Release 0.10.0 (#1854 )

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>
fix: update the tool call schema for nova (#1850 )
2026-07-01 22:14:03 -04:00 · 2025-04-16 17:02:34 +07:00 · 2025-04-16 16:52:29 +07:00 · 2025-04-16 15:57:25 +07:00 · 2025-04-16 15:47:30 +07:00 · 2025-04-15 23:51:38 -07:00
414 changed files with 17490 additions and 4555 deletions
@@ -1,5 +1,105 @@
 # @llamaindex/doc

+## 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
@@ -4,6 +4,8 @@ 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,
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/doc",
-  "version": "0.2.0",
+  "version": "0.2.11",
  "private": true,
  "scripts": {
    "postinstall": "fumadocs-mdx",
@@ -14,6 +14,7 @@
  },
  "dependencies": {
    "@icons-pack/react-simple-icons": "^10.1.0",
+    "@llama-flow/docs": "0.0.3",
    "@llamaindex/chat-ui": "0.2.0",
    "@llamaindex/cloud": "workspace:*",
    "@llamaindex/core": "workspace:*",
@@ -36,20 +37,20 @@
    "clsx": "2.1.1",
    "foxact": "^0.2.41",
    "framer-motion": "^11.11.17",
-    "fumadocs-core": "^15.0.15",
+    "fumadocs-core": "^15.2.7",
    "fumadocs-docgen": "^2.0.0",
-    "fumadocs-mdx": "^11.5.6",
+    "fumadocs-mdx": "^11.6.0",
    "fumadocs-openapi": "^6.3.0",
-    "fumadocs-twoslash": "^3.1.0",
+    "fumadocs-twoslash": "^3.1.1",
    "fumadocs-typescript": "^3.1.0",
-    "fumadocs-ui": "^15.0.15",
+    "fumadocs-ui": "^15.2.7",
    "hast-util-to-jsx-runtime": "^2.3.2",
    "llamaindex": "workspace:*",
    "lucide-react": "^0.460.0",
-    "next": "^15.2.1",
+    "next": "^15.3.0",
    "next-themes": "^0.4.3",
-    "react": "^19.0.0",
-    "react-dom": "^19.0.0",
+    "react": "^19.1.0",
+    "react-dom": "^19.1.0",
    "react-icons": "^5.3.0",
    "react-monaco-editor": "^0.56.2",
    "react-use-measure": "^2.1.1",
@@ -68,7 +69,7 @@
    "zod": "^3.23.8"
  },
  "devDependencies": {
-    "@next/env": "^15.2.1",
+    "@next/env": "^15.3.0",
    "@tailwindcss/postcss": "^4.0.9",
    "@types/mdx": "^2.0.13",
    "@types/node": "22.9.0",
@@ -7,7 +7,7 @@ import rehypeKatex from "rehype-katex";
 import remarkMath from "remark-math";

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

 export default defineConfig({
@@ -11,7 +11,7 @@ import { NpmInstall } from "@/components/npm-install";
 import { Supports } from "@/components/supports";
 import { Button } from "@/components/ui/button";
 import { Skeleton } from "@/components/ui/skeleton";
-import { LEGACY_DOCUMENT_URL } from "@/lib/const";
+import { DOCUMENT_URL } from "@/lib/const";
 import { SiStackblitz } from "@icons-pack/react-simple-icons";
 import {
  CodeBlock as FumaCodeBlock,
@@ -39,7 +39,7 @@ export default function HomePage() {
      </div>

      <div className="flex flex-wrap justify-center gap-4">
-        <Link href={LEGACY_DOCUMENT_URL}>
+        <Link href={DOCUMENT_URL}>
          <Button variant="outline">Get Started</Button>
        </Link>
        <NpmInstall />
@@ -2,6 +2,7 @@ import { createMetadata, metadataImage } from "@/lib/metadata";
 import { openapi, source } from "@/lib/source";
 import { Popup, PopupContent, PopupTrigger } from "fumadocs-twoslash/ui";
 import { createTypeTable } from "fumadocs-typescript/ui";
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
 import defaultMdxComponents from "fumadocs-ui/mdx";
 import {
  DocsBody,
@@ -31,6 +32,7 @@ export default async function Page(props: {
      editOnGithub={{
        owner: "run-llama",
        repo: "LlamaIndexTS",
+        sha: "main",
        path: `apps/next/src/content/docs/${page.file.path}`,
      }}
    >
@@ -41,6 +43,8 @@ export default async function Page(props: {
          components={{
            ...defaultMdxComponents,
            APIPage: openapi.APIPage,
+            Tab,
+            Tabs,
            Popup,
            PopupContent,
            PopupTrigger,
@@ -1,7 +1,7 @@
@import "tailwindcss";
@import "fumadocs-ui/css/neutral.css";
@import "fumadocs-ui/css/preset.css";
-@import "../../node_modules/fumadocs-twoslash/dist/twoslash.css";
+@import "../../node_modules/fumadocs-twoslash/styles/twoslash.css";
@plugin "tailwindcss-animate";
@source '../../node_modules/fumadocs-ui/dist/**/*.js';
@source "../../node_modules/fumadocs-openapi/dist/**/*.js",
@@ -1,4 +1,4 @@
-import { LEGACY_DOCUMENT_URL } from "@/lib/const";
+import { DOCUMENT_URL } from "@/lib/const";
 import type { BaseLayoutProps } from "fumadocs-ui/layouts/shared";
 import Image from "next/image";

@@ -28,7 +28,7 @@ export const baseOptions: BaseLayoutProps = {
  links: [
    {
      text: "Docs",
-      url: LEGACY_DOCUMENT_URL,
+      url: DOCUMENT_URL,
      active: "nested-url",
    },
  ],
@@ -1,7 +1,6 @@
 "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";
@@ -11,6 +10,7 @@ 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 { useShiki } from "fumadocs-core/highlight/client";

