from 'lucide-react';
-class ComputeEvent extends WorkflowEvent<number> {
-	constructor(data: number) {
-		super(data);
-	}
-}
-class ComputeResultEvent extends WorkflowEvent<number> {
-	constructor(data: number) {
-		super(data);
-	}
-}
-
-
-type ContextData = {
-	sum: number;
-}
-
-const workflow = new Workflow<ContextData, number, number>();
-// ---cut---
-async function compute() {
-	const context = workflow.run(1000, {
-		sum: 0
-	});
-	for await (const event of context) {
-		if (event instanceof ComputeResultEvent) {
-			return event.data;
-		}
-	}
-	throw new Error('UNREACHABLE');
-}
-
-const result = await compute();
-```
-
-### Streaming with UI
-
-You can use the `Workflow` API with UI libraries like React.
-
-```tsx twoslash
-// @filename: utils.ts
-export async function runWithoutBlocking(fn: () => Promise<void>) {
-	fn();
-}
-// @filename: action.ts
-// ---cut---
-'use server';
-// "use server" is required to enable server side feature in React
-import { createStreamableUI } from 'ai/rsc';
-import { runWithoutBlocking } from './utils';
-// ---cut-start---
-import { Workflow, WorkflowEvent, StartEvent, StopEvent } from '@llamaindex/workflow';
-class ComputeEvent extends WorkflowEvent<number> {
-	constructor(data: number) {
-		super(data);
-	}
-}
-class ComputeResultEvent extends WorkflowEvent<number> {
-	constructor(data: number) {
-		super(data);
-	}
-}
-
-
-type ContextData = {
-	sum: number;
-}
-
-const workflow = new Workflow<ContextData, number, number>();
-const min = 100;
-const max = 1000;
-workflow.addStep(
-	{
-		inputs: [ComputeEvent],
-		outputs: [ComputeResultEvent]
-	},
-	async (context, event) => {
-		await new Promise((resolve) =>
-			setTimeout(resolve, Math.floor(Math.random() * (max - min + 1) + min))
-		);
-		return new ComputeResultEvent(event.data);
-	}
-);
-// ---cut-end---
-export async function compute() {
-	'use server';
-	const ui = createStreamableUI();
-	const context = workflow.run(100, {
-		sum: 0
-	});
-	runWithoutBlocking(async () => {
-		for await (const event of context) {
-			if (event instanceof ComputeResultEvent) {
-				// Update UI
-			} else if (event instanceof StopEvent) {
-				// Update UI
-			}
-			// ...
-		}
-	});
-	return ui.value;
-}
-```
-
-<WorkflowStreamingDemo />

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

@@ -1,3 +1,3 @@
 {
-  "pages": ["llamaindex", "llamaflow", "cloud", "api"]
+  "pages": ["llamaindex", "api"]
 }

apps/next/src/deps/type.ts

@@ -1,5 +0,0 @@
-export type {
-  HandlerContext,
-  Workflow,
-  WorkflowContext,
-} from "@llamaindex/workflow";

apps/next/turbo.json

@@ -3,12 +3,19 @@
   "extends": ["//"],
   "tasks": {
     "build": {
+      "inputs": [
+        "node_modules/@llama-flow/docs/**",
+        "src/**/*.ts",
+        "src/**/*.tsx",
+        "src/**/*.mdx",
+        "src/**/*.md"
+      ],
       "outputs": [
         ".next",
         ".source",
         "next-env.d.ts",
-        "src/content/docs/cloud/api/**",
-        "src/content/docs/api/**"
+        "src/content/docs/api/**",
+        "tsconfig.json"
       ],
       "env": [
         "LLAMA_CLOUD_API_KEY",

apps/next/typedoc.json

@@ -2,12 +2,10 @@
   "plugin": ["typedoc-plugin-markdown", "typedoc-plugin-merge-modules"],
   "entryPoints": [
     "../../packages/{,**/}index.ts",
-    "../../packages/readers/src/*.ts",
-    "../../packages/cloud/src/{reader,utils}.ts"
+    "../../packages/readers/src/*.ts"
   ],
   "exclude": [
     "../../packages/autotool/**/src/index.ts",
-    "../../packages/cloud/src/client/index.ts",
     "**/node_modules/**",
     "**/dist/**",
     "**/test/**",
@@ -22,7 +20,7 @@
   "categoryOrder": ["Classes", "Enums", "Functions", "Interfaces", "Types"],
   "sort": ["source-order"],
   "entryFileName": "index.md",
-  "fileExtension": ".mdx",
+  "fileExtension": ".md",
   "hidePageTitle": true,
   "hidePageHeader": true,
   "hideGroupHeadings": true,

e2e/examples/cloudflare-worker-agent/CHANGELOG.md

@@ -1,5 +1,24 @@
 # @llamaindex/cloudflare-worker-agent-test
 
+## 0.0.157
+
+### Patch Changes
+
+- Updated dependencies [3ee8c83]
+  - llamaindex@0.10.3
+
+## 0.0.156
+
+### Patch Changes
+
+- llamaindex@0.10.2
+
+## 0.0.155
+
+### Patch Changes
+
+- llamaindex@0.10.1
+
 ## 0.0.154
 
 ### Patch Changes

e2e/examples/cloudflare-worker-agent/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/cloudflare-worker-agent-test",
-  "version": "0.0.154",
+  "version": "0.0.157",
   "type": "module",
   "private": true,
   "scripts": {

e2e/examples/llama-parse-browser/CHANGELOG.md

@@ -1,5 +1,11 @@
 # @llamaindex/llama-parse-browser-test
 
+## 0.0.59
+
+### Patch Changes
+
+- @llamaindex/cloud@4.0.4
+
 ## 0.0.58
 
 ### Patch Changes

e2e/examples/llama-parse-browser/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/llama-parse-browser-test",
   "private": true,
-  "version": "0.0.58",
+  "version": "0.0.59",
   "type": "module",
   "scripts": {
     "dev": "vite",
@@ -10,8 +10,8 @@
   },
   "devDependencies": {
     "typescript": "^5.7.3",
-    "vite": "^5.4.16",
-    "vite-plugin-wasm": "^3.3.0"
+    "vite": "^6.3.3",
+    "vite-plugin-wasm": "^3.4.1"
   },
   "dependencies": {
     "@llamaindex/cloud": "workspace:*"

e2e/examples/nextjs-agent/CHANGELOG.md

@@ -1,5 +1,24 @@
 # @llamaindex/next-agent-test
 
+## 0.1.157
+
+### Patch Changes
+
+- Updated dependencies [3ee8c83]
+  - llamaindex@0.10.3
+
+## 0.1.156
+
+### Patch Changes
+
+- llamaindex@0.10.2
+
+## 0.1.155
+
+### Patch Changes
+
+- llamaindex@0.10.1
+
 ## 0.1.154
 
