Release 0.8.27 (#1545)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: himself65 <himself65@users.noreply.github.com>

.eslintrc.cjs

@@ -1,79 +0,0 @@
-const { join } = require("node:path");
-
-module.exports = {
-  root: true,
-  extends: [
-    "turbo",
-    "prettier",
-    "plugin:@typescript-eslint/recommended-type-checked-only",
-  ],
-  parserOptions: {
-    project: join(__dirname, "tsconfig.eslint.json"),
-    __tsconfigRootDir: __dirname,
-  },
-  settings: {
-    react: {
-      version: "999.999.999",
-    },
-  },
-  rules: {
-    "max-params": ["error", 4],
-    "prefer-const": "error",
-    "@typescript-eslint/no-floating-promises": [
-      "error",
-      {
-        ignoreIIFE: true,
-      },
-    ],
-    "no-debugger": "error",
-    "@typescript-eslint/await-thenable": "off",
-    "@typescript-eslint/ban-ts-comment": "off",
-    "@typescript-eslint/ban-types": "off",
-    "no-array-constructor": "off",
-    "@typescript-eslint/no-array-constructor": "off",
-    "@typescript-eslint/no-base-to-string": "off",
-    "@typescript-eslint/no-duplicate-enum-values": "off",
-    "@typescript-eslint/no-duplicate-type-constituents": "off",
-    "@typescript-eslint/no-explicit-any": "off",
-    "@typescript-eslint/no-extra-non-null-assertion": "off",
-    "@typescript-eslint/no-for-in-array": "off",
-    "no-implied-eval": "off",
-    "@typescript-eslint/no-implied-eval": "off",
-    "no-loss-of-precision": "off",
-    "@typescript-eslint/no-loss-of-precision": "off",
-    "@typescript-eslint/no-misused-new": "off",
-    "@typescript-eslint/no-misused-promises": "off",
-    "@typescript-eslint/no-namespace": "off",
-    "@typescript-eslint/no-non-null-asserted-optional-chain": "off",
-    "@typescript-eslint/no-redundant-type-constituents": "off",
-    "@typescript-eslint/no-this-alias": "off",
-    "@typescript-eslint/no-unnecessary-type-assertion": "off",
-    "@typescript-eslint/no-unnecessary-type-constraint": "off",
-    "@typescript-eslint/no-unsafe-argument": "off",
-    "@typescript-eslint/no-unsafe-assignment": "off",
-    "@typescript-eslint/no-unsafe-call": "off",
-    "@typescript-eslint/no-unsafe-declaration-merging": "off",
-    "@typescript-eslint/no-unsafe-enum-comparison": "off",
-    "@typescript-eslint/no-unsafe-member-access": "off",
-    "@typescript-eslint/no-unsafe-return": "off",
-    "no-unused-vars": "off",
-    "@typescript-eslint/no-unused-vars": "off",
-    "@typescript-eslint/no-var-requires": "off",
-    "@typescript-eslint/prefer-as-const": "off",
-    "require-await": "off",
-    "@typescript-eslint/require-await": "off",
-    "@typescript-eslint/restrict-plus-operands": "off",
-    "@typescript-eslint/restrict-template-expressions": "off",
-    "@typescript-eslint/triple-slash-reference": "off",
-    "@typescript-eslint/unbound-method": "off",
-  },
-  overrides: [
-    {
-      files: ["examples/**/*.ts"],
-      rules: {
-        "turbo/no-undeclared-env-vars": "off",
-      },
-    },
-  ],
-  ignorePatterns: ["dist/", "lib/", "deps/"],
-};

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,46 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ""
+labels: bug
+assignees: ""
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Code to reproduce the behavior:
+
+```ts
+// paste the code here
+```
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+
+- OS: [e.g. macOS, Linux]
+- JS Runtime / Framework / Bundler (select all applicable)
+- [ ] Node.js
+- [ ] Deno
+- [ ] Bun
+- [ ] Next.js
+- [ ] ESBuild
+- [ ] Rollup
+- [ ] Webpack
+- [ ] Turbopack
+- [ ] Vite
+- [ ] Waku
+- [ ] Edge Runtime
+- [ ] AWS Lambda
+- [ ] Cloudflare Worker
+- [ ] Others (please elaborate on this)
+- Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.

.github/workflows/lint_on_push_or_pull.yml

@@ -13,7 +13,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v3
+      - uses: pnpm/action-setup@v4
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:

.github/workflows/preview.yml

@@ -10,7 +10,7 @@ jobs:
       - name: Checkout Repo
         uses: actions/checkout@v4
 
-      - uses: pnpm/action-setup@v3
+      - uses: pnpm/action-setup@v4
 
       - name: Setup Node.js
         uses: actions/setup-node@v4
@@ -25,4 +25,4 @@ jobs:
         run: pnpm run build
 
       - name: Pre Release
-        run: pnpx pkg-pr-new publish ./packages/*
+        run: pnpx pkg-pr-new publish ./packages/* ./packages/providers/*

.github/workflows/publish_github.yml

@@ -12,7 +12,7 @@ jobs:
       - name: Checkout Repo
         uses: actions/checkout@v4
 
-      - uses: pnpm/action-setup@v3
+      - uses: pnpm/action-setup@v4
 
       - name: Setup Node.js
         uses: actions/setup-node@v4

.github/workflows/release.yml

@@ -15,7 +15,7 @@ jobs:
       - name: Checkout Repo
         uses: actions/checkout@v4
 
-      - uses: pnpm/action-setup@v3
+      - uses: pnpm/action-setup@v4
 
       - name: Setup Node.js
         uses: actions/setup-node@v4
@@ -67,3 +67,4 @@ jobs:
         with:
           commit_message: "chore: update lock file"
           branch: changeset-release/main
+          file_pattern: "pnpm-lock.yaml"

.github/workflows/test.yml

@@ -12,19 +12,33 @@ concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
 
+env:
+  POSTGRES_HOST_AUTH_METHOD: trust
+  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
+  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
+  TURBO_REMOTE_ONLY: true
+
 jobs:
   e2e:
     strategy:
       fail-fast: false
       matrix:
-        node-version: [18.x, 20.x, 22.x]
+        node-version: [18.x, 20.x, 22.x, 23.x]
     name: E2E on Node.js ${{ matrix.node-version }}
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-
-      - uses: pnpm/action-setup@v3
-
+      - uses: ankane/setup-postgres@v1
+        with:
+          database: llamaindex_node_test
+          dev-files: true
+      - run: |
+          cd /tmp
+          git clone --branch v0.7.0 https://github.com/pgvector/pgvector.git
+          cd pgvector
+          make
+          sudo make install
+      - uses: pnpm/action-setup@v4
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
@@ -39,13 +53,12 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        node-version: [18.x, 20.x, 22.x]
+        node-version: [18.x, 20.x, 22.x, 23.x]
     name: Test on Node.js ${{ matrix.node-version }}
     runs-on: ubuntu-latest
-
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v3
+      - uses: pnpm/action-setup@v4
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
@@ -60,7 +73,7 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v3
+      - uses: pnpm/action-setup@v4
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
@@ -71,18 +84,14 @@ jobs:
       - name: Build
         run: pnpm run build
       - name: Use Build For Examples
-        run: pnpm link ../packages/llamaindex/
+        run: |
+          pnpm link ../packages/llamaindex/
+          cd readers && pnpm link ../../packages/llamaindex/
         working-directory: ./examples
       - name: Run Type Check
         run: pnpm run type-check
       - name: Run Circular Dependency Check
-        run: pnpm dlx turbo run circular-check
-      - uses: actions/upload-artifact@v3
-        if: failure()
-        with:
-          name: typecheck-build-dist
-          path: ./packages/llamaindex/dist
-          if-no-files-found: error
+        run: pnpm run circular-check
   e2e-llamaindex-examples:
     strategy:
       fail-fast: false
@@ -92,12 +101,13 @@ jobs:
           - nextjs-agent
           - nextjs-edge-runtime
           - nextjs-node-runtime
-    #          - waku-query-engine
+          - waku-query-engine
+          - llama-parse-browser
     runs-on: ubuntu-latest
     name: Build LlamaIndex Example (${{ matrix.packages }})
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v3
+      - uses: pnpm/action-setup@v4
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
@@ -109,14 +119,14 @@ jobs:
         run: pnpm run build
       - name: Build ${{ matrix.packages }}
         run: pnpm run build
-        working-directory: packages/llamaindex/e2e/examples/${{ matrix.packages }}
+        working-directory: e2e/examples/${{ matrix.packages }}
 
   typecheck-examples:
     runs-on: ubuntu-latest
 
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v3
+      - uses: pnpm/action-setup@v4
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
@@ -128,21 +138,36 @@ jobs:
         run: pnpm run build
       - name: Copy examples
         run: rsync -rv --exclude=node_modules ./examples ${{ runner.temp }}
-      - name: Pack @llamaindex/cloud
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/cloud
-      - name: Pack @llamaindex/core
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/core
-      - name: Pack @llamaindex/env
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/env
-      - name: Pack llamaindex
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/llamaindex
+      - name: Pack packages
+        run: |
+          for dir in packages/*; do
+            if [ -d "$dir" ] && [ -f "$dir/package.json" ] && [[ ! "$dir" =~ autotool ]]; then
+              echo "Packing $dir"
+              pnpm pack --pack-destination ${{ runner.temp }} -C $dir
+            else
+              echo "Skipping $dir, no package.json found"
+            fi
+          done
+      - name: Pack provider packages
+        run: |
+          for dir in packages/providers/*; do
+            if [ -d "$dir" ] && [ -f "$dir/package.json" ]; then
+              echo "Packing $dir"
+              pnpm pack --pack-destination ${{ runner.temp }} -C $dir
+            else
+              echo "Skipping $dir, no package.json found"
+            fi
+          done
       - name: Install
         run: npm add ${{ runner.temp }}/*.tgz
         working-directory: ${{ runner.temp }}/examples
       - name: Run Type Check
         run: npx tsc --project ./tsconfig.json
         working-directory: ${{ runner.temp }}/examples
+      - uses: actions/upload-artifact@v4
+        if: failure()
+        with:
+          name: build-dist
+          path: |
+            ${{ runner.temp }}/*.tgz
+          if-no-files-found: error

.prettierignore

@@ -4,3 +4,6 @@ pnpm-lock.yaml
 lib/
 dist/
 .docusaurus/
+.source/
+# prttier doesn't support mdx3 we are using
+*.mdx

.vscode/settings.json

@@ -13,5 +13,6 @@
   },
   "[json]": {
     "editor.defaultFormatter": "esbenp.prettier-vscode"
-  }
+  },
+  "prettier.prettierPath": "./node_modules/prettier"
 }

CONTRIBUTING.md

@@ -2,86 +2,58 @@
 
 ## Structure
 
-This is a monorepo built with Turborepo
+LlamaIndex.TS uses pnpm monorepo.
 
-Right now there are two packages of importance:
+We recommend you to understand the basics of Node.js, TypeScript, pnpm, and of course, LLM before contributing.
 
-packages/llamaindex which is the main NPM library llamaindex
+There are some important folders in the repository:
 
-examples is where the demo code lives
-
-### Turborepo docs
-
-You can checkout how Turborepo works using the default [README-turborepo.md](/README-turborepo.md)
+- `packages/*`: Contains the source code of the packages. Each package is a separate npm package.
+  - `llamaindex`: The starter package for LlamaIndex.TS, which contains the all sub-packages.
+  - `core`: The core package of LlamaIndex.TS, which contains the abstract classes and interfaces. It is designed for
+    all JS runtime environments.
+  - `env`: The environment package of LlamaIndex.TS, which contains the environment-specific classes and interfaces. It
+    includes compatibility layers for Node.js, Deno, Vercel Edge Runtime, Cloudflare Workers...
+- `apps/*`: The applications based on LlamaIndex.TS.
+  - `next`: Our documentation website based on Next.js.
+- `examples`: The code examples of LlamaIndex.TS using Node.js.
 
 ## Getting Started
 
-Install NodeJS. Preferably v18 using nvm or n.
-
-Inside the LlamaIndexTS directory:
+Make sure you have Node.js LIS (Long-term Support) installed. You can check your Node.js version by running:
 
+```shell
+node -v
+# v20.x.x
 ```
-npm i -g pnpm ts-node
+
+### Use pnpm
+
+```shell
+corepack enable
+```
+
+### Install dependencies
+
+```shell
 pnpm install
 ```
 
-Note: we use pnpm in this repo, which has a lot of the same functionality and CLI options as npm but it does do some things better in a monorepo, like centralizing dependencies and caching.
+### Build the packages
 
-PNPM's has documentation on its [workspace feature](https://pnpm.io/workspaces) and Turborepo had some [useful documentation also](https://turbo.build/repo/docs/core-concepts/monorepos/running-tasks).
-
-### Running Typescript
-
-When we publish to NPM we will have a tsc compiled version of the library in JS. For now, the easiest thing to do is use ts-node.
-
-### Test cases
-
-To run them, run
-
-```
-pnpm run test
-```
-
-To write new test cases write them in [packages/llamaindex/tests](/packages/llamaindex/tests)
-
-We use Jest https://jestjs.io/ to write our test cases. Jest comes with a bunch of built in assertions using the expect function: https://jestjs.io/docs/expect
-
-### Demo applications
-
-There is an existing ["example"](/examples/README.md) demos folder with mainly NodeJS scripts. Feel free to add additional demos to that folder. If you would like to try out your changes in the core package with a new demo, you need to run the build command in the README.
-
-You can create new demo applications in the apps folder. Just run pnpm init in the folder after you create it to create its own package.json
-
-### Installing packages
-
-To install packages for a specific package or demo application, run
-
-```
-pnpm add [NPM Package] --filter [package or application i.e. llamaindex or docs]
-```
-
-To install packages for every package or application run
-
-```
-pnpm add -w [NPM Package]
+```shell
+# Build all packages
+turbo build --filter "./packages/*"
 ```
 
 ### Docs
 
-To contribute to the docs, go to the docs website folder and run the Docusaurus instance.
-
-```bash
-cd apps/docs
-pnpm install
-pnpm start
-```
-
-That should start a webserver which will serve the docs on https://localhost:3000
-
-Any changes you make should be reflected in the browser. If you need to regenerate the API docs and find that your TSDoc isn't getting the updates, feel free to remove apps/docs/api. It will automatically regenerate itself when you run pnpm start again.
+See the [docs](./apps/next/README.md) for more information.
 
 ## Changeset
 
-We use [changesets](https://github.com/changesets/changesets) for managing versions and changelogs. To create a new changeset, run:
+We use [changesets](https://github.com/changesets/changesets) for managing versions and changelogs. To create a new
+changeset, run in the root folder:
 
 ```
 pnpm changeset
@@ -95,6 +67,6 @@ The [Release Github Action](.github/workflows/release.yml) is automatically gene
 PR called "Release {version}".
 
 This PR will update the `package.json` and `CHANGELOG.md` files of each package according to
-the current changesets in the [.changeset](.changeset/) folder.
+the current changesets in the [.changeset](.changeset) folder.
 
 If this PR is merged it will automatically add version tags to the repository and publish the updated packages to NPM.

README.md

@@ -1,13 +1,17 @@
-# LlamaIndex.TS
+<p align="center">
+  <img height="100" width="100" alt="LlamaIndex logo" src="https://ts.llamaindex.ai/square.svg" />
+</p>
+<h1 align="center">LlamaIndex.TS</h1>
+<h3 align="center">
+  Data framework for your LLM application.
+</h3>
 
 [![NPM Version](https://img.shields.io/npm/v/llamaindex)](https://www.npmjs.com/package/llamaindex)
 [![NPM License](https://img.shields.io/npm/l/llamaindex)](https://www.npmjs.com/package/llamaindex)
 [![NPM Downloads](https://img.shields.io/npm/dm/llamaindex)](https://www.npmjs.com/package/llamaindex)
 [![Discord](https://img.shields.io/discord/1059199217496772688)](https://discord.com/invite/eN6D2HQ4aX)
 
-LlamaIndex is a data framework for your LLM application.
-
-Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.
+Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in JS runtime environments with TypeScript support.
 
 Documentation: https://ts.llamaindex.ai/
 
@@ -19,164 +23,51 @@ Try examples online:
 
 LlamaIndex.TS aims to be a lightweight, easy to use set of libraries to help you integrate large language models into your applications with your own data.
 
