Release 0.8.37 (#1642)

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

.github/workflows/test.yml

@@ -150,7 +150,7 @@ jobs:
           done
       - name: Pack provider packages
         run: |
-          for dir in packages/providers/*; do
+          for dir in packages/providers/* packages/providers/storage/*; do
             if [ -d "$dir" ] && [ -f "$dir/package.json" ]; then
               echo "Packing $dir"
               pnpm pack --pack-destination ${{ runner.temp }} -C $dir

README.md

@@ -76,7 +76,7 @@ If you need any of those classes, you have to import them instead directly thoug
 Here's an example for importing the `PineconeVectorStore` class:
 
 ```typescript
-import { PineconeVectorStore } from "llamaindex/storage/vectorStore/PineconeVectorStore";
+import { PineconeVectorStore } from "llamaindex/vector-store/PineconeVectorStore";
 ```
 
 As the `PDFReader` is not working with the Edge runtime, here's how to use the `SimpleDirectoryReader` with the `LlamaParseReader` to load PDFs:

apps/next/CHANGELOG.md

@@ -1,5 +1,75 @@
 # @llamaindex/doc
 
+## 0.0.41
+
+### Patch Changes
+
+- Updated dependencies [1c908fd]
+  - @llamaindex/openai@0.1.51
+  - @llamaindex/node-parser@0.0.24
+  - @llamaindex/workflow@0.0.10
+  - @llamaindex/readers@1.0.25
+  - @llamaindex/cloud@2.0.24
+  - @llamaindex/core@0.4.23
+  - llamaindex@0.8.37
+
+## 0.0.40
+
+### Patch Changes
+
+- Updated dependencies [cb608b5]
+  - @llamaindex/openai@0.1.50
+  - @llamaindex/node-parser@0.0.23
+  - @llamaindex/workflow@0.0.9
+  - @llamaindex/readers@1.0.24
+  - @llamaindex/cloud@2.0.23
+  - @llamaindex/core@0.4.22
+  - llamaindex@0.8.36
+
+## 0.0.39
+
+### Patch Changes
+
+- 6d4d96f: chore: update examples and docs to use unified imports
+- Updated dependencies [15563a0]
+  - @llamaindex/openai@0.1.49
+  - llamaindex@0.8.35
+
+## 0.0.38
+
+### Patch Changes
+
+- Updated dependencies [9f8ad37]
+- Updated dependencies [7265f74]
+  - llamaindex@0.8.34
+  - @llamaindex/openai@0.1.48
+
+## 0.0.37
+
+### Patch Changes
+
+- Updated dependencies [2019a04]
+  - @llamaindex/openai@0.1.47
+  - llamaindex@0.8.33
+
+## 0.0.36
+
+### Patch Changes
+
+- f02621e: Fix internal links between chapters
+- Updated dependencies [34faf48]
+- Updated dependencies [4df1fe6]
+- Updated dependencies [9456616]
+- Updated dependencies [d6c270e]
+- Updated dependencies [1892e1c]
+- Updated dependencies [1931bbc]
+  - llamaindex@0.8.32
+  - @llamaindex/core@0.4.21
+  - @llamaindex/cloud@2.0.22
+  - @llamaindex/openai@0.1.46
+  - @llamaindex/node-parser@0.0.22
+  - @llamaindex/readers@1.0.23
+
 ## 0.0.35
 
 ### Patch Changes

apps/next/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/doc",
-  "version": "0.0.35",
+  "version": "0.0.41",
   "private": true,
   "scripts": {
     "build": "pnpm run build:docs && next build",

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

@@ -76,15 +76,19 @@ export default function HomePage() {
           >
             <MagicMove
               code={[
-                `import { OpenAI } from "llamaindex";
+                `import { OpenAI } from "@llamaindex/openai";
+
 const llm = new OpenAI();
 const response = await llm.complete({ prompt: "How are you?" });`,
-                `import { OpenAI } from "llamaindex";
+                `import { OpenAI } from "@llamaindex/openai";
+
 const llm = new OpenAI();
 const response = await llm.chat({
   messages: [{ content: "Tell me a joke.", role: "user" }],
 });`,
-                `import { OpenAI, ChatMemoryBuffer } from "llamaindex";
+                `import { ChatMemoryBuffer } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+
 const llm = new OpenAI({ model: 'gpt4o-turbo' });
 const buffer = new ChatMemoryBuffer({
   tokenLimit: 128_000,
@@ -94,7 +98,9 @@ const response = await llm.chat({
   messages: buffer.getMessages(),
   stream: true
 });`,
-                `import { OpenAIAgent, ChatMemoryBuffer } from "llamaindex";
+                `import { ChatMemoryBuffer } from "llamaindex";
+import { OpenAIAgent } from "@llamaindex/openai";
+
 const agent = new OpenAIAgent({
   llm,
   tools: [...myTools]

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

@@ -5,4 +5,24 @@ title: Gemini Agent
 import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
 import CodeSourceGemini from "!raw-loader!../../../../../../../examples/gemini/agent.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/google
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/google
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/google
+	```
+</Tabs>
+
+## Source 
+
 <DynamicCodeBlock lang="ts" code={CodeSourceGemini} />

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

@@ -12,9 +12,8 @@ Here's a simple example of how to use the Context-Aware Agent:
 import {
   Document,
   VectorStoreIndex,
-  OpenAIContextAwareAgent,
-  OpenAI,
 } from "llamaindex";
+import { OpenAI, OpenAIContextAwareAgent } from "@llamaindex/openai";
 
 async function createContextAwareAgent() {
   // Create and index some documents

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

@@ -7,14 +7,36 @@ import CodeSource from "!raw-loader!../../../../../../../examples/mistral";
 
 By default LlamaIndex.TS uses OpenAI's LLMs and embedding models, but we support [lots of other LLMs](../modules/llms) including models from Mistral (Mistral, Mixtral), Anthropic (Claude) and Google (Gemini).
 
-If you don't want to use an API at all you can [run a local model](../../examples/local_llm)
+If you don't want to use an API at all you can [run a local model](../../examples/local_llm).
+
+This example runs you through the process of setting up a Mistral model:
+
+
+## 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>
 
 ## Using another LLM
 
 You can specify what LLM LlamaIndex.TS will use on the `Settings` object, like this:
 
 ```typescript
-import { MistralAI, Settings } from "llamaindex";
+import { MistralAI } from "@llamaindex/mistral";
+import { Settings } from "llamaindex";
 
 Settings.llm = new MistralAI({
   model: "mistral-tiny",
@@ -29,7 +51,8 @@ You can see examples of other APIs we support by checking out "Available LLMs" i
 A frequent gotcha when trying to use a different API as your LLM is that LlamaIndex will also by default index and embed your data using OpenAI's embeddings. To completely switch away from OpenAI you will need to set your embedding model as well, for example:
 
 ```typescript
-import { MistralAIEmbedding, Settings } from "llamaindex";
+import { MistralAIEmbedding } from "@llamaindex/mistral";
+import { Settings } from "llamaindex";
 
 Settings.embedModel = new MistralAIEmbedding();
 ```

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

@@ -5,6 +5,8 @@ description: Install llamaindex by running a single command.
 
 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
@@ -19,6 +21,25 @@ import { Tab, Tabs } from "fumadocs-ui/components/tabs";
 	```
 </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 [Using other LLM APIs](/docs/llamaindex/examples/other_llms) to find out how to use other LLMs.
+
+
 ## What's next?
 
 <Cards>

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

@@ -9,7 +9,7 @@ LlamaIndex.TS is written in TypeScript and designed to be used in TypeScript pro
 We do lots of work on strong typing to make sure you have a great typing experience with LlamaIndex.TS.
 
 ```ts twoslash
-import { PromptTemplate } from '@llamaindex/core/prompts'
+import { PromptTemplate } from 'llamaindex'
 const promptTemplate = new PromptTemplate({
   template: `Context information from multiple sources is below.
 ---------------------
@@ -29,7 +29,7 @@ promptTemplate.format({
 ```
 
 ```ts twoslash
-import { FunctionTool } from '@llamaindex/core/tools'
+import { FunctionTool } from 'llamaindex'
 import { z } from 'zod'
 
 // ---cut-before---

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

@@ -20,7 +20,7 @@ npm install llamaindex
 
 ## Choose your model
 
-By default we'll be using OpenAI with GPT-4, as it's a powerful model and easy to get started with. If you'd prefer to run a local model, see [using a local model](local_model).
+By default we'll be using OpenAI with GPT-4, as it's a powerful model and easy to get started with. If you'd prefer to run a local model, see [using a local model](3_local_model).
 
