test: crypto compatity with node18

2026-07-02 20:13:52 -04:00 · 2025-03-19 07:42:58 +07:00
448 changed files with 10484 additions and 19688 deletions
@@ -0,0 +1,7 @@
+---
+"@llamaindex/readers": patch
+"@llamaindex/core": patch
+"@llamaindex/doc": patch
+---
+
+Expose more content to fix the issue with unavailable documentation links, and adjust the documentation based on the latest code.
@@ -0,0 +1,5 @@
+---
+"@llamaindex/google": patch
+---
+
+Added saftey setting parameter for gemini
@@ -0,0 +1,5 @@
+---
+"@llamaindex/pinecone": minor
+---
+
+Fix deleting of document by id in PineconeVectorStore
@@ -0,0 +1,13 @@
+---
+"@llamaindex/huggingface": minor
+"@llamaindex/anthropic": minor
+"@llamaindex/mistral": minor
+"@llamaindex/google": minor
+"@llamaindex/ollama": minor
+"@llamaindex/openai": minor
+"@llamaindex/core": minor
+"@llamaindex/examples": minor
+---
+
+Added support for structured output in the chat api of openai and ollama
+Added structured output parameter in the provider
@@ -0,0 +1,5 @@
+---
+"@llamaindex/cloud": patch
+---
+
+chore: bump sdk openapi.json
@@ -0,0 +1,5 @@
+---
+"@llamaindex/azure": patch
+---
+
+Add `fromConnectionString` method to azure storage libs to track the usage vCore.
@@ -0,0 +1,5 @@
+---
+"@llamaindex/tools": patch
+---
+
+feat: @llamaindex/tools
@@ -0,0 +1,8 @@
+---
+"@llamaindex/mistral": minor
+"@llamaindex/examples": minor
+---
+
+Added support for function calling in mistral provider
+Update model list for mistral provider
+Added example for the tool call in mistral
@@ -0,0 +1,8 @@
+---
+"@llamaindex/cloud": patch
+"@llamaindex/community": patch
+"@llamaindex/core": patch
+"@llamaindex/readers": patch
+---
+
+fix: add retry handling logic to parser reader and fix lint issues
@@ -8,11 +8,6 @@ on:
    branches:
      - main

-env:
-  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
-  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
-  TURBO_REMOTE_ONLY: true
-
 jobs:
  lint:
    runs-on: ubuntu-latest
@@ -1,11 +1,6 @@
 name: Publish Preview
 on: [pull_request]

-env:
-  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
-  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
-  TURBO_REMOTE_ONLY: true
-
 jobs:
  pre_release:
    name: Pre Release
@@ -7,4 +7,3 @@ dist/
 .source/
 # prttier doesn't support mdx3 we are using
 *.mdx
-packages/server/server/
@@ -1,166 +1,5 @@
 # @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
-
- 6cf928f: chore: use bunchee for llamaindex
- Updated dependencies [6cf928f]
-  - llamaindex@0.10.0
-
-## 0.2.10
-
-### Patch Changes
-
- 411dcea: Add Nova Premier to AWS Nova models. Add EU endpoints
-
-## 0.2.9
-
-### Patch Changes
-
- Updated dependencies [d365eb2]
-  - @llamaindex/openai@0.3.2
-  - llamaindex@0.9.19
-
-## 0.2.8
-
-### Patch Changes
-
- 2ffdb27: docs: correct the CondenseQuestionChatEngine path
- Updated dependencies [88b7046]
-  - @llamaindex/openai@0.3.1
-  - llamaindex@0.9.18
-
-## 0.2.7
-
-### Patch Changes
-
- 3ffee26: feat: enhance config params for LlamaIndexServer
-
-## 0.2.6
-
-### Patch Changes
-
- Updated dependencies [3534c37]
- Updated dependencies [41191d0]
-  - llamaindex@0.9.17
-  - @llamaindex/workflow@1.0.3
-  - @llamaindex/cloud@4.0.3
-
-## 0.2.5
-
-### Patch Changes
-
- 4999df1: bump nextjs
- Updated dependencies [f5e4d09]
-  - llamaindex@0.9.16
-
-## 0.2.4
-
-### Patch Changes
-
- 9c63f3f: Add support for openai responses api
- Updated dependencies [9c63f3f]
- Updated dependencies [c515a32]
-  - @llamaindex/openai@0.3.0
-  - @llamaindex/core@0.6.2
-  - @llamaindex/workflow@1.0.2
-  - llamaindex@0.9.15
-  - @llamaindex/cloud@4.0.2
-  - @llamaindex/node-parser@2.0.2
-  - @llamaindex/readers@3.0.2
-
-## 0.2.3
-
-### Patch Changes
-
- 648cfb5: Add support for supabase vector store
-  Added doc for the supbase vector store
- Updated dependencies [1b6f368]
- Updated dependencies [eaf326e]
- Updated dependencies [9d951b2]
-  - @llamaindex/core@0.6.1
-  - llamaindex@0.9.14
-  - @llamaindex/cloud@4.0.1
-  - @llamaindex/node-parser@2.0.1
-  - @llamaindex/openai@0.2.1
-  - @llamaindex/readers@3.0.1
-  - @llamaindex/workflow@1.0.1
-
-## 0.2.2
-
-### Patch Changes
-
- e98033e: docs: correct the number of indexes
-
-## 0.2.1
-
-### Patch Changes
-
- Updated dependencies [75d6e29]
-  - llamaindex@0.9.13
-
-## 0.2.0
-
-### Minor Changes
-
- f1db9b3: Adding an options parameter to vercel tool to tailor responses
-
-### Patch Changes
-
- 21bebfc: Expose more content to fix the issue with unavailable documentation links, and adjust the documentation based on the latest code.
- 2b39cef: Added documentation for structured output in openai and ollama
- Updated dependencies [21bebfc]
- Updated dependencies [93bc0ff]
- Updated dependencies [91a18e7]
- Updated dependencies [bf56fc0]
- Updated dependencies [f8a86e4]
- Updated dependencies [5189b44]
- Updated dependencies [58a9446]
-  - @llamaindex/readers@3.0.0
-  - @llamaindex/core@0.6.0
-  - @llamaindex/openai@0.2.0
-  - @llamaindex/cloud@4.0.0
-  - @llamaindex/workflow@1.0.0
-  - llamaindex@0.9.12
-  - @llamaindex/node-parser@2.0.0
-
 ## 0.1.11

 ### Patch Changes
@@ -1,2 +0,0 @@
-// fallback for `fs` usage in `web-tree-sitter`
-module.exports = {};
@@ -1,10 +1,9 @@
 import { createMDX } from "fumadocs-mdx/next";
+import MonacoWebpackPlugin from "monaco-editor-webpack-plugin";
 const withMDX = createMDX();

 /** @type {import('next').NextConfig} */
 const config = {
-  // default timeout for static generation is 60s, but we need to increase it to 10 minutes due to the large number of document pages
-  staticPageGenerationTimeout: 600,
  reactStrictMode: true,
  eslint: {
    ignoreDuringBuilds: true,
@@ -15,12 +14,7 @@ const config = {
    "twoslash",
    "typescript",
  ],
-  turbopack: {
-    resolveAlias: {
-      fs: { browser: "./fallback.js" },
-    },
-  },
-  webpack: (config) => {
+  webpack: (config, { isServer }) => {
    if (Array.isArray(config.target) && config.target.includes("web")) {
      config.target = ["web", "es2020"];
    }
@@ -32,6 +26,14 @@ const config = {
    };
    config.resolve.fallback ??= {};
    config.resolve.fallback.fs = false;
+    if (!isServer) {
+      config.plugins.push(
+        new MonacoWebpackPlugin({
+          languages: ["typescript"],
+          filename: "static/[name].worker.js",
+        }),
+      );
+    }
    config.resolve.alias["replicate"] = false;
    return config;
  },
@@ -1,21 +1,19 @@
 {
  "name": "@llamaindex/doc",
-  "version": "0.2.15",
+  "version": "0.1.11",
  "private": true,
  "scripts": {
    "postinstall": "fumadocs-mdx",
    "prebuild": "pnpm run build:docs",
    "build": "next build",
-    "dev": "next dev --turbo",
+    "dev": "next dev",
    "start": "next start",
    "postbuild": "tsx scripts/post-build.mts && tsx scripts/validate-links.mts",
    "build:docs": "cross-env NODE_OPTIONS=\"--max-old-space-size=8192\" typedoc && tsx scripts/generate-docs.mts",
    "validate-links": "tsx scripts/validate-links.mts"
  },
  "dependencies": {
-    "@huggingface/transformers": "^3.5.0",
    "@icons-pack/react-simple-icons": "^10.1.0",
-    "@llama-flow/docs": "0.0.5",
    "@llamaindex/chat-ui": "0.2.0",
    "@llamaindex/cloud": "workspace:*",
    "@llamaindex/core": "workspace:*",
@@ -24,7 +22,6 @@
    "@llamaindex/readers": "workspace:*",
    "@llamaindex/workflow": "workspace:*",
    "@mdx-js/mdx": "^3.1.0",
-    "@monaco-editor/react": "^4.7.0",
    "@number-flow/react": "^0.3.4",
    "@radix-ui/react-dialog": "^1.1.2",
    "@radix-ui/react-icons": "^1.3.2",
@@ -39,21 +36,22 @@
    "clsx": "2.1.1",
    "foxact": "^0.2.41",
    "framer-motion": "^11.11.17",
-    "fumadocs-core": "^15.2.7",
+    "fumadocs-core": "^15.0.15",
    "fumadocs-docgen": "^2.0.0",
-    "fumadocs-mdx": "^11.6.0",
-    "fumadocs-openapi": "^8.0.1",
-    "fumadocs-twoslash": "^3.1.1",
-    "fumadocs-typescript": "^4.0.2",
-    "fumadocs-ui": "^15.2.7",
+    "fumadocs-mdx": "^11.5.6",
+    "fumadocs-openapi": "^6.3.0",
+    "fumadocs-twoslash": "^3.1.0",
+    "fumadocs-typescript": "^3.1.0",
+    "fumadocs-ui": "^15.0.15",
    "hast-util-to-jsx-runtime": "^2.3.2",
    "llamaindex": "workspace:*",
    "lucide-react": "^0.460.0",
-    "next": "^15.3.0",
+    "next": "^15.2.1",
    "next-themes": "^0.4.3",
-    "react": "^19.1.0",
-    "react-dom": "^19.1.0",
+    "react": "^19.0.0",
+    "react-dom": "^19.0.0",
    "react-icons": "^5.3.0",
+    "react-monaco-editor": "^0.56.2",
    "react-use-measure": "^2.1.1",
    "rehype-katex": "^7.0.1",
    "remark-math": "^6.0.0",
@@ -65,14 +63,12 @@
    "tailwindcss-animate": "^1.0.7",
    "tree-sitter": "^0.22.1",
    "tree-sitter-typescript": "^0.23.2",
-    "ts-morph": "^25.0.1",
-    "twoslash": "^0.3.1",
    "use-stick-to-bottom": "^1.0.42",
    "web-tree-sitter": "^0.24.4",
    "zod": "^3.23.8"
  },
  "devDependencies": {
-    "@next/env": "^15.3.0",
+    "@next/env": "^15.2.1",
    "@tailwindcss/postcss": "^4.0.9",
    "@types/mdx": "^2.0.13",
    "@types/node": "22.9.0",
@@ -82,6 +78,7 @@
    "cross-env": "^7.0.3",
    "fast-glob": "^3.3.2",
    "gray-matter": "^4.0.3",
+    "monaco-editor-webpack-plugin": "^7.1.0",
    "postcss": "^8.5.3",
    "raw-loader": "^4.0.2",
    "remark": "^15.0.1",
@@ -90,9 +87,9 @@
    "remark-stringify": "^11.0.0",
    "tailwindcss": "^4.0.9",
    "tsx": "^4.19.3",
-    "typedoc": "0.28.3",
-    "typedoc-plugin-markdown": "^4.6.2",
-    "typedoc-plugin-merge-modules": " ^7.0.0",
+    "typedoc": "0.27.4",
+    "typedoc-plugin-markdown": "^4.3.1",
+    "typedoc-plugin-merge-modules": "^6.1.0",
    "typescript": "^5.7.3"
  }
 }
@@ -1,24 +1,27 @@
-import {
-  createGenerator,
-  generateFiles as typescriptGenerateFiles,
-} from "fumadocs-typescript";
+import { generateFiles as openapiGenerateFiles } from "fumadocs-openapi";
+import { generateFiles as typescriptGenerateFiles } from "fumadocs-typescript";
 import fs from "node:fs";
 import * as path from "node:path";
 import { rimrafSync } from "rimraf";