-## Multiple JS Environment Support
+## Compatibility
+
+### Multiple JS Environment Support
 
 LlamaIndex.TS supports multiple JS environments, including:
 
-- Node.js (18, 20, 22) ✅
+- Node.js >= 20 ✅
 - Deno ✅
 - Bun ✅
-- React Server Components (Next.js) ✅
+- Nitro ✅
+- Vercel Edge Runtime ✅ (with some limitations)
+- Cloudflare Workers ✅ (with some limitations)
 
 For now, browser support is limited due to the lack of support for [AsyncLocalStorage-like APIs](https://github.com/tc39/proposal-async-context)
 
+### Supported LLMs:
+
+- OpenAI LLms
+- Anthropic LLms
+- Groq LLMs
+- Llama2, Llama3, Llama3.1 LLMs
+- MistralAI LLMs
+- Fireworks LLMs
+- DeepSeek LLMs
+- ReplicateAI LLMs
+- TogetherAI LLMs
+- HuggingFace LLms
+- DeepInfra LLMs
+- Gemini LLMs
+
 ## Getting started
 
 ```shell
 npm install llamaindex
 pnpm install llamaindex
 yarn add llamaindex
-jsr install @llamaindex/core
 ```
 
-### Node.js
+### Setup in Node.js, Deno, Bun, TypeScript...?
 
-```ts
-import fs from "fs/promises";
-import { Document, VectorStoreIndex } from "llamaindex";
+See our official document: <https://ts.llamaindex.ai/docs/llamaindex/setup/getting-started>
 
-async function main() {
-  // Load essay from abramov.txt in Node
-  const essay = await fs.readFile(
-    "node_modules/llamaindex/examples/abramov.txt",
-    "utf-8",
-  );
+### Tips when using in non-Node.js environments
 
-  // Create Document object with essay
-  const document = new Document({ text: essay });
-
-  // Split text and create embeddings. Store them in a VectorStoreIndex
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  // Query the index
-  const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query({
-    query: "What did the author do in college?",
-  });
-
-  // Output response
-  console.log(response.toString());
-}
-
-main();
-```
-
-```bash
-# `pnpm install tsx` before running the script
-node --import tsx ./main.ts
-```
-
-### React Server Component (Next.js, Waku, Redwood.JS...)
-
-First, you will need to add a llamaindex plugin to your Next.js project.
-
-```js
-// next.config.js
-const withLlamaIndex = require("llamaindex/next");
-
-module.exports = withLlamaIndex({
-  // your next.js config
-});
-```
-
-You can combine `ai` with `llamaindex` in Next.js with RSC (React Server Components).
-
-```tsx
-// src/apps/page.tsx
-"use client";
-import { chatWithAgent } from "@/actions";
-import type { JSX } from "react";
-import { useFormState } from "react-dom";
-
-// You can use the Edge runtime in Next.js by adding this line:
-// export const runtime = "edge";
-
-export default function Home() {
-  const [ui, action] = useFormState<JSX.Element | null>(async () => {
-    return chatWithAgent("hello!", []);
-  }, null);
-  return (
-    <main>
-      {ui}
-      <form action={action}>
-        <button>Chat</button>
-      </form>
-    </main>
-  );
-}
-```
-
-```tsx
-// src/actions/index.ts
-"use server";
-import { createStreamableUI } from "ai/rsc";
-import { OpenAIAgent } from "llamaindex";
-import type { ChatMessage } from "llamaindex/llm/types";
-
-export async function chatWithAgent(
-  question: string,
-  prevMessages: ChatMessage[] = [],
-) {
-  const agent = new OpenAIAgent({
-    tools: [
-      // ... adding your tools here
-    ],
-  });
-  const responseStream = await agent.chat({
-    stream: true,
-    message: question,
-    chatHistory: prevMessages,
-  });
-  const uiStream = createStreamableUI(<div>loading...</div>);
-  responseStream
-    .pipeTo(
-      new WritableStream({
-        start: () => {
-          uiStream.update("response:");
-        },
-        write: async (message) => {
-          uiStream.append(message.response.delta);
-        },
-      }),
-    )
-    .catch(console.error);
-  return uiStream.value;
-}
-```
-
-## Playground
-
-Check out our NextJS playground at https://llama-playground.vercel.app/. The source is available at https://github.com/run-llama/ts-playground
-
-## Core concepts for getting started:
-
-- [Document](/packages/llamaindex/src/Node.ts): A document represents a text file, PDF file or other contiguous piece of data.
-
-- [Node](/packages/llamaindex/src/Node.ts): The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
-
-- [Embedding](/packages/llamaindex/src/embeddings/OpenAIEmbedding.ts): Embeddings are sets of floating point numbers which represent the data in a Node. By comparing the similarity of embeddings, we can derive an understanding of the similarity of two pieces of data. One use case is to compare the embedding of a question with the embeddings of our Nodes to see which Nodes may contain the data needed to answer that quesiton. Because the default service context is OpenAI, the default embedding is `OpenAIEmbedding`. If using different models, say through Ollama, use this [Embedding](/packages/llamaindex/src/embeddings/OllamaEmbedding.ts) (see all [here](/packages/llamaindex/src/embeddings)).
-
-- [Indices](/packages/llamaindex/src/indices/): Indices store the Nodes and the embeddings of those nodes. QueryEngines retrieve Nodes from these Indices using embedding similarity.
-
-- [QueryEngine](/packages/llamaindex/src/engines/query/RetrieverQueryEngine.ts): Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected Nodes from your Index to give the LLM the context it needs to answer your query. To build a query engine from your Index (recommended), use the [`asQueryEngine`](/packages/llamaindex/src/indices/BaseIndex.ts) method on your Index. See all query engines [here](/packages/llamaindex/src/engines/query).
-
-- [ChatEngine](/packages/llamaindex/src/engines/chat/SimpleChatEngine.ts): A ChatEngine helps you build a chatbot that will interact with your Indices. See all chat engines [here](/packages/llamaindex/src/engines/chat).
-
-- [SimplePrompt](/packages/llamaindex/src/Prompt.ts): A simple standardized function call definition that takes in inputs and formats them in a template literal. SimplePrompts can be specialized using currying and combined using other SimplePrompt functions.
-
-## Tips when using in non-Node.js environments
-
-When you are importing `llamaindex` in a non-Node.js environment(such as React Server Components, Cloudflare Workers, etc.)
+When you are importing `llamaindex` 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`).
@@ -212,19 +103,31 @@ export async function getDocuments() {
 
 You'll find a complete example with LlamaIndexTS here: https://github.com/run-llama/create_llama_projects/tree/main/nextjs-edge-llamaparse
 
-## Supported LLMs:
+## Playground
 
-- OpenAI GPT-3.5-turbo and GPT-4
-- Anthropic Claude 3 (Opus, Sonnet, and Haiku) and the legacy models (Claude 2 and Instant)
-- Groq LLMs
-- Llama2/3 Chat LLMs (70B, 13B, and 7B parameters)
-- MistralAI Chat LLMs
-- Fireworks Chat LLMs
+Check out our NextJS playground at https://llama-playground.vercel.app/. The source is available at https://github.com/run-llama/ts-playground
+
+## Core concepts for getting started:
+
+- [Document](/packages/llamaindex/src/Node.ts): A document represents a text file, PDF file or other contiguous piece of data.
+
+- [Node](/packages/llamaindex/src/Node.ts): The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
+
+- [Embedding](/packages/llamaindex/src/embeddings/OpenAIEmbedding.ts): Embeddings are sets of floating point numbers which represent the data in a Node. By comparing the similarity of embeddings, we can derive an understanding of the similarity of two pieces of data. One use case is to compare the embedding of a question with the embeddings of our Nodes to see which Nodes may contain the data needed to answer that question. Because the default service context is OpenAI, the default embedding is `OpenAIEmbedding`. If using different models, say through Ollama, use this [Embedding](/packages/llamaindex/src/embeddings/OllamaEmbedding.ts) (see all [here](/packages/llamaindex/src/embeddings)).
+
+- [Indices](/packages/llamaindex/src/indices/): Indices store the Nodes and the embeddings of those nodes. QueryEngines retrieve Nodes from these Indices using embedding similarity.
+
+- [QueryEngine](/packages/llamaindex/src/engines/query/RetrieverQueryEngine.ts): Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected Nodes from your Index to give the LLM the context it needs to answer your query. To build a query engine from your Index (recommended), use the [`asQueryEngine`](/packages/llamaindex/src/indices/BaseIndex.ts) method on your Index. See all query engines [here](/packages/llamaindex/src/engines/query).
+
+- [ChatEngine](/packages/llamaindex/src/engines/chat/SimpleChatEngine.ts): A ChatEngine helps you build a chatbot that will interact with your Indices. See all chat engines [here](/packages/llamaindex/src/engines/chat).
+
+- [SimplePrompt](/packages/llamaindex/src/Prompt.ts): A simple standardized function call definition that takes in inputs and formats them in a template literal. SimplePrompts can be specialized using currying and combined using other SimplePrompt functions.
 
 ## Contributing:
 
-We are in the very early days of LlamaIndex.TS. If you’re interested in hacking on it with us check out our [contributing guide](/CONTRIBUTING.md)
+Please see our [contributing guide](CONTRIBUTING.md) for more information.
+You are highly encouraged to contribute to LlamaIndex.TS!
 
-## Bugs? Questions?
+## Community
 
 Please join our Discord! https://discord.com/invite/eN6D2HQ4aX

apps/docs/.changeset/tall-parents-add.md

@@ -0,0 +1,6 @@
+---
+"llamaindex": minor
+"docs": minor
+---
+
+Add deepseek llm class

apps/docs/CHANGELOG.md

@@ -1,5 +1,719 @@
 # docs
 
+## 0.0.133
+
+### Patch Changes
+
+- c1850ee: feat: Amazon Nova support via Bedrock
+- Updated dependencies [b504303]
+- Updated dependencies [a0e6f57]
+  - llamaindex@0.8.27
+  - @llamaindex/examples@0.0.20
+
+## 0.0.132
+
+### Patch Changes
+
+- Updated dependencies [3d1808b]
+  - llamaindex@0.8.26
+  - @llamaindex/examples@0.0.19
+
+## 0.0.131
+
+### Patch Changes
+
+- llamaindex@0.8.25
+- @llamaindex/examples@0.0.18
+
+## 0.0.130
+
+### Patch Changes
+
+- Updated dependencies [fd38a25]
+  - @llamaindex/examples@0.0.17
+
+## 0.0.129
+
+### Patch Changes
+
+- Updated dependencies [515f2c1]
+  - llamaindex@0.8.24
+
+## 0.0.128
+
+### Patch Changes
+
+- llamaindex@0.8.23
+
+## 0.0.127
+
+### Patch Changes
+
+- Updated dependencies [819af45]
+  - llamaindex@0.8.22
+
+## 0.0.126
+
+### Patch Changes
+
+- Updated dependencies [83c3897]
+- Updated dependencies [efa2211]
+  - llamaindex@0.8.21
+
+## 0.0.125
+
+### Patch Changes
+
+- Updated dependencies [02b22da]
+  - llamaindex@0.8.20
+
+## 0.0.124
+
+### Patch Changes
+
+- Updated dependencies [90d265c]
+  - llamaindex@0.8.19
+
+## 0.0.123
+
+### Patch Changes
+
+- Updated dependencies [d17450f]
+  - llamaindex@0.8.18
+
+## 0.0.122
+
+### Patch Changes
+
+- llamaindex@0.8.17
+
+## 0.0.121
+
+### Patch Changes
+
+- llamaindex@0.8.16
+
+## 0.0.120
+
+### Patch Changes
+
+- Updated dependencies [3d503cb]
+- Updated dependencies [5dae534]
+  - llamaindex@0.8.15
+
+## 0.0.119
+
+### Patch Changes
+
+- Updated dependencies [630b425]
+  - llamaindex@0.8.14
+
+## 0.0.118
+
+### Patch Changes
+
+- llamaindex@0.8.13
+- @llamaindex/examples@0.0.16
+
+## 0.0.117
+
+### Patch Changes
+
+- @llamaindex/examples@0.0.15
+
+## 0.0.116
+
+### Patch Changes
+
+- llamaindex@0.8.12
+
+## 0.0.115
+
+### Patch Changes
+
+- llamaindex@0.8.11
+
+## 0.0.114
+
+### Patch Changes
+
+- Updated dependencies [f066e50]
+  - llamaindex@0.8.10
+  - @llamaindex/examples@0.0.14
+
+## 0.0.113
+
+### Patch Changes
+
+- Updated dependencies [4fc001c]
+- Updated dependencies [4d4cd8a]
+  - llamaindex@0.8.9
+
+## 0.0.112
+
+### Patch Changes
+
+- Updated dependencies [ad85bd0]
+  - llamaindex@0.8.8
+  - @llamaindex/examples@0.0.13
+
+## 0.0.111
+
+### Patch Changes
+
+- llamaindex@0.8.7
+
+## 0.0.110
+
+### Patch Changes
+
+- Updated dependencies [95a5cc6]
+  - llamaindex@0.8.6
+
+## 0.0.109
+
+### Patch Changes
+
+- Updated dependencies [14cc9eb]
+- Updated dependencies [a6db5dd]
+- Updated dependencies [396b1e1]
+  - llamaindex@0.8.5
+
+## 0.0.108
+
+### Patch Changes
+
+- Updated dependencies [35430d3]
+  - llamaindex@0.8.4
+
+## 0.0.107
+
+### Patch Changes
+
+- llamaindex@0.8.3
+
+## 0.0.106
+
+### Patch Changes
+
+- @llamaindex/examples@0.0.12
+
+## 0.0.105
+
+### Patch Changes
+
+- Updated dependencies [c7a918c]
+  - llamaindex@0.8.2
+
+## 0.0.104
+
+### Patch Changes
+
+- llamaindex@0.8.1
+
+## 0.0.103
+
+### Patch Changes
+
+- Updated dependencies [359fd33]
+- Updated dependencies [efb7e1b]
+- Updated dependencies [98ba1e7]
+- Updated dependencies [620c63c]
+  - llamaindex@0.8.0
+  - @llamaindex/examples@0.0.11
+
+## 0.0.102
+
+### Patch Changes
+
+- Updated dependencies [9df9a8f]
+  - llamaindex@0.7.10
+
+## 0.0.101
+
+### Patch Changes
+
+- Updated dependencies [691c5bc]
+  - llamaindex@0.7.9
+
+## 0.0.100
+
+### Patch Changes
+
+- llamaindex@0.7.8
+
+## 0.0.99
+
+### Patch Changes
+
+- Updated dependencies [2486bd8]
+  - @llamaindex/examples@0.0.10
+  - llamaindex@0.7.7
+
+## 0.0.98
+
+### Patch Changes
+
+- Updated dependencies [534d550]
+  - llamaindex@0.7.6
+
+## 0.0.97
+
+### Patch Changes
+
+- Updated dependencies [e9a111d]
+- Updated dependencies [9f22aae]
+  - llamaindex@0.7.5
+
+## 0.0.96
+
+### Patch Changes
+
+- llamaindex@0.7.4
+
+## 0.0.95
+
+### Patch Changes
+
+- llamaindex@0.7.3
+
+## 0.0.94
+
+### Patch Changes
+
+- llamaindex@0.7.2
+
+## 0.0.93
+
+### Patch Changes
+
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+
+## 0.0.92
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+  - @llamaindex/examples@0.0.9
+
+## 0.0.91
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+
+## 0.0.90
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+
+## 0.0.89
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+
+## 0.0.88
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+
+## 0.0.87
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+
+## 0.0.86
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+
+## 0.0.85
+
+### Patch Changes
+
+- Updated dependencies [63e9846]
+- Updated dependencies [6f3a31c]
+  - llamaindex@0.6.16
+
+## 0.0.84
+
+### Patch Changes
+
+- Updated dependencies [2a82413]
+  - llamaindex@0.6.15
+
+## 0.0.83
+
+### Patch Changes
+
+- llamaindex@0.6.14
+
+## 0.0.82
+
+### Patch Changes
+
+- llamaindex@0.6.13
+
+## 0.0.81
+
+### Patch Changes
+
+- Updated dependencies [f7b4e94]
+- Updated dependencies [78037a6]
+- Updated dependencies [1d9e3b1]
+  - llamaindex@0.6.12
+
+## 0.0.80
+
+### Patch Changes
+
+- Updated dependencies [df441e2]
+  - llamaindex@0.6.11
+
+## 0.0.79
+
+### Patch Changes
+
+- Updated dependencies [ebc5105]
+- Updated dependencies [6cce3b1]
+  - llamaindex@0.6.10
+
+## 0.0.78
+
+### Patch Changes
+
+- llamaindex@0.6.9
+
+## 0.0.77
+
+### Patch Changes
+
+- Updated dependencies [8b7fdba]
+  - llamaindex@0.6.8
+
+## 0.0.76
+
+### Patch Changes
+
+- Updated dependencies [23bcc37]
+  - llamaindex@0.6.7
+
+## 0.0.75
+
+### Patch Changes
+
+- Updated dependencies [d902cc3]
+- Updated dependencies [025ffe6]
+- Updated dependencies [a659574]
+  - llamaindex@0.6.6
+
+## 0.0.74
+
+### Patch Changes
+
+- Updated dependencies [e9714db]
+  - llamaindex@0.6.5
+
+## 0.0.73
+
+### Patch Changes
+
+- Updated dependencies [b48bcc3]
+  - llamaindex@0.6.4
+
+## 0.0.72
+
+### Patch Changes
+
+- Updated dependencies [2cd1383]
+- Updated dependencies [5c4badb]
+  - llamaindex@0.6.3
+
+## 0.0.71
+
+### Patch Changes
+
+- Updated dependencies [749b43a]
+  - llamaindex@0.6.2
+
+## 0.0.70
+
+### Patch Changes
+
+- Updated dependencies [fbd5e01]
+- Updated dependencies [6b70c54]
+- Updated dependencies [1a6137b]
+- Updated dependencies [85c2e19]
+  - llamaindex@0.6.1
+
+## 0.0.69
+
+### Patch Changes
+
+- Updated dependencies [11feef8]
+  - llamaindex@0.6.0
+  - @llamaindex/examples@0.0.8
+
+## 0.0.68
+
+### Patch Changes
+
+- Updated dependencies [7edeb1c]
+  - llamaindex@0.5.27
+
+## 0.0.67
+
+### Patch Changes
+
+- Updated dependencies [ffe0cd1]
+- Updated dependencies [ffe0cd1]
+  - llamaindex@0.5.26
+
+## 0.0.66
+
+### Patch Changes
+
+- Updated dependencies [4810364]
+- Updated dependencies [d3bc663]
+  - llamaindex@0.5.25
+
+## 0.0.65
+
+### Patch Changes
+
+- llamaindex@0.5.24
+
+## 0.0.64
+
+### Patch Changes
+
+- llamaindex@0.5.23
+
+## 0.0.63
+
+### Patch Changes
+
+- Updated dependencies [4648da6]
+  - llamaindex@0.5.22
+
+## 0.0.62
+
+### Patch Changes
+
+- Updated dependencies [ae1149f]
+- Updated dependencies [2411c9f]
+- Updated dependencies [e8f229c]
+- Updated dependencies [11b3856]
+- Updated dependencies [83d7f41]
+- Updated dependencies [0148354]
+- Updated dependencies [1711f6d]
+  - llamaindex@0.5.21
+
+## 0.0.61
+
+### Patch Changes
+
+- Updated dependencies [d9d6c56]
+- Updated dependencies [22ff486]
+- Updated dependencies [eed0b04]
+  - llamaindex@0.5.20
+
+## 0.0.60
+
+### Patch Changes
+
+- Updated dependencies [fcbf183]
+  - llamaindex@0.5.19
+
+## 0.0.59
+
+### Patch Changes
+
+- Updated dependencies [8b66cf4]
+  - llamaindex@0.5.18
+
+## 0.0.58
+
+### Patch Changes
+
+- Updated dependencies [c654398]
+  - llamaindex@0.5.17
+
+## 0.0.57
+
+### Patch Changes
+
+- Updated dependencies [58abc57]
+  - llamaindex@0.5.16
+
+## 0.0.56
+
+### Patch Changes
+
+- Updated dependencies [01c184c]
+- Updated dependencies [07a275f]
+  - llamaindex@0.5.15
+
+## 0.0.55
+
+### Patch Changes
+
+- Updated dependencies [c825a2f]
+  - llamaindex@0.5.14
+
+## 0.0.54
+
+### Patch Changes
+
+- llamaindex@0.5.13
+
+## 0.0.53
+
+### Patch Changes
+
+- Updated dependencies [345300f]
+- Updated dependencies [da5cfc4]
+- Updated dependencies [da5cfc4]
+  - llamaindex@0.5.12
+
+## 0.0.52
+
+### Patch Changes
+
+- 376d29a: feat: added tool calling and agent support for llama3.1 504B
+  - llamaindex@0.5.11
+
+## 0.0.51
+
+### Patch Changes
+
+- 086b940: feat: add DeepSeek LLM
+- 5d5716b: feat: add a reader for JSON data
+- Updated dependencies [086b940]
+- Updated dependencies [5d5716b]
+- Updated dependencies [91d02a4]
+- Updated dependencies [fb6db45]
+  - llamaindex@0.5.10
+
+## 0.0.50
+
+### Patch Changes
+
+- Updated dependencies [15962b3]
+  - llamaindex@0.5.9
+
+## 0.0.49
+
+### Patch Changes
+
+- Updated dependencies [3d5ba08]
+- Updated dependencies [d917cdc]
+  - llamaindex@0.5.8
+
+## 0.0.48
+
+### Patch Changes
+
+- Updated dependencies [ec59acd]
+  - llamaindex@0.5.7
+
+## 0.0.47
+
+### Patch Changes
+
+- Updated dependencies [2562244]
+- Updated dependencies [325aa51]
+- Updated dependencies [ab700ea]
+- Updated dependencies [92f0782]
+- Updated dependencies [6cf6ae6]
+- Updated dependencies [b7cfe5b]
+  - llamaindex@0.5.6
+
+## 0.0.46
+
+### Patch Changes
+
+- Updated dependencies [b974eea]
+  - llamaindex@0.5.5
+
+## 0.0.45
+
+### Patch Changes
+
+- Updated dependencies [1a65ead]
+  - llamaindex@0.5.4
+
+## 0.0.44
+
+### Patch Changes
+
+- Updated dependencies [9bbbc67]
+- Updated dependencies [b3681bf]
+  - llamaindex@0.5.3
+
+## 0.0.43
+
+### Patch Changes
+
+- llamaindex@0.5.2
+
+## 0.0.42
+
+### Patch Changes
+
+- 2774681: Add mixedbread's embeddings and reranking API
+- Updated dependencies [2774681]
+- Updated dependencies [a0f424e]
+  - llamaindex@0.5.1
+
+## 0.0.41
+
+### Patch Changes
+
+- 36ddec4: fix: typo in custom page separator parameter for LlamaParse
+- Updated dependencies [16ef5dd]
+- Updated dependencies [16ef5dd]
+- Updated dependencies [36ddec4]
+  - llamaindex@0.5.0
+  - @llamaindex/examples@0.0.7
+
+## 0.0.40
+
+### Patch Changes
+
+- llamaindex@0.4.14
+
+## 0.0.39
+
+### Patch Changes
+
+- Updated dependencies [e8f8bea]
+- Updated dependencies [304484b]
+  - llamaindex@0.4.13
+
 ## 0.0.38
 