 let promise: Promise<CodeSplitter>;
 if (typeof window !== "undefined") {
@@ -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 install
+npm i
 ```

 Then you can run any example in the folder with `tsx`, e.g.:
@@ -1,42 +0,0 @@
---
-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>
@@ -1,6 +0,0 @@
-{
-  "title": "Framework",
-  "description": "The setup guide",
-  "defaultOpen": true,
-  "pages": ["node", "typescript", "next", "vite", "cloudflare"]
-}
@@ -1,56 +0,0 @@
---
-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>
@@ -14,7 +14,7 @@ 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/frameworks/node"
+  href="/docs/llamaindex/getting_started/installation/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>.
@@ -69,7 +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, insteadof 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, instead of 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 +1,77 @@
+---
+title: Installation
+description: How to install llamaindex packages.
+---
+
+import {
+	SiNodedotjs,
+	SiTypescript,
+	SiNextdotjs,
+	SiCloudflareworkers,
+	SiVite
+} from "@icons-pack/react-simple-icons";
+
+To install llamaindex, run the following command:
+
+```package-install
+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>
@@ -0,0 +1,4 @@
+{
+  "title": "Installation",
+  "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/frameworks/node"
+  href="/docs/llamaindex/getting_started/installation/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.
-LlamaIndex.TS has lots of upstream dependencies, some of them are not compatible with Next.js.
+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. 

-You might need to use `withNext` to make sure that LlamaIndex.TS works well with Next.js.
+Make sure to use `withLlamaIndex` 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/frameworks/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/installation/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.
@@ -3,8 +3,6 @@ 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.
@@ -28,19 +26,9 @@ 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:

-<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>
+```package-install
+npm i gpt-tokenizer
+```

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

@@ -48,5 +36,5 @@ By the default, we are using `js-tiktoken` for tokenization. You can install `gp

 <Card
 	title="Getting Started with LlamaIndex.TS in TypeScript"
-	href="/docs/llamaindex/getting_started/frameworks/typescript"
+	href="/docs/llamaindex/getting_started/installation/typescript"
 />
@@ -2,11 +2,10 @@
 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 do lots of work on strong typing to make sure you have a great typing experience with LlamaIndex.TS.
+We put a lot of work on strong typing to make sure you have a great typing experience with code completion such as:

 ```ts twoslash
 import { PromptTemplate } from 'llamaindex'
@@ -28,70 +27,32 @@ 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 "node16"
+    moduleResolution: "bundler", // or "nodenext" | "node16" | "node"
  },
 }
 ```

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

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

-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"
-	}
-}
+```ts
+import { Settings } 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)
+The simplest way to fix this without changing `moduleResolution` is to import directly 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>
+```ts
+import { Settings } from "llamaindex";
+```

 ## 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/frameworks/node"
+  href="/docs/llamaindex/getting_started/installation/node"
 />

 Also, make sure you have a basic understanding of [Vite](https://vitejs.dev/).
@@ -1,4 +1,4 @@
 {
  "title": "Getting Started",
-  "pages": ["index", "create_llama", "examples", "frameworks"]
+  "pages": ["installation", "create_llama", "examples"]
 }
@@ -2,7 +2,6 @@
 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.

@@ -10,19 +9,9 @@ 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

-<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>
+```package-install
+npm i @langtrase/typescript-sdk
+```

 ## Initialize

@@ -2,27 +2,15 @@
 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

-
-<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>
+```package-install
+npm i @traceloop/node-server-sdk
+```

 ```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:

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

 ## Using Vercel AI's Model Providers 
@@ -2,8 +2,6 @@
 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
@@ -33,21 +31,11 @@ import { OpenAI } from "@llamaindex/openai";

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

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

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

 ### 2. Storage Providers

@@ -61,7 +49,7 @@ 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

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

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

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

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

-**Note**: Agents are deprecated, use [Agent Workflows](/docs/llamaindex/modules/agent_workflow) instead.
+**Note**: Agents are deprecated, use [Agent Workflows](/docs/llamaindex/modules/agents/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/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/agents/workflows) system and provides a streamlined interface for agent interactions.

 ## Usage

@@ -0,0 +1,4 @@
+{
+  "title": "Agents",
+  "pages": ["tool", "agent_workflow", "workflows"]
+}
@@ -0,0 +1,107 @@
+---
+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()],
+});
+```
+
+## 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
+})
+```
@@ -3,7 +3,7 @@ title: Workflows
 ---

 import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../examples/workflow/joke.ts";
+import CodeSource from "!raw-loader!@/examples/workflow/joke.ts";

 A `Workflow` in LlamaIndexTS is an event-driven abstraction used to chain together several events. Workflows are made up of `steps`, with each step responsible for handling certain event types and emitting new events.

@@ -13,21 +13,10 @@ When a step function is added to a workflow, you need to specify the input and o

 You can create a `Workflow` to do anything! Build an agent, a RAG flow, an extraction flow, or anything else you want.

-import { Tab, Tabs } from "fumadocs-ui/components/tabs";

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

 ## Getting Started

@@ -2,7 +2,8 @@
 title: Index
 ---

-An index is the basic container and organization for your data. LlamaIndex.TS supports two indexes:
+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:
+

 - `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
@@ -0,0 +1,36 @@
+---
+title: Managed Index
+description: Managed index using LlamaCloud
+---
+
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/cloud/chat.ts";
+import CodeSource2 from "!raw-loader!@/examples/cloud/from-documents.ts";
+
+LlamaCloud is a new generation of managed parsing, ingestion, and retrieval services, designed to bring production-grade context-augmentation to your LLM and RAG applications.
+
+LlamaCloud supports
+
+- 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:
+
+<DynamicCodeBlock lang="ts" code={CodeSource2} />
+
+## Use a Managed Index
+
+Here's an example of how to use a managed index together with a chat engine:
+
+<DynamicCodeBlock lang="ts" code={CodeSource} />
+
+## API Reference
+
+- [LlamaCloudIndex](/docs/api/classes/LlamaCloudIndex)
+- [LlamaCloudRetriever](/docs/api/classes/LlamaCloudRetriever)
@@ -0,0 +1,17 @@
+---
+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)
@@ -7,21 +7,9 @@ These `Transformations` are applied to your input data, and the resulting nodes

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/openai @llamaindex/qdrant
+```

 ## 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/api/classes/SentenceSplitter)
- [MetadataExtractor](/docs/llamaindex/modules/documents_and_nodes/metadata_extraction)
- [Embeddings](/docs/llamaindex/modules/embeddings)
+- [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)

 ## Usage Pattern

@@ -1,5 +1,5 @@
 ---
-title: Metadata Extraction Usage Pattern
+title: Metadata Extraction 
 ---

 You can use LLMs to automate metadata extraction with our `Metadata Extractor` modules.
@@ -2,17 +2,16 @@
 title: Node Parsers / Text Splitters
 description: Learn how to use Node Parsers and Text Splitters to extract data from documents.
 ---
-import { CodeNodeParserDemo } from '../../../../../components/demo/code-node-parser.tsx';
-import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+import { CodeNodeParserDemo } from '@/components/demo/code-node-parser.tsx';

-Node parsers are a simple abstraction that take a list of documents, and chunk them into `Node` objects, such that each node is a specific chunk of the parent document. When a document is broken into nodes, all of it's attributes are inherited to the children nodes (i.e. `metadata`, text and metadata templates, etc.). You can read more about `Node` and `Document` properties [here](/docs/llamaindex/modules/loading).
-
-## NodeParser
-
-The `NodeParser` in LlamaIndex is responsible for splitting `Document` objects into more manageable `Node` objects.
+Node parsers are a simple abstraction that take a list of `Document` objects, and chunk them into `Node` objects, such that each node is a specific chunk of the parent document. When a document is broken into nodes, all of it's attributes are inherited to the children nodes (i.e. `metadata`, text and metadata templates, etc.). You can read more about `Node` and `Document` properties [here](/docs/llamaindex/modules/data).

