Merge main into sweep/svelte-frontend-option

.cjs.swcrc

@@ -1,16 +0,0 @@
-{
-  "jsc": {
-    "parser": {
-      "syntax": "typescript",
-      "decorators": true
-    },
-    "target": "esnext",
-    "transform": {
-      "decoratorVersion": "2022-03"
-    }
-  },
-  "module": {
-    "type": "commonjs",
-    "ignoreDynamic": true
-  }
-}

.eslintrc.js

@@ -0,0 +1,10 @@
+module.exports = {
+  root: true,
+  // This tells ESLint to load the config from the package `eslint-config-custom`
+  extends: ["custom"],
+  settings: {
+    next: {
+      rootDir: ["apps/*/"],
+    },
+  },
+};

.gitattributes

@@ -1,2 +0,0 @@
-examples/readers/data/** binary
-examples/data/** binary

.github/ISSUE_TEMPLATE/bug_report.md

@@ -1,46 +0,0 @@
----
-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/ISSUE_TEMPLATE/bugfix.yml

@@ -1,6 +1,6 @@
 name: Bugfix
 title: ""
-description: Write something like "We notice ... behavior when ... happens instead of ..." If you would like to use sweep.dev prefix with "Sweep:"
+description: Write something like "We notice ... behavior when ... happens instead of ...""
 body:
   - type: textarea
     id: description

.github/workflows/lint_on_push_or_pull.yml

@@ -8,25 +8,18 @@ on:
     branches:
       - main
 
-env:
-  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
-  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
-  TURBO_REMOTE_ONLY: true
-
 jobs:
   lint:
     runs-on: ubuntu-latest
     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: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Install pnpm
+        run: npm install -g pnpm
+
       - name: Install dependencies
         run: pnpm install
+
       - name: Run lint
         run: pnpm run lint
-      - name: Run Prettier
-        run: pnpm run format

.github/workflows/preview.yml

@@ -1,33 +0,0 @@
-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
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout Repo
-        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
-        run: pnpm run build
-
-      - name: Pre Release
-        run: pnpx pkg-pr-new publish --pnpm ./packages/* ./packages/providers/* ./packages/providers/storage/*

.github/workflows/publish_github.yml

@@ -1,37 +0,0 @@
-name: Publish to GitHub Releases
-
-on:
-  push:
-    tags:
-      - "llamaindex@*"
-
-jobs:
-  build-and-publish:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout Repo
-        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 tarball
-        run: |
-          pnpm pack
-        working-directory: packages/llamaindex
-
-      - name: Create release
-        uses: ncipollo/release-action@v1
-        with:
-          artifacts: "packages/llamaindex/llamaindex-*.tgz"
-          name: Release ${{ github.ref }}
-          bodyFile: "packages/llamaindex/CHANGELOG.md"
-          token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/release.yml

@@ -1,70 +0,0 @@
-name: Release
-
-on:
-  push:
-    branches:
-      - main
-
-concurrency: ${{ github.workflow }}-${{ github.ref }}
-
-jobs:
-  release:
-    name: Release
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout Repo
-        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: Add auth token to .npmrc file
-        run: |
-          cat << EOF >> ".npmrc"
-            //registry.npmjs.org/:_authToken=$NPM_TOKEN
-          EOF
-        env:
-          NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
-
-      - name: Get changeset status
-        id: get-changeset-status
-        run: |
-          pnpm changeset status --output .changeset/status.json
-          new_version=$(jq -r '.releases[] | select(.name == "llamaindex") | .newVersion' < .changeset/status.json)
-          rm -v .changeset/status.json
-          echo "new-version=${new_version}" >> "$GITHUB_OUTPUT"
-
-      - name: Create Release Pull Request or Publish to npm
-        id: changesets
-        uses: changesets/action@v1
-        with:
-          commit: Release ${{ steps.get-changeset-status.outputs.new-version }}
-          title: Release ${{ steps.get-changeset-status.outputs.new-version }}
-          # update version PR with the latest changesets
-          version: pnpm new-version
-          # build package and call changeset publish
-          publish: pnpm release
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          NPM_TOKEN: ${{ secrets.NPM_TOKEN }}
-
-      # Refs: https://github.com/changesets/changesets/issues/421
-      - name: Update lock file
-        continue-on-error: true
-        run: pnpm install --lockfile-only
-
-      - name: Commit lock file
-        continue-on-error: true
-        uses: stefanzweifel/git-auto-commit-action@v5
-        with:
-          commit_message: "chore: update lock file"
-          branch: changeset-release/main
-          file_pattern: "pnpm-lock.yaml"

.github/workflows/test.yml

@@ -1,217 +1,24 @@
 name: Run Tests
 
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - main
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
-env:
-  POSTGRES_HOST_AUTH_METHOD: trust
-  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
-  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
-  TURBO_REMOTE_ONLY: true
+on: [push, pull_request]
 
 jobs:
-  e2e:
-    strategy:
-      fail-fast: false
-      matrix:
-        node-version: [20.x, 22.x, 23.x]
-    name: E2E on Node.js ${{ matrix.node-version }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: ankane/setup-postgres@v1
-        with:
-          database: llamaindex_node_test
-          dev-files: true
-      - run: |
-          cd /tmp
-          git clone --branch v0.7.0 https://github.com/pgvector/pgvector.git
-          cd pgvector
-          make
-          sudo make install
-      - uses: pnpm/action-setup@v4
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: ${{ matrix.node-version }}
-          cache: "pnpm"
-      - name: Install dependencies
-        run: pnpm install
-      - name: Run E2E Tests
-        run: pnpm run e2e
-
   test:
-    strategy:
-      fail-fast: false
-      matrix:
-        node-version: [20.x, 22.x, 23.x]
-    name: Test on Node.js ${{ matrix.node-version }}
     runs-on: ubuntu-latest
+
     steps:
-      - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v4
+      - name: Checkout code
+        uses: actions/checkout@v2
+
       - name: Setup Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v2
         with:
-          node-version: ${{ matrix.node-version }}
-          cache: "pnpm"
+          node-version: "18"
+
       - name: Install dependencies
-        run: pnpm install
+        run: |
+          npm i -g pnpm
+          pnpm install
+
       - name: Run tests
         run: pnpm run test
-  typecheck:
-    runs-on: ubuntu-latest
-
-    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
-        run: pnpm run build
-      - 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 ${{ runner.temp }}/*.tgz
-        working-directory: e2e/npm
-      - name: Run tests
-        run: npm test
-        working-directory: e2e/npm
-  e2e-llamaindex-examples:
-    strategy:
-      fail-fast: false
-      matrix:
-        packages:
-          - cloudflare-worker-agent
-          - nextjs-agent
-          - nextjs-edge-runtime
-          - nextjs-node-runtime
-          - waku-query-engine
-          - llama-parse-browser
-          - vite-import-llamaindex
-    runs-on: ubuntu-latest
-    name: Build LlamaIndex Example (${{ matrix.packages }})
-    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
-      - name: Build ${{ matrix.packages }}
-        run: pnpm run build
-        working-directory: e2e/examples/${{ 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
-
-    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
-        run: pnpm run build
-      - name: Copy examples
-        run: rsync -rv --exclude=node_modules ./examples ${{ runner.temp }}
-      - name: Pack packages
-        run: |
-          for dir in packages/*; do
-            if [ -d "$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
-      - name: Run Type Check
-        run: npx tsc --project ./tsconfig.json
-        working-directory: ${{ runner.temp }}/examples
-      - uses: actions/upload-artifact@v4
-        if: failure()
-        with:
-          name: build-dist
-          path: |
-            ${{ runner.temp }}/*.tgz
-          if-no-files-found: error

.gitignore

@@ -37,17 +37,6 @@ yarn-error.log*
 .vercel
 
 dist/
-lib/
 
-.cache
-test-results/
-playwright-report/
-blob-report/
-playwright/.cache/
-.tsbuildinfo
-
-# intellij
-**/.idea
-
-# generated API
-packages/cloud/src/client
+# vs code
+.vscode/launch.json

.husky/pre-commit

@@ -1 +1,5 @@
-pnpm run lint-staged
+#!/usr/bin/env sh
+. "$(dirname -- "$0")/_/husky.sh"
+
+pnpm lint
+npx lint-staged

.husky/pre-push

@@ -0,0 +1,4 @@
+#!/usr/bin/env sh
+. "$(dirname -- "$0")/_/husky.sh"
+
+pnpm test

.npmrc

@@ -1,5 +1 @@
 auto-install-peers = true
-enable-pre-post-scripts = true
-prefer-workspace-packages = true
-save-workspace-protocol = true
-link-workspace-packages = true

.nvmrc

@@ -1 +0,0 @@
-22

.prettierignore

@@ -1,10 +0,0 @@
-apps/docs/i18n
-apps/docs/docs/api
-pnpm-lock.yaml
-lib/
-dist/
-.docusaurus/
-.source/
-# prttier doesn't support mdx3 we are using
-*.mdx
-packages/server/server/

.swcrc

@@ -1,12 +0,0 @@
-{
-  "jsc": {
-    "parser": {
-      "syntax": "typescript",
-      "decorators": true
-    },
-    "target": "esnext",
-    "transform": {
-      "decoratorVersion": "2022-03"
-    }
-  }
-}

.vscode/launch.json

@@ -1,18 +0,0 @@
-{
-  // Use IntelliSense to learn about possible attributes.
-  // Hover to view descriptions of existing attributes.
-  // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
-  "version": "0.2.0",
-  "configurations": [
-    {
-      "type": "node",
-      "request": "launch",
-      "name": "Debug Example",
-      "skipFiles": ["<node_internals>/**"],
-      "runtimeExecutable": "pnpm",
-      "console": "integratedTerminal",
-      "cwd": "${workspaceFolder}/examples",
-      "runtimeArgs": ["npx", "tsx", "${file}"]
-    }
-  ]
-}

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

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

.vscode/settings.json

@@ -5,15 +5,5 @@
   "[xml]": {
     "editor.defaultFormatter": "redhat.vscode-xml"
   },
-  "[python]": {
-    "editor.defaultFormatter": "ms-python.black-formatter"
-  },
-  "[jsonc]": {
-    "editor.defaultFormatter": "esbenp.prettier-vscode"
-  },
-  "[json]": {
-    "editor.defaultFormatter": "esbenp.prettier-vscode"
-  },
-  "prettier.prettierPath": "./node_modules/prettier",
-  "prettier.configPath": "prettier.config.mjs"
-}
+  "jest.rootPath": "./packages/core"
+}

CLAUDE.md

@@ -1,92 +0,0 @@
-# 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

@@ -2,111 +2,79 @@
 
 ## Structure
 
-LlamaIndex.TS uses pnpm monorepo.
+This is a monorepo built with Turborepo
 
-We recommend you to understand the basics of Node.js, TypeScript, pnpm, and of course, LLM before contributing.
+Right now there are two packages of importance:
 
-There are some important folders in the repository:
+packages/core which is the main NPM library llamaindex
 
-- `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.
+apps/simple is where the demo code lives
+
+### Turborepo docs
+
+You can checkout how Turborepo works using the default [README-turborepo.md](/README-turborepo.md)
 
 ## Getting Started
 
-Make sure you have Node.js LTS (Long-term Support) installed. You can check your Node.js version by running:
+Install NodeJS. Preferably v18 using nvm or n.
+
+Inside the LlamaIndexTS directory:
 
-```shell
-node -v
-# v20.x.x
 ```
-
-### Use pnpm
-
-```shell
-npm install -g pnpm
-```
-
-### Install dependencies
-
-```shell
+npm i -g pnpm ts-node
 pnpm install
 ```
 
-### Build the packages
+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.
 
-To build all packages, run:
+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).
 