 ### Patch Changes

apps/docs/docs/examples/context_aware_agent.mdx

@@ -0,0 +1,63 @@
+---
+sidebar_position: 14
+---
+
+# Context-Aware Agent
+
+The Context-Aware Agent enhances the capabilities of standard LLM agents by incorporating relevant context from a retriever for each query. This allows the agent to provide more informed and specific responses based on the available information.
+
+## Usage
+
+Here's a simple example of how to use the Context-Aware Agent:
+
+```typescript
+import {
+  Document,
+  VectorStoreIndex,
+  OpenAIContextAwareAgent,
+  OpenAI,
+} from "llamaindex";
+
+async function createContextAwareAgent() {
+  // Create and index some documents
+  const documents = [
+    new Document({
+      text: "LlamaIndex is a data framework for LLM applications.",
+      id_: "doc1",
+    }),
+    new Document({
+      text: "The Eiffel Tower is located in Paris, France.",
+      id_: "doc2",
+    }),
+  ];
+
+  const index = await VectorStoreIndex.fromDocuments(documents);
+  const retriever = index.asRetriever({ similarityTopK: 1 });
+
+  // Create the Context-Aware Agent
+  const agent = new OpenAIContextAwareAgent({
+    llm: new OpenAI({ model: "gpt-3.5-turbo" }),
+    contextRetriever: retriever,
+  });
+
+  // Use the agent to answer queries
+  const response = await agent.chat({
+    message: "What is LlamaIndex used for?",
+  });
+
+  console.log("Agent Response:", response.response);
+}
+
+createContextAwareAgent().catch(console.error);
+```
+
+In this example, the Context-Aware Agent uses the retriever to fetch relevant context for each query, allowing it to provide more accurate and informed responses based on the indexed documents.
+
+## Key Components
+
+- `contextRetriever`: A retriever (e.g., from a VectorStoreIndex) that fetches relevant documents or passages for each query.
+
+## Available Context-Aware Agents
+
+- `OpenAIContextAwareAgent`: A context-aware agent using OpenAI's models.
+- `AnthropicContextAwareAgent`: A context-aware agent using Anthropic's models.

apps/docs/docs/getting_started/concepts.md

@@ -62,7 +62,7 @@ These building blocks can be customized to reflect ranking preferences, as well
 
 [**Retrievers**](../modules/retriever.md):
 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 difference indices, the most popular being dense retrieval against a vector index.
+The specific retrieval logic differs for different indices, the most popular being dense retrieval against a vector index.
 
 [**Response Synthesizers**](../modules/response_synthesizer.md):
 A response synthesizer generates a response from an LLM, using a user query and a given set of retrieved text chunks.

apps/docs/docs/getting_started/environments.md

@@ -6,10 +6,17 @@ sidebar_position: 2
 
 We support Node.JS versions 18, 20 and 22, with experimental support for Deno, Bun and Vercel Edge functions.
 
-## NextJS App Router
+## NextJS
 
-If you're using NextJS App Router route handlers/serverless functions, you'll need to use the NodeJS mode:
+If you're using NextJS you'll need to add `withLlamaIndex` to your `next.config.js` file. This will add the necessary configuration for included 3rd-party libraries to your build:
 
 ```js
-export const runtime = "nodejs"; // default
+// next.config.js
+const withLlamaIndex = require("llamaindex/next");
+
+module.exports = withLlamaIndex({
+  // your next.js config
+});
 ```
+
+For details, check the latest [withLlamaIndex](https://github.com/run-llama/LlamaIndexTS/blob/main/packages/llamaindex/src/next.ts) implementation.

apps/docs/docs/guides/agents/2_create_agent.mdx

@@ -50,10 +50,10 @@ We want to see what our agent is up to, so we're going to hook into some events
 
 ```javascript
 Settings.callbackManager.on("llm-tool-call", (event) => {
-  console.log(event.detail.payload);
+  console.log(event.detail);
 });
 Settings.callbackManager.on("llm-tool-result", (event) => {
-  console.log(event.detail.payload);
+  console.log(event.detail);
 });
 ```
 

apps/docs/docs/guides/agents/4_agentic_rag.mdx

@@ -4,22 +4,23 @@ While an agent that can perform math is nifty (LLMs are usually not very good at
 
 To learn more about RAG, we recommend this [introduction](https://docs.llamaindex.ai/en/stable/getting_started/concepts/) from our Python docs. We'll assume you know the basics:
 
-- You need to parse your source data into chunks of text
-- You need to encode that text as numbers, called embeddings
-- You need to search your embeddings for the most relevant chunks of text
-- You feed your relevant chunks and a query to an LLM to answer a question
+- Parse your source data into chunks of text.
+- Encode that text as numbers, called embeddings.
+- Search your embeddings for the most relevant chunks of text.
+- Use the relevant chunks along with a query to ask an LLM to generate an answer.
 
 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).
 
 ### New dependencies
 
-We'll be bringing in `SimpleDirectoryReader`, `HuggingFaceEmbedding`, `VectorStoreIndex`, and `QueryEngineTool` from LlamaIndex.TS, as well as the dependencies we previously used.
+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,
@@ -41,7 +42,7 @@ Settings.embedModel = new HuggingFaceEmbedding({
 
 ### Load data using SimpleDirectoryReader
 
-SimpleDirectoryReader is a flexible tool that can read a variety of file formats. We're going to point it at our data directory, which contains just the single PDF file, and get it to return a set of documents.
+`SimpleDirectoryReader` is a flexible tool that can read various file formats. We will point it at our data directory, which contains a single PDF file, and retrieve a set of documents.
 
 ```javascript
 const reader = new SimpleDirectoryReader();
@@ -50,7 +51,7 @@ const documents = await reader.loadData("../data");
 
 ### Index our data
 
-Now we turn our text into embeddings. The `VectorStoreIndex` class takes care of this for us when we use the `fromDocuments` method (it uses the embedding model we defined in `Settings` earlier).
+We will convert our text into embeddings using the `VectorStoreIndex` class through the `fromDocuments` method, which utilizes the embedding model defined earlier in `Settings`.
 
 ```javascript
 const index = await VectorStoreIndex.fromDocuments(documents);
@@ -72,21 +73,35 @@ By default LlamaIndex will retrieve just the 2 most relevant chunks of text. Thi
 retriever.similarityTopK = 10;
 ```
 
-### Create a query engine
+### Approach 1: Create a Context-Aware Agent
 
-And our final step in creating a RAG pipeline is to create a query engine that will use the retriever to find the most relevant chunks of text, and then use the LLM to answer the question.
+With the retriever ready, you can create a **context-aware agent**.
 
 ```javascript
-const queryEngine = await index.asQueryEngine({
-  retriever,
+const agent = new OpenAIContextAwareAgent({
+  contextRetriever: retriever,
 });
+
+// Example query to the context-aware agent
+let response = await agent.chat({
+  message: `What's the budget of San Francisco in 2023-2024?`,
+});
+
+console.log(response);
 ```
 
-### Define the query engine as a tool
+**Expected Output:**
 
-Just as before we created a `FunctionTool`, we're going to create a `QueryEngineTool` that uses our `queryEngine`.
+```md
+The total budget for the City and County of San Francisco for the fiscal year 2023-2024 is $14.6 billion. This represents a $611.8 million, or 4.4 percent, increase over the previous fiscal year's budget. The budget covers various expenditures across different departments and services, including significant allocations to public works, transportation, commerce, public protection, and health services.
+```
+
+### Approach 2: Using QueryEngineTool (Alternative Approach)
+
+If you prefer more flexibility and don't mind additional complexity, you can create a `QueryEngineTool`. This approach allows you to define the query logic, providing a more tailored way to interact with the data, but note that it introduces a delay due to the extra tool call.
 