 ### Patch Changes

e2e/examples/nextjs-agent/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/next-agent-test",
-  "version": "0.1.154",
+  "version": "0.1.157",
   "private": true,
   "scripts": {
     "dev": "next dev",

e2e/examples/nextjs-edge-runtime/CHANGELOG.md

@@ -1,5 +1,24 @@
 # test-edge-runtime
 
+## 0.1.156
+
+### Patch Changes
+
+- Updated dependencies [3ee8c83]
+  - llamaindex@0.10.3
+
+## 0.1.155
+
+### Patch Changes
+
+- llamaindex@0.10.2
+
+## 0.1.154
+
+### Patch Changes
+
+- llamaindex@0.10.1
+
 ## 0.1.153
 
 ### Patch Changes

e2e/examples/nextjs-edge-runtime/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/nextjs-edge-runtime-test",
-  "version": "0.1.153",
+  "version": "0.1.156",
   "private": true,
   "scripts": {
     "dev": "next dev",

e2e/examples/nextjs-node-runtime/CHANGELOG.md

@@ -1,5 +1,35 @@
 # @llamaindex/next-node-runtime
 
+## 0.1.24
+
+### Patch Changes
+
+- Updated dependencies [3ee8c83]
+  - llamaindex@0.10.3
+  - @llamaindex/huggingface@0.1.7
+  - @llamaindex/readers@3.1.1
+
+## 0.1.23
+
+### Patch Changes
+
+- Updated dependencies [1e59695]
+  - @llamaindex/readers@3.1.0
+
+## 0.1.22
+
+### Patch Changes
+
+- llamaindex@0.10.2
+- @llamaindex/huggingface@0.1.6
+
+## 0.1.21
+
+### Patch Changes
+
+- llamaindex@0.10.1
+- @llamaindex/huggingface@0.1.5
+
 ## 0.1.20
 
 ### Patch Changes

e2e/examples/nextjs-node-runtime/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/next-node-runtime-test",
-  "version": "0.1.20",
+  "version": "0.1.24",
   "private": true,
   "scripts": {
     "dev": "next dev",

e2e/examples/vite-import-llamaindex/CHANGELOG.md

@@ -1,5 +1,24 @@
 # vite-import-llamaindex
 
+## 0.0.23
+
+### Patch Changes
+
+- Updated dependencies [3ee8c83]
+  - llamaindex@0.10.3
+
+## 0.0.22
+
+### Patch Changes
+
+- llamaindex@0.10.2
+
+## 0.0.21
+
+### Patch Changes
+
+- llamaindex@0.10.1
+
 ## 0.0.20
 
 ### Patch Changes

e2e/examples/vite-import-llamaindex/package.json

@@ -1,7 +1,7 @@
 {
   "name": "vite-import-llamaindex",
   "private": true,
-  "version": "0.0.20",
+  "version": "0.0.23",
   "type": "module",
   "scripts": {
     "build": "vite build",
@@ -16,7 +16,7 @@
     "@size-limit/preset-big-lib": "^11.1.6",
     "size-limit": "^11.1.6",
     "typescript": "^5.7.3",
-    "vite": "^5.4.16"
+    "vite": "^6.3.3"
   },
   "dependencies": {
     "llamaindex": "workspace:*"

e2e/examples/waku-query-engine/CHANGELOG.md

@@ -1,5 +1,24 @@
 # @llamaindex/waku-query-engine-test
 
+## 0.0.157
+
+### Patch Changes
+
+- Updated dependencies [3ee8c83]
+  - llamaindex@0.10.3
+
+## 0.0.156
+
+### Patch Changes
+
+- llamaindex@0.10.2
+
+## 0.0.155
+
+### Patch Changes
+
+- llamaindex@0.10.1
+
 ## 0.0.154
 
 ### Patch Changes

e2e/examples/waku-query-engine/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/waku-query-engine-test",
-  "version": "0.0.154",
+  "version": "0.0.157",
   "type": "module",
   "private": true,
   "scripts": {
@@ -14,13 +14,14 @@
     "react": "19.0.0",
     "react-dom": "19.0.0",
     "react-server-dom-webpack": "19.0.0",
-    "waku": "0.21.20"
+    "waku": "0.22.2"
   },
   "devDependencies": {
+    "@tailwindcss/postcss": "^4.1.4",
     "@types/react": "19.0.10",
     "@types/react-dom": "19.0.4",
-    "autoprefixer": "^10.4.20",
-    "tailwindcss": "^4.0.9",
+    "rollup": "4.38.0",
+    "tailwindcss": "^4.1.4",
     "typescript": "5.7.3"
   }
 }

e2e/node/embedding/clip.e2e.ts

@@ -21,19 +21,21 @@ test.beforeEach(() => {
   callback.mock.resetCalls();
 });
 