 ## Get an OpenAI API key
 
@@ -36,4 +36,4 @@ We'll use `dotenv` to pull the API key out of that .env file, so also run:
 npm install dotenv
 ```
 
-Now you're ready to [create your agent](create_agent).
+Now you're ready to [create your agent](2_create_agent).

apps/next/src/content/docs/llamaindex/guide/agents/2_create_agent.mdx

@@ -31,7 +31,8 @@ First we'll need to pull in our dependencies. These are:
 - Dotenv to load our API key from the .env file
 
 ```javascript
-import { OpenAI, FunctionTool, OpenAIAgent, Settings } from "llamaindex";
+import { FunctionTool, Settings } from "llamaindex";
+import { OpenAI, OpenAIAgent } from "@llamaindex/openai";
 import "dotenv/config";
 ```
 
@@ -177,5 +178,5 @@ The second piece of output is the response from the LLM itself, where the `messa
 Great! We've built an agent with tool use! Next you can:
 
 - [See the full code](https://github.com/run-llama/ts-agents/blob/main/1_agent/agent.ts)
-- [Switch to a local LLM](local_model)
-- Move on to [add Retrieval-Augmented Generation to your agent](agentic_rag)
+- [Switch to a local LLM](3_local_model)
+- Move on to [add Retrieval-Augmented Generation to your agent](4_agentic_rag)

apps/next/src/content/docs/llamaindex/guide/agents/3_local_model.mdx

@@ -89,4 +89,4 @@ You can use a ReActAgent instead of an OpenAIAgent in any of the further example
 
 ### Next steps
 
-Now you've got a local agent, you can [add Retrieval-Augmented Generation to your agent](agentic_rag).
+Now you've got a local agent, you can [add Retrieval-Augmented Generation to your agent](4_agentic_rag).

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

@@ -13,22 +13,34 @@ To learn more about RAG, we recommend this [introduction](https://docs.llamainde
 
 We're going to start with the same agent we [built in step 1](https://github.com/run-llama/ts-agents/blob/main/1_agent/agent.ts), but make a few changes. You can find the finished version [in the repository](https://github.com/run-llama/ts-agents/blob/main/2_agentic_rag/agent.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/openai @llamaindex/huggingface 
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai @llamaindex/huggingface 
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai @llamaindex/huggingface 
+	```
+</Tabs>
+
+
 ### New dependencies
 
 We'll be bringing in `SimpleDirectoryReader`, `HuggingFaceEmbedding`, `VectorStoreIndex`, and `QueryEngineTool`, `OpenAIContextAwareAgent` from LlamaIndex.TS, as well as the dependencies we previously used.
 
 ```javascript
-import {
-  OpenAI,
-  FunctionTool,
-  OpenAIAgent,
-  OpenAIContextAwareAgent,
-  Settings,
-  SimpleDirectoryReader,
-  HuggingFaceEmbedding,
-  VectorStoreIndex,
-  QueryEngineTool,
-} from "llamaindex";
+import { FunctionTool, QueryEngineTool, Settings, VectorStoreIndex } from "llamaindex";
+import { OpenAI, OpenAIAgent } from "@llamaindex/openai";
+import { HuggingFaceEmbedding } from "@llamaindex/huggingface";
+import { SimpleDirectoryReader } from "llamaindex";
 ```
 
 ### Add an embedding model
@@ -153,4 +165,4 @@ The `OpenAIContextAwareAgent` approach simplifies the setup by allowing you to d
 
 On the other hand, using the `QueryEngineTool` offers more flexibility and power. This method allows for customization in how queries are constructed and executed, enabling you to query data from various storages and process them in different ways. However, this added flexibility comes with increased complexity and response time due to the separate tool call and queryEngine generating tool output by LLM that is then passed to the agent.
 
-So now we have an agent that can index complicated documents and answer questions about them. Let's [combine our math agent and our RAG agent](rag_and_tools)!
+So now we have an agent that can index complicated documents and answer questions about them. Let's [combine our math agent and our RAG agent](5_rag_and_tools)!

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

@@ -127,4 +127,4 @@ In the final tool call, it used the `sumNumbers` function to add the two budgets
 }
 ```
 
-Great! Now let's improve accuracy by improving our parsing with [LlamaParse](llamaparse).
+Great! Now let's improve accuracy by improving our parsing with [LlamaParse](6_llamaparse).

apps/next/src/content/docs/llamaindex/guide/agents/6_llamaparse.mdx

@@ -17,4 +17,4 @@ const documents = await reader.loadData("../data/sf_budget_2023_2024.pdf");
 
 Now you will be able to ask more complicated questions of the same PDF and get better results. You can find this code [in our repo](https://github.com/run-llama/ts-agents/blob/main/4_llamaparse/agent.ts).
 
-Next up, let's persist our embedded data so we don't have to re-parse every time by [using a vector store](qdrant).
+Next up, let's persist our embedded data so we don't have to re-parse every time by [using a vector store](7_qdrant).

apps/next/src/content/docs/llamaindex/guide/agents/7_qdrant.mdx

@@ -65,13 +65,13 @@ Since parsing a PDF can be slow, especially a large one, using the pre-parsed ch
 
 In this guide you've learned how to
 
-- [Create an agent](create_agent)
+- [Create an agent](2_create_agent)
 - Use remote LLMs like GPT-4
-- [Use local LLMs like Mixtral](local_model)
-- [Create a RAG query engine](agentic_rag)
-- [Turn functions and query engines into agent tools](rag_and_tools)
+- [Use local LLMs like Mixtral](3_local_model)
+- [Create a RAG query engine](4_agentic_rag)
+- [Turn functions and query engines into agent tools](5_rag_and_tools)
 - Combine those tools
-- [Enhance your parsing with LlamaParse](llamaparse)
+- [Enhance your parsing with LlamaParse](6_llamaparse)
 - Persist your data in a vector store
 
 The next steps are up to you! Try creating more complex functions and query engines, and set your agent loose on the world.

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

@@ -33,11 +33,11 @@ We offer readers for different file formats.
 
 <Tabs groupId="llamaindex-or-readers" items={["llamaindex", "@llamaindex/readers"]} persist>
 	```ts twoslash tab="llamaindex"
-	import { CSVReader } from 'llamaindex'
-	import { PDFReader } from 'llamaindex'
-	import { JSONReader } from 'llamaindex'
-	import { MarkdownReader } from 'llamaindex'
-	import { HTMLReader } from 'llamaindex'
+	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
 	```
 
@@ -71,7 +71,7 @@ We offer readers for different file formats.
 	```
 
 	```ts twoslash tab="@llamaindex/readers"
-	import { SimpleDirectoryReader } from "@llamaindex/readers/directory";
+	import { SimpleDirectoryReader } from "llamaindex";
 
 	const reader = new SimpleDirectoryReader()
 	const documents = await reader.loadData("./data")

apps/next/src/content/docs/llamaindex/guide/loading/node-parser.mdx

@@ -15,7 +15,7 @@ By default, we will use `Settings.nodeParser` to split the document into nodes.
 
 ```ts twoslash
 import { TextFileReader } from '@llamaindex/readers/text'
-import { SentenceSplitter } from '@llamaindex/core/node-parser';
+import { SentenceSplitter } from 'llamaindex';
 import { Settings } from 'llamaindex';
 
 const nodeParser = new SentenceSplitter();
@@ -28,7 +28,7 @@ Settings.nodeParser = nodeParser;
 The underlying text splitter will split text by sentences. It can also be used as a standalone module for splitting raw text.
 
 ```ts twoslash
-import { SentenceSplitter } from "@llamaindex/core/node-parser";
+import { SentenceSplitter } from "llamaindex";
 
 const splitter = new SentenceSplitter({ chunkSize: 1 });
 
@@ -42,7 +42,7 @@ The `MarkdownNodeParser` is a more advanced `NodeParser` that can handle markdow
 
 <Tabs items={["with reader", "with node:fs"]}>
 	```ts twoslash tab="with reader"
-	import { MarkdownNodeParser } from "@llamaindex/core/node-parser";
+	import { MarkdownNodeParser } from "llamaindex";
 	import { MarkdownReader } from '@llamaindex/readers/markdown'
 
 	const reader = new MarkdownReader();
@@ -56,8 +56,7 @@ The `MarkdownNodeParser` is a more advanced `NodeParser` that can handle markdow
 
 	```ts twoslash tab="with node:fs"
 	import fs from 'node:fs/promises';
-	import { MarkdownNodeParser } from "@llamaindex/core/node-parser";
-	import { Document } from '@llamaindex/core/schema';
+	import { MarkdownNodeParser, Document } from "llamaindex";
 
 	const markdownNodeParser = new MarkdownNodeParser();
 	const text = await fs.readFile('path/to/file.md', 'utf-8');

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

@@ -69,7 +69,7 @@ streamText({
 For production deployments, you can use LlamaCloud to store and manage your documents:
 
 ```typescript
-import { LlamaCloudIndex } from "llamaindex";
+import { LlamaCloudIndex } from "@llamaindex/cloud";
 
 // Create a LlamaCloud index
 const index = await LlamaCloudIndex.fromDocuments({

apps/next/src/content/docs/llamaindex/modules/data_loaders/json.mdx

@@ -6,10 +6,28 @@ A simple JSON data loader with various options.
 Either parses the entire string, cleaning it and treat each line as an embedding or performs a recursive depth-first traversal yielding JSON paths.
 Supports streaming of large JSON data using [@discoveryjs/json-ext](https://github.com/discoveryjs/json-ext)
 
+## 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>
+
 ## Usage
 
 ```ts
-import { JSONReader } from "llamaindex";
+import { JSONReader } from "@llamaindex/readers/json";
 
 const file = "../../PATH/TO/FILE";
 const content = new TextEncoder().encode("JSON_CONTENT");

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

@@ -4,6 +4,24 @@ title: Image Retrieval
 
 LlamaParse `json` mode supports extracting any images found in a page object by using the `getImages` function. They are downloaded to a local folder and can then be sent to a multimodal LLM for further processing.
 
+## 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>
+
 ## Usage
 
 We use the `getImages` method to input our array of JSON objects, download the images to a specified folder and get a list of ImageNodes.
@@ -19,14 +37,10 @@ const imageDicts = await reader.getImages(jsonObjs, "images");
 You can create an index across both text and image nodes by requesting alternative text for the image from a multimodal LLM.
 