 ```javascript
+const queryEngine = await index.asQueryEngine({ retriever });
 const tools = [
   new QueryEngineTool({
     queryEngine: queryEngine,
@@ -96,28 +111,18 @@ const tools = [
     },
   }),
 ];
-```
 
-As before, we've created an array of tools with just one tool in it. The metadata is slightly different: we don't need to define our parameters, we just give the tool a name and a natural-language description.
-
-### Create the agent as before
-
-Creating the agent and asking a question is exactly the same as before, but we'll ask a different question.
-
-```javascript
-// create the agent
+// Create an agent using the tools array
 const agent = new OpenAIAgent({ tools });
 
-let response = await agent.chat({
+let toolResponse = await agent.chat({
   message: "What's the budget of San Francisco in 2023-2024?",
 });
 
-console.log(response);
+console.log(toolResponse);
 ```
 
-Once again we'll run `npx tsx agent.ts` and see what we get:
-
-**_Output_**
+**Expected Output:**
 
 ```javascript
 {
@@ -138,28 +143,12 @@ Once again we'll run `npx tsx agent.ts` and see what we get:
 }
 ```
 
-```javascript
-{
-  response: {
-    raw: {
-      id: 'chatcmpl-9KxUkwizVCYCmxwFQcZFSHrInzNFU',
-      object: 'chat.completion',
-      created: 1714782286,
-      model: 'gpt-4-turbo-2024-04-09',
-      choices: [Array],
-      usage: [Object],
-      system_fingerprint: 'fp_ea6eb70039'
-    },
-    message: {
-      content: "The total budget for the City and County of San Francisco for the fiscal year 2023-2024 is $14.6 billion. This represents a $611.8 million, or 4.4 percent, increase over the previous fiscal year's budget. The budget covers various expenditures across different departments and services, including significant allocations to public works, transportation, commerce, public protection, and health services.",
-      role: 'assistant',
-      options: {}
-    }
-  },
-  sources: [Getter]
-}
-```
-
 Once again we see a `toolResult`. You can see the query the LLM decided to send to the query engine ("total budget"), and the output the engine returned. In `response.message` you see that the LLM has returned the output from the tool almost verbatim, although it trimmed out the bit about 2024-2025 since we didn't ask about that year.
 
+### Comparison of Approaches
+
+The `OpenAIContextAwareAgent` approach simplifies the setup by allowing you to directly link the retriever to the agent, making it straightforward to access relevant context for your queries. This is ideal for situations where you want easy integration with existing data sources, like a context chat engine.
+
+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)!

apps/docs/docs/introduction.md

@@ -21,7 +21,7 @@ LlamaIndex.TS handles several major use cases:
 
 - **Structured Data Extraction**: turning complex, unstructured and semi-structured data into uniform, programmatically accessible formats.
 - **Retrieval-Augmented Generation (RAG)**: answering queries across your internal data by providing LLMs with up-to-date, semantically relevant context including Question and Answer systems and chat bots.
-- **Autonomous Agents**: building software that is capable of intelligently selecting and using tools to accomplish tasks in an interative, unsupervised manner.
+- **Autonomous Agents**: building software that is capable of intelligently selecting and using tools to accomplish tasks in an interactive, unsupervised manner.
 
 ## 👨‍👩‍👧‍👦 Who is LlamaIndex for?
 

apps/docs/docs/modules/agent/_category_.yml

@@ -1,2 +1,2 @@
 label: "Agents"
-position: 3
+position: 10

apps/docs/docs/modules/agent/index.md

@@ -15,6 +15,7 @@ LlamaIndex.TS comes with a few built-in agents, but you can also create your own
 - Anthropic Agent both via Anthropic and Bedrock (in `@llamaIndex/community`)
 - Gemini Agent
 - ReACT Agent
+- Meta3.1 504B via Bedrock (in `@llamaIndex/community`)
 
 ## Examples
 

apps/docs/docs/modules/chat_engine.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 4
+sidebar_position: 13
 ---
 
 # ChatEngine
@@ -27,3 +27,4 @@ for await (const chunk of stream) {
 
 - [ContextChatEngine](../api/classes/ContextChatEngine.md)
 - [CondenseQuestionChatEngine](../api/classes/ContextChatEngine.md)
+- [SimpleChatEngine](../api/classes/SimpleChatEngine.md)

apps/docs/docs/modules/data_index.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 4
+sidebar_position: 12
 ---
 
 # Index
@@ -8,6 +8,7 @@ An index is the basic container and organization for your data. LlamaIndex.TS su
 
 - `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
+- `KeywordTableIndex` extracts and provides keywords from `Node`s to the LLM
 
 ```typescript
 import { Document, VectorStoreIndex } from "llamaindex";
@@ -21,3 +22,4 @@ const index = await VectorStoreIndex.fromDocuments([document]);
 
 - [SummaryIndex](../api/classes/SummaryIndex.md)
 - [VectorStoreIndex](../api/classes/VectorStoreIndex.md)
+- [KeywordTableIndex](../api/classes/KeywordTableIndex.md)

apps/docs/docs/modules/data_loaders/discord.mdx

@@ -0,0 +1,34 @@
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../../examples/readers/src/discord";
+
+# DiscordReader
+
+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.
+
+## Usage
+
+First step is to create a Discord Application and generating a bot token [here](https://discord.com/developers/applications).
+In your Discord Application, go to the `OAuth2` tab and generate an invite URL by selecting `bot` and click `Read Messages/View Channels` as wells as `Read Message History`.
+This will invite the bot with the necessary permissions to read messages.
+Copy the URL in your browser and select the server you want your bot to join.
+
+<CodeBlock language="ts">{CodeSource}</CodeBlock>
+
+### Params
+
+#### DiscordReader()
+
+- `discordToken?`: The Discord bot token.
+- `requestHandler?`: Optionally provide a custom request function for edge environments, e.g. `fetch`. See discord.js for more info.
+
+#### DiscordReader.loadData
+
+- `channelIDs`: The ID(s) of discord channels as an array of strings.
+- `limit?`: Optionally limit the number of messages to read
+- `additionalInfo?`: An optional flag to include embedded messages and attachment urls in the document.
+- `oldestFirst?`: An optional flag to return the oldest messages first.
+
+## API Reference
+
+- [DiscordReader](../../api/classes/DiscordReader.md)

apps/docs/docs/modules/data_loaders/index.mdx

@@ -6,6 +6,19 @@ import CodeSource2 from "!raw-loader!../../../../../examples/readers/src/custom-
 
 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](../../api/classes/AudioTranscriptReader.md): loads entire transcript as a single document.
+  - [AudioTranscriptParagraphsReader](../../api/classes/AudioTranscriptParagraphsReader.md): creates a document per paragraph.
+  - [AudioTranscriptSentencesReader](../../api/classes/AudioTranscriptSentencesReader.md): creates a document per sentence.
+  - [AudioSubtitlesReader](../../api/classes/AudioTranscriptParagraphsReader.md): creates a document containing the subtitles of a transcript.
+- [NotionReader](../../api/classes/NotionReader.md) loads [Notion](https://www.notion.so/) pages.
+- [SimpleMongoReader](../../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://developer.stackblitz.com/img/open_in_stackblitz.svg)](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples/readers?file=src/simple-directory-reader.ts&title=Simple%20Directory%20Reader)
@@ -16,7 +29,15 @@ It is a simple reader that reads all files from a directory and its subdirectori
 
 <CodeBlock language="ts">{CodeSource}</CodeBlock>
 
-Currently, it supports reading `.txt`, `.pdf`, `.csv`, `.md`, `.docx`, `.htm`, `.html`, `.jpg`, `.jpeg`, `.png` and `.gif` files, but support for other file types is planned.
+Currently, the following readers are mapped to specific file types:
+
+- [TextFileReader](../../api/classes/TextFileReader.md): `.txt`
+- [PDFReader](../../api/classes/PDFReader.md): `.pdf`
+- [PapaCSVReader](../../api/classes/PapaCSVReader.md): `.csv`
+- [MarkdownReader](../../api/classes/MarkdownReader.md): `.md`
+- [DocxReader](../../api/classes/DocxReader.md): `.docx`
+- [HTMLReader](../../api/classes/HTMLReader.md): `.htm`, `.html`
+- [ImageReader](../../api/classes/ImageReader.md): `.jpg`, `.jpeg`, `.png`, `.gif`
 
 You can modify the reader three different ways:
 

apps/docs/docs/modules/data_loaders/json.md

@@ -0,0 +1,149 @@
+# JSONReader
+
+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)
+
+## Usage
+
+```ts
+import { JSONReader } from "llamaindex";
+
+const file = "../../PATH/TO/FILE";
+const content = new TextEncoder().encode("JSON_CONTENT");
+
+const reader = new JSONReader({ levelsBack: 0, collapseLength: 100 });
+const docsFromFile = reader.loadData(file);
+const docsFromContent = reader.loadDataAsContent(content);
+```
+
+### Options
+
+Basic:
+
+- `streamingThreshold?`: The threshold for using streaming mode in MB of the JSON Data. CEstimates characters by calculating bytes: `(streamingThreshold * 1024 * 1024) / 2` and comparing against `.length` of the JSON string. Set `undefined` to disable streaming or `0` to always use streaming. Default is `50` MB.
+
+- `ensureAscii?`: Wether to ensure only ASCII characters be present in the output by converting non-ASCII characters to their unicode escape sequence. Default is `false`.
+
+- `isJsonLines?`: Wether the JSON is in JSON Lines format. If true, will split into lines, remove empty one and parse each line as JSON. Note: Uses a custom streaming parser, most likely less robust than json-ext. Default is `false`
+
+- `cleanJson?`: Whether to clean the JSON by filtering out structural characters (`{}, [], and ,`). If set to false, it will just parse the JSON, not removing structural characters. Default is `true`.
+
+- `logger?`: A placeholder for a custom logger function.
+
+Depth-First-Traversal:
+
+- `levelsBack?`: Specifies how many levels up the JSON structure to include in the output. `cleanJson` will be ignored. If set to 0, all levels are included. If undefined, parses the entire JSON, treat each line as an embedding and create a document per top-level array. Default is `undefined`
+
+- `collapseLength?`: The maximum length of JSON string representation to be collapsed into a single line. Only applicable when `levelsBack` is set. Default is `undefined`
+
+#### Examples
+
+<!-- prettier-ignore-start -->
+Input:
+
+```json
+{"a": {"1": {"key1": "value1"}, "2": {"key2": "value2"}}, "b": {"3": {"k3": "v3"}, "4": {"k4": "v4"}}}
+```
+
+Default options:
+
+`LevelsBack` = `undefined` & `cleanJson` = `true`
+
+Output:
+
+```json
+"a": {
+"1": {
+"key1": "value1"
+"2": {
+"key2": "value2"
+"b": {
+"3": {
+"k3": "v3"
+"4": {
+"k4": "v4"
+```
+
+Depth-First Traversal all levels:
+
+`levelsBack` = `0`
+
+Output:
+
+```json
+a 1 key1 value1
+a 2 key2 value2
+b 3 k3 v3
+b 4 k4 v4
+```
+
+Depth-First Traversal and Collapse:
+
+`levelsBack` = `0` & `collapseLength` = `35`
+
+Output:
+
+```json
+a 1 {"key1":"value1"}
+a 2 {"key2":"value2"}
+b {"3":{"k3":"v3"},"4":{"k4":"v4"}}
+```
+
+Depth-First Traversal limited levels:
+
+`levelsBack` = `2`
+
+Output:
+
+```json
+1 key1 value1
+2 key2 value2
+3 k3 v3
+4 k4 v4
+```
+
+Uncleaned JSON:
+
+`levelsBack` = `undefined` & `cleanJson` = `false`
+
+Output:
+
+```json
+{"a":{"1":{"key1":"value1"},"2":{"key2":"value2"}},"b":{"3":{"k3":"v3"},"4":{"k4":"v4"}}}
+```
+
+ASCII-Conversion:
+
+Input:
+
+```json
+{ "message": "こんにちは世界" }
+```
+
+Output:
+
+```json
+"message": "\u3053\u3093\u306b\u3061\u306f\u4e16\u754c"
+```
+
+JSON Lines Format:
+
+Input:
+
+```json
+{"tweet": "Hello world"}\n{"tweet": "こんにちは世界"}
+```
+
+Output:
+
+```json
+"tweet": "Hello world"
+
+"tweet": "こんにちは世界"
+```
+<!-- prettier-ignore-end -->
+
+## API Reference
+
+- [JSONReader](../../api/classes/JSONReader.md)

apps/docs/docs/modules/data_loaders/llama_parse/index.mdx

@@ -13,7 +13,7 @@ Official documentation for LlamaParse can be found [here](https://docs.cloud.lla
 ## Usage
 
 You can then use the `LlamaParseReader` class to load local files and convert them into a parsed document that can be used by LlamaIndex.
-See [LlamaParseReader.ts](https://github.com/run-llama/LlamaIndexTS/blob/main/packages/llamaindex/src/readers/LlamaParseReader.ts) for a list of supported file types:
+See [reader.ts](https://github.com/run-llama/LlamaIndexTS/blob/main/packages/cloud/src/reader.ts) for a list of supported file types:
 
 <CodeBlock language="ts">{CodeSource}</CodeBlock>
 
@@ -27,23 +27,31 @@ They can be divided into two groups.
 
 - `apiKey` is required. Can be set as an environment variable `LLAMA_CLOUD_API_KEY`
 - `checkInterval` is the interval in seconds to check if the parsing is done. Default is `1`.
-- `maxTimeout` is the maximum timout to wait for parsing to finish. Default is `2000`
+- `maxTimeout` is the maximum timeout to wait for parsing to finish. Default is `2000`
 - `verbose` shows progress of the parsing. Default is `true`
 - `ignoreErrors` set to false to get errors while parsing. Default is `true` and returns an empty array on error.
 
 #### Advanced params:
 
 - `resultType` can be set to `markdown`, `text` or `json`. Defaults to `text`. More information about `json` mode on the next pages.
-- `language` primarly helps with OCR recognition. Defaults to `en`. Click [here](../../../api/type-aliases/Language.md) for a list of supported languages.
+- `language` primarily helps with OCR recognition. Defaults to `en`. Click [here](../../../api/type-aliases/Language.md) for a list of supported languages.
 - `parsingInstructions?` Optional. Can help with complicated document structures. See this [LlamaIndex Blog Post](https://www.llamaindex.ai/blog/launching-the-first-genai-native-document-parsing-platform) for an example.
 - `skipDiagonalText?` Optional. Set to true to ignore diagonal text. (Text that is not rotated 0, 90, 180 or 270 degrees)
 - `invalidateCache?` Optional. Set to true to ignore the LlamaCloud cache. All document are kept in cache for 48hours after the job was completed to avoid processing the same document twice. Can be useful for testing when trying to re-parse the same document with, e.g. different `parsingInstructions`.
 - `doNotCache?` Optional. Set to true to not cache the document.
 - `fastMode?` Optional. Set to true to use the fast mode. This mode will skip OCR of images, and table/heading reconstruction. Note: Non-compatible with `gpt4oMode`.
-- `doNotUnrollColumns?` Optional. Set to true to keep the text according to document layout. Reduce reconstruction accuracy, and LLM's/embedings performances in most cases.
-- `pageSeperator?` Optional. The page seperator to use. Defaults is `\\n---\\n`.
-- `gpt4oMode` set to true to use GPT-4o to extract content. Default is `false`.
-- `gpt4oApiKey?` Optional. Set the GPT-4o API key. Lowers the cost of parsing by using your own API key. Your OpenAI account will be charged. Can also be set in the environment variable `LLAMA_CLOUD_GPT4O_API_KEY`.
+- `doNotUnrollColumns?` Optional. Set to true to keep the text according to document layout. Reduce reconstruction accuracy, and LLMs/embeddings performances in most cases.
+- `pageSeparator?` Optional. A templated page separator to use to split the text. If the results contain `{page_number}` (e.g. JSON mode), it will be replaced by the next page number. If not set the default separator `\\n---\\n` will be used.
+- `pagePrefix?` Optional. A templated prefix to add to the beginning of each page. If the results contain `{page_number}`, it will be replaced by the page number.
+- `pageSuffix?` Optional. A templated suffix to add to the end of each page. If the results contain `{page_number}`, it will be replaced by the page number.
+- `gpt4oMode` Deprecated. Use vendorMultimodal params. Set to true to use GPT-4o to extract content. Default is `false`.
+- `gpt4oApiKey?` Deprecated. Use vendorMultimodal params. Optional. Set the GPT-4o API key. Lowers the cost of parsing by using your own API key. Your OpenAI account will be charged. Can also be set in the environment variable `LLAMA_CLOUD_GPT4O_API_KEY`.
+- `boundingBox?` Optional. Specify an area of the document to parse. Expects the bounding box margins as a string in clockwise order, e.g. `boundingBox = "0.1,0,0,0"` to not parse the top 10% of the document.
+- `targetPages?` Optional. Specify which pages to parse by specifying them as a comma-separated list. First page is `0`.
+- `splitByPage` Wether to split the results, creating one document per page. Uses the set `pageSeparator` or `\n---\n` as fallback. Default is true.
+- `useVendorMultimodalModel` set to true to use a multimodal model. Default is `false`.
+- `vendorMultimodalModel?` Optional. Specify which multimodal model to use. Default is GPT4o. See [here](https://docs.cloud.llamaindex.ai/llamaparse/features/multimodal) for a list of available models and cost.
+- `vendorMultimodalApiKey?` Optional. Set the multimodal model API key. Can also be set in the environment variable `LLAMA_CLOUD_VENDOR_MULTIMODAL_API_KEY`.
 - `numWorkers` as in the python version, is set in `SimpleDirectoryReader`. Default is 1.
 