 By default, we will use `Settings.nodeParser` to split the document into nodes. You can also assign a custom `NodeParser` to the `Settings` object.

+## SentenceSplitter
+
+The `SentenceSplitter` is the default `NodeParser` in LlamaIndex. It will split the text from a `Document` into sentences.
+
 ```ts twoslash
 import { TextFileReader } from '@llamaindex/readers/text'
 import { SentenceSplitter } from 'llamaindex';
@@ -23,8 +22,6 @@ Settings.nodeParser = nodeParser;
 //		     ^?
 ```

-## TextSplitter
-
 The underlying text splitter will split text by sentences. It can also be used as a standalone module for splitting raw text.

 ```ts twoslash
@@ -68,6 +65,46 @@ The `MarkdownNodeParser` is a more advanced `NodeParser` that can handle markdow
 	```
 </Tabs>

+The output metadata will be something like:
+
+```bash
+[
+  TextNode {
+    id_: '008e41a8-b097-487c-bee8-bd88b9455844',
+    metadata: { 'Header 1': 'Main Header' },
+    excludedEmbedMetadataKeys: [],
+    excludedLlmMetadataKeys: [],
+    relationships: { PARENT: [Array] },
+    hash: 'KJ5e/um/RkHaNR6bonj9ormtZY7I8i4XBPVYHXv1A5M=',
+    text: 'Main Header\nMain content',
+    textTemplate: '',
+    metadataSeparator: '\n'
+  },
+  TextNode {
+    id_: '0f5679b3-ba63-4aff-aedc-830c4208d0b5',
+    metadata: { 'Header 1': 'Header 2' },
+    excludedEmbedMetadataKeys: [],
+    excludedLlmMetadataKeys: [],
+    relationships: { PARENT: [Array] },
+    hash: 'IP/g/dIld3DcbK+uHzDpyeZ9IdOXY4brxhOIe7wc488=',
+    text: 'Header 2\nHeader 2 content',
+    textTemplate: '',
+    metadataSeparator: '\n'
+  },
+  TextNode {
+    id_: 'e81e9bd0-121c-4ead-8ca7-1639d65fdf90',
+    metadata: { 'Header 1': 'Header 2', 'Header 2': 'Sub-header' },
+    excludedEmbedMetadataKeys: [],
+    excludedLlmMetadataKeys: [],
+    relationships: { PARENT: [Array] },
+    hash: 'B3kYNnxaYi9ghtAgwza0ZEVKF4MozobkNUlcekDL7JQ=',
+    text: 'Sub-header\nSub-header content',
+    textTemplate: '',
+    metadataSeparator: '\n'
+  }
+]
+```
+
 ## CodeSplitter