-```shell
-pnpm build
+### 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
 ```
 
-### Run tests
+To write new test cases write them in [packages/core/src/tests](/packages/core/src/tests)
 
-#### Unit tests
+We use Jest https://jestjs.io/ to write our test cases. Jest comes with a bunch of built in assertions using the expect function: https://jestjs.io/docs/expect
 
-After build, to run all unit tests, call:
+### Demo applications
 
-```shell
-pnpm test
+There is an existing ["simple"](/apps/simple/README.md) demos folder with mainly NodeJS scripts. Feel free to add additional demos to that folder. If you would like to try out your changes in the core package with a new demo, you need to run the build command in the README.
+
+You can create new demo applications in the apps folder. Just run pnpm init in the folder after you create it to create its own package.json
+
+### Installing packages
+
+To install packages for a specific package or demo application, run
+
+```
+pnpm add [NPM Package] --filter [package or application i.e. core or simple]
 ```
 
-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.
+To install packages for every package or application run
 
-#### E2E tests
-
-To run all E2E tests, call:
-
-```shell
-pnpm e2e
 ```
-
-All E2E tests are in the `e2e` folder.
+pnpm add -w [NPM Package]
+```
 
 ### Docs
 
-See the [docs](./apps/next/README.md) for more information.
+To contribute to the docs, go to the docs website folder and run the Docusaurus instance.
 
-## Adding a new package
-
-Please follow these steps to add a new package:
-
-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.
-
-## Before sending a PR
-
-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:
-
-### Changesets
-
-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
+```bash
+cd apps/docs
+pnpm install
+pnpm start
 ```
 
-## Publishing (maintainers only)
+That should start a webserver which will serve the docs on https://localhost:3000
 
-The [Release Github Action](.github/workflows/release.yml) is automatically generating and updating a
-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.
-
-If this PR is merged it will automatically add version tags to the repository and publish the updated packages to NPM.
+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.

README-turborepo.md

@@ -0,0 +1,81 @@
+# Turborepo starter
+
+This is an official starter Turborepo.
+
+## Using this example
+
+Run the following command:
+
+```sh
+npx create-turbo@latest
+```
+
+## What's inside?
+
+This Turborepo includes the following packages/apps:
+
+### Apps and Packages
+
+- `docs`: a [Next.js](https://nextjs.org/) app
+- `web`: another [Next.js](https://nextjs.org/) app
+- `ui`: a stub React component library shared by both `web` and `docs` applications
+- `eslint-config-custom`: `eslint` configurations (includes `eslint-config-next` and `eslint-config-prettier`)
+- `tsconfig`: `tsconfig.json`s used throughout the monorepo
+
+Each package/app is 100% [TypeScript](https://www.typescriptlang.org/).
+
+### Utilities
+
+This Turborepo has some additional tools already setup for you:
+
+- [TypeScript](https://www.typescriptlang.org/) for static type checking
+- [ESLint](https://eslint.org/) for code linting
+- [Prettier](https://prettier.io) for code formatting
+
+### Build
+
+To build all apps and packages, run the following command:
+
+```
+cd my-turborepo
+pnpm build
+```
+
+### Develop
+
+To develop all apps and packages, run the following command:
+
+```
+cd my-turborepo
+pnpm dev
+```
+
+### Remote Caching
+
+Turborepo can use a technique known as [Remote Caching](https://turbo.build/repo/docs/core-concepts/remote-caching) to share cache artifacts across machines, enabling you to share build caches with your team and CI/CD pipelines.
+
+By default, Turborepo will cache locally. To enable Remote Caching you will need an account with Vercel. If you don't have an account you can [create one](https://vercel.com/signup), then enter the following commands:
+
+```
+cd my-turborepo
+npx turbo login
+```
+
+This will authenticate the Turborepo CLI with your [Vercel account](https://vercel.com/docs/concepts/personal-accounts/overview).
+
+Next, you can link your Turborepo to your Remote Cache by running the following command from the root of your Turborepo:
+
+```
+npx turbo link
+```
+
+## Useful Links
+
+Learn more about the power of Turborepo:
+
+- [Tasks](https://turbo.build/repo/docs/core-concepts/monorepos/running-tasks)
+- [Caching](https://turbo.build/repo/docs/core-concepts/caching)
+- [Remote Caching](https://turbo.build/repo/docs/core-concepts/remote-caching)
+- [Filtering](https://turbo.build/repo/docs/core-concepts/monorepos/filtering)
+- [Configuration Options](https://turbo.build/repo/docs/reference/configuration)
+- [CLI Usage](https://turbo.build/repo/docs/reference/command-line-reference)

README.md

@@ -1,81 +1,67 @@
-<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>
+# LlamaIndex.TS
 
-[![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://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)
-[![Twitter](https://img.shields.io/twitter/follow/llama_index)](https://x.com/llama_index)
+LlamaIndex is a data framework for your LLM application.
 
-Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in JS runtime environments with TypeScript support.
+Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.
 
 Documentation: https://ts.llamaindex.ai/
 
-Try examples online:
-
-[![Open in Stackblitz](https://developer.stackblitz.com/img/open_in_stackblitz.svg)](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples)
-
 ## What is LlamaIndex.TS?
 
 LlamaIndex.TS aims to be a lightweight, easy to use set of libraries to help you integrate large language models into your applications with your own data.
 
-## Compatibility
+## Getting started with an example:
 
-### Multiple JS Environment Support
+LlamaIndex.TS requries Node v18 or higher. You can download it from https://nodejs.org or use https://nvm.sh (our preferred option).
 
-LlamaIndex.TS supports multiple JS environments, including:
+In a new folder:
 
-- Node.js >= 20 ✅
-- Deno ✅
-- Bun ✅
-- Nitro ✅
-- Vercel Edge Runtime ✅ (with some limitations)
-- Cloudflare Workers ✅ (with some limitations)
-
-For now, browser support is limited due to the lack of support for [AsyncLocalStorage-like APIs](https://github.com/tc39/proposal-async-context)
-
-### Supported LLMs:
-
-- OpenAI LLms
-- Anthropic LLms
-- Groq LLMs
-- Llama2, Llama3, Llama3.1 LLMs
-- MistralAI LLMs
-- Fireworks LLMs
-- DeepSeek LLMs
-- ReplicateAI LLMs
-- TogetherAI LLMs
-- HuggingFace LLms
-- DeepInfra LLMs
-- Gemini LLMs
-
-## Getting started
-
-```shell
-npm install llamaindex
+```bash
+export OPENAI_API_KEY="sk-......" # Replace with your key from https://platform.openai.com/account/api-keys
+pnpm init
+pnpm install typescript
+pnpm exec tsc --init # if needed
 pnpm install llamaindex
-yarn add llamaindex
+pnpm install @types/node
 ```
 
-### Setup in Node.js, Deno, Bun, TypeScript...?
+Create the file example.ts
 
-See our official document: https://ts.llamaindex.ai/docs/llamaindex/getting_started
+```ts
+// example.ts
+import fs from "fs/promises";
+import { Document, VectorStoreIndex } from "llamaindex";
 
-### Adding provider packages
+async function main() {
+  // Load essay from abramov.txt in Node
+  const essay = await fs.readFile(
+    "node_modules/llamaindex/examples/abramov.txt",
+    "utf-8",
+  );
 
-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.
+  // Create Document object with essay
+  const document = new Document({ text: essay });
 
-For example, to use the OpenAI LLM, you would install the following package:
+  // Split text and create embeddings. Store them in a VectorStoreIndex
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
-```shell
-npm install @llamaindex/openai
-pnpm install @llamaindex/openai
-yarn add @llamaindex/openai
+  // Query the index
+  const queryEngine = index.asQueryEngine();
+  const response = await queryEngine.query(
+    "What did the author do in college?",
+  );
+
+  // Output response
+  console.log(response.toString());
+}
+
+main();
+```
+
+Then you can run it using
+
+```bash
+pnpx ts-node example.ts
 ```
 
 ## Playground
@@ -84,13 +70,50 @@ Check out our NextJS playground at https://llama-playground.vercel.app/. The sou
 
 ## Core concepts for getting started:
 
-See our documentation: https://ts.llamaindex.ai/docs/llamaindex/getting_started/concepts
+- [Document](/packages/core/src/Node.ts): A document represents a text file, PDF file or other contiguous piece of data.
+
+- [Node](/packages/core/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/core/src/Embedding.ts): Embeddings are sets of floating point numbers which represent the data in a Node. By comparing the similarity of embeddings, we can derive an understanding of the similarity of two pieces of data. One use case is to compare the embedding of a question with the embeddings of our Nodes to see which Nodes may contain the data needed to answer that quesiton.
+
+- [Indices](/packages/core/src/indices/): Indices store the Nodes and the embeddings of those nodes. QueryEngines retrieve Nodes from these Indices using embedding similarity.
+
+- [QueryEngine](/packages/core/src/QueryEngine.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.
+
+- [ChatEngine](/packages/core/src/ChatEngine.ts): A ChatEngine helps you build a chatbot that will interact with your Indices.
+
+- [SimplePrompt](/packages/core/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.
+
+## Note: NextJS:
+
+If you're using NextJS App Router, you'll need to use the NodeJS runtime (default) and add the follow config to your next.config.js to have it use imports/exports in the same way Node does.
+
+```js
+export const runtime = "nodejs"; // default
+```
+
+```js
+// next.config.js
+/** @type {import('next').NextConfig} */
+const nextConfig = {
+  experimental: {
+    serverComponentsExternalPackages: ["pdf-parse"], // Puts pdf-parse in actual NodeJS mode with NextJS App Router
+  },
+};
+
+module.exports = nextConfig;
+```
+
+## Supported LLMs:
+
+- OpenAI GPT-3.5-turbo and GPT-4
+- Anthropic Claude Instant and Claude 2
+- Llama2 Chat LLMs (70B, 13B, and 7B parameters)
 
 ## Contributing:
 
-Please see our [contributing guide](CONTRIBUTING.md) for more information.
-You are highly encouraged to contribute to LlamaIndex.TS!
+We are in the very early days of LlamaIndex.TS. If you’re interested in hacking on it with us check out our [contributing guide](/CONTRIBUTING.md)
 
-## Community
+## Bugs? Questions?
 
 Please join our Discord! https://discord.com/invite/eN6D2HQ4aX

apps/docs/.gitignore

@@ -0,0 +1,20 @@
+# Dependencies
+/node_modules
+
+# Production
+/build
+
+# Generated files
+.docusaurus
+.cache-loader
+
+# 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

@@ -0,0 +1,47 @@
+# 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

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

apps/docs/docs/.gitignore

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

apps/docs/docs/concepts.md

@@ -1,7 +1,9 @@
 ---
-title: Concepts
+sidebar_position: 3
 ---
 
+# High-Level Concepts
+
 LlamaIndex.TS helps you build LLM-powered applications (e.g. Q&A, chatbot) over custom data.
 
 In this high-level concepts guide, you will learn:
@@ -16,7 +18,7 @@ LlamaIndex uses a two stage method when using an LLM with your data:
 1. **indexing stage**: preparing a knowledge base, and
 2. **querying stage**: retrieving relevant context from the knowledge to assist the LLM in responding to a question
 
-![](/_static/concepts/rag.jpg)
+![](./_static/concepts/rag.jpg)
 
 This process is also known as Retrieval Augmented Generation (RAG).
 
@@ -28,14 +30,14 @@ Let's explore each stage in detail.
 
 LlamaIndex.TS help you prepare the knowledge base with a suite of data connectors and indexes.
 
-![](/_static/concepts/indexing.jpg)
+![](./_static/concepts/indexing.jpg)
 
-[**Data Loaders**](/docs/llamaindex/modules/data/readers):
+[**Data Loaders**](./modules/high_level/data_loader.md):
 A data connector (i.e. `Reader`) ingest data from different data sources and data formats into a simple `Document` representation (text and simple metadata).
 
-[**Documents / Nodes**](/docs/llamaindex/modules/data): A `Document` is a generic container around any data source - for instance, a PDF, an API output, or retrieved data from a database. A `Node` is the atomic unit of data in LlamaIndex and represents a "chunk" of a source `Document`. It's a rich representation that includes metadata and relationships (to other nodes) to enable accurate and expressive retrieval operations.
+[**Documents / Nodes**](./modules/high_level/documents_and_nodes.md): A `Document` is a generic container around any data source - for instance, a PDF, an API output, or retrieved data from a database. A `Node` is the atomic unit of data in LlamaIndex and represents a "chunk" of a source `Document`. It's a rich representation that includes metadata and relationships (to other nodes) to enable accurate and expressive retrieval operations.
 
-[**Data Indexes**](/docs/llamaindex/modules/data/data_index):
+[**Data Indexes**](./modules/high_level/data_index.md):
 Once you've ingested your data, LlamaIndex helps you index data into a format that's easy to retrieve.
 
 Under the hood, LlamaIndex parses the raw documents into intermediate representations, calculates vector embeddings, and stores your data in-memory or to disk.
@@ -54,23 +56,23 @@ LlamaIndex provides composable modules that help you build and integrate RAG pip
 
 These building blocks can be customized to reflect ranking preferences, as well as composed to reason over multiple knowledge bases in a structured way.
 
