Release 0.11.20 (#2121)

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: himself65 <14026360+himself65@users.noreply.github.com>

.eslintrc.cjs

@@ -1,84 +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": [
-      "error",
-      {
-        ignoredTypeNames: ["Error", "RegExp", "URL", "URLSearchParams"],
-      },
-    ],
-    "@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

@@ -8,6 +8,11 @@ on:
     branches:
       - main
 
+env:
+  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
+  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
+  TURBO_REMOTE_ONLY: true
+
 jobs:
   lint:
     runs-on: ubuntu-latest

.github/workflows/preview.yml

@@ -1,6 +1,11 @@
 name: Publish Preview
 on: [pull_request]
 
+env:
+  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
+  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
+  TURBO_REMOTE_ONLY: true
+
 jobs:
   pre_release:
     name: Pre Release
@@ -25,4 +30,4 @@ jobs:
         run: pnpm run build
 
       - name: Pre Release
-        run: pnpx pkg-pr-new publish ./packages/*
+        run: pnpx pkg-pr-new publish --pnpm ./packages/* ./packages/providers/* ./packages/providers/storage/*

.github/workflows/test.yml

@@ -23,7 +23,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        node-version: [18.x, 20.x, 22.x]
+        node-version: [20.x, 22.x, 23.x]
     name: E2E on Node.js ${{ matrix.node-version }}
     runs-on: ubuntu-latest
     steps:
@@ -53,7 +53,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        node-version: [18.x, 20.x, 22.x]
+        node-version: [20.x, 22.x, 23.x]
     name: Test on Node.js ${{ matrix.node-version }}
     runs-on: ubuntu-latest
     steps:
@@ -83,13 +83,34 @@ jobs:
         run: pnpm install
       - name: Build
         run: pnpm run build
-      - name: Use Build For Examples
-        run: pnpm link ../packages/llamaindex/
-        working-directory: ./examples
       - name: Run Type Check
         run: pnpm run type-check
       - name: Run Circular Dependency Check
         run: pnpm run circular-check
+  e2e-npm:
+    runs-on: ubuntu-latest
+    name: Test using packages with npm
+    steps:
+      - uses: actions/checkout@v4
+      - uses: pnpm/action-setup@v4
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: ".nvmrc"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Build packages
+        run: pnpm run build
+      - name: Pack packages
+        run: |
+          pnpm pack --pack-destination ${{ runner.temp }} -C packages/llamaindex
+          pnpm pack --pack-destination ${{ runner.temp }} -C packages/workflow
+      - name: Install packed packages
+        run: npm add ${{ runner.temp }}/*.tgz
+        working-directory: e2e/npm
+      - name: Run tests
+        run: npm test
+        working-directory: e2e/npm
   e2e-llamaindex-examples:
     strategy:
       fail-fast: false
@@ -101,6 +122,7 @@ jobs:
           - nextjs-node-runtime
           - waku-query-engine
           - llama-parse-browser
+          - vite-import-llamaindex
     runs-on: ubuntu-latest
     name: Build LlamaIndex Example (${{ matrix.packages }})
     steps:
@@ -117,7 +139,31 @@ 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 }}
+
+  size-limit:
+    runs-on: ubuntu-latest
+    if: github.event_name == 'pull_request'
+    name: Size Limit
+    steps:
+      - uses: actions/checkout@v4
+      - uses: pnpm/action-setup@v4
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: ".nvmrc"
+          cache: "pnpm"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Build llamaindex
+        run: pnpm run build
+      - uses: andresz1/size-limit-action@94bc357df29c36c8f8d50ea497c3e225c3c95d1d
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          directory: e2e/examples/vite-import-llamaindex
+          skip_step: "install"
+          build_script: build
+          package_manager: pnpm
 
   typecheck-examples:
     runs-on: ubuntu-latest
@@ -136,27 +182,26 @@ 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/openai
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/llm/openai
-      - name: Pack @llamaindex/groq
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/llm/groq
-      - name: Pack @llamaindex/ollama
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/llm/ollama
-      - 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 $dir, no package.json found"
+            fi
+          done
+      - name: Pack provider packages
+        run: |
+          for dir in packages/providers/* packages/providers/storage/*; do
+            if [ -d "$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

.husky/pre-commit

@@ -1,3 +1 @@
-pnpm format
-pnpm lint
-npx lint-staged
+pnpm run lint-staged

.nvmrc

@@ -1 +1 @@
-20
+22

.prettierignore

@@ -4,3 +4,7 @@ pnpm-lock.yaml
 lib/
 dist/
 .docusaurus/
+.source/
+# prttier doesn't support mdx3 we are using
+*.mdx
+packages/server/server/

.vscode/ltex.dictionary.en-US.txt

@@ -0,0 +1 @@
+LlamaIndexTS

.vscode/settings.json

@@ -13,5 +13,7 @@
   },
   "[json]": {
     "editor.defaultFormatter": "esbenp.prettier-vscode"
-  }
+  },
+  "prettier.prettierPath": "./node_modules/prettier",
+  "prettier.configPath": "prettier.config.mjs"
 }

CLAUDE.md

@@ -0,0 +1,92 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Development Commands
+
+This project uses pnpm as the package manager and Turbo for build orchestration:
+
+- `pnpm install` - Install all dependencies
+- `pnpm build` - Build all packages using Turbo
+- `pnpm dev` - Start development mode for all packages
+- `pnpm test` - Run all unit tests
+- `pnpm e2e` - Run end-to-end tests
+- `pnpm lint` - Run ESLint across all packages
+- `pnpm type-check` - Run TypeScript type checking across workspace
+- `pnpm format` - Check code formatting with Prettier
+- `pnpm format:write` - Auto-fix formatting issues
+- `pnpm circular-check` - Check for circular dependencies using madge
+
+For individual package development:
+
+- `turbo run build --filter="@llamaindex/core"` - Build specific package
+- `turbo run test --filter="@llamaindex/core"` - Test specific package
+- Navigate to specific package directory and run `pnpm test` for focused testing
+- `pnpm clean` - Remove all build artifacts and node_modules across workspace
+
+## Architecture Overview
+
+LlamaIndex.TS is a TypeScript data framework for LLM applications organized as a pnpm monorepo with multiple runtime environment support (Node.js, Deno, Bun, Vercel Edge, Cloudflare Workers).
+
+### Package Structure
+
+**Core Packages:**
+
+- `packages/core/` - Abstract base classes and interfaces for all runtime environments
+- `packages/llamaindex/` - Main package that aggregates core functionality
+- `packages/env/` - Environment-specific compatibility layers for different JS runtimes
+
+**Provider Packages (`packages/providers/`):**
+
+- LLM providers: `openai/`, `anthropic/`, `ollama/`, `google/`, `groq/`, etc.
+- Vector stores: `storage/pinecone/`, `storage/chroma/`, `storage/qdrant/`, etc.
+- Embeddings: Various embedding providers integrated within LLM packages
+- Readers: `assemblyai/`, `discord/`, `notion/` for data ingestion
+
+**Specialized Packages:**
+
+- `packages/cloud/` - LlamaCloud integration for managed services
+- `packages/tools/` - Function calling tools and utilities
+- `packages/workflow/` - Agent workflow orchestration
+- `packages/readers/` - File format readers (PDF, DOCX, etc.)
+
+### Key Architectural Patterns
+
+**Runtime Abstraction:** Core functionality is runtime-agnostic, with environment-specific implementations in separate entry points (`index.ts`, `index.edge.ts`, `index.workerd.ts`).
+
+**Provider Pattern:** LLMs, embeddings, and vector stores implement common interfaces from `@llamaindex/core`, allowing easy swapping between providers.
+
+**Modular Design:** Each provider is a separate package to minimize bundle size - users install only what they need.
+
+**Data Flow:** Document → NodeParser → Embedding → VectorStore → Retriever → QueryEngine → Response
+
+### Core Components
+
+- **Agents and Workflows:** Abstractions for building agentic workflows and agents in `packages/workflow`
+- **Chat Engines:** Conversational interfaces in `core/chat-engine/`
+- **Query Engines:** Document querying with retrieval in `core/query-engine/`
+- **Indices:** VectorStoreIndex, SummaryIndex, KeywordTable in `llamaindex/indices/`
+- **Node Parsers:** Text splitting and chunking in `core/node-parser/`
+- **Ingestion Pipeline:** Document processing workflows in `llamaindex/ingestion/`
+- **Storage:** Chat stores, document stores, index stores, and KV stores in `core/storage/`
+
+### Deprecated Components
+
+- **Agents:** ReAct and function calling agents in `core/agent/` and `llamaindex/agent/`
+
+### Testing Structure
+
+- Unit tests in each package's `tests/` directory
+- E2E tests in `e2e/` directory with runtime-specific examples
+- Tests depend on build artifacts, so always run `pnpm build` before testing
+
+### Multi-Runtime Support
+
+The codebase supports multiple JavaScript runtimes through conditional exports and separate entry points. When making changes, consider compatibility across Node.js, Deno, Bun, and edge runtimes.
+
+### Development Notes
+
+- The project uses Husky for git hooks with lint-staged for pre-commit formatting and linting
+- All packages use bunchee for building with dual CJS/ESM support
+- Core package exports are organized as sub-modules (e.g., `@llamaindex/core/llms`, `@llamaindex/core/embeddings`)
+- Always run `pnpm build` before running tests, as tests depend on build artifacts

CONTRIBUTING.md

@@ -2,92 +2,157 @@
 
 ## Structure
 
-This is a monorepo built with Turborepo
+LlamaIndex.TS uses pnpm monorepo.
 
-Right now, for first-time contributors, these three packages are of the highest 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`
-- `examples` is where the demo code lives
-- `apps/docs` is where the code for the documentation of https://ts.llamaindex.ai/ is located
+There are some important folders in the repository:
 
-### 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...
+  - `providers/*`: The providers package of LlamaIndex.TS, which contains the providers for LLM and other services.
+- `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 LTS (Long-term Support) installed. You can check your Node.js version by running:
 
+```shell
+node -v
+# v22.x.x
 ```
-npm i -g pnpm ts-node
+
+### Use pnpm
+
+```shell
+npm install -g pnpm
+```
+
+### Install dependencies
+
+```shell
 pnpm install
+pnpm install -g tsx
 ```
 
-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).
+To build all packages, run:
 
-### 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
+```shell
+pnpm build
 ```
 
-To write new test cases write them in [packages/llamaindex/tests](/packages/llamaindex/tests)
+### Start Developing
 
-We use Vitest https://vitest.dev to write our test cases. Vitest comes with a bunch of built-in assertions using the expect function: https://vitest.dev/api/expect.html#expect
+You can launch the package in dev-mode by running:
 
-### 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 `llamaindex` 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]
+```shell
+pnpm dev
 ```
 
-To install packages for every package or application run
+This will use turbo to run all packages in watch-mode. This means you can make changes and have them automatically built.
 
+If you want to customize what packages are built/watched, you can run turbo directly and adjust the filter:
+
+```shell
+pnpm turbo run dev --filter="./packages/core" --concurrency=100
 ```
-pnpm add -w [NPM Package]
+
+In another terminal, you can write and run any script needed to quickly test your changes. For example:
+
+```typescript
+import { createMemory, staticBlock } from "@llamaindex/core/memory";
+
+// Create memory with predefined context
+const memory = createMemory({
+  memoryBlocks: [
+    staticBlock({
+      content:
+        "The user is a software engineer who loves TypeScript and LlamaIndex.",
+      messageRole: "system",
+    }),
+  ],
+});
+
+async function main() {
+  const result = await memory.getLLM();
+  console.log(result);
+}
+
+void main().catch(console.error);
 ```
 
+And run it with:
+
+```shell
+pnpm exec tsx my_script.ts
+```
+
+This flow allows you to easily test your changes without having to build the entire project.
+
+Once you are happy with your changes, be sure to add tests (and confirm existing tests are passing!).
+
+### Run tests
+
+#### Unit tests
+
+After build, to run all unit tests, call:
+
+```shell
+pnpm test
+```
+
+Unit tests are located in the `tests` folder of each package. They are using their own package (e.g. `@llamaindex/core-tests` for `@llamaindex/core`). The tests are importing the package under test and the test package is not published.
+
+#### E2E tests
+
+To run all E2E tests, call:
+
+```shell
+pnpm e2e
+```
+
+All E2E tests are in the `e2e` folder.
+
 ### Docs
 
-To contribute to the docs, go to the docs website folder and run the Docusaurus instance.
+See the [docs](./apps/next/README.md) for more information.
 