 The `CodeSplitter` is a more advanced `NodeParser` that can handle code documents.
@@ -155,3 +192,9 @@ import { Accordion, Accordions } from 'fumadocs-ui/components/accordion';
 		```
 	</Accordion>
 </Accordions>
+
+## API Reference
+
+- [SentenceSplitter](/docs/api/classes/SentenceSplitter)
+- [MarkdownNodeParser](/docs/api/classes/MarkdownNodeParser)
+- [CodeSplitter](/docs/api/classes/CodeSplitter)
@@ -0,0 +1,4 @@
+{
+  "title": "Data",
+  "pages": ["index", "readers", "data_index", "ingestion_pipeline", "stores"]
+}
@@ -3,7 +3,7 @@ title: DiscordReader
 ---

 import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../../examples/readers/src/discord";
+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.
@@ -0,0 +1,132 @@
+---
+title: Loading Data
+description: Loading data using Readers into Documents
+---
+
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/readers/src/simple-directory-reader";
+import CodeSource2 from "!raw-loader!@/examples/readers/src/custom-simple-directory-reader";
+import { Accordion, Accordions } from 'fumadocs-ui/components/accordion';
+
+Before you can start indexing your documents, you need to load them into memory.
+A reader is a module that loads data from a file into a `Document` object.
+
+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 { PDFReader } from '@llamaindex/readers/pdf'
+import { JSONReader } from '@llamaindex/readers/json'
+import { MarkdownReader } from '@llamaindex/readers/markdown'
+import { HTMLReader } from '@llamaindex/readers/html'
+// you can find more readers in the documentation
+```
+
+Additionally the following loaders exist without separate documentation:
+
+- `AssemblyAIReader` transcribes audio using [AssemblyAI](https://www.assemblyai.com/).
+  - [AudioTranscriptReader](/docs/api/classes/AudioTranscriptReader): loads entire transcript as a single document.
+  - [AudioTranscriptParagraphsReader](/docs/api/classes/AudioTranscriptParagraphsReader): creates a document per paragraph.
+  - [AudioTranscriptSentencesReader](/docs/api/classes/AudioTranscriptSentencesReader): creates a document per sentence.
+  - [AudioSubtitlesReader](/docs/api/classes/AudioTranscriptParagraphsReader): creates a document containing the subtitles of a transcript.
+- [NotionReader](/docs/api/classes/NotionReader) loads [Notion](https://www.notion.so/) pages.
+- [SimpleMongoReader](/docs/api/classes/SimpleMongoReader) loads data from a [MongoDB](https://www.mongodb.com/).
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
+## SimpleDirectoryReader
+
+[Open in StackBlitz](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples/readers?file=src/simple-directory-reader.ts&title=Simple%20Directory%20Reader)
+
+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.
+
+<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} />
+
+## 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)
@@ -8,21 +8,9 @@ Supports streaming of large JSON data using [@discoveryjs/json-ext](https://gith

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/readers
+```

 ## Usage

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

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/cloud @llamaindex/openai
+```

 ## Usage

@@ -3,8 +3,8 @@ 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";
+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.

@@ -6,21 +6,9 @@ In JSON mode, LlamaParse will return a data structure representing the parsed ob

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/cloud
+```

 ## Usage

@@ -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.

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

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

-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>
+```package-install
+npm i llamaindex @llamaindex/postgres
+```

 You can configure the `schemaName`, `tableName`, `namespace`, and
 `connectionString`. If a `connectionString` is not
@@ -2,32 +2,20 @@
 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_index) itself.
+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.

 ## 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

-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>
+```package-install
+npm i llamaindex @llamaindex/postgres
+```

 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/)
@@ -13,21 +13,9 @@ docker run -p 6333:6333 qdrant/qdrant

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/qdrant
+```

 ## Importing the modules

@@ -0,0 +1,154 @@
+---
+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)
@@ -1,58 +0,0 @@
---
-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)
@@ -1,16 +0,0 @@
---
-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)
@@ -10,21 +10,9 @@ This is useful for measuring if the response was correct. The evaluator returns

 Firstly, you need to install the package:

-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>
+```package-install
+npm i llamaindex @llamaindex/openai
+```

 Set the OpenAI API key:

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

 Firstly, you need to install the package:

-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>
-
+```package-install
+npm i llamaindex @llamaindex/openai
+```

 Set the OpenAI API key:

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

 Firstly, you need to install the package:

-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>
-
+```package-install
+npm i llamaindex @llamaindex/openai
+```

 Set the OpenAI API key:

@@ -1,34 +0,0 @@
---
-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)
@@ -1,137 +0,0 @@
---
-title: OpenAI
---
-
-## Installation
-
-import { Tab, Tabs } from "fumadocs-ui/components/tabs";
-
-<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
-	```shell tab="npm"
-	npm install llamaindex @llamaindex/openai
-	```
-
-	```shell tab="yarn"
-	yarn add llamaindex @llamaindex/openai
-	```
-
-	```shell tab="pnpm"
-	pnpm add llamaindex @llamaindex/openai
-	```
-</Tabs>
-
-
-```ts
-import { OpenAI } from "@llamaindex/openai";
-import { Settings } from "llamaindex";
-
-Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0, apiKey: <YOUR_API_KEY> });
-```
-
-You can setup the apiKey on the environment variables, like:
-
-```bash
-export OPENAI_API_KEY="<YOUR_API_KEY>"
-```
-
-You can optionally set a custom base URL, like:
-
-```bash
-export OPENAI_BASE_URL="https://api.scaleway.ai/v1"
-```
-
-or
-
-```ts
-Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0, apiKey: <YOUR_API_KEY>, baseURL: "https://api.scaleway.ai/v1" });
-```
-
-## Using JSON Response Format
-
-You can configure OpenAI to return responses in JSON format:
-
-```ts
-Settings.llm = new OpenAI({ 
-  model: "gpt-4o", 
-  temperature: 0,
-  responseFormat: { type: "json_object" }  
-});
-
-// You can also use a Zod schema to validate the response structure
-import { z } from "zod";
-
-const responseSchema = z.object({
-  summary: z.string(),  
-  topics: z.array(z.string()),
-  sentiment: z.enum(["positive", "negative", "neutral"])
-});
-
-Settings.llm = new OpenAI({ 
-  model: "gpt-4o", 
-  temperature: 0,
-  responseFormat: responseSchema  
-});
-```
-
-## Load and index documents
-
-For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
-
-```ts
-import { Document, VectorStoreIndex } from "llamaindex";
-
-const document = new Document({ text: essay, id_: "essay" });
-
-const index = await VectorStoreIndex.fromDocuments([document]);
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import { OpenAI } from "@llamaindex/openai";
-import { Document, Settings, VectorStoreIndex } from "llamaindex";
-
-// Use the OpenAI LLM
-Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
-
-async function main() {
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  // get retriever
-  const retriever = index.asRetriever();
-
-  // Create a query engine
-  const queryEngine = index.asQueryEngine({
-    retriever,
-  });
-
-  const query = "What is the meaning of life?";
-
-  // Query
-  const response = await queryEngine.query({
-    query,
-  });
-
-  // Log the response
-  console.log(response.response);
-}
-```
-
-## API Reference
-
- [OpenAI](/docs/api/classes/OpenAI)
@@ -1,106 +0,0 @@
---
-title: Document and Nodes
-description: llamaindex readers is a collection of readers for different file formats.
---
-
-import { Tab, Tabs } from "fumadocs-ui/components/tabs";
-
-import { Accordion, Accordions } from 'fumadocs-ui/components/accordion';
-
-<Accordions>
-	<Accordion title="Install @llamaindex/readers">
-
-		If you want to use the reader module, you need to install `@llamaindex/readers`
-
-		<Tabs groupId="install-llamaindex" items={["npm", "yarn", "pnpm"]} persist>
-			```shell tab="npm"
-			npm install @llamaindex/readers
-			```
-
-			```shell tab="yarn"
-			yarn add @llamaindex/readers
-			```
-
-			```shell tab="pnpm"
-			pnpm add @llamaindex/readers
-			```
-
-		</Tabs>
-	</Accordion>
-</Accordions>
-
-We offer readers for different file formats.
-
-```ts twoslash 
-import { CSVReader } from '@llamaindex/readers/csv'
-import { PDFReader } from '@llamaindex/readers/pdf'
-import { JSONReader } from '@llamaindex/readers/json'
-import { MarkdownReader } from '@llamaindex/readers/markdown'
-import { HTMLReader } from '@llamaindex/readers/html'
-// you can find more readers in the documentation
-```
-
-## SimpleDirectoryReader
-
-`SimpleDirectoryReader` is the simplest way to load data from local files into LlamaIndex.
-
-```ts twoslash 
-import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
-
-const reader = new SimpleDirectoryReader()
-const documents = await reader.loadData("./data")
-//    ^?
-
-
-const texts = documents.map(doc => doc.getText())
-//    ^?
-```
-
-
-## 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()
-```
@@ -1,5 +0,0 @@
-{
-  "title": "Loading Data",
-  "description": "Loading Data using LlamaIndex.TS",
-  "pages": ["index", "node-parser"]
-}
@@ -0,0 +1,4 @@
+{
+  "title": "Modules",
+  "pages": ["models", "agents", "data", "rag", "ui", "evaluation"]
+}
@@ -7,21 +7,9 @@ Check out available embedding models [here](https://deepinfra.com/models/embeddi

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/deepinfra
+```

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

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/google
+```

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

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/huggingface
+```

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

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/openai
+```

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

 ## Local Embedding

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

 ## Local Ollama Embeddings With Remote Host

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

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/mistral
+```

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

 ## Setup

-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>
-
+```package-install
+npm i llamaindex @llamaindex/mixedbread
+```

 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.

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

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/ollama
+```

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

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/openai
+```

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

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/voyage-ai
+```

 ```ts
 import { VoyageAIEmbedding } from "@llamaindex/voyage-ai";
@@ -4,21 +4,9 @@ title: Anthropic

 ## Installation

-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>
+```shell tab="npm"
+npm i llamaindex @llamaindex/anthropic
+```

 ## Usage

@@ -16,21 +16,9 @@ export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/openai
+```