-await test("clip embedding", async (t) => {
+await test.skip("clip embedding", async (t) => {
   const major = parseInt(process.versions.node.split(".")[0] ?? "0", 10);
   if (major < 20) {
     t.skip("Skip CLIP tests on Node.js < 20");
     return;
   }
+  const imageUrl = new URL(
+    "../../fixtures/img/llamaindex-white.png",
+    import.meta.url,
+  );
+
   await t.test("should trigger load transformer event", async () => {
     const nodes = [
       new ImageNode({
-        image: new URL(
-          "../../fixtures/img/llamaindex-white.png",
-          import.meta.url,
-        ),
+        image: imageUrl,
       }),
     ];
     assert.equal(callback.mock.callCount(), 0);
@@ -46,21 +48,14 @@ await test("clip embedding", async (t) => {
 
   await t.test("init & get image embedding", async () => {
     const clipEmbedding = new ClipEmbedding();
-    const imgUrl = new URL(
-      "../../fixtures/img/llamaindex-white.png",
-      import.meta.url,
-    );
-    const vec = await clipEmbedding.getImageEmbedding(imgUrl);
+    const vec = await clipEmbedding.getImageEmbedding(imageUrl);
     assert.ok(vec);
   });
 
   await t.test("load image document", async () => {
     const nodes = [
       new ImageNode({
-        image: new URL(
-          "../../fixtures/img/llamaindex-white.png",
-          import.meta.url,
-        ),
+        image: imageUrl,
       }),
     ];
     const clipEmbedding = new ClipEmbedding();
@@ -80,12 +75,8 @@ await test("clip embedding", async (t) => {
       }),
     );
     const clipEmbedding = new ClipEmbedding();
-    const imgUrl = new URL(
-      "../../fixtures/img/llamaindex-white.png",
-      import.meta.url,
-    );
     assert.equal(getter.mock.callCount(), 0);
-    const vec = await clipEmbedding.getImageEmbedding(imgUrl);
+    const vec = await clipEmbedding.getImageEmbedding(imageUrl);
     assert.ok(vec);
     assert.ok(getter.mock.callCount() > 0);
   });

e2e/package.json

@@ -10,7 +10,7 @@
   },
   "devDependencies": {
     "@faker-js/faker": "^9.2.0",
-    "@huggingface/transformers": "^3.0.2",
+    "@huggingface/transformers": "^3.5.0",
     "@llamaindex/anthropic": "workspace:*",
     "@llamaindex/clip": "workspace:*",
     "@llamaindex/core": "workspace:*",

examples/CHANGELOG.md

@@ -1,5 +1,125 @@
 # examples
 
+## 0.3.13
+
+### Patch Changes
+
+- Updated dependencies [3ee8c83]
+  - @llamaindex/core@0.6.3
+  - llamaindex@0.10.3
+  - @llamaindex/anthropic@0.3.4
+  - @llamaindex/google@0.2.5
+  - @llamaindex/openai@0.3.5
+  - @llamaindex/vercel@0.1.3
+  - @llamaindex/cloud@4.0.4
+  - @llamaindex/node-parser@2.0.3
+  - @llamaindex/assemblyai@0.1.2
+  - @llamaindex/clip@0.0.53
+  - @llamaindex/cohere@0.0.17
+  - @llamaindex/deepinfra@0.0.53
+  - @llamaindex/discord@0.1.2
+  - @llamaindex/huggingface@0.1.7
+  - @llamaindex/jinaai@0.0.13
+  - @llamaindex/mistral@0.1.3
+  - @llamaindex/mixedbread@0.0.17
+  - @llamaindex/notion@0.1.2
+  - @llamaindex/ollama@0.1.3
+  - @llamaindex/perplexity@0.0.10
+  - @llamaindex/portkey-ai@0.0.45
+  - @llamaindex/replicate@0.0.45
+  - @llamaindex/astra@0.0.17
+  - @llamaindex/azure@0.1.13
+  - @llamaindex/chroma@0.0.17
+  - @llamaindex/elastic-search@0.1.3
+  - @llamaindex/firestore@1.0.10
+  - @llamaindex/milvus@0.1.12
+  - @llamaindex/mongodb@0.0.18
+  - @llamaindex/pinecone@0.1.3
+  - @llamaindex/postgres@0.0.46
+  - @llamaindex/qdrant@0.1.12
+  - @llamaindex/supabase@0.1.2
+  - @llamaindex/upstash@0.0.17
+  - @llamaindex/weaviate@0.0.17
+  - @llamaindex/voyage-ai@1.0.9
+  - @llamaindex/readers@3.1.1
+  - @llamaindex/tools@0.0.8
+  - @llamaindex/workflow@1.0.4
+  - @llamaindex/deepseek@0.0.13
+  - @llamaindex/fireworks@0.0.13
+  - @llamaindex/groq@0.0.68
+  - @llamaindex/together@0.0.13
+  - @llamaindex/vllm@0.0.39
+
+## 0.3.12
+
+### Patch Changes
+
+- Updated dependencies [82d4b46]
+  - @llamaindex/server@0.1.6
+  - @llamaindex/tools@0.0.7
+
+## 0.3.11
+
+### Patch Changes
+
+- Updated dependencies [294f502]
+  - @llamaindex/tools@0.0.6
+
+## 0.3.10
+
+### Patch Changes
+
+- Updated dependencies [1e59695]
+- Updated dependencies [1e59695]
+- Updated dependencies [1e59695]
+- Updated dependencies [1e59695]
+- Updated dependencies [1e59695]
+- Updated dependencies [1e59695]
+  - @llamaindex/assemblyai@0.1.1
+  - @llamaindex/mongodb@0.0.17
+  - @llamaindex/azure@0.1.12
+  - @llamaindex/readers@3.1.0
+  - @llamaindex/notion@0.1.1
+  - @llamaindex/discord@0.1.1
+
+## 0.3.9
+
+### Patch Changes
+
+- Updated dependencies [96dac4d]
+- Updated dependencies [e5c3f95]
+  - @llamaindex/google@0.2.4
+  - @llamaindex/openai@0.3.4
+  - llamaindex@0.10.2
+  - @llamaindex/clip@0.0.52
+  - @llamaindex/deepinfra@0.0.52
+  - @llamaindex/deepseek@0.0.12
+  - @llamaindex/fireworks@0.0.12
+  - @llamaindex/groq@0.0.67
+  - @llamaindex/huggingface@0.1.6
+  - @llamaindex/jinaai@0.0.12
+  - @llamaindex/perplexity@0.0.9
+  - @llamaindex/together@0.0.12
+  - @llamaindex/vllm@0.0.38
+
+## 0.3.8
+
+### Patch Changes
+
+- Updated dependencies [96dd798]
+  - @llamaindex/openai@0.3.3
+  - llamaindex@0.10.1
+  - @llamaindex/clip@0.0.51
+  - @llamaindex/deepinfra@0.0.51
+  - @llamaindex/deepseek@0.0.11
+  - @llamaindex/fireworks@0.0.11
+  - @llamaindex/groq@0.0.66
+  - @llamaindex/huggingface@0.1.5
+  - @llamaindex/jinaai@0.0.11
+  - @llamaindex/perplexity@0.0.8
+  - @llamaindex/together@0.0.11
+  - @llamaindex/vllm@0.0.37
+
 ## 0.3.7
 