-```bash
-cd apps/docs
-pnpm install
-pnpm start
-```
+## Adding a new package
 
-That should start a webserver which will serve the docs on https://localhost:3000
+Please follow these steps to add a new package:
 
-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.
+1. Only add new packages to the `packages/providers` folder.
+2. Use the `package.json` and `tsconfig.json` of an existing packages as template.
+3. Reference your new package in the root `tsconfig.json` file
+4. Add your package to the `examples/package.json` file if you add a new example.
 
-## Changeset
+## Before sending a PR
 
-We use [changesets](https://github.com/changesets/changesets) for managing versions and changelogs. To create a new changeset, run in the root folder:
+Before sending a PR, make sure of the following:
 
-```
+1. Tests are all running and you added meaningful tests for your change.
+2. If you have a new feature, document it in the `apps/next` docs folder.
+3. If you have a new feature, add a new example in the `examples` folder.
+4. You have a descriptive changeset for each PR:
+
+### Bumping the versions of packages you've modified
+
+We use [changesets](https://github.com/changesets/changesets) for managing versions and changelogs. To create a new
+changeset, run in the root folder:
+
+```shell
 pnpm changeset
 ```
 
-Please send a descriptive changeset for each PR.
+You will be prompted to choose what packages need their versions bumped, and what kind of bump (major, minor or patch) is needed. Once you carry out this operation, the bumping will be automatic after the PR is merged.
 
 ## Publishing (maintainers only)
 
@@ -95,6 +160,6 @@ 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

@@ -1,11 +1,16 @@
-# 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 License](https://img.shields.io/npm/l/llamaindex)](https://github.com/run-llama/LlamaIndexTS/blob/main/LICENSE)
 [![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.
+[![Twitter](https://img.shields.io/twitter/follow/llama_index)](https://x.com/llama_index)
 
 Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in JS runtime environments with TypeScript support.
 
@@ -25,7 +30,7 @@ LlamaIndex.TS aims to be a lightweight, easy to use set of libraries to help you
 
 LlamaIndex.TS supports multiple JS environments, including:
 
-- Node.js (18, 20, 22) ✅
+- Node.js >= 20 ✅
 - Deno ✅
 - Bun ✅
 - Nitro ✅
@@ -57,271 +62,29 @@ pnpm install llamaindex
 yarn add llamaindex
 ```
 
-### Setup TypeScript
+### Setup in Node.js, Deno, Bun, TypeScript...?
 