 ## Usage

@@ -4,21 +4,9 @@ title: Bedrock

 ## Installation

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

 ## Usage

@@ -57,6 +45,7 @@ META_LLAMA3_2_1B_INSTRUCT = "meta.llama3-2-1b-instruct-v1:0"; // only available
 META_LLAMA3_2_3B_INSTRUCT = "meta.llama3-2-3b-instruct-v1:0"; // only available via inference endpoints (see below)
 META_LLAMA3_2_11B_INSTRUCT = "meta.llama3-2-11b-instruct-v1:0"; // only available via inference endpoints (see below), multimodal and function call supported
 META_LLAMA3_2_90B_INSTRUCT = "meta.llama3-2-90b-instruct-v1:0"; // only available via inference endpoints (see below), multimodal and function call supported
+AMAZON_NOVA_PREMIER_1 = "amazon.nova-premier-v1:0";
 AMAZON_NOVA_PRO_1 = "amazon.nova-pro-v1:0";
 AMAZON_NOVA_LITE_1 = "amazon.nova-lite-v1:0";
 AMAZON_NOVA_MICRO_1 = "amazon.nova-micro-v1:0";
@@ -76,6 +65,7 @@ US_META_LLAMA_3_2_1B_INSTRUCT = "us.meta.llama3-2-1b-instruct-v1:0";
 US_META_LLAMA_3_2_3B_INSTRUCT = "us.meta.llama3-2-3b-instruct-v1:0";
 US_META_LLAMA_3_2_11B_INSTRUCT = "us.meta.llama3-2-11b-instruct-v1:0";
 US_META_LLAMA_3_2_90B_INSTRUCT = "us.meta.llama3-2-90b-instruct-v1:0";
+US_AMAZON_NOVA_PRO_1 = "us.amazon.nova-premier-v1:0";
 US_AMAZON_NOVA_PRO_1 = "us.amazon.nova-pro-v1:0";
 US_AMAZON_NOVA_LITE_1 = "us.amazon.nova-lite-v1:0";
 US_AMAZON_NOVA_MICRO_1 = "us.amazon.nova-micro-v1:0";
@@ -86,6 +76,10 @@ EU_ANTHROPIC_CLAUDE_3_SONNET = "eu.anthropic.claude-3-sonnet-20240229-v1:0";
 EU_ANTHROPIC_CLAUDE_3_5_SONNET = "eu.anthropic.claude-3-5-sonnet-20240620-v1:0";
 EU_META_LLAMA_3_2_1B_INSTRUCT = "eu.meta.llama3-2-1b-instruct-v1:0";
 EU_META_LLAMA_3_2_3B_INSTRUCT = "eu.meta.llama3-2-3b-instruct-v1:0";
+EU_AMAZON_NOVA_PRO_1 = "eu.amazon.nova-premier-v1:0";
+EU_AMAZON_NOVA_PRO_1 = "eu.amazon.nova-pro-v1:0";
+EU_AMAZON_NOVA_LITE_1 = "eu.amazon.nova-lite-v1:0";
+EU_AMAZON_NOVA_MICRO_1 = "eu.amazon.nova-micro-v1:0";
 ```

 Sonnet, Haiku and Opus are multimodal, image_url only supports base64 data url format, e.g. `data:image/jpeg;base64,SGVsbG8sIFdvcmxkIQ==`
@@ -6,21 +6,9 @@ Check out available LLMs [here](https://deepinfra.com/models/text-generation).

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/deepinfra
+```

 ```ts
 import { DeepInfra } from "@llamaindex/deepinfra";
@@ -4,21 +4,9 @@ title: Gemini

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/google
+```

 ## Usage

@@ -69,7 +57,7 @@ const gemini = new Gemini({
 To authenticate for local development:

 ```bash
-npm install @google-cloud/vertexai
+npm i @google-cloud/vertexai
 gcloud auth application-default login
 ```

@@ -3,25 +3,13 @@ title: Groq
 ---

 import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../../examples/groq.ts";
+import CodeSource from "!raw-loader!@/examples/groq.ts";

 ## Installation

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

 ## Usage

@@ -8,21 +8,9 @@ The LLM can be explicitly updated through `Settings`.