-![](/_static/concepts/querying.jpg)
+![](./_static/concepts/querying.jpg)
 
 #### Building Blocks
 
-[**Retrievers**](/docs/llamaindex/modules/rag/retriever):
+[**Retrievers**](./modules/low_level/retriever.md):
 A retriever defines how to efficiently retrieve relevant context from a knowledge base (i.e. index) when given a query.
-The specific retrieval logic differs for different indices, the most popular being dense retrieval against a vector index.
+The specific retrieval logic differs for difference indices, the most popular being dense retrieval against a vector index.
 
-[**Response Synthesizers**](/docs/llamaindex/modules/rag/response_synthesizer):
+[**Response Synthesizers**](./modules/low_level/response_synthesizer.md):
 A response synthesizer generates a response from an LLM, using a user query and a given set of retrieved text chunks.
 
 #### Pipelines
 
-[**Query Engines**](/docs/llamaindex/modules/rag/query_engines):
+[**Query Engines**](./modules/high_level/query_engine.md):
 A query engine is an end-to-end pipeline that allow you to ask question over your data.
 It takes in a natural language query, and returns a response, along with reference context retrieved and passed to the LLM.
 
-[**Chat Engines**](/docs/llamaindex/modules/rag/chat_engine):
+[**Chat Engines**](./modules/high_level/chat_engine.md):
 A chat engine is an end-to-end pipeline for having a conversation with your data
 (multiple back-and-forth instead of a single question & answer).

apps/docs/docs/end_to_end.md

@@ -0,0 +1,45 @@
+---
+sidebar_position: 4
+---
+
+# End to End Examples
+
+We include several end-to-end examples using LlamaIndex.TS in the repository
+
+Check out the examples below or try them out and complete them in minutes with interactive Github Codespace tutorials provided by Dev-Docs [here](https://codespaces.new/team-dev-docs/lits-dev-docs-playground?devcontainer_path=.devcontainer%2Fjavascript_ltsquickstart%2Fdevcontainer.json):
+
+## [Chat Engine](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/chatEngine.ts)
+
+Read a file and chat about it with the LLM.
+
+## [Vector Index](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/vectorIndex.ts)
+
+Create a vector index and query it. The vector index will use embeddings to fetch the top k most relevant nodes. By default, the top k is 2.
+
+## [Summary Index](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/summaryIndex.ts)
+
+Create a list index and query it. This example also use the `LLMRetriever`, which will use the LLM to select the best nodes to use when generating answer.
+
+## [Save / Load an Index](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/storageContext.ts)
+
+Create and load a vector index. Persistance to disk in LlamaIndex.TS happens automatically once a storage context object is created.
+
+## [Customized Vector Index](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/vectorIndexCustomize.ts)
+
+Create a vector index and query it, while also configuring the the `LLM`, the `ServiceContext`, and the `similarity_top_k`.
+
+## [OpenAI LLM](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/openai.ts)
+
+Create an OpenAI LLM and directly use it for chat.
+
+## [Llama2 DeuceLLM](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/llamadeuce.ts)
+
+Create a Llama-2 LLM and directly use it for chat.
+
+## [SubQuestionQueryEngine](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/subquestion.ts)
+
+Uses the `SubQuestionQueryEngine`, which breaks complex queries into multiple questions, and then aggreates a response across the answers to all sub-questions.
+
+## [Low Level Modules](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/lowlevel.ts)
+
+This example uses several low-level components, which removes the need for an actual query engine. These components can be used anywhere, in any application, or customized and sub-classed to meet your own needs.

apps/docs/docs/environments.md

@@ -0,0 +1,29 @@
+---
+sidebar_position: 5
+---
+
+# Environments
+
+LlamaIndex currently officially supports NodeJS 18 and NodeJS 20.
+
+## NextJS App Router
+
+If you're using NextJS App Router route handlers/serverless functions, you'll need to use the NodeJS mode:
+
+```js
+export const runtime = "nodejs"; // default
+```
+
+and you'll need to add an exception for pdf-parse in your next.config.js
+
+```js
+// next.config.js
+/** @type {import('next').NextConfig} */
+const nextConfig = {
+  experimental: {
+    serverComponentsExternalPackages: ["pdf-parse"], // Puts pdf-parse in actual NodeJS mode with NextJS App Router
+  },
+};
+
+module.exports = nextConfig;
+```

apps/docs/docs/installation.md

@@ -0,0 +1,25 @@
+---
+sidebar_position: 1
+---
+
+# Installation and Setup
+
+## Installation from NPM
+
+Make sure you have NodeJS v18 or higher.
+
+```bash npm2yarn
+npm install llamaindex
+```
+
+## Environment variables
+
+Our examples use OpenAI by default. You'll need to set up your Open AI key like so:
+
+```bash
+export OPENAI_API_KEY="sk-......" # Replace with your key from https://platform.openai.com/account/api-keys
+```
+
+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.

apps/docs/docs/introduction.md

@@ -0,0 +1,56 @@
+---
+sidebar_position: 0
+slug: /
+---
+
+# What is LlamaIndex.TS?
+
+LlamaIndex.TS is a data framework for LLM applications to ingest, structure, and access private or domain-specific data. While a python package is also available (see [here](https://gpt-index.readthedocs.io/en/latest/)), LlamaIndex.TS offers core features in a simple package, optimized for usage with TypeScript.
+
+## 🚀 Why LlamaIndex.TS?
+
+At their core, 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. Unfortunately, that data can be distributed across siloed applications and data stores. It's behind APIs, in SQL databases, or trapped in PDFs and slide decks.
+
+That's where **LlamaIndex.TS** comes in.
+
+## 🦙 How can LlamaIndex.TS help?
+
+LlamaIndex.TS provides the following tools:
+
+- **Data loading** ingest your existing `.txt`, `.pdf`, `.csv`, `.md` and `.docx` data directly
+- **Data indexes** structure your data in intermediate representations that are easy and performant for LLMs to consume.
+- **Engines** provide natural language access to your data. For example:
+  - Query engines are powerful retrieval interfaces for knowledge-augmented output.
+  - Chat engines are conversational interfaces for multi-message, "back and forth" interactions with your data.
+
+## 👨‍👩‍👧‍👦 Who is LlamaIndex for?
+
+LlamaIndex.TS provides a core set of tools, essential for anyone building LLM apps with JavaScript and TypeScript.
+
+Our high-level API allows beginner users to use LlamaIndex.TS to ingest and query their data.
+
+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](./installation.md) and a [Starter Tutorial](./starter.md) to build your first application.
+
+Once you're up and running, [High-Level Concepts](./concepts.md) has an overview of LlamaIndex's modular architecture. For more hands-on practical examples, look through our [End-to-End Tutorials](./end_to_end.md).
+
+## 🗺️ 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

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

apps/docs/docs/modules/high_level/_category_.yml

@@ -0,0 +1 @@
+label: High-Level Modules

apps/docs/docs/modules/high_level/chat_engine.md

@@ -0,0 +1,20 @@
+---
+sidebar_position: 4
+---
+
+# 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(query);
+```
+
+## Api References
+
+- [ContextChatEngine](../../api/classes/ContextChatEngine.md)
+- [CondenseQuestionChatEngine](../../api/classes/ContextChatEngine.md)

apps/docs/docs/modules/high_level/data_index.md

@@ -0,0 +1,23 @@
+---
+sidebar_position: 2
+---
+
+# Index
+
+An index is the basic container and organization for your data. LlamaIndex.TS supports two indexes:
+
+- `VectorStoreIndex` - will send the top-k `Node`s to the LLM when generating a response. The default top-k is 2.
+- `SummaryIndex` - will send every `Node` in the index to the LLM in order to generate a response
+
+```typescript
+import { Document, VectorStoreIndex } from "llamaindex";
+
+const document = new Document({ text: "test" });
+
+const index = await VectorStoreIndex.fromDocuments([document]);
+```
+
+## API Reference
+
+- [SummaryIndex](../../api/classes/SummaryIndex.md)
+- [VectorStoreIndex](../../api/classes/VectorStoreIndex.md)

apps/docs/docs/modules/high_level/data_loader.md

@@ -0,0 +1,17 @@
+---
+sidebar_position: 1
+---
+
+# Reader / Loader
+
+LlamaIndex.TS supports easy loading of files from folders using the `SimpleDirectoryReader` class. Currently, `.txt`, `.pdf`, `.csv`, `.md` and `.docx` files are supported, with more planned in the future!
+
+```typescript
+import { SimpleDirectoryReader } from "llamaindex";
+
+documents = new SimpleDirectoryReader().loadData("./data");
+```
+
+## API Reference
+
+- [SimpleDirectoryReader](../../api/classes/SimpleDirectoryReader.md)

apps/docs/docs/modules/high_level/documents_and_nodes.md

@@ -0,0 +1,18 @@
+---
+sidebar_position: 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/high_level/query_engine.md

@@ -1,21 +1,14 @@
 ---
-title: QueryEngine
+sidebar_position: 3
 ---
 
-A query engine wraps a `Retriever` and a `ResponseSynthesizer` into a pipeline, that will use the query string to fetch nodes and then send them to the LLM to generate a response.
+# QueryEngine
+
+A query engine wraps a `Retriever` and a `ResponseSynthesizer` into a pipeline, that will use the query string to fetech nodes and then send them to the LLM to generate a response.
 
 ```typescript
 const queryEngine = index.asQueryEngine();
-const response = await queryEngine.query({ query: "query string" });
-```
-
-The `query` function also supports streaming, just add `stream: true` as an option:
-
-```typescript
-const stream = await queryEngine.query({ query: "query string", stream: true });
-for await (const chunk of stream) {
-  process.stdout.write(chunk.response);
-}
+const response = await queryEngine.query("query string");
 ```
 
 ## Sub Question Query Engine
@@ -24,7 +17,7 @@ The basic concept of the Sub Question Query Engine is that it splits a single qu
 