 ### LlamaParse with SimpleDirectoryReader

apps/docs/docs/modules/data_loaders/llama_parse/json_mode.mdx

@@ -8,7 +8,7 @@ In JSON mode, LlamaParse will return a data structure representing the parsed ob
 
 ## Usage
 
-For Json mode, you need to use `loadJson`. The `resultType` is automatically set with this method. Currently it can't be used with `SimpleDirectoryReader`.
+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

apps/docs/docs/modules/data_stores/_category_.yml

@@ -0,0 +1,2 @@
+label: "Data Stores"
+position: 2

apps/docs/docs/modules/data_stores/chat_stores/_category_.yml

@@ -0,0 +1 @@
+label: "Chat Stores"

apps/docs/docs/modules/data_stores/chat_stores/index.md

@@ -0,0 +1,13 @@
+# Chat Stores
+
+Chat stores manage chat history by storing sequences of messages in a structured way, ensuring the order of messages is maintained for accurate conversation flow.
+
+## Available Chat Stores
+
+- [SimpleChatStore](../../../api/classes/SimpleChatStore.md): A simple in-memory chat store with support for [persisting](../index.md#local-storage) data to disk.
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
+## API Reference
+
+- [BaseChatStore](../../../api/interfaces/BaseChatStore.md)

apps/docs/docs/modules/data_stores/doc_stores/_category_.yml

@@ -0,0 +1,2 @@
+label: "Document Stores"
+position: 2

apps/docs/docs/modules/data_stores/doc_stores/index.md

@@ -0,0 +1,14 @@
+# Document Stores
+
+Document stores contain ingested document chunks, i.e. [Node](../../documents_and_nodes/index.md)s.
+
+## Available Document Stores
+
+- [SimpleDocumentStore](../../../api/classes/SimpleDocumentStore.md): A simple in-memory document store with support for [persisting](../index.md#local-storage) data to disk.
+- [PostgresDocumentStore](../../../api/classes/PostgresDocumentStore.md): A PostgreSQL document store, see [PostgreSQL Storage](../index.md#postgresql-storage).
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
+## API Reference
+
+- [BaseDocumentStore](../../../api/classes/BaseDocumentStore.md)

apps/docs/docs/modules/data_stores/index.md

@@ -0,0 +1,56 @@
+# Storage
+
+Storage in LlamaIndex.TS works automatically once you've configured a
+`StorageContext` object.
+
+## Local Storage
+
+You can configure the `persistDir` and attach it to an index.
+
+```typescript
+import {
+  Document,
+  VectorStoreIndex,
+  storageContextFromDefaults,
+} from "llamaindex";
+
+const storageContext = await storageContextFromDefaults({
+  persistDir: "./storage",
+});
+
+const document = new Document({ text: "Test Text" });
+const index = await VectorStoreIndex.fromDocuments([document], {
+  storageContext,
+});
+```
+
+## 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](../../api/interfaces/StorageContext.md)

apps/docs/docs/modules/data_stores/index_stores/_category_.yml

@@ -0,0 +1,2 @@
+label: "Index Stores"
+position: 3

apps/docs/docs/modules/data_stores/index_stores/index.md

@@ -0,0 +1,14 @@
+# Index Stores
+
+Index stores are underlying storage components that contain metadata(i.e. information created when indexing) about the [index](../../data_index.md) itself.
+
+## Available Index Stores
+
+- [SimpleIndexStore](../../../api/classes/SimpleIndexStore.md): A simple in-memory index store with support for [persisting](../index.md#local-storage) data to disk.
+- [PostgresIndexStore](../../../api/classes/PostgresIndexStore.md): A PostgreSQL index store, , see [PostgreSQL Storage](../index.md#postgresql-storage).
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
+## API Reference
+
+- [BaseIndexStore](../../../api/classes/BaseIndexStore.md)

apps/docs/docs/modules/data_stores/kv_stores/_category_.yml

@@ -0,0 +1,2 @@
+label: "Key-Value Stores"
+position: 4

apps/docs/docs/modules/data_stores/kv_stores/index.md

@@ -0,0 +1,14 @@
+# Key-Value Stores
+
+Key-Value Stores represent underlying storage components used in [Document Stores](../doc_stores/index.md) and [Index Stores](../index_stores/index.md)
+
+## Available Key-Value Stores
+
+- [SimpleKVStore](../../../api/classes/SimpleKVStore.md): A simple Key-Value store with support of [persisting](../index.md#local-storage) data to disk.
+- [PostgresKVStore](../../../api/classes/PostgresKVStore.md): A PostgreSQL Key-Value store, see [PostgreSQL Storage](../index.md#postgresql-storage).
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
+## API Reference
+
+- [BaseKVStore](../../../api/classes/BaseKVStore.md)

apps/docs/docs/modules/data_stores/vector_stores/index.md

@@ -0,0 +1,22 @@
+# Vector Stores
+
+Vector stores save embedding vectors of your ingested document chunks.
+
+## Available Vector Stores
+
+Available Vector Stores are shown on the sidebar to the left. Additionally the following integrations exist without separate documentation:
+
+- [SimpleVectorStore](../../../api/classes/SimpleVectorStore.md): A simple in-memory vector store with optional [persistance](../index.md#local-storage) to disk.
+- [AstraDBVectorStore](../../../api/classes/AstraDBVectorStore.md): A cloud-native, scalable Database-as-a-Service built on Apache Cassandra, see [datastax.com](https://www.datastax.com/products/datastax-astra)
+- [ChromaVectorStore](../../../api/classes/ChromaVectorStore.md): An open-source vector database, focused on ease of use and performance, see [trychroma.com](https://www.trychroma.com/)
+- [MilvusVectorStore](../../../api/classes/MilvusVectorStore.md): An open-source, high-performance, highly scalable vector database, see [milvus.io](https://milvus.io/)
+- [MongoDBAtlasVectorSearch](../../../api/classes/MongoDBAtlasVectorSearch.md): A cloud-based vector search solution for MongoDB, see [mongodb.com](https://www.mongodb.com/products/platform/atlas-vector-search)
+- [PGVectorStore](../../../api/classes/PGVectorStore.md): An open-source vector store built on PostgreSQL, see [pgvector Github](https://github.com/pgvector/pgvector)
+- [PineconeVectorStore](../../../api/classes/PineconeVectorStore.md): A managed, cloud-native vector database, see [pinecone.io](https://www.pinecone.io/)
+- [WeaviateVectorStore](../../../api/classes/WeaviateVectorStore.md): An open-source, ai-native vector database, see [weaviate.io](https://weaviate.io/)
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
+## API Reference
+
+- [VectorStoreBase](../../../api/classes/VectorStoreBase.md)

apps/docs/docs/modules/data_stores/vector_stores/qdrant.md

@@ -1,5 +1,7 @@
 # Qdrant Vector Store
 
+[qdrant.tech](https://qdrant.tech/)
+
 To run this example, you need to have a Qdrant instance running. You can run it with Docker:
 
 ```bash
@@ -87,4 +89,4 @@ main().catch(console.error);
 
 ## API Reference
 
-- [QdrantVectorStore](../../api/classes/QdrantVectorStore.md)
+- [QdrantVectorStore](../../../api/classes/QdrantVectorStore.md)

apps/docs/docs/modules/documents_and_nodes/index.md

@@ -1,7 +1,3 @@
----
-sidebar_position: 1
----
-
 # 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.

apps/docs/docs/modules/embeddings/_category_.yml

@@ -1,2 +1,2 @@
 label: "Embeddings"
-position: 3
+position: 6

apps/docs/docs/modules/embeddings/available_embeddings/mixedbreadai.md

@@ -0,0 +1,104 @@
+# MixedbreadAI
+
+Welcome to the mixedbread embeddings guide! This guide will help you use the mixedbread ai's API to generate embeddings for your text documents, ensuring you get the most relevant information, just like picking the freshest bread from the bakery.
+
+To find out more about the latest features, updates, and available models, visit [mixedbread.ai](https://mixedbread-ai.com/).
+
+## Table of Contents
+
+1. [Setup](#setup)
+2. [Usage with LlamaIndex](#usage-with-llamaindex)
+3. [Embeddings with Custom Parameters](#embeddings-with-custom-parameters)
+
+## Setup
+
+First, you will need to install the `llamaindex` package.
+
+```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";
+```
+
+## Usage with LlamaIndex
+
+This section will guide you through integrating mixedbread embeddings with LlamaIndex for more advanced usage.
+
+### Step 1: 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, like a variety of breads in a bakery.
+
+```ts
+Settings.embedModel = new MixedbreadAIEmbeddings({
+  apiKey: "<MIXEDBREAD_API_KEY>",
+  model: "mixedbread-ai/mxbai-embed-large-v1",
+});
+
+const document = new Document({
+  text: "The true source of happiness.",
+  id_: "bread",
+});
+
+const index = await VectorStoreIndex.fromDocuments([document]);
+```
+
+### Step 2: Create a Query Engine
+
+Combine the retriever and the embed model to create a query engine. This setup ensures that your queries are processed to provide the best results, like arranging the bread in the order of freshness and quality.
+
+Models can require prompts to generate embeddings for queries, in the 'mixedbread-ai/mxbai-embed-large-v1' model's case, the prompt is `Represent this sentence for searching relevant passages:`.
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const query =
+  "Represent this sentence for searching relevant passages: What is bread?";
+
+// Log the response
+const results = await queryEngine.query(query);
+console.log(results); // Serving up the freshest, most relevant results.
+```
+
+## Embeddings with Custom Parameters
+
+This section will guide you through generating embeddings with custom parameters and usage with f.e. matryoshka and binary embeddings.
+
+### Step 1: Create an Instance of MixedbreadAIEmbeddings
+
+Create a new instance of the `MixedbreadAIEmbeddings` class with custom parameters. For example, to use the `mixedbread-ai/mxbai-embed-large-v1` model with a batch size of 64, normalized embeddings, and binary encoding format:
+
+```ts
+const embeddings = new MixedbreadAIEmbeddings({
+  apiKey: "<MIXEDBREAD_API_KEY>",
+  model: "mixedbread-ai/mxbai-embed-large-v1",
+  batchSize: 64,
+  normalized: true,
+  dimensions: 512,
+  encodingFormat: MixedbreadAI.EncodingFormat.Binary,
+});
+```
+
+### Step 2: Define Texts
+
+Define the texts you want to generate embeddings for.
+
+```ts
+const texts = ["Bread is life", "Bread is love"];
+```
+
+### Step 3: Generate Embeddings
+
+Use the `embedDocuments` method to generate embeddings for the texts.
+
+```ts
+const result = await embeddings.embedDocuments(texts);
+console.log(result); // Perfectly customized embeddings, ready to serve.
+```
+
+## API Reference
+
+- [MixedbreadAIEmbeddings](../../../api/classes/MixedbreadAIEmbeddings.md)

apps/docs/docs/modules/embeddings/index.md

@@ -16,6 +16,16 @@ Settings.embedModel = new OpenAIEmbedding({
 
 For local embeddings, you can use the [HuggingFace](./available_embeddings/huggingface.md) embedding model.
 
+## Available Embeddings
+
+Most available embeddings are listed in the sidebar on the left.
+Additionally the following integrations exist without separate documentation:
+
+- [ClipEmbedding](../../api/classes/ClipEmbedding.md) using `@xenova/transformers`
+- [FireworksEmbedding](../../api/classes/FireworksEmbedding.md) see [fireworks.ai](https://fireworks.ai/)
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
 ## API Reference
 
 - [OpenAIEmbedding](../../api/classes/OpenAIEmbedding.md)

apps/docs/docs/modules/evaluation/_category_.yml

@@ -1,2 +1,2 @@
 label: "Evaluating"
-position: 3
+position: 9

apps/docs/docs/modules/evaluation/index.md

@@ -2,7 +2,7 @@
 
 ## Concept
 
-Evaluation and benchmarking are crucial concepts in LLM development. To improve the perfomance of an LLM app (RAG, agents) you must have a way to measure it.
+Evaluation and benchmarking are crucial concepts in LLM development. To improve the performance of an LLM app (RAG, agents) you must have a way to measure it.
 
 LlamaIndex offers key modules to measure the quality of generated results. We also offer key modules to measure retrieval quality.
 

apps/docs/docs/modules/ingestion_pipeline/_category_.yml

@@ -1,2 +1,2 @@
 label: "Ingestion Pipeline"
-position: 2
+position: 4

apps/docs/docs/modules/ingestion_pipeline/index.md

@@ -16,7 +16,7 @@ import {
   MetadataMode,
   OpenAIEmbedding,
   TitleExtractor,
-  SimpleNodeParser,
+  SentenceSplitter,
 } from "llamaindex";
 
 async function main() {
@@ -29,7 +29,7 @@ async function main() {
   const document = new Document({ text: essay, id_: path });
   const pipeline = new IngestionPipeline({
     transformations: [
-      new SimpleNodeParser({ chunkSize: 1024, chunkOverlap: 20 }),
+      new SentenceSplitter({ chunkSize: 1024, chunkOverlap: 20 }),
       new TitleExtractor(),
       new OpenAIEmbedding(),
     ],
@@ -62,7 +62,7 @@ import {
   MetadataMode,
   OpenAIEmbedding,
   TitleExtractor,
-  SimpleNodeParser,
+  SentenceSplitter,
   QdrantVectorStore,
   VectorStoreIndex,
 } from "llamaindex";
@@ -81,7 +81,7 @@ async function main() {
   const document = new Document({ text: essay, id_: path });
   const pipeline = new IngestionPipeline({
     transformations: [
-      new SimpleNodeParser({ chunkSize: 1024, chunkOverlap: 20 }),
+      new SentenceSplitter({ chunkSize: 1024, chunkOverlap: 20 }),
       new TitleExtractor(),
       new OpenAIEmbedding(),
     ],

apps/docs/docs/modules/ingestion_pipeline/transformations.md

@@ -4,7 +4,7 @@ A transformation is something that takes a list of nodes as an input, and return
 
 Currently, the following components are Transformation objects:
 
-- [SimpleNodeParser](../../api/classes/SimpleNodeParser.md)
+- [SentenceSplitter](../../api/classes/SentenceSplitter.md)
 - [MetadataExtractor](../documents_and_nodes/metadata_extraction.md)
 - [Embeddings](../embeddings/index.md)
 
@@ -13,10 +13,10 @@ Currently, the following components are Transformation objects:
 While transformations are best used with with an IngestionPipeline, they can also be used directly.
 
 ```ts
-import { SimpleNodeParser, TitleExtractor, Document } from "llamaindex";
+import { SentenceSplitter, TitleExtractor, Document } from "llamaindex";
 
 async function main() {
-  let nodes = new SimpleNodeParser().getNodesFromDocuments([
+  let nodes = new SentenceSplitter().getNodesFromDocuments([
     new Document({ text: "I am 10 years old. John is 20 years old." }),
   ]);
 
@@ -34,15 +34,15 @@ main().catch(console.error);
 
 ## Custom Transformations
 
-You can implement any transformation yourself by implementing the `TransformerComponent`.
+You can implement any transformation yourself by implementing the `TransformComponent`.
 
-The following custom transformation will remove any special characters or punctutaion in text.
+The following custom transformation will remove any special characters or punctuation in text.
 
 ```ts
-import { TransformerComponent, Node } from "llamaindex";
+import { TransformComponent, TextNode } from "llamaindex";
 
-class RemoveSpecialCharacters extends TransformerComponent {
-  async transform(nodes: Node[]): Promise<Node[]> {
+export class RemoveSpecialCharacters extends TransformComponent {
+  async transform(nodes: TextNode[]): Promise<TextNode[]> {
     for (const node of nodes) {
       node.text = node.text.replace(/[^\w\s]/gi, "");
     }
@@ -75,3 +75,7 @@ async function main() {
 
 main().catch(console.error);
 ```
+
+## API Reference
+
+- [TransformComponent](../../api/classes/TransformComponent.md)