 ```ts
-import {
-  Document,
-  ImageNode,
-  LlamaParseReader,
-  OpenAI,
-  VectorStoreIndex,
-} from "llamaindex";
-import { createMessageContent } from "llamaindex/synthesizers/utils";
+import { Document, ImageNode, VectorStoreIndex } from "llamaindex";
+import { LlamaParseReader } from "@llamaindex/cloud";
+import { OpenAI } from "@llamaindex/openai";
+import { createMessageContent } from "llamaindex";
 
 const reader = new LlamaParseReader();
 async function main() {

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

@@ -4,12 +4,32 @@ title: JSON Mode
 
 In JSON mode, LlamaParse will return a data structure representing the parsed object.
 
+## 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>
+
 ## Usage
 
 For Json mode, you need to use `loadJson`. The `resultType` is automatically set with this method.
 More information about indexing the results on the next page.
 
 ```ts
+import { LlamaParseReader } from "@llamaindex/cloud";
+
 const reader = new LlamaParseReader();
 async function main() {
   // Load the file and return an array of json objects
@@ -59,7 +79,8 @@ All Readers share a `loadData` method with `SimpleDirectoryReader` that promises
 However, a simple work around is to create a new reader class that extends `LlamaParseReader` and adds a new method or overrides `loadData`, wrapping around JSON mode, extracting the required values, and returning a Document object.
 
 ```ts
-import { LlamaParseReader, Document } from "llamaindex";
+import { Document } from "llamaindex";
+import { LlamaParseReader } from "@llamaindex/cloud";
 
 class LlamaParseReaderWithJson extends LlamaParseReader {
   // Override the loadData method

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

@@ -11,6 +11,38 @@ Document stores contain ingested document chunks, i.e. [Node](/docs/llamaindex/m
 
 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>
+
+You can configure the `schemaName`, `tableName`, `namespace`, and
+`connectionString`. If a `connectionString` is not
+provided, it will use the environment variables `PGHOST`, `PGUSER`,
+`PGPASSWORD`, `PGDATABASE` and `PGPORT`.
+
+```typescript
+import { Document, VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { PostgresDocumentStore } from "@llamaindex/postgres";
+
+const storageContext = await storageContextFromDefaults({
+  docStore: new PostgresDocumentStore(),
+});
+```
+
 ## API Reference
 
 - [BaseDocumentStore](/docs/api/classes/BaseDocumentStore)

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

@@ -5,9 +5,13 @@ title: Storage
 Storage in LlamaIndex.TS works automatically once you've configured a
 `StorageContext` object.
 
+Per default a local directory is used for storage. Depening on the storage type (i.e. doc stores, index stores or vector stores), you can configure a different persistence layer. 
+Most commonly a vector database is used as vector store. 
+
+
 ## Local Storage
 
-You can configure the `persistDir` and attach it to an index.
+You can configure the `persistDir` to define where to store the data locally.
 
 ```typescript
 import {
@@ -26,33 +30,6 @@ const index = await VectorStoreIndex.fromDocuments([document], {
 });
 ```
 
-## PostgreSQL Storage
-
-You can configure the `schemaName`, `tableName`, `namespace`, and
-`connectionString`. If a `connectionString` is not
-provided, it will use the environment variables `PGHOST`, `PGUSER`,
-`PGPASSWORD`, `PGDATABASE` and `PGPORT`.
-
-```typescript
-import {
-  Document,
-  VectorStoreIndex,
-  PostgresDocumentStore,
-  PostgresIndexStore,
-  storageContextFromDefaults,
-} from "llamaindex";
-
-const storageContext = await storageContextFromDefaults({
-  docStore: new PostgresDocumentStore(),
-  indexStore: new PostgresIndexStore(),
-});
-
-const document = new Document({ text: "Test Text" });
-const index = await VectorStoreIndex.fromDocuments([document], {
-  storageContext,
-});
-```
-
 ## API Reference
 
 - [StorageContext](/docs/api/interfaces/StorageContext)

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

@@ -11,6 +11,38 @@ Index stores are underlying storage components that contain metadata(i.e. inform
 
 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>
+
+You can configure the `schemaName`, `tableName`, `namespace`, and
+`connectionString`. If a `connectionString` is not
+provided, it will use the environment variables `PGHOST`, `PGUSER`,
+`PGPASSWORD`, `PGDATABASE` and `PGPORT`.
+
+```typescript
+import { Document, VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { PostgresIndexStore } from "@llamaindex/postgres";
+
+const storageContext = await storageContextFromDefaults({
+  indexStore: new PostgresIndexStore(),
+});
+```
+
 ## API Reference
 
 - [BaseIndexStore](/docs/api/classes/BaseIndexStore)

apps/next/src/content/docs/llamaindex/modules/data_stores/vector_stores/index.mdx

@@ -21,4 +21,4 @@ Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for t
 
 ## API Reference
 
-- [VectorStoreBase](/docs/api/classes/VectorStoreBase)
+- [BaseVectorStore](/docs/api/classes/BaseVectorStore)

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

@@ -11,11 +11,30 @@ docker pull qdrant/qdrant
 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>
+
 ## Importing the modules
 
 ```ts
 import fs from "node:fs/promises";
-import { Document, VectorStoreIndex, QdrantVectorStore } from "llamaindex";
+import { Document, VectorStoreIndex } from "llamaindex";
+import { QdrantVectorStore } from "@llamaindex/qdrant";
 ```
 
 ## Load the documents
@@ -60,7 +79,8 @@ console.log(response.toString());
 
 ```ts
 import fs from "node:fs/promises";
-import { Document, VectorStoreIndex, QdrantVectorStore } from "llamaindex";
+import { Document, VectorStoreIndex } from "llamaindex";
+import { QdrantVectorStore } from "@llamaindex/qdrant";
 
 async function main() {
   const path = "node_modules/llamaindex/examples/abramov.txt";

apps/next/src/content/docs/llamaindex/modules/documents_and_nodes/metadata_extraction.mdx

@@ -14,13 +14,8 @@ Our metadata extractor modules include the following "feature extractors":
 Then you can chain the `Metadata Extractors` with the `IngestionPipeline` to extract metadata from a set of documents.
 
 ```ts
-import {
-  IngestionPipeline,
-  TitleExtractor,
-  QuestionsAnsweredExtractor,
-  Document,
-  OpenAI,
-} from "llamaindex";
+import { Document, IngestionPipeline, TitleExtractor, QuestionsAnsweredExtractor } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
 
 async function main() {
   const pipeline = new IngestionPipeline({

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

@@ -5,13 +5,27 @@ title: DeepInfra
 To use DeepInfra embeddings, you need to import `DeepInfraEmbedding` from llamaindex.
 Check out available embedding models [here](https://deepinfra.com/models/embeddings).
 
+## 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>
+
 ```ts
-import {
-  DeepInfraEmbedding,
-  Settings,
-  Document,
-  VectorStoreIndex,
-} from "llamaindex";
+import { Document, Settings, VectorStoreIndex } from "llamaindex";
+import { DeepInfraEmbedding } from "@llamaindex/deepinfra";
 
 // Update Embed Model
 Settings.embedModel = new DeepInfraEmbedding();
@@ -33,7 +47,7 @@ By default, DeepInfraEmbedding is using the sentence-transformers/clip-ViT-B-32
 For example:
 
 ```ts
-import { DeepInfraEmbedding } from "llamaindex";
+import { DeepInfraEmbedding } from "@llamaindex/deepinfra";
 
 const model = "intfloat/e5-large-v2";
 Settings.embedModel = new DeepInfraEmbedding({
@@ -46,7 +60,8 @@ You can also set the `maxRetries` and `timeout` parameters when initializing `De
 For example:
 
 ```ts
-import { DeepInfraEmbedding, Settings } from "llamaindex";
+import { Settings } from "llamaindex";
+import { DeepInfraEmbedding } from "@llamaindex/deepinfra";
 
 const model = "intfloat/e5-large-v2";
 const maxRetries = 5;
@@ -62,7 +77,7 @@ Settings.embedModel = new DeepInfraEmbedding({
 Standalone usage:
 
 ```ts
-import { DeepInfraEmbedding } from "llamaindex";
+import { DeepInfraEmbedding } from "@llamaindex/deepinfra";
 import { config } from "dotenv";
 // For standalone usage, you need to configure DEEPINFRA_API_TOKEN in .env file
 config();

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

@@ -2,10 +2,29 @@
 title: Gemini
 ---
 
-To use Gemini embeddings, you need to import `GeminiEmbedding` from `llamaindex`.
+To use Gemini embeddings, you need to import `GeminiEmbedding` from `@llamaindex/google`.
+
+## 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>
 
 ```ts
-import { GeminiEmbedding, Settings } from "llamaindex";
+import { Document, Settings, VectorStoreIndex } from "llamaindex";
+import { GeminiEmbedding, GEMINI_MODEL } from "@llamaindex/google";
 
 // Update Embed Model
 Settings.embedModel = new GeminiEmbedding();
@@ -27,7 +46,7 @@ Per default, `GeminiEmbedding` is using the `gemini-pro` model. You can change t
 For example:
 
 ```ts
-import { GEMINI_MODEL, GeminiEmbedding } from "llamaindex";
+import { GEMINI_MODEL, GeminiEmbedding } from "@llamaindex/google";
 
 Settings.embedModel = new GeminiEmbedding({
   model: GEMINI_MODEL.GEMINI_PRO_LATEST,