 ### Getting Started
 
-The easiest way to start trying the Sub Question Query Engine is running the subquestion.ts file in [examples](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/subquestion.ts).
+The easiest way to start trying the Sub Question Query Engine is running the subquestion.ts file in [apps/simple](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/subquestion.ts).
 
 ```bash
 npx ts-node subquestion.ts
@@ -38,6 +31,6 @@ You can learn more about Tools by taking a look at the LlamaIndex Python documen
 
 ## API Reference
 
-- [RetrieverQueryEngine](/docs/api/classes/RetrieverQueryEngine)
-- [SubQuestionQueryEngine](/docs/api/classes/SubQuestionQueryEngine)
-- [QueryEngineTool](/docs/api/classes/QueryEngineTool)
+- [RetrieverQueryEngine](../../api/classes/RetrieverQueryEngine.md)
+- [SubQuestionQueryEngine](../../api/classes/SubQuestionQueryEngine.md)
+- [QueryEngineTool](../../api/interfaces/QueryEngineTool.md)

apps/docs/docs/modules/index.md

@@ -0,0 +1,31 @@
+# Core Modules
+
+LlamaIndex.TS offers several core modules, seperated into high-level modules for quickly getting started, and low-level modules for customizing key components as you need.
+
+## High-Level Modules
+
+- [**Document**](./high_level/documents_and_nodes.md): A document represents a text file, PDF file or other contiguous piece of data.
+
+- [**Node**](./high_level/documents_and_nodes.md): 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.
+
+- [**Reader/Loader**](./high_level/data_loader.md): A reader or loader is something that takes in a document in the real world and transforms into a Document class that can then be used in your Index and queries. We currently support plain text files and PDFs with many many more to come.
+
+- [**Indexes**](./high_level/data_index.md): indexes store the Nodes and the embeddings of those nodes.
+
+- [**QueryEngine**](./high_level/query_engine.md): 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.
+
+- [**ChatEngine**](./high_level/chat_engine.md): A ChatEngine helps you build a chatbot that will interact with your Indexes.
+
+## Low Level Module
+
+- [**LLM**](./low_level/llm.md): The LLM class is a unified interface over a large language model provider such as OpenAI GPT-4, Anthropic Claude, or Meta LLaMA. You can subclass it to write a connector to your own large language model.
+
+- [**Embedding**](./low_level/embedding.md): An embedding is represented as a vector of floating point numbers. OpenAI's text-embedding-ada-002 is our default embedding model and each embedding it generates consists of 1,536 floating point numbers. Another popular embedding model is BERT which uses 768 floating point numbers to represent each Node. We provide a number of utilities to work with embeddings including 3 similarity calculation options and Maximum Marginal Relevance
+
+- [**TextSplitter/NodeParser**](./low_level/node_parser.md): Text splitting strategies are incredibly important to the overall efficacy of the embedding search. Currently, while we do have a default, there's no one size fits all solution. Depending on the source documents, you may want to use different splitting sizes and strategies. Currently we support spliltting by fixed size, splitting by fixed size with overlapping sections, splitting by sentence, and splitting by paragraph. The text splitter is used by the NodeParser when splitting `Document`s into `Node`s.
+
+- [**Retriever**](./low_level/retriever.md): The Retriever is what actually chooses the Nodes to retrieve from the index. Here, you may wish to try retrieving more or fewer Nodes per query, changing your similarity function, or creating your own retriever for each individual use case in your application. For example, you may wish to have a separate retriever for code content vs. text content.
+
+- [**ResponseSynthesizer**](./low_level/response_synthesizer.md): The ResponseSynthesizer is responsible for taking a query string, and using a list of `Node`s to generate a response. This can take many forms, like iterating over all the context and refining an answer, or building a tree of summaries and returning the root summary.
+
+- [**Storage**](./low_level/storage.md): At some point you're going to want to store your indexes, data and vectors instead of re-running the embedding models every time. IndexStore, DocStore, VectorStore, and KVStore are abstractions that let you do that. Combined, they form the StorageContext. Currently, we allow you to persist your embeddings in files on the filesystem (or a virtual in memory file system), but we are also actively adding integrations to Vector Databases.

apps/docs/docs/modules/low_level/_category_.yml

@@ -0,0 +1 @@
+label: Low-Level Modules

apps/docs/docs/modules/low_level/embedding.md

@@ -0,0 +1,22 @@
+---
+sidebar_position: 1
+---
+
+# 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 set in the `ServiceContext` object.
+
+```typescript
+import { OpenAIEmbedding, serviceContextFromDefaults } from "llamaindex";
+
+const openaiEmbeds = new OpenAIEmbedding();
+
+const serviceContext = serviceContextFromDefaults({ embedModel: openaiEmbeds });
+```
+
+## API Reference
+
+- [OpenAIEmbedding](../../api/classes/OpenAIEmbedding.md)
+- [ServiceContext](../../api/interfaces/ServiceContext.md)

apps/docs/docs/modules/low_level/llm.md

@@ -0,0 +1,22 @@
+---
+sidebar_position: 0
+---
+
+# LLM
+
+The LLM is responsible for reading text and generating natural language responses to queries. By default, LlamaIndex.TS uses `gpt-3.5-turbo`.
+
+The LLM can be explicitly set in the `ServiceContext` object.
+
+```typescript
+import { OpenAI, serviceContextFromDefaults } from "llamaindex";
+
+const openaiLLM = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
+
+const serviceContext = serviceContextFromDefaults({ llm: openaiLLM });
+```
+
+## API Reference
+
+- [OpenAI](../../api/classes/OpenAI.md)
+- [ServiceContext](../../api/interfaces/ServiceContext.md)

apps/docs/docs/modules/low_level/node_parser.md

@@ -0,0 +1,33 @@
+---
+sidebar_position: 3
+---
+
+# NodeParser
+
+The `NodeParser` in LlamaIndex is responbile for splitting `Document` objects into more manageable `Node` objects. When you call `.fromDocuments()`, the `NodeParser` from the `ServiceContext` is used to do this automatically for you. Alternatively, you can use it to split documents ahead of time.
+
+```typescript
+import { Document, SimpleNodeParser } from "llamaindex";
+
+const nodeParser = new SimpleNodeParser();
+const nodes = nodeParser.getNodesFromDocuments([
+  new Document({ text: "I am 10 years old. John is 20 years old." }),
+]);
+```
+
+## 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");
+```
+
+## API Reference
+
+- [SimpleNodeParser](../../api/classes/SimpleNodeParser.md)
+- [SentenceSplitter](../../api/classes/SentenceSplitter.md)

apps/docs/docs/modules/low_level/response_synthesizer.md

@@ -0,0 +1,51 @@
+---
+sidebar_position: 6
+---
+
+# ResponseSynthesizer
+
+The ResponseSynthesizer is responsible for sending the query, nodes, and prompt templates to the LLM to generate a response. There are a few key modes for generating a response:
+
+- `Refine`: "create and refine" an answer by sequentially going through each retrieved text chunk.
+  This makes a separate LLM call per Node. Good for more detailed answers.
+- `CompactAndRefine` (default): "compact" the prompt during each LLM call by stuffing as
+  many text chunks that can fit within the maximum prompt size. If there are
+  too many chunks to stuff in one prompt, "create and refine" an answer by going through
+  multiple compact prompts. The same as `refine`, but should result in less LLM calls.
+- `TreeSummarize`: Given a set of text chunks and the query, recursively construct a tree
+  and return the root node as the response. Good for summarization purposes.
+- `SimpleResponseBuilder`: Given a set of text chunks and the query, apply the query to each text
+  chunk while accumulating the responses into an array. Returns a concatenated string of all
+  responses. Good for when you need to run the same query separately against each text
+  chunk.
+
+```typescript
+import { NodeWithScore, ResponseSynthesizer, TextNode } from "llamaindex";
+
+const responseSynthesizer = new ResponseSynthesizer();
+
+const nodesWithScore: NodeWithScore[] = [
+  {
+    node: new TextNode({ text: "I am 10 years old." }),
+    score: 1,
+  },
+  {
+    node: new TextNode({ text: "John is 20 years old." }),
+    score: 0.5,
+  },
+];
+
+const response = await responseSynthesizer.synthesize(
+  "What age am I?",
+  nodesWithScore,
+);
+console.log(response.response);
+```
+
+## API Reference
+
+- [ResponseSynthesizer](../../api/classes/ResponseSynthesizer.md)
+- [Refine](../../api/classes/Refine.md)
+- [CompactAndRefine](../../api/classes/CompactAndRefine.md)
+- [TreeSummarize](../../api/classes/TreeSummarize.md)
+- [SimpleResponseBuilder](../../api/classes/SimpleResponseBuilder.md)

apps/docs/docs/modules/low_level/retriever.md

@@ -0,0 +1,21 @@
+---
+sidebar_position: 5
+---
+
+# Retriever
+
+A retriever in LlamaIndex is what is used to fetch `Node`s from an index using a query string. Aa `VectorIndexRetriever` will fetch the top-k most similar nodes. Meanwhile, a `SummaryIndexRetriever` will fetch all nodes no matter the query.
+
+```typescript
+const retriever = vector_index.asRetriever();
+retriever.similarityTopK = 3;
+
+// Fetch nodes!
+const nodesWithScore = await retriever.retrieve("query string");
+```
+
+## API Reference
+
+- [SummaryIndexRetriever](../../api/classes/SummaryIndexRetriever.md)
+- [SummaryIndexLLMRetriever](../../api/classes/SummaryIndexLLMRetriever.md)
+- [VectorIndexRetriever](../../api/classes/VectorIndexRetriever.md)

apps/docs/docs/modules/low_level/storage.md

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

apps/docs/docs/starter.md

@@ -0,0 +1,56 @@
+---
+sidebar_position: 2
+---
+
+# Starter Tutorial
+
+Once you have [installed LlamaIndex.TS using NPM](installation.md) and set up your OpenAI key, you're ready to start your first app:
+
+In a new folder:
+
+```bash npm2yarn
+npm install typescript
+npm install @types/node
+npx tsc --init # if needed
+```
+
+Create the file `example.ts`. This code will load some example data, create a document, index it (which creates embeddings using OpenAI), and then creates query engine to answer questions about the data.
+
+```ts
+// example.ts
+import fs from "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(
+    "What did the author do in college?",
+  );
+
+  // Output response
+  console.log(response.toString());
+}
+
+main();
+```
+
+Then you can run it using
+
+```bash
+npx ts-node example.ts
+```
+
+Ready to learn more? Check out our NextJS playground at https://llama-playground.vercel.app/. The source is available at https://github.com/run-llama/ts-playground

apps/docs/docusaurus.config.js

@@ -0,0 +1,152 @@
+// @ts-check
+// Note: type annotations allow type checking and IDEs autocompletion
+
+const lightCodeTheme = require("prism-react-renderer/themes/github");
+const darkCodeTheme = require("prism-react-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",
+
+  // Even if you don't use internalization, you can use this field to set useful
+  // metadata like html lang. For example, if your site is Chinese, you may want
+  // to replace "en" with "zh-Hans".
+  i18n: {
+    defaultLocale: "en",
+    locales: ["en"],
+  },
+
+  presets: [
+    [
+      "classic",
+      /** @type {import('@docusaurus/preset-classic').Options} */
+      ({
+        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: false,
+      }),
+    ],
+  ],
+
+  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",
+          },
+          {
+            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/core/src/index.ts"],
+        tsconfig: "../../packages/core/tsconfig.json",
+        readme: "none",
+        sourceLinkTemplate:
+          "https://github.com/run-llama/LlamaIndexTS/blob/{gitRevision}/{path}#L{line}",
+        sidebar: {
+          position: 6,
+        },
+      },
+    ],
+  ],
+};
+
+module.exports = config;

apps/docs/package.json

@@ -0,0 +1,53 @@
+{
+  "name": "docs",
+  "version": "0.0.0",
+  "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": "2.4.3",
+    "@docusaurus/preset-classic": "2.4.3",
+    "@docusaurus/remark-plugin-npm2yarn": "^2.4.3",
+    "@mdx-js/react": "^1.6.22",
+    "clsx": "^1.2.1",
+    "postcss": "^8.4.31",
+    "prism-react-renderer": "^1.3.5",
+    "raw-loader": "^4.0.2",
+    "react": "^17.0.2",
+    "react-dom": "^17.0.2"
+  },
+  "devDependencies": {
+    "@docusaurus/module-type-aliases": "2.4.3",
+    "@docusaurus/types": "^2.4.3",
+    "@tsconfig/docusaurus": "^2.0.1",
+    "docusaurus-plugin-typedoc": "^0.19.2",
+    "typedoc": "^0.24.8",
+    "typedoc-plugin-markdown": "^3.16.0",
+    "typescript": "^4.9.5"
+  },
+  "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": ">=16.14"
+  }
+}

apps/docs/sidebars.js

@@ -0,0 +1,33 @@
+/**
+ * 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

@@ -0,0 +1,59 @@
+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

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

apps/docs/src/css/custom.css

@@ -0,0 +1,30 @@
+/**
+ * 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

@@ -0,0 +1,23 @@
+/**
+ * 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

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

apps/docs/static/img/logo.svg

@@ -0,0 +1,37 @@
+<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
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+      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

@@ -0,0 +1,171 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="1088" height="687.962" viewBox="0 0 1088 687.962">
+  <title>Easy to Use</title>
+  <g id="Group_12" data-name="Group 12" transform="translate(-57 -56)">
+    <g id="Group_11" data-name="Group 11" transform="translate(57 56)">
+      <path id="Path_83" data-name="Path 83" d="M1017.81,560.461c-5.27,45.15-16.22,81.4-31.25,110.31-20,38.52-54.21,54.04-84.77,70.28a193.275,193.275,0,0,1-27.46,11.94c-55.61,19.3-117.85,14.18-166.74,3.99a657.282,657.282,0,0,0-104.09-13.16q-14.97-.675-29.97-.67c-15.42.02-293.07,5.29-360.67-131.57-16.69-33.76-28.13-75-32.24-125.27-11.63-142.12,52.29-235.46,134.74-296.47,155.97-115.41,369.76-110.57,523.43,7.88C941.15,276.621,1036.99,396.031,1017.81,560.461Z" transform="translate(-56 -106.019)" fill="#3f3d56"/>
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+      <circle id="Ellipse_12" data-name="Ellipse 12" cx="3" cy="3" r="3" transform="translate(396 201.962)" fill="#f2f2f2"/>
+      <circle id="Ellipse_13" data-name="Ellipse 13" cx="2" cy="2" r="2" transform="translate(600 220.962)" fill="#f2f2f2"/>
+      <circle id="Ellipse_14" data-name="Ellipse 14" cx="2" cy="2" r="2" transform="translate(180 265.962)" fill="#f2f2f2"/>
+      <circle id="Ellipse_15" data-name="Ellipse 15" cx="2" cy="2" r="2" transform="translate(612 96.962)" fill="#f2f2f2"/>
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+</svg>

apps/docs/tsconfig.json

@@ -0,0 +1,7 @@
+{
+  // This file is not used in compilation. It is here just for a nice editor experience.
+  "extends": "@tsconfig/docusaurus/tsconfig.json",
+  "compilerOptions": {
+    "baseUrl": "."
+  }
+}

apps/mongodb/1_import.ts

@@ -1,3 +1,4 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
 import * as dotenv from "dotenv";
 import * as fs from "fs";
 import { MongoClient } from "mongodb";
@@ -5,7 +6,7 @@ import { MongoClient } from "mongodb";
 // Load environment variables from local .env file
 dotenv.config();
 
-const jsonFile = "./data/tinytweets.json";
+const jsonFile = "tinytweets.json";
 const mongoUri = process.env.MONGODB_URI!;
 const databaseName = process.env.MONGODB_DATABASE!;
 const collectionName = process.env.MONGODB_COLLECTION!;
@@ -30,4 +31,4 @@ async function importJsonToMongo() {
 }
 
 // Run the import function
-void importJsonToMongo();
+importJsonToMongo();

apps/mongodb/2_load_and_index.ts

@@ -1,9 +1,11 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import * as dotenv from "dotenv";
 import {
   MongoDBAtlasVectorSearch,
   SimpleMongoReader,
-} from "@llamaindex/mongodb";
-import * as dotenv from "dotenv";
-import { storageContextFromDefaults, VectorStoreIndex } from "llamaindex";
+  storageContextFromDefaults,
+  VectorStoreIndex,
+} from "llamaindex";
 import { MongoClient } from "mongodb";
 
 // Load environment variables from local .env file
@@ -26,23 +28,12 @@ async function loadAndIndex() {
     "full_text",
   ]);
 
-  const FILTER_METADATA_FIELD = "content_type";
-
-  documents.forEach((document, index) => {
-    const contentType = ["tweet", "post", "story"][index % 3]; // assign a random content type to each document
-    document.metadata = {
-      ...document.metadata,
-      [FILTER_METADATA_FIELD]: contentType,
-    };
-  });
-
   // create Atlas as a vector store
   const vectorStore = new MongoDBAtlasVectorSearch({
     mongodbClient: client,
     dbName: databaseName,
     collectionName: vectorCollectionName, // this is where your embeddings will be stored
     indexName: indexName, // this is the name of the index you will need to create
-    indexedMetadataFields: [FILTER_METADATA_FIELD], // this is the field that will be used for the query
   });
 
   // now create an index from all the Documents and store them in Atlas
@@ -54,4 +45,6 @@ async function loadAndIndex() {
   await client.close();
 }
 
-loadAndIndex().catch(console.error);
+loadAndIndex();
+
+// you can't query your index yet because you need to create a vector search index in mongodb's UI now

apps/mongodb/3_query.ts

@@ -0,0 +1,34 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import * as dotenv from "dotenv";
+import {
+  MongoDBAtlasVectorSearch,
+  serviceContextFromDefaults,
+  VectorStoreIndex,
+} from "llamaindex";
+import { MongoClient } from "mongodb";
+
+// Load environment variables from local .env file
+dotenv.config();
+
+async function query() {
+  const client = new MongoClient(process.env.MONGODB_URI!);
+  const serviceContext = serviceContextFromDefaults();
+  const store = new MongoDBAtlasVectorSearch({
+    mongodbClient: client,
+    dbName: process.env.MONGODB_DATABASE!,
+    collectionName: process.env.MONGODB_VECTORS!,
+    indexName: process.env.MONGODB_VECTOR_INDEX!,
+  });
+
+  const index = await VectorStoreIndex.fromVectorStore(store, serviceContext);
+
+  const retriever = index.asRetriever({ similarityTopK: 20 });
+  const queryEngine = index.asQueryEngine({ retriever });
+  const result = await queryEngine.query(
+    "What does the author think of web frameworks?",
+  );
+  console.log(result.response);
+  await client.close();
+}
+
+query();

apps/mongodb/CHANGELOG.md

@@ -0,0 +1,20 @@
+# mongodb-llamaindexts
+
+## 0.0.3
+
+### Patch Changes
+
+- Updated dependencies [3bab231]
+  - llamaindex@0.0.37
+
+## 0.0.2
+
+### Patch Changes
+
+- Updated dependencies
+- Updated dependencies
+- Updated dependencies
+- Updated dependencies
+- Updated dependencies
+- Updated dependencies
+  - llamaindex@0.0.36

apps/mongodb/README.md

@@ -2,7 +2,12 @@
 
 ### Prepare Environment
 
-Read and follow the instructions in the [README.md](../README.md) file located one directory up to make sure your JS/TS dependencies are set up. The commands listed below are also run from that parent directory.
+Make sure to run `pnpm install` and set your OpenAI environment variable before running these examples.
+
+```
+pnpm install
+export OPENAI_API_KEY="sk-..."
+```
 
 ### Sign up for MongoDB Atlas
 
@@ -16,7 +21,7 @@ The signup process will walk you through the process of creating your cluster an
 
 ### Set up environment variables
 
-Copy the connection string (make sure you include your password) and put it into a file called `.env` in the parent folder of this directory. It should look like this:
+Copy the connection string (make sure you include your password) and put it into a file called `.env` in the root of this repo. It should look like this:
 