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/openai
+```

 ```typescript
 import { OpenAI } from "@llamaindex/openai";
@@ -45,7 +33,7 @@ export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name

 ## Local LLM

-For local LLMs, currently we recommend the use of [Ollama](/docs/llamaindex/modules/llms/ollama) LLM.
+For local LLMs, currently we recommend the use of [Ollama](/docs/llamaindex/modules/models/llms/ollama) LLM.

 ## Available LLMs

@@ -4,21 +4,9 @@ title: LLama2

 ## Installation

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

 ## Usage

@@ -4,21 +4,9 @@ title: Mistral

 ## Installation

-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>
+```package-install
+npm i llamaindex @llamaindex/mistral
+```

 ## Usage

@@ -4,22 +4,9 @@ title: Ollama

 ## Installation

-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>
-
+```package-install
+npm i llamaindex @llamaindex/ollama
+```

 ## Usage

@@ -0,0 +1,380 @@
+---
+title: OpenAI
+---
+
+## Installation
+
+```package-install
+npm i llamaindex @llamaindex/openai
+```
+
+```ts
+import { OpenAI } from "@llamaindex/openai";
+import { Settings } from "llamaindex";
+
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0, apiKey: <YOUR_API_KEY> });
+```
+
+You can setup the apiKey on the environment variables, like:
+
+```bash
+export OPENAI_API_KEY="<YOUR_API_KEY>"
+```
+
+You can optionally set a custom base URL, like:
+
+```bash
+export OPENAI_BASE_URL="https://api.scaleway.ai/v1"
+```
+
+or
+
+```ts
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0, apiKey: <YOUR_API_KEY>, baseURL: "https://api.scaleway.ai/v1" });
+```
+
+## Using OpenAI Responses API
+
+The OpenAI Responses API provides enhanced functionality for handling complex interactions, including built-in tools, annotations, and streaming responses. Here's how to use it:
+
+### Basic Setup
+
+```ts
+import { openaiResponses } from "@llamaindex/openai";
+
+const llm = openaiResponses({
+  model: "gpt-4o",
+  temperature: 0.1,
+  maxOutputTokens: 1000
+});
+```
+
+### Message Content Types
+
+The API supports different types of message content, including text and images:
+
+```ts
+const response = await llm.chat({
+  messages: [
+    {
+      role: "user",
+      content: [
+        {
+          type: "input_text",
+          text: "What's in this image?"
+        },
+        {
+          type: "input_image",
+          image_url: "https://example.com/image.jpg",
+          detail: "auto" // Optional: can be "auto", "low", or "high"
+        }
+      ]
+    }
+  ]
+});
+```
+
+### Advanced Features
+
+#### Built-in Tools
+
+```ts
+const llm = openaiResponses({
+  model: "gpt-4o",
+  builtInTools: [
+    {
+      type: "function",
+      name: "search_files",
+      description: "Search through available files"
+    }
+  ],
+  strict: true // Enable strict mode for tool calls
+});
+```
+
+#### Response Tracking and Storage
+
+```ts
+const llm = openaiResponses({
+  trackPreviousResponses: true, // Enable response tracking
+  store: true, // Store responses for future reference
+  user: "user-123", // Associate responses with a user
+  callMetadata: { // Add custom metadata
+    sessionId: "session-123",
+    context: "customer-support"
+  }
+});
+```
+
+#### Streaming Responses
+
+```ts
+const response = await llm.chat({
+  messages: [
+    {
+      role: "user",
+      content: "Generate a long response"
+    }
+  ],
+  stream: true // Enable streaming
+});
+
+for await (const chunk of response) {
+  console.log(chunk.delta); // Process each chunk of the response
+}
+```
+
+### Configuration Options
+
+The OpenAI Responses API supports various configuration options:
+
+```ts
+const llm = openaiResponses({
+  // Model and basic settings
+  model: "gpt-4o",
+  temperature: 0.1,
+  topP: 1,
+  maxOutputTokens: 1000,
+  
+  // API configuration
+  apiKey: "your-api-key",
+  baseURL: "custom-endpoint",
+  maxRetries: 10,
+  timeout: 60000,
+  
+  // Response handling
+  trackPreviousResponses: false,
+  store: false,
+  strict: false,
+  
+  // Additional options
+  instructions: "Custom instructions for the model",
+  truncation: "auto", // Can be "auto", "disabled", or null
+  include: ["citations", "reasoning"] // Specify what to include in responses
+});
+```
+
+### Response Structure
+
+The API returns responses with rich metadata and optional annotations:
+
+```ts
+interface ResponseStructure {
+  message: {
+    content: string;
+    role: "assistant";
+    options: {
+      built_in_tool_calls: Array<ToolCall>;
+      annotations?: Array<Citation | URLCitation | FilePath>;
+      refusal?: string;
+      reasoning?: ReasoningItem;
+      usage?: ResponseUsage;
+      toolCall?: Array<PartialToolCall>;
+    }
+  }
+}
+```
+
+### Best Practices
+
+1. Use `trackPreviousResponses` when you need conversation continuity
+2. Enable `strict` mode when using tools to ensure accurate function calls
+3. Set appropriate `maxOutputTokens` to control response length
+4. Use `annotations` to track citations and references in responses
+5. Implement error handling for potential API failures and retries
+
+## Using JSON Response Format
+
+You can configure OpenAI to return responses in JSON format:
+
+```ts
+Settings.llm = new OpenAI({ 
+  model: "gpt-4o", 
+  temperature: 0,
+  responseFormat: { type: "json_object" }  
+});
+
+// You can also use a Zod schema to validate the response structure
+import { z } from "zod";
+
+const responseSchema = z.object({
+  summary: z.string(),  
+  topics: z.array(z.string()),
+  sentiment: z.enum(["positive", "negative", "neutral"])
+});
+
+Settings.llm = new OpenAI({ 
+  model: "gpt-4o", 
+  temperature: 0,
+  responseFormat: responseSchema  
+});
+```
+
+## Response Formats
+
+The OpenAI LLM supports different response formats to structure the output in specific ways. There are two main approaches to formatting responses:
+
+### 1. JSON Object Format
+
+The simplest way to get structured JSON responses is using the `json_object` response format:
+
+```ts
+Settings.llm = new OpenAI({ 
+  model: "gpt-4o", 
+  temperature: 0,
+  responseFormat: { type: "json_object" }  
+});
+
+const response = await llm.chat({
+  messages: [
+    {
+      role: "system",
+      content: "You are a helpful assistant that outputs JSON."
+    },
+    {
+      role: "user", 
+      content: "Summarize this meeting transcript"
+    }
+  ]
+});
+
+// Response will be valid JSON
+console.log(response.message.content);
+```
+
+### 2. Schema Validation with Zod
+
+For more robust type safety and validation, you can use Zod schemas to define the expected response structure:
+
+```ts
+import { z } from "zod";
+
+// Define the response schema
+const meetingSchema = z.object({
+  summary: z.string(),
+  participants: z.array(z.string()),
+  actionItems: z.array(z.string()),
+  nextSteps: z.string()
+});
+
+// Configure the LLM with the schema
+Settings.llm = new OpenAI({ 
+  model: "gpt-4o", 
+  temperature: 0,
+  responseFormat: meetingSchema
+});
+
+const response = await llm.chat({
+  messages: [
+    {
+      role: "user",
+      content: "Summarize this meeting transcript" 