-const generator = createGenerator();
 const out = "./src/content/docs/cloud/api";
 const apiRefOut = "./src/content/docs/api";

 // clean generated files
 rimrafSync(out, {
  filter(v) {
-    return !v.endsWith("index.md") && !v.endsWith("meta.json");
+    return !v.endsWith("index.mdx") && !v.endsWith("meta.json");
  },
 });

-void typescriptGenerateFiles(generator, {
-  input: ["./src/content/docs/api/**/*.md"],
+void openapiGenerateFiles({
+  input: ["../../packages/cloud/openapi.json"],
+  output: "./src/content/docs/cloud/api",
+  groupBy: "tag",
+});
+
+void typescriptGenerateFiles({
+  input: ["./src/content/docs/api/**/*.mdx"],
  output: (file) => path.resolve(path.dirname(file), path.basename(file)),
  transformOutput,
 });
@@ -27,22 +30,19 @@ function transformOutput(filePath: string, content: string) {
  const fileName = path.basename(filePath);
  let title = fileName.split(".")[0];
  if (title === "index") title = "LlamaIndex API Reference";
-  return `---\ntitle: ${title}\n---\n\n${transformAbsoluteUrl(
-    content.replace(/(?<!\\)\{([^}]+)(?<!\\)}/g, "\\{$1\\}"),
-    filePath,
-  )}`;
+  return `---\ntitle: ${title}\n---\n\n${transformAbsoluteUrl(content, filePath)}`;
 }

 /**
- * Transforms the content by converting relative MD links to absolute docs API links
- * Example: [text](../type-aliases/TaskHandler.md) -> [text](/docs/api/type-aliases/TaskHandler)
- * [text](BaseChatEngine.md) -> [text](/docs/api/classes/BaseChatEngine)
- * [text](BaseVectorStore.md#constructors) -> [text](/docs/api/classes/BaseVectorStore#constructors)
- * [text](TaskStep.md) -> [text](/docs/api/type-aliases/TaskStep)
+ * Transforms the content by converting relative MDX links to absolute docs API links
+ * Example: [text](../type-aliases/TaskHandler.mdx) -> [text](/docs/api/type-aliases/TaskHandler)
+ * [text](BaseChatEngine.mdx) -> [text](/docs/api/classes/BaseChatEngine)
+ * [text](BaseVectorStore.mdx#constructors) -> [text](/docs/api/classes/BaseVectorStore#constructors)
+ * [text](TaskStep.mdx) -> [text](/docs/api/type-aliases/TaskStep)
 */
 function transformAbsoluteUrl(content: string, filePath: string) {
  const group = path.dirname(filePath).split(path.sep).pop();
-  return content.replace(/\]\(([^)]+)\.md([^)]*)\)/g, (_, slug, anchor) => {
+  return content.replace(/\]\(([^)]+)\.mdx([^)]*)\)/g, (_, slug, anchor) => {
    const slugParts = slug.split("/");
    const fileName = slugParts[slugParts.length - 1];
    const fileGroup = slugParts[slugParts.length - 2] ?? group;
@@ -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[] = [];
@@ -1,18 +1,13 @@
-import {
-  rehypeCodeDefaultOptions,
-  remarkStructure,
-} from "fumadocs-core/mdx-plugins";
+import { rehypeCodeDefaultOptions } from "fumadocs-core/mdx-plugins";
 import { fileGenerator, remarkDocGen, remarkInstall } from "fumadocs-docgen";
 import { defineConfig, defineDocs } from "fumadocs-mdx/config";
 import { transformerTwoslash } from "fumadocs-twoslash";
+import { createFileSystemTypesCache } from "fumadocs-twoslash/cache-fs";
 import rehypeKatex from "rehype-katex";
 import remarkMath from "remark-math";

 export const docs = defineDocs({
-  dir: ["./src/content/docs", "./node_modules/@llama-flow/docs"],
-  docs: {
-    async: true,
-  },
+  dir: "./src/content/docs",
 });

 export default defineConfig({
@@ -26,7 +21,11 @@ export default defineConfig({
      },
      transformers: [
        ...(rehypeCodeDefaultOptions.transformers ?? []),
-        transformerTwoslash(),
+        transformerTwoslash({
+          typesCache: createFileSystemTypesCache({
+            dir: ".next/cache/twoslash",
+          }),
+        }),
        {
          name: "transformers:remove-notation-escape",
          code(hast) {
@@ -47,7 +46,6 @@ export default defineConfig({
      ],
    },
    remarkPlugins: [
-      remarkStructure,
      remarkMath,
      [remarkInstall, { persist: { id: "package-manager" } }],
      [remarkDocGen, { generators: [fileGenerator()] }],
@@ -10,55 +10,16 @@ import { MagicMove } from "@/components/magic-move";
 import { NpmInstall } from "@/components/npm-install";
 import { Supports } from "@/components/supports";
 import { Button } from "@/components/ui/button";
-import { DOCUMENT_URL } from "@/lib/const";
+import { Skeleton } from "@/components/ui/skeleton";
+import { LEGACY_DOCUMENT_URL } from "@/lib/const";
 import { SiStackblitz } from "@icons-pack/react-simple-icons";
+import {
+  CodeBlock as FumaCodeBlock,
+  Pre,
+} from "fumadocs-ui/components/codeblock";
 import { Blocks, Bot, Footprints, Terminal } from "lucide-react";
 import Link from "next/link";
-
-const codes = [
-  `import { openai } from "@llamaindex/openai";
-
-const llm = openai();
-const response = await llm.complete({ prompt: "How are you?" });`,
-  `import { openai } from "@llamaindex/openai";
-
-const llm = openai();
-const response = await llm.chat({
-  messages: [{ content: "Tell me a joke.", role: "user" }],
-});`,
-  `import { agent } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-
-const analyseAgent = agent({
-  llm: openai({ model: "gpt-4o" }),
-  tools: [analyseTools],
-  systemPrompt,
-});
-const response = await analyseAgent.run(\`Analyse the given data:
-\${data}\`);`,
-  `import { agent, multiAgent } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-
-const analyseAgent = agent({
-  name: "AnalyseAgent",
-  llm: openai({ model: "gpt-4o" }),
-  tools: [analyseTools],
-});
-const reporterAgent = agent({
-  name: "ReporterAgent",
-  llm: openai({ model: "gpt-4o" }),
-  tools: [reporterTools],
-  canHandoffTo: [analyseAgent],
-});
-
-const agents = multiAgent({
-  agents: [analyseAgent, reporterAgent],
-  rootAgent: reporterAgent,
-});
-
-const response = await agents.run(\`Analyse the given data:
-\${data}\`);`,
-];
+import { Suspense } from "react";

 export default function HomePage() {
  return (
@@ -78,7 +39,7 @@ export default function HomePage() {
      </div>

      <div className="flex flex-wrap justify-center gap-4">
-        <Link href={DOCUMENT_URL}>
+        <Link href={LEGACY_DOCUMENT_URL}>
          <Button variant="outline">Get Started</Button>
        </Link>
        <NpmInstall />
@@ -101,10 +62,65 @@ export default function HomePage() {
          heading="From the simplest to the most complex"
          description="LlamaIndex.TS is designed to be simple to get started, but powerful enough to build complex, agentic AI applications using multi-agents."
        >
-          <MagicMove
-            placeholder={<CodeBlock lang="ts" code={codes[0]} />}
-            code={codes}
-          />
+          <Suspense
+            fallback={
+              <FumaCodeBlock allowCopy={false}>
+                <Pre>
+                  <div className="space-y-2">
+                    <Skeleton className="h-4 w-[250px]" />
+                    <Skeleton className="h-4 w-[200px]" />
+                  </div>
+                </Pre>
+              </FumaCodeBlock>
+            }
+          >
+            <MagicMove
+              code={[
+                `import { openai } from "@llamaindex/openai";
+
+const llm = openai();
+const response = await llm.complete({ prompt: "How are you?" });`,
+                `import { openai } from "@llamaindex/openai";
+
+const llm = openai();
+const response = await llm.chat({
+  messages: [{ content: "Tell me a joke.", role: "user" }],
+});`,
+                `import { agent } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+
+const analyseAgent = agent({
+  llm: openai({ model: "gpt-4o" }),
+  tools: [analyseTools],
+  systemPrompt,
+});
+const response = await analyseAgent.run(\`Analyse the given data:
+\${data}\`);`,
+                `import { agent, multiAgent } from "llamaindex";
+import { openai } from "@llamaindex/openai";
+
+const analyseAgent = agent({
+  name: "AnalyseAgent",
+  llm: openai({ model: "gpt-4o" }),
+  tools: [analyseTools],
+});
+const reporterAgent = agent({
+  name: "ReporterAgent",
+  llm: openai({ model: "gpt-4o" }),
+  tools: [reporterTools],
+  canHandoffTo: [analyseAgent],
+});
+
+const agents = multiAgent({
+  agents: [analyseAgent, reporterAgent],
+  rootAgent: reporterAgent,
+});
+
+const response = await agents.run(\`Analyse the given data:
+\${data}\`);`,
+              ]}
+            />
+          </Suspense>
        </Feature>
        <Feature
          icon={Bot}
@@ -1,12 +1,4 @@
 import { source } from "@/lib/source";
-import { structure } from "fumadocs-core/mdx-plugins";
 import { createFromSource } from "fumadocs-core/search/server";

-// TODO: migrate to another search service, I don't think Vercel can handle that many of documents.
-export const { GET } = createFromSource(source, (page) => ({
-  id: page.file.path,
-  title: page.data.title,
-  description: page.data.description,
-  url: page.url,
-  structuredData: structure(page.data.content),
-}));
+export const { GET } = createFromSource(source);
@@ -1,14 +1,7 @@
-import { ChatDemoRSC } from "@/components/demo/chat/rsc/demo";
-import * as demos from "@/components/demo/lazy";
 import { createMetadata, metadataImage } from "@/lib/metadata";
 import { openapi, source } from "@/lib/source";
-import * as Icons from "@icons-pack/react-simple-icons";
-import { APIPage } from "fumadocs-openapi/ui";
 import { Popup, PopupContent, PopupTrigger } from "fumadocs-twoslash/ui";
-import { createGenerator } from "fumadocs-typescript";
-import { AutoTypeTable } from "fumadocs-typescript/ui";
-import { Accordion, Accordions } from "fumadocs-ui/components/accordion";
-import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+import { createTypeTable } from "fumadocs-typescript/ui";
 import defaultMdxComponents from "fumadocs-ui/mdx";
 import {
  DocsBody,
@@ -18,8 +11,6 @@ import {
 } from "fumadocs-ui/page";
 import { notFound } from "next/navigation";

