Release @llamaindex/community@0.0.95 (#1848 )

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Add Nova Premier model. Add EU endpoints for Nova models (#1841 )
2026-07-01 22:14:03 -04:00 · 2025-04-15 16:00:59 +07:00 · 2025-04-15 15:48:11 +07:00 · 2025-04-15 01:20:29 -07:00 · 2025-04-15 00:31:31 -07:00 · 2025-04-15 09:07:38 +02:00
320 changed files with 13387 additions and 3550 deletions
@@ -1,5 +1,67 @@
 # @llamaindex/doc

+## 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
@@ -1,6 +1,6 @@
 {
  "name": "@llamaindex/doc",
-  "version": "0.2.3",
+  "version": "0.2.10",
  "private": true,
  "scripts": {
    "postinstall": "fumadocs-mdx",
@@ -36,20 +36,20 @@
    "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 +68,7 @@
    "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",
@@ -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",
@@ -1,7 +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") {
@@ -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 +1,98 @@
+---
+title: Installation
+description: How to install llamaindex packages.
+---
+
+import {
+	SiNodedotjs,
+	SiTypescript,
+	SiNextdotjs,
+	SiCloudflareworkers,
+	SiVite
+} from "@icons-pack/react-simple-icons";
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+To install llamaindex, run the following command:
+
+<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/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.
@@ -48,5 +48,5 @@ 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/Settings";
 ```

-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"]
 }
@@ -47,7 +47,7 @@ import { OpenAI } from "@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 +61,7 @@ Now:
 import { PineconeVectorStore } from "@llamaindex/pinecone";
 ```

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

 ### 3. Data Loaders

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

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

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

-An index is the basic container and organization for your data. LlamaIndex.TS supports three 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)
@@ -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 +2,17 @@
 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 { CodeNodeParserDemo } from '@/components/demo/code-node-parser.tsx';
 import { Tab, Tabs } from "fumadocs-ui/components/tabs";

-Node parsers are a simple abstraction that take a list of 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 +23,6 @@ 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 +66,46 @@ 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 +193,9 @@ 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 +1,144 @@
+---
+title: Loading Data
+description: Loading data using Readers into Documents
+---
+
+import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
+import CodeSource from "!raw-loader!@/examples/readers/src/simple-directory-reader";
+import CodeSource2 from "!raw-loader!@/examples/readers/src/custom-simple-directory-reader";
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+import { Accordion, Accordions } from 'fumadocs-ui/components/accordion';
+
+Before you can start indexing your documents, you need to load them into memory.
+A reader is a module that loads data from a file into a `Document` object.
+
+To install readers call:
+
+<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
+```
+
+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)
@@ -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.

@@ -6,7 +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.

@@ -2,12 +2,12 @@
 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.

@@ -2,12 +2,12 @@
 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.

@@ -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/)
@@ -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)
@@ -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"]
+}
@@ -35,7 +35,7 @@ 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

@@ -57,6 +57,7 @@ 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 +77,7 @@ 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 +88,10 @@ 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==`
@@ -3,7 +3,7 @@ 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

@@ -45,7 +45,7 @@ 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

@@ -0,0 +1,393 @@
+---
+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 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)
@@ -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"
+  ]
+}
@@ -4,6 +4,7 @@ title: Retriever

 A retriever in LlamaIndex is what is used to fetch `Node`s from an index using a query string.