apps/docs/docs/modules/llms/_category_.yml

@@ -1,2 +1,2 @@
 label: "LLMs"
-position: 3
+position: 5

apps/docs/docs/modules/llms/available_llms/bedrock.md

@@ -25,10 +25,47 @@ ANTHROPIC_CLAUDE_3_SONNET = "anthropic.claude-3-sonnet-20240229-v1:0";
 ANTHROPIC_CLAUDE_3_HAIKU = "anthropic.claude-3-haiku-20240307-v1:0";
 ANTHROPIC_CLAUDE_3_OPUS = "anthropic.claude-3-opus-20240229-v1:0"; // available on us-west-2
 ANTHROPIC_CLAUDE_3_5_SONNET = "anthropic.claude-3-5-sonnet-20240620-v1:0";
+ANTHROPIC_CLAUDE_3_5_HAIKU = "anthropic.claude-3-5-haiku-20241022-v1:0";
 META_LLAMA2_13B_CHAT = "meta.llama2-13b-chat-v1";
 META_LLAMA2_70B_CHAT = "meta.llama2-70b-chat-v1";
 META_LLAMA3_8B_INSTRUCT = "meta.llama3-8b-instruct-v1:0";
 META_LLAMA3_70B_INSTRUCT = "meta.llama3-70b-instruct-v1:0";
+META_LLAMA3_1_8B_INSTRUCT = "meta.llama3-1-8b-instruct-v1:0"; // available on us-west-2
+META_LLAMA3_1_70B_INSTRUCT = "meta.llama3-1-70b-instruct-v1:0"; // available on us-west-2
+META_LLAMA3_1_405B_INSTRUCT = "meta.llama3-1-405b-instruct-v1:0"; // available on us-west-2, tool calling supported
+META_LLAMA3_2_1B_INSTRUCT = "meta.llama3-2-1b-instruct-v1:0"; // only available via inference endpoints (see below)
+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_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";
+```
+
+You can also use Bedrock's Inference endpoints by using the model names:
+
+```ts
+// US
+US_ANTHROPIC_CLAUDE_3_HAIKU = "us.anthropic.claude-3-haiku-20240307-v1:0";
+US_ANTHROPIC_CLAUDE_3_OPUS = "us.anthropic.claude-3-opus-20240229-v1:0";
+US_ANTHROPIC_CLAUDE_3_SONNET = "us.anthropic.claude-3-sonnet-20240229-v1:0";
+US_ANTHROPIC_CLAUDE_3_5_SONNET = "us.anthropic.claude-3-5-sonnet-20240620-v1:0";
+US_ANTHROPIC_CLAUDE_3_5_SONNET_V2 =
+  "us.anthropic.claude-3-5-sonnet-20241022-v2:0";
+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-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";
+
+// EU
+EU_ANTHROPIC_CLAUDE_3_HAIKU = "eu.anthropic.claude-3-haiku-20240307-v1:0";
+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";
 ```
 
 Sonnet, Haiku and Opus are multimodal, image_url only supports base64 data url format, e.g. `data:image/jpeg;base64,SGVsbG8sIFdvcmxkIQ==`
@@ -64,3 +101,72 @@ async function main() {
   console.log(response.response);
 }
 ```
+
+## Agent Example
+
+```ts
+import { BEDROCK_MODELS, Bedrock } from "@llamaindex/community";
+import { FunctionTool, LLMAgent } from "llamaindex";
+
+const sumNumbers = FunctionTool.from(
+  ({ a, b }: { a: number; b: number }) => `${a + b}`,
+  {
+    name: "sumNumbers",
+    description: "Use this function to sum two numbers",
+    parameters: {
+      type: "object",
+      properties: {
+        a: {
+          type: "number",
+          description: "The first number",
+        },
+        b: {
+          type: "number",
+          description: "The second number",
+        },
+      },
+      required: ["a", "b"],
+    },
+  },
+);
+
+const divideNumbers = FunctionTool.from(
+  ({ a, b }: { a: number; b: number }) => `${a / b}`,
+  {
+    name: "divideNumbers",
+    description: "Use this function to divide two numbers",
+    parameters: {
+      type: "object",
+      properties: {
+        a: {
+          type: "number",
+          description: "The dividend a to divide",
+        },
+        b: {
+          type: "number",
+          description: "The divisor b to divide by",
+        },
+      },
+      required: ["a", "b"],
+    },
+  },
+);
+
+const bedrock = new Bedrock({
+  model: BEDROCK_MODELS.META_LLAMA3_1_405B_INSTRUCT,
+  ...
+});
+
+async function main() {
+  const agent = new LLMAgent({
+    llm: bedrock,
+    tools: [sumNumbers, divideNumbers],
+  });
+
+  const response = await agent.chat({
+    message: "How much is 5 + 5? then divide by 2",
+  });
+
+  console.log(response.message);
+}
+```

apps/docs/docs/modules/llms/available_llms/deepseek.md

@@ -0,0 +1,52 @@
+# DeepSeek LLM
+
+[DeepSeek Platform](https://platform.deepseek.com/)
+
+## Usage
+
+```ts
+import { DeepSeekLLM, Settings } from "llamaindex";
+
+Settings.llm = new DeepSeekLLM({
+  apiKey: "<YOUR_API_KEY>",
+  model: "deepseek-coder", // or "deepseek-chat"
+});
+```
+
+## Example
+
+```ts
+import { DeepSeekLLM, Document, VectorStoreIndex, Settings } from "llamaindex";
+
+const deepseekLlm = new DeepSeekLLM({
+  apiKey: "<YOUR_API_KEY>",
+  model: "deepseek-coder", // or "deepseek-chat"
+});
+
+async function main() {
+  const response = await llm.deepseekLlm.chat({
+    messages: [
+      {
+        role: "system",
+        content: "You are an AI assistant",
+      },
+      {
+        role: "user",
+        content: "Tell me about San Francisco",
+      },
+    ],
+    stream: false,
+  });
+  console.log(response);
+}
+```
+
+# Limitations
+
+Currently does not support function calling.
+
+[Currently does not support json-output param while still is very good at json generating.](https://platform.deepseek.com/api-docs/faq#does-your-api-support-json-output)
+
+## API Reference
+
+- [DeepSeekLLM](../../../api/classes/DeepSeekLLM.md)

apps/docs/docs/modules/llms/available_llms/fireworks.md

@@ -1,6 +1,6 @@
 # Fireworks LLM
 
-Fireworks.ai focus on production use cases for open source LLMs, offering speed and quality.
+[Fireworks.ai](https://fireworks.ai/) focus on production use cases for open source LLMs, offering speed and quality.
 
 ## Usage
 

apps/docs/docs/modules/llms/index.md

@@ -1,7 +1,3 @@
----
-sidebar_position: 3
----
-
 # 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`.
@@ -30,6 +26,15 @@ export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name
 
 For local LLMs, currently we recommend the use of [Ollama](./available_llms/ollama.md) LLM.
 
+## Available LLMs
+
+Most available LLMs are listed in the sidebar on the left. Additionally the following integrations exist without separate documentation:
+
+- [HuggingFaceLLM](../../api/classes/HuggingFaceLLM.md) and [HuggingFaceInferenceAPI](../../api/classes/HuggingFaceInferenceAPI.md).
+- [ReplicateLLM](../../api/classes/ReplicateLLM.md) see [replicate.com](https://replicate.com/)
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
 ## API Reference
 
 - [OpenAI](../../api/classes/OpenAI.md)

apps/docs/docs/modules/node_parser.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 4
+sidebar_position: 11
 ---
 
 # NodeParser
@@ -7,9 +7,9 @@ sidebar_position: 4
 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, SimpleNodeParser } from "llamaindex";
+import { Document, SentenceSplitter } from "llamaindex";
 
-const nodeParser = new SimpleNodeParser();
+const nodeParser = new SentenceSplitter();
 
 Settings.nodeParser = nodeParser;
 ```
@@ -93,6 +93,5 @@ The output metadata will be something like:
 
 ## API Reference
 
-- [SimpleNodeParser](../api/classes/SimpleNodeParser.md)
 - [SentenceSplitter](../api/classes/SentenceSplitter.md)
 - [MarkdownNodeParser](../api/classes/MarkdownNodeParser.md)

apps/docs/docs/modules/node_postprocessors/cohere_reranker.md

@@ -39,8 +39,9 @@ const index = await VectorStoreIndex.fromDocuments([document]);
 The default value for `similarityTopK` is 2. This means that only the most similar document will be returned. To retrieve more results, you can increase the value of `similarityTopK`.
 
 ```ts
-const retriever = index.asRetriever();
-retriever.similarityTopK = 5;
+const retriever = index.asRetriever({
+  similarityTopK: 5,
+});
 ```
 
 ## Create a new instance of the CohereRerank class

apps/docs/docs/modules/node_postprocessors/index.md

@@ -107,3 +107,4 @@ const filteredNodes = processor.postprocessNodes(nodes);
 ## API Reference
 
 - [SimilarityPostprocessor](../../api/classes/SimilarityPostprocessor.md)
+- [MetadataReplacementPostProcessor](../../api/classes/MetadataReplacementPostProcessor.md)

apps/docs/docs/modules/node_postprocessors/jinaai_reranker.md

@@ -39,8 +39,9 @@ const index = await VectorStoreIndex.fromDocuments([document]);
 The default value for `similarityTopK` is 2. This means that only the most similar document will be returned. To retrieve more results, you can increase the value of `similarityTopK`.
 
 ```ts
-const retriever = index.asRetriever();
-retriever.similarityTopK = 5;
+const retriever = index.asRetriever({
+  similarityTopK: 5,
+});
 ```
 
 ## Create a new instance of the JinaAIReranker class

apps/docs/docs/modules/node_postprocessors/mixedbreadiai_reranker.md

@@ -0,0 +1,169 @@
+# MixedbreadAI
+
+Welcome to the mixedbread ai reranker guide! This guide will help you use mixedbread ai's API to rerank search query results, ensuring you get the most relevant information, just like picking the freshest bread from the bakery.
+
+To find out more about the latest features and updates, visit the [mixedbread.ai](https://mixedbread.ai/).
+
+## Table of Contents
+
+1. [Setup](#setup)
+2. [Usage with LlamaIndex](#usage-with-llamaindex)
+3. [Simple Reranking Guide](#simple-reranking-guide)
+4. [Reranking with Objects](#reranking-with-objects)
+
+## Setup
+
+First, you will need to install the `llamaindex` package.
+
+```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 `MixedbreadAIReranker` class.
+
+```ts
+import {
+  MixedbreadAIReranker,
+  Document,
+  OpenAI,
+  VectorStoreIndex,
+  Settings,
+} from "llamaindex";
+```
+
+## Usage with LlamaIndex
+
+This section will guide you through integrating mixedbread's reranker with LlamaIndex.
+
+### Step 1: 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, like a variety of breads in a bakery.
+
+```ts
+const document = new Document({
+  text: "This is a sample document.",
+  id_: "sampleDoc",
+});
+
+Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 });
+
+const index = await VectorStoreIndex.fromDocuments([document]);
+```
+
+### Step 2: Increase Similarity TopK
+
+The default value for `similarityTopK` is 2, which means only the most similar document will be returned. To get more results, like picking a variety of fresh breads, you can increase the value of `similarityTopK`.
+
+```ts
+const retriever = index.asRetriever({
+  similarityTopK: 5,
+});
+```
+
+### Step 3: Create a MixedbreadAIReranker Instance
+
+Create a new instance of the `MixedbreadAIReranker` class.
+
+```ts
+const nodePostprocessor = new MixedbreadAIReranker({
+  apiKey: "<MIXEDBREAD_API_KEY>",
+  topN: 4,
+});
+```
+
+### Step 4: Create a Query Engine
+
+Combine the retriever and node postprocessor to create a query engine. This setup ensures that your queries are processed and reranked to provide the best results, like arranging the bread in the order of freshness and quality.
+
+```ts
+const queryEngine = index.asQueryEngine({
+  retriever,
+  nodePostprocessors: [nodePostprocessor],
+});
+
+// Log the response
+const response = await queryEngine.query("Where did the author grow up?");
+console.log(response);
+```
+
+With mixedbread's Reranker, you're all set to serve up the most relevant and well-ordered results, just like a skilled baker arranging their best breads for eager customers. Enjoy the perfect blend of technology and culinary delight!
+
+## Simple Reranking Guide
+
+This section will guide you through a simple reranking process using mixedbread ai.
+
+### Step 1: Create an Instance of MixedbreadAIReranker
+
+Create a new instance of the `MixedbreadAIReranker` class, passing in your API key and the number of results you want to return. It's like setting up your bakery to offer a specific number of freshly baked items.
+
+```ts
+const reranker = new MixedbreadAIReranker({
+  apiKey: "<MIXEDBREAD_API_KEY>",
+  topN: 4,
+});
+```
+
+### Step 2: Define Nodes and Query
+
+Define the nodes (documents) you want to rerank and the query.
+
+```ts
+const nodes = [
+  { node: new BaseNode("To bake bread you need flour") },
+  { node: new BaseNode("To bake bread you need yeast") },
+];
+const query = "What do you need to bake bread?";
+```
+
+### Step 3: Perform Reranking
+
+Use the `postprocessNodes` method to rerank the nodes based on the query.
+
+```ts
+const result = await reranker.postprocessNodes(nodes, query);
+console.log(result); // Like pulling freshly baked nodes out of the oven.
+```
+
+## Reranking with Objects
+
+This section will guide you through reranking when working with objects.
+
+### Step 1: Create an Instance of MixedbreadAIReranker
+
+Create a new instance of the `MixedbreadAIReranker` class, just like before.
+
+```ts
+const reranker = new MixedbreadAIReranker({
+  apiKey: "<MIXEDBREAD_API_KEY>",
+  model: "mixedbread-ai/mxbai-rerank-large-v1",
+  topK: 5,
+  rankFields: ["title", "content"],
+  returnInput: true,
+  maxRetries: 5,
+});
+```
+
+### Step 2: Define Documents and Query
+
+Define the documents (objects) you want to rerank and the query.
+
+```ts
+const documents = [
+  { title: "Bread Recipe", content: "To bake bread you need flour" },
+  { title: "Bread Recipe", content: "To bake bread you need yeast" },
+];
+const query = "What do you need to bake bread?";
+```
+
+### Step 3: Perform Reranking
+
+Use the `rerank` method to reorder the documents based on the query.
+
+```ts
+const result = await reranker.rerank(documents, query);
+console.log(result); // Perfectly customized results, ready to serve.
+```
+
+## API Reference
+
+- [MixedbreadAIReranker](../../api/classes/MixedbreadAIReranker.md)

apps/docs/docs/modules/prompt/_category_.yml

@@ -1,2 +1,2 @@
 label: "Prompts"
-position: 0
+position: 7

apps/docs/docs/modules/prompt/index.md

@@ -73,6 +73,5 @@ const response = await queryEngine.query({
 
 ## API Reference
 
-- [TextQaPrompt](../../api/type-aliases/TextQaPrompt.md)
 - [ResponseSynthesizer](../../api/classes/ResponseSynthesizer.md)
 - [CompactAndRefine](../../api/classes/CompactAndRefine.md)

apps/docs/docs/modules/query_engines/_category_.yml

@@ -1,2 +1,2 @@
 label: "Query Engines"
-position: 2
+position: 8

apps/docs/docs/modules/query_engines/index.md

@@ -1,6 +1,6 @@
 # QueryEngine
 
-A query engine wraps a `Retriever` and a `ResponseSynthesizer` into a pipeline, that will use the query string to fetech nodes and then send them to the LLM to generate a response.
+A query engine wraps a `Retriever` and a `ResponseSynthesizer` into a pipeline, that will use the query string to fetch nodes and then send them to the LLM to generate a response.
 
 ```typescript
 const queryEngine = index.asQueryEngine();

apps/docs/docs/modules/query_engines/metadata_filtering.md

@@ -75,7 +75,7 @@ const queryEngine = index.asQueryEngine({
       {
         key: "dogId",
         value: "2",
-        filterType: "ExactMatch",
+        operator: "==",
       },
     ],
   },
@@ -88,6 +88,8 @@ const response = await queryEngine.query({
 console.log(response.toString());
 ```
 
+Besides using the equal operator (`==`), you can also use a whole set of different [operators](../../api/interfaces/MetadataFilter.md#operator) to filter your documents.
+
 ## Full Code
 
 ```ts
@@ -135,7 +137,7 @@ async function main() {
           {
             key: "dogId",
             value: "2",
-            filterType: "ExactMatch",
+            operator: "==",
           },
         ],
       },
@@ -156,3 +158,4 @@ main();
 
 - [VectorStoreIndex](../../api/classes/VectorStoreIndex.md)
 - [ChromaVectorStore](../../api/classes/ChromaVectorStore.md)
+- [MetadataFilter](../../api/interfaces/MetadataFilter.md)

apps/docs/docs/modules/query_engines/router_query_engine.md

@@ -15,7 +15,7 @@ import {
   OpenAI,
   RouterQueryEngine,
   SimpleDirectoryReader,
-  SimpleNodeParser,
+  SentenceSplitter,
   SummaryIndex,
   VectorStoreIndex,
   Settings,
@@ -34,11 +34,11 @@ const documents = await new SimpleDirectoryReader().loadData({
 
 ## Service Context
 
-Next, we need to define some basic rules and parse the documents into nodes. We will use the `SimpleNodeParser` to parse the documents into nodes and `Settings` to define the rules (eg. LLM API key, chunk size, etc.):
+Next, we need to define some basic rules and parse the documents into nodes. We will use the `SentenceSplitter` to parse the documents into nodes and `Settings` to define the rules (eg. LLM API key, chunk size, etc.):
 
 ```ts
 Settings.llm = new OpenAI();
-Settings.nodeParser = new SimpleNodeParser({
+Settings.nodeParser = new SentenceSplitter({
   chunkSize: 1024,
 });
 ```
@@ -104,14 +104,14 @@ import {
   OpenAI,
   RouterQueryEngine,
   SimpleDirectoryReader,
-  SimpleNodeParser,
+  SentenceSplitter,
   SummaryIndex,
   VectorStoreIndex,
   Settings,
 } from "llamaindex";
 
 Settings.llm = new OpenAI();
-Settings.nodeParser = new SimpleNodeParser({
+Settings.nodeParser = new SentenceSplitter({
   chunkSize: 1024,
 });
 

apps/docs/docs/modules/response_synthesizer.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 6
+sidebar_position: 15
 ---
 