-const generator = createGenerator();
-
 export const revalidate = false;

 export default async function Page(props: {
@@ -29,17 +20,17 @@ export default async function Page(props: {
  const page = source.getPage(params.slug);
  if (!page) notFound();

-  const { body: MDX, toc, lastModified } = await page.data.load();
+  const { AutoTypeTable } = createTypeTable();
+  const MDX = page.data.body;

  return (
    <DocsPage
-      toc={toc}
+      toc={page.data.toc}
      full={page.data.full}
-      lastUpdate={lastModified}
+      lastUpdate={page.data.lastModified}
      editOnGithub={{
        owner: "run-llama",
        repo: "LlamaIndexTS",
-        sha: "main",
        path: `apps/next/src/content/docs/${page.file.path}`,
      }}
    >
@@ -48,21 +39,12 @@ export default async function Page(props: {
      <DocsBody>
        <MDX
          components={{
-            ...Icons,
            ...defaultMdxComponents,
-            ...demos,
-            ChatDemoRSC,
-            Accordion,
-            Accordions,
-            APIPage: (props) => <APIPage {...openapi.getAPIPageProps(props)} />,
-            Tab,
-            Tabs,
+            APIPage: openapi.APIPage,
            Popup,
            PopupContent,
            PopupTrigger,
-            AutoTypeTable: (props) => (
-              <AutoTypeTable generator={generator} {...props} />
-            ),
+            AutoTypeTable,
          }}
        />
      </DocsBody>
@@ -1,7 +1,11 @@
 import { baseOptions } from "@/app/layout.config";
+import { AITrigger } from "@/components/ai-chat";
+import { buttonVariants } from "@/components/ui/button";
 import { source } from "@/lib/source";
+import { cn } from "@/lib/utils";
 import "fumadocs-twoslash/twoslash.css";
 import { DocsLayout } from "fumadocs-ui/layouts/docs";
+import { MessageCircle } from "lucide-react";
 import type { ReactNode } from "react";

 export default function Layout({ children }: { children: ReactNode }) {
@@ -9,9 +13,23 @@ export default function Layout({ children }: { children: ReactNode }) {
    <DocsLayout
      tree={source.pageTree}
      {...baseOptions}
-      links={[]}
      nav={{
        ...baseOptions.nav,
+        children: (
+          <AITrigger
+            className={cn(
+              buttonVariants({
+                variant: "secondary",
+                size: "xs",
+                className:
+                  "text-fd-muted-foreground ms-2 gap-1.5 rounded-full px-2 md:flex-1",
+              }),
+            )}
+          >
+            <MessageCircle className="size-3" />
+            Ask LlamaCloud
+          </AITrigger>
+        ),
      }}
    >
      {children}
@@ -1,7 +1,7 @@
@import "tailwindcss";
@import "fumadocs-ui/css/neutral.css";
@import "fumadocs-ui/css/preset.css";
-@import "../../node_modules/fumadocs-twoslash/styles/twoslash.css";
+@import "../../node_modules/fumadocs-twoslash/dist/twoslash.css";
@plugin "tailwindcss-animate";
@source '../../node_modules/fumadocs-ui/dist/**/*.js';
@source "../../node_modules/fumadocs-openapi/dist/**/*.js",
@@ -1,4 +1,4 @@
-import { DOCUMENT_URL } from "@/lib/const";
+import { LEGACY_DOCUMENT_URL } from "@/lib/const";
 import type { BaseLayoutProps } from "fumadocs-ui/layouts/shared";
 import Image from "next/image";

@@ -27,19 +27,9 @@ export const baseOptions: BaseLayoutProps = {
  githubUrl: "https://github.com/run-llama/LlamaIndexTS",
  links: [
    {
-      text: "TypeScript",
-      url: DOCUMENT_URL,
+      text: "Docs",
+      url: LEGACY_DOCUMENT_URL,
      active: "nested-url",
    },
-    {
-      text: "Python",
-      url: "https://docs.llamaindex.ai",
-      active: "url",
-    },
-    {
-      text: "LlamaCloud",
-      url: "https://docs.cloud.llamaindex.ai/",
-      active: "url",
-    },
  ],
 };
@@ -13,7 +13,11 @@ import remarkStringify from "remark-stringify";
 export const revalidate = false;

 export async function GET() {
-  const files = await fg(["./src/content/docs/**/*.mdx"]);
+  const files = await fg([
+    "./src/content/docs/**/*.mdx",
+    // remove generated openapi files
+    "!./src/content/docs/cloud/api/**/*",
+  ]);

  const scan = files.map(async (file) => {
    const fileContent = await fs.readFile(file);
@@ -1,26 +1,24 @@
 "use client";
+import { createContextState } from "foxact/context-state";
+import { useIsClient } from "foxact/use-is-client";
+import { useShiki } from "fumadocs-core/utils/use-shiki";
+import { CodeBlock, Pre } from "fumadocs-ui/components/codeblock";
+import { lazy, Suspense, use, useMemo } from "react";
+import { StickToBottom, useStickToBottomContext } from "use-stick-to-bottom";
+import Parser from "web-tree-sitter";
+
 import { Label } from "@/components/ui/label";
 import { Skeleton } from "@/components/ui/skeleton";
 import { Slider } from "@/components/ui/slider";
 import { CodeSplitter } from "@llamaindex/node-parser/code";
-import { Editor } from "@monaco-editor/react";
-import { createContextState } from "foxact/context-state";
-import { useIsClient } from "foxact/use-is-client";
-import { useShiki } from "fumadocs-core/highlight/client";
-import { CodeBlock, Pre } from "fumadocs-ui/components/codeblock";
-import { Suspense, use, useMemo } from "react";
-import { StickToBottom, useStickToBottomContext } from "use-stick-to-bottom";

 let promise: Promise<CodeSplitter>;
 if (typeof window !== "undefined") {
-  async function run() {
-    const { default: Parser } = await import("web-tree-sitter");
-    await Parser.init({
-      locateFile(scriptName: string) {
-        return "/" + scriptName;
-      },
-    });
-
+  promise = Parser.init({
+    locateFile(scriptName: string) {
+      return "/" + scriptName;
+    },
+  }).then(async () => {
    const parser = new Parser();
    const Lang = await Parser.Language.load("/tree-sitter-typescript.wasm");
    parser.setLanguage(Lang);
@@ -28,9 +26,7 @@ if (typeof window !== "undefined") {
      getParser: () => parser,
      maxChars: 100,
    });
-  }
-
-  promise = run();
+  });
 }

 const [SliderProvider, useSlider, useSetSlider] = createContextState(100);
@@ -52,6 +48,8 @@ const john: Person = {

 console.log(greet(john));`);

+const Editor = lazy(() => import("react-monaco-editor"));
+
 export const IDE = () => {
  const codeSplitter = use(promise);
  const code = useCode();
@@ -75,6 +73,21 @@ export const IDE = () => {
        />
      </div>
      <Editor
+        editorWillMount={() => {}}
+        editorDidMount={() => {
+          window.MonacoEnvironment!.getWorkerUrl = (
+            _moduleId: string,
+            label: string,
+          ) => {
+            if (label === "json") return "/_next/static/json.worker.js";
+            if (label === "css") return "/_next/static/css.worker.js";
+            if (label === "html") return "/_next/static/html.worker.js";
+            if (label === "typescript" || label === "javascript")
+              return "/_next/static/ts.worker.js";
+            return "/_next/static/editor.worker.js";
+          };
+        }}
+        editorWillUnmount={() => {}}
        options={{
          minimap: {
            enabled: false,
@@ -84,9 +97,7 @@ export const IDE = () => {
        height="100%"
        width="100%"
        language="typescript"
-        onChange={(v) => {
-          if (v) setCode(v);
-        }}
+        onChange={setCode}
        value={code}
      />
    </div>
@@ -1,13 +0,0 @@
-"use client";
-import dynamic from "next/dynamic";
-
-// lazy load client components
-export const ChatDemo = dynamic(() =>
-  import("@/components/demo/chat/api/demo").then((mod) => mod.ChatDemo),
-);
-
-export const CodeNodeParserDemo = dynamic(() =>
-  import("@/components/demo/code-node-parser").then(
-    (mod) => mod.CodeNodeParserDemo,
-  ),
-);
@@ -0,0 +1,152 @@
+"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>
+  );
+}
@@ -1,27 +1,25 @@
 "use client";
 import { Button } from "@/components/ui/button";
 import { cn } from "@/lib/utils";
-import { CodeBlock } from "fumadocs-ui/components/codeblock";
+import { CodeBlock, Pre } from "fumadocs-ui/components/codeblock";
 import { RotateCcw } from "lucide-react";
 import { useTheme } from "next-themes";
-import { type ReactNode, use, useCallback, useEffect, useState } from "react";
-import { createJavaScriptRegexEngine, getSingletonHighlighter } from "shiki";
+import { use, useCallback, useEffect, useState } from "react";
+import { getSingletonHighlighter } from "shiki";
 import { ShikiMagicMove } from "shiki-magic-move/react";
+import { createOnigurumaEngine } from "shiki/engine/oniguruma";

-const engine = createJavaScriptRegexEngine();
 const highlighterPromise = getSingletonHighlighter({
-  engine,
+  engine: createOnigurumaEngine(() => import("shiki/wasm")),
  themes: ["vesper", "github-light"],
  langs: ["js", "ts", "tsx"],
 });

 export type MagicMoveProps = {
  code: string[];
-  placeholder: ReactNode;
 };

 export function MagicMove(props: MagicMoveProps) {
-  const [mounted, setMounted] = useState(false);
  const [move, setMove] = useState<number>(0);
  const currentCode = props.code[move];
  const highlighter = use(highlighterPromise);
@@ -40,27 +38,24 @@ export function MagicMove(props: MagicMoveProps) {
    }
  }, [animate, move, props.code]);

-  useEffect(() => {
-    setMounted(true);
-  }, []);
-
-  if (!mounted) return props.placeholder;
-
  return (
    <CodeBlock allowCopy={false}>
-      <ShikiMagicMove
-        className="shiki !block p-4 *:!inline"
-        lang="ts"
-        theme={resolvedTheme === "dark" ? "vesper" : "github-light"}
-        highlighter={highlighter}
-        code={currentCode}
-        options={{
-          duration: 800,
-          stagger: 0.3,
-          lineNumbers: false,
-          containerStyle: false,
-        }}
-      />
+      {highlighter && (
+        <Pre>
+          <ShikiMagicMove
+            lang="ts"
+            theme={resolvedTheme === "dark" ? "vesper" : "github-light"}
+            highlighter={highlighter}
+            code={currentCode}
+            options={{
+              duration: 800,
+              stagger: 0.3,
+              lineNumbers: false,
+              containerStyle: false,
+            }}
+          />
+        </Pre>
+      )}
      <Button
        className={cn(
          "absolute bottom-2 right-2",
@@ -0,0 +1,8 @@
+---
+title: LlamaCloud
+description: LlamaCloud is a new generation of managed parsing, ingestion, and retrieval services, designed to bring production-grade context-augmentation to your LLM and RAG applications.
+---
+
+This is TypeScript binding for LlamaCloud API. It provides a simple way to interact with LlamaCloud API.
+
+If you are looking for the official documentation, please visit the [Official Document](https://docs.cloud.llamaindex.ai/)
@@ -0,0 +1,6 @@
+{
+  "title": "LlamaCloud",
+  "description": "The Cloud framework for LLM",
+  "root": true,
+  "pages": ["---Guide---", "index", "..."]
+}
@@ -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)
@@ -11,7 +11,7 @@ It may be useful to check out all the examples at once so you can try them out l
 ```bash npm2yarn
 npx degit run-llama/LlamaIndexTS/examples my-new-project
 cd my-new-project
-npm i
+npm install
 ```