-```json5
-{
-  compilerOptions: {
-    // ⬇️ add this line to your tsconfig.json
-    moduleResolution: "bundler", // or "node16"
-  },
-}
+See our official document: https://ts.llamaindex.ai/docs/llamaindex/getting_started
+
+### Adding provider packages
+
+In most cases, you'll also need to install provider packages to use LlamaIndexTS. These are for adding AI models, file readers for ingestion or storing documents, e.g. in vector databases.
+
+For example, to use the OpenAI LLM, you would install the following package:
+
+```shell
+npm install @llamaindex/openai
+pnpm install @llamaindex/openai
+yarn add @llamaindex/openai
 ```
 
-<details>
-   <summary>Why?</summary>
-  We are shipping both ESM and CJS module, and compatible with Vercel Edge, Cloudflare Workers, and other serverless platforms.
-
-So we are using [conditional exports](https://nodejs.org/api/packages.html#conditional-exports) to support all environments.
-
-This is a kind of modern way of shipping packages, but might cause TypeScript type check to fail because of legacy module resolution.
-
-Imaging you put output file into `/dist/openai.js` but you are importing `llamaindex/openai` in your code, and set `package.json` like this:
-
-```json
-{
-  "exports": {
-    "./openai": "./dist/openai.js"
-  }
-}
-```
-
-In old module resolution, TypeScript will not be able to find the module because it is not follow the file structure, even you run `node index.js` successfully. (on Node.js >=16)
-
-See more about [moduleResolution](https://www.typescriptlang.org/docs/handbook/modules/theory.html#module-resolution) or
-[TypeScript 5.0 blog](https://devblogs.microsoft.com/typescript/announcing-typescript-5-0/#--moduleresolution-bundler7).
-
-</details>
-
-### Node.js
-
-```ts
-import fs from "node:fs/promises";
-import { Document, VectorStoreIndex } from "llamaindex";
-
-async function main() {
-  // Load essay from abramov.txt in Node
-  const essay = await fs.readFile(
-    "node_modules/llamaindex/examples/abramov.txt",
-    "utf-8",
-  );
-
-  // 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
-```
-
-### Next.js
-
-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
-});
-```
-
-### React Server Actions
-
-You can combine `ai` with `llamaindex` in Next.js, Waku or Redwood.js with RSC (React Server Components).
-
-```tsx
-"use client";
-import { chatWithAgent } from "@/actions";
-import type { JSX } from "react";
-import { useActionState } from "react";
-
-export default function Home() {
-  const [ui, action] = useActionState<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(
-    {
-      message: question,
-      chatHistory: prevMessages,
-    },
-    true,
-  );
-  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;
-}
-```
-
-### Cloudflare Workers
-
-> [!TIP]
-> Some modules are not supported in Cloudflare Workers which require Node.js APIs.
-
-```ts
-// add `OPENAI_API_KEY` to the `.dev.vars` file
-interface Env {
-  OPENAI_API_KEY: string;
-}
-
-export default {
-  async fetch(
-    request: Request,
-    env: Env,
-    ctx: ExecutionContext,
-  ): Promise<Response> {
-    const { OpenAIAgent, OpenAI } = await import("@llamaindex/openai");
-    const text = await request.text();
-    const agent = new OpenAIAgent({
-      llm: new OpenAI({
-        apiKey: env.OPENAI_API_KEY,
-      }),
-      tools: [],
-    });
-    const responseStream = await agent.chat({
-      stream: true,
-      message: text,
-    });
-    const textEncoder = new TextEncoder();
-    const response = responseStream.pipeThrough<Uint8Array>(
-      new TransformStream({
-        transform: (chunk, controller) => {
-          controller.enqueue(textEncoder.encode(chunk.delta));
-        },
-      }),
-    );
-    return new Response(response);
-  },
-};
-```
-
-### Vite
-
-We have some wasm dependencies for better performance. You can use `vite-plugin-wasm` to load them.
-
-```ts
-import wasm from "vite-plugin-wasm";
-
-export default {
-  plugins: [wasm()],
-  ssr: {
-    external: ["tiktoken"],
-  },
-};
-```
-
-### Tips when using in non-Node.js environments
-
-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`).
-
-If you need any of those classes, you have to import them instead directly though their file path in the package.
-Here's an example for importing the `PineconeVectorStore` class:
-
-```typescript
-import { PineconeVectorStore } from "llamaindex/storage/vectorStore/PineconeVectorStore";
-```
-
-As the `PDFReader` is not working with the Edge runtime, here's how to use the `SimpleDirectoryReader` with the `LlamaParseReader` to load PDFs:
-
-```typescript
-import { SimpleDirectoryReader } from "llamaindex/readers/SimpleDirectoryReader";
-import { LlamaParseReader } from "llamaindex/readers/LlamaParseReader";
-
-export const DATA_DIR = "./data";
-
-export async function getDocuments() {
-  const reader = new SimpleDirectoryReader();
-  // Load PDFs using LlamaParseReader
-  return await reader.loadData({
-    directoryPath: DATA_DIR,
-    fileExtToReader: {
-      pdf: new LlamaParseReader({ resultType: "markdown" }),
-    },
-  });
-}
-```
-
-> _Note_: Reader classes have to be added explictly to the `fileExtToReader` map in the Edge version of the `SimpleDirectoryReader`.
-
-You'll find a complete example with LlamaIndexTS here: https://github.com/run-llama/create_llama_projects/tree/main/nextjs-edge-llamaparse
-
 ## 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 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.
+See our documentation: https://ts.llamaindex.ai/docs/llamaindex/getting_started/concepts
 
 ## Contributing:
 

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

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

apps/docs/.gitignore

@@ -1,21 +0,0 @@
-# Dependencies
-/node_modules
-
-# Production
-/build
-
-# Generated files
-.docusaurus
-.cache-loader
-lib
-
-# Misc
-.DS_Store
-.env.local
-.env.development.local
-.env.test.local
-.env.production.local
-
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*

apps/docs/README-docusaurus.md

@@ -1,47 +0,0 @@
-# Website
-
-This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator.
-
-### Installation
-
-```
-$ pnpm
-```
-
-### Local Development
-
-```
-$ pnpm start
-```
-
-This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
-
-However, the searchbar may not function with `yarn start`. Instead, run `yarn build` and launch a server:
-
-```
-$ npx http-server ./build
-```
-
-### Build
-
-```
-$ pnpm build
-```
-
-This command generates static content into the `build` directory and can be served using any static contents hosting service.
-
-### Deployment
-
-Using SSH:
-
-```
-$ USE_SSH=true pnpm deploy
-```
-
-Not using SSH:
-
-```
-$ GIT_USER=<Your GitHub username> pnpm deploy
-```
-
-If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.

apps/docs/babel.config.js

@@ -1,3 +0,0 @@
-module.exports = {
-  presets: [require.resolve("@docusaurus/core/lib/babel/preset")],
-};

apps/docs/blog/2024-04-26-v0.3.0.md

@@ -1,493 +0,0 @@
----
-title: LlamaIndexTS v0.3.0
-description: This is my first post on Docusaurus.
-slug: welcome-llamaindexts-v0.3
-authors:
-  - name: Alex Yang
-    title: LlamaIndexTS maintainer, Node.js Member
-    url: https://github.com/himself65
-    image_url: https://github.com/himself65.png
-tags: [llamaindex, agent]
-hide_table_of_contents: false
----
-
-- [What's new in LlamaIndexTS v0.3.0](#whats-new-in-llamaindexts-v030)
-- [Improvement in LlamaIndexTS v0.3.0](#improvement-in-llamaindexts-v030)
-- [What's the next?](#whats-the-next)
-
-## What's new in LlamaIndexTS v0.3.0
-
-## Agents
-
-In this release, we've not only ported the Agent module from the LlamaIndex Python version but have significantly
-enhanced it to be more powerful and user-friendly for JavaScript/TypeScript applications.
-
-Starting from v0.3.0, we are introducing multiple agents specifically designed for RAG applications, including:
-
-- `OpenAIAgent`
-- `AnthropicAgent`
-- `ReActAgent`:
-
-```ts
-import { OpenAIAgent } from "llamaindex";
-import { tools } from "./tools";
-
-const agent = new OpenAIAgent({
-  tools: [...tools],
-});
-const { response } = await agent.chat({
-  message: "What is weather today?",
-  stream: false,
-});
-
-console.log(response.message.content);
-```
-
-We are also introducing the abstract AgentRunner class, which allows you to create your own agent by simply implementing
-the task handler.
-
-```ts
-import { AgentRunner, OpenAI } from "llamaindex";
-
-class MyLLM extends OpenAI {}
-
-export class MyAgentWorker extends AgentWorker<MyLLM> {
-  taskHandler = MyAgent.taskHandler;
-}
-
-export class MyAgent extends AgentRunner<MyLLM> {
-  constructor(params: Params) {
-    super({
-      llm: params.llm,
-      chatHistory: params.chatHistory ?? [],
-      systemPrompt: params.systemPrompt ?? null,
-      runner: new MyAgentWorker(),
-      tools:
-        "tools" in params
-          ? params.tools
-          : params.toolRetriever.retrieve.bind(params.toolRetriever),
-    });
-  }
-
-  // create store is a function to create a store for each task, by default it only includes `messages` and `toolOutputs`
-  createStore = AgentRunner.defaultCreateStore;
-
-  static taskHandler: TaskHandler<Anthropic> = async (step, enqueueOutput) => {
-    const { llm, stream } = step.context;
-    // initialize the input
-    const response = await llm.chat({
-      stream,
-      messages: step.context.store.messages,
-    });
-    // store the response for next task step
-    step.context.store.messages = [
-      ...step.context.store.messages,
-      response.message,
-    ];
-    // your logic here to decide whether to continue the task
-    const shouldContinue = Math.random(); /* <-- replace with your logic here */
-    enqueueOutput({
-      taskStep: step,
-      output: response,
-      isLast: !shouldContinue,
-    });
-    if (shouldContinue) {
-      const content = await someHeavyFunctionCall();
-      // if you want to continue the task, you can insert your new context for the next task step
-      step.context.store.messages = [
-        ...step.context.store.messages,
-        {
-          content,
-          role: "user",
-        },
-      ];
-    }
-  };
-}
-```
-
-### Web Stream API for Streaming response
-
-Web Stream is a web standard utilized in many modern web frameworks and libraries (like React 19, Deno, Node 22). We
-have migrated streaming responses to Web Stream to ensure broader compatibility.
-
-For instance, you can use the streaming response in a simple HTTP Server:
-
-```ts
-import { createServer } from "http";
-import { OpenAIAgent } from "llamaindex";
-import { OpenAIStream, streamToResponse } from "ai";
-import { tools } from "./tools";
-
-const agent = new OpenAIAgent({
-  tools: [...tools],
-});
-
-const server = createServer(async (req, res) => {
-  const response = await agent.chat({
-    message: "What is weather today?",
-    stream: true,
-  });
-
-  // Transform the response into a string readable stream
-  const stream: ReadableStream<string> = response.pipeThrough(
-    new TransformStream({
-      transform: (chunk, controller) => {
-        controller.enqueue(chunk.response.delta);
-      },
-    }),
-  );
-
-  // Pipe the stream to the response
-  streamToResponse(stream, res);
-});
-
-server.listen(3000);
-```
-
-Or it can be integrated into React Server Components (RSC) in Next.js:
-
-```tsx
-// app/actions/index.tsx
-"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: [],
-  });
-  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(uiStream.error);
-  return uiStream.value;
-}
-```
-
-```tsx
-// app/src/page.tsx
-"use client";
-import { chatWithAgent } from "@/actions";
-import type { JSX } from "react";
-import { useFormState } from "react-dom";
-
-export const runtime = "edge";
-
-export default function Home() {
-  const [state, action] = useFormState<JSX.Element | null>(async () => {
-    return chatWithAgent("hello!", []);
-  }, null);
-  return (
-    <main>
-      {state}
-      <form action={action}>
-        <button>Chat</button>
-      </form>
-    </main>
-  );
-}
-```
-
-## Improvement in LlamaIndexTS v0.3.0
-
-### Better TypeScript support
-
-We have made significant improvements to the type system to ensure that all code is thoroughly checked before it is
-published. This ongoing enhancement has already resulted in better module reliability and developer experience.
-
-For example, we have improved `FunctionTool` type with generic support:
-
-```ts
-type Input = {
-  a: number;
-  b: number;
-};
-
-const sumNumbers = FunctionTool.from<Input>(
-  ({ a, b }) => `${a + b}`, // a and b will be checked as number
-  // JSON schema will be an error if you type wrong.
-  {
-    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"],
-    },
-  },
-);
-```
-
-![type checking](./img/function_tool_example.png)
-
-### Better Next.js, Deno, Cloudflare Worker, and Waku(Vite) support
-
-In addition to Node.js, LlamaIndexTS now offers enhanced support for Next.js, Deno, and Cloudflare Workers, making it
-more versatile across different platforms.
-
-For now, you can install llamaindex and directly import it into your existing Next.js, Deno or Cloudflare Worker project
-**without any extra configuration**.
-
-#### [Deno](https://deno.com/)
-
-You can use LlamaIndexTS in Deno by installation through JSR:
-
-```sh
-jsr add @llamaindex/core
-```
-
-#### [Cloudflare Worker](https://developers.cloudflare.com/workers/)
-
-For Cloudflare Workers, here is a starter template:
-
-```typescript
-export default {
-  async fetch(
-    request: Request,
-    env: Env,
-    ctx: ExecutionContext,
-  ): Promise<Response> {