 ```
 MONGODB_URI=mongodb+srv://seldo:xxxxxxxxxxx@llamaindexdemocluster.xfrdhpz.mongodb.net/?retryWrites=true&w=majority
@@ -34,7 +39,7 @@ MONGODB_COLLECTION=tiny_tweets_collection
 You are now ready to import our ready-made data set into Mongo. This is the file `tinytweets.json`, a selection of approximately 1000 tweets from @seldo on Twitter in mid-2019. With your environment set up you can do this by running
 
 ```
-npx tsx mongodb/1_import.ts
+pnpm ts-node 1_import.ts
 ```
 
 If you don't want to use tweets, you can replace `json_file` with any other array of JSON objects, but you will need to modify some code later to make sure the correct field gets indexed. There is no LlamaIndex-specific code here; you can load your data into Mongo any way you want to.
@@ -59,7 +64,7 @@ MONGODB_VECTOR_INDEX=tiny_tweets_vector_index
 If the data you're indexing is the tweets we gave you, you're ready to go:
 
 ```bash
-npx tsx mongodb/2_load_and_index.ts
+pnpm ts-node 2_load_and_index.ts
 ```
 
 > Note: this script is running a couple of minutes and currently doesn't show any progress.
@@ -68,12 +73,53 @@ What you're doing here is creating a Reader which loads the data out of Mongo in
 
 Now you're creating a vector search client for Mongo. In addition to a MongoDB client object, you again tell it what database everything is in. This time you give it the name of the collection where you'll store the vector embeddings, and the name of the vector search index you'll create in the next step.
 
+### Create a vector search index
+
+Now if all has gone well you should be able to log in to the Mongo Atlas UI and see two collections in your database: the original data in `tiny_tweets_collection`, and the vector embeddings in `tiny_tweets_vectors`.
+
+![MongoDB Atlas collections](./docs/3_vectors_in_db.png)
+
+Now it's time to create the vector search index so that you can query the data.
+It's not yet possible to programmatically create a vector search index using the [`createIndex`](https://www.mongodb.com/docs/manual/reference/method/db.collection.createIndex/) function, therefore we have to create one manually in the UI.
+To do so, first, click the Search tab, and then click "Create Search Index":
+
+![MongoDB Atlas create search index](./docs/4_search_tab.png)
+
+We have to use the JSON editor, as the Visual Editor does not yet support to create a vector search index:
+
+![MongoDB Atlas JSON editor](./docs/5_json_editor.png)
+
+Now under "database and collection" select `tiny_tweets_db` and within that select `tiny_tweets_vectors`. Then under "Index name" enter `tiny_tweets_vector_index` (or whatever value you put for MONGODB_VECTOR_INDEX in `.env`). Under that, you'll want to enter this JSON object:
+
+```json
+{
+  "mappings": {
+    "dynamic": true,
+    "fields": {
+      "embedding": {
+        "dimensions": 1536,
+        "similarity": "cosine",
+        "type": "knnVector"
+      }
+    }
+  }
+}
+```
+
+This tells Mongo that the `embedding` field in each document (in the `tiny_tweets_vectors` collection) is a vector of 1536 dimensions (this is the size of embeddings used by OpenAI), and that we want to use cosine similarity to compare vectors. You don't need to worry too much about these values unless you want to use a different LLM to OpenAI entirely.
+
+The UI will ask you to review and confirm your choices, then you need to wait a minute or two while it generates the index. If all goes well, you should see something like this screen:
+
+![MongoDB Atlas index created](./docs/7_index_created.png)
+
+Now you're ready to query your data!
+
 ### Run a test query
 
 You can do this by running
 
 ```bash
-npx tsx mongodb/3_query.ts
+pnpm ts-node 3_query.ts
 ```
 
 This sets up a connection to Atlas just like `2_load_and_index.ts` did, then it creates a [query engine](https://docs.llamaindex.ai/en/stable/understanding/querying/querying.html#getting-started) and runs a query against it.

apps/mongodb/package.json

@@ -0,0 +1,17 @@
+{
+  "version": "0.0.3",
+  "private": true,
+  "name": "mongodb-llamaindexts",
+  "dependencies": {
+    "llamaindex": "workspace:*",
+    "dotenv": "^16.3.1",
+    "mongodb": "^6.2.0"
+  },
+  "devDependencies": {
+    "@types/node": "^18.18.6",
+    "ts-node": "^10.9.1"
+  },
+  "scripts": {
+    "lint": "eslint ."
+  }
+}

apps/next/.gitignore

@@ -1,33 +0,0 @@
-# deps
-/node_modules
-
-# generated content
-.contentlayer
-.content-collections
-.source
-
-# test & build
-/coverage
-/.next/
-/out/
-/build
-*.tsbuildinfo
-
-# misc
-.DS_Store
-*.pem
-/.pnp
-.pnp.js
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-
-# others
-.env*.local
-.vercel
-next-env.d.ts
-
-# build
-/src/content/docs/cloud/api
-/src/content/docs/api
-./types