 Then you can run any example in the folder with `tsx`, e.g.:
@@ -3,11 +3,18 @@ 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
  title="Getting Started with LlamaIndex.TS in Node.js"
-  href="/docs/llamaindex/getting_started/installation/node"
+  href="/docs/llamaindex/getting_started/frameworks/node"
 />

 Also, you need have the basic understanding of <a href='https://developers.cloudflare.com/workers/'><SiCloudflareworkers className="inline mr-2" color="#F38020" />Cloudflare Worker</a>.
@@ -62,7 +69,7 @@ export default {
 In Cloudflare Worker and similar serverless JS environment, you need to be aware of the following differences:

 - Some Node.js modules are not available in Cloudflare Worker, such as `node:fs`, `node:child_process`, `node:cluster`...
- You are recommend to design your code using network request, such as use `fetch` API to communicate with database, instead of a long-running process in Node.js.
+- You are recommend to design your code using network request, such as use `fetch` API to communicate with database, insteadof a long-running process in Node.js.
 - Some of LlamaIndex.TS packages are not available in Cloudflare Worker, for example `@llamaindex/readers` and `@llamaindex/huggingface`.
 - The main `llamaindex` is designed to work in all JavaScript environment, including Cloudflare Worker. If you find any issue, please report to us.
 - `@llamaindex/env` is a JS environment binding module, which polyfill some Node.js/Modern Web API (for example, we have a memory based `fs` module, and Crypto API polyfill). It is designed to work in all JavaScript environment, including Cloudflare Worker.
@@ -0,0 +1,42 @@
+---
+title: Frameworks
+description: We support multiple JS runtime and frameworks, bundlers.
+---
+import {
+	SiNodedotjs,
+	SiTypescript,
+	SiNextdotjs,
+	SiCloudflareworkers,
+	SiVite
+} from "@icons-pack/react-simple-icons";
+
+<Cards>
+	<Card title={
+		<>
+			<SiNodedotjs className="inline" color="#5FA04E" /> Node.js
+		</>
+	} href="/docs/llamaindex/getting_started/frameworks/node" />
+	<Card title={
+		<>
+			<SiTypescript className="inline" color="#3178C6" /> TypeScript
+		</>
+	} href="/docs/llamaindex/getting_started/frameworks/typescript" />
+	<Card title={
+		<>
+			<SiVite className='inline' color='#646CFF' /> Vite
+		</>
+	} href="/docs/llamaindex/getting_started/frameworks/vite" />
+	<Card
+		title={
+			<>
+				<SiNextdotjs className='inline' /> Next.js (React Server Component)
+			</>
+		}
+		href="/docs/llamaindex/getting_started/frameworks/next"
+	/>
+	<Card title={
+		<>
+			<SiCloudflareworkers className='inline' color='#F38020' /> Cloudflare Workers
+		</>
+	} href="/docs/llamaindex/getting_started/frameworks/cloudflare" />
+</Cards>
@@ -0,0 +1,6 @@
+{
+  "title": "Framework",
+  "description": "The setup guide",
+  "defaultOpen": true,
+  "pages": ["node", "typescript", "next", "vite", "cloudflare"]
+}
@@ -7,7 +7,7 @@ Before you start, make sure you have try LlamaIndex.TS in Node.js to make sure y

 <Card
  title="Getting Started with LlamaIndex.TS in Node.js"
-  href="/docs/llamaindex/getting_started/installation/node"
+  href="/docs/llamaindex/getting_started/frameworks/node"
 />

 ## Differences between Node.js and Next.js
@@ -17,9 +17,9 @@ This means that you need to be careful when using LlamaIndex.TS in Next.js.
 Don't leak the import data like API keys to the client side.

 Also, in Next.js, there is build time and runtime. Some computations can be done at build time like Document embedding could be done at build time for better performance.
-Where as the `llamaindex` package is working with Next.js, some provider packages like `@llamaindex/huggingface` are not working well with Next.js. This is due to the upstream dependencies used by the provider package. 
+LlamaIndex.TS has lots of upstream dependencies, some of them are not compatible with Next.js.

-Make sure to use `withLlamaIndex` to make sure that LlamaIndex.TS works well with Next.js.
+You might need to use `withNext` to make sure that LlamaIndex.TS works well with Next.js.

 ```js
 // next.config.mjs / next.config.ts
@@ -35,7 +35,7 @@ If you see any dependency issues, you are welcome to open an issue on the GitHub

 ## Edge Runtime

-[Vercel Edge Runtime](https://edge-runtime.vercel.app/) is a subset of Node.js APIs. Similar to [Cloudflare Workers](/docs/llamaindex/getting_started/installation/cloudflare#difference-between-nodejs-and-cloudflare-worker),
+[Vercel Edge Runtime](https://edge-runtime.vercel.app/) is a subset of Node.js APIs. Similar to [Cloudflare Workers](/docs/llamaindex/getting_started/frameworks/cloudflare#difference-between-nodejs-and-cloudflare-worker),
 it is a serverless platform that runs your code on the edge.

 Not all features of Node.js are supported in Vercel Edge Runtime, so does LlamaIndex.TS, we are working on more compatibility with all JavaScript runtimes.
@@ -3,6 +3,8 @@ title: With Node.js/Bun/Deno
 description: In this guide, you'll learn how to use LlamaIndex with Node.js, Bun, and Deno.
 ---

+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
 ## Adding environment variables

 By default, LlamaIndex uses OpenAI provider, which requires an API key. You can set the `OPENAI_API_KEY` environment variable to authenticate with OpenAI.
@@ -26,9 +28,19 @@ For more information, see the [How to read environment variables from Node.js](h

 By the default, we are using `js-tiktoken` for tokenization. You can install `gpt-tokenizer` which is then automatically used by LlamaIndex to get a 60x speedup for tokenization:

-```package-install
-npm i gpt-tokenizer
-```
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install gpt-tokenizer
+	```
+
+	```shell tab="yarn"
+	yarn add gpt-tokenizer
+	```
+
+	```shell tab="pnpm"
+	pnpm add gpt-tokenizer
+	```
+</Tabs>

 **Note**: This only works for Node.js

@@ -36,5 +48,5 @@ npm i gpt-tokenizer

 <Card
 	title="Getting Started with LlamaIndex.TS in TypeScript"
-	href="/docs/llamaindex/getting_started/installation/typescript"
+	href="/docs/llamaindex/getting_started/frameworks/typescript"
 />
@@ -2,10 +2,11 @@
 title: With TypeScript
 description: In this guide, you'll learn how to use LlamaIndex with TypeScript
 ---
+import { Accordion, Accordions } from 'fumadocs-ui/components/accordion';

 LlamaIndex.TS is written in TypeScript and designed to be used in TypeScript projects.

-We put a lot of work on strong typing to make sure you have a great typing experience with code completion such as:
+We do lots of work on strong typing to make sure you have a great typing experience with LlamaIndex.TS.

 ```ts twoslash
 import { PromptTemplate } from 'llamaindex'
@@ -27,32 +28,70 @@ promptTemplate.format({
 })
 ```

+```ts twoslash
+import { FunctionTool } from 'llamaindex'
+import { z } from 'zod'
+
+// ---cut-before---
+const inputSchema = z.object({
+	time: z.string(),
+	city: z.string(),
+})
+
+type Input = z.infer<typeof inputSchema>
+
+FunctionTool.from<Input>((input) => {
+// @noErrors
+	input.t
+//      ^|
+}, {
+	name: 'getWeather',
+	description: 'Get the weather information',
+	parameters: inputSchema,
+})
+```
+
 ## Enable TypeScript

-Make sure to set [moduleResolution](https://www.typescriptlang.org/docs/handbook/modules/theory.html#module-resolution) in your `tsconfig.json` file:

 ```json5
 {
  compilerOptions: {
    // ⬇️ add this line to your tsconfig.json
-    moduleResolution: "bundler", // or "nodenext" | "node16" | "node"
+    moduleResolution: "bundler", // or "node16"
  },
 }
 ```

-We recommend using `bundler` or `nodenext`, but due to popularity of `node`, we still added support for it, but with import path limitations.
+<Accordions>
+	<Accordion
+		title="Why modify tsconfig.json"
+	>

-So you may encounter type errors when importing sub paths from the `llamaindex` package like:
+We are shipping both ESM and CJS module, and compatible with Vercel Edge, Cloudflare Workers, and other serverless platforms.