+- [LlamaCloudRetriever](/docs/api/classes/LlamaCloudRetriever) to retrieve nodes from a [managed index](/docs/llamaindex/modules/data/data_index/managed)
 - [VectorIndexRetriever](/docs/api/classes/VectorIndexRetriever) will fetch the top-k most similar nodes. Ideal for dense retrieval to find most relevant nodes.
 - [SummaryIndexRetriever](/docs/api/classes/SummaryIndexRetriever) will fetch all nodes no matter the query. Ideal when complete context is necessary, e.g. analyzing large datasets.
 - [SummaryIndexLLMRetriever](/docs/api/classes/SummaryIndexLLMRetriever) utilizes an LLM to score and filter nodes based on relevancy to the query.
@@ -0,0 +1,165 @@
+---
+title: Using LlamaIndex Server
+description: Running LlamaIndex workflows with both API endpoints and a user interface for interaction
+---
+
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+# LlamaIndex Server
+
+LlamaIndexServer is a Next.js-based application that allows you to quickly launch your [LlamaIndex Workflows](https://ts.llamaindex.ai/docs/llamaindex/modules/agents/workflows) and [Agent Workflows](https://ts.llamaindex.ai/docs/llamaindex/modules/agents/agent_workflow) as an API server with an optional chat UI. It provides a complete environment for running LlamaIndex workflows with both API endpoints and a user interface for interaction.
+
+## Features
+
+- Serving a workflow as a chatbot
+- Built on Next.js for high performance and easy API development
+- Optional built-in chat UI with extendable UI components
+- Prebuilt development code
+
+## Installation
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install @llamaindex/server
+	```
+
+	```shell tab="yarn"
+	yarn add @llamaindex/server
+	```
+
+	```shell tab="pnpm"
+	pnpm add @llamaindex/server
+	```
+</Tabs>
+
+## Quick Start
+
+Create index.ts file and add the following code:
+
+```ts
+import { LlamaIndexServer } from "@llamaindex/server";
+import { wiki } from "@llamaindex/tools"; // or any other tool
+
+const createWorkflow = () => agent({ tools: [wiki()] })
+
+new LlamaIndexServer({
+  workflow: createWorkflow,
+  uiConfig: {
+    appTitle: "LlamaIndex App",
+    starterQuestions: ["Who is the first president of the United States?"],
+  },
+}).start();
+```
+
+## Running the Server
+
+In the same directory as `index.ts`, run the following command to start the server:
+
+  ```bash
+  tsx index.ts
+  ```
+The server will start at `http://localhost:3000`
+
+You can also make a request to the server:
+
+  ```bash
+  curl -X POST "http://localhost:3000/api/chat" -H "Content-Type: application/json" -d '{"message": "Who is the first president of the United States?"}'
+  ```
+
+## Configuration Options
+
+The LlamaIndexServer accepts the following configuration
+
+- `workflow`: A callable function that creates a workflow instance for each request
+- `uiConfig`: An object to configure the chat UI containing the following properties:
+  - `appTitle`: The title of the application (default: `"LlamaIndex App"`)
+  - `starterQuestions`: List of starter questions for the chat UI (default: `[]`)
+  - `componentsDir`: The directory for custom UI components rendering events emitted by the workflow. The default is undefined, which does not render custom UI components.
+  - `llamaCloudIndexSelector`: Whether to show the LlamaCloud index selector in the chat UI (requires `LLAMA_CLOUD_API_KEY` to be set in the environment variables) (default: `false`)
+
+LlamaIndexServer accepts all the configuration options from Nextjs Custom Server such as `port`, `hostname`, `dev`, etc.
+See all Nextjs Custom Server options [here](https://nextjs.org/docs/app/building-your-application/configuring/custom-server).
+
+## Default Endpoints and Features
+
+### Chat Endpoint
+
+The server includes a default chat endpoint at `/api/chat` for handling chat interactions.
+
+### Chat UI
+
+The server always provides a chat interface at the root path (`/`) with:
+
+- Configurable starter questions
+- Real-time chat interface
+- API endpoint integration
+
+### Static File Serving
+
+- The server automatically mounts the `data` and `output` folders at `{server_url}{api_prefix}/files/data` (default: `/api/files/data`) and `{server_url}{api_prefix}/files/output` (default: `/api/files/output`) respectively.