apps/next/CHANGELOG.md

@@ -1,882 +0,0 @@
-# @llamaindex/doc
-
-## 0.2.24
-
-### Patch Changes
-
-- Updated dependencies [766054b]
-- Updated dependencies [71598f8]
-  - @llamaindex/workflow@1.1.6
-  - @llamaindex/core@0.6.9
-  - llamaindex@0.11.5
-  - @llamaindex/cloud@4.0.13
-  - @llamaindex/node-parser@2.0.9
-  - @llamaindex/openai@0.4.3
-  - @llamaindex/readers@3.1.7
-
-## 0.2.23
-
-### Patch Changes
-
-- Updated dependencies [c927457]
-  - @llamaindex/openai@0.4.2
-  - @llamaindex/core@0.6.8
-  - @llamaindex/cloud@4.0.12
-  - llamaindex@0.11.4
-  - @llamaindex/node-parser@2.0.8
-  - @llamaindex/readers@3.1.6
-  - @llamaindex/workflow@1.1.5
-
-## 0.2.22
-
-### Patch Changes
-
-- Updated dependencies [76ff23d]
-  - @llamaindex/cloud@4.0.11
-  - llamaindex@0.11.3
-
-## 0.2.21
-
-### Patch Changes
-
-- Updated dependencies [59601dd]
-  - @llamaindex/openai@0.4.1
-  - @llamaindex/core@0.6.7
-  - @llamaindex/cloud@4.0.10
-  - llamaindex@0.11.2
-  - @llamaindex/node-parser@2.0.7
-  - @llamaindex/readers@3.1.5
-  - @llamaindex/workflow@1.1.4
-
-## 0.2.20
-
-### Patch Changes
-
-- Updated dependencies [3703f90]
-  - @llamaindex/cloud@4.0.9
-  - llamaindex@0.11.1
-
-## 0.2.19
-
-### Patch Changes
-
-- Updated dependencies [680b529]
-- Updated dependencies [b0cd530]
-- Updated dependencies [361a685]
-- Updated dependencies [3e66ddc]
-  - @llamaindex/workflow@1.1.3
-  - @llamaindex/core@0.6.6
-  - llamaindex@0.11.0
-  - @llamaindex/openai@0.4.0
-  - @llamaindex/cloud@4.0.8
-  - @llamaindex/node-parser@2.0.6
-  - @llamaindex/readers@3.1.4
-
-## 0.2.18
-
-### Patch Changes
-
-- d671ed6: Add functionality for search params when querying Qdrant vector store.
-- Updated dependencies [76c9a80]
-- Updated dependencies [168d11f]
-- Updated dependencies [d671ed6]
-- Updated dependencies [40f5f41]
-  - @llamaindex/openai@0.3.7
-  - @llamaindex/workflow@1.1.2
-  - @llamaindex/core@0.6.5
-  - @llamaindex/cloud@4.0.7
-  - llamaindex@0.10.6
-  - @llamaindex/node-parser@2.0.5
-  - @llamaindex/readers@3.1.3
-
-## 0.2.17
-
-### Patch Changes
-
-- Updated dependencies [9b2e25a]
-  - @llamaindex/openai@0.3.6
-  - @llamaindex/core@0.6.4
-  - llamaindex@0.10.5
-  - @llamaindex/cloud@4.0.6
-  - @llamaindex/node-parser@2.0.4
-  - @llamaindex/readers@3.1.2
-  - @llamaindex/workflow@1.1.1
-
-## 0.2.16
-
-### Patch Changes
-
-- Updated dependencies [7e8e454]
-- Updated dependencies [2225ffd]
-- Updated dependencies [6ddf1c1]
-- Updated dependencies [bc53342]
-- Updated dependencies [41953a3]
-  - @llamaindex/workflow@1.1.0
-  - @llamaindex/cloud@4.0.5
-  - llamaindex@0.10.4
-
-## 0.2.15
-
-### Patch Changes
-
-- Updated dependencies [3ee8c83]
-  - @llamaindex/core@0.6.3
-  - llamaindex@0.10.3
-  - @llamaindex/openai@0.3.5
-  - @llamaindex/cloud@4.0.4
-  - @llamaindex/node-parser@2.0.3
-  - @llamaindex/readers@3.1.1
-  - @llamaindex/workflow@1.0.4
-
-## 0.2.14
-
-### Patch Changes
-
-- Updated dependencies [1e59695]
-  - @llamaindex/readers@3.1.0
-
-## 0.2.13
-
-### Patch Changes
-
-- Updated dependencies [e5c3f95]
-  - @llamaindex/openai@0.3.4
-  - llamaindex@0.10.2
-
-## 0.2.12
-
-### Patch Changes
-
-- Updated dependencies [96dd798]
-  - @llamaindex/openai@0.3.3
-  - llamaindex@0.10.1
-
-## 0.2.11
-
-### Patch Changes
-
-- 6cf928f: chore: use bunchee for llamaindex
-- Updated dependencies [6cf928f]
-  - llamaindex@0.10.0
-
-## 0.2.10
-
-### Patch Changes
-
-- 411dcea: Add Nova Premier to AWS Nova models. Add EU endpoints
-
-## 0.2.9
-
-### Patch Changes
-
-- Updated dependencies [d365eb2]
-  - @llamaindex/openai@0.3.2
-  - llamaindex@0.9.19
-
-## 0.2.8
-
-### Patch Changes
-
-- 2ffdb27: docs: correct the CondenseQuestionChatEngine path
-- Updated dependencies [88b7046]
-  - @llamaindex/openai@0.3.1
-  - llamaindex@0.9.18
-
-## 0.2.7
-
-### Patch Changes
-
-- 3ffee26: feat: enhance config params for LlamaIndexServer
-
-## 0.2.6
-
-### Patch Changes
-
-- Updated dependencies [3534c37]
-- Updated dependencies [41191d0]
-  - llamaindex@0.9.17
-  - @llamaindex/workflow@1.0.3
-  - @llamaindex/cloud@4.0.3
-
-## 0.2.5
-
-### Patch Changes
-
-- 4999df1: bump nextjs
-- Updated dependencies [f5e4d09]
-  - llamaindex@0.9.16
-
-## 0.2.4
-
-### Patch Changes
-
-- 9c63f3f: Add support for openai responses api
-- Updated dependencies [9c63f3f]
-- Updated dependencies [c515a32]
-  - @llamaindex/openai@0.3.0
-  - @llamaindex/core@0.6.2
-  - @llamaindex/workflow@1.0.2
-  - llamaindex@0.9.15
-  - @llamaindex/cloud@4.0.2
-  - @llamaindex/node-parser@2.0.2
-  - @llamaindex/readers@3.0.2
-
-## 0.2.3
-
-### Patch Changes
-
-- 648cfb5: Add support for supabase vector store
-  Added doc for the supbase vector store
-- Updated dependencies [1b6f368]
-- Updated dependencies [eaf326e]
-- Updated dependencies [9d951b2]
-  - @llamaindex/core@0.6.1
-  - llamaindex@0.9.14
-  - @llamaindex/cloud@4.0.1
-  - @llamaindex/node-parser@2.0.1
-  - @llamaindex/openai@0.2.1
-  - @llamaindex/readers@3.0.1
-  - @llamaindex/workflow@1.0.1
-
-## 0.2.2
-
-### Patch Changes
-
-- e98033e: docs: correct the number of indexes
-
-## 0.2.1
-
-### Patch Changes
-
-- Updated dependencies [75d6e29]
-  - llamaindex@0.9.13
-
-## 0.2.0
-
-### Minor Changes
-
-- f1db9b3: Adding an options parameter to vercel tool to tailor responses
-
-### Patch Changes
-
-- 21bebfc: Expose more content to fix the issue with unavailable documentation links, and adjust the documentation based on the latest code.
-- 2b39cef: Added documentation for structured output in openai and ollama
-- Updated dependencies [21bebfc]
-- Updated dependencies [93bc0ff]
-- Updated dependencies [91a18e7]
-- Updated dependencies [bf56fc0]
-- Updated dependencies [f8a86e4]
-- Updated dependencies [5189b44]
-- Updated dependencies [58a9446]
-  - @llamaindex/readers@3.0.0
-  - @llamaindex/core@0.6.0
-  - @llamaindex/openai@0.2.0
-  - @llamaindex/cloud@4.0.0
-  - @llamaindex/workflow@1.0.0
-  - llamaindex@0.9.12
-  - @llamaindex/node-parser@2.0.0
-
-## 0.1.11
-
-### Patch Changes
-
-- a8c0637: feat: simplify to provide base URL to OpenAI
-- a654f58: Added docs for using perplexity
-- 98eebf7: Add RequestOptions parameter passing to support Gemini proxy calls.
-  Add a usage example for the RequestOptions parameter.
-- Updated dependencies [a8c0637]
-  - @llamaindex/openai@0.1.61
-  - llamaindex@0.9.11
-
-## 0.1.10
-
-### Patch Changes
-
-- Updated dependencies [aea550a]
-- Updated dependencies [c1b5be5]
-- Updated dependencies [40ee761]
-- Updated dependencies [40ee761]
-  - @llamaindex/openai@0.1.60
-  - llamaindex@0.9.10
-  - @llamaindex/workflow@0.0.16
-  - @llamaindex/core@0.5.8
-  - @llamaindex/cloud@3.0.9
-  - @llamaindex/node-parser@1.0.8
-  - @llamaindex/readers@2.0.8
-
-## 0.1.9
-
-### Patch Changes
-
-- 4bac71d: Support binding additional argument to function tool
-- Updated dependencies [4bac71d]
-  - @llamaindex/core@0.5.7
-  - @llamaindex/cloud@3.0.8
-  - llamaindex@0.9.9
-  - @llamaindex/node-parser@1.0.7
-  - @llamaindex/openai@0.1.59
-  - @llamaindex/readers@2.0.7
-  - @llamaindex/workflow@0.0.15
-
-## 0.1.8
-
-### Patch Changes
-
-- Updated dependencies [4b49428]
-- Updated dependencies [bbc8c87]
-  - @llamaindex/workflow@0.0.14
-  - llamaindex@0.9.8
-
-## 0.1.7
-
-### Patch Changes
-
-- Updated dependencies [beb922b]
-  - @llamaindex/core@0.5.6
-  - llamaindex@0.9.7
-  - @llamaindex/cloud@3.0.7
-  - @llamaindex/node-parser@1.0.6
-  - @llamaindex/openai@0.1.58
-  - @llamaindex/readers@2.0.6
-  - @llamaindex/workflow@0.0.13
-
-## 0.1.6
-
-### Patch Changes
-
-- Updated dependencies [5668970]
-  - @llamaindex/core@0.5.5
-  - @llamaindex/workflow@0.0.12
-  - @llamaindex/cloud@3.0.6
-  - llamaindex@0.9.6
-  - @llamaindex/node-parser@1.0.5
-  - @llamaindex/openai@0.1.57
-  - @llamaindex/readers@2.0.5
-
-## 0.1.5
-
-### Patch Changes
-
-- Updated dependencies [ad3c7f1]
-  - @llamaindex/core@0.5.4
-  - @llamaindex/cloud@3.0.5
-  - llamaindex@0.9.5
-  - @llamaindex/node-parser@1.0.4
-  - @llamaindex/openai@0.1.56
-  - @llamaindex/readers@2.0.4
-
-## 0.1.4
-
-### Patch Changes
-
-- Updated dependencies [cb256f2]
-- Updated dependencies [cb021e7]
-  - @llamaindex/openai@0.1.55
-  - @llamaindex/core@0.5.3
-  - llamaindex@0.9.4
-  - @llamaindex/cloud@3.0.4
-  - @llamaindex/node-parser@1.0.3
-  - @llamaindex/readers@2.0.3
-
-## 0.1.3
-
-### Patch Changes
-
-- Updated dependencies [d952e68]
-  - @llamaindex/core@0.5.2
-  - @llamaindex/cloud@3.0.3
-  - llamaindex@0.9.3
-  - @llamaindex/node-parser@1.0.2
-  - @llamaindex/openai@0.1.54
-  - @llamaindex/readers@2.0.2
-
-## 0.1.2
-
-### Patch Changes
-
-- Updated dependencies [c902fcb]
-- Updated dependencies [88d776f]
-  - @llamaindex/cloud@3.0.2
-  - llamaindex@0.9.2
-
-## 0.1.1
-
-### Patch Changes
-
-- Updated dependencies [6d37d44]
-  - llamaindex@0.9.1
-  - @llamaindex/cloud@3.0.1
-  - @llamaindex/core@0.5.1
-  - @llamaindex/node-parser@1.0.1
-  - @llamaindex/openai@0.1.53
-  - @llamaindex/readers@2.0.1
-  - @llamaindex/workflow@0.0.11
-
-## 0.1.0
-
-### Minor Changes
-
-- 6a4a737: Remove re-exports from llamaindex main package
-- f4588bc: Remove readers package from llamaindex
-
-### Patch Changes
-
-- c7c0800: fix: fumadoc build fail
-- a87efb9: docs: update chat engine docs
-- 7bd5d93: docs: update workflow doc
-- Updated dependencies [6a4a737]