-```ts
-import { Settings } from "llamaindex";
+So we are using [conditional exports](https://nodejs.org/api/packages.html#conditional-exports) to support all environments.
+
+This is a kind of modern way of shipping packages, but might cause TypeScript type check to fail because of legacy module resolution.
+
+Imaging you put output file into `/dist/openai.js` but you are importing `llamaindex/openai` in your code, and set `package.json` like this:
+
+```json5
+{
+	"exports": {
+		"./openai": "./dist/openai.js"
+	}
+}
 ```

-The simplest way to fix this without changing `moduleResolution` is to import directly from `llamaindex`:
+In old module resolution, TypeScript will not be able to find the module because it is not following the file structure, even you run `node index.js` successfully. (on Node.js >=16)

-```ts
-import { Settings } from "llamaindex";
-```
+See more about [moduleResolution](https://www.typescriptlang.org/docs/handbook/modules/theory.html#module-resolution) or
+[TypeScript 5.0 blog](https://devblogs.microsoft.com/typescript/announcing-typescript-5-0/#--moduleresolution-bundler7).
+
+
+	</Accordion>
+</Accordions>

 ## Enable AsyncIterable for `Web Stream` API

@@ -7,7 +7,7 @@ Before you start, make sure you have try LlamaIndex.TS in Node.js to make sure y

 <Card
  title="Getting Started with LlamaIndex.TS in Node.js"
-  href="/docs/llamaindex/getting_started/installation/node"
+  href="/docs/llamaindex/getting_started/frameworks/node"
 />

 Also, make sure you have a basic understanding of [Vite](https://vitejs.dev/).
@@ -0,0 +1,56 @@
+---
+title: Installation
+description: How to install llamaindex packages.
+---
+
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+To install llamaindex, run the following command:
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex
+	```
+</Tabs>
+
+In most cases, you'll also need an LLM package to use LlamaIndex. For example, to use the OpenAI LLM, you would install the following:
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add @llamaindex/openai
+	```
+</Tabs>
+
+Go to [LLM APIs](/docs/llamaindex/modules/llms) to find out how to use other LLMs.
+
+
+## What's next?
+
+<Cards>
+	<Card
+		title="Learn LlamaIndex.TS"
+		description="Learn how to use LlamaIndex.TS by starting with one of our tutorials."
+		href="/docs/llamaindex/tutorials/rag"
+	/>
+	<Card
+		title="Show me code examples"
+		description="Explore code examples using LlamaIndex.TS."
+		href="/docs/llamaindex/getting_started/examples"
+	/>
+</Cards>
@@ -1,69 +0,0 @@
---
-title: Installation
-description: How to install llamaindex packages.
---
-
-To install llamaindex, run the following command:
-
-```package-install
-npm i llamaindex
-```
-
-In most cases, you'll also need an LLM package to use LlamaIndex. For example, to use the OpenAI LLM, you would install the following:
-
-```package-install
-npm i @llamaindex/openai
-```
-
-Go to [LLM APIs](/docs/llamaindex/modules/models/llms) to find out how to use other LLMs.
-
-
-## Frameworks
-
-LlamaIndex supports a wide range of frameworks and runtimes. Click on the card below to learn more.
-
-<Cards>
-	<Card title={
-		<>
-			<SiNodedotjs className="inline" color="#5FA04E" /> Node.js
-		</>
-	} href="/docs/llamaindex/getting_started/installation/node" />
-	<Card title={
-		<>
-			<SiTypescript className="inline" color="#3178C6" /> TypeScript
-		</>
-	} href="/docs/llamaindex/getting_started/installation/typescript" />
-	<Card title={
-		<>
-			<SiVite className='inline' color='#646CFF' /> Vite
-		</>
-	} href="/docs/llamaindex/getting_started/installation/vite" />
-	<Card
-		title={
-			<>
-				<SiNextdotjs className='inline' /> Next.js (React Server Component)
-			</>
-		}
-		href="/docs/llamaindex/getting_started/installation/next"
-	/>
-	<Card title={
-		<>
-			<SiCloudflareworkers className='inline' color='#F38020' /> Cloudflare Workers
-		</>
-	} href="/docs/llamaindex/getting_started/installation/cloudflare" />
-</Cards>
-
-## What's next?
-
-<Cards>
-	<Card
-		title="Learn LlamaIndex.TS"
-		description="Learn how to use LlamaIndex.TS by starting with one of our tutorials."
-		href="/docs/llamaindex/tutorials/rag"
-	/>
-	<Card
-		title="Show me code examples"
-		description="Explore code examples using LlamaIndex.TS."
-		href="/docs/llamaindex/getting_started/examples"
-	/>
-</Cards>
@@ -1,4 +0,0 @@
-{
-  "title": "Installation",
-  "pages": ["node", "typescript", "next", "vite", "cloudflare"]
-}
@@ -1,4 +1,4 @@
 {
  "title": "Getting Started",
-  "pages": ["installation", "create_llama", "examples"]
+  "pages": ["index", "create_llama", "examples", "frameworks"]
 }
@@ -3,6 +3,13 @@ title: What is LlamaIndex.TS
 description: LlamaIndex is the leading data framework for building LLM applications
 ---

+import {
+  SiNodedotjs,
+  SiDeno,
+  SiBun,
+  SiCloudflareworkers,
+} from "@icons-pack/react-simple-icons";
+
 LlamaIndex is a framework for building context-augmented generative AI applications with LLMs including agents and workflows.

 The TypeScript implementation is designed for JavaScript server side applications using <SiNodedotjs className="inline" color="#5FA04E" /> Node.js, <SiDeno className="inline" color="#70FFAF" /> Deno, <SiBun className="inline" /> Bun, <SiCloudflareworkers className="inline" color="#F38020" /> Cloudflare Workers, and more.
@@ -2,6 +2,7 @@
 title: Langtrace
 description: Learn how to integrate LlamaIndex.TS with Langtrace.
 ---
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";

 Enhance your observability with Langtrace, a robust open-source tool supports OpenTelemetry and is designed to trace, evaluate, and manage LLM applications seamlessly. Langtrace integrates directly with LlamaIndex, offering detailed, real-time insights into performance metrics such as accuracy, evaluations, and latency.

@@ -9,9 +10,19 @@ Enhance your observability with Langtrace, a robust open-source tool supports Op

 - Self-host or sign-up and generate an API key using [Langtrace](https://www.langtrace.ai) Cloud

-```package-install
-npm i @langtrase/typescript-sdk
-```
+<Tabs groupId="install-langtrase" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @langtrase/typescript-sdk
+	```
+
+	```shell tab="yarn"
+	yarn add @langtrase/typescript-sdk
+	```
+
+	```shell tab="pnpm"
+	pnpm add @langtrase/typescript-sdk
+	```
+</Tabs>

 ## Initialize

@@ -2,15 +2,27 @@
 title: OpenLLMetry
 description: Learn how to integrate LlamaIndex.TS with OpenLLMetry.
 ---
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";

 [OpenLLMetry](https://github.com/traceloop/openllmetry-js) is an open-source project based on OpenTelemetry for tracing and monitoring
 LLM applications. It connects to [all major observability platforms](https://www.traceloop.com/docs/openllmetry/integrations/introduction) and installs in minutes.

 ### Usage Pattern

-```package-install
-npm i @traceloop/node-server-sdk
-```
+
+<Tabs groupId="install-traceloop" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @traceloop/node-server-sdk
+	```
+
+	```shell tab="yarn"
+	yarn add @traceloop/node-server-sdk
+	```
+
+	```shell tab="pnpm"
+	pnpm add @traceloop/node-server-sdk
+	```
+</Tabs>

 ```js
 import * as traceloop from "@traceloop/node-server-sdk";
@@ -11,8 +11,8 @@ LlamaIndex provides integration with Vercel's AI SDK, allowing you to create pow

 First, install the required dependencies:

-```package-install
-npm i @llamaindex/vercel ai
+```bash
+npm install @llamaindex/vercel ai
 ```

 ## Using Vercel AI's Model Providers 
@@ -84,7 +84,6 @@ const queryTool = llamaindex({
  model: openai("gpt-4"),
  index,
  description: "Search through the documents",
-  options: { fields: ["sourceNodes", "messages"]}
 });

 // Use the tool with Vercel's AI SDK
@@ -2,6 +2,8 @@
 title: Migrating from v0.8 to v0.9
 ---

+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
 Version 0.9 of LlamaIndex.TS introduces significant architectural changes to improve package size and runtime compatibility. The main goals of this release are:

 1. Reduce the package size of the main `llamaindex` package by moving dependencies into provider packages, making it more suitable for serverless environments
@@ -31,11 +33,21 @@ import { OpenAI } from "@llamaindex/openai";

 > Note: This examples requires installing the `@llamaindex/openai` package: 

-```package-install
-npm i @llamaindex/openai
-```
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @llamaindex/openai
+	```

-For more details on available AI model providers and their configuration, see the [LLMs documentation](/docs/llamaindex/modules/models/llms) and the [Embedding Models documentation](/docs/llamaindex/modules/models/embeddings).
+	```shell tab="yarn"
+	yarn add @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add @llamaindex/openai
+	```
+</Tabs>
+
+For more details on available AI model providers and their configuration, see the [LLMs documentation](/docs/llamaindex/modules/llms) and the [Embedding Models documentation](/docs/llamaindex/modules/embeddings).

 ### 2. Storage Providers

@@ -49,7 +61,7 @@ Now:
 import { PineconeVectorStore } from "@llamaindex/pinecone";
 ```

-For more information about available storage options, refer to the [Data Stores documentation](/docs/llamaindex/modules/data/stores).
+For more information about available storage options, refer to the [Data Stores documentation](/docs/llamaindex/modules/data_stores).

 ### 3. Data Loaders

@@ -63,7 +75,7 @@ Now:
 import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
 ```

-For more details about available data loaders and their usage, check the [Loading Data](/docs/llamaindex/modules/data/readers).
+For more details about available data loaders and their usage, check the [Loading Data](/docs/llamaindex/modules/loading).

 ### 4. Prefer using `llamaindex` instead of `@llamaindex/core`

@@ -2,7 +2,7 @@
 title: Agents
 ---

-**Note**: Agents are deprecated, use [Agent Workflows](/docs/llamaindex/modules/agents/agent_workflow) instead.
+**Note**: Agents are deprecated, use [Agent Workflows](/docs/llamaindex/modules/agent_workflow) instead.

 An “agent” is an automated reasoning and decision engine. It takes in a user input/query and can make internal decisions for executing that query in order to return the correct result. The key agent components can include, but are not limited to:

@@ -3,7 +3,7 @@ title: Agent Workflows
 ---


-Agent Workflows are a powerful system that enables you to create and orchestrate one or multiple agents with tools to perform specific tasks. It's built on top of the base [`Workflow`](/docs/llamaindex/modules/agents/workflows) system and provides a streamlined interface for agent interactions.
+Agent Workflows are a powerful system that enables you to create and orchestrate one or multiple agents with tools to perform specific tasks. It's built on top of the base [`Workflow`](/docs/llamaindex/modules/workflows) system and provides a streamlined interface for agent interactions.

 ## Usage

@@ -1,4 +0,0 @@
-{
-  "title": "Agents",
-  "pages": ["tool", "agent_workflow", "workflows"]
-}
@@ -1,142 +0,0 @@
---
-title: Tools
---
-
-A "tool" is a utility that can be called by an agent on behalf of an LLM.
-A tool can be called to perform custom actions, or retrieve extra information based on the LLM-generated input.
-A result from a tool call can be used by subsequent steps in a workflow, or to compute a final answer.
-For example, a "weather tool" could fetch some live weather information from a geographical location.
-
-## Tool Function
-
-The `tool` function is a utility provided to define a tool that can be used by an agent. It takes a function and a configuration object as arguments. The configuration object includes the tool's name, description, and parameters.
-
-### Parameters with Zod
-
-The `parameters` field in the tool configuration is defined using `zod`, a TypeScript-first schema declaration and validation library. `zod` allows you to specify the expected structure and types of the input parameters, ensuring that the data passed to the tool is valid.
-
-Example:
-```ts
-import { agent, tool } from "llamaindex";
-import { z } from "zod";
-
-// first arg is LLM input, second is bound arg
-const queryKnowledgeBase = async ({ question }, { userToken }) => {
-  const response = await fetch(`https://knowledge-base.com?token=${userToken}&query=${question}`);
-  // ...
-};
-
-// define tool with zod validation
-const kbTool = tool(queryKnowledgeBase, {
-  name: 'queryKnowledgeBase',
-  description: 'Query knowledge base',
-  parameters: z.object({
-    question: z.string({
-      description: 'The user question',
-    }),
-  }),
-});
-
-```
-In this example, `z.object` is used to define a schema for the `parameters` where `question` is expected to be a string. This ensures that any input to the tool adheres to the specified structure, providing a layer of type safety and validation.
-
-
-## Built-in tools
-
-You can import built-in tools from the `@llamaindex/tools` package.
-
-```ts
-import { agent } from "llamaindex";
-import { wiki } from "@llamaindex/tools";
-
-const researchAgent = agent({
-  name: "WikiAgent",
-  description: "Gathering information from the internet",
-  systemPrompt: `You are a research agent. Your role is to gather information from the internet using the provided tools.`,
-  tools: [wiki()],
-});
-```
-
-## 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.
-A `FunctionTool` is constructed from a function with signature
-```ts
-(input: T, additionalArg?: AdditionalToolArgument) => R
-```
-where
- `input` is generated by the LLM, `T` is the type defined by the tool `parameters`
- `additionalArg` is an optional extra argument, see "Binding" below
- `R` is the return type
-
-### Binding
-
-An additional argument can be bound to a tool, each tool call will be passed
- the input provided by the LLM
- the additional argument (extends object)
-
-Note: calling the `bind` method will return a new `FunctionTool` instance, without modifying the tool which `bind` is called on.
-
-Example to pass a `userToken` as additional argument:
-```ts
-import { agent, tool } from "llamaindex";
-
-// first arg is LLM input, second is bound arg
-const queryKnowledgeBase = async ({ question }, { userToken }) => {
-  const response = await fetch(`https://knowledge-base.com?token=${userToken}&query=${question}`);
-  // ...
-};
-
-// define tool as usual
-const kbTool = tool(queryKnowledgeBase, {
-  name: 'queryKnowledgeBase',
-  description: 'Query knowledge base',
-  parameters: z.object({
-    question: z.string({
-      description: 'The user question',
-    }),
-  }),
-});
-
-// create an agent
-const additionalArg = { userToken: 'abcd1234' };
-const workflow = agent({
-  tools: [kbTool.bind(additionalArg)],
-  // llm, systemPrompt etc
-})
-```
@@ -1,261 +0,0 @@
---
-title: Workflows
---
-
-A `Workflow` in LlamaIndex is a lightweight, event-driven abstraction used to chain together several events. Workflows are made up of `handlers`, with each one responsible for processing specific event types and emitting new events.
-
-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 @llama-flow/core @llamaindex/openai
-```
-
-## Getting Started
-
-Let's explore a simple workflow example where a joke is generated and then critiqued and iterated on:
-
-```typescript
-import { OpenAI } from "@llamaindex/openai";
-import { createWorkflow, workflowEvent } from "@llama-flow/core";
-import { withStore } from "@llama-flow/core/middleware/store";
-
-// 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
-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 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 with Store Middleware
-
-```typescript
-const jokeFlow = withStore(
-    () => ({
-        numIterations: 0,
-        maxIterations: 3,
-    }),
-    createWorkflow()
-);
-```
-
-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
-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 });
-});
-```
-
-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.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
-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);
-}
-```
-
-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
-
-### Using Stream Utilities
-
-Workflows provide utility functions to make working with event streams easier:
-
-```typescript
-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
-```
-
-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)
-
-You can combine these utilities with other stream operators like `filter` and `map` to create powerful processing pipelines.
-
-## Next Steps
-
-To learn more about workflows, check out [the documentation in the tutorial section](../../../llamaflow).
@@ -2,6 +2,7 @@
 title: Using API Route
 description: Chat interface for your LlamaIndexTS application using API Route
 ---
+import { ChatDemo } from '../../../../../components/demo/chat/api/demo';