+- Your workflows can use both folders to store and access files. As a convention, the `data` folder is used for documents that are ingested and the `output` folder is used for documents that are generated by the workflow.
+
+
+## Custom UI Components
+
+The LlamaIndex server provides support for rendering workflow events using custom UI components, allowing you to extend and customize the chat interface.
+
+### Overview
+
+Custom UI components are a powerful feature that enables you to:
+
+- Add custom interface elements to the chat UI using React JSX or TSX files
+- Extend the default chat interface functionality
+- Create specialized visualizations or interactions
+
+### Configuration
+
+Your workflow must emit events that fit this structure, allowing the LlamaIndex server to display the right UI components based on the event type.
+
+```json
+{
+    "type": "<event_name>",
+    "data": <data model>
+}
+```
+
+### Server Setup
+
+1. Initialize the LlamaIndex server with a component directory:
+
+```ts
+new LlamaIndexServer({
+  workflow: createWorkflow,
+  uiConfig: {
+    appTitle: "LlamaIndex App",
+    componentsDir: "components",
+  },
+}).start();
+```
+
+2. Add the custom component code to the directory following the naming pattern:
+
+   - File Extension: `.jsx` and `.tsx` for React components
+   - File Name: Should match the event type from your workflow (e.g., `deep_research_event.jsx` for handling `deep_research_event` type that you defined in your workflow). If there are TSX and JSX files with the same name, the TSX file will be used.
+   - Component Name: Export a default React component named `Component` that receives props from the event data
+
+   Example component structure:
+
+   ```jsx
+   function Component({ events }) {
+       // Your component logic here
+       return (
+           // Your UI code here
+       );
+   }
+   ```
+
+## Best Practices
+
+1. Always provide a workflow factory that creates fresh workflow instances
+2. Use environment variables for sensitive configuration
+3. Use starter questions to guide users in the chat UI
+
+## Getting Started with a New Project
+
+Want to start a new project with LlamaIndexServer? Check out our [create-llama](https://github.com/run-llama/create-llama) tool to quickly generate a new project with LlamaIndexServer.
@@ -2,5 +2,5 @@
  "title": "Chat UI",
  "description": "Use chat-ui to add a chat interface to your LlamaIndexTS application.",
  "defaultOpen": false,
-  "pages": ["install", "chat", "rsc"]
+  "pages": ["install", "chat", "rsc", "llamaindex-server"]
 }
@@ -6,7 +6,7 @@ import { ChatDemoRSC } from '../../../../../components/demo/chat/rsc/demo';

 Using [chat-ui](https://github.com/run-llama/chat-ui), it's easy to add a chat interface to your LlamaIndexTS application using [Next.js RSC](https://nextjs.org/docs/app/building-your-application/rendering/server-components) and [Vercel AI RSC](https://sdk.vercel.ai/docs/ai-sdk-rsc/overview).

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

 For implementing a chat interface with RSC, you need to create an AI action and then connect the chat interface to use it.
@@ -3,7 +3,7 @@ title: Basic Agent
 ---

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

 We have a comprehensive, step-by-step [guide to building agents in LlamaIndex.TS](/docs/llamaindex/tutorials/agents/1_setup) that we recommend to learn what agents are and how to build them for production. But building a basic agent is simple:

@@ -4,7 +4,7 @@ title: Local LLMs

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

-LlamaIndex.TS supports OpenAI and [other remote LLM APIs](/docs/llamaindex/modules/llms). You can also run a local LLM on your machine!
+LlamaIndex.TS supports OpenAI and [other remote LLM APIs](/docs/llamaindex/modules/models/llms). You can also run a local LLM on your machine!

 ## Using a local model via Ollama

@@ -30,12 +30,12 @@ LlamaIndex.TS help you prepare the knowledge base with a suite of data connector

 ![](./_static/concepts/indexing.jpg)

-[**Data Loaders**](/docs/llamaindex/modules/data_loaders):
+[**Data Loaders**](/docs/llamaindex/modules/data/readers):
 A data connector (i.e. `Reader`) ingest data from different data sources and data formats into a simple `Document` representation (text and simple metadata).

