Change any to Usage for class ChatMessage

.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/deploy-developer-hub.yml

@@ -1,13 +0,0 @@
-name: Trigger Vercel Deploy
-
-on:
-  push:
-    branches: [main]
-
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Trigger Vercel deployment
-        run: |
-          curl -X POST "${{ secrets.DEVELOPER_HUB_DEPLOY_HOOK }}"

.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
+      - uses: pnpm/action-setup@v2
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
-          node-version-file: ".nvmrc"
+          node-version-file: '.nvmrc'
           cache: "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 +1,20 @@
 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
+      - uses: pnpm/action-setup@v2
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: ${{ matrix.node-version }}
+          node-version-file: '.nvmrc'
           cache: "pnpm"
       - name: Install dependencies
         run: 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
-          pnpm pack --pack-destination ${{ runner.temp }} -C packages/core
-      - name: Install packed packages
-        run: npm add ${{ runner.temp }}/*.tgz
-        working-directory: e2e/npm
-      - name: Run tests
-        run: npm test
-        working-directory: e2e/npm
-  e2e-llamaindex-examples:
-    strategy:
-      fail-fast: false
-      matrix:
-        packages:
-          - cloudflare-worker-agent
-          - nextjs-agent
-          - nextjs-edge-runtime
-          - nextjs-node-runtime
-          - waku-query-engine
-          - vite-import-llamaindex
-    runs-on: ubuntu-latest
-    name: Build LlamaIndex Example (${{ 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: ci-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 --legacy-peer-deps
-        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 +1 @@
-22
+18

.prettierignore

@@ -1,11 +0,0 @@
-apps/docs/i18n
-apps/docs/docs/api
-pnpm-lock.yaml
-lib/
-dist/
-docs/
-.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

@@ -2,164 +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
-# v22.x.x
 ```
-
-### Use pnpm
-
-```shell
-npm install -g pnpm
-```
-
-### Install dependencies
-
-```shell
+npm i -g pnpm ts-node
 pnpm install
-pnpm install -g tsx
 ```
 
-### 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
 ```
 
-### Start Developing
+To write new test cases write them in [packages/core/src/tests](/packages/core/src/tests)
 
-You can launch the package in dev-mode by running:
+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
 
-```shell
-pnpm dev
+### Demo applications
+
+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]
 ```
 
-This will use turbo to run all packages in watch-mode. This means you can make changes and have them automatically built.
+To install packages for every package or application run
 
-If you want to customize what packages are built/watched, you can run turbo directly and adjust the filter:
-
-```shell
-pnpm turbo run dev --filter="./packages/core" --concurrency=100
 ```
-
-In another terminal, you can write and run any script needed to quickly test your changes. For example:
-
-```typescript
-import { createMemory, staticBlock } from "@llamaindex/core/memory";
-
-// Create memory with predefined context
-const memory = createMemory({
-  memoryBlocks: [
-    staticBlock({
-      content:
-        "The user is a software engineer who loves TypeScript and LlamaIndex.",
-      messageRole: "system",
-    }),
-  ],
-});
-
-async function main() {
-  const result = await memory.getLLM();
-  console.log(result);
-}
-
-void main().catch(console.error);
+pnpm add -w [NPM Package]
 ```
 
-And run it with:
-
-```shell
-pnpm exec tsx my_script.ts
-```
-
-This flow allows you to easily test your changes without having to build the entire project.
-
-Once you are happy with your changes, be sure to add tests (and confirm existing tests are passing!).
-
-### Run tests
-
-#### Unit tests
-
-After build, to run all unit tests, call:
-
-```shell
-pnpm test
-```
-
-Unit tests are located in the `tests` folder of each package. They are using their own package (e.g. `@llamaindex/core-tests` for `@llamaindex/core`). The tests are importing the package under test and the test package is not published.
-
-#### E2E tests
-
-To run all E2E tests, call:
-
-```shell
-pnpm e2e
-```
-
-All E2E tests are in the `e2e` folder.
-
 ### Docs
 