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

@@ -2,10 +2,29 @@
 title: HuggingFace
 ---
 
-To use HuggingFace embeddings, you need to import `HuggingFaceEmbedding` from `llamaindex`.
+To use HuggingFace embeddings, you need to import `HuggingFaceEmbedding` from `@llamaindex/huggingface`.
+
+## 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>
 
 ```ts
-import { HuggingFaceEmbedding, Settings } from "llamaindex";
+import { Document, Settings, VectorStoreIndex } from "llamaindex";
+import { HuggingFaceEmbedding } from "@llamaindex/huggingface";
 
 // Update Embed Model
 Settings.embedModel = new HuggingFaceEmbedding();
@@ -29,6 +48,8 @@ If you're not using a quantized model, set the `quantized` parameter to `false`.
 For example, to use the not quantized `BAAI/bge-small-en-v1.5` model, you can use the following code:
 
 ```ts
+import { HuggingFaceEmbedding } from "@llamaindex/huggingface";
+
 Settings.embedModel = new HuggingFaceEmbedding({
   modelType: "BAAI/bge-small-en-v1.5",
   quantized: false,

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

@@ -2,10 +2,29 @@
 title: MistralAI
 ---
 
-To use MistralAI embeddings, you need to import `MistralAIEmbedding` from `llamaindex`.
+To use MistralAI embeddings, you need to import `MistralAIEmbedding` from `@llamaindex/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>
 
 ```ts
-import { MistralAIEmbedding, Settings } from "llamaindex";
+import { Document, Settings, VectorStoreIndex } from "llamaindex";
+import { MistralAIEmbedding } from "@llamaindex/mistral";
 
 // Update Embed Model
 Settings.embedModel = new MistralAIEmbedding({

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

@@ -14,16 +14,28 @@ To find out more about the latest features, updates, and available models, visit
 
 ## Setup
 
-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/mixedbread
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/mixedbread
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/mixedbread
+	```
+</Tabs>
 
-```bash
-pnpm install llamaindex
-```
 
 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.
 
 ```ts
-import { MixedbreadAIEmbeddings, Document, Settings } from "llamaindex";
+import { MixedbreadAIEmbeddings } from "@llamaindex/mixedbread";
+import { Document, Settings } from "llamaindex";
 ```
 
 ## Usage with LlamaIndex

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

@@ -2,7 +2,7 @@
 title: Ollama
 ---
 
-To use Ollama embeddings, you need to import `OllamaEmbedding` from `llamaindex`.
+To use Ollama embeddings, you need to import `OllamaEmbedding` from `@llamaindex/ollama`.
 
 Note that you need to pull the embedding model first before using it.
 
@@ -12,8 +12,27 @@ In the example below, we're using the [`nomic-embed-text`](https://ollama.com/li
 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>
+
 ```ts
-import { OllamaEmbedding, Settings } from "llamaindex";
+import { OllamaEmbedding } from "@llamaindex/ollama";
+import { Document, Settings, VectorStoreIndex } from "llamaindex";
 
 Settings.embedModel = new OllamaEmbedding({ model: "nomic-embed-text" });
 

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

@@ -2,10 +2,29 @@
 title: OpenAI
 ---
 
-To use OpenAI embeddings, you need to import `OpenAIEmbedding` from `llamaindex`.
+To use OpenAI embeddings, you need to import `OpenAIEmbedding` from `@llamaindex/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 { OpenAIEmbedding, Settings } from "llamaindex";
+import { OpenAIEmbedding } from "@llamaindex/openai";
+import { Document, Settings, VectorStoreIndex } from "llamaindex";
 
 Settings.embedModel = new OpenAIEmbedding();
 

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

@@ -6,8 +6,27 @@ The embedding model in LlamaIndex is responsible for creating numerical represen
 
 This 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>
+
 ```typescript
-import { OpenAIEmbedding, Settings } from "llamaindex";
+import { OpenAIEmbedding } from "@llamaindex/openai";
+import { Settings } from "llamaindex";
 
 Settings.embedModel = new OpenAIEmbedding({
   model: "text-embedding-ada-002",

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

@@ -10,9 +10,21 @@ This is useful for measuring if the response was correct. The evaluator returns
 
 Firstly, you need to install the package:
 
-```bash
-pnpm i llamaindex
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
 
 Set the OpenAI API key:
 
@@ -23,7 +35,8 @@ export OPENAI_API_KEY=your-api-key
 Import the required modules:
 
 ```ts
-import { CorrectnessEvaluator, OpenAI, Settings, Response } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+import { CorrectnessEvaluator, Settings, Response } from "llamaindex";
 ```
 
 Let's setup gpt-4 for better results:

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

@@ -12,9 +12,22 @@ This is useful for measuring if the response was hallucinated. The evaluator ret
 
 Firstly, you need to install the package:
 
-```bash
-pnpm i llamaindex
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
+
 
 Set the OpenAI API key:
 
@@ -25,12 +38,12 @@ export OPENAI_API_KEY=your-api-key
 Import the required modules:
 
 ```ts
+import { OpenAI } from "@llamaindex/openai";
 import {
   Document,
   FaithfulnessEvaluator,
-  OpenAI,
-  VectorStoreIndex,
   Settings,
+  VectorStoreIndex,
 } from "llamaindex";
 ```
 

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

@@ -10,9 +10,22 @@ It is useful for measuring if the response was relevant to the query. The evalua
 
 Firstly, you need to install the package:
 
-```bash
-pnpm i llamaindex
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
+
 
 Set the OpenAI API key:
 
@@ -23,11 +36,11 @@ export OPENAI_API_KEY=your-api-key
 Import the required modules:
 
 ```ts
+import { OpenAI } from "@llamaindex/openai";
 import {
-  RelevancyEvaluator,
-  OpenAI,
-  Settings,
   Document,
+  RelevancyEvaluator,
+  Settings,
   VectorStoreIndex,
 } from "llamaindex";
 ```

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

@@ -5,18 +5,35 @@ title: Ingestion Pipeline
 An `IngestionPipeline` uses a concept of `Transformations` that are applied to input data.
 These `Transformations` are applied to your input data, and the resulting nodes are either returned or inserted into a vector database (if given).
 
+## 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>
+
 ## Usage Pattern
 
 The simplest usage is to instantiate an IngestionPipeline like so:
 
 ```ts
 import fs from "node:fs/promises";
-
+import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
 import {
   Document,
   IngestionPipeline,
   MetadataMode,
-  OpenAIEmbedding,
   TitleExtractor,
   SentenceSplitter,
 } from "llamaindex";
@@ -58,14 +75,14 @@ Then, you can construct an index from that vector store later on.
 ```ts
 import fs from "node:fs/promises";
 
+import { OpenAIEmbedding } from "@llamaindex/openai";
+import { QdrantVectorStore } from "@llamaindex/qdrant";
 import {
   Document,
   IngestionPipeline,
   MetadataMode,
-  OpenAIEmbedding,
   TitleExtractor,
   SentenceSplitter,
-  QdrantVectorStore,
   VectorStoreIndex,
 } from "llamaindex";
 

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

@@ -2,10 +2,29 @@
 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>
+
 ## Usage
 
 ```ts
-import { Anthropic, Settings } from "llamaindex";
+import { Settings } from "llamaindex";
+import { Anthropic } from "@llamaindex/anthropic";
 
 Settings.llm = new Anthropic({
   apiKey: "<YOUR_API_KEY>",
@@ -37,7 +56,8 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import { Anthropic, Document, VectorStoreIndex, Settings } from "llamaindex";
+import { Document, VectorStoreIndex, Settings } from "llamaindex";
+import { Anthropic } from "@llamaindex/anthropic";
 
 Settings.llm = new Anthropic({
   apiKey: "<YOUR_API_KEY>",

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

@@ -14,10 +14,29 @@ export AZURE_OPENAI_ENDPOINT="<YOUR ENDPOINT, see https://learn.microsoft.com/en
 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>
+
 ## Usage
 
 ```ts
-import { OpenAI, Settings } from "llamaindex";
+import { Settings } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
 
 Settings.llm = new OpenAI({ model: "gpt-4", temperature: 0 });
 ```
@@ -47,7 +66,8 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import { OpenAI, Document, VectorStoreIndex, Settings } from "llamaindex";
+import { Document, VectorStoreIndex, Settings } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
 
 Settings.llm = new OpenAI({ model: "gpt-4", temperature: 0 });
 

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

@@ -2,6 +2,24 @@
 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>
+
 ## Usage
 
 ```ts

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

@@ -4,8 +4,27 @@ title: DeepInfra
 
 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>
+
 ```ts
-import { DeepInfra, Settings } from "llamaindex";
+import { DeepInfra } from "@llamaindex/deepinfra";
+import { Settings } from "llamaindex";
 
 // Get the API key from `DEEPINFRA_API_TOKEN` environment variable
 import { config } from "dotenv";
@@ -28,6 +47,8 @@ export DEEPINFRA_API_TOKEN="<YOUR_API_KEY>"
 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]);
@@ -48,7 +69,8 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import { DeepInfra, Document, VectorStoreIndex, Settings } from "llamaindex";
+import { DeepInfra } from "@llamaindex/deepinfra";
+import { Document, VectorStoreIndex, Settings } from "llamaindex";
 
 // Use custom LLM
 const model = "meta-llama/Meta-Llama-3-8B-Instruct";

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

@@ -2,10 +2,29 @@
 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>
+
 ## Usage
 