 Using [chat-ui](https://github.com/run-llama/chat-ui), it's easy to add a chat interface to your LlamaIndexTS application.
 You just need to create an API route that provides an `api/chat` endpoint and a chat component to consume the API.
@@ -16,7 +16,7 @@ npx shadcn@latest add https://ui.llamaindex.ai/r/chat.json
 To install the package, run the following command in your project directory:

 ```sh
-npm i @llamaindex/chat-ui
+npm install @llamaindex/chat-ui
 ```

 For more information, check out the [github.comrun-llama/chat-ui](https://github.com/run-llama/chat-ui)
@@ -2,5 +2,5 @@
  "title": "Chat UI",
  "description": "Use chat-ui to add a chat interface to your LlamaIndexTS application.",
  "defaultOpen": false,
-  "pages": ["install", "chat", "rsc", "llamaindex-server"]
+  "pages": ["install", "chat", "rsc"]
 }
@@ -2,10 +2,11 @@
 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).

-With RSC, the chat messages are not returned as JSON from the server (like when using an [API route](/docs/llamaindex/modules/ui/chat)), instead the chat message components are rendered on the server side.
+With RSC, the chat messages are not returned as JSON from the server (like when using an [API route](/docs/llamaindex/modules/chat/chat)), instead the chat message components are rendered on the server side.
 This is for example useful for rendering a whole chat history on the server before sending it to the client. [Check here](https://sdk.vercel.ai/docs/getting-started/navigating-the-library#when-to-use-ai-sdk-rsc), for a discussion of when to use use RSC.

 For implementing a chat interface with RSC, you need to create an AI action and then connect the chat interface to use it.
@@ -41,5 +41,5 @@ for await (const chunk of stream) {
 ## Api References

 - [ContextChatEngine](/docs/api/classes/ContextChatEngine)
- [CondenseQuestionChatEngine](/docs/api/classes/CondenseQuestionChatEngine)
+- [CondenseQuestionChatEngine](/docs/api/classes/ContextChatEngine)
 - [SimpleChatEngine](/docs/api/classes/SimpleChatEngine)
@@ -1,32 +0,0 @@
---
-title: Managed Index
-description: Managed index using LlamaCloud
---
-
-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
-
- Managed Ingestion API, handling parsing and document management
- Managed Retrieval API, configuring optimal retrieval for your RAG system
-
-## Access
-
-Visit [LlamaCloud](https://cloud.llamaindex.ai) to sign in and get an API key.
-
-## Create a Managed Index
-
-Here's an example of how to create a managed index by ingesting a couple of documents:
-
-<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:
-
-<include cwd>../../examples/cloud/from-documents.ts</include>
-
-## API Reference
-
- [LlamaCloudIndex](/docs/api/classes/LlamaCloudIndex)
- [LlamaCloudRetriever](/docs/api/classes/LlamaCloudRetriever)
@@ -1,17 +0,0 @@
---
-title: Documents and Nodes
-description: Data structure for storing data in LlamaIndex
---
-
-`Document`s and `Node`s are the basic building blocks of data in LlamaIndexTS. While the API for these objects is similar, `Document` objects represent entire files, while `Node`s are smaller pieces of that original document, that are suitable for an LLM and Q&A.
-
-```typescript
-import { Document } from "llamaindex";
-
-document = new Document({ text: "text", metadata: { key: "val" } });
-```
-
-## API Reference
-
- [Document](/docs/api/classes/Document)
- [TextNode](/docs/api/classes/TextNode)
@@ -1,4 +0,0 @@
-{
-  "title": "Data",
-  "pages": ["index", "readers", "data_index", "ingestion_pipeline", "stores"]
-}
@@ -1,118 +0,0 @@
---
-title: Loading Data
-description: Loading data using Readers into Documents
---
-
-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.
-
-To install readers call:
-
-<Accordions>
-	<Accordion title="Install @llamaindex/readers">
-
-		If you want to use the reader module, you need to install `@llamaindex/readers`
-
-    ```package-install
-    npm i @llamaindex/readers
-    ```
-	</Accordion>
-</Accordions>
-
-We offer readers for different file formats.
-
-```ts twoslash
-import { CSVReader } from '@llamaindex/readers/csv';
-import { DocxReader } from '@llamaindex/readers/docx';
-import { HTMLReader } from '@llamaindex/readers/html';
-import { ImageReader } from '@llamaindex/readers/image';
-import { JSONReader } from '@llamaindex/readers/json';
-import { MarkdownReader } from '@llamaindex/readers/markdown';
-import { ObsidianReader } from '@llamaindex/readers/obsidian';
-import { PDFReader } from '@llamaindex/readers/pdf';
-import { TextFileReader } from '@llamaindex/readers/text';
-```
-
-## 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)
-
-LlamaIndex.TS supports easy loading of files from folders using the `SimpleDirectoryReader` class.
-
-It is a simple reader that reads all files from a directory and its subdirectories and delegates the actual reading to the reader specified in the `fileExtToReader` map.
-
-<include cwd>../../examples/readers/src/simple-directory-reader.ts</include>
-
-Currently, the following readers are mapped to specific file types:
-
- [TextFileReader](/docs/api/classes/TextFileReader): `.txt`
- [PDFReader](/docs/api/classes/PDFReader): `.pdf`
- [CSVReader](/docs/api/classes/CSVReader): `.csv`
- [MarkdownReader](/docs/api/classes/MarkdownReader): `.md`
- [DocxReader](/docs/api/classes/DocxReader): `.docx`
- [HTMLReader](/docs/api/classes/HTMLReader): `.htm`, `.html`
- [ImageReader](/docs/api/classes/ImageReader): `.jpg`, `.jpeg`, `.png`, `.gif`
-
-You can modify the reader three different ways:
-
- `overrideReader` overrides the reader for all file types, including unsupported ones.
- `fileExtToReader` maps a reader to a specific file type. Can override reader for existing file types or add support for new file types.
- `defaultReader` sets a fallback reader for files with unsupported extensions. By default it is `TextFileReader`.
-
-SimpleDirectoryReader supports up to 9 concurrent requests. Use the `numWorkers` option to set the number of concurrent requests. By default it runs in sequential mode, i.e. set to 1.
-
-### Example
-
-<include cwd>../../examples/readers/src/custom-simple-directory-reader.ts</include>
-
-## Tips when using in non-Node.js environments
-
-When using `@llamaindex/readers` in a non-Node.js environment (such as Vercel Edge, Cloudflare Workers, etc.)
-Some classes are not exported from top-level entry file.
-
-The reason is that some classes are only compatible with Node.js runtime, (e.g. `PDFReader`) which uses Node.js specific APIs (like `fs`, `child_process`, `crypto`).
-
-If you need any of those classes, you have to import them instead directly through their file path in the package.
-
-As the `PDFReader` is not working with the Edge runtime, here's how to use the `SimpleDirectoryReader` with the `LlamaParseReader` to load PDFs:
-
-```typescript
-import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
-import { LlamaParseReader } from "@llamaindex/cloud";
-
-export const DATA_DIR = "./data";
-
-export async function getDocuments() {
-  const reader = new SimpleDirectoryReader();
-  // Load PDFs using LlamaParseReader
-  return await reader.loadData({
-    directoryPath: DATA_DIR,
-    fileExtToReader: {
-      pdf: new LlamaParseReader({ resultType: "markdown" }),
-    },
-  });
-}
-```
-
-> _Note_: Reader classes have to be added explicitly to the `fileExtToReader` map in the Edge version of the `SimpleDirectoryReader`.
-
-You'll find a complete example with LlamaIndexTS here: https://github.com/run-llama/create_llama_projects/tree/main/nextjs-edge-llamaparse
-
-
-## Load file natively using Node.js Customization Hooks
-
-We have a helper utility to allow you to import a file in Node.js script.
-
-```shell 
-node --import @llamaindex/readers/node ./script.js
-```
-
-```ts
-import csv from './path/to/data.csv';
-
-const text = csv.getText()
-```
-
-## API Reference
-
- [SimpleDirectoryReader](/docs/api/classes/SimpleDirectoryReader)
@@ -1,154 +0,0 @@
---
-title: Supabase Vector Store
---
-
-[supabase.com](https://supabase.com/)
-
-To use this vector store, you need a Supabase project. You can create one at [supabase.com](https://supabase.com/).
-
-## Installation
-
-```package-install
-npm i llamaindex @llamaindex/supabase
-```
-
-## Database Setup
-
-Before using the vector store, you need to:
-1. Enable the `pgvector` extension
-2. Create a table for storing vectors
-3. Create a vector similarity search function
-
-```sql
-create table documents (
-id uuid primary key,
-content text,
-metadata jsonb,
-embedding vector(1536)
-);
-```
-
-- Create a function for similarity search
-```sql
-create function match_documents (
-query_embedding vector(1536),
-match_count int
-) returns table (
-id uuid,
-content text,
-metadata jsonb,
-embedding vector(1536),
-similarity float
-)
-language plpgsql
-as $$
-begin
-return query
-select
-id,
-content,
-metadata,
-embedding,
-1 - (embedding <=> query_embedding) as similarity
-from documents
-order by embedding <=> query_embedding
-limit match_count;
-end;
-$$;
-```
-
-## Importing the modules
-
-```ts
-import { Document, VectorStoreIndex } from "llamaindex";
-import { SupabaseVectorStore } from "@llamaindex/supabase";
-```
-
-## Setup Supabase
-
-```ts
-const vectorStore = new SupabaseVectorStore({
-  supabaseUrl: process.env.SUPABASE_URL,
-  supabaseKey: process.env.SUPABASE_KEY,
-  table: "documents",
-});
-```
-
-## Setup the index
-
-```ts
-const documents = [
-  new Document({ 
-    text: "Sample document text",
-    metadata: { source: "example" }
-  })
-];
-
-const storageContext = await storageContextFromDefaults({ vectorStore });
-const index = await VectorStoreIndex.fromDocuments(documents, {
-  storageContext,
-});
-```
-
-## Query the index
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const response = await queryEngine.query({
-  query: "What is in the document?",
-});
-
-// Output response
-console.log(response.toString());
-```
-
-## Full code
-
-```ts
-import { Document, VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
-import { SupabaseVectorStore } from "@llamaindex/supabase";
-
-async function main() {
-  // Initialize the vector store
-  const vectorStore = new SupabaseVectorStore({
-    supabaseUrl: process.env.SUPABASE_URL,
-    supabaseKey: process.env.SUPABASE_KEY,
-    table: "documents",
-  });
-
-  // Create sample documents
-  const documents = [
-    new Document({
-      text: "Vector search enables semantic similarity search",
-      metadata: {
-        source: "research_paper",
-        author: "Jane Smith",
-      },
-    }),
-  ];
-
-  // Create storage context
-  const storageContext = await storageContextFromDefaults({ vectorStore });
-
-  // Create and store embeddings
-  const index = await VectorStoreIndex.fromDocuments(documents, {
-    storageContext,
-  });
-
-  // Query the index
-  const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query({
-    query: "What is vector search?",
-  });
-
-  // Output response
-  console.log(response.toString());
-}
-
-main().catch(console.error);
-```
-
-## API Reference
-
- [SupabaseVectorStore](/docs/api/classes/SupabaseVectorStore)
@@ -2,8 +2,7 @@
 title: Index
 ---