-[**Documents / Nodes**](/docs/llamaindex/modules/documents_and_nodes): A `Document` is a generic container around any data source - for instance, a PDF, an API output, or retrieved data from a database. A `Node` is the atomic unit of data in LlamaIndex and represents a "chunk" of a source `Document`. It's a rich representation that includes metadata and relationships (to other nodes) to enable accurate and expressive retrieval operations.
+[**Documents / Nodes**](/docs/llamaindex/modules/data): A `Document` is a generic container around any data source - for instance, a PDF, an API output, or retrieved data from a database. A `Node` is the atomic unit of data in LlamaIndex and represents a "chunk" of a source `Document`. It's a rich representation that includes metadata and relationships (to other nodes) to enable accurate and expressive retrieval operations.

-[**Data Indexes**](/docs/llamaindex/modules/data_index):
+[**Data Indexes**](/docs/llamaindex/modules/data/data_index):
 Once you've ingested your data, LlamaIndex helps you index data into a format that's easy to retrieve.

 Under the hood, LlamaIndex parses the raw documents into intermediate representations, calculates vector embeddings, and stores your data in-memory or to disk.
@@ -58,19 +58,19 @@ These building blocks can be customized to reflect ranking preferences, as well

 #### Building Blocks

-[**Retrievers**](/docs/llamaindex/modules/retriever):
+[**Retrievers**](/docs/llamaindex/modules/rag/retriever):
 A retriever defines how to efficiently retrieve relevant context from a knowledge base (i.e. index) when given a query.
 The specific retrieval logic differs for different indices, the most popular being dense retrieval against a vector index.

-[**Response Synthesizers**](/docs/llamaindex/modules/response_synthesizer):
+[**Response Synthesizers**](/docs/llamaindex/modules/rag/response_synthesizer):
 A response synthesizer generates a response from an LLM, using a user query and a given set of retrieved text chunks.

 #### Pipelines

-[**Query Engines**](/docs/llamaindex/modules/query_engines):
+[**Query Engines**](/docs/llamaindex/modules/rag/query_engines):
 A query engine is an end-to-end pipeline that allow you to ask question over your data.
 It takes in a natural language query, and returns a response, along with reference context retrieved and passed to the LLM.

-[**Chat Engines**](/docs/llamaindex/modules/chat_engine):
+[**Chat Engines**](/docs/llamaindex/modules/rag/chat_engine):
 A chat engine is an end-to-end pipeline for having a conversation with your data
 (multiple back-and-forth instead of a single question & answer).
@@ -3,8 +3,8 @@ title: Retrieval Augmented Generation (RAG)
 ---

 import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
-import CodeSource from "!raw-loader!../../../../../../../../examples/vectorIndex";
-import TSConfigSource from "!!raw-loader!../../../../../../../../examples/tsconfig.json";
+import CodeSource from "!raw-loader!@/examples/vectorIndex";
+import TSConfigSource from "!!raw-loader!@/examples/tsconfig.json";

 One of the most common use-cases for LlamaIndex is Retrieval-Augmented Generation or RAG, in which your data is indexed and selectively retrieved to be given to an LLM as source material for responding to a query. You can learn more about the [concepts behind RAG](/docs/llamaindex/tutorials/rag/concepts).

@@ -17,9 +17,9 @@ npm init
 npm install -D typescript @types/node
 ```

-Then, check out the [installation](/docs/llamaindex/getting_started) steps to install LlamaIndex.TS and prepare an OpenAI key.
+Then, check out the [installation](/docs/llamaindex/getting_started/installation) steps to install LlamaIndex.TS and prepare an OpenAI key.

-You can use [other LLMs](/docs/llamaindex/modules/llms) via their APIs; if you would prefer to use local models check out our [local LLM example](/docs/llamaindex/tutorials/local_llm).
+You can use [other LLMs](/docs/llamaindex/modules/models/llms) via their APIs; if you would prefer to use local models check out our [local LLM example](/docs/llamaindex/tutorials/local_llm).

 ## Run queries

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
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