-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:
-
-### Bumping the versions of packages you've modified
-
-We use [changesets](https://github.com/changesets/changesets) for managing versions and changelogs. To create a new
-changeset, run in the root folder:
-
-```shell
-pnpm changeset
+```bash
+cd apps/docs
+pnpm install
+pnpm start
 ```
 
-You will be prompted to choose what packages need their versions bumped, and what kind of bump (major, minor or patch) is needed. Once you carry out this operation, the bumping will be automatic after the PR is merged.
+That should start a webserver which will serve the docs on https://localhost:3000
 
-## Publishing (maintainers only)
-
-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**](/typescript/framework/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**](/typescript/framework/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**](/typescript/framework/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**](/typescript/framework/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**](/typescript/framework/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**](/typescript/framework/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**](/typescript/framework/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/examples/chatEngine.ts)
+
+Read a file and chat about it with the LLM.
+
+## [Vector Index](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/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/examples/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/examples/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/examples/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/examples/openai.ts)
+
+Create an OpenAI LLM and directly use it for chat.
+
+## [Llama2 DeuceLLM](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/llamadeuce.ts)
+
+Create a Llama-2 LLM and directly use it for chat.
+
+## [SubQuestionQueryEngine](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/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/examples/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.mdx

@@ -0,0 +1,63 @@
+---
+sidebar_position: 1
+---
+
+# Installation and Setup
+
+Make sure you have NodeJS v18 or higher.
+
+## Using create-llama
+
+The easiest way to get started with LlamaIndex is by using `create-llama`. This CLI tool enables you to quickly start building a new LlamaIndex application, with everything set up for you.
+
+Just run
+
+<Tabs>
+<TabItem value="1" label="npm" default>
+
+```bash
+npx create-llama@latest
+```
+
+</TabItem>
+<TabItem value="2" label="Yarn">
+
+```bash
+yarn create llama
+```
+
+</TabItem>
+<TabItem value="3" label="pnpm">
+
+```bash
+pnpm create llama@latest
+```
+
+</TabItem>
+</Tabs>
+
+to get started. Once your app is generated, run
+
+```bash npm2yarn
+npm run dev
+```
+
+to start the development server. You can then visit [http://localhost:3000](http://localhost:3000) to see your app
+
+## Installation from NPM
+
+```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://docs.llamaindex.ai/en/stable/)), 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
@@ -38,6 +31,6 @@ You can learn more about Tools by taking a look at the LlamaIndex Python documen
 
 ## API Reference
 
-- [RetrieverQueryEngine](/typescript/framework-api-reference/classes/retrieverqueryengine/)
-- [SubQuestionQueryEngine](/typescript/framework-api-reference/classes/subquestionqueryengine/)
-- [QueryEngineTool](/typescript/framework-api-reference/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 +1,62 @@
+---
+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)
+
+## Usage
+
+The LLM object tracks API consumption across all your code. This is done through the `llm.usage` property.
+
+_Note: Usage is not supported for stream calls_
+
+```javascript
+import { OpenAI } from "llamaindex";