-    const { setEnvs } = await import("@llamaindex/env");
-    setEnvs(env);
-    const { OpenAIAgent } = await import("llamaindex");
-    const agent = new OpenAIAgent({
-      tools: [],
-    });
-    const responseStream = await agent.chat({
-      stream: true,
-      message: "Hello? What is the weather today?",
-    });
-    const textEncoder = new TextEncoder();
-    const response = responseStream.pipeThrough(
-      new TransformStream({
-        transform: (chunk, controller) => {
-          controller.enqueue(textEncoder.encode(chunk.response.delta));
-        },
-      }),
-    );
-    return new Response(response);
-  },
-};
-```
-
-### [Waku (Vite)](https://waku.gg/)
-
-Waku powered by Vite is a minimal React framework that supports multiple JS environments, including Deno, Cloudflare, and
-Node.js.
-
-You can use LlamaIndexTS with Node.js output to enable full Node.js support with React.
-
-```sh
-npm install llamaindex
-```
-
-```ts
-// file: src/actions.ts
-"use server";
-import { Document, VectorStoreIndex } from "llamaindex";
-import { readFile } from "node:fs/promises";
-
-const path = "node_modules/llamaindex/examples/abramov.txt";
-
-const essay = await readFile(path, "utf-8");
-
-// Create Document object with essay
-const document = new Document({ text: essay, id_: path });
-
-// Split text and create embeddings. Store them in a VectorStoreIndex
-const index = await VectorStoreIndex.fromDocuments([document]);
-
-const queryEngine = index.asQueryEngine();
-
-export async function chatWithAI(question: string): Promise<string> {
-  const { response } = await queryEngine.query({ query: question });
-  return response;
-}
-```
-
-```tsx
-// file: src/pages/index.tsx
-import { chatWithAI } from "./actions";
-
-export default async function HomePage() {
-  return (
-    <div>
-      <Chat askQuestion={chatWithAI} />
-    </div>
-  );
-}
-```
-
-```tsx
-// file: src/components/Chat.tsx
-"use client";
-
-export type ChatProps = {
-  askQuestion: (question: string) => Promise<string>;
-};
-
-export const Chat = (props: ChatProps) => {
-  const [response, setResponse] = useState<string | null>(null);
-
-  return (
-    <section className="border-blue-400 -mx-4 mt-4 rounded border border-dashed p-4">
-      <h2 className="text-lg font-bold">Chat with AI</h2>
-      {response ? (
-        <p className="text-sm text-gray-600 max-w-sm">{response}</p>
-      ) : null}
-      <form
-        action={async (formData) => {
-          const question = formData.get("question") as string | null;
-          if (question) {
-            setResponse(await props.askQuestion(question));
-          }
-        }}
-      >
-        <input
-          type="text"
-          name="question"
-          className="border border-gray-400 rounded-sm px-2 py-0.5 text-sm"
-        />
-        <button className="rounded-sm bg-black px-2 py-0.5 text-sm text-white">
-          Ask
-        </button>
-      </form>
-    </section>
-  );
-};
-```
-
-```shell
-waku dev # development mode
-waku build # build for production
-waku start # start the production server
-```
-
-Note that not all the modules are supported in all JS environments because of
-lack of the file system, network API,
-and incompatibility with the Node.js API by upstream dependencies.
-
-But we are trying to make it more compatible with all the environments.
-
-## What's the next?
-
-As we continue to develop LlamaIndexTS, our focus remains on providing more comprehensive and powerful tools for
-creating custom agents.
-
-### Align with the Python `llama-index`
-
-We aim to align LlamaIndexTS with the Python version to ensure API consistency and ease of use for developers familiar
-with the Python ecosystem.
-
-### Align with the Web Standard and JS development
-
-Not all python APIs are compatible and easy to use in JavaScript/TypeScript.
-We are trying to make the API more compatible with the Web Standard and JavaScript modern development.
-
-### More Agents
-
-Future releases will introduce more agents from the Python Llama-Index and explore APIs tailored to real-world use
-cases.
-
-### 🧪 `@llamaindex/tool`
-
-We are exploring innovative ways to create tools for agents. The `@llamaindex/tool` library allows you to transform any
-function into a tool for an agent, simplifying the development process and reducing runtime costs.
-
-```ts
-export function getWeather(city: string) {
-  return `The weather in ${city} is sunny.`;
-}
-
-// you don't need to worry about the shcema with different llm tools
-export function getTemperature(city: string) {
-  return `The temperature in ${city} is 25°C.`;
-}
-
-export function getCurrentCity() {
-  return "New York";
-}
-```
-
-These functions can be easily integrated into your applications, such as Next.js:
-
-```ts
-"use server";
-import { OpenAI } from "openai";
-import { getTools } from "@llamaindex/tool";
-
-export async function chat(message: string) {
-  const openai = new OpenAI();
-  openai.chat.completions.create({
-    messages: [
-      {
-        role: "user",
-        content: "What is the weather in the current city?",
-      },
-    ],
-    tools: getTools("openai"),
-  });
-}
-```
-
-```ts
-// next.config.js
-const withTool = require("@llamaindex/tool/next");
-
-const config = {
-  // Your original Next.js config
-};
-module.exports = withTool(config);
-```
-
-The functions are automatically transformed into tools for the agent at compile time, which eliminates any extra runtime
-costs. This feature is particularly beneficial when you need to debug or deploy your assistant.
-
-For deploying your local functions into OpenAI, you can use a simple command:
-
-```sh
-npm install -g @llamaindex/tool
-mkai --tools ./src/index.llama.ts
-# Successfully created assistant: asst_XXX
-# chat with your assistant by `chatai --assistant asst_XXX`
-chatai --assistant asst_XXX
-# Open your browser and chat with your assistant
-# Running at http://localhost:3000
-```
-
-This deployment process simplifies the testing and implementation of your custom tools in a live environment.
-
-As this project is still in its early stages, we continue to explore the best ways to create and integrate tools for
-agents. For more information and updates, visit the @llamaindex/tool repository.
-
-This release of LlamaIndexTS v0.3.0 marks a significant step forward in our journey to provide developers with robust,
-flexible tools for building advanced agents. We are excited to see how our community utilizes these new capabilities to
-create innovative solutions and look forward to continuing to support and enhance LlamaIndexTS in future updates.

apps/docs/docs/.gitignore

@@ -1 +0,0 @@
-api/

apps/docs/docs/examples/_category_.yml

@@ -1,2 +0,0 @@
-label: Examples
-position: 3

apps/docs/docs/examples/agent.mdx

@@ -1,10 +0,0 @@
-# Agents
-
-A built-in agent that can take decisions and reasoning based on the tools provided to it.
-
-## OpenAI Agent
-
-import CodeBlock from "@theme/CodeBlock";
-import CodeSource from "!raw-loader!../../../../examples/agent/openai";
-
-<CodeBlock language="ts">{CodeSource}</CodeBlock>

apps/docs/docs/examples/agent_gemini.mdx

@@ -1,6 +0,0 @@
-# Gemini Agent
-
-import CodeBlock from "@theme/CodeBlock";
-import CodeSourceGemini from "!raw-loader!../../../../examples/gemini/agent.ts";
-
-<CodeBlock language="ts">{CodeSourceGemini}</CodeBlock>

apps/docs/docs/examples/chat_engine.mdx

@@ -1,12 +0,0 @@
----
-sidebar_position: 2
----
-
-import CodeBlock from "@theme/CodeBlock";
-import CodeSource from "!raw-loader!../../../../examples/chatEngine";
-
-# Chat Engine
-
-Chat Engine is a class that allows you to create a chatbot from a retriever. It is a wrapper around a retriever that allows you to chat with it in a conversational manner.
-
-<CodeBlock language="ts">{CodeSource}</CodeBlock>

apps/docs/docs/examples/other_llms.mdx

@@ -1,41 +0,0 @@
-import CodeBlock from "@theme/CodeBlock";
-import CodeSource from "!raw-loader!../../../../examples/mistral";
-
-# Using other LLM APIs
-
-By default LlamaIndex.TS uses OpenAI's LLMs and embedding models, but we support [lots of other LLMs](../modules/llms) including models from Mistral (Mistral, Mixtral), Anthropic (Claude) and Google (Gemini).
-
-If you don't want to use an API at all you can [run a local model](../../examples/local_llm)
-
-## Using another LLM
-
-You can specify what LLM LlamaIndex.TS will use on the `Settings` object, like this:
-
-```typescript
-import { MistralAI, Settings } from "llamaindex";
-
-Settings.llm = new MistralAI({
-  model: "mistral-tiny",
-  apiKey: "<YOUR_API_KEY>",
-});
-```
-
-You can see examples of other APIs we support by checking out "Available LLMs" in the sidebar of our [LLMs section](../modules/llms).
-
-## Using another embedding model
-
-A frequent gotcha when trying to use a different API as your LLM is that LlamaIndex will also by default index and embed your data using OpenAI's embeddings. To completely switch away from OpenAI you will need to set your embedding model as well, for example:
-
-```typescript
-import { MistralAIEmbedding, Settings } from "llamaindex";
-
-Settings.embedModel = new MistralAIEmbedding();
-```
-
-We support [many different embeddings](../modules/embeddings).
-
-## Full example
-
-This example uses Mistral's `mistral-tiny` model as the LLM and Mistral for embeddings as well.
-
-<CodeBlock language="ts">{CodeSource}</CodeBlock>

apps/docs/docs/examples/save_load_index.mdx

@@ -1,10 +0,0 @@
----
-sidebar_position: 4
----
-
-import CodeBlock from "@theme/CodeBlock";
-import CodeSource from "!raw-loader!../../../../examples/storageContext";
-
-# Save/Load an Index
-
-<CodeBlock language="ts">{CodeSource}</CodeBlock>

apps/docs/docs/examples/summary_index.mdx

@@ -1,10 +0,0 @@
----
-sidebar_position: 3
----
-
-import CodeBlock from "@theme/CodeBlock";
-import CodeSource from "!raw-loader!../../../../examples/summaryIndex";
-
-# Summary Index
-
-<CodeBlock language="ts">{CodeSource}</CodeBlock>

apps/docs/docs/examples/vector_index.mdx

@@ -1,10 +0,0 @@
----
-sidebar_position: 2
----
-
-import CodeBlock from "@theme/CodeBlock";
-import CodeSource from "!raw-loader!../../../../examples/vectorIndex";
-
-# Vector Index
-
-<CodeBlock language="ts">{CodeSource}</CodeBlock>

apps/docs/docs/getting_started/_category_.yml

@@ -1,2 +0,0 @@
-label: Getting Started
-position: 1

apps/docs/docs/getting_started/environments.md

@@ -1,22 +0,0 @@
----
-sidebar_position: 2
----
-
-# Environments
-
-We support Node.JS versions 18, 20 and 22, with experimental support for Deno, Bun and Vercel Edge functions.
-
-## NextJS
-
-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
-// 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/getting_started/installation.mdx

@@ -1,34 +0,0 @@
----
-sidebar_position: 0
----
-
-# Installation and Setup
-
-We support Node.JS versions 18, 20 and 22, with experimental support for Deno, Bun and Vercel Edge functions.
-
-## Installation from NPM
-
-```bash npm2yarn
-npm install llamaindex
-```
-
-### Environment variables
-
-Our examples use OpenAI by default. You can use [other LLMs](../examples/other_llms) via their APIs; if you would prefer to use local models check out our [local LLM example](../examples/local_llm).
-
-To use OpenAI, you'll need to [get an OpenAI API key](https://platform.openai.com/account/api-keys) and then make it available as an environment variable this way:
-
-```bash
-export OPENAI_API_KEY="sk-......" # Replace with your key
-```
-
-If you want to have it automatically loaded every time, add it to your `.zshrc/.bashrc`.
-
-**WARNING:** do not check in your OpenAI key into version control. GitHub automatically invalidates OpenAI keys checked in by accident.
-
-## What next?
-
-- The easiest way to started is to [build a full-stack chat app with `create-llama`](starter_tutorial/chatbot).
-- Try our other [getting started tutorials](starter_tutorial/retrieval_augmented_generation)
-- Learn more about the [high level concepts](concepts) behind how LlamaIndex works
-- Check out our [many examples](../examples/more_examples) of LlamaIndex.TS in action

apps/docs/docs/getting_started/starter_tutorial/_category_.yml

@@ -1,2 +0,0 @@
-label: Starter Tutorials
-position: 1

apps/docs/docs/getting_started/starter_tutorial/agent.mdx

@@ -1,49 +0,0 @@
----
-sidebar_position: 4
----
-
-import CodeBlock from "@theme/CodeBlock";
-import CodeSource from "!raw-loader!../../../../../examples/agent/openai";
-
-# Agent tutorial
-
-We have a comprehensive, step-by-step [guide to building agents in LlamaIndex.TS](../../guides/agents/setup) that we recommend to learn what agents are and how to build them for production. But building a basic agent is simple:
-
-## Set up
-
-In a new folder:
-
-```bash npm2yarn
-npm init
-npm install -D typescript @types/node
-```
-
-## Run agent
-
-Create the file `example.ts`. This code will:
-
-- Create two tools for use by the agent:
-  - A `sumNumbers` tool that adds two numbers
-  - A `divideNumbers` tool that divides numbers
--
-- Give an example of the data structure we wish to generate
-- Prompt the LLM with instructions and the example, plus a sample transcript
-
-<CodeBlock language="ts">{CodeSource}</CodeBlock>
-
-To run the code:
-
-```bash
-npx tsx example.ts
-```
-
-You should expect output something like:
-
-```
-{
-  content: 'The sum of 5 + 5 is 10. When you divide 10 by 2, you get 5.',
-  role: 'assistant',
-  options: {}
-}
-Done
-```

apps/docs/docs/guides/_category_.yml

@@ -1,2 +0,0 @@
-label: Guides
-position: 2

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

@@ -1,179 +0,0 @@
-# Create a basic agent
-
-We want to use `await` so we're going to wrap all of our code in a `main` function, like this:
-
-```typescript
-// Your imports go here
-
-async function main() {
-  // the rest of your code goes here
-}
-
-main().catch(console.error);
-```
-
-For the rest of this guide we'll assume your code is wrapped like this so we can use `await`. You can run the code this way:
-
-```bash
-npx tsx example.ts
-```
-
-### Load your dependencies
-
-First we'll need to pull in our dependencies. These are:
-
-- The OpenAI class to use the OpenAI LLM
-- FunctionTool to provide tools to our agent
-- OpenAIAgent to create the agent itself
-- Settings to define some global settings for the library
-- Dotenv to load our API key from the .env file
-
-```javascript
-import { OpenAI, FunctionTool, OpenAIAgent, Settings } from "llamaindex";
-import "dotenv/config";
-```
-
-### Initialize your LLM
-
-We need to tell our OpenAI class where its API key is, and which of OpenAI's models to use. We'll be using `gpt-4o`, which is capable while still being pretty cheap. This is a global setting, so anywhere an LLM is needed will use the same model.
-
-```javascript
-Settings.llm = new OpenAI({
-  apiKey: process.env.OPENAI_API_KEY,
-  model: "gpt-4o",
-});
-```
-
-### Turn on logging
-
-We want to see what our agent is up to, so we're going to hook into some events that the library generates and print them out. There are several events possible, but we'll specifically tune in to `llm-tool-call` (when a tool is called) and `llm-tool-result` (when it responds).
-
-```javascript
-Settings.callbackManager.on("llm-tool-call", (event) => {
-  console.log(event.detail);
-});
-Settings.callbackManager.on("llm-tool-result", (event) => {
-  console.log(event.detail);
-});
-```
-
-### Create a function
-
-We're going to create a very simple function that adds two numbers together. This will be the tool we ask our agent to use.
-
-```javascript
-const sumNumbers = ({ a, b }) => {
-  return `${a + b}`;
-};
-```
-
-Note that we're passing in an object with two named parameters, `a` and `b`. This is a little unusual, but important for defining a tool that an LLM can use.
-
-### Turn the function into a tool for the agent
-
-This is the most complicated part of creating an agent. We need to define a `FunctionTool`. We have to pass in:
-
-- The function itself (`sumNumbers`)
-- A name for the function, which the LLM will use to call it
-- A description of the function. The LLM will read this description to figure out what the tool does, and if it needs to call it
-- A schema for function. We tell the LLM that the parameter is an `object`, and we tell it about the two named parameters we gave it, `a` and `b`. We describe each parameter as a `number`, and we say that both are required.
-- You can see [more examples of function schemas](https://cookbook.openai.com/examples/how_to_call_functions_with_chat_models).
-
-```javascript
-const tool = FunctionTool.from(sumNumbers, {
-  name: "sumNumbers",
-  description: "Use this function to sum two numbers",
-  parameters: {
-    type: "object",
-    properties: {
-      a: {
-        type: "number",
-        description: "First number to sum",
-      },
-      b: {
-        type: "number",
-        description: "Second number to sum",
-      },
-    },
-    required: ["a", "b"],
-  },
-});
-```
-
-We then wrap up the tools into an array. We could provide lots of tools this way, but for this example we're just using the one.
-
-```javascript
-const tools = [tool];
-```
-
-### Create the agent
-
-With your LLM already set up and your tools defined, creating an agent is simple:
-
-```javascript
-const agent = new OpenAIAgent({ tools });
-```
-
-### Ask the agent a question
-
-We can use the `chat` interface to ask our agent a question, and it will use the tools we've defined to find an answer.
-
-```javascript
-let response = await agent.chat({
-  message: "Add 101 and 303",
-});
-
-console.log(response);
-```
-
-Let's see what running this looks like using `npx tsx agent.ts`
-
-**_Output_**
-
-```javascript
-{
-  toolCall: {
-    id: 'call_ze6A8C3mOUBG4zmXO8Z4CPB5',
-    name: 'sumNumbers',
-    input: { a: 101, b: 303 }
-  },
-  toolResult: {
-    tool: FunctionTool { _fn: [Function: sumNumbers], _metadata: [Object] },
-    input: { a: 101, b: 303 },
-    output: '404',
-    isError: false
-  }
-}
-```
-
-```javascript
-{
-  response: {
-    raw: {
-      id: 'chatcmpl-9KwauZku3QOvH78MNvxJs81mDvQYK',
-      object: 'chat.completion',
-      created: 1714778824,
-      model: 'gpt-4-turbo-2024-04-09',
-      choices: [Array],
-      usage: [Object],
-      system_fingerprint: 'fp_ea6eb70039'
-    },
-    message: {
-      content: 'The sum of 101 and 303 is 404.',
-      role: 'assistant',
-      options: {}
-    }
-  },
-  sources: [Getter]
-}
-```
-
-We're seeing two pieces of output here. The first is our callback firing when the tool is called. You can see in `toolResult` that the LLM has correctly passed `101` and `303` to our `sumNumbers` function, which adds them up and returns `404`.
-
-The second piece of output is the response from the LLM itself, where the `message.content` key is giving us the answer.
-
-Great! We've built an agent with tool use! Next you can:
-
-- [See the full code](https://github.com/run-llama/ts-agents/blob/main/1_agent/agent.ts)
-- [Switch to a local LLM](local_model)
-- Move on to [add Retrieval-Augmented Generation to your agent](agentic_rag)

apps/docs/docs/guides/agents/3_local_model.mdx

@@ -1,90 +0,0 @@
-# Using a local model via Ollama
-
-If you're happy using OpenAI, you can skip this section, but many people are interested in using models they run themselves. The easiest way to do this is via the great work of our friends at [Ollama](https://ollama.com/), who provide a simple to use client that will download, install and run a [growing range of models](https://ollama.com/library) for you.
-
-### Install Ollama
-
-They provide a one-click installer for Mac, Linux and Windows on their [home page](https://ollama.com/).
-
-### Pick and run a model
-
-Since we're going to be doing agentic work, we'll need a very capable model, but the largest models are hard to run on a laptop. We think `mixtral 8x7b` is a good balance between power and resources, but `llama3` is another great option. You can run it simply by running
-
-```bash
-ollama run mixtral:8x7b
-```
-
-The first time you run it will also automatically download and install the model for you.
-
-### Switch the LLM in your code
-
-There are two changes you need to make to the code we already wrote in `1_agent` to get Mixtral 8x7b to work. First, you need to switch to that model. Replace the call to `Settings.llm` with this:
-
-```javascript
-Settings.llm = new Ollama({
-  model: "mixtral:8x7b",
-});
-```
-
-### Swap to a ReActAgent
-
-In our original code we used a specific OpenAIAgent, so we'll need to switch to a more generic agent pattern, the ReAct pattern. This is simple: change the `const agent` line in your code to read
-
-```javascript
-const agent = new ReActAgent({ tools });
-```
-
-(You will also need to bring in `Ollama` and `ReActAgent` in your imports)
-
-### Run your totally local agent
-
-Because your embeddings were already local, your agent can now run entirely locally without making any API calls.
-
-```bash
-node agent.mjs
-```
-
-Note that your model will probably run a lot slower than OpenAI, so be prepared to wait a while!
-
-**_Output_**
-
-```javascript
-{
-  response: {
-    message: {
-      role: 'assistant',
-      content: ' Thought: I need to use a tool to add the numbers 101 and 303.\n' +
-        'Action: sumNumbers\n' +
-        'Action Input: {"a": 101, "b": 303}\n' +
-        '\n' +
-        'Observation: 404\n' +
-        '\n' +
-        'Thought: I can answer without using any more tools.\n' +
-        'Answer: The sum of 101 and 303 is 404.'
-    },
-    raw: {
-      model: 'mixtral:8x7b',
-      created_at: '2024-05-09T00:24:30.339473Z',
-      message: [Object],
-      done: true,
-      total_duration: 64678371209,
-      load_duration: 57394551334,
-      prompt_eval_count: 475,
-      prompt_eval_duration: 4163981000,
-      eval_count: 94,
-      eval_duration: 3116692000
-    }
-  },
-  sources: [Getter]
-}
-```
-
-Tada! You can see all of this in the folder `1a_mixtral`.
-
-### Extending to other examples
-
-You can use a ReActAgent instead of an OpenAIAgent in any of the further examples below, but keep in mind that GPT-4 is a lot more capable than Mixtral 8x7b, so you may see more errors or failures in reasoning if you are using an entirely local setup.
-
-### Next steps
-
-Now you've got a local agent, you can [add Retrieval-Augmented Generation to your agent](agentic_rag).

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

@@ -1,165 +0,0 @@
-# Adding Retrieval-Augmented Generation (RAG)
-
-While an agent that can perform math is nifty (LLMs are usually not very good at math), LLM-based applications are always more interesting when they work with large amounts of data. In this case, we're going to use a 200-page PDF of the proposed budget of the city of San Francisco for fiscal years 2024-2024 and 2024-2025. It's a great example because it's extremely wordy and full of tables of figures, which present a challenge for humans and LLMs alike.
-
-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
-
-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.
-
-```javascript
-import {
-  OpenAI,
-  FunctionTool,
-  OpenAIAgent,
-  Settings,
-  SimpleDirectoryReader,
-  HuggingFaceEmbedding,
-  VectorStoreIndex,
-  QueryEngineTool,
-} from "llamaindex";
-```
-
-### Add an embedding model
-
-To encode our text into embeddings, we'll need an embedding model. We could use OpenAI for this but to save on API calls we're going to use a local embedding model from HuggingFace.
-
-```javascript
-Settings.embedModel = new HuggingFaceEmbedding({
-  modelType: "BAAI/bge-small-en-v1.5",
-  quantized: false,
-});
-```
-
-### 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.
-
-```javascript
-const reader = new SimpleDirectoryReader();
-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).
-
-```javascript
-const index = await VectorStoreIndex.fromDocuments(documents);
-```
-
-### Configure a retriever
-
-Before LlamaIndex can send a query to the LLM, it needs to find the most relevant chunks to send. That's the purpose of a `Retriever`. We're going to get `VectorStoreIndex` to act as a retriever for us
-
-```javascript
-const retriever = await index.asRetriever();
-```
-
-### Configure how many documents to retrieve
-
-By default LlamaIndex will retrieve just the 2 most relevant chunks of text. This document is complex though, so we'll ask for more context.
-
-```javascript
-retriever.similarityTopK = 10;
-```
-
-### Create a query engine
-
-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.
-
-```javascript
-const queryEngine = await index.asQueryEngine({
-  retriever,
-});
-```
-
-### Define the query engine as a tool
-
-Just as before we created a `FunctionTool`, we're going to create a `QueryEngineTool` that uses our `queryEngine`.
-
-```javascript
-const tools = [
-  new QueryEngineTool({
-    queryEngine: queryEngine,
-    metadata: {
-      name: "san_francisco_budget_tool",
-      description: `This tool can answer detailed questions about the individual components of the budget of San Francisco in 2023-2024.`,
-    },
-  }),
-];
-```
-
-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
-const agent = new OpenAIAgent({ tools });
-
-let response = await agent.chat({
-  message: "What's the budget of San Francisco in 2023-2024?",
-});
-
-console.log(response);
-```
-
-Once again we'll run `npx tsx agent.ts` and see what we get:
-
-**_Output_**
-
-```javascript
-{
-  toolCall: {
-    id: 'call_iNo6rTK4pOpOBbO8FanfWLI9',
-    name: 'san_francisco_budget_tool',
-    input: { query: 'total budget' }
-  },
-  toolResult: {
-    tool: QueryEngineTool {
-      queryEngine: [RetrieverQueryEngine],
-      metadata: [Object]
-    },
-    input: { query: 'total budget' },
-    output: 'The total budget for the City and County of San Francisco for Fiscal Year (FY) 2023-24 is $14.6 billion, which represents a $611.8 million, or 4.4 percent, increase over the FY 2022-23 budget. For FY 2024-25, the total budget is also projected to be $14.6 billion, reflecting a $40.5 million, or 0.3 percent, decrease from the FY 2023-24 proposed budget. This budget includes various expenditures across different departments and services, with significant allocations to public works, transportation, commerce, public protection, and health services.',
-    isError: false
-  }
-}
-```
-
-```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.
-
-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/guides/agents/_category_.yml

@@ -1,2 +0,0 @@
-label: Agents
-position: 1

apps/docs/docs/introduction.md

@@ -1,54 +0,0 @@
----
-sidebar_position: 0
-slug: /
----
-
-# What is LlamaIndex?
-
-LlamaIndex is a framework for building LLM-powered applications. LlamaIndex helps you ingest, structure, and access private or domain-specific data. It's available [as a Python package](https://docs.llamaindex.ai/en/stable/) and in TypeScript (this package). LlamaIndex.TS offers the core features of LlamaIndex for popular runtimes like Node.js (official support), Vercel Edge Functions (experimental), and Deno (experimental).
-
-## 🚀 Why LlamaIndex.TS?
-
-LLMs offer a natural language interface between humans and inferred data. Widely available models come pre-trained on huge amounts of publicly available data, from Wikipedia and mailing lists to textbooks and source code.
-