 ```ts
-import { Gemini, Settings, GEMINI_MODEL } from "llamaindex";
+import { Gemini, GEMINI_MODEL } from "@llamaindex/google";
+import { Settings } from "llamaindex";
 
 Settings.llm = new Gemini({
   model: GEMINI_MODEL.GEMINI_PRO,
@@ -19,7 +38,7 @@ To use Gemini via Vertex AI you can use `GeminiVertexSession`.
 GeminiVertexSession accepts the env variables: `GOOGLE_VERTEX_LOCATION` and `GOOGLE_VERTEX_PROJECT`
 
 ```ts
-import { Gemini, GEMINI_MODEL, GeminiVertexSession } from "llamaindex";
+import { Gemini, GEMINI_MODEL, GeminiVertexSession } from "@llamaindex/google";
 
 const gemini = new Gemini({
   model: GEMINI_MODEL.GEMINI_PRO,
@@ -47,6 +66,8 @@ To authenticate for production you'll have to use a [service account](https://cl
 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]);
@@ -67,13 +88,8 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import {
-  Gemini,
-  Document,
-  VectorStoreIndex,
-  Settings,
-  GEMINI_MODEL,
-} from "llamaindex";
+import { Gemini, GEMINI_MODEL } from "@llamaindex/google";
+import { Document, VectorStoreIndex, Settings } from "llamaindex";
 
 Settings.llm = new Gemini({
   model: GEMINI_MODEL.GEMINI_PRO,

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

@@ -5,6 +5,24 @@ title: Groq
 import { DynamicCodeBlock } from 'fumadocs-ui/components/dynamic-codeblock';
 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>
+
 ## Usage
 
 First, create an API key at the [Groq Console](https://console.groq.com/keys). Then save it in your environment:
@@ -16,7 +34,8 @@ export GROQ_API_KEY=<your-api-key>
 The initialize the Groq module.
 
 ```ts
-import { Groq, Settings } from "llamaindex";
+import { Groq } from "@llamaindex/groq";
+import { Settings } from "llamaindex";
 
 Settings.llm = new Groq({
   // If you do not wish to set your API key in the environment, you may
@@ -30,6 +49,8 @@ Settings.llm = new Groq({
 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]);

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

@@ -2,10 +2,29 @@
 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>
+
 ## Usage
 
 ```ts
-import { Ollama, Settings, DeuceChatStrategy } from "llamaindex";
+import { LlamaDeuce, DeuceChatStrategy } from "@llamaindex/replicate";
+import { Document, VectorStoreIndex, Settings } from "llamaindex";
 
 Settings.llm = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
 ```
@@ -13,12 +32,8 @@ Settings.llm = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
 ## Usage with Replication
 
 ```ts
-import {
-  Ollama,
-  ReplicateSession,
-  Settings,
-  DeuceChatStrategy,
-} from "llamaindex";
+import { Settings } from "llamaindex";
+import { LlamaDeuce, DeuceChatStrategy, ReplicateSession } from "@llamaindex/replicate";
 
 const replicateSession = new ReplicateSession({
   replicateKey,
@@ -55,13 +70,8 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import {
-  LlamaDeuce,
-  Document,
-  VectorStoreIndex,
-  Settings,
-  DeuceChatStrategy,
-} from "llamaindex";
+import { LlamaDeuce, DeuceChatStrategy } from "@llamaindex/replicate";
+import { Document, VectorStoreIndex, Settings } from "llamaindex";
 
 // Use the LlamaDeuce LLM
 Settings.llm = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });

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

@@ -2,10 +2,29 @@
 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>
+
 ## Usage
 
 ```ts
-import { MistralAI, Settings } from "llamaindex";
+import { MistralAI } from "@llamaindex/mistral";
+import { Settings } from "llamaindex";
 
 Settings.llm = new MistralAI({
   model: "mistral-tiny",
@@ -18,6 +37,8 @@ Settings.llm = new MistralAI({
 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]);
@@ -38,7 +59,8 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import { MistralAI, Document, VectorStoreIndex, Settings } from "llamaindex";
+import { MistralAI } from "@llamaindex/mistral";
+import { Document, VectorStoreIndex, Settings } from "llamaindex";
 
 // Use the MistralAI LLM
 Settings.llm = new MistralAI({ model: "mistral-tiny" });

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

@@ -2,10 +2,30 @@
 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>
+
+
 ## Usage
 
 ```ts
-import { Ollama, Settings } from "llamaindex";
+import { Ollama } from "@llamaindex/ollama";
+import { Settings } from "llamaindex";
 
 Settings.llm = ollamaLLM;
 Settings.embedModel = ollamaLLM;
@@ -16,6 +36,8 @@ Settings.embedModel = ollamaLLM;
 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]);
@@ -36,7 +58,8 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import { Ollama, Document, VectorStoreIndex, Settings } from "llamaindex";
+import { Ollama } from "@llamaindex/ollama";
+import { Document, VectorStoreIndex, Settings } from "llamaindex";
 
 import fs from "fs/promises";
 

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

@@ -2,8 +2,28 @@
 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, Settings } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+import { Settings } from "llamaindex";
 
 Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0, apiKey: <YOUR_API_KEY> });
 ```
@@ -19,6 +39,8 @@ export OPENAI_API_KEY="<YOUR_API_KEY>"
 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]);
@@ -39,7 +61,8 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import { OpenAI, Document, VectorStoreIndex, Settings } from "llamaindex";
+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 });

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

@@ -2,10 +2,30 @@
 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>
+
+
 ## Usage
 
 ```ts
-import { Portkey, Settings } from "llamaindex";
+import { Portkey } from "@llamaindex/portkey-ai";
+import { Settings } from "llamaindex";
 
 Settings.llm = new Portkey({
   apiKey: "<YOUR_API_KEY>",
@@ -17,6 +37,8 @@ Settings.llm = new Portkey({
 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]);
@@ -37,7 +59,8 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import { Portkey, Document, VectorStoreIndex, Settings } from "llamaindex";
+import { Portkey } from "@llamaindex/portkey-ai";
+import { Document, Settings, VectorStoreIndex } from "llamaindex";
 
 // Use the Portkey LLM
 Settings.llm = new Portkey({

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

@@ -2,10 +2,28 @@
 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
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex
+	```
+</Tabs>
+
 ## Usage
 
 ```ts
-import { TogetherLLM, Settings } from "llamaindex";
+import { Settings, TogetherLLM } from "llamaindex";
 
 Settings.llm = new TogetherLLM({
   apiKey: "<YOUR_API_KEY>",
@@ -17,6 +35,8 @@ Settings.llm = new TogetherLLM({
 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]);
@@ -37,7 +57,8 @@ const results = await queryEngine.query({
 ## Full Example
 
 ```ts
-import { TogetherLLM, Document, VectorStoreIndex, Settings } from "llamaindex";
+import { TogetherLLM } from "@llamaindex/together";
+import { Document, Settings, VectorStoreIndex } from "llamaindex";
 
 Settings.llm = new TogetherLLM({
   apiKey: "<YOUR_API_KEY>",

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

@@ -2,12 +2,31 @@
 title: Large Language Models (LLMs)
 ---
 
-The LLM is responsible for reading text and generating natural language responses to queries. By default, LlamaIndex.TS uses `gpt-3.5-turbo`.
+The LLM is responsible for reading text and generating natural language responses to queries. By default, LlamaIndex.TS uses `gpt-4o`.
 
 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>
+
 ```typescript
-import { OpenAI, Settings } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+import { Settings } from "llamaindex";
 
 Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
 ```

apps/next/src/content/docs/llamaindex/modules/node_parser.mdx

@@ -5,7 +5,8 @@ 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, SentenceSplitter } from "llamaindex";
+import { Document } from "llamaindex";
+import { SentenceSplitter } from "llamaindex";
 
 const nodeParser = new SentenceSplitter();
 
@@ -30,6 +31,7 @@ The `MarkdownNodeParser` is a more advanced `NodeParser` that can handle markdow
 
 ```typescript
 import { MarkdownNodeParser } from "llamaindex";
+import { Document } from "llamaindex";
 
 const nodeParser = new MarkdownNodeParser();
 

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

@@ -8,20 +8,28 @@ The Cohere Reranker is a postprocessor that uses the Cohere API to rerank the re
 
 Firstly, you will need to install the `llamaindex` package.
 
-```bash
-pnpm install llamaindex
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/cohere @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/cohere @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/cohere @llamaindex/openai
+	```
+</Tabs>
 
 Now, you will need to sign up for an API key at [Cohere](https://cohere.ai/). Once you have your API key you can import the necessary modules and create a new instance of the `CohereRerank` class.
 
 ```ts
-import {
-  CohereRerank,
-  Document,
-  OpenAI,
-  VectorStoreIndex,
-  Settings,
-} from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+import { CohereRerank } from "@llamaindex/cohere";
+import { Document, Settings, VectorStoreIndex } from "llamaindex";
 ```
 
 ## Load and index documents

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

@@ -2,6 +2,24 @@
 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>
+
 ## Concept
 
 Node postprocessors are a set of modules that take a set of nodes, and apply some kind of transformation or filtering before returning them.
@@ -15,12 +33,8 @@ LlamaIndex offers several node postprocessors for immediate use, while also prov
 An example of using a node postprocessors is below:
 
 ```ts
-import {
-  Node,
-  NodeWithScore,
-  SimilarityPostprocessor,
-  CohereRerank,
-} from "llamaindex";
+import { CohereRerank } from "@llamaindex/cohere";
+import { Node, NodeWithScore, SimilarityPostprocessor, TextNode } from "llamaindex";
 
 const nodes: NodeWithScore[] = [
   {
@@ -60,7 +74,9 @@ Most commonly, node-postprocessors will be used in a query engine, where they ar
 ### Using Node Postprocessors in a Query Engine
 
 ```ts
-import { Node, NodeWithScore, SimilarityPostprocessor, CohereRerank, Settings } from "llamaindex";
+import { CohereRerank } from "@llamaindex/cohere";
+import { OpenAI } from "@llamaindex/openai";
+import { Node, NodeWithScore, SimilarityPostprocessor, Settings, TextNode } from "llamaindex";
 
 // Use OpenAI LLM
 Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 });
@@ -78,9 +94,9 @@ const nodes: NodeWithScore[] = [
 
 // cohere rerank: rerank nodes given query using trained model
 const reranker = new CohereRerank({
-  apiKey: "<COHERE_API_KEY>,
+  apiKey: "<COHERE_API_KEY>",
   topN: 2,
-})
+});
 
 const document = new Document({ text: "essay", id_: "essay" });
 

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

@@ -8,20 +8,28 @@ 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.
 