 ### Patch Changes

examples/agent/mcp_tools.ts

@@ -9,6 +9,12 @@ async function main() {
     args: ["-y", "@modelcontextprotocol/server-filesystem", "."],
     verbose: true,
   });
+  // You can also connect to the MCP server using SSE
+  // See: https://modelcontextprotocol.io/docs/concepts/transports#server-sent-events-sse
+  // const server = mcp({
+  //   url: "http://localhost:8000/mcp",
+  //   verbose: true,
+  // });
 
   try {
     // Create an agent that uses the MCP tools
@@ -21,9 +27,7 @@ async function main() {
     });
 
     // Run a task
-    const response = await myAgent.run(
-      "what are the files in the current directory?",
-    );
+    const response = await myAgent.run("What are the available tools?");
 
     console.log("Agent response:", response.data);
   } finally {

examples/anthropic/chat-with-file.ts

@@ -0,0 +1,39 @@
+import { Anthropic } from "@llamaindex/anthropic";
+import fs from "fs";
+
+// Note that: Anthropic only supports PDF files for now with limited models
+// See: https://docs.anthropic.com/en/docs/build-with-claude/pdf-support?q=pdf#supported-platforms-and-models
+
+async function main() {
+  if (!process.env.ANTHROPIC_API_KEY) {
+    throw new Error("Please set the ANTHROPIC_API_KEY environment variable.");
+  }
+
+  const llm = new Anthropic({
+    apiKey: process.env.ANTHROPIC_API_KEY,
+    model: "claude-3-7-sonnet",
+  });
+
+  const result = await llm.chat({
+    messages: [
+      {
+        role: "user",
+        content: [
+          {
+            type: "text",
+            text: "What's in this document? Describe it in detail.",
+          },
+          {
+            type: "file",
+            data: fs.readFileSync("./data/manga.pdf"),
+            mimeType: "application/pdf",
+          },
+        ],
+      },
+    ],
+  });
+
+  console.log(result.message);
+}
+
+void main().catch(console.error);

examples/assemblyai/reader.ts

@@ -1,7 +1,7 @@
 import {
   AudioTranscriptReader,
   TranscribeParams,
-} from "@llamaindex/readers/assembly-ai";
+} from "@llamaindex/assemblyai";
 import { program } from "commander";
 import { VectorStoreIndex } from "llamaindex";
 import { stdin as input, stdout as output } from "node:process";

examples/cosmosdb/loadVectorData.ts

@@ -1,11 +1,11 @@
 import { CosmosClient } from "@azure/cosmos";
 import { DefaultAzureCredential } from "@azure/identity";
-import { AzureCosmosDBNoSQLConfig } from "@llamaindex/azure";
-import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
 import {
+  AzureCosmosDBNoSQLConfig,
   SimpleCosmosDBReader,
   SimpleCosmosDBReaderLoaderConfig,
-} from "@llamaindex/readers/cosmosdb";
+} from "@llamaindex/azure";
+import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
 import * as dotenv from "dotenv";
 import {
   Settings,

examples/discord/reader.ts

@@ -1,4 +1,4 @@
-import { DiscordReader } from "@llamaindex/readers/discord";
+import { DiscordReader } from "@llamaindex/discord";
 
 async function main() {
   // Create an instance of the DiscordReader. Set token here or DISCORD_TOKEN environment variable

examples/gemini/chat.ts

@@ -1,11 +1,12 @@
 import { Gemini, GEMINI_MODEL } from "@llamaindex/google";
+import fs from "fs";
 
 (async () => {
   if (!process.env.GOOGLE_API_KEY) {
     throw new Error("Please set the GOOGLE_API_KEY environment variable.");
   }
   const gemini = new Gemini({
-    model: GEMINI_MODEL.GEMINI_PRO,
+    model: GEMINI_MODEL.GEMINI_PRO_1_5,
   });
   const result = await gemini.chat({
     messages: [
@@ -18,4 +19,26 @@ import { Gemini, GEMINI_MODEL } from "@llamaindex/google";
     ],
   });
   console.log(result);
+
+  // chat with file
+  const resultWithFile = await gemini.chat({
+    messages: [
+      {
+        role: "user",
+        content: [
+          {
+            type: "text",
+            text: "What's in this document? Describe it in detail.",
+          },
+          {
+            type: "file",
+            data: fs.readFileSync("./data/manga.pdf"),
+            mimeType: "application/pdf",
+          },
+        ],
+      },
+    ],
+  });
+
+  console.log(resultWithFile);
 })();

examples/llamaindex-server/README.md

@@ -0,0 +1,17 @@
+# Run LlamaIndex Server with simple steps
+
+1. Setup environment variables
+
+```bash
+export OPENAI_API_KEY=<your-openai-api-key>
+```
+
+2. Run the server
+
+```bash
+npx tsx llamaindex-server/simple-workflow/index.ts
+```
+
+3. Open the app at `http://localhost:4000` and start chatting with the agent
+
+![Screenshot](./screenshot.png)

examples/llamaindex-server/simple-workflow/index.ts

@@ -0,0 +1,19 @@
+import { OpenAI } from "@llamaindex/openai";
+import { LlamaIndexServer } from "@llamaindex/server";
+import { weather } from "@llamaindex/tools";
+import "dotenv/config";
+import { agent } from "llamaindex";
+
+const weatherAgent = agent({
+  tools: [weather()],
+  llm: new OpenAI({ model: "gpt-4o-mini" }),
+});
+
+new LlamaIndexServer({
+  workflow: () => weatherAgent,
+  uiConfig: {
+    appTitle: "Weather Agent",
+    starterQuestions: ["Ho Chi Minh city weather", "New York weather"],
+  },
+  port: 4000,
+}).start();