 # ResponseSynthesizer

apps/docs/docs/modules/retriever.md

@@ -1,21 +1,23 @@
 ---
-sidebar_position: 5
+sidebar_position: 14
 ---
 
 # Retriever
 
-A retriever in LlamaIndex is what is used to fetch `Node`s from an index using a query string. Aa `VectorIndexRetriever` will fetch the top-k most similar nodes. Meanwhile, a `SummaryIndexRetriever` will fetch all nodes no matter the query.
+A retriever in LlamaIndex is what is used to fetch `Node`s from an index using a query string.
+
+- [VectorIndexRetriever](../api/classes/VectorIndexRetriever.md) will fetch the top-k most similar nodes. Ideal for dense retrieval to find most relevant nodes.
+- [SummaryIndexRetriever](../api/classes/SummaryIndexRetriever.md) will fetch all nodes no matter the query. Ideal when complete context is necessary, e.g. analyzing large datasets.
+- [SummaryIndexLLMRetriever](../api/classes/SummaryIndexLLMRetriever.md) utilizes an LLM to score and filter nodes based on relevancy to the query.
+- [KeywordTableLLMRetriever](../api/classes/KeywordTableLLMRetriever.md) uses an LLM to extract keywords from the query and retrieve relevant nodes based on keyword matches.
+- [KeywordTableSimpleRetriever](../api/classes/KeywordTableSimpleRetriever.md) uses a basic frequency-based approach to extract keywords and retrieve nodes.
+- [KeywordTableRAKERetriever](../api/classes/KeywordTableRAKERetriever.md) uses the RAKE (Rapid Automatic Keyword Extraction) algorithm to extract keywords from the query, focusing on co-occurrence and context for keyword-based retrieval.
 
 ```typescript
-const retriever = vector_index.asRetriever();
-retriever.similarityTopK = 3;
+const retriever = vectorIndex.asRetriever({
+  similarityTopK: 3,
+});
 
 // Fetch nodes!
 const nodesWithScore = await retriever.retrieve({ query: "query string" });
 ```
-
-## API Reference
-
-- [SummaryIndexRetriever](../api/classes/SummaryIndexRetriever.md)
-- [SummaryIndexLLMRetriever](../api/classes/SummaryIndexLLMRetriever.md)
-- [VectorIndexRetriever](../api/classes/VectorIndexRetriever.md)

apps/docs/docs/modules/storage.md

@@ -1,26 +0,0 @@
----
-sidebar_position: 7
----
-
-# Storage
-
-Storage in LlamaIndex.TS works automatically once you've configured a `StorageContext` object. Just configure the `persistDir` and attach it to an index.
-
-Right now, only saving and loading from disk is supported, with future integrations planned!
-
-```typescript
-import { Document, VectorStoreIndex, storageContextFromDefaults } from "./src";
-
-const storageContext = await storageContextFromDefaults({
-  persistDir: "./storage",
-});
-
-const document = new Document({ text: "Test Text" });
-const index = await VectorStoreIndex.fromDocuments([document], {
-  storageContext,
-});
-```
-
-## API Reference
-
-- [StorageContext](../api/interfaces/StorageContext.md)

apps/docs/docs/modules/workflows.mdx

@@ -0,0 +1,168 @@
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../examples/workflow/joke.ts";
+
+# Workflows
+
+A `Workflow` in LlamaIndexTS is an event-driven abstraction used to chain together several events. Workflows are made up of `steps`, with each step responsible for handling certain event types and emitting new events.
+
+Workflows in LlamaIndexTS work by defining step functions that handle specific event types and emit new events.
+
+When a step function is added to a workflow, you need to specify the input and optionally the output event types (used for validation). The specification of the input events ensures each step only runs when an accepted event is ready.
+
+You can create a `Workflow` to do anything! Build an agent, a RAG flow, an extraction flow, or anything else you want.
+
+## Getting Started
+
+As an illustrative example, let's consider a naive workflow where a joke is generated and then critiqued.
+
+<CodeBlock language="ts">{CodeSource}</CodeBlock>
+
+There's a few moving pieces here, so let's go through this piece by piece.
+
+### Defining Workflow Events
+
+```typescript
+export class JokeEvent extends WorkflowEvent<{ joke: string }> {}
+```
+
+Events are user-defined classes that extend `WorkflowEvent` and contain arbitrary data provided as template argument. In this case, our workflow relies on a single user-defined event, the `JokeEvent` with a `joke` attribute of type `string`.
+
+### Setting up the Workflow Class
+
+```typescript
+const llm = new OpenAI();
+...
+const jokeFlow = new Workflow({ verbose: true });
+```
+
+Our workflow is implemented by initiating the `Workflow` class. For simplicity, we created a `OpenAI` llm instance.
+
+### Workflow Entry Points
+
+```typescript
+const generateJoke = async (_context: Context, ev: StartEvent) => {
+  const prompt = `Write your best joke about ${ev.data.input}.`;
+  const response = await llm.complete({ prompt });
+  return new JokeEvent({ joke: response.text });
+};
+```
+
+Here, we come to the entry-point of our workflow. While events are user-defined, there are two special-case events, the `StartEvent` and the `StopEvent`. Here, the `StartEvent` signifies where to send the initial workflow input.
+
+The `StartEvent` is a bit of a special object since it can hold arbitrary attributes. Here, we accessed the topic with `ev.data.input`.
+
+At this point, you may have noticed that we haven't explicitly told the workflow what events are handled by which steps.
+
+To do so, we use the `addStep` method which adds a step to the workflow. The first argument is the event type that the step will handle, and the second argument is the previously defined step function:
+
+```typescript
+jokeFlow.addStep(StartEvent, generateJoke);
+```
+
+### Workflow Exit Points
+
+```typescript
+const critiqueJoke = async (_context: Context, ev: JokeEvent) => {
+  const prompt = `Give a thorough critique of the following joke: ${ev.data.joke}`;
+  const response = await llm.complete({ prompt });
+  return new StopEvent({ result: response.text });
+};
+```
+
+Here, we have our second, and last step, in the workflow. We know its the last step because the special `StopEvent` is returned. When the workflow encounters a returned `StopEvent`, it immediately stops the workflow and returns whatever the result was.
+
+In this case, the result is a string, but it could be a map, array, or any other object.
+
+Don't forget to add the step to the workflow:
+
+```typescript
+jokeFlow.addStep(JokeEvent, critiqueJoke);
+```
+
+### Running the Workflow
+
+```typescript
+const result = await jokeFlow.run("pirates");
+console.log(result.data.result);
+```
+
+Lastly, we run the workflow. The `.run()` method is async, so we use await here to wait for the result.
+
+### Validating Workflows
+
+To tell the workflow what events are produced by each step, you can optionally provide a third argument to `addStep` to specify the output event type:
+
+```typescript
+jokeFlow.addStep(StartEvent, generateJoke, { outputs: JokeEvent });
+jokeFlow.addStep(JokeEvent, critiqueJoke, { outputs: StopEvent });
+```
+
+To validate a workflow, you need to call the `validate` method:
+
+```typescript
+jokeFlow.validate();
+```
+
+To automatically validate a workflow when you run it, you can set the `validate` flag to `true` at initialization:
+
+```typescript
+const jokeFlow = new Workflow({ verbose: true, validate: true });
+```
+
+## Working with Global Context/State
+
+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";
+
+const query = async (context: Context, ev: MyEvent) => {
+  // get the query from the context
+  const query = context.get("query");
+  // do something with context and event
+  const val = ...
+  const result = ...
+  // store in context
+  context.set("key", val);
+
+  return new StopEvent({ result });
+};
+```
+
+## Waiting for Multiple Events
+
+The context does more than just hold data, it also provides utilities to buffer and wait for multiple events.
+
+For example, you might have a step that waits for a query and retrieved nodes before synthesizing a response:
+
+```typescript
+const synthesize = async (context: Context, ev: QueryEvent | RetrieveEvent) => {
+  const events = context.collectEvents(ev, [QueryEvent | RetrieveEvent]);
+  if (!events) {
+    return;
+  }
+  const prompt = events
+    .map((event) => {
+      if (event instanceof QueryEvent) {
+        return `Answer this query using the context provided: ${event.data.query}`;
+      } else if (event instanceof RetrieveEvent) {
+        return `Context: ${event.data.context}`;
+      }
+      return "";
+    })
+    .join("\n");
+
+  const response = await llm.complete({ prompt });
+  return new StopEvent({ result: response.text });
+};
+```
+
+Using `ctx.collectEvents()` we can buffer and wait for ALL expected events to arrive. This function will only return events (in the requested order) once all events have arrived.
+
+## Manually Triggering Events
+
+Normally, events are triggered by returning another event during a step. However, events can also be manually dispatched using the `ctx.sendEvent(event)` method within a workflow.
+
+## Examples
+
+You can find many useful examples of using workflows in the [examples folder](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/workflow).

apps/docs/docusaurus.config.js

@@ -1,6 +1,7 @@
 // @ts-check
 // Note: type annotations allow type checking and IDEs autocompletion
 
+// eslint-disable-next-line @typescript-eslint/no-require-imports
 const renderer = require("prism-react-renderer");
 const lightCodeTheme = renderer.themes.github;
 const darkCodeTheme = renderer.themes.dracula;
@@ -39,6 +40,7 @@ const config = {
           // editUrl:
           //   "https://github.com/facebook/docusaurus/tree/main/packages/create-docusaurus/templates/shared/",
           remarkPlugins: [
+            // eslint-disable-next-line @typescript-eslint/no-require-imports
             [require("@docusaurus/remark-plugin-npm2yarn"), { sync: true }],
           ],
         },
@@ -60,6 +62,12 @@ const config = {
     ({
       // Replace with your project's social card
       image: "img/favicon.png", // TODO change this
+      announcementBar: {
+        id: "migrate_to_next",
+        content:
+          'We are migrating to Next.js based documentation. Check it out <a href="https://ts.llamaindex.ai/docs/llamaindex">here</a>!',
+        isCloseable: false,
+      },
       navbar: {
         title: "LlamaIndex.TS",
         logo: {

apps/docs/package.json

@@ -1,6 +1,6 @@
 {
   "name": "docs",
-  "version": "0.0.38",
+  "version": "0.0.133",
   "private": true,
   "scripts": {
     "docusaurus": "docusaurus",
@@ -15,29 +15,29 @@
     "typecheck": "tsc"
   },
   "dependencies": {
-    "@docusaurus/core": "^3.3.2",
-    "@docusaurus/remark-plugin-npm2yarn": "^3.3.2",
+    "@docusaurus/core": "3.6.1",
+    "@docusaurus/remark-plugin-npm2yarn": "3.6.1",
     "@llamaindex/examples": "workspace:*",
-    "@mdx-js/react": "^3.0.1",
+    "@mdx-js/react": "^3.1.0",
     "clsx": "^2.1.1",
     "llamaindex": "workspace:*",
-    "postcss": "^8.4.38",
-    "prism-react-renderer": "^2.3.1",
+    "postcss": "^8.4.49",
+    "prism-react-renderer": "^2.4.0",
     "raw-loader": "^4.0.2",
     "react": "^18.3.1",
-    "react-dom": "^18.3.1"
+    "react-dom": "18.3.1"
   },
   "devDependencies": {
-    "@docusaurus/module-type-aliases": "3.3.2",
-    "@docusaurus/preset-classic": "^3.3.2",
-    "@docusaurus/theme-classic": "^3.3.2",
-    "@docusaurus/types": "^3.3.2",
-    "@tsconfig/docusaurus": "^2.0.3",
-    "@types/node": "^20.12.11",
-    "docusaurus-plugin-typedoc": "^1.0.1",
-    "typedoc": "^0.25.13",
-    "typedoc-plugin-markdown": "^4.0.1",
-    "typescript": "^5.5.2"
+    "@docusaurus/module-type-aliases": "3.6.1",
+    "@docusaurus/preset-classic": "3.6.1",
+    "@docusaurus/theme-classic": "3.6.1",
+    "@docusaurus/types": "3.6.1",
+    "@tsconfig/docusaurus": "2.0.3",
+    "@types/node": "^22.9.0",
+    "docusaurus-plugin-typedoc": "1.0.5",
+    "typedoc": "0.26.11",
+    "typedoc-plugin-markdown": "4.2.10",
+    "typescript": "^5.6.3"
   },
   "browserslist": {
     "production": [
@@ -52,6 +52,6 @@
     ]
   },
   "engines": {
-    "node": ">=18"
+    "node": ">=20.0.0"
   }
 }

apps/docs/src/components/HomepageFeatures/index.tsx

@@ -11,16 +11,19 @@ type FeatureItem = {
 const FeatureList: FeatureItem[] = [
   {
     title: "Data Driven",
+    // eslint-disable-next-line @typescript-eslint/no-require-imports
     Svg: require("@site/static/img/undraw_docusaurus_mountain.svg").default,
     description: <>LlamaIndex.TS is all about using your data with LLMs.</>,
   },
   {
     title: "Typescript Native",
+    // eslint-disable-next-line @typescript-eslint/no-require-imports
     Svg: require("@site/static/img/undraw_docusaurus_tree.svg").default,
     description: <>We ❤️ Typescript, and so do our users.</>,
   },
   {
     title: "Built by the Community",
+    // eslint-disable-next-line @typescript-eslint/no-require-imports
     Svg: require("@site/static/img/undraw_docusaurus_react.svg").default,
     description: (
       <>

apps/docs/turbo.json

@@ -0,0 +1,8 @@
+{
+  "extends": ["//"],
+  "tasks": {
+    "build": {
+      "outputs": ["build/**", ".docusaurus/**"]
+    }
+  }
+}

apps/next/.gitignore

@@ -0,0 +1,32 @@
+# deps
+/node_modules
+
+# generated content
+.contentlayer
+.content-collections
+.source
+
+# test & build
+/coverage
+/.next/
+/out/
+/build
+*.tsbuildinfo
+
+# misc
+.DS_Store
+*.pem
+/.pnp
+.pnp.js
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# others
+.env*.local
+.vercel
+next-env.d.ts
+
+# build
+/src/content/docs/cloud/api
+./types