-Applications built on top of LLMs often require augmenting these models with private or domain-specific data. That data is often distributed across siloed applications and data stores. It's behind APIs, in SQL databases, or trapped in PDFs and slide decks.
-
-LlamaIndex.TS helps you unlock that data and then build powerful applications with it.
-
-## 🦙 What is LlamaIndex for?
-
-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 interactive, unsupervised manner.
-
-## 👨‍👩‍👧‍👦 Who is LlamaIndex for?
-
-LlamaIndex targets the "AI Engineer": developers building software in any domain that can be enhanced by LLM-powered functionality, without needing to be an expert in machine learning or natural language processing.
-
-Our high-level API allows beginner users to use LlamaIndex.TS to ingest, index, and query their data in just a few lines of code.
-
-For more complex applications, our lower-level APIs allow advanced users to customize and extend any module—data connectors, indices, retrievers, and query engines, to fit their needs.
-
-## Getting Started
-
-`npm install llamaindex`
-
-Our documentation includes [Installation Instructions](./getting_started/installation.mdx) and a [Starter Tutorial](./getting_started/starter_tutorial/retrieval_augmented_generation.mdx) to build your first application.
-
-Once you're up and running, [High-Level Concepts](./getting_started/concepts.md) has an overview of LlamaIndex's modular architecture. For more hands-on practical examples, look through our Examples section on the sidebar.
-
-## 🗺️ Ecosystem
-
-To download or contribute, find LlamaIndex on:
-
-- Github: https://github.com/run-llama/LlamaIndexTS
-- NPM: https://www.npmjs.com/package/llamaindex
-
-## Community
-
-Need help? Have a feature suggestion? Join the LlamaIndex community:
-
-- Twitter: https://twitter.com/llama_index
-- Discord https://discord.gg/dGcwcsnxhU

apps/docs/docs/modules/_category_.yml

@@ -1,3 +0,0 @@
-label: "Modules"
-collapsed: false
-position: 5

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

@@ -1,2 +0,0 @@
-label: "Agents"
-position: 10

apps/docs/docs/modules/chat_engine.md

@@ -1,30 +0,0 @@
----
-sidebar_position: 13
----
-
-# ChatEngine
-
-The chat engine is a quick and simple way to chat with the data in your index.
-
-```typescript
-const retriever = index.asRetriever();
-const chatEngine = new ContextChatEngine({ retriever });
-
-// start chatting
-const response = await chatEngine.chat({ message: query });
-```
-
-The `chat` function also supports streaming, just add `stream: true` as an option:
-
-```typescript
-const stream = await chatEngine.chat({ message: query, stream: true });
-for await (const chunk of stream) {
-  process.stdout.write(chunk.response);
-}
-```
-
-## Api References
-
-- [ContextChatEngine](../api/classes/ContextChatEngine.md)
-- [CondenseQuestionChatEngine](../api/classes/ContextChatEngine.md)
-- [SimpleChatEngine](../api/classes/SimpleChatEngine.md)

apps/docs/docs/modules/data_loaders/_category_.yml

@@ -1,2 +0,0 @@
-label: "Loaders"
-position: 1

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

@@ -1,58 +0,0 @@
-import CodeBlock from "@theme/CodeBlock";
-import CodeSource from "!raw-loader!../../../../../examples/readers/src/simple-directory-reader";
-import CodeSource2 from "!raw-loader!../../../../../examples/readers/src/custom-simple-directory-reader";
-
-# Loader
-
-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)
-
-LlamaIndex.TS supports easy loading of files from folders using the `SimpleDirectoryReader` class.
-
-It is a simple reader that reads all files from a directory and its subdirectories.
-
-<CodeBlock language="ts">{CodeSource}</CodeBlock>
-
-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:
-
-- `overrideReader` overrides the reader for all file types, including unsupported ones.
-- `fileExtToReader` maps a reader to a specific file type. Can override reader for existing file types or add support for new file types.
-- `defaultReader` sets a fallback reader for files with unsupported extensions. By default it is `TextFileReader`.
-
-SimpleDirectoryReader supports up to 9 concurrent requests. Use the `numWorkers` option to set the number of concurrent requests. By default it runs in sequential mode, i.e. set to 1.
-
-### Example
-
-<CodeBlock language="ts" showLineNumbers metastring="{8-12,17-21}">
-  {CodeSource2}
-</CodeBlock>
-
-## API Reference
-
-- [SimpleDirectoryReader](../../api/classes/SimpleDirectoryReader.md)

apps/docs/docs/modules/data_loaders/llama_parse/_category_.yml

@@ -1 +0,0 @@
-label: "LlamaParse"

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

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

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

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

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

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

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

@@ -1,14 +0,0 @@
-# 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

@@ -1,56 +0,0 @@
-# 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

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

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

@@ -1,14 +0,0 @@
-# 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

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

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

@@ -1,14 +0,0 @@
-# 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/_category_.yml

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

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

@@ -1,22 +0,0 @@
-# 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/documents_and_nodes/_category_.yml

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

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

@@ -1,14 +0,0 @@
-# Documents and Nodes
-
-`Document`s and `Node`s are the basic building blocks of any index. While the API for these objects is similar, `Document` objects represent entire files, while `Node`s are smaller pieces of that original document, that are suitable for an LLM and Q&A.
-
-```typescript
-import { Document } from "llamaindex";
-
-document = new Document({ text: "text", metadata: { key: "val" } });
-```
-
-## API Reference
-
-- [Document](../../api/classes/Document.md)
-- [TextNode](../../api/classes/TextNode.md)

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

@@ -1,2 +0,0 @@
-label: "Embeddings"
-position: 6

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

@@ -1 +0,0 @@
-label: "Available Embeddings"

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

@@ -1,31 +0,0 @@
-# Embedding
-
-The embedding model in LlamaIndex is responsible for creating numerical representations of text. By default, LlamaIndex will use the `text-embedding-ada-002` model from OpenAI.
-
-This can be explicitly updated through `Settings`
-
-```typescript
-import { OpenAIEmbedding, Settings } from "llamaindex";
-
-Settings.embedModel = new OpenAIEmbedding({
-  model: "text-embedding-ada-002",
-});
-```
-
-## Local Embedding
-
-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 +0,0 @@
-label: "Evaluating"
-position: 9

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

@@ -1 +0,0 @@
-label: "Modules"

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

@@ -1,2 +0,0 @@
-label: "Ingestion Pipeline"
-position: 4

apps/docs/docs/modules/llamacloud.mdx

@@ -1,32 +0,0 @@
-import CodeBlock from "@theme/CodeBlock";
-import CodeSource from "!raw-loader!../../../../examples/cloud/chat.ts";
-
-# LlamaCloud
-
-LlamaCloud is a new generation of managed parsing, ingestion, and retrieval services, designed to bring production-grade context-augmentation to your LLM and RAG applications.
-
-Currently, LlamaCloud supports
-
-- Managed Ingestion API, handling parsing and document management
-- Managed Retrieval API, configuring optimal retrieval for your RAG system
-
-## Access
-
-We are opening up a private beta to a limited set of enterprise partners for the managed ingestion and retrieval API. If you’re interested in centralizing your data pipelines and spending more time working on your actual RAG use cases, come [talk to us.](https://www.llamaindex.ai/contact)
-
-If you have access to LlamaCloud, you can visit [LlamaCloud](https://cloud.llamaindex.ai) to sign in and get an API key.
-
-## Create a Managed Index
-
-Currently, you can't create a managed index on LlamaCloud using LlamaIndexTS, but you can use an existing managed index for retrieval that was created by the Python version of LlamaIndex. See [the LlamaCloudIndex documentation](https://docs.llamaindex.ai/en/stable/module_guides/indexing/llama_cloud_index.html#usage) for more information on how to create a managed index.
-
-## Use a Managed Index
-
-Here's an example of how to use a managed index together with a chat engine:
-
-<CodeBlock language="ts">{CodeSource}</CodeBlock>
-
-## API Reference
-
-- [LlamaCloudIndex](../api/classes/LlamaCloudIndex.md)
-- [LlamaCloudRetriever](../api/classes/LlamaCloudRetriever.md)

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

@@ -1,2 +0,0 @@
-label: "LLMs"
-position: 5

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

@@ -1 +0,0 @@
-label: "Available LLMs"

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

@@ -1,80 +0,0 @@
-# Azure OpenAI
-
-To use Azure OpenAI, you only need to set a few environment variables together with the `OpenAI` class.
-
-For example:
-
-## Environment Variables
-
-```
-export AZURE_OPENAI_KEY="<YOUR KEY HERE>"
-export AZURE_OPENAI_ENDPOINT="<YOUR ENDPOINT, see https://learn.microsoft.com/en-us/azure/ai-services/openai/quickstart?tabs=command-line%2Cpython&pivots=rest-api>"
-export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name
-```
-
-## Usage
-
-```ts
-import { OpenAI, Settings } from "llamaindex";
-
-Settings.llm = new OpenAI({ model: "gpt-4", temperature: 0 });
-```
-
-## Load and index documents
-
-For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
-
-```ts
-const document = new Document({ text: essay, id_: "essay" });
-
-const index = await VectorStoreIndex.fromDocuments([document]);
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import { OpenAI, Document, VectorStoreIndex, Settings } from "llamaindex";
-
-Settings.llm = new OpenAI({ model: "gpt-4", temperature: 0 });
-
-async function main() {
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  // get retriever
-  const retriever = index.asRetriever();
-
-  // Create a query engine
-  const queryEngine = index.asQueryEngine({
-    retriever,
-  });
-
-  const query = "What is the meaning of life?";
-
-  // Query
-  const response = await queryEngine.query({
-    query,
-  });
-
-  // Log the response
-  console.log(response.response);
-}
-```
-
-## API Reference
-
-- [OpenAI](../../../api/classes/OpenAI.md)

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

@@ -1,138 +0,0 @@
-# Bedrock
-
-## Usage
-
-```ts
-import { BEDROCK_MODELS, Bedrock } from "@llamaindex/community";
-
-Settings.llm = new Bedrock({
-  model: BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU,
-  region: "us-east-1", // can be provided via env AWS_REGION
-  credentials: {
-    accessKeyId: "...", // optional and can be provided via env AWS_ACCESS_KEY_ID
-    secretAccessKey: "...", // optional and can be provided via env AWS_SECRET_ACCESS_KEY
-  },
-});
-```
-
-Currently only supports Anthropic and Meta models:
-
-```ts
-ANTHROPIC_CLAUDE_INSTANT_1 = "anthropic.claude-instant-v1";
-ANTHROPIC_CLAUDE_2 = "anthropic.claude-v2";
-ANTHROPIC_CLAUDE_2_1 = "anthropic.claude-v2:1";
-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";
-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"; // preview only, available on us-west-2, tool calling supported
-```
-
-Sonnet, Haiku and Opus are multimodal, image_url only supports base64 data url format, e.g. `data:image/jpeg;base64,SGVsbG8sIFdvcmxkIQ==`
-
-## Full Example
-
-```ts
-import { BEDROCK_MODELS, Bedrock } from "llamaindex";
-
-Settings.llm = new Bedrock({
-  model: BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU,
-});
-
-async function main() {
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  // Create a query engine
-  const queryEngine = index.asQueryEngine({
-    retriever,
-  });
-
-  const query = "What is the meaning of life?";
-
-  // Query
-  const response = await queryEngine.query({
-    query,
-  });
-
-  // Log the response
-  console.log(response.response);
-}
-```
-
-## 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/gemini.md

@@ -1,105 +0,0 @@
-# Gemini
-
-## Usage
-
-```ts
-import { Gemini, Settings, GEMINI_MODEL } from "llamaindex";
-
-Settings.llm = new Gemini({
-  model: GEMINI_MODEL.GEMINI_PRO,
-});
-```
-
-### Usage with Vertex AI
-
-To use Gemini via Vertex AI you can use `GeminiVertexSession`.
-
-GeminiVertexSession accepts the env variables: `GOOGLE_VERTEX_LOCATION` and `GOOGLE_VERTEX_PROJECT`
-
-```ts
-import { Gemini, GEMINI_MODEL, GeminiVertexSession } from "llamaindex";
-
-const gemini = new Gemini({
-  model: GEMINI_MODEL.GEMINI_PRO,
-  session: new GeminiVertexSession({
-    location: "us-central1",      // optional if provided by GOOGLE_VERTEX_LOCATION env variable
-    project: "project1",          // optional if provided by GOOGLE_VERTEX_PROJECT env variable
-    googleAuthOptions: {...},     // optional, but useful for production. It accepts all values from `GoogleAuthOptions`
-  }),
-});
-```
-
-[GoogleAuthOptions](https://github.com/googleapis/google-auth-library-nodejs/blob/main/src/auth/googleauth.ts)
-
-To authenticate for local development:
-
-```bash
-npm install @google-cloud/vertexai
-gcloud auth application-default login