examples/mongo.ts

@@ -1,4 +1,4 @@
-import { SimpleMongoReader } from "@llamaindex/readers/mongo";
+import { SimpleMongoReader } from "@llamaindex/mongodb";
 import { Document, VectorStoreIndex } from "llamaindex";
 import { MongoClient } from "mongodb";
 

examples/mongodb/2_load_and_index.ts

@@ -1,5 +1,7 @@
-import { MongoDBAtlasVectorSearch } from "@llamaindex/mongodb";
-import { SimpleMongoReader } from "@llamaindex/readers/mongo";
+import {
+  MongoDBAtlasVectorSearch,
+  SimpleMongoReader,
+} from "@llamaindex/mongodb";
 import * as dotenv from "dotenv";
 import { storageContextFromDefaults, VectorStoreIndex } from "llamaindex";
 import { MongoClient } from "mongodb";

examples/node/workflow/basic.ts

@@ -11,7 +11,6 @@ const workflow = new Workflow<ContextData, string, string>();
 workflow.addStep(
   {
     inputs: [StartEvent<string>],
-    outputs: [StopEvent<string>],
   },
   async (context, startEvent) => {
     const input = startEvent.data;

examples/notion/reader.ts

@@ -1,4 +1,4 @@
-import { NotionReader } from "@llamaindex/readers/notion";
+import { NotionReader } from "@llamaindex/notion";
 import { Client } from "@notionhq/client";
 import { program } from "commander";
 import { VectorStoreIndex } from "llamaindex";
@@ -8,8 +8,6 @@ import { createInterface } from "node:readline/promises";
 program
   .argument("[page]", "Notion page id (must be provided)")
   .action(async (page, _options) => {
-    // Initializing a client
-
     if (!process.env.NOTION_TOKEN) {
       console.log(
         "No NOTION_TOKEN found in environment variables. You will need to register an integration https://www.notion.com/my-integrations and put it in your NOTION_TOKEN environment variable.",
@@ -64,10 +62,8 @@ program
     const documents = await reader.loadData(page);
     console.log(documents);
 
-    // Split text and create embeddings. Store them in a VectorStoreIndex
     const index = await VectorStoreIndex.fromDocuments(documents);
 
-    // Create query engine
     const queryEngine = index.asQueryEngine();
 
     const rl = createInterface({ input, output });
@@ -80,7 +76,6 @@ program
 
       const response = await queryEngine.query({ query });
 
-      // Output response
       console.log(response.toString());
     }
   });

examples/openai/openai.ts

@@ -1,7 +1,8 @@
 import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
+import fs from "fs";
 
 (async () => {
-  const llm = new OpenAI({ model: "gpt-4.5-preview", temperature: 0.1 });
+  const llm = new OpenAI({ model: "gpt-4o" });
 
   // complete api
   const response1 = await llm.complete({ prompt: "How are you?" });
@@ -13,7 +14,51 @@ import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
   });
   console.log(response2.message.content);
 
-  // embeddings
+  // chat with file
+  const response3 = await llm.chat({
+    messages: [
+      {
+        role: "user",
+        content: [
+          {
+            type: "text",
+            text: "What's in this document? Describe it in detail.",
+          },
+          {
+            type: "file",
+            data: fs.readFileSync("./data/manga.pdf"),
+            mimeType: "application/pdf",
+          },
+        ],
+      },
+    ],
+  });
+  console.log(response3.message.content);
+
+  // chat with image
+  const response4 = await llm.chat({
+    messages: [
+      {
+        role: "user",
+        content: [
+          {
+            type: "text",
+            text: "What's in this image? Describe it in detail.",
+          },
+          {
+            type: "image_url",
+            image_url: {
+              url: "https://storage.googleapis.com/cloud-samples-data/vision/face/faces.jpeg",
+            },
+          },
+        ],
+      },
+    ],
+  });
+
+  console.log("Single Image Analysis:", response4.message.content);
+
+  // // embeddings
   const embedModel = new OpenAIEmbedding();
   const texts = ["hello", "world"];
   const embeddings = await embedModel.getTextEmbeddingsBatch(texts);

examples/openai/responses/chat-with-file.ts

@@ -0,0 +1,35 @@
+import { openaiResponses } from "@llamaindex/openai";
+import fs from "fs";
+
+async function main() {
+  if (!process.env.OPENAI_API_KEY) {
+    throw new Error("Please set the OPENAI_API_KEY environment variable.");
+  }
+
+  const llm = openaiResponses({
+    apiKey: process.env.OPENAI_API_KEY,
+  });
+
+  const result = await llm.chat({
+    messages: [
+      {
+        role: "user",
+        content: [
+          {
+            type: "text",
+            text: "What's in this document? Describe it in detail.",
+          },
+          {
+            type: "file",
+            data: fs.readFileSync("./data/manga.pdf"),
+            mimeType: "application/pdf",
+          },
+        ],
+      },
+    ],
+  });
+
+  console.log(result);
+}
+
+void main().catch(console.error);