-- Updated dependencies [d924c63]
-- Updated dependencies [b490376]
-- Updated dependencies [f4588bc]
-  - llamaindex@0.9.0
-  - @llamaindex/core@0.5.0
-  - @llamaindex/cloud@3.0.0
-  - @llamaindex/node-parser@1.0.0
-  - @llamaindex/openai@0.1.52
-  - @llamaindex/readers@2.0.0
-
-## 0.0.41
-
-### Patch Changes
-
-- Updated dependencies [1c908fd]
-  - @llamaindex/openai@0.1.51
-  - @llamaindex/node-parser@0.0.24
-  - @llamaindex/workflow@0.0.10
-  - @llamaindex/readers@1.0.25
-  - @llamaindex/cloud@2.0.24
-  - @llamaindex/core@0.4.23
-  - llamaindex@0.8.37
-
-## 0.0.40
-
-### Patch Changes
-
-- Updated dependencies [cb608b5]
-  - @llamaindex/openai@0.1.50
-  - @llamaindex/node-parser@0.0.23
-  - @llamaindex/workflow@0.0.9
-  - @llamaindex/readers@1.0.24
-  - @llamaindex/cloud@2.0.23
-  - @llamaindex/core@0.4.22
-  - llamaindex@0.8.36
-
-## 0.0.39
-
-### Patch Changes
-
-- 6d4d96f: chore: update examples and docs to use unified imports
-- Updated dependencies [15563a0]
-  - @llamaindex/openai@0.1.49
-  - llamaindex@0.8.35
-
-## 0.0.38
-
-### Patch Changes
-
-- Updated dependencies [9f8ad37]
-- Updated dependencies [7265f74]
-  - llamaindex@0.8.34
-  - @llamaindex/openai@0.1.48
-
-## 0.0.37
-
-### Patch Changes
-
-- Updated dependencies [2019a04]
-  - @llamaindex/openai@0.1.47
-  - llamaindex@0.8.33
-
-## 0.0.36
-
-### Patch Changes
-
-- f02621e: Fix internal links between chapters
-- Updated dependencies [34faf48]
-- Updated dependencies [4df1fe6]
-- Updated dependencies [9456616]
-- Updated dependencies [d6c270e]
-- Updated dependencies [1892e1c]
-- Updated dependencies [1931bbc]
-  - llamaindex@0.8.32
-  - @llamaindex/core@0.4.21
-  - @llamaindex/cloud@2.0.22
-  - @llamaindex/openai@0.1.46
-  - @llamaindex/node-parser@0.0.22
-  - @llamaindex/readers@1.0.23
-
-## 0.0.35
-
-### Patch Changes
-
-- Updated dependencies [5dec9f9]
-- Updated dependencies [fd9c829]
-- Updated dependencies [d211b7a]
-- Updated dependencies [0ebbfc1]
-  - @llamaindex/cloud@2.0.21
-  - llamaindex@0.8.31
-  - @llamaindex/core@0.4.20
-  - @llamaindex/node-parser@0.0.21
-  - @llamaindex/openai@0.1.45
-  - @llamaindex/readers@1.0.22
-
-## 0.0.34
-
-### Patch Changes
-
-- Updated dependencies [012495b]
-  - @llamaindex/cloud@2.0.20
-  - llamaindex@0.8.30
-
-## 0.0.33
-
-### Patch Changes
-
-- Updated dependencies [dd596a0]
-  - llamaindex@0.8.29
-
-## 0.0.32
-
-### Patch Changes
-
-- a9b5b99: feat: build api reference pages for new documentation site
-- Updated dependencies [a9b5b99]
-  - @llamaindex/core@0.4.19
-  - @llamaindex/readers@1.0.21
-  - @llamaindex/cloud@2.0.19
-  - llamaindex@0.8.28
-  - @llamaindex/node-parser@0.0.20
-  - @llamaindex/openai@0.1.44
-
-## 0.0.31
-
-### Patch Changes
-
-- Updated dependencies [b504303]
-- Updated dependencies [e0f6cc3]
-- Updated dependencies [a0e6f57]
-  - llamaindex@0.8.27
-  - @llamaindex/core@0.4.18
-  - @llamaindex/cloud@2.0.18
-  - @llamaindex/node-parser@0.0.19
-  - @llamaindex/openai@0.1.43
-  - @llamaindex/readers@1.0.20
-  - @llamaindex/workflow@0.0.8
-
-## 0.0.30
-
-### Patch Changes
-
-- Updated dependencies [3d1808b]
-  - @llamaindex/core@0.4.17
-  - llamaindex@0.8.26
-  - @llamaindex/openai@0.1.42
-  - @llamaindex/cloud@2.0.17
-  - @llamaindex/node-parser@0.0.18
-  - @llamaindex/readers@1.0.19
-
-## 0.0.29
-
-### Patch Changes
-
-- Updated dependencies [7e8230b]
-- Updated dependencies [8be4589]
-  - @llamaindex/readers@1.0.18
-  - @llamaindex/cloud@2.0.16
-  - @llamaindex/core@0.4.16
-  - @llamaindex/node-parser@0.0.17
-  - @llamaindex/openai@0.1.41
-  - @llamaindex/workflow@0.0.7
-  - llamaindex@0.8.25
-
-## 0.0.28
-
-### Patch Changes
-
-- fd38a25: Add vercel tool adapter to use query engine tool
-
-## 0.0.27
-
-### Patch Changes
-
-- Updated dependencies [515f2c1]
-  - llamaindex@0.8.24
-
-## 0.0.26
-
-### Patch Changes
-
-- @llamaindex/cloud@2.0.15
-- @llamaindex/core@0.4.15
-- llamaindex@0.8.23
-- @llamaindex/node-parser@0.0.16
-- @llamaindex/openai@0.1.40
-- @llamaindex/readers@1.0.17
-
-## 0.0.25
-
-### Patch Changes
-
-- Updated dependencies [819af45]
-  - llamaindex@0.8.22
-  - @llamaindex/cloud@2.0.14
-  - @llamaindex/core@0.4.14
-  - @llamaindex/node-parser@0.0.15
-  - @llamaindex/openai@0.1.39
-  - @llamaindex/readers@1.0.16
-
-## 0.0.24
-
-### Patch Changes
-
-- Updated dependencies [83c3897]
-- Updated dependencies [efa2211]
-  - llamaindex@0.8.21
-
-## 0.0.23
-
-### Patch Changes
-
-- Updated dependencies [02b22da]
-  - llamaindex@0.8.20
-
-## 0.0.22
-
-### Patch Changes
-
-- Updated dependencies [90d265c]
-  - @llamaindex/cloud@2.0.13
-  - @llamaindex/core@0.4.13
-  - llamaindex@0.8.19
-  - @llamaindex/node-parser@0.0.14
-  - @llamaindex/readers@1.0.15
-  - @llamaindex/openai@0.1.38
-
-## 0.0.21
-
-### Patch Changes
-
-- Updated dependencies [d17450f]
-- Updated dependencies [ef4f63d]
-  - llamaindex@0.8.18
-  - @llamaindex/core@0.4.12
-  - @llamaindex/cloud@2.0.12
-  - @llamaindex/node-parser@0.0.13
-  - @llamaindex/openai@0.1.37
-  - @llamaindex/readers@1.0.14
-
-## 0.0.20
-
-### Patch Changes
-
-- Updated dependencies [6d22fa2]
-  - @llamaindex/core@0.4.11
-  - @llamaindex/cloud@2.0.11
-  - llamaindex@0.8.17
-  - @llamaindex/node-parser@0.0.12
-  - @llamaindex/openai@0.1.36
-  - @llamaindex/readers@1.0.13
-
-## 0.0.19
-
-### Patch Changes
-
-- Updated dependencies [e60328b]
-  - @llamaindex/readers@1.0.12
-  - llamaindex@0.8.16
-
-## 0.0.18
-
-### Patch Changes
-
-- Updated dependencies [3d503cb]
-- Updated dependencies [5dae534]
-  - llamaindex@0.8.15
-
-## 0.0.17
-
-### Patch Changes
-
-- Updated dependencies [630b425]
-  - llamaindex@0.8.14
-
-## 0.0.16
-
-### Patch Changes
-
-- Updated dependencies [a7b0ac3]
-- Updated dependencies [ee20c44]
-- Updated dependencies [c69605f]
-  - @llamaindex/core@0.4.10
-  - @llamaindex/workflow@0.0.6
-  - llamaindex@0.8.13
-  - @llamaindex/cloud@2.0.10
-  - @llamaindex/node-parser@0.0.11
-  - @llamaindex/openai@0.1.35
-  - @llamaindex/readers@1.0.11
-
-## 0.0.15
-
-### Patch Changes
-
-- Updated dependencies [ea92b69]
-- Updated dependencies [fadc8b8]
-  - @llamaindex/workflow@0.0.5
-
-## 0.0.14
-
-### Patch Changes
-
-- Updated dependencies [7ae6eaa]
-  - @llamaindex/core@0.4.9
-  - @llamaindex/openai@0.1.34
-  - @llamaindex/cloud@2.0.9
-  - llamaindex@0.8.12
-  - @llamaindex/node-parser@0.0.10
-  - @llamaindex/readers@1.0.10
-
-## 0.0.13
-
-### Patch Changes
-
-- Updated dependencies [f865c98]
-  - @llamaindex/core@0.4.8
-  - @llamaindex/cloud@2.0.8
-  - llamaindex@0.8.11
-  - @llamaindex/node-parser@0.0.9
-  - @llamaindex/openai@0.1.33
-  - @llamaindex/readers@1.0.9
-
-## 0.0.12
-
-### Patch Changes
-
-- Updated dependencies [f066e50]
-- Updated dependencies [d89ebe0]
-- Updated dependencies [fd8c882]
-- Updated dependencies [fd8c882]
-  - llamaindex@0.8.10
-  - @llamaindex/core@0.4.7
-  - @llamaindex/workflow@0.0.4
-  - @llamaindex/cloud@2.0.7
-  - @llamaindex/node-parser@0.0.8
-  - @llamaindex/openai@0.1.32
-  - @llamaindex/readers@1.0.8
-
-## 0.0.11
-
-### Patch Changes
-
-- Updated dependencies [4fc001c]
-- Updated dependencies [4d4cd8a]
-  - llamaindex@0.8.9
-  - @llamaindex/cloud@2.0.6
-  - @llamaindex/core@0.4.6
-  - @llamaindex/node-parser@0.0.7
-  - @llamaindex/openai@0.1.31
-  - @llamaindex/readers@1.0.7
-
-## 0.0.10
-
-### Patch Changes
-
-- Updated dependencies [ad85bd0]
-  - @llamaindex/core@0.4.5
-  - llamaindex@0.8.8
-  - @llamaindex/node-parser@0.0.6
-  - @llamaindex/workflow@0.0.3
-  - @llamaindex/cloud@2.0.5
-  - @llamaindex/openai@0.1.30
-  - @llamaindex/readers@1.0.6
-
-## 0.0.9
-
-### Patch Changes
-
-- @llamaindex/cloud@2.0.4
-- @llamaindex/core@0.4.4
-- llamaindex@0.8.7
-- @llamaindex/node-parser@0.0.5
-- @llamaindex/openai@0.1.29
-- @llamaindex/readers@1.0.5
-
-## 0.0.8
-
-### Patch Changes
-
-- Updated dependencies [95a5cc6]
-  - @llamaindex/core@0.4.3
-  - llamaindex@0.8.6
-  - @llamaindex/cloud@2.0.3
-  - @llamaindex/node-parser@0.0.4
-  - @llamaindex/openai@0.1.28
-  - @llamaindex/readers@1.0.4
-
-## 0.0.7
-
-### Patch Changes
-
-- Updated dependencies [14cc9eb]
-- Updated dependencies [a6db5dd]
-- Updated dependencies [396b1e1]
-  - llamaindex@0.8.5
-  - @llamaindex/cloud@2.0.2
-  - @llamaindex/core@0.4.2
-  - @llamaindex/node-parser@0.0.3
-  - @llamaindex/openai@0.1.27
-  - @llamaindex/readers@1.0.3
-
-## 0.0.6
-
-### Patch Changes
-
-- Updated dependencies [35430d3]
-  - llamaindex@0.8.4
-  - @llamaindex/readers@1.0.2
-
-## 0.0.5
-
-### Patch Changes
-
-- Updated dependencies [2ff0a89]
-  - @llamaindex/node-parser@0.0.2
-  - llamaindex@0.8.3
-
-## 0.0.4
-
-### Patch Changes
-
-- Updated dependencies [0765742]
-  - @llamaindex/workflow@0.0.2
-
-## 0.0.3
-
-### Patch Changes
-
-- Updated dependencies [c7a918c]
-  - llamaindex@0.8.2
-
-## 0.0.2
-
-### Patch Changes
-
-- Updated dependencies [9c73f0a]
-  - @llamaindex/core@0.4.1
-  - @llamaindex/cloud@2.0.1
-  - llamaindex@0.8.1
-  - @llamaindex/openai@0.1.26
-  - @llamaindex/readers@1.0.1
-
-## 0.0.1
-
-### Patch Changes
-
-- Updated dependencies [359fd33]
-- Updated dependencies [efb7e1b]
-- Updated dependencies [98ba1e7]
-- Updated dependencies [620c63c]
-  - @llamaindex/core@0.4.0
-  - llamaindex@0.8.0
-  - @llamaindex/cloud@2.0.0
-  - @llamaindex/openai@0.1.25
-  - @llamaindex/readers@1.0.0