-```bash
-pnpm install llamaindex
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai
+	```
+</Tabs>
+
 
 Now, you will need to sign up for an API key at [Jina AI](https://jina.ai/reranker). Once you have your API key you can import the necessary modules and create a new instance of the `JinaAIReranker` class.
 
 ```ts
-import {
-  JinaAIReranker,
-  Document,
-  OpenAI,
-  VectorStoreIndex,
-  Settings,
-} from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+import { Document, Settings, VectorStoreIndex, JinaAIReranker } from "llamaindex";
 ```
 
 ## Load and index documents

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

@@ -17,20 +17,33 @@ To find out more about the latest features and updates, visit the [mixedbread.ai
 
 First, you will need to install the `llamaindex` package.
 
-```bash
-pnpm install llamaindex
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai @llamaindex/mixedbread
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai @llamaindex/mixedbread
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai @llamaindex/mixedbread
+	```
+</Tabs>
+
 
 Next, sign up for an API key at [mixedbread.ai](https://mixedbread.ai/). Once you have your API key, you can import the necessary modules and create a new instance of the `MixedbreadAIReranker` class.
 
 ```ts
 import {
-  MixedbreadAIReranker,
   Document,
-  OpenAI,
   VectorStoreIndex,
   Settings,
 } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+import { MixedbreadAIReranker } from "@llamaindex/mixedbread";
 ```
 
 ## Usage with LlamaIndex

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

@@ -10,19 +10,27 @@ 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:
 
-```bash
-pnpm i llamaindex
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai @llamaindex/chroma
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai @llamaindex/chroma
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai @llamaindex/chroma
+	```
+</Tabs>
 
 Then you can import the necessary modules from `llamaindex`:
 
 ```ts
-import {
-  ChromaVectorStore,
-  Document,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
+import { Document, VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { ChromaVectorStore } from "@llamaindex/chroma";
 
 const collectionName = "dog_colors";
 ```
@@ -95,12 +103,8 @@ Besides using the equal operator (`==`), you can also use a whole set of differe
 ## Full Code
 
 ```ts
-import {
-  ChromaVectorStore,
-  Document,
-  VectorStoreIndex,
-  storageContextFromDefaults,
-} from "llamaindex";
+import { Document, VectorStoreIndex, storageContextFromDefaults } from "llamaindex";
+import { ChromaVectorStore } from "@llamaindex/chroma";
 
 const collectionName = "dog_colors";
 

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

@@ -8,13 +8,24 @@ In this tutorial, we define a custom router query engine that selects one out of
 
 First, we need to install import the necessary modules from `llamaindex`:
 
-```bash
-pnpm i lamaindex
-```
+import { Tab, Tabs } from "fumadocs-ui/components/tabs";
+
+<Tabs groupId="install" items={["npm", "yarn", "pnpm"]} persist>
+	```shell tab="npm"
+	npm install llamaindex @llamaindex/openai @llamaindex/readers
+	```
+
+	```shell tab="yarn"
+	yarn add llamaindex @llamaindex/openai @llamaindex/readers
+	```
+
+	```shell tab="pnpm"
+	pnpm add llamaindex @llamaindex/openai @llamaindex/readers
+	```
+</Tabs>
 
 ```ts
 import {
-  OpenAI,
   RouterQueryEngine,
   SimpleDirectoryReader,
   SentenceSplitter,
@@ -22,6 +33,8 @@ import {
   VectorStoreIndex,
   Settings,
 } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+import { SimpleDirectoryReader } from "llamaindex";
 ```
 
 ## Loading Data
@@ -103,7 +116,6 @@ console.log({
 
 ```ts
 import {
-  OpenAI,
   RouterQueryEngine,
   SimpleDirectoryReader,
   SentenceSplitter,
@@ -111,6 +123,8 @@ import {
   VectorStoreIndex,
   Settings,
 } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+import { SimpleDirectoryReader } from "llamaindex";
 
 Settings.llm = new OpenAI();
 Settings.nodeParser = new SentenceSplitter({

apps/next/src/content/docs/llamaindex/modules/response_synthesizer.mdx

@@ -18,7 +18,7 @@ The ResponseSynthesizer is responsible for sending the query, nodes, and prompt
   chunk.
 
 ```typescript
-import { NodeWithScore, ResponseSynthesizer, TextNode } from "llamaindex";
+import { NodeWithScore, TextNode, ResponseSynthesizer } from "llamaindex";
 
 const responseSynthesizer = new ResponseSynthesizer();
 