examples/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/examples",
-  "version": "0.3.7",
+  "version": "0.3.13",
   "private": true,
   "scripts": {
     "lint": "eslint .",
@@ -11,56 +11,60 @@
     "@azure/cosmos": "^4.1.1",
     "@azure/identity": "^4.4.1",
     "@azure/search-documents": "^12.1.0",
-    "@llamaindex/anthropic": "^0.3.3",
-    "@llamaindex/astra": "^0.0.16",
-    "@llamaindex/azure": "^0.1.11",
-    "@llamaindex/chroma": "^0.0.16",
-    "@llamaindex/clip": "^0.0.50",
-    "@llamaindex/cloud": "^4.0.3",
-    "@llamaindex/cohere": "^0.0.16",
-    "@llamaindex/core": "^0.6.2",
-    "@llamaindex/deepinfra": "^0.0.50",
+    "@llamaindex/anthropic": "^0.3.4",
+    "@llamaindex/astra": "^0.0.17",
+    "@llamaindex/azure": "^0.1.13",
+    "@llamaindex/chroma": "^0.0.17",
+    "@llamaindex/clip": "^0.0.53",
+    "@llamaindex/cloud": "^4.0.4",
+    "@llamaindex/cohere": "^0.0.17",
+    "@llamaindex/core": "^0.6.3",
+    "@llamaindex/deepinfra": "^0.0.53",
     "@llamaindex/env": "^0.1.29",
-    "@llamaindex/firestore": "^1.0.9",
-    "@llamaindex/google": "^0.2.3",
-    "@llamaindex/groq": "^0.0.65",
-    "@llamaindex/huggingface": "^0.1.4",
-    "@llamaindex/milvus": "^0.1.11",
-    "@llamaindex/mistral": "^0.1.2",
-    "@llamaindex/mixedbread": "^0.0.16",
-    "@llamaindex/mongodb": "^0.0.16",
-    "@llamaindex/elastic-search": "^0.1.2",
-    "@llamaindex/node-parser": "^2.0.2",
-    "@llamaindex/ollama": "^0.1.2",
-    "@llamaindex/openai": "^0.3.2",
-    "@llamaindex/pinecone": "^0.1.2",
-    "@llamaindex/portkey-ai": "^0.0.44",
-    "@llamaindex/postgres": "^0.0.45",
-    "@llamaindex/qdrant": "^0.1.11",
-    "@llamaindex/readers": "^3.0.2",
-    "@llamaindex/replicate": "^0.0.44",
-    "@llamaindex/upstash": "^0.0.16",
-    "@llamaindex/vercel": "^0.1.2",
-    "@llamaindex/vllm": "^0.0.36",
-    "@llamaindex/voyage-ai": "^1.0.8",
-    "@llamaindex/weaviate": "^0.0.16",
-    "@llamaindex/workflow": "^1.0.3",
-    "@llamaindex/deepseek": "^0.0.10",
-    "@llamaindex/fireworks": "^0.0.10",
-    "@llamaindex/together": "^0.0.10",
-    "@llamaindex/jinaai": "^0.0.10",
-    "@llamaindex/perplexity": "^0.0.7",
-    "@llamaindex/supabase": "^0.1.1",
-    "@llamaindex/tools": "^0.0.5",
+    "@llamaindex/firestore": "^1.0.10",
+    "@llamaindex/google": "^0.2.5",
+    "@llamaindex/groq": "^0.0.68",
+    "@llamaindex/huggingface": "^0.1.7",
+    "@llamaindex/milvus": "^0.1.12",
+    "@llamaindex/mistral": "^0.1.3",
+    "@llamaindex/mixedbread": "^0.0.17",
+    "@llamaindex/mongodb": "^0.0.18",
+    "@llamaindex/elastic-search": "^0.1.3",
+    "@llamaindex/node-parser": "^2.0.3",
+    "@llamaindex/ollama": "^0.1.3",
+    "@llamaindex/openai": "^0.3.5",
+    "@llamaindex/pinecone": "^0.1.3",
+    "@llamaindex/portkey-ai": "^0.0.45",
+    "@llamaindex/postgres": "^0.0.46",
+    "@llamaindex/qdrant": "^0.1.12",
+    "@llamaindex/readers": "^3.1.1",
+    "@llamaindex/replicate": "^0.0.45",
+    "@llamaindex/upstash": "^0.0.17",
+    "@llamaindex/vercel": "^0.1.3",
+    "@llamaindex/vllm": "^0.0.39",
+    "@llamaindex/voyage-ai": "^1.0.9",
+    "@llamaindex/weaviate": "^0.0.17",
+    "@llamaindex/workflow": "^1.0.4",
+    "@llamaindex/deepseek": "^0.0.13",
+    "@llamaindex/fireworks": "^0.0.13",
+    "@llamaindex/together": "^0.0.13",
+    "@llamaindex/jinaai": "^0.0.13",
+    "@llamaindex/perplexity": "^0.0.10",
+    "@llamaindex/server": "^0.1.6",
+    "@llamaindex/supabase": "^0.1.2",
+    "@llamaindex/tools": "^0.0.8",
     "@notionhq/client": "^2.2.15",
     "@pinecone-database/pinecone": "^4.0.0",
+    "@llamaindex/assemblyai": "^0.1.2",
+    "@llamaindex/discord": "^0.1.2",
+    "@llamaindex/notion": "^0.1.2",
     "@vercel/postgres": "^0.10.0",
     "ai": "^4.0.0",
     "ajv": "^8.17.1",
     "commander": "^12.1.0",
     "dotenv": "^16.4.5",
     "js-tiktoken": "^1.0.14",
-    "llamaindex": "^0.10.0",
+    "llamaindex": "^0.10.3",
     "mongodb": "6.7.0",
     "postgres": "^3.4.4",
     "wikipedia": "^2.1.2",

examples/readers/package.json

@@ -11,7 +11,6 @@
     "start:pdf": "node --import tsx ./src/pdf.ts",
     "start:llamaparse": "node --import tsx ./src/llamaparse.ts",
     "start:notion": "node --import tsx ./src/notion.ts",
-    "start:assemblyai": "node --import tsx ./src/assemblyai.ts",
     "start:llamaparse-dir": "node --import tsx ./src/simple-directory-reader-with-llamaparse.ts",
     "start:llamaparse-json": "node --import tsx ./src/llamaparse-json.ts",
     "start:discord": "node --import tsx ./src/discord.ts",

examples/workflow/README.md

@@ -1,7 +0,0 @@
-# Workflow Examples
-
-These examples demonstrate LlamaIndexTS's workflow system. Check out [its documentation](https://ts.llamaindex.ai/modules/workflows) for more information.
-
-## Running the Examples
-
-To run the examples, make sure to run them from the parent folder called `examples`). For example, to run the joke workflow, run `npx tsx workflow/joke.ts`.