+
+// Create a new instance of OpenAI with the specified model and temperature
+const llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
+
+async function ask() {
+  // Send a chat request to the OpenAI API
+  const response = await llm.chat([
+    {
+      role: "system",
+      content: "You are a helpful llama.",
+    },
+    {
+      role: "user",
+      content: "Where do llama live?",
+    },
+  ]);
+
+  // Log the response from the API
+  console.log(response);
+  /**
+  The response includes a message from the assistant and usage information.
+  The usage information includes the number of prompt tokens, completion tokens, and total tokens used.
+  */
+
+  // Log the usage information from the LLM object
+  console.log(llm.usage);
+  /** 
+  The usage object includes the number of prompt tokens, completion tokens, the cost, and the compute seconds.
+  */
+}
+```

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) 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

@@ -1,55 +1,50 @@
-import Heading from "@theme/Heading";
 import clsx from "clsx";
+import React from "react";
 import styles from "./styles.module.css";
 
-const FeatureList = [
+type FeatureItem = {
+  title: string;
+  Svg: React.ComponentType<React.ComponentProps<"svg">>;
+  description: JSX.Element;
+};
+
+const FeatureList: FeatureItem[] = [
   {
-    title: "Easy to Use",
+    title: "Data Driven",
     Svg: require("@site/static/img/undraw_docusaurus_mountain.svg").default,
-    description: (
-      <>
-        Docusaurus was designed from the ground up to be easily installed and
-        used to get your website up and running quickly.
-      </>
-    ),
+    description: <>LlamaIndex.TS is all about using your data with LLMs.</>,
   },
   {
-    title: "Focus on What Matters",
+    title: "Typescript Native",
     Svg: require("@site/static/img/undraw_docusaurus_tree.svg").default,
-    description: (
-      <>
-        Docusaurus lets you focus on your docs, and we&apos;ll do the chores. Go
-        ahead and move your docs into the <code>docs</code> directory.
-      </>
-    ),
+    description: <>We ❤️ Typescript, and so do our users.</>,
   },
   {
-    title: "Powered by React",
+    title: "Built by the Community",
     Svg: require("@site/static/img/undraw_docusaurus_react.svg").default,
     description: (
       <>
-        Extend or customize your website layout by reusing React. Docusaurus can
-        be extended while reusing the same header and footer.
+        LlamaIndex.TS is a community project, and we welcome your contributions!
       </>
     ),
   },
 ];
 
-function Feature({ Svg, title, description }) {
+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">
-        <Heading as="h3">{title}</Heading>
+        <h3>{title}</h3>
         <p>{description}</p>
       </div>
     </div>
   );
 }
 
-export default function HomepageFeatures() {
+export default function HomepageFeatures(): JSX.Element {
   return (
     <section className={styles.features}>
       <div className="container">

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
+      d="M133 115.5a2.5 2.5 0 1 0 0-5c-.109 0-.214.019-.319.032-.02-.075-.037-.15-.058-.225a2.501 2.501 0 0 0-.963-4.807c-.569 0-1.088.197-1.508.518a6.653 6.653 0 0 0-.168-.168c.314-.417.506-.931.506-1.494a2.5 2.5 0 0 0-4.8-.979A9.987 9.987 0 0 0 123 103c-5.522 0-10 4.478-10 10s4.478 10 10 10c.934 0 1.833-.138 2.69-.377a2.5 2.5 0 0 0 4.8-.979c0-.563-.192-1.077-.506-1.494.057-.055.113-.111.168-.168.42.321.939.518 1.508.518a2.5 2.5 0 0 0 .963-4.807c.021-.074.038-.15.058-.225.105.013.21.032.319.032"
+      fill="#44D860" />
+    <path