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

@@ -116,7 +116,7 @@ const jokeFlow = new Workflow({ verbose: true, validate: true });
 Optionally, you can choose to use global context between steps. For example, maybe multiple steps access the original `query` input from the user. You can store this in global context so that every step has access.
 
 ```typescript
-import { Context } from "@llamaindex/core/workflow";
+import { Context } from "llamaindex";
 
 const query = async (context: Context, ev: MyEvent) => {
   // get the query from the context

e2e/CHANGELOG.md

@@ -1,5 +1,12 @@
 # @llamaindex/core-e2e
 
+## 0.0.8
+
+### Patch Changes
+
+- 34faf48: chore: move vector stores to their own packages
+- 9456616: refactor: @llamaindex/postgres
+
 ## 0.0.7
 
 ### Patch Changes

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

@@ -1,5 +1,47 @@
 # @llamaindex/cloudflare-worker-agent-test
 
+## 0.0.133
+
+### Patch Changes
+
+- llamaindex@0.8.37
+
+## 0.0.132
+
+### Patch Changes
+
+- Updated dependencies [cb608b5]
+  - llamaindex@0.8.36
+
+## 0.0.131
+
+### Patch Changes
+
+- llamaindex@0.8.35
+
+## 0.0.130
+
+### Patch Changes
+
+- Updated dependencies [9f8ad37]
+  - llamaindex@0.8.34
+
+## 0.0.129
+
+### Patch Changes
+
+- llamaindex@0.8.33
+
+## 0.0.128
+
+### Patch Changes
+
+- Updated dependencies [34faf48]
+- Updated dependencies [4df1fe6]
+- Updated dependencies [9456616]
+- Updated dependencies [1931bbc]
+  - llamaindex@0.8.32
+
 ## 0.0.127
 
 ### Patch Changes

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

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/cloudflare-worker-agent-test",
-  "version": "0.0.127",
+  "version": "0.0.133",
   "type": "module",
   "private": true,
   "scripts": {

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

@@ -1,5 +1,26 @@
 # @llamaindex/llama-parse-browser-test
 
+## 0.0.44
+
+### Patch Changes
+
+- Updated dependencies [1c908fd]
+  - @llamaindex/cloud@2.0.24
+
+## 0.0.43
+
+### Patch Changes
+
+- Updated dependencies [cb608b5]
+  - @llamaindex/cloud@2.0.23
+
+## 0.0.42
+
+### Patch Changes
+
+- Updated dependencies [d6c270e]
+  - @llamaindex/cloud@2.0.22
+
 ## 0.0.41
 
 ### Patch Changes

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

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/llama-parse-browser-test",
   "private": true,
-  "version": "0.0.41",
+  "version": "0.0.44",
   "type": "module",
   "scripts": {
     "dev": "vite",
@@ -10,7 +10,7 @@
   },
   "devDependencies": {
     "typescript": "^5.7.2",
-    "vite": "^5.4.11",
+    "vite": "^5.4.12",
     "vite-plugin-wasm": "^3.3.0"
   },
   "dependencies": {

e2e/examples/llama-parse-browser/src/main.ts

@@ -1,4 +1,4 @@
-import { LlamaParseReader } from "@llamaindex/cloud/reader";
+import { LlamaParseReader } from "@llamaindex/cloud";
 import "./style.css";
 
 new LlamaParseReader();

e2e/examples/nextjs-agent/CHANGELOG.md

@@ -1,5 +1,47 @@
 # @llamaindex/next-agent-test
 
+## 0.1.133
+
+### Patch Changes
+
+- llamaindex@0.8.37
+
+## 0.1.132
+
+### Patch Changes
+
+- Updated dependencies [cb608b5]
+  - llamaindex@0.8.36
+
+## 0.1.131
+
+### Patch Changes
+
+- llamaindex@0.8.35
+
+## 0.1.130
+
+### Patch Changes
+
+- Updated dependencies [9f8ad37]
+  - llamaindex@0.8.34
+
+## 0.1.129
+
+### Patch Changes
+
+- llamaindex@0.8.33
+
+## 0.1.128
+
+### Patch Changes
+
+- Updated dependencies [34faf48]
+- Updated dependencies [4df1fe6]
+- Updated dependencies [9456616]
+- Updated dependencies [1931bbc]
+  - llamaindex@0.8.32
+
 ## 0.1.127
 
 ### Patch Changes

e2e/examples/nextjs-agent/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/next-agent-test",
-  "version": "0.1.127",
+  "version": "0.1.133",
   "private": true,
   "scripts": {
     "dev": "next dev",

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

@@ -1,5 +1,47 @@
 # test-edge-runtime
 
+## 0.1.132
+
+### Patch Changes
+
+- llamaindex@0.8.37
+
+## 0.1.131
+
+### Patch Changes
+
+- Updated dependencies [cb608b5]
+  - llamaindex@0.8.36
+
+## 0.1.130
+
+### Patch Changes
+
+- llamaindex@0.8.35
+
+## 0.1.129
+
+### Patch Changes
+
+- Updated dependencies [9f8ad37]
+  - llamaindex@0.8.34
+
+## 0.1.128
+
+### Patch Changes
+
+- llamaindex@0.8.33
+
+## 0.1.127
+
+### Patch Changes
+
+- Updated dependencies [34faf48]
+- Updated dependencies [4df1fe6]
+- Updated dependencies [9456616]
+- Updated dependencies [1931bbc]
+  - llamaindex@0.8.32
+
 ## 0.1.126
 
 ### Patch Changes

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

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/nextjs-edge-runtime-test",
-  "version": "0.1.126",
+  "version": "0.1.132",
   "private": true,
   "scripts": {
     "dev": "next dev",

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

@@ -1,5 +1,47 @@
 # @llamaindex/next-node-runtime
 
+## 0.0.114
+
+### Patch Changes
+
+- llamaindex@0.8.37
+
+## 0.0.113
+
+### Patch Changes
+
+- Updated dependencies [cb608b5]
+  - llamaindex@0.8.36
+
+## 0.0.112
+
+### Patch Changes
+
+- llamaindex@0.8.35
+
+## 0.0.111
+
+### Patch Changes
+
+- Updated dependencies [9f8ad37]
+  - llamaindex@0.8.34
+
+## 0.0.110
+
+### Patch Changes
+
+- llamaindex@0.8.33
+
+## 0.0.109
+
+### Patch Changes
+
+- Updated dependencies [34faf48]
+- Updated dependencies [4df1fe6]
+- Updated dependencies [9456616]
+- Updated dependencies [1931bbc]
+  - llamaindex@0.8.32
+
 ## 0.0.108
 
 ### Patch Changes

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

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/next-node-runtime-test",
-  "version": "0.0.108",
+  "version": "0.0.114",
   "private": true,
   "scripts": {
     "dev": "next dev",

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

@@ -1,5 +1,47 @@
 # @llamaindex/waku-query-engine-test
 
+## 0.0.133
+
+### Patch Changes
+
+- llamaindex@0.8.37
+
+## 0.0.132
+
+### Patch Changes
+
+- Updated dependencies [cb608b5]
+  - llamaindex@0.8.36
+
+## 0.0.131
+
+### Patch Changes
+
+- llamaindex@0.8.35
+
+## 0.0.130
+
+### Patch Changes
+
+- Updated dependencies [9f8ad37]
+  - llamaindex@0.8.34
+
+## 0.0.129
+
+### Patch Changes
+
+- llamaindex@0.8.33
+
+## 0.0.128
+
+### Patch Changes
+
+- Updated dependencies [34faf48]
+- Updated dependencies [4df1fe6]
+- Updated dependencies [9456616]
+- Updated dependencies [1931bbc]
+  - llamaindex@0.8.32
+
 ## 0.0.127
 
 ### Patch Changes

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

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/waku-query-engine-test",
-  "version": "0.0.127",
+  "version": "0.0.133",
   "type": "module",
   "private": true,
   "scripts": {

e2e/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/e2e",
   "private": true,
-  "version": "0.0.7",
+  "version": "0.0.8",
   "type": "module",
   "scripts": {
     "e2e": "node --import tsx --import ./mock-register.js --test ./node/**/*.e2e.ts",

examples/CHANGELOG.md

@@ -1,5 +1,116 @@
 # examples
 
+## 0.1.3
+
+### Patch Changes
+
+- Updated dependencies [1c908fd]
+  - @llamaindex/pinecone@0.0.4
+  - @llamaindex/postgres@0.0.32
+  - @llamaindex/weaviate@0.0.4
+  - @llamaindex/mongodb@0.0.4
+  - @llamaindex/upstash@0.0.4
+  - @llamaindex/chroma@0.0.4
+  - @llamaindex/milvus@0.0.4
+  - @llamaindex/qdrant@0.0.4
+  - @llamaindex/astra@0.0.4
+  - @llamaindex/azure@0.0.4
+  - @llamaindex/huggingface@0.0.35
+  - @llamaindex/mixedbread@0.0.4
+  - @llamaindex/portkey-ai@0.0.32
+  - @llamaindex/anthropic@0.0.33
+  - @llamaindex/deepinfra@0.0.35
+  - @llamaindex/replicate@0.0.32
+  - @llamaindex/mistral@0.0.4
+  - @llamaindex/cohere@0.0.4
+  - @llamaindex/google@0.0.6
+  - @llamaindex/ollama@0.0.39
+  - @llamaindex/openai@0.1.51
+  - @llamaindex/vercel@0.0.10
+  - @llamaindex/clip@0.0.35
+  - @llamaindex/groq@0.0.50
+  - @llamaindex/vllm@0.0.21
+  - @llamaindex/node-parser@0.0.24
+  - @llamaindex/workflow@0.0.10
+  - @llamaindex/readers@1.0.25
+  - @llamaindex/cloud@2.0.24
+  - llamaindex@0.8.37
+
+## 0.1.2
+
+### Patch Changes
+
+- Updated dependencies [cb608b5]
+  - @llamaindex/pinecone@0.0.3
+  - @llamaindex/postgres@0.0.31
+  - @llamaindex/weaviate@0.0.3
+  - @llamaindex/mongodb@0.0.3
+  - @llamaindex/upstash@0.0.3
+  - @llamaindex/chroma@0.0.3
+  - @llamaindex/milvus@0.0.3
+  - @llamaindex/qdrant@0.0.3
+  - @llamaindex/astra@0.0.3
+  - @llamaindex/azure@0.0.3
+  - @llamaindex/huggingface@0.0.34
+  - @llamaindex/mixedbread@0.0.3
+  - @llamaindex/portkey-ai@0.0.31
+  - @llamaindex/anthropic@0.0.32
+  - @llamaindex/deepinfra@0.0.34
+  - @llamaindex/replicate@0.0.31
+  - @llamaindex/mistral@0.0.3
+  - @llamaindex/cohere@0.0.3
+  - @llamaindex/google@0.0.5
+  - @llamaindex/ollama@0.0.38
+  - @llamaindex/openai@0.1.50
+  - @llamaindex/vercel@0.0.9
+  - @llamaindex/clip@0.0.34
+  - @llamaindex/groq@0.0.49
+  - @llamaindex/vllm@0.0.20
+  - @llamaindex/node-parser@0.0.23
+  - @llamaindex/workflow@0.0.9
+  - @llamaindex/readers@1.0.24
+  - @llamaindex/cloud@2.0.23
+  - llamaindex@0.8.36
+
+## 0.1.1
+
+### Patch Changes
+
+- Updated dependencies [b6ea2bf]