-```
-
-To authenticate for production you'll have to use a [service account](https://cloud.google.com/docs/authentication/). `googleAuthOptions` has `credentials` which might be useful for you.
-
-## Load and index documents
-
-For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
-
-```ts
-const document = new Document({ text: essay, id_: "essay" });
-
-const index = await VectorStoreIndex.fromDocuments([document]);
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import {
-  Gemini,
-  Document,
-  VectorStoreIndex,
-  Settings,
-  GEMINI_MODEL,
-} from "llamaindex";
-
-Settings.llm = new Gemini({
-  model: GEMINI_MODEL.GEMINI_PRO,
-});
-
-async function main() {
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  // Create a query engine
-  const queryEngine = index.asQueryEngine({
-    retriever,
-  });
-
-  const query = "What is the meaning of life?";
-
-  // Query
-  const response = await queryEngine.query({
-    query,
-  });
-
-  // Log the response
-  console.log(response.response);
-}
-```
-
-## API Reference
-
-- [Gemini](../../../api/classes/Gemini.md)

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

@@ -1,73 +0,0 @@
-# OpenAI
-
-```ts
-import { OpenAI, Settings } from "llamaindex";
-
-Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0, apiKey: <YOUR_API_KEY> });
-```
-
-You can setup the apiKey on the environment variables, like:
-
-```bash
-export OPENAI_API_KEY="<YOUR_API_KEY>"
-```
-
-## Load and index documents
-
-For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
-
-```ts
-const document = new Document({ text: essay, id_: "essay" });
-
-const index = await VectorStoreIndex.fromDocuments([document]);
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import { OpenAI, Document, VectorStoreIndex, Settings } from "llamaindex";
-
-// Use the OpenAI LLM
-Settings.llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
-
-async function main() {
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  // get retriever
-  const retriever = index.asRetriever();
-
-  // Create a query engine
-  const queryEngine = index.asQueryEngine({
-    retriever,
-  });
-
-  const query = "What is the meaning of life?";
-
-  // Query
-  const response = await queryEngine.query({
-    query,
-  });
-
-  // Log the response
-  console.log(response.response);
-}
-```
-
-## API Reference
-
-- [OpenAI](../../../api/classes/OpenAI.md)

apps/docs/docs/modules/node_parser.md

@@ -1,97 +0,0 @@
----
-sidebar_position: 11
----
-
-# NodeParser
-
-The `NodeParser` in LlamaIndex is responsible for splitting `Document` objects into more manageable `Node` objects. When you call `.fromDocuments()`, the `NodeParser` from the `Settings` is used to do this automatically for you. Alternatively, you can use it to split documents ahead of time.
-
-```typescript
-import { Document, SentenceSplitter } from "llamaindex";
-
-const nodeParser = new SentenceSplitter();
-
-Settings.nodeParser = nodeParser;
-```
-
-## TextSplitter
-
-The underlying text splitter will split text by sentences. It can also be used as a standalone module for splitting raw text.
-
-```typescript
-import { SentenceSplitter } from "llamaindex";
-
-const splitter = new SentenceSplitter({ chunkSize: 1 });
-
-const textSplits = splitter.splitText("Hello World");
-```
-
-## MarkdownNodeParser
-
-The `MarkdownNodeParser` is a more advanced `NodeParser` that can handle markdown documents. It will split the markdown into nodes and then parse the nodes into a `Document` object.
-
-```typescript
-import { MarkdownNodeParser } from "llamaindex";
-
-const nodeParser = new MarkdownNodeParser();
-
-const nodes = nodeParser.getNodesFromDocuments([
-  new Document({
-    text: `# Main Header
-Main content
-
-# Header 2
-Header 2 content
-
-## Sub-header
-Sub-header content
-
-  `,
-  }),
-]);
-```
-
-The output metadata will be something like:
-
-```bash
-[
-  TextNode {
-    id_: '008e41a8-b097-487c-bee8-bd88b9455844',
-    metadata: { 'Header 1': 'Main Header' },
-    excludedEmbedMetadataKeys: [],
-    excludedLlmMetadataKeys: [],
-    relationships: { PARENT: [Array] },
-    hash: 'KJ5e/um/RkHaNR6bonj9ormtZY7I8i4XBPVYHXv1A5M=',
-    text: 'Main Header\nMain content',
-    textTemplate: '',
-    metadataSeparator: '\n'
-  },
-  TextNode {
-    id_: '0f5679b3-ba63-4aff-aedc-830c4208d0b5',
-    metadata: { 'Header 1': 'Header 2' },
-    excludedEmbedMetadataKeys: [],
-    excludedLlmMetadataKeys: [],
-    relationships: { PARENT: [Array] },
-    hash: 'IP/g/dIld3DcbK+uHzDpyeZ9IdOXY4brxhOIe7wc488=',
-    text: 'Header 2\nHeader 2 content',
-    textTemplate: '',
-    metadataSeparator: '\n'
-  },
-  TextNode {
-    id_: 'e81e9bd0-121c-4ead-8ca7-1639d65fdf90',
-    metadata: { 'Header 1': 'Header 2', 'Header 2': 'Sub-header' },
-    excludedEmbedMetadataKeys: [],
-    excludedLlmMetadataKeys: [],
-    relationships: { PARENT: [Array] },
-    hash: 'B3kYNnxaYi9ghtAgwza0ZEVKF4MozobkNUlcekDL7JQ=',
-    text: 'Sub-header\nSub-header content',
-    textTemplate: '',
-    metadataSeparator: '\n'
-  }
-]
-```
-
-## API Reference
-
-- [SentenceSplitter](../api/classes/SentenceSplitter.md)
-- [MarkdownNodeParser](../api/classes/MarkdownNodeParser.md)

apps/docs/docs/modules/node_postprocessors/_category_.yml

@@ -1,2 +0,0 @@
-label: "Node Postprocessors"
-position: 3

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

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

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

@@ -1,2 +0,0 @@
-label: "Query Engines"
-position: 8

apps/docs/docs/modules/retriever.md

@@ -1,23 +0,0 @@
----
-sidebar_position: 14
----
-
-# Retriever
-
-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 = vectorIndex.asRetriever({
-  similarityTopK: 3,
-});
-
-// Fetch nodes!
-const nodesWithScore = await retriever.retrieve({ query: "query string" });
-```

apps/docs/docs/modules/workflows.mdx

@@ -1,168 +0,0 @@
-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/docs/observability/_category_.yml

@@ -1,2 +0,0 @@
-label: Observability
-position: 5

apps/docs/docs/observability/index.md

@@ -1,64 +0,0 @@
-# Observability
-
-LlamaIndex provides **one-click observability** 🔭 to allow you to build principled LLM applications in a production setting.
-
-A key requirement for principled development of LLM applications over your data (RAG systems, agents) is being able to observe, debug, and evaluate
-your system - both as a whole and for each component.
-
-This feature allows you to seamlessly integrate the LlamaIndex library with powerful observability/evaluation tools offered by our partners.
-Configure a variable once, and you'll be able to do things like the following:
-
-- View LLM/prompt inputs/outputs
-- Ensure that the outputs of any component (LLMs, embeddings) are performing as expected
-- View call traces for both indexing and querying
-
-Each provider has similarities and differences. Take a look below for the full set of guides for each one!
-
-- [OpenLLMetry](#openllmetry)
-- [Langtrace](#langtrace)
-
-## OpenLLMetry
-
-[OpenLLMetry](https://github.com/traceloop/openllmetry-js) is an open-source project based on OpenTelemetry for tracing and monitoring
-LLM applications. It connects to [all major observability platforms](https://www.traceloop.com/docs/openllmetry/integrations/introduction) and installs in minutes.
-
-### Usage Pattern
-
-```bash
-npm install @traceloop/node-server-sdk
-```
-
-```js
-import * as traceloop from "@traceloop/node-server-sdk";
-
-traceloop.initialize({
-  apiKey: process.env.TRACELOOP_API_KEY,
-  disableBatch: true,
-});
-```
-
-## Langtrace
-
-Enhance your observability with Langtrace, a robust open-source tool supports OpenTelemetry and is designed to trace, evaluate, and manage LLM applications seamlessly. Langtrace integrates directly with LlamaIndex, offering detailed, real-time insights into performance metrics such as accuracy, evaluations, and latency.
-
-#### Install
-
-- Self-host or sign-up and generate an API key using [Langtrace](https://www.langtrace.ai) Cloud
-
-```bash
-npm install @langtrase/typescript-sdk
-```
-
-#### Initialize
-
-```js
-import * as Langtrace from "@langtrase/typescript-sdk";
-Langtrace.init({ api_key: "<YOUR_API_KEY>" });
-```
-
-Features:
-
-- OpenTelemetry compliant, ensuring broad compatibility with observability platforms.
-- Provides comprehensive logs and detailed traces of all components.
-- Real-time monitoring of accuracy, evaluations, usage, costs, and latency.
-- For more configuration options and details, visit [Langtrace Docs](https://docs.langtrace.ai/introduction).

apps/docs/docs/recipes/_category_.yml

@@ -1,2 +0,0 @@
-label: Recipes
-position: 3

apps/docs/docs/recipes/cost-analysis.mdx

@@ -1,14 +0,0 @@
-# Cost Analysis
-
-This page shows how to track LLM cost using APIs.
-
-## Callback Manager
-
-The callback manager is a class that manages the callback functions.
-
-You can register `llm-start`, `llm-end`, and `llm-stream` callbacks to the callback manager for tracking the cost.
-
-import CodeBlock from "@theme/CodeBlock";
-import CodeSource from "!raw-loader!../../../../examples/recipes/cost-analysis";
-
-<CodeBlock language="ts">{CodeSource}</CodeBlock>

apps/docs/docusaurus.config.js

@@ -1,162 +0,0 @@
-// @ts-check
-// Note: type annotations allow type checking and IDEs autocompletion
-
-const renderer = require("prism-react-renderer");
-const lightCodeTheme = renderer.themes.github;
-const darkCodeTheme = renderer.themes.dracula;
-
-/** @type {import('@docusaurus/types').Config} */
-const config = {
-  title: "LlamaIndex.TS",
-  tagline: "Unleash the power of LLMs over your data in TypeScript",
-  favicon: "img/favicon.png",
-
-  // Set the production url of your site here
-  url: "https://ts.llamaindex.ai",
-  // Set the /<baseUrl>/ pathname under which your site is served
-  // For GitHub pages deployment, it is often '/<projectName>/'
-  baseUrl: "/",
-
-  // GitHub pages deployment config.
-  // If you aren't using GitHub pages, you don't need these.
-  organizationName: "run-llama", // Usually your GitHub org/user name.
-  projectName: "LlamaIndex.TS", // Usually your repo name.
-
-  onBrokenLinks: "warn",
-  onBrokenMarkdownLinks: "warn",
-
-  presets: [
-    [
-      "@docusaurus/preset-classic",
-      /** @type {import('@docusaurus/preset-classic').Options} */
-      ({
-        docs: {
-          path: "docs",
-          routeBasePath: "/",
-          sidebarPath: require.resolve("./sidebars.js"),
-          // Please change this to your repo.
-          // Remove this to remove the "edit this page" links.
-          // editUrl:
-          //   "https://github.com/facebook/docusaurus/tree/main/packages/create-docusaurus/templates/shared/",
-          remarkPlugins: [
-            [require("@docusaurus/remark-plugin-npm2yarn"), { sync: true }],
-          ],
-        },
-        blog: {
-          blogTitle: "LlamaIndexTS blog",
-          blogDescription: "The official blog of LlamaIndexTS",
-          postsPerPage: "ALL",
-        },
-        gtag: {
-          trackingID: "G-NB9B8LW9W5",
-          anonymizeIP: true,
-        },
-      }),
-    ],
-  ],
-
-  themeConfig:
-    /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
-    ({
-      // Replace with your project's social card
-      image: "img/favicon.png", // TODO change this
-      navbar: {
-        title: "LlamaIndex.TS",
-        logo: {
-          alt: "LlamaIndex.TS",
-          src: "img/favicon.png",
-        },
-        items: [
-          {
-            type: "docSidebar",
-            sidebarId: "mySidebar",
-            position: "left",
-            label: "Docs",
-          },
-          {
-            type: "localeDropdown",
-            position: "left",
-          },
-          { to: "blog", label: "Blog", position: "right" },
-          {
-            href: "https://github.com/run-llama/LlamaIndexTS",
-            label: "GitHub",
-            position: "right",
-          },
-        ],
-      },
-      footer: {
-        style: "dark",
-        links: [
-          {
-            title: "Docs",
-            items: [
-              {
-                label: "API",
-                to: "/api",
-              },
-            ],
-          },
-          {
-            title: "Community",
-            items: [
-              {
-                label: "Discord",
-                href: "https://discord.com/invite/eN6D2HQ4aX",
-              },
-              {
-                label: "Twitter",
-                href: "https://twitter.com/LlamaIndex",
-              },
-            ],
-          },
-          {
-            title: "More",
-            items: [
-              {
-                label: "GitHub",
-                href: "https://github.com/run-llama/LlamaIndexTS",
-              },
-            ],
-          },
-        ],
-        copyright: `Copyright © ${new Date().getFullYear()} LlamaIndex. Built with Docusaurus.`,
-      },
-      prism: {
-        theme: lightCodeTheme,
-        darkTheme: darkCodeTheme,
-      },
-      algolia: {
-        // The application ID provided by Algolia
-        appId: "DYKPM6G4CX",
-
-        // Public API key: it is safe to commit it
-        apiKey: "c4ff3789f20bb72a5d735082aef17719",
-
-        indexName: "ts-llamaindex",
-
-        // Optional: see doc section below
-        contextualSearch: true,
-      },
-    }),
-  plugins: [
-    [
-      "docusaurus-plugin-typedoc",
-      {
-        entryPoints: ["../../packages/llamaindex/src/index.ts"],
-        tsconfig: "../../tsconfig.json",
-        readme: "none",
-        sourceLinkTemplate:
-          "https://github.com/run-llama/LlamaIndexTS/blob/{gitRevision}/{path}#L{line}",