+      d="M143 41.75c-.16 0-.33-.02-.49-.05a2.52 2.52 0 0 1-.47-.14c-.15-.06-.29-.14-.431-.23-.13-.09-.259-.2-.38-.31-.109-.12-.219-.24-.309-.38s-.17-.28-.231-.43a2.619 2.619 0 0 1-.189-.96c0-.16.02-.33.05-.49.03-.16.08-.31.139-.47.061-.15.141-.29.231-.43.09-.13.2-.26.309-.38.121-.11.25-.22.38-.31.141-.09.281-.17.431-.23.149-.06.31-.11.47-.14.32-.07.65-.07.98 0 .159.03.32.08.47.14.149.06.29.14.43.23.13.09.259.2.38.31.11.12.22.25.31.38.09.14.17.28.23.43.06.16.11.31.14.47.029.16.05.33.05.49 0 .66-.271 1.31-.73 1.77-.121.11-.25.22-.38.31-.14.09-.281.17-.43.23a2.565 2.565 0 0 1-.96.19m20-1.25c-.66 0-1.3-.27-1.771-.73a3.802 3.802 0 0 1-.309-.38c-.09-.14-.17-.28-.231-.43a2.619 2.619 0 0 1-.189-.96c0-.66.27-1.3.729-1.77.121-.11.25-.22.38-.31.141-.09.281-.17.431-.23.149-.06.31-.11.47-.14.32-.07.66-.07.98 0 .159.03.32.08.47.14.149.06.29.14.43.23.13.09.259.2.38.31.459.47.73 1.11.73 1.77 0 .16-.021.33-.05.49-.03.16-.08.32-.14.47-.07.15-.14.29-.23.43-.09.13-.2.26-.31.38-.121.11-.25.22-.38.31-.14.09-.281.17-.43.23a2.565 2.565 0 0 1-.96.19"
+      fill="#000" />
+  </g>
+</svg>

apps/docs/static/img/undraw_docusaurus_mountain.svg

@@ -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"/>
+      <path id="Path_84" data-name="Path 84" d="M986.56,670.771c-20,38.52-47.21,64.04-77.77,80.28a193.272,193.272,0,0,1-27.46,11.94c-55.61,19.3-117.85,14.18-166.74,3.99a657.3,657.3,0,0,0-104.09-13.16q-14.97-.675-29.97-.67-23.13.03-46.25,1.72c-100.17,7.36-253.82-6.43-321.42-143.29L382,283.981,444.95,445.6l20.09,51.59,55.37-75.98L549,381.981l130.2,149.27,36.8-81.27L970.78,657.9l14.21,11.59Z" transform="translate(-56 -106.019)" fill="#f2f2f2"/>
+      <path id="Path_85" data-name="Path 85" d="M302,282.962l26-57,36,83-31-60Z" opacity="0.1"/>
+      <path id="Path_86" data-name="Path 86" d="M610.5,753.821q-14.97-.675-29.97-.67L465.04,497.191Z" transform="translate(-56 -106.019)" opacity="0.1"/>
+      <path id="Path_87" data-name="Path 87" d="M464.411,315.191,493,292.962l130,150-132-128Z" opacity="0.1"/>
+      <path id="Path_88" data-name="Path 88" d="M908.79,751.051a193.265,193.265,0,0,1-27.46,11.94L679.2,531.251Z" transform="translate(-56 -106.019)" opacity="0.1"/>
+      <circle id="Ellipse_11" data-name="Ellipse 11" cx="3" cy="3" r="3" transform="translate(479 98.962)" fill="#f2f2f2"/>
+      <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"/>
+      <circle id="Ellipse_16" data-name="Ellipse 16" cx="2" cy="2" r="2" transform="translate(736 192.962)" fill="#f2f2f2"/>
+      <circle id="Ellipse_17" data-name="Ellipse 17" cx="2" cy="2" r="2" transform="translate(858 344.962)" fill="#f2f2f2"/>
+      <path id="Path_89" data-name="Path 89" d="M306,121.222h-2.76v-2.76h-1.48v2.76H299V122.7h2.76v2.759h1.48V122.7H306Z" fill="#f2f2f2"/>
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+  <path d="M410.74039,545.70532a3.31768,3.31768,0,1,0,0-6.63536,3.41133,3.41133,0,0,0-.42333.04247c-.02655-.09953-.04911-.19907-.077-.29859a3.319,3.319,0,0,0-1.278-6.37923,3.28174,3.28174,0,0,0-2.00122.68742q-.10947-.11346-.22294-.22295a3.282,3.282,0,0,0,.67149-1.98265,3.31768,3.31768,0,0,0-6.37-1.2992,13.27078,13.27078,0,1,0,0,25.54082,3.31768,3.31768,0,0,0,6.37-1.2992,3.282,3.282,0,0,0-.67149-1.98265q.11347-.10947.22294-.22294a3.28174,3.28174,0,0,0,2.00122.68742,3.31768,3.31768,0,0,0,1.278-6.37923c.02786-.0982.05042-.19907.077-.29859a3.41325,3.41325,0,0,0,.42333.04246" transform="translate(-35.5 -118.5)" fill="#44d860" fill-rule="evenodd" />
+  <path d="M424.01111,447.8338a3.60349,3.60349,0,0,1-.65028-.06636,3.34415,3.34415,0,0,1-.62372-.18579,3.44679,3.44679,0,0,1-.572-.30522,5.02708,5.02708,0,0,1-.50429-.4114,3.88726,3.88726,0,0,1-.41007-.50428,3.27532,3.27532,0,0,1-.55737-1.84463,3.60248,3.60248,0,0,1,.06636-.65027,3.82638,3.82638,0,0,1,.18447-.62373,3.48858,3.48858,0,0,1,.30656-.57064,3.197,3.197,0,0,1,.91436-.91568,3.44685,3.44685,0,0,1,.572-.30523,3.344,3.344,0,0,1,.62372-.18578,3.06907,3.06907,0,0,1,1.30053,0,3.22332,3.22332,0,0,1,1.19436.491,5.02835,5.02835,0,0,1,.50429.41139,4.8801,4.8801,0,0,1,.41139.50429,3.38246,3.38246,0,0