+- Updated dependencies [15563a0]
+  - @llamaindex/google@0.0.4
+  - @llamaindex/openai@0.1.49
+  - llamaindex@0.8.35
+  - @llamaindex/clip@0.0.33
+  - @llamaindex/deepinfra@0.0.33
+  - @llamaindex/groq@0.0.48
+  - @llamaindex/huggingface@0.0.33
+  - @llamaindex/vllm@0.0.19
+
+## 0.1.0
+
+### Minor Changes
+
+- 21769c8: Update deprecated response property of query engine to message.content propery
+
+### Patch Changes
+
+- llamaindex@0.8.33
+
+## 0.0.23
+
+### Patch Changes
+
+- 8744796: Update the chromadb npm client to support the latest chromadb image (0.6.3)
+- Updated dependencies [34faf48]
+- Updated dependencies [4df1fe6]
+- Updated dependencies [9456616]
+- Updated dependencies [1931bbc]
+  - llamaindex@0.8.32
+  - @llamaindex/core@0.4.21
+  - @llamaindex/vercel@0.0.8
+  - @llamaindex/readers@1.0.23
+
 ## 0.0.22
 
 ### Patch Changes

examples/Settings.ts

@@ -1,6 +1,7 @@
 import fs from "node:fs/promises";
 
-import { Document, OpenAI, Settings, VectorStoreIndex } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+import { Document, Settings, VectorStoreIndex } from "llamaindex";
 
 Settings.llm = new OpenAI({ model: "gpt-4" });
 

examples/agent/large_toolcall.ts

@@ -1,4 +1,5 @@
-import { FunctionTool, OpenAI, OpenAIAgent } from "llamaindex";
+import { OpenAI, OpenAIAgent } from "@llamaindex/openai";
+import { FunctionTool } from "llamaindex";
 
 const csvData =
   "TITLE,RELEASE_YEAR,SCORE,NUMBER_OF_VOTES,DURATION,MAIN_GENRE,MAIN_PRODUCTION\nDavid Attenborough: A Life on Our Planet,2020,9,31180,83,documentary,GB\nInception,2010,8.8,2268288,148,scifi,GB\nForrest Gump,1994,8.8,1994599,142,drama,US\nAnbe Sivam,2003,8.7,20595,160,comedy,IN\nBo Burnham: Inside,2021,8.7,44074,87,comedy,US\nSaving Private Ryan,1998,8.6,1346020,169,drama,US\nDjango Unchained,2012,8.4,1472668,165,western,US\nDangal,2016,8.4,180247,161,action,IN\nBo Burnham: Make Happy,2016,8.4,14356,60,comedy,US\nLouis C.K.: Hilarious,2010,8.4,11973,84,comedy,US\nDave Chappelle: Sticks & Stones,2019,8.4,25687,65,comedy,US\n3 Idiots,2009,8.4,385782,170,comedy,IN\nBlack Friday,2004,8.4,20611,143,crime,IN\nSuper Deluxe,2019,8.4,13680,176,thriller,IN\nWinter on Fire: Ukraine's Fight for Freedom,2015,8.3,17710,98,documentary,UA\nOnce Upon a Time in America,1984,8.3,342335,229,drama,US\nTaxi Driver,1976,8.3,795222,113,crime,US\nLike Stars on Earth,2007,8.3,188234,165,drama,IN\nBo Burnham: What.,2013,8.3,11488,60,comedy,US\nFull Metal Jacket,1987,8.3,723306,116,drama,GB\nWarrior,2011,8.2,463276,140,drama,US\nDrishyam,2015,8.2,79075,163,thriller,IN\nQueen,2014,8.2,64805,146,drama,IN\nPaan Singh Tomar,2012,8.2,35888,135,drama,IN";

examples/agent/large_toolcall_with_gpt4o.ts

@@ -1,4 +1,5 @@
-import { FunctionTool, OpenAI, ToolCallOptions } from "llamaindex";
+import { OpenAI } from "@llamaindex/openai";
+import { FunctionTool, ToolCallOptions } from "llamaindex";
 
 (async () => {
   // The tool call will generate a partial JSON for `gpt-4-turbo`

examples/agent/multi_document_agent.ts

@@ -1,10 +1,9 @@
 import fs from "node:fs/promises";
 
+import { OpenAI, OpenAIAgent } from "@llamaindex/openai";
 import {
   Document,
   ObjectIndex,
-  OpenAI,
-  OpenAIAgent,
   QueryEngineTool,
   SentenceSplitter,
   Settings,

examples/agent/openai-task.ts

@@ -1,4 +1,5 @@
-import { ChatResponseChunk, OpenAIAgent } from "llamaindex";
+import { OpenAIAgent } from "@llamaindex/openai";
+import { ChatResponseChunk } from "llamaindex";
 import {
   getCurrentIDTool,
   getUserInfoTool,

examples/agent/openai.ts

@@ -1,4 +1,5 @@
-import { FunctionTool, OpenAIAgent } from "llamaindex";
+import { OpenAIAgent } from "@llamaindex/openai";
+import { FunctionTool } from "llamaindex";
 
 const sumNumbers = FunctionTool.from(
   ({ a, b }: { a: number; b: number }) => `${a + b}`,

examples/agent/query_openai_agent.ts

@@ -1,5 +1,5 @@
+import { OpenAIAgent } from "@llamaindex/openai";
 import {
-  OpenAIAgent,
   QueryEngineTool,
   SimpleDirectoryReader,
   VectorStoreIndex,
@@ -33,12 +33,12 @@ async function main() {
   });
 
   // Chat with the agent
-  const response = await agent.chat({
+  const { message } = await agent.chat({
     message: "What was his first salary?",
   });
 
   // Print the response
-  console.log(response.response);
+  console.log(message.content);
 }
 
 void main().then(() => {

examples/agent/react_agent.ts

@@ -1,4 +1,5 @@
-import { Anthropic, FunctionTool, ReActAgent } from "llamaindex";
+import { Anthropic } from "@llamaindex/anthropic";
+import { FunctionTool, ReActAgent } from "llamaindex";
 
 // Define a function to sum two numbers
 function sumNumbers({ a, b }: { a: number; b: number }) {

examples/agent/retriever_openai_agent.ts

@@ -1,8 +1,8 @@
+import { OpenAIAgent } from "@llamaindex/openai";
 import {
   FunctionTool,
   MetadataMode,
   NodeWithScore,
-  OpenAIAgent,
   SimpleDirectoryReader,
   VectorStoreIndex,
 } from "llamaindex";
@@ -52,12 +52,12 @@ async function main() {
   });
 
   // Chat with the agent
-  const response = await agent.chat({
+  const { message } = await agent.chat({
     message: "What was his first salary?",
   });
 
   // Print the response
-  console.log(response.response);
+  console.log(message.content);
 }
 
 void main().then(() => {

examples/agent/step_wise_query_tool.ts

@@ -1,5 +1,5 @@
+import { OpenAIAgent } from "@llamaindex/openai";
 import {
-  OpenAIAgent,
   QueryEngineTool,
   SimpleDirectoryReader,
   VectorStoreIndex,

examples/agent/step_wise_react.ts

@@ -1,4 +1,5 @@
-import { Anthropic, FunctionTool, ReActAgent } from "llamaindex";
+import { Anthropic } from "@llamaindex/anthropic";
+import { FunctionTool, ReActAgent } from "llamaindex";
 
 // Define a function to sum two numbers
 function sumNumbers({ a, b }: { a: number; b: number }) {

examples/agent/stream_openai_agent.ts

@@ -1,4 +1,5 @@
-import { FunctionTool, OpenAIAgent } from "llamaindex";
+import { OpenAIAgent } from "@llamaindex/openai";
+import { FunctionTool } from "llamaindex";
 
 // Define a function to sum two numbers
 function sumNumbers({ a, b }: { a: number; b: number }) {

examples/agent/wiki.ts

@@ -1,4 +1,5 @@
-import { OpenAI, OpenAIAgent, WikipediaTool } from "llamaindex";
+import { OpenAI, OpenAIAgent } from "@llamaindex/openai";
+import { WikipediaTool } from "../wiki";
 
 async function main() {
   const llm = new OpenAI({ model: "gpt-4-turbo" });

examples/anthropic/agent.ts

@@ -1,5 +1,6 @@
-import { Anthropic, FunctionTool, Settings, WikipediaTool } from "llamaindex";
-import { AnthropicAgent } from "llamaindex/agent/anthropic";
+import { Anthropic, AnthropicAgent } from "@llamaindex/anthropic";
+import { FunctionTool, Settings } from "llamaindex";
+import { WikipediaTool } from "../wiki";
 
 Settings.callbackManager.on("llm-tool-call", (event) => {
   console.log("llm-tool-call", event.detail.toolCall);
@@ -37,12 +38,12 @@ const agent = new AnthropicAgent({
 });
 
 async function main() {
-  const { response } = await agent.chat({
+  const { message } = await agent.chat({
     message:
       "What is the weather in New York? What's the history of New York from Wikipedia in 3 sentences?",
   });
 
-  console.log(response);
+  console.log(message.content);
 }
 
 void main();

examples/anthropic/chat.ts

@@ -1,4 +1,4 @@
-import { Anthropic } from "llamaindex";
+import { Anthropic } from "@llamaindex/anthropic";
 
 (async () => {
   const anthropic = new Anthropic({

examples/anthropic/chat_interactive.ts

@@ -1,4 +1,5 @@
-import { Anthropic, ChatMemoryBuffer, SimpleChatEngine } from "llamaindex";
+import { Anthropic } from "@llamaindex/anthropic";
+import { ChatMemoryBuffer, SimpleChatEngine } from "llamaindex";
 import { stdin as input, stdout as output } from "node:process";
 import readline from "node:readline/promises";
 

examples/anthropic/haiku.ts

@@ -1,4 +1,4 @@
-import { Anthropic } from "llamaindex";
+import { Anthropic } from "@llamaindex/anthropic";
 
 (async () => {
   const anthropic = new Anthropic({

examples/anthropic/prompt-caching.ts

@@ -1,4 +1,4 @@
-import { Anthropic } from "llamaindex";
+import { Anthropic } from "@llamaindex/anthropic";
 
 async function main() {
   const anthropic = new Anthropic({

examples/anthropic/stream.ts

@@ -1,4 +1,4 @@
-import { Anthropic } from "llamaindex";
+import { Anthropic } from "@llamaindex/anthropic";
 
 (async () => {
   const anthropic = new Anthropic({

examples/astradb/example.ts

@@ -1,5 +1,5 @@
+import { AstraDBVectorStore } from "@llamaindex/astra";
 import {
-  AstraDBVectorStore,
   Document,
   MetadataFilters,
   storageContextFromDefaults,

examples/astradb/load.ts

@@ -1,9 +1,6 @@
-import {
-  AstraDBVectorStore,
-  CSVReader,
-  storageContextFromDefaults,
-  VectorStoreIndex,
-} from "llamaindex";
+import { AstraDBVectorStore } from "@llamaindex/astra";
+import { CSVReader } from "@llamaindex/readers/csv";
+import { storageContextFromDefaults, VectorStoreIndex } from "llamaindex";
 
 const collectionName = "movie_reviews";