+    }
+  ]
+});
+
+// Response will be typed and validated according to the schema
+const result = response.message.content;
+console.log(result.summary);
+console.log(result.actionItems);
+```
+
+### Response Format Options
+
+The response format can be configured in two ways:
+
+1. At LLM initialization:
+```ts
+const llm = new OpenAI({
+  model: "gpt-4o",
+  responseFormat: { type: "json_object" } // or a Zod schema
+});
+```
+
+2. Per request:
+```ts
+const response = await llm.chat({
+  messages: [...],
+  responseFormat: { type: "json_object" } // or a Zod schema
+});
+```
+
+The response format options are:
+
+- `{ type: "json_object" }` - Returns responses as JSON objects
+- `zodSchema` - A Zod schema that defines and validates the response structure
+
+### Best Practices
+
+1. Use JSON object format for simple structured responses
+2. Use Zod schemas when you need:
+   - Type safety
+   - Response validation
+   - Complex nested structures
+   - Specific field constraints
+3. Set a low temperature (e.g. 0) when using structured outputs for more reliable formatting
+4. Include clear instructions in system or user messages about the expected response format
+5. Handle potential parsing errors when working with JSON responses
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+import { Document, VectorStoreIndex } from "llamaindex";
+
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document]);
+```
+
+## Query
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+## Full Example
+
+```ts
+import { OpenAI } from "@llamaindex/openai";
+import { Document, Settings, VectorStoreIndex } from "llamaindex";
+
+// Use the OpenAI LLM
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
+
+async function main() {
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document]);
+
+  // get retriever
+  const retriever = index.asRetriever();
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```
+
+## API Reference
+
+- [OpenAI](/docs/api/classes/OpenAI)
@@ -4,21 +4,9 @@ title: Perplexity LLM

 ## Installation

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

 ## Usage

@@ -4,22 +4,9 @@ title: Portkey LLM

 ## Installation

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

 ## Usage

@@ -4,21 +4,9 @@ title: Together LLM

 ## Installation

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

 ## Usage

@@ -0,0 +1,4 @@
+{
+  "title": "Models",
+  "pages": ["embeddings", "llms", "prompt"]
+}
@@ -82,5 +82,5 @@ const response = await queryEngine.query({

 ## API Reference

- [Response Synthesizer](/docs/llamaindex/modules/response_synthesizer)
+- [Response Synthesizer](/docs/llamaindex/modules/rag/response_synthesizer)
 - [CompactAndRefine](/docs/api/classes/CompactAndRefine)
@@ -1,97 +0,0 @@
---
-title: NodeParser
---
-
-The `NodeParser` in LlamaIndex is responsible for splitting `Document` objects into more manageable `Node` objects. When you call `.fromDocuments()`, the `NodeParser` from the `Settings` is used to do this automatically for you. Alternatively, you can use it to split documents ahead of time.
-
-```typescript
-import { Document } from "llamaindex";
-import { SentenceSplitter } from "llamaindex";
-
-const nodeParser = new SentenceSplitter();
-
-Settings.nodeParser = nodeParser;
-```
-
-## TextSplitter
-
-The underlying text splitter will split text by sentences. It can also be used as a standalone module for splitting raw text.
-
-```typescript
-import { SentenceSplitter } from "llamaindex";
-
-const splitter = new SentenceSplitter({ chunkSize: 1 });
-
-const textSplits = splitter.splitText("Hello World");
-```
-
-## MarkdownNodeParser
-
-The `MarkdownNodeParser` is a more advanced `NodeParser` that can handle markdown documents. It will split the markdown into nodes and then parse the nodes into a `Document` object.
-
-```typescript
-import { MarkdownNodeParser } from "llamaindex";
-import { Document } from "llamaindex";
-
-const nodeParser = new MarkdownNodeParser();
-
-const nodes = nodeParser.getNodesFromDocuments([
-  new Document({
-    text: `# Main Header
-Main content
-
-# Header 2
-Header 2 content
-
-## Sub-header
-Sub-header content
-
-  `,
-  }),
-]);
-```
-
-The output metadata will be something like:
-
-```bash
-[
-  TextNode {
-    id_: '008e41a8-b097-487c-bee8-bd88b9455844',
-    metadata: { 'Header 1': 'Main Header' },
-    excludedEmbedMetadataKeys: [],
-    excludedLlmMetadataKeys: [],
-    relationships: { PARENT: [Array] },
-    hash: 'KJ5e/um/RkHaNR6bonj9ormtZY7I8i4XBPVYHXv1A5M=',
-    text: 'Main Header\nMain content',
-    textTemplate: '',
-    metadataSeparator: '\n'
-  },
-  TextNode {
-    id_: '0f5679b3-ba63-4aff-aedc-830c4208d0b5',
-    metadata: { 'Header 1': 'Header 2' },
-    excludedEmbedMetadataKeys: [],
-    excludedLlmMetadataKeys: [],
-    relationships: { PARENT: [Array] },
-    hash: 'IP/g/dIld3DcbK+uHzDpyeZ9IdOXY4brxhOIe7wc488=',
-    text: 'Header 2\nHeader 2 content',
-    textTemplate: '',
-    metadataSeparator: '\n'
-  },
-  TextNode {
-    id_: 'e81e9bd0-121c-4ead-8ca7-1639d65fdf90',
-    metadata: { 'Header 1': 'Header 2', 'Header 2': 'Sub-header' },
-    excludedEmbedMetadataKeys: [],
-    excludedLlmMetadataKeys: [],
-    relationships: { PARENT: [Array] },
-    hash: 'B3kYNnxaYi9ghtAgwza0ZEVKF4MozobkNUlcekDL7JQ=',
-    text: 'Sub-header\nSub-header content',
-    textTemplate: '',
-    metadataSeparator: '\n'
-  }
-]
-```
-
-## API Reference
-
- [SentenceSplitter](/docs/api/classes/SentenceSplitter)
- [MarkdownNodeParser](/docs/api/classes/MarkdownNodeParser)
@@ -41,5 +41,5 @@ for await (const chunk of stream) {
 ## Api References

 - [ContextChatEngine](/docs/api/classes/ContextChatEngine)
- [CondenseQuestionChatEngine](/docs/api/classes/ContextChatEngine)
+- [CondenseQuestionChatEngine](/docs/api/classes/CondenseQuestionChatEngine)
 - [SimpleChatEngine](/docs/api/classes/SimpleChatEngine)
@@ -0,0 +1,11 @@
+{
+  "title": "RAG",
+  "pages": [
+    "retriever",
+    "response_synthesizer",
+    "query_engines",
+    "chat_engine",
+    "node_postprocessors",
+    "evaluation"
+  ]
+}
@@ -8,21 +8,9 @@ The Cohere Reranker is a postprocessor that uses the Cohere API to rerank the re

 Firstly, you will need to install the `llamaindex` package.

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

 Now, you will need to sign up for an API key at [Cohere](https://cohere.ai/). Once you have your API key you can import the necessary modules and create a new instance of the `CohereRerank` class.

@@ -4,21 +4,9 @@ title: Node Postprocessors

 ## Installation

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

 ## Concept

@@ -8,22 +8,9 @@ The Jina AI Reranker is a postprocessor that uses the Jina AI Reranker API to re

 Firstly, you will need to install the `llamaindex` package.

-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>
-
+```package-install
+npm i llamaindex @llamaindex/openai
+```

 Now, you will need to sign up for an API key at [Jina AI](https://jina.ai/reranker). Once you have your API key you can import the necessary modules and create a new instance of the `JinaAIReranker` class.