,1,.30522.57064,3.47806,3.47806,0,0,1,.25215,1.274A3.36394,3.36394,0,0,1,426.36,446.865a5.02708,5.02708,0,0,1-.50429.4114,3.3057,3.3057,0,0,1-1.84463.55737m26.54143-1.65884a3.38754,3.38754,0,0,1-2.35024-.96877,5.04185,5.04185,0,0,1-.41007-.50428,3.27532,3.27532,0,0,1-.55737-1.84463,3.38659,3.38659,0,0,1,.96744-2.34892,5.02559,5.02559,0,0,1,.50429-.41139,3.44685,3.44685,0,0,1,.572-.30523,3.3432,3.3432,0,0,1,.62373-.18579,3.06952,3.06952,0,0,1,1.30052,0,3.22356,3.22356,0,0,1,1.19436.491,5.02559,5.02559,0,0,1,.50429.41139,3.38792,3.38792,0,0,1,.96876,2.34892,3.72635,3.72635,0,0,1-.06636.65026,3.37387,3.37387,0,0,1-.18579.62373,4.71469,4.71469,0,0,1-.30522.57064,4.8801,4.8801,0,0,1-.41139.50429,5.02559,5.02559,0,0,1-.50429.41139,3.30547,3.30547,0,0,1-1.84463.55737" transform="translate(-35.5 -118.5)" fill-rule="evenodd" />
+</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/simple/assemblyai.ts

@@ -1,14 +1,16 @@
-import {
-  AudioTranscriptReader,
-  TranscribeParams,
-} from "@llamaindex/assemblyai";
 import { program } from "commander";
-import { VectorStoreIndex } from "llamaindex";
+import { AudioTranscriptReader, TranscribeParams } from "llamaindex";
 import { stdin as input, stdout as output } from "node:process";
-import { createInterface } from "node:readline/promises";
+// readline/promises is still experimental so not in @types/node yet
+// @ts-ignore
+import readline from "node:readline/promises";
+import { VectorStoreIndex } from "../../packages/core/src/indices";
 
 program
-  .option("-a, --audio [string]", "URL or path of the audio file to transcribe")
+  .option(
+    "-a, --audio-url [string]",
+    "URL or path of the audio file to transcribe",
+  )
   .option("-i, --transcript-id [string]", "ID of the AssemblyAI transcript")
   .action(async (options) => {
     if (!process.env.ASSEMBLYAI_API_KEY) {
@@ -18,14 +20,16 @@ program
 
     const reader = new AudioTranscriptReader();
     let params: TranscribeParams | string;
-    if (options.audio) {
+    if (options.audioUrl) {
       params = {
-        audio: options.audio,
+        audio: options.audioUrl,
       };
     } else if (options.transcriptId) {
       params = options.transcriptId;
     } else {
-      console.log("You must provide either an --audio or a --transcript-id");
+      console.log(
+        "You must provide either an --audio-url or a --transcript-id",
+      );
       return;
     }
 
@@ -38,7 +42,7 @@ program
     // Create query engine
     const queryEngine = index.asQueryEngine();
 
-    const rl = createInterface({ input, output });
+    const rl = readline.createInterface({ input, output });
     while (true) {
       const query = await rl.question("Ask a question: ");
 
@@ -46,7 +50,7 @@ program
         break;
       }
 
-      const response = await queryEngine.query({ query });
+      const response = await queryEngine.query(query);
 
       console.log(response.toString());
     }

assets/tslogo.svg

@@ -0,0 +1,7 @@
+<svg fill="none" height="50" viewBox="0 0 512 512" width="50" xmlns="http://www.w3.org/2000/svg">
+  <rect fill="#3178c6" height="512" rx="50" width="512" />
+  <rect fill="#3178c6" height="512" rx="50" width="512" />
+  <path clip-rule="evenodd"
+    d="m316.939 407.424v50.061c8.138 4.172 17.763 7.3 28.875 9.386s22.823 3.129 35.135 3.129c11.999 0 23.397-1.147 34.196-3.442 10.799-2.294 20.268-6.075 28.406-11.342 8.138-5.266 14.581-12.15 19.328-20.65s7.121-19.007 7.121-31.522c0-9.074-1.356-17.026-4.069-23.857s-6.625-12.906-11.738-18.225c-5.112-5.319-11.242-10.091-18.389-14.315s-15.207-8.213-24.18-11.967c-6.573-2.712-12.468-5.345-17.685-7.9-5.217-2.556-9.651-5.163-13.303-7.822-3.652-2.66-6.469-5.476-8.451-8.448-1.982-2.973-2.974-6.336-2.974-10.091 0-3.441.887-6.544 2.661-9.308s4.278-5.136 7.512-7.118c3.235-1.981 7.199-3.52 11.894-4.615 4.696-1.095 9.912-1.642 15.651-1.642 