apps/next/CLAUDE.md

@@ -1,143 +0,0 @@
-# CLAUDE.md
-
-This file provides guidance to Claude Code (claude.ai/code) when working with the LlamaIndex.TS documentation site.
-
-## Application Overview
-
-This is a Next.js documentation site (`@llamaindex/doc`) that serves as the official documentation for LlamaIndex.TS. It's built using Fumadocs, a modern documentation framework, and includes interactive features, API documentation generation, and AI-powered chat functionality.
-
-## Development Commands
-
-From this directory (`apps/next/`):
-
-- `pnpm dev` - Start development server with Turbo
-- `pnpm build` - Build the documentation site (includes `prebuild` step)
-- `pnpm start` - Start production server
-- `pnpm build:docs` - Generate API documentation from TypeScript source
-- `pnpm validate-links` - Validate all internal and external links
-
-Key build process:
-
-1. `prebuild` runs `build:docs` to generate API documentation using TypeDoc
-2. `build` runs Next.js build process
-3. `postbuild` runs post-processing scripts and link validation
-
-## Architecture
-
-### Framework Stack
-
-- **Next.js 15.3** - React framework with App Router
-- **Fumadocs** - Documentation framework with MDX support
-- **React Server Components** - AI chat functionality with server actions
-- **Tailwind CSS** - Styling with custom design system
-- **TypeScript** - Full type safety
-
-### Key Dependencies
-
-- **Fumadocs ecosystem**: `fumadocs-ui`, `fumadocs-mdx`, `fumadocs-core`, `fumadocs-openapi`
-- **AI features**: `ai` package for React Server Components chat
-- **Code features**: Monaco Editor, Shiki syntax highlighting, Twoslash TypeScript integration
-- **UI components**: Radix UI primitives, Framer Motion animations
-- **Content processing**: MDX, remark/rehype plugins, TypeDoc for API generation
-
-### Directory Structure
-
-**Content Management:**
-
-- `src/content/docs/` - MDX documentation files organized by topic
-- `src/content/docs/api/` - Auto-generated API documentation from TypeScript
-- `scripts/` - Build-time documentation generation and validation
-
-**Application Code:**
-
-- `src/app/` - Next.js App Router pages and API routes
-- `src/components/` - Reusable React components including UI library
-- `src/lib/` - Utilities, constants, and configuration
-
-**Configuration:**
-
-- `source.config.ts` - Fumadocs MDX configuration with plugins
-- `next.config.mjs` - Next.js configuration with MDX integration
-- `tailwind.config.mjs` - Tailwind CSS customization
-
-### Key Features
-
-**Documentation Features:**
-
-- MDX-based content with TypeScript code highlighting
-- Auto-generated API documentation from TypeScript source
-- Interactive code examples with Monaco Editor
-- Math equation support with KaTeX
-- Link validation and build-time checks
-
-**Interactive Features:**
-
-- AI-powered chat interface using React Server Components
-- Code demos with live TypeScript execution
-- Interactive UI components and animations
-- Search functionality across all documentation
-
-**Build Process:**
-
-- TypeDoc generates API documentation from workspace packages
-- Custom scripts transform and validate generated content
-- Link checking ensures all internal/external links work
-- Static site generation with 10-minute timeout for large documentation set
-
-### Configuration Files
-
-**source.config.ts**: Defines MDX processing pipeline with:
-
-- Code highlighting themes (Catppuccin)
-- Twoslash TypeScript integration
-- Remark/rehype plugins for enhanced Markdown
-- Content directories including external docs
-
-**next.config.mjs**: Next.js configuration with:
-
-- Extended static generation timeout (10 minutes)
-- Monaco Editor transpilation
-- Server external packages for build optimization
-- Webpack/Turbopack aliases for browser compatibility
-
-### Content Organization
-
-**Documentation Structure:**
-
-- `/docs/llamaindex/` - Core LlamaIndex.TS documentation
-- `/docs/cloud/` - LlamaCloud integration guides
-- `/docs/api/` - Auto-generated TypeScript API reference
-
-**Content Sources:**
-
-- Local MDX files in `src/content/docs/`
-- External docs from `@llama-flow/docs` package
-- Generated API docs from TypeScript source
-
-### Development Notes
-
-- Documentation content is sourced from multiple locations including external packages
-- API documentation is regenerated on each build from TypeScript source
-- The site uses advanced MDX features including custom transformers and plugins
-- Build process includes comprehensive link validation
-- Large memory allocation needed for TypeDoc generation (`--max-old-space-size=8192`)
-- Chat functionality uses React Server Components with streaming responses
-
-### AI Chat Integration
-
-The documentation includes an AI chat feature that:
-
-- Uses React Server Components for server-side AI processing
-- Integrates with LlamaIndex.TS packages for demonstrations
-- Provides interactive examples and code generation
-- Streams responses for better user experience
-
-### Content Authoring
-
-When adding new documentation:
-
-- Create MDX files in appropriate `src/content/docs/` subdirectories
-- Follow existing content structure and frontmatter conventions
-- Use Fumadocs MDX features like code blocks, callouts, and tabs
-- API documentation is auto-generated - edit TypeScript source comments instead
-- Run `pnpm validate-links` to check all links before publishing

apps/next/README.md

@@ -1,20 +0,0 @@
-# Docs
-
-This is a Next.js application generated with
-[Create Fumadocs](https://github.com/fuma-nama/fumadocs).
-
-Run development server:
-
-```bash
-pnpm run dev
-```
-
-## Learn More
-
-To learn more about Next.js and Fumadocs, take a look at the following
-resources:
-
-- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js
-  features and API.
-- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
-- [Fumadocs](https://fumadocs.vercel.app) - learn about Fumadocs

apps/next/components.json

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

apps/next/fallback.js

@@ -1,2 +0,0 @@
-// fallback for `fs` usage in `web-tree-sitter`
-module.exports = {};

apps/next/next.config.mjs

@@ -1,40 +0,0 @@
-import { createMDX } from "fumadocs-mdx/next";
-const withMDX = createMDX();
-
-/** @type {import('next').NextConfig} */
-const config = {
-  // default timeout for static generation is 60s, but we need to increase it to 10 minutes due to the large number of document pages
-  staticPageGenerationTimeout: 600,
-  reactStrictMode: true,
-  eslint: {
-    ignoreDuringBuilds: true,
-  },
-  transpilePackages: ["monaco-editor"],
-  serverExternalPackages: [
-    "@huggingface/transformers",
-    "twoslash",
-    "typescript",
-  ],
-  turbopack: {
-    resolveAlias: {
-      fs: { browser: "./fallback.js" },
-    },
-  },
-  webpack: (config) => {
-    if (Array.isArray(config.target) && config.target.includes("web")) {
-      config.target = ["web", "es2020"];
-    }
-
-    config.resolve.alias = {
-      ...config.resolve.alias,
-      sharp$: false,
-      "onnxruntime-node$": false,
-    };
-    config.resolve.fallback ??= {};
-    config.resolve.fallback.fs = false;
-    config.resolve.alias["replicate"] = false;
-    return config;
-  },
-};
-
-export default withMDX(config);

apps/next/package.json

@@ -1,98 +0,0 @@
-{
-  "name": "@llamaindex/doc",
-  "version": "0.2.24",
-  "private": true,
-  "scripts": {
-    "postinstall": "fumadocs-mdx",
-    "prebuild": "pnpm run build:docs",
-    "build": "next build",
-    "dev": "next dev --turbo",
-    "start": "next start",
-    "postbuild": "tsx scripts/post-build.mts && tsx scripts/validate-links.mts",
-    "build:docs": "cross-env NODE_OPTIONS=\"--max-old-space-size=8192\" typedoc && tsx scripts/generate-docs.mts",
-    "validate-links": "tsx scripts/validate-links.mts"
-  },
-  "dependencies": {
-    "@huggingface/transformers": "^3.5.0",
-    "@icons-pack/react-simple-icons": "^10.1.0",
-    "@llama-flow/docs": "0.0.8",
-    "@llamaindex/chat-ui-docs": "0.0.3",
-    "@llamaindex/cloud": "workspace:*",
-    "@llamaindex/core": "workspace:*",
-    "@llamaindex/node-parser": "workspace:*",
-    "@llamaindex/openai": "workspace:*",
-    "@llamaindex/readers": "workspace:*",
-    "@llamaindex/workflow": "workspace:*",
-    "@mdx-js/mdx": "^3.1.0",
-    "@monaco-editor/react": "^4.7.0",
-    "@number-flow/react": "^0.3.4",
-    "@radix-ui/react-dialog": "^1.1.2",
-    "@radix-ui/react-icons": "^1.3.2",
-    "@radix-ui/react-label": "^2.1.0",
-    "@radix-ui/react-slider": "^1.2.1",
-    "@radix-ui/react-slot": "^1.1.0",
-    "@radix-ui/react-tooltip": "^1.1.4",
-    "@scalar/api-client-react": "^1.1.25",
-    "@vercel/functions": "^1.5.0",
-    "ai": "^3.4.33",
-    "class-variance-authority": "^0.7.0",
-    "clsx": "2.1.1",
-    "foxact": "^0.2.41",
-    "framer-motion": "^11.11.17",
-    "fumadocs-core": "^15.2.7",
-    "fumadocs-docgen": "^2.0.0",
-    "fumadocs-mdx": "^11.6.0",
-    "fumadocs-openapi": "^8.0.1",
-    "fumadocs-twoslash": "^3.1.1",
-    "fumadocs-typescript": "^4.0.2",
-    "fumadocs-ui": "^15.2.7",
-    "hast-util-to-jsx-runtime": "^2.3.2",
-    "llamaindex": "workspace:*",
-    "lucide-react": "^0.460.0",
-    "next": "^15.3.0",
-    "next-themes": "^0.4.3",
-    "react": "^19.1.0",
-    "react-dom": "^19.1.0",
-    "react-icons": "^5.3.0",
-    "react-use-measure": "^2.1.1",
-    "rehype-katex": "^7.0.1",
-    "remark-math": "^6.0.0",
-    "rimraf": "^6.0.1",
-    "shiki": "^3.1.0",
-    "shiki-magic-move": "^1.0.1",
-    "swr": "^2.2.5",
-    "tailwind-merge": "^2.5.2",
-    "tailwindcss-animate": "^1.0.7",
-    "tree-sitter": "^0.22.1",
-    "tree-sitter-typescript": "^0.23.2",
-    "ts-morph": "^25.0.1",
-    "twoslash": "^0.3.1",
-    "use-stick-to-bottom": "^1.0.42",
-    "web-tree-sitter": "^0.24.4",
-    "zod": "^3.23.8"
-  },
-  "devDependencies": {
-    "@next/env": "^15.3.0",
-    "@tailwindcss/postcss": "^4.0.9",
-    "@types/mdx": "^2.0.13",
-    "@types/node": "22.9.0",
-    "@types/react": "^19.0.10",
-    "@types/react-dom": "^19.0.4",
-    "autoprefixer": "^10.4.20",
-    "cross-env": "^7.0.3",
-    "fast-glob": "^3.3.2",
-    "gray-matter": "^4.0.3",
-    "postcss": "^8.5.3",
-    "raw-loader": "^4.0.2",
-    "remark": "^15.0.1",
-    "remark-gfm": "^4.0.0",
-    "remark-mdx": "^3.1.0",
-    "remark-stringify": "^11.0.0",
-    "tailwindcss": "^4.0.9",
-    "tsx": "^4.19.3",
-    "typedoc": "0.28.3",
-    "typedoc-plugin-markdown": "^4.6.2",
-    "typedoc-plugin-merge-modules": " ^7.0.0",
-    "typescript": "^5.7.3"
-  }
-}

apps/next/postcss.config.mjs

@@ -1,5 +0,0 @@
-export default {
-  plugins: {
-    "@tailwindcss/postcss": {},
-  },
-};

apps/next/public/icons/Amazon_Web_Services_Logo.svg

@@ -1,38 +0,0 @@
-<?xml version="1.0" encoding="utf-8"?>
-<!-- Generator: Adobe Illustrator 19.0.1, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
-<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
-	 viewBox="0 0 304 182" style="enable-background:new 0 0 304 182;" xml:space="preserve">
-<style type="text/css">
-	.st0{fill:#252F3E;}
-	.st1{fill-rule:evenodd;clip-rule:evenodd;fill:#FF9900;}
-</style>
-<g>
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