apps/next/CHANGELOG.md

@@ -0,0 +1,334 @@
+# @llamaindex/doc
+
+## 0.0.31
+
+### Patch Changes
+
+- Updated dependencies [b504303]
+- Updated dependencies [e0f6cc3]
+- Updated dependencies [a0e6f57]
+  - llamaindex@0.8.27
+  - @llamaindex/core@0.4.18
+  - @llamaindex/cloud@2.0.18
+  - @llamaindex/node-parser@0.0.19
+  - @llamaindex/openai@0.1.43
+  - @llamaindex/readers@1.0.20
+  - @llamaindex/workflow@0.0.8
+
+## 0.0.30
+
+### Patch Changes
+
+- Updated dependencies [3d1808b]
+  - @llamaindex/core@0.4.17
+  - llamaindex@0.8.26
+  - @llamaindex/openai@0.1.42
+  - @llamaindex/cloud@2.0.17
+  - @llamaindex/node-parser@0.0.18
+  - @llamaindex/readers@1.0.19
+
+## 0.0.29
+
+### Patch Changes
+
+- Updated dependencies [7e8230b]
+- Updated dependencies [8be4589]
+  - @llamaindex/readers@1.0.18
+  - @llamaindex/cloud@2.0.16
+  - @llamaindex/core@0.4.16
+  - @llamaindex/node-parser@0.0.17
+  - @llamaindex/openai@0.1.41
+  - @llamaindex/workflow@0.0.7
+  - llamaindex@0.8.25
+
+## 0.0.28
+
+### Patch Changes
+
+- fd38a25: Add vercel tool adapter to use query engine tool
+
+## 0.0.27
+
+### Patch Changes
+
+- Updated dependencies [515f2c1]
+  - llamaindex@0.8.24
+
+## 0.0.26
+
+### Patch Changes
+
+- @llamaindex/cloud@2.0.15
+- @llamaindex/core@0.4.15
+- llamaindex@0.8.23
+- @llamaindex/node-parser@0.0.16
+- @llamaindex/openai@0.1.40
+- @llamaindex/readers@1.0.17
+
+## 0.0.25
+
+### Patch Changes
+
+- Updated dependencies [819af45]
+  - llamaindex@0.8.22
+  - @llamaindex/cloud@2.0.14
+  - @llamaindex/core@0.4.14
+  - @llamaindex/node-parser@0.0.15
+  - @llamaindex/openai@0.1.39
+  - @llamaindex/readers@1.0.16
+
+## 0.0.24
+
+### Patch Changes
+
+- Updated dependencies [83c3897]
+- Updated dependencies [efa2211]
+  - llamaindex@0.8.21
+
+## 0.0.23
+
+### Patch Changes
+
+- Updated dependencies [02b22da]
+  - llamaindex@0.8.20
+
+## 0.0.22
+
+### Patch Changes
+
+- Updated dependencies [90d265c]
+  - @llamaindex/cloud@2.0.13
+  - @llamaindex/core@0.4.13
+  - llamaindex@0.8.19
+  - @llamaindex/node-parser@0.0.14
+  - @llamaindex/readers@1.0.15
+  - @llamaindex/openai@0.1.38
+
+## 0.0.21
+
+### Patch Changes
+
+- Updated dependencies [d17450f]
+- Updated dependencies [ef4f63d]
+  - llamaindex@0.8.18
+  - @llamaindex/core@0.4.12
+  - @llamaindex/cloud@2.0.12
+  - @llamaindex/node-parser@0.0.13
+  - @llamaindex/openai@0.1.37
+  - @llamaindex/readers@1.0.14
+
+## 0.0.20
+
+### Patch Changes
+
+- Updated dependencies [6d22fa2]
+  - @llamaindex/core@0.4.11
+  - @llamaindex/cloud@2.0.11
+  - llamaindex@0.8.17
+  - @llamaindex/node-parser@0.0.12
+  - @llamaindex/openai@0.1.36
+  - @llamaindex/readers@1.0.13
+
+## 0.0.19
+
+### Patch Changes
+
+- Updated dependencies [e60328b]
+  - @llamaindex/readers@1.0.12
+  - llamaindex@0.8.16
+
+## 0.0.18
+
+### Patch Changes
+
+- Updated dependencies [3d503cb]
+- Updated dependencies [5dae534]
+  - llamaindex@0.8.15
+
+## 0.0.17
+
+### Patch Changes
+
+- Updated dependencies [630b425]
+  - llamaindex@0.8.14
+
+## 0.0.16
+
+### Patch Changes
+
+- Updated dependencies [a7b0ac3]
+- Updated dependencies [ee20c44]
+- Updated dependencies [c69605f]
+  - @llamaindex/core@0.4.10
+  - @llamaindex/workflow@0.0.6
+  - llamaindex@0.8.13
+  - @llamaindex/cloud@2.0.10
+  - @llamaindex/node-parser@0.0.11
+  - @llamaindex/openai@0.1.35
+  - @llamaindex/readers@1.0.11
+
+## 0.0.15
+
+### Patch Changes
+
+- Updated dependencies [ea92b69]
+- Updated dependencies [fadc8b8]
+  - @llamaindex/workflow@0.0.5
+
+## 0.0.14
+
+### Patch Changes
+
+- Updated dependencies [7ae6eaa]
+  - @llamaindex/core@0.4.9
+  - @llamaindex/openai@0.1.34
+  - @llamaindex/cloud@2.0.9
+  - llamaindex@0.8.12
+  - @llamaindex/node-parser@0.0.10
+  - @llamaindex/readers@1.0.10
+
+## 0.0.13
+
+### Patch Changes
+
+- Updated dependencies [f865c98]
+  - @llamaindex/core@0.4.8
+  - @llamaindex/cloud@2.0.8
+  - llamaindex@0.8.11
+  - @llamaindex/node-parser@0.0.9
+  - @llamaindex/openai@0.1.33
+  - @llamaindex/readers@1.0.9
+
+## 0.0.12
+
+### Patch Changes
+
+- Updated dependencies [f066e50]
+- Updated dependencies [d89ebe0]
+- Updated dependencies [fd8c882]
+- Updated dependencies [fd8c882]
+  - llamaindex@0.8.10
+  - @llamaindex/core@0.4.7
+  - @llamaindex/workflow@0.0.4
+  - @llamaindex/cloud@2.0.7
+  - @llamaindex/node-parser@0.0.8
+  - @llamaindex/openai@0.1.32
+  - @llamaindex/readers@1.0.8
+
+## 0.0.11
+
+### Patch Changes
+
+- Updated dependencies [4fc001c]
+- Updated dependencies [4d4cd8a]
+  - llamaindex@0.8.9
+  - @llamaindex/cloud@2.0.6
+  - @llamaindex/core@0.4.6
+  - @llamaindex/node-parser@0.0.7
+  - @llamaindex/openai@0.1.31
+  - @llamaindex/readers@1.0.7
+
+## 0.0.10
+
+### Patch Changes
+
+- Updated dependencies [ad85bd0]
+  - @llamaindex/core@0.4.5
+  - llamaindex@0.8.8
+  - @llamaindex/node-parser@0.0.6
+  - @llamaindex/workflow@0.0.3
+  - @llamaindex/cloud@2.0.5
+  - @llamaindex/openai@0.1.30
+  - @llamaindex/readers@1.0.6
+
+## 0.0.9
+
+### Patch Changes
+
+- @llamaindex/cloud@2.0.4
+- @llamaindex/core@0.4.4
+- llamaindex@0.8.7
+- @llamaindex/node-parser@0.0.5
+- @llamaindex/openai@0.1.29
+- @llamaindex/readers@1.0.5
+
+## 0.0.8
+
+### Patch Changes
+
+- Updated dependencies [95a5cc6]
+  - @llamaindex/core@0.4.3
+  - llamaindex@0.8.6
+  - @llamaindex/cloud@2.0.3
+  - @llamaindex/node-parser@0.0.4
+  - @llamaindex/openai@0.1.28
+  - @llamaindex/readers@1.0.4
+
+## 0.0.7
+
+### Patch Changes
+
+- Updated dependencies [14cc9eb]
+- Updated dependencies [a6db5dd]
+- Updated dependencies [396b1e1]
+  - llamaindex@0.8.5
+  - @llamaindex/cloud@2.0.2
+  - @llamaindex/core@0.4.2
+  - @llamaindex/node-parser@0.0.3
+  - @llamaindex/openai@0.1.27
+  - @llamaindex/readers@1.0.3
+
+## 0.0.6
+
+### Patch Changes
+
+- Updated dependencies [35430d3]
+  - llamaindex@0.8.4
+  - @llamaindex/readers@1.0.2
+
+## 0.0.5
+
+### Patch Changes
+
+- Updated dependencies [2ff0a89]
+  - @llamaindex/node-parser@0.0.2
+  - llamaindex@0.8.3
+
+## 0.0.4
+
+### Patch Changes
+
+- Updated dependencies [0765742]
+  - @llamaindex/workflow@0.0.2
+
+## 0.0.3
+
+### Patch Changes
+
+- Updated dependencies [c7a918c]
+  - llamaindex@0.8.2
+
+## 0.0.2
+
+### Patch Changes
+
+- Updated dependencies [9c73f0a]
+  - @llamaindex/core@0.4.1
+  - @llamaindex/cloud@2.0.1
+  - llamaindex@0.8.1
+  - @llamaindex/openai@0.1.26
+  - @llamaindex/readers@1.0.1
+
+## 0.0.1
+
+### Patch Changes
+
+- Updated dependencies [359fd33]
+- Updated dependencies [efb7e1b]
+- Updated dependencies [98ba1e7]
+- Updated dependencies [620c63c]
+  - @llamaindex/core@0.4.0
+  - llamaindex@0.8.0
+  - @llamaindex/cloud@2.0.0
+  - @llamaindex/openai@0.1.25
+  - @llamaindex/readers@1.0.0

apps/next/README.md

@@ -0,0 +1,21 @@
+# Docs
+
+This is a Next.js application generated with
+[Create Fumadocs](https://github.com/fuma-nama/fumadocs).
+
+Run development server:
+
+```bash
+turbo run dev
+# turbo will build all required packages before running the dev server
+```
+
+## Learn More
+
+To learn more about Next.js and Fumadocs, take a look at the following
+resources:
+
+- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js
+  features and API.
+- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
+- [Fumadocs](https://fumadocs.vercel.app) - learn about Fumadocs

apps/next/components.json

@@ -0,0 +1,20 @@
+{
+  "$schema": "https://ui.shadcn.com/schema.json",
+  "style": "new-york",
+  "rsc": true,
+  "tsx": true,
+  "tailwind": {
+    "config": "tailwind.config.js",
+    "css": "app/global.css",
+    "baseColor": "neutral",
+    "cssVariables": true,
+    "prefix": ""
+  },
+  "aliases": {
+    "components": "@/components",
+    "utils": "@/lib/utils",
+    "ui": "@/components/ui",
+    "lib": "@/lib",
+    "hooks": "@/hooks"
+  }
+}

apps/next/next.config.mjs

@@ -0,0 +1,35 @@
+import { createMDX } from "fumadocs-mdx/next";
+import MonacoWebpackPlugin from "monaco-editor-webpack-plugin";
+const withMDX = createMDX();
+
+/** @type {import('next').NextConfig} */
+const config = {
+  reactStrictMode: true,
+  transpilePackages: ["monaco-editor"],
+  serverExternalPackages: ["@huggingface/transformers"],
+  webpack: (config, { isServer }) => {
+    if (Array.isArray(config.target) && config.target.includes("web")) {
+      config.target = ["web", "es2020"];
+    }
+
+    config.resolve.alias = {
+      ...config.resolve.alias,
+      sharp$: false,
+      "onnxruntime-node$": false,
+    };
+    config.resolve.fallback ??= {};
+    config.resolve.fallback.fs = false;
+    if (!isServer) {
+      config.plugins.push(
+        new MonacoWebpackPlugin({
+          languages: ["typescript"],
+          filename: "static/[name].worker.js",
+        }),
+      );
+    }
+    config.resolve.alias["replicate"] = false;
+    return config;
+  },
+};
+
+export default withMDX(config);

apps/next/package.json

@@ -0,0 +1,87 @@
+{
+  "name": "@llamaindex/doc",
+  "version": "0.0.31",
+  "private": true,
+  "scripts": {
+    "build": "pnpm run build:docs && next build",
+    "dev": "next dev",
+    "start": "next start",
+    "postdev": "fumadocs-mdx",
+    "postbuild": "fumadocs-mdx && tsx scripts/post-build.mts",
+    "build:docs": "node ./scripts/generate-docs.mjs"
+  },
+  "dependencies": {
+    "@icons-pack/react-simple-icons": "^10.1.0",
+    "@llamaindex/chat-ui": "0.0.9",
+    "@llamaindex/cloud": "workspace:*",
+    "@llamaindex/core": "workspace:*",
+    "@llamaindex/node-parser": "workspace:*",
+    "@llamaindex/openai": "workspace:*",
+    "@llamaindex/readers": "workspace:*",
+    "@llamaindex/workflow": "workspace:*",
+    "@mdx-js/mdx": "^3.1.0",
+    "@number-flow/react": "^0.3.4",
+    "@radix-ui/react-dialog": "^1.1.2",
+    "@radix-ui/react-icons": "^1.3.2",
+    "@radix-ui/react-label": "^2.1.0",
+    "@radix-ui/react-slider": "^1.2.1",
+    "@radix-ui/react-slot": "^1.1.0",
+    "@radix-ui/react-tooltip": "^1.1.4",
+    "@vercel/functions": "^1.5.0",
+    "ai": "^3.4.33",
+    "class-variance-authority": "^0.7.0",
+    "clsx": "2.1.1",
+    "foxact": "^0.2.41",
+    "framer-motion": "^11.11.17",
+    "fumadocs-core": "14.4.2",
+    "fumadocs-docgen": "^1.3.2",
+    "fumadocs-mdx": "^11.1.1",
+    "fumadocs-openapi": "^5.7.0",
+    "fumadocs-twoslash": "^2.0.1",
+    "fumadocs-typescript": "^3.0.2",
+    "fumadocs-ui": "14.4.2",
+    "hast-util-to-jsx-runtime": "^2.3.2",
+    "llamaindex": "workspace:*",
+    "lucide-react": "^0.460.0",
+    "next": "15.0.3",
+    "next-themes": "^0.4.3",
+    "react": "^18.3.1",
+    "react-dom": "^18.3.1",
+    "react-icons": "^5.3.0",
+    "react-monaco-editor": "^0.56.2",
+    "react-text-transition": "^3.1.0",
+    "react-use-measure": "^2.1.1",
+    "rehype-katex": "^7.0.1",
+    "remark-math": "^6.0.0",
+    "rimraf": "^6.0.1",
+    "shiki": "^1.23.1",
+    "shiki-magic-move": "^0.5.0",
+    "swr": "^2.2.5",
+    "tailwind-merge": "^2.5.2",
+    "tailwindcss-animate": "^1.0.7",
+    "tree-sitter": "^0.22.1",
+    "tree-sitter-typescript": "^0.23.2",
+    "use-stick-to-bottom": "^1.0.42",
+    "web-tree-sitter": "^0.24.4",
+    "zod": "^3.23.8"
+  },
+  "devDependencies": {
+    "@next/env": "^15.0.3",
+    "@types/mdx": "^2.0.13",
+    "@types/node": "22.9.0",
+    "@types/react": "^18.3.12",
+    "@types/react-dom": "^18.3.1",
+    "autoprefixer": "^10.4.20",
+    "fast-glob": "^3.3.2",
+    "gray-matter": "^4.0.3",
+    "monaco-editor-webpack-plugin": "^7.1.0",
+    "postcss": "^8.4.49",
+    "remark": "^15.0.1",
+    "remark-gfm": "^4.0.0",
+    "remark-mdx": "^3.1.0",
+    "remark-stringify": "^11.0.0",
+    "tailwindcss": "^3.4.15",
+    "tsx": "^4.19.2",
+    "typescript": "^5.6.3"
+  }
+}

apps/next/postcss.config.js

@@ -0,0 +1,6 @@
+module.exports = {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+};

apps/next/public/icons/Amazon_Web_Services_Logo.svg

@@ -0,0 +1,38 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 19.0.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 304 182" style="enable-background:new 0 0 304 182;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill:#252F3E;}
+	.st1{fill-rule:evenodd;clip-rule:evenodd;fill:#FF9900;}
+</style>
+<g>
+	<path class="st0" d="M86.4,66.4c0,3.7,0.4,6.7,1.1,8.9c0.8,2.2,1.8,4.6,3.2,7.2c0.5,0.8,0.7,1.6,0.7,2.3c0,1-0.6,2-1.9,3l-6.3,4.2
+		c-0.9,0.6-1.8,0.9-2.6,0.9c-1,0-2-0.5-3-1.4C76.2,90,75,88.4,74,86.8c-1-1.7-2-3.6-3.1-5.9c-7.8,9.2-17.6,13.8-29.4,13.8
+		c-8.4,0-15.1-2.4-20-7.2c-4.9-4.8-7.4-11.2-7.4-19.2c0-8.5,3-15.4,9.1-20.6c6.1-5.2,14.2-7.8,24.5-7.8c3.4,0,6.9,0.3,10.6,0.8
+		c3.7,0.5,7.5,1.3,11.5,2.2v-7.3c0-7.6-1.6-12.9-4.7-16c-3.2-3.1-8.6-4.6-16.3-4.6c-3.5,0-7.1,0.4-10.8,1.3c-3.7,0.9-7.3,2-10.8,3.4
+		c-1.6,0.7-2.8,1.1-3.5,1.3c-0.7,0.2-1.2,0.3-1.6,0.3c-1.4,0-2.1-1-2.1-3.1v-4.9c0-1.6,0.2-2.8,0.7-3.5c0.5-0.7,1.4-1.4,2.8-2.1
+		c3.5-1.8,7.7-3.3,12.6-4.5c4.9-1.3,10.1-1.9,15.6-1.9c11.9,0,20.6,2.7,26.2,8.1c5.5,5.4,8.3,13.6,8.3,24.6V66.4z M45.8,81.6
+		c3.3,0,6.7-0.6,10.3-1.8c3.6-1.2,6.8-3.4,9.5-6.4c1.6-1.9,2.8-4,3.4-6.4c0.6-2.4,1-5.3,1-8.7v-4.2c-2.9-0.7-6-1.3-9.2-1.7
+		c-3.2-0.4-6.3-0.6-9.4-0.6c-6.7,0-11.6,1.3-14.9,4c-3.3,2.7-4.9,6.5-4.9,11.5c0,4.7,1.2,8.2,3.7,10.6
+		C37.7,80.4,41.2,81.6,45.8,81.6z M126.1,92.4c-1.8,0-3-0.3-3.8-1c-0.8-0.6-1.5-2-2.1-3.9L96.7,10.2c-0.6-2-0.9-3.3-0.9-4
+		c0-1.6,0.8-2.5,2.4-2.5h9.8c1.9,0,3.2,0.3,3.9,1c0.8,0.6,1.4,2,2,3.9l16.8,66.2l15.6-66.2c0.5-2,1.1-3.3,1.9-3.9c0.8-0.6,2.2-1,4-1
+		h8c1.9,0,3.2,0.3,4,1c0.8,0.6,1.5,2,1.9,3.9l15.8,67l17.3-67c0.6-2,1.3-3.3,2-3.9c0.8-0.6,2.1-1,3.9-1h9.3c1.6,0,2.5,0.8,2.5,2.5
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