-        sidebar: {
-          position: 6,
-        },
-      },
-    ],
-  ],
-  markdown: {
-    format: "detect",
-  },
-};
-
-module.exports = config;

apps/docs/package.json

@@ -1,57 +0,0 @@
-{
-  "name": "docs",
-  "version": "0.0.82",
-  "private": true,
-  "scripts": {
-    "docusaurus": "docusaurus",
-    "start": "docusaurus start",
-    "build": "docusaurus build",
-    "swizzle": "docusaurus swizzle",
-    "deploy": "docusaurus deploy",
-    "clear": "docusaurus clear",
-    "serve": "docusaurus serve",
-    "write-translations": "docusaurus write-translations",
-    "write-heading-ids": "docusaurus write-heading-ids",
-    "typecheck": "tsc"
-  },
-  "dependencies": {
-    "@docusaurus/core": "3.5.2",
-    "@docusaurus/remark-plugin-npm2yarn": "3.5.2",
-    "@llamaindex/examples": "workspace:*",
-    "@mdx-js/react": "3.0.1",
-    "clsx": "2.1.1",
-    "llamaindex": "workspace:*",
-    "postcss": "8.4.41",
-    "prism-react-renderer": "2.4.0",
-    "raw-loader": "4.0.2",
-    "react": "18.3.1",
-    "react-dom": "18.3.1"
-  },
-  "devDependencies": {
-    "@docusaurus/module-type-aliases": "3.5.2",
-    "@docusaurus/preset-classic": "3.5.2",
-    "@docusaurus/theme-classic": "3.5.2",
-    "@docusaurus/types": "3.5.2",
-    "@tsconfig/docusaurus": "2.0.3",
-    "@types/node": "^22.5.1",
-    "docusaurus-plugin-typedoc": "1.0.5",
-    "typedoc": "0.26.6",
-    "typedoc-plugin-markdown": "4.2.6",
-    "typescript": "^5.6.2"
-  },
-  "browserslist": {
-    "production": [
-      ">0.5%",
-      "not dead",
-      "not op_mini all"
-    ],
-    "development": [
-      "last 1 chrome version",
-      "last 1 firefox version",
-      "last 1 safari version"
-    ]
-  },
-  "engines": {
-    "node": ">=18"
-  }
-}

apps/docs/sidebars.js

@@ -1,33 +0,0 @@
-/**
- * Creating a sidebar enables you to:
- - create an ordered group of docs
- - render a sidebar for each doc of that group
- - provide next/previous navigation
-
- The sidebars can be generated from the filesystem, or explicitly defined here.
-
- Create as many sidebars as you want.
- */
-
-// @ts-check
-
-/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */
-const sidebars = {
-  // By default, Docusaurus generates a sidebar from the docs folder structure
-  mySidebar: [{ type: "autogenerated", dirName: "." }],
-
-  // But you can create a sidebar manually
-  /*
-  tutorialSidebar: [
-    'intro',
-    'hello',
-    {
-      type: 'category',
-      label: 'Tutorial',
-      items: ['tutorial-basics/create-a-document'],
-    },
-  ],
-   */
-};
-
-module.exports = sidebars;

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

@@ -1,59 +0,0 @@
-import clsx from "clsx";
-import React from "react";
-import styles from "./styles.module.css";
-
-type FeatureItem = {
-  title: string;
-  Svg: React.ComponentType<React.ComponentProps<"svg">>;
-  description: JSX.Element;
-};
-
-const FeatureList: FeatureItem[] = [
-  {
-    title: "Data Driven",
-    Svg: require("@site/static/img/undraw_docusaurus_mountain.svg").default,
-    description: <>LlamaIndex.TS is all about using your data with LLMs.</>,
-  },
-  {
-    title: "Typescript Native",
-    Svg: require("@site/static/img/undraw_docusaurus_tree.svg").default,
-    description: <>We ❤️ Typescript, and so do our users.</>,
-  },
-  {
-    title: "Built by the Community",
-    Svg: require("@site/static/img/undraw_docusaurus_react.svg").default,
-    description: (
-      <>
-        LlamaIndex.TS is a community project, and we welcome your contributions!
-      </>
-    ),
-  },
-];
-
-function Feature({ title, Svg, description }: FeatureItem) {
-  return (
-    <div className={clsx("col col--4")}>
-      <div className="text--center">
-        <Svg className={styles.featureSvg} role="img" />
-      </div>
-      <div className="text--center padding-horiz--md">
-        <h3>{title}</h3>
-        <p>{description}</p>
-      </div>
-    </div>
-  );
-}
-
-export default function HomepageFeatures(): JSX.Element {
-  return (
-    <section className={styles.features}>
-      <div className="container">
-        <div className="row">
-          {FeatureList.map((props, idx) => (
-            <Feature key={idx} {...props} />
-          ))}
-        </div>
-      </div>
-    </section>
-  );
-}

apps/docs/src/components/HomepageFeatures/styles.module.css

@@ -1,11 +0,0 @@
-.features {
-  display: flex;
-  align-items: center;
-  padding: 2rem 0;
-  width: 100%;
-}
-
-.featureSvg {
-  height: 200px;
-  width: 200px;
-}

apps/docs/src/css/custom.css

@@ -1,30 +0,0 @@
-/**
- * Any CSS included here will be global. The classic template
- * bundles Infima by default. Infima is a CSS framework designed to
- * work well for content-centric websites.
- */
-
-/* You can override the default Infima variables here. */
-:root {
-  --ifm-color-primary: #2e8555;
-  --ifm-color-primary-dark: #29784c;
-  --ifm-color-primary-darker: #277148;
-  --ifm-color-primary-darkest: #205d3b;
-  --ifm-color-primary-light: #33925d;
-  --ifm-color-primary-lighter: #359962;
-  --ifm-color-primary-lightest: #3cad6e;
-  --ifm-code-font-size: 95%;
-  --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.1);
-}
-
-/* For readability concerns, you should choose a lighter palette in dark mode. */
-[data-theme="dark"] {
-  --ifm-color-primary: #25c2a0;
-  --ifm-color-primary-dark: #21af90;
-  --ifm-color-primary-darker: #1fa588;
-  --ifm-color-primary-darkest: #1a8870;
-  --ifm-color-primary-light: #29d5b0;
-  --ifm-color-primary-lighter: #32d8b4;
-  --ifm-color-primary-lightest: #4fddbf;
-  --docusaurus-highlighted-code-line-bg: rgba(0, 0, 0, 0.3);
-}

apps/docs/src/pages/index.module.css

@@ -1,23 +0,0 @@
-/**
- * CSS files with the .module.css suffix will be treated as CSS modules
- * and scoped locally.
- */
-
-.heroBanner {
-  padding: 4rem 0;
-  text-align: center;
-  position: relative;
-  overflow: hidden;
-}
-
-@media screen and (max-width: 996px) {
-  .heroBanner {
-    padding: 2rem;
-  }
-}
-
-.buttons {
-  display: flex;
-  align-items: center;
-  justify-content: center;
-}

apps/docs/src/pages/markdown-page.md

@@ -1,7 +0,0 @@
----
-title: Markdown page example
----
-
-# Markdown page example
-
-You don't need React to write simple standalone pages.

apps/docs/static/img/logo.svg

@@ -1,37 +0,0 @@
-<svg width="200" height="200" viewBox="0 0 200 200" xmlns="http://www.w3.org/2000/svg">
-  <g fill="none" fill-rule="evenodd">
-    <path fill="#FFF" d="M99 52h84v34H99z" />
-    <path
-      d="M23 163c-7.398 0-13.843-4.027-17.303-10A19.886 19.886 0 0 0 3 163c0 11.046 8.954 20 20 20h20v-20H23z"
-      fill="#3ECC5F" />
-    <path
-      d="M112.98 57.376L183 53V43c0-11.046-8.954-20-20-20H73l-2.5-4.33c-1.112-1.925-3.889-1.925-5 0L63 23l-2.5-4.33c-1.111-1.925-3.889-1.925-5 0L53 23l-2.5-4.33c-1.111-1.925-3.889-1.925-5 0L43 23c-.022 0-.042.003-.065.003l-4.142-4.141c-1.57-1.571-4.252-.853-4.828 1.294l-1.369 5.104-5.192-1.392c-2.148-.575-4.111 1.389-3.535 3.536l1.39 5.193-5.102 1.367c-2.148.576-2.867 3.259-1.296 4.83l4.142 4.142c0 .021-.003.042-.003.064l-4.33 2.5c-1.925 1.111-1.925 3.889 0 5L23 53l-4.33 2.5c-1.925 1.111-1.925 3.889 0 5L23 63l-4.33 2.5c-1.925 1.111-1.925 3.889 0 5L23 73l-4.33 2.5c-1.925 1.111-1.925 3.889 0 5L23 83l-4.33 2.5c-1.925 1.111-1.925 3.889 0 5L23 93l-4.33 2.5c-1.925 1.111-1.925 3.889 0 5L23 103l-4.33 2.5c-1.925 1.111-1.925 3.889 0 5L23 113l-4.33 2.5c-1.925 1.111-1.925 3.889 0 5L23 123l-4.33 2.5c-1.925 1.111-1.925 3.889 0 5L23 133l-4.33 2.5c-1.925 1.111-1.925 3.889 0 5L23 143l-4.33 2.5c-1.925 1.111-1.925 3.889 0 5L23 153l-4.33 2.5c-1.925 1.111-1.925 3.889 0 5L23 163c0 11.046 8.954 20 20 20h120c11.046 0 20-8.954 20-20V83l-70.02-4.376A10.645 10.645 0 0 1 103 68c0-5.621 4.37-10.273 9.98-10.624"
-      fill="#3ECC5F" />
-    <path fill="#3ECC5F" d="M143 183h30v-40h-30z" />
-    <path
-      d="M193 158c-.219 0-.428.037-.639.064-.038-.15-.074-.301-.116-.451A5 5 0 0 0 190.32 148a4.96 4.96 0 0 0-3.016 1.036 26.531 26.531 0 0 0-.335-.336 4.955 4.955 0 0 0 1.011-2.987 5 5 0 0 0-9.599-1.959c-.148-.042-.297-.077-.445-.115.027-.211.064-.42.064-.639a5 5 0 0 0-5-5 5 5 0 0 0-5 5c0 .219.037.428.064.639-.148.038-.297.073-.445.115a4.998 4.998 0 0 0-9.599 1.959c0 1.125.384 2.151 1.011 2.987-3.717 3.632-6.031 8.693-6.031 14.3 0 11.046 8.954 20 20 20 9.339 0 17.16-6.41 19.361-15.064.211.027.42.064.639.064a5 5 0 0 0 5-5 5 5 0 0 0-5-5"
-      fill="#44D860" />
-    <path fill="#3ECC5F" d="M153 123h30v-20h-30z" />
-    <path
-      d="M193 115.5a2.5 2.5 0 1 0 0-5c-.109 0-.214.019-.319.032-.02-.075-.037-.15-.058-.225a2.501 2.501 0 0 0-.963-4.807c-.569 0-1.088.197-1.508.518a6.653 6.653 0 0 0-.168-.168c.314-.417.506-.931.506-1.494a2.5 2.5 0 0 0-4.8-.979A9.987 9.987 0 0 0 183 103c-5.522 0-10 4.478-10 10s4.478 10 10 10c.934 0 1.833-.138 2.69-.377a2.5 2.5 0 0 0 4.8-.979c0-.563-.192-1.077-.506-1.494.057-.055.113-.111.168-.168.42.321.939.518 1.508.518a2.5 2.5 0 0 0 .963-4.807c.021-.074.038-.15.058-.225.105.013.21.032.319.032"
-      fill="#44D860" />
-    <path
-      d="M63 55.5a2.5 2.5 0 0 1-2.5-2.5c0-4.136-3.364-7.5-7.5-7.5s-7.5 3.364-7.5 7.5a2.5 2.5 0 1 1-5 0c0-6.893 5.607-12.5 12.5-12.5S65.5 46.107 65.5 53a2.5 2.5 0 0 1-2.5 2.5"
-      fill="#000" />
-    <path d="M103 183h60c11.046 0 20-8.954 20-20V93h-60c-11.046 0-20 8.954-20 20v70z" fill="#FFFF50" />
-    <path
-      d="M168.02 124h-50.04a1 1 0 1 1 0-2h50.04a1 1 0 1 1 0 2m0 20h-50.04a1 1 0 1 1 0-2h50.04a1 1 0 1 1 0 2m0 20h-50.04a1 1 0 1 1 0-2h50.04a1 1 0 1 1 0 2m0-49.814h-50.04a1 1 0 1 1 0-2h50.04a1 1 0 1 1 0 2m0 19.814h-50.04a1 1 0 1 1 0-2h50.04a1 1 0 1 1 0 2m0 20h-50.04a1 1 0 1 1 0-2h50.04a1 1 0 1 1 0 2M183 61.611c-.012 0-.022-.006-.034-.005-3.09.105-4.552 3.196-5.842 5.923-1.346 2.85-2.387 4.703-4.093 4.647-1.889-.068-2.969-2.202-4.113-4.46-1.314-2.594-2.814-5.536-5.963-5.426-3.046.104-4.513 2.794-5.807 5.167-1.377 2.528-2.314 4.065-4.121 3.994-1.927-.07-2.951-1.805-4.136-3.813-1.321-2.236-2.848-4.75-5.936-4.664-2.994.103-4.465 2.385-5.763 4.4-1.373 2.13-2.335 3.428-4.165 3.351-1.973-.07-2.992-1.51-4.171-3.177-1.324-1.873-2.816-3.993-5.895-3.89-2.928.1-4.399 1.97-5.696 3.618-1.232 1.564-2.194 2.802-4.229 2.724a1 1 0 0 0-.072 2c3.017.101 4.545-1.8 5.872-3.487 1.177-1.496 2.193-2.787 4.193-2.855 1.926-.082 2.829 1.115 4.195 3.045 1.297 1.834 2.769 3.914 5.731 4.021 3.103.104 4.596-2.215 5.918-4.267 1.182-1.834 2.202-3.417 4.15-3.484 1.793-.067 2.769 1.35 4.145 3.681 1.297 2.197 2.766 4.686 5.787 4.796 3.125.108 4.634-2.62 5.949-5.035 1.139-2.088 2.214-4.06 4.119-4.126 1.793-.042 2.728 1.595 4.111 4.33 1.292 2.553 2.757 5.445 5.825 5.556l.169.003c3.064 0 4.518-3.075 5.805-5.794 1.139-2.41 2.217-4.68 4.067-4.773v-2z"
-      fill="#000" />
-    <path fill="#3ECC5F" d="M83 183h40v-40H83z" />
-    <path
-      d="M143 158c-.219 0-.428.037-.639.064-.038-.15-.074-.301-.116-.451A5 5 0 0 0 140.32 148a4.96 4.96 0 0 0-3.016 1.036 26.531 26.531 0 0 0-.335-.336 4.955 4.955 0 0 0 1.011-2.987 5 5 0 0 0-9.599-1.959c-.148-.042-.297-.077-.445-.115.027-.211.064-.42.064-.639a5 5 0 0 0-5-5 5 5 0 0 0-5 5c0 .219.037.428.064.639-.148.038-.297.073-.445.115a4.998 4.998 0 0 0-9.599 1.959c0 1.125.384 2.151 1.011 2.987-3.717 3.632-6.031 8.693-6.031 14.3 0 11.046 8.954 20 20 20 9.339 0 17.16-6.41 19.361-15.064.211.027.42.064.639.064a5 5 0 0 0 5-5 5 5 0 0 0-5-5"
-      fill="#44D860" />
-    <path fill="#3ECC5F" d="M83 123h40v-20H83z" />
-    <path
-      d="M133 115.5a2.5 2.5 0 1 0 0-5c-.109 0-.214.019-.319.032-.02-.075-.037-.15-.058-.225a2.501 2.501 0 0 0-.963-4.807c-.569 0-1.088.197-1.508.518a6.653 6.653 0 0 0-.168-.168c.314-.417.506-.931.506-1.494a2.5 2.5 0 0 0-4.8-.979A9.987 9.987 0 0 0 123 103c-5.522 0-10 4.478-10 10s4.478 10 10 10c.934 0 1.833-.138 2.69-.377a2.5 2.5 0 0 0 4.8-.979c0-.563-.192-1.077-.506-1.494.057-.055.113-.111.168-.168.42.321.939.518 1.508.518a2.5 2.5 0 0 0 .963-4.807c.021-.074.038-.15.058-.225.105.013.21.032.319.032"
-      fill="#44D860" />
-    <path
-      d="M143 41.75c-.16 0-.33-.02-.49-.05a2.52 2.52 0 0 1-.47-.14c-.15-.06-.29-.14-.431-.23-.13-.09-.259-.2-.38-.31-.109-.12-.219-.24-.309-.38s-.17-.28-.231-.43a2.619 2.619 0 0 1-.189-.96c0-.16.02-.33.05-.49.03-.16.08-.31.139-.47.061-.15.141-.29.231-.43.09-.13.2-.26.309-.38.121-.11.25-.22.38-.31.141-.09.281-.17.431-.23.149-.06.31-.11.47-.14.32-.07.65-.07.98 0 .159.03.32.08.47.14.149.06.29.14.43.23.13.09.259.2.38.31.11.12.22.25.31.38.09.14.17.28.23.43.06.16.11.31.14.47.029.16.05.33.05.49 0 .66-.271 1.31-.73 1.77-.121.11-.25.22-.38.31-.14.09-.281.17-.43.23a2.565 2.565 0 0 1-.96.19m20-1.25c-.66 0-1.3-.27-1.771-.73a3.802 3.802 0 0 1-.309-.38c-.09-.14-.17-.28-.231-.43a2.619 2.619 0 0 1-.189-.96c0-.66.27-1.3.729-1.77.121-.11.25-.22.38-.31.141-.09.281-.17.431-.23.149-.06.31-.11.47-.14.32-.07.66-.07.98 0 .159.03.32.08.47.14.149.06.29.14.43.23.13.09.259.2.38.31.459.47.73 1.11.73 1.77 0 .16-.021.33-.05.49-.03.16-.08.32-.14.47-.07.15-.14.29-.23.43-.09.13-.2.26-.31.38-.121.11-.25.22-.38.31-.14.09-.281.17-.43.23a2.565 2.565 0 0 1-.96.19"
-      fill="#000" />
-  </g>
-</svg>

apps/docs/static/img/undraw_docusaurus_mountain.svg

@@ -1,171 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" width="1088" height="687.962" viewBox="0 0 1088 687.962">
-  <title>Easy to Use</title>
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apps/docs/tsconfig.json

@@ -1,11 +0,0 @@
-{
-  // This file is not used in compilation. It is here just for a nice editor experience.
-  "extends": "./node_modules/@tsconfig/docusaurus/tsconfig.json",
-  "compilerOptions": {
-    "baseUrl": ".",
-    "composite": true,
-    "incremental": true,
-    "outDir": "./lib",
-    "tsBuildInfoFile": "./lib/.tsbuildinfo"
-  }
-}

apps/docs/turbo.json

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