-An index is the basic container for organizing your data. Besides managed indexes using [LlamaCloud](/docs/llamaindex/modules/data/data_index/managed), LlamaIndex.TS supports three indexes:
-
+An index is the basic container and organization for your data. LlamaIndex.TS supports two indexes:

 - `VectorStoreIndex` - will send the top-k `Node`s to the LLM when generating a response. The default top-k is 2.
 - `SummaryIndex` - will send every `Node` in the index to the LLM in order to generate a response
@@ -2,15 +2,12 @@
 title: DiscordReader
 ---

+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!../../../../../../../../examples/readers/src/discord";
+
 DiscordReader is a simple data loader that reads all messages in a given Discord channel and returns them as Document objects.
 It uses the [@discordjs/rest](https://github.com/discordjs/discord.js/tree/main/packages/rest) library to fetch the messages.

-## Installation
-
-```package-install
-npm install @llamaindex/discord
-```
-
 ## Usage

 First step is to create a Discord Application and generating a bot token [here](https://discord.com/developers/applications).
@@ -18,7 +15,7 @@ In your Discord Application, go to the `OAuth2` tab and generate an invite URL b
 This will invite the bot with the necessary permissions to read messages.
 Copy the URL in your browser and select the server you want your bot to join.

-<include cwd>../../examples/discord/reader.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource} />

 ### Params

@@ -0,0 +1,58 @@
+---
+title: Loader
+---
+
+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";
+
+Before you can start indexing your documents, you need to load them into memory.
+
+All "basic" data loaders can be seen below, mapped to their respective filetypes in `SimpleDirectoryReader`. More loaders are shown in the sidebar on the left.
+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)
+
+LlamaIndex.TS supports easy loading of files from folders using the `SimpleDirectoryReader` class.
+
+It is a simple reader that reads all files from a directory and its subdirectories.
+
+<DynamicCodeBlock lang="ts" code={CodeSource} />
+
+Currently, the following readers are mapped to specific file types:
+
+- [TextFileReader](/docs/api/classes/TextFileReader): `.txt`
+- [PDFReader](/docs/api/classes/PDFReader): `.pdf`
+- [CSVReader](/docs/api/classes/CSVReader): `.csv`
+- [MarkdownReader](/docs/api/classes/MarkdownReader): `.md`
+- [DocxReader](/docs/api/classes/DocxReader): `.docx`
+- [HTMLReader](/docs/api/classes/HTMLReader): `.htm`, `.html`
+- [ImageReader](/docs/api/classes/ImageReader): `.jpg`, `.jpeg`, `.png`, `.gif`
+
+You can modify the reader three different ways:
+
+- `overrideReader` overrides the reader for all file types, including unsupported ones.
+- `fileExtToReader` maps a reader to a specific file type. Can override reader for existing file types or add support for new file types.
+- `defaultReader` sets a fallback reader for files with unsupported extensions. By default it is `TextFileReader`.
+
+SimpleDirectoryReader supports up to 9 concurrent requests. Use the `numWorkers` option to set the number of concurrent requests. By default it runs in sequential mode, i.e. set to 1.
+
+### Example
+
+<DynamicCodeBlock lang="ts" code={CodeSource2} />
+
+## API Reference
+
+- [SimpleDirectoryReader](/docs/api/classes/SimpleDirectoryReader)
@@ -8,9 +8,21 @@ Supports streaming of large JSON data using [@discoveryjs/json-ext](https://gith

 ## Installation

-```package-install
-npm i llamaindex @llamaindex/readers
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/readers
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/readers
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/readers
+	```
+</Tabs>

 ## Usage

@@ -6,9 +6,21 @@ LlamaParse `json` mode supports extracting any images found in a page object by

 ## Installation

-```package-install
-npm i llamaindex @llamaindex/cloud @llamaindex/openai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/cloud @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/cloud @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/cloud @llamaindex/openai
+	```
+</Tabs>

 ## Usage

@@ -112,3 +124,6 @@ The returned `imageDocs` have the alt text assigned as text and the image path a

 You can see the full example file [here](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/readers/src/llamaparse-json.ts).

+## API Reference
+
+- [LlamaParseReader](/docs/api/classes/LlamaParseReader)
@@ -2,6 +2,10 @@
 title: LlamaParse
 ---

+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!../../../../../../../../../examples/readers/src/llamaparse";
+import CodeSource2 from "!raw-loader!../../../../../../../../../examples/readers/src/simple-directory-reader-with-llamaparse.ts";
+
 LlamaParse is an API created by LlamaIndex to efficiently parse files, e.g. it's great at converting PDF tables into markdown.

 To use it, first login and get an API key from https://cloud.llamaindex.ai. Make sure to store the key as `apiKey` parameter or in the environment variable `LLAMA_CLOUD_API_KEY`.
@@ -13,7 +17,7 @@ Official documentation for LlamaParse can be found [here](https://docs.cloud.lla
 You can then use the `LlamaParseReader` class to load local files and convert them into a parsed document that can be used by LlamaIndex.
 See [reader.ts](https://github.com/run-llama/LlamaIndexTS/blob/main/packages/cloud/src/reader.ts) for a list of supported file types:

-<include cwd>../../examples/readers/src/llamaparse.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource} />

 ### Params

@@ -32,7 +36,7 @@ They can be divided into two groups.
 #### Advanced params:

 - `resultType` can be set to `markdown`, `text` or `json`. Defaults to `text`. More information about `json` mode on the next pages.
- `language` primarily helps with OCR recognition. Defaults to `en`.
+- `language` primarily helps with OCR recognition. Defaults to `en`. Click [here](/docs/api/type-aliases/Language) for a list of supported languages.
 - `parsingInstructions?` Optional. Can help with complicated document structures. See this [LlamaIndex Blog Post](https://www.llamaindex.ai/blog/launching-the-first-genai-native-document-parsing-platform) for an example.
 - `skipDiagonalText?` Optional. Set to true to ignore diagonal text. (Text that is not rotated 0, 90, 180 or 270 degrees)
 - `invalidateCache?` Optional. Set to true to ignore the LlamaCloud cache. All document are kept in cache for 48hours after the job was completed to avoid processing the same document twice. Can be useful for testing when trying to re-parse the same document with, e.g. different `parsingInstructions`.
@@ -56,8 +60,9 @@ They can be divided into two groups.

 Below a full example of `LlamaParse` integrated in `SimpleDirectoryReader` with additional options.

-<include cwd>../../examples/readers/src/simple-directory-reader-with-llamaparse.ts</include>
+<DynamicCodeBlock lang="ts" code={CodeSource2} />

 ## API Reference

 - [SimpleDirectoryReader](/docs/api/classes/SimpleDirectoryReader)
+- [LlamaParseReader](/docs/api/classes/LlamaParseReader)
@@ -6,9 +6,21 @@ In JSON mode, LlamaParse will return a data structure representing the parsed ob

 ## Installation

-```package-install
-npm i llamaindex @llamaindex/cloud
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/cloud
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/cloud
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/cloud
+	```
+</Tabs>

 ## Usage

@@ -98,4 +110,5 @@ You can assign any other values of the JSON response to the Document as needed.

 ## API Reference

+- [LlamaParseReader](/docs/api/classes/LlamaParseReader)
 - [SimpleDirectoryReader](/docs/api/classes/SimpleDirectoryReader)
@@ -6,7 +6,7 @@ Chat stores manage chat history by storing sequences of messages in a structured

 ## Available Chat Stores

- [SimpleChatStore](/docs/api/classes/SimpleChatStore): A simple in-memory chat store with support for [persisting](/docs/llamaindex/modules/data/stores#local-storage) data to disk.
+- [SimpleChatStore](/docs/api/classes/SimpleChatStore): A simple in-memory chat store with support for [persisting](/docs/llamaindex/modules/data_stores#local-storage) data to disk.

 Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.

@@ -2,20 +2,32 @@
 title: Document Stores
 ---

-Document stores contain ingested document chunks, i.e. [Node](/docs/llamaindex/modules/data)s.
+Document stores contain ingested document chunks, i.e. [Node](/docs/llamaindex/modules/documents_and_nodes)s.

 ## Available Document Stores

- [SimpleDocumentStore](/docs/api/classes/SimpleDocumentStore): A simple in-memory document store with support for [persisting](/docs/llamaindex/modules/data/stores#local-storage) data to disk.
- [PostgresDocumentStore](/docs/api/classes/PostgresDocumentStore): A PostgreSQL document store, see [PostgreSQL Storage](/docs/llamaindex/modules/data/stores#postgresql-storage).
+- [SimpleDocumentStore](/docs/api/classes/SimpleDocumentStore): A simple in-memory document store with support for [persisting](/docs/llamaindex/modules/data_stores#local-storage) data to disk.
+- [PostgresDocumentStore](/docs/api/classes/PostgresDocumentStore): A PostgreSQL document store, see [PostgreSQL Storage](/docs/llamaindex/modules/data_stores#postgresql-storage).

 Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.

 ## Using PostgreSQL as Document Store

-```package-install
-npm i llamaindex @llamaindex/postgres
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/postgres
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/postgres
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/postgres
+	```
+</Tabs>

 You can configure the `schemaName`, `tableName`, `namespace`, and
 `connectionString`. If a `connectionString` is not
@@ -2,20 +2,32 @@
 title: Index Stores
 ---

-Index stores are underlying storage components that contain metadata(i.e. information created when indexing) about the [index](/docs/llamaindex/modules/data/data_index) itself.
+Index stores are underlying storage components that contain metadata(i.e. information created when indexing) about the [index](/docs/llamaindex/modules/data_index) itself.