@@ -17,22 +17,9 @@ To find out more about the latest features and updates, visit the [mixedbread.ai

 First, you will need to install the `llamaindex` package.

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

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

@@ -10,21 +10,9 @@ You can also check our multi-tenancy blog post to see how metadata filtering can

 Firstly if you haven't already, you need to install the `llamaindex` package:

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

 Then you can import the necessary modules from `llamaindex`:

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
github-actions[bot]	cde403be58	Release 0.10.0 (#1854 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-04-16 17:02:34 +07:00
Parham Saidi	e9bf4424e2	fix: update the tool call schema for nova (#1850 ) Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2025-04-16 16:52:29 +07:00
Thuc Pham	edb8b87d86	fix: shadcn components cannot be used in next server (#1853 )	2025-04-16 15:57:25 +07:00
Thuc Pham	6cf928f390	chore: use bunchee for llamaindex (#1821 )	2025-04-16 15:47:30 +07:00
Alex Yang	8e27fd2009	fix(docs): sha on edit page (#1852 )	2025-04-15 23:51:38 -07:00
Alex Yang	c84036bbdd	fix(doc): use install shortcut (#1849 )	2025-04-15 09:08:32 -07:00
github-actions[bot]	f43406fc9b	Release @llamaindex/community@0.0.95 (#1848 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-04-15 16:00:59 +07:00
Peter Goldstein	411dceaa41	Add Nova Premier model. Add EU endpoints for Nova models (#1841 )	2025-04-15 15:48:11 +07:00
Alex Yang	2447384f31	chore: bump fumadocs & next & react (#1845 )	2025-04-15 01:20:29 -07:00
github-actions[bot]	5f3eb457e6	Release 0.9.19 (#1844 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-04-15 00:31:31 -07:00
Peter Goldstein	d365eb2e54	Add GPT-4.1 models to OpenAI (#1842 )	2025-04-15 09:07:38 +02:00
Thuc Pham	bb34ade6d4	feat: support cn utils for server UI (#1843 )	2025-04-15 09:06:39 +02:00
github-actions[bot]	c540df5069	Release 0.9.18 (#1836 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-04-14 20:52:09 +07:00
Marcus Schiesser	400b3b54bf	feat: use full-source code with import statements for custom comps (#1838 ) Co-authored-by: thucpn <thucsh2@gmail.com> Co-authored-by: Thuc Pham <51660321+thucpn@users.noreply.github.com>	2025-04-14 13:48:21 +02:00
Marcus Schiesser	88b7046c68	chore: Move zod to peer deps (#1837 )	2025-04-10 18:17:26 +07:00
Zhanghao	2ffdb274f2	docs: correct the `CondenseQuestionChatEngine` path (#1834 )	2025-04-10 16:07:07 +07:00
github-actions[bot]	139eb050f9	Release @llamaindex/server@0.1.0 (#1835 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-04-10 15:38:40 +07:00
Thuc Pham	3ffee26b77	feat: enhance config params for LlamaIndexServer (#1833 )	2025-04-10 15:21:51 +07:00
Marcus Schiesser	dc6e774d78	chore: remove deepresearch events (#1831 )	2025-04-09 20:45:49 +07:00
github-actions[bot]	6716188e10	Release @llamaindex/server@0.0.9 (#1830 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-04-09 17:44:13 +07:00
Thuc Pham	0b75bd6d92	feat: component dir in llamaindex server (#1828 )	2025-04-09 17:25:21 +07:00
github-actions[bot]	045b267d1b	Release 0.9.17 (#1823 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: himself65 <14026360+himself65@users.noreply.github.com>	2025-04-08 17:35:55 -07:00
Alex Yang	41191d074a	fix(parse): file input (#1829 )	2025-04-08 14:15:07 -07:00
Marcus Schiesser	8b2914c8b7	docs: reorganize modules structure in docs (#1827 )	2025-04-08 22:42:00 +07:00
Marcus Schiesser	4c24dfcbce	docs: Move framework docs to installation directory and simplify gett… (#1826 )	2025-04-07 22:54:12 +07:00
r3rer3	0dfa371fc9	Add "thinking" and "thinking_signature" to chat response, and "thinking_signature" to chat stream (#1825 )	2025-04-07 21:57:55 +07:00
Peter Goldstein	0d852d6fdc	Add the Gemini 2.5 Pro Preview model (#1822 )	2025-04-07 16:08:40 +07:00
ANKIT VARSHNEY	2410527e64	feat: reader for postgres (#1813 ) Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>	2025-04-07 15:57:14 +07:00
Marcus Schiesser	7d2be8c640	fix: mcp test	2025-04-07 10:44:54 +02:00
Thuc Pham	3534c373f2	feat: support multi-resolution compatibility (#1816 )	2025-04-05 18:41:39 +07:00
github-actions[bot]	2cbdf71669	Release 0.9.16 (#1811 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-04-04 11:12:27 +02:00
Huu Le	ead657aedd	feat: add MCP tools integration and example usage (#1819 )	2025-04-04 11:03:10 +02:00
Marcus Schiesser	f5e4d098b0	chore: remove gpt-tokenizer (#1815 )	2025-04-03 14:23:31 +02:00
dependabot[bot]	4d97226e50	chore(deps): bump next from 15.2.3 to 15.2.4 (#1812 ) Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2025-04-03 13:57:39 +07:00
Marcus Schiesser	4999df18cc	chore: bump nextjs to "^15.2.3" (#1810 )	2025-04-02 21:24:57 +07:00
github-actions[bot]	9a27b6d94a	Release 0.9.15 (#1807 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-04-02 17:08:47 +07:00
Thuc Pham	8c02684f0f	fix: handle error when streaming workflow (#1808 )	2025-04-02 16:26:01 +07:00
ANKIT VARSHNEY	9c63f3f94e	feat: openai responses api (#1801 )	2025-04-02 16:21:43 +07:00
Thuc Pham	c515a324f6	feat: return raw output for agent toolcall result (#1806 )	2025-04-01 22:20:06 +07:00
github-actions[bot]	c70d7b9930	Release 0.9.14 (#1799 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>	2025-04-01 12:59:10 +02:00
Marcus Schiesser	1b6f368a3f	feat: Support loading from URLs for all readers extending FileReader (#1805 )	2025-04-01 17:39:59 +07:00
Thuc Pham	9d951b288f	feat: support llamacloud in @llamaindex/server (#1796 )	2025-04-01 17:39:39 +07:00
dependabot[bot]	5fe16697a2	chore(deps-dev): bump vite from 5.4.15 to 5.4.16 (#1804 ) Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2025-04-01 16:49:36 +07:00
Marcus Schiesser	189d8a83ac	chore: use node 20 for examples (#1803 )	2025-03-31 17:21:19 +07:00
ANKIT VARSHNEY	648cfb5cb5	feat: supbase vector store (#1790 )	2025-03-29 15:14:28 +07:00
Marcus Schiesser	eaf326ee90	fix: passing right llm setting from SimpleChatEngine to ChatMemoryBuffer (#1798 )	2025-03-28 18:20:52 +07:00
github-actions[bot]	fc1bedf438	Release (#1794 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-03-28 15:22:44 +07:00
Thuc Pham	164cf7a6df	fix: custom next server start fail (#1795 )	2025-03-28 15:09:57 +07:00
Zhanghao	e98033e2cc	docs: correct the number of indexes (#1793 )	2025-03-27 16:33:52 +02:00
dependabot[bot]	c0ffc7b434	chore(deps-dev): bump vite from 5.4.14 to 5.4.15 (#1787 ) Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>	2025-03-26 20:43:49 +07:00
github-actions[bot]	9cf88e9f3f	Release 0.9.13 (#1783 ) Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>	2025-03-26 10:31:29 +02:00
Thuc Pham	75d6e29187	feat: response source nodes in query tool output (#1784 )	2025-03-26 15:24:53 +07:00
Parham Saidi	132517877e	fix: stringify all tool results for anthropic on bedrock (#1786 )	2025-03-25 21:16:44 +07:00
Thuc Pham	299008b34f	feat: copy create-llama to @llamaindex/servers (#1780 )	2025-03-25 11:55:44 +02:00
Thuc Pham	482ed67690	fix: document deployment fail due to static generation timed out (#1779 )	2025-03-24 11:12:09 +02:00