examples/workflow/app-creator.ts

@@ -1,157 +0,0 @@
-import { OpenAI } from "@llamaindex/openai";
-import {
-  HandlerContext,
-  StartEvent,
-  StopEvent,
-  Workflow,
-  WorkflowEvent,
-} from "llamaindex";
-
-const MAX_REVIEWS = 3;
-
-type Context = {
-  specification: string;
-  numberReviews: number;
-};
-
-// Using the o1-preview model (see https://platform.openai.com/docs/guides/reasoning?reasoning-prompt-examples=coding-planning)
-const llm = new OpenAI({ model: "o1-preview", temperature: 1 });
-
-// example specification from https://platform.openai.com/docs/guides/reasoning?reasoning-prompt-examples=coding-planning
-const specification = `Python app that takes user questions and looks them up in a 
-database where they are mapped to answers. If there is a close match, it retrieves 
-the matched answer. If there isn't, it asks the user to provide an answer and 
-stores the question/answer pair in the database.`;
-
-// Create custom event types
-export class MessageEvent extends WorkflowEvent<{ msg: string }> {}
-
-export class CodeEvent extends WorkflowEvent<{ code: string }> {}
-
-export class ReviewEvent extends WorkflowEvent<{
-  review: string;
-  code: string;
-}> {}
-
-// Helper function to truncate long strings
-const truncate = (str: string) => {
-  const MAX_LENGTH = 60;
-  if (str.length <= MAX_LENGTH) return str;
-  return str.slice(0, MAX_LENGTH) + "...";
-};
-
-// the architect is responsible for writing the structure and the initial code based on the specification
-const architect = async (
-  context: HandlerContext<Context>,
-  _: StartEvent<string>,
-) => {
-  const spec = context.data.specification;
-  // write a message to send an update to the user
-  context.sendEvent(
-    new MessageEvent({
-      msg: `Writing app using this specification: ${truncate(spec)}`,
-    }),
-  );
-  const prompt = `Build an app for this specification: <spec>${spec}</spec>. Make a plan for the directory structure you'll need, then return each file in full. Don't supply any reasoning, just code.`;
-  const code = await llm.complete({ prompt });
-  return new CodeEvent({ code: code.text });
-};
-
-// the coder is responsible for updating the code based on the review
-const coder = async (context: HandlerContext<Context>, ev: ReviewEvent) => {
-  // get the specification from the context
-  const spec = context.data.specification;
-  // get the latest review and code
-  const { review, code } = ev.data;
-  // write a message to send an update to the user
-  context.sendEvent(
-    new MessageEvent({
-      msg: `Update code based on review: ${truncate(review)}`,
-    }),
-  );
-  const prompt = `We need to improve code that should implement this specification: <spec>${spec}</spec>. Here is the current code: <code>${code}</code>. And here is a review of the code: <review>${review}</review>. Improve the code based on the review, keep the specification in mind, and return the full updated code. Don't supply any reasoning, just code.`;
-  const updatedCode = await llm.complete({ prompt });
-  return new CodeEvent({ code: updatedCode.text });
-};
-
-// the reviewer is responsible for reviewing the code and providing feedback
-const reviewer = async (context: HandlerContext<Context>, ev: CodeEvent) => {
-  // get the specification from the context
-  const spec = context.data.specification;
-  // get latest code from the event
-  const { code } = ev.data;
-  // update and check the number of reviews
-  context.data.numberReviews++;
-  if (context.data.numberReviews > MAX_REVIEWS) {
-    // the we've done this too many times - return the code
-    context.sendEvent(
-      new MessageEvent({
-        msg: `Already reviewed ${
-          context.data.numberReviews - 1
-        } times, stopping!`,
-      }),
-    );
-    return new StopEvent({ result: code });
-  }
-  // write a message to send an update to the user
-  context.sendEvent(
-    new MessageEvent({
-      msg: `Review #${context.data.numberReviews}: ${truncate(code)}`,
-    }),
-  );
-  const prompt = `Review this code: <code>${code}</code>. Check if the code quality and whether it correctly implements this specification: <spec>${spec}</spec>. If you're satisfied, just return 'Looks great', nothing else. If not, return a review with a list of changes you'd like to see.`;
-  const review = (await llm.complete({ prompt })).text;
-  if (review.includes("Looks great")) {
-    // the reviewer is satisfied with the code, let's return the review
-    context.sendEvent(
-      new MessageEvent({
-        msg: `Reviewer says: ${review}`,
-      }),
-    );
-    return new StopEvent({ result: code });
-  }
-
-  return new ReviewEvent({ review, code });
-};
-
-const codeAgent = new Workflow<Context, string, string>();
-codeAgent.addStep(
-  {
-    inputs: [StartEvent<string>],
-    outputs: [CodeEvent],
-  },
-  architect,
-);
-codeAgent.addStep(
-  {
-    inputs: [ReviewEvent],
-    outputs: [CodeEvent],
-  },
-  coder,
-);
-codeAgent.addStep(
-  {
-    inputs: [CodeEvent],
-    outputs: [ReviewEvent, StopEvent],
-  },
-  reviewer,
-);
-
-// Usage
-async function main() {
-  const run = codeAgent.run(specification).with({
-    specification,
-    numberReviews: 0,
-  });
-  for await (const event of run) {
-    if (event instanceof MessageEvent) {
-      const msg = (event as MessageEvent).data.msg;
-      console.log(`${msg}\n`);
-    } else if (event instanceof StopEvent) {
-      const result = (event as StopEvent<string>).data;
-      console.log("Final code:\n", result);
-    }
-  }
-}
-
-main().catch(console.error);