 ## Available Index Stores

- [SimpleIndexStore](/docs/api/classes/SimpleIndexStore): A simple in-memory index store with support for [persisting](/docs/llamaindex/modules/data/stores#local-storage) data to disk.
- [PostgresIndexStore](/docs/api/classes/PostgresIndexStore): A PostgreSQL index store, , see [PostgreSQL Storage](/docs/llamaindex/modules/data/stores#postgresql-storage).
+- [SimpleIndexStore](/docs/api/classes/SimpleIndexStore): A simple in-memory index store with support for [persisting](/docs/llamaindex/modules/data_stores#local-storage) data to disk.
+- [PostgresIndexStore](/docs/api/classes/PostgresIndexStore): A PostgreSQL index store, , see [PostgreSQL Storage](/docs/llamaindex/modules/data_stores#postgresql-storage).

 Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.

 ## Using PostgreSQL as Index Store

-```package-install
-npm i llamaindex @llamaindex/postgres
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/postgres
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/postgres
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/postgres
+	```
+</Tabs>

 You can configure the `schemaName`, `tableName`, `namespace`, and
 `connectionString`. If a `connectionString` is not
@@ -2,12 +2,12 @@
 title: Key-Value Stores
 ---

-Key-Value Stores represent underlying storage components used in [Document Stores](/docs/llamaindex/modules/data/stores/doc_stores) and [Index Stores](/docs/llamaindex/modules/data/stores/index_stores)
+Key-Value Stores represent underlying storage components used in [Document Stores](/docs/llamaindex/modules/data_stores/doc_stores) and [Index Stores](/docs/llamaindex/modules/data_stores/index_stores)

 ## Available Key-Value Stores

- [SimpleKVStore](/docs/api/classes/SimpleKVStore): A simple Key-Value store with support of [persisting](/docs/llamaindex/modules/data/stores#local-storage) data to disk.
- [PostgresKVStore](/docs/api/classes/PostgresKVStore): A PostgreSQL Key-Value store, see [PostgreSQL Storage](/docs/llamaindex/modules/data/stores#postgresql-storage).
+- [SimpleKVStore](/docs/api/classes/SimpleKVStore): A simple Key-Value store with support of [persisting](/docs/llamaindex/modules/data_stores#local-storage) data to disk.
+- [PostgresKVStore](/docs/api/classes/PostgresKVStore): A PostgreSQL Key-Value store, see [PostgreSQL Storage](/docs/llamaindex/modules/data_stores#postgresql-storage).

 Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.

@@ -8,7 +8,7 @@ Vector stores save embedding vectors of your ingested document chunks.

 Available Vector Stores are shown on the sidebar to the left. Additionally the following integrations exist without separate documentation:

- [SimpleVectorStore](/docs/api/classes/SimpleVectorStore): A simple in-memory vector store with optional [persistance](/docs/llamaindex/modules/data/stores#local-storage) to disk.
+- [SimpleVectorStore](/docs/api/classes/SimpleVectorStore): A simple in-memory vector store with optional [persistance](/docs/llamaindex/modules/data_stores#local-storage) to disk.
 - [AstraDBVectorStore](/docs/api/classes/AstraDBVectorStore): A cloud-native, scalable Database-as-a-Service built on Apache Cassandra, see [datastax.com](https://www.datastax.com/products/datastax-astra)
 - [ChromaVectorStore](/docs/api/classes/ChromaVectorStore): An open-source vector database, focused on ease of use and performance, see [trychroma.com](https://www.trychroma.com/)
 - [MilvusVectorStore](/docs/api/classes/MilvusVectorStore): An open-source, high-performance, highly scalable vector database, see [milvus.io](https://milvus.io/)
@@ -13,9 +13,21 @@ docker run -p 6333:6333 qdrant/qdrant

 ## Installation

-```package-install
-npm i llamaindex @llamaindex/qdrant
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/qdrant
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/qdrant
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/qdrant
+	```
+</Tabs>

 ## Importing the modules

@@ -44,10 +56,10 @@ const vectorStore = new QdrantVectorStore({

 ```ts
 const document = new Document({ text: essay, id_: path });
-const storageContext = await storageContextFromDefaults({ vectorStore });
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    storageContext,
-  });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  vectorStore,
+});
 ```

 ## Query the index
@@ -79,11 +91,11 @@ async function main() {
  });

  const document = new Document({ text: essay, id_: path });
-  const storageContext = await storageContextFromDefaults({ vectorStore });
+
  const index = await VectorStoreIndex.fromDocuments([document], {
-    storageContext,
+    vectorStore,
  });
-  
+
  const queryEngine = index.asQueryEngine();

  const response = await queryEngine.query({
@@ -0,0 +1,16 @@
+---
+title: Documents and Nodes
+---
+
+`Document`s and `Node`s are the basic building blocks of any index. While the API for these objects is similar, `Document` objects represent entire files, while `Node`s are smaller pieces of that original document, that are suitable for an LLM and Q&A.
+
+```typescript
+import { Document } from "llamaindex";
+
+document = new Document({ text: "text", metadata: { key: "val" } });
+```
+
+## API Reference
+
+- [Document](/docs/api/classes/Document)
+- [TextNode](/docs/api/classes/TextNode)
@@ -1,5 +1,5 @@
 ---
-title: Metadata Extraction 
+title: Metadata Extraction Usage Pattern
 ---

 You can use LLMs to automate metadata extraction with our `Metadata Extractor` modules.
@@ -7,9 +7,21 @@ Check out available embedding models [here](https://deepinfra.com/models/embeddi

 ## Installation

-```package-install
-npm i llamaindex @llamaindex/deepinfra
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/deepinfra
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/deepinfra
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/deepinfra
+	```
+</Tabs>

 ```ts
 import { Document, Settings, VectorStoreIndex } from "llamaindex";
@@ -6,9 +6,21 @@ To use Gemini embeddings, you need to import `GeminiEmbedding` from `@llamaindex

 ## Installation

-```package-install
-npm i llamaindex @llamaindex/google
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/google
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/google
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/google
+	```
+</Tabs>

 ```ts
 import { Document, Settings, VectorStoreIndex } from "llamaindex";
@@ -6,9 +6,21 @@ To use HuggingFace embeddings, you need to import `HuggingFaceEmbedding` from `@

 ## Installation

-```package-install
-npm i llamaindex @llamaindex/huggingface
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/huggingface
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/huggingface
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/huggingface
+	```
+</Tabs>

 ```ts
 import { Document, Settings, VectorStoreIndex } from "llamaindex";
@@ -8,9 +8,21 @@ This can be explicitly updated through `Settings.embedModel`.

 ## Installation

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

 ```typescript
 import { OpenAIEmbedding } from "@llamaindex/openai";
@@ -23,7 +35,7 @@ Settings.embedModel = new OpenAIEmbedding({

 ## Local Embedding

-For local embeddings, you can use the [HuggingFace](/docs/llamaindex/modules/models/embeddings/huggingface) embedding model.
+For local embeddings, you can use the [HuggingFace](/docs/llamaindex/modules/embeddings/huggingface) embedding model.

 ## Local Ollama Embeddings With Remote Host

@@ -6,9 +6,21 @@ To use MistralAI embeddings, you need to import `MistralAIEmbedding` from `@llam

 ## Installation

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

 ```ts
 import { Document, Settings, VectorStoreIndex } from "llamaindex";
@@ -14,9 +14,22 @@ To find out more about the latest features, updates, and available models, visit

 ## Setup

-```package-install
-npm i llamaindex @llamaindex/mixedbread
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/mixedbread
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/mixedbread
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/mixedbread
+	```
+</Tabs>
+

 Next, sign up for an API key at [mixedbread.ai](https://mixedbread.ai/). Once you have your API key, you can import the necessary modules and create a new instance of the `MixedbreadAIEmbeddings` class.

@@ -14,9 +14,21 @@ ollama pull nomic-embed-text

 ## Installation

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

 ```ts
 import { OllamaEmbedding } from "@llamaindex/ollama";
@@ -6,9 +6,21 @@ To use OpenAI embeddings, you need to import `OpenAIEmbedding` from `@llamaindex

 ## Installation

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

 ```ts
 import { OpenAIEmbedding } from "@llamaindex/openai";
@@ -6,9 +6,21 @@ To use VoyageAI embeddings, you need to import `VoyageAIEmbedding` from `@llamai

 ## Installation

-```package-install
-npm i llamaindex @llamaindex/voyage-ai
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/voyage-ai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/voyage-ai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/voyage-ai
+	```
+</Tabs>

 ```ts
 import { VoyageAIEmbedding } from "@llamaindex/voyage-ai";
@@ -10,9 +10,21 @@ This is useful for measuring if the response was correct. The evaluator returns

 Firstly, you need to install the package:

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

 Set the OpenAI API key:

@@ -12,9 +12,22 @@ This is useful for measuring if the response was hallucinated. The evaluator ret

 Firstly, you need to install the package:

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

 Set the OpenAI API key:

@@ -10,9 +10,22 @@ It is useful for measuring if the response was relevant to the query. The evalua

 Firstly, you need to install the package:

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

 Set the OpenAI API key:

@@ -7,9 +7,21 @@ These `Transformations` are applied to your input data, and the resulting nodes

 ## Installation

-```package-install
-npm i llamaindex @llamaindex/openai @llamaindex/qdrant
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai @llamaindex/qdrant
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai @llamaindex/qdrant
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai @llamaindex/qdrant
+	```
+</Tabs>

 ## Usage Pattern

@@ -6,9 +6,9 @@ A transformation is something that takes a list of nodes as an input, and return

 Currently, the following components are Transformation objects:

- [SentenceSplitter](/docs/llamaindex/modules/data/ingestion_pipeline/transformations/node-parser)
- [MetadataExtractor](/docs/llamaindex/modules/data/ingestion_pipeline/transformations/metadata_extraction)
- [Embeddings](/docs/llamaindex/modules/models/embeddings)
+- [SentenceSplitter](/docs/api/classes/SentenceSplitter)
+- [MetadataExtractor](/docs/llamaindex/modules/documents_and_nodes/metadata_extraction)
+- [Embeddings](/docs/llamaindex/modules/embeddings)

 ## Usage Pattern

@@ -0,0 +1,34 @@
+---
+title: LlamaCloud
+---
+
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!../../../../../../../examples/cloud/chat.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.
+
+Currently, LlamaCloud supports
+
+- Managed Ingestion API, handling parsing and document management
+- Managed Retrieval API, configuring optimal retrieval for your RAG system
+
+## Access
+
+We are opening up a private beta to a limited set of enterprise partners for the managed ingestion and retrieval API. If you’re interested in centralizing your data pipelines and spending more time working on your actual RAG use cases, come [talk to us.](https://www.llamaindex.ai/contact)
+
+If you have access to LlamaCloud, you can visit [LlamaCloud](https://cloud.llamaindex.ai) to sign in and get an API key.
+
+## Create a Managed Index
+
+Currently, you can't create a managed index on LlamaCloud using LlamaIndexTS, but you can use an existing managed index for retrieval that was created by the Python version of LlamaIndex. See [the LlamaCloudIndex documentation](https://docs.llamaindex.ai/en/stable/module_guides/indexing/llama_cloud_index.html#usage) for more information on how to create a managed index.
+
+## 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} />
+
+## API Reference
+
+- [LlamaCloudIndex](/docs/api/classes/LlamaCloudIndex)
+- [LlamaCloudRetriever](/docs/api/classes/LlamaCloudRetriever)
@@ -4,9 +4,21 @@ title: Anthropic

 ## Installation

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

 ## Usage

--- a/Show More
+++ b/Show More