examples/workflow/conditional.ts

@@ -1,88 +0,0 @@
-import { OpenAI } from "@llamaindex/openai";
-import {
-  HandlerContext,
-  StartEvent,
-  StopEvent,
-  Workflow,
-  WorkflowEvent,
-} from "llamaindex";
-
-// Create LLM instance
-const llm = new OpenAI();
-
-// Create custom event types
-export class JokeEvent extends WorkflowEvent<{ joke: string }> {}
-
-export class CritiqueEvent extends WorkflowEvent<{ critique: string }> {}
-
-export class AnalysisEvent extends WorkflowEvent<{ analysis: string }> {}
-
-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 });
-};
-
-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 });
-  return new CritiqueEvent({ critique: response.text });
-};
-
-const analyzeJoke = async (_: unknown, ev: JokeEvent) => {
-  const prompt = `Give a thorough analysis of the following joke: ${ev.data.joke}`;
-  const response = await llm.complete({ prompt });
-  return new AnalysisEvent({ analysis: response.text });
-};
-
-const reportJoke = async (
-  context: HandlerContext,
-  ev1: AnalysisEvent,
-  ev2: CritiqueEvent,
-) => {
-  const subPrompts = [ev1.data.analysis, ev2.data.critique];
-
-  const prompt = `Based on the following information about a joke:\n${subPrompts.join(
-    "\n",
-  )}\nProvide a comprehensive report on the joke's quality and impact.`;
-  const response = await llm.complete({ prompt });
-  return new StopEvent(response.text);
-};
-
-const jokeFlow = new Workflow<unknown, string, string>();
-jokeFlow.addStep(
-  {
-    inputs: [StartEvent<string>],
-    outputs: [JokeEvent],
-  },
-  generateJoke,
-);
-jokeFlow.addStep(
-  {
-    inputs: [JokeEvent],
-    outputs: [CritiqueEvent],
-  },
-  critiqueJoke,
-);
-jokeFlow.addStep(
-  {
-    inputs: [JokeEvent],
-    outputs: [AnalysisEvent],
-  },
-  analyzeJoke,
-);
-jokeFlow.addStep(
-  {
-    inputs: [AnalysisEvent, CritiqueEvent],
-    outputs: [StopEvent<string>],
-  },
-  reportJoke,
-);
-
-// Usage
-async function main() {
-  const result = await jokeFlow.run("pirates");
-  console.log(result.data);
-}
-
-main().catch(console.error);

examples/workflow/joke.ts

@@ -1,44 +0,0 @@
-import { OpenAI } from "@llamaindex/openai";
-import { StartEvent, StopEvent, Workflow, WorkflowEvent } from "llamaindex";
-
-// Create LLM instance
-const llm = new OpenAI();
-
-// Create a custom event type
-export class JokeEvent extends WorkflowEvent<{ joke: string }> {}
-
-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 });
-};
-
-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 });
-  return new StopEvent(response.text);
-};
-
-const jokeFlow = new Workflow<unknown, string, string>();
-jokeFlow.addStep(
-  {
-    inputs: [StartEvent<string>],
-    outputs: [JokeEvent],
-  },
-  generateJoke,
-);
-jokeFlow.addStep(
-  {
-    inputs: [JokeEvent],
-    outputs: [StopEvent<string>],
-  },
-  critiqueJoke,
-);
-
-// Usage
-async function main() {
-  const result = await jokeFlow.run("pirates");
-  console.log(result.data);
-}
-
-main().catch(console.error);

examples/workflow/stream-events.ts

@@ -1,66 +0,0 @@
-import { OpenAI } from "@llamaindex/openai";
-import {
-  HandlerContext,
-  StartEvent,
-  StopEvent,
-  Workflow,
-  WorkflowEvent,
-} from "llamaindex";
-
-// Create LLM instance
-const llm = new OpenAI();
-
-// Create custom event types
-export class JokeEvent extends WorkflowEvent<{ joke: string }> {}
-
-export class MessageEvent extends WorkflowEvent<{ msg: string }> {}
-
-const generateJoke = async (context: HandlerContext, ev: StartEvent) => {
-  context.sendEvent(
-    new MessageEvent({ msg: `Generating a joke about: ${ev.data}` }),
-  );
-  const prompt = `Write your best joke about ${ev.data}.`;
-  const response = await llm.complete({ prompt });
-  return new JokeEvent({ joke: response.text });
-};
-
-const critiqueJoke = async (context: HandlerContext, ev: JokeEvent) => {
-  context.sendEvent(
-    new MessageEvent({ msg: `Write a critique of this joke: ${ev.data.joke}` }),
-  );
-  const prompt = `Give a thorough critique of the following joke: ${ev.data.joke}`;
-  const response = await llm.complete({ prompt });
-  return new StopEvent(response.text);
-};
-
-const jokeFlow = new Workflow();
-jokeFlow.addStep(
-  {
-    inputs: [StartEvent<string>],
-    outputs: [JokeEvent],
-  },
-  generateJoke,
-);
-jokeFlow.addStep(
-  {
-    inputs: [JokeEvent],
-    outputs: [StopEvent<string>],
-  },
-  critiqueJoke,
-);
-
-// Usage
-async function main() {
-  const run = jokeFlow.run("pirates");
-  for await (const event of run) {
-    if (event instanceof MessageEvent) {
-      console.log("Message:");
-      console.log((event as MessageEvent).data.msg);
-    } else if (event instanceof StopEvent) {
-      console.log("Result:");
-      console.log((event as StopEvent<string>).data);
-    }
-  }
-}
-
-main().catch(console.error);

examples/workflow/timeout.ts

@@ -1,48 +0,0 @@
-import { StartEvent, StopEvent, Workflow } from "llamaindex";
-
-const longRunning = async (_: unknown, ev: StartEvent<string>) => {
-  await new Promise((resolve) => setTimeout(resolve, 2000)); // Wait for 2 seconds
-  return new StopEvent("We waited 2 seconds");
-};
-
-async function timeout() {
-  const workflow = new Workflow<unknown, string, string>({
-    timeout: 1,
-  });
-  workflow.addStep(
-    {
-      inputs: [StartEvent<string>],
-      outputs: [StopEvent<string>],
-    },
-    longRunning,
-  );
-  try {
-    await workflow.run("Let's start");
-  } catch (error) {
-    console.error(error);
-  }
-}
-
-async function notimeout() {
-  // Increase timeout to 3 seconds - no timeout
-  const workflow = new Workflow<unknown, string, string>({
-    timeout: 3,
-  });
-  workflow.addStep(
-    {
-      inputs: [StartEvent<string>],
-      outputs: [StopEvent<string>],
-    },
-    longRunning,
-  );
-  const result = await workflow.run("Let's start");
-  console.log(result.data);
-}
-
-async function main() {
-  await timeout();
-  console.log("---");
-  await notimeout();
-}
-
-main().catch(console.error);