4.173 0 8.581.313 13.224.938 4.643.626 9.312 1.591 14.008 2.894 4.695 1.304 9.259 2.947 13.694 4.928 4.434 1.982 8.529 4.276 12.285 6.884v-46.776c-7.616-2.92-15.937-5.084-24.962-6.492s-19.381-2.112-31.066-2.112c-11.895 0-23.163 1.278-33.805 3.833s-20.006 6.544-28.093 11.967c-8.086 5.424-14.476 12.333-19.171 20.729-4.695 8.395-7.043 18.433-7.043 30.114 0 14.914 4.304 27.638 12.912 38.172 8.607 10.533 21.675 19.45 39.204 26.751 6.886 2.816 13.303 5.579 19.25 8.291s11.086 5.528 15.415 8.448c4.33 2.92 7.747 6.101 10.252 9.543 2.504 3.441 3.756 7.352 3.756 11.733 0 3.233-.783 6.231-2.348 8.995s-3.939 5.162-7.121 7.196-7.147 3.624-11.894 4.771c-4.748 1.148-10.303 1.721-16.668 1.721-10.851 0-21.597-1.903-32.24-5.71-10.642-3.806-20.502-9.516-29.579-17.13zm-84.159-123.342h64.22v-41.082h-179v41.082h63.906v182.918h50.874z"
+    fill="#fff" fill-rule="evenodd" />
+</svg>

docs/.gitignore

@@ -1,36 +0,0 @@
-# build output
-dist/
-# generated types
-.astro/
-
-# dependencies
-node_modules/
-
-# logs
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-pnpm-debug.log*
-
-
-# environment variables
-.env
-.env.production
-.env.local
-
-# macOS-specific files
-.DS_Store
-
-# built files
-.build
-docs-python/src/content/docs/cloud
-docs-python/src/content/docs/framework
-docs-python/src/content/docs/workflows
-docs-python/src/content/docs/examples
-docs-typescript/src/content/docs/cloud
-docs-typescript/src/content/docs/framework
-docs-typescript/src/content/docs/examples
-docs-typescript/src/content/docs/workflows
-public/python-api-reference
-public/typescript-api-reference
-public/cloud-api-reference

docs/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 `@llamaindex/workflow-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

docs/astro.config.mjs

@@ -1,115 +0,0 @@
-// @ts-check
-import { defineConfig, passthroughImageService } from 'astro/config';
-import starlight from '@astrojs/starlight';
-import react from '@astrojs/react';
-import AutoImport from 'astro-auto-import';
-import starlightAutoSidebar from 'starlight-auto-sidebar'
-import path from 'path';
-
-// https://astro.build/config
-export default defineConfig({
-	site: "https://developers.llamaindex.ai",
-	base: "/typescript/",
-	outDir: path.resolve('../dist/typescript/'),
-	integrations: [
-		starlight({
-			plugins: [starlightAutoSidebar()],
-			title: 'LlamaIndex Documentation',
-			head: [
-				{
-					tag: 'script',
-					content: `
-					(function (w, d, s, l, i) {
-					  w[l] = w[l] || [];
-					  w[l].push({ "gtm.start": new Date().getTime(), event: "gtm.js" });
-					  var f = d.getElementsByTagName(s)[0],
-						j = d.createElement(s),
-						dl = l != "dataLayer" ? "&l=" + l : "";
-					  j.async = true;
-					  j.src = "https://www.googletagmanager.com/gtm.js?id=" + i + dl;
-					  f.parentNode.insertBefore(j, f);
-					})(window, document, "script", "dataLayer", "GTM-WWRFB36R");
-				  `,
-				},
-				{
-					tag: 'script',
-					content: `
-						document.addEventListener("DOMContentLoaded", function () {
-							var script = document.createElement("script");
-							script.type = "module";
-							script.id = "runllm-widget-script"
-							script.src = "https://widget.runllm.com";
-							script.setAttribute("version", "stable");
-							script.setAttribute("crossorigin", "true");
-							script.setAttribute("runllm-keyboard-shortcut", "Mod+j");
-							script.setAttribute("runllm-name", "LlamaIndex");
-							script.setAttribute("runllm-position", "BOTTOM_RIGHT");
-							script.setAttribute("runllm-assistant-id", "1450");
-							script.setAttribute("runllm-disable-ask-a-person", true);
-							script.setAttribute(
-								"runllm-slack-community-url",
-								"https://discord.com/invite/eN6D2HQ4aX"
-							);
-							script.async = true;
-							document.head.appendChild(script);
-						});
-					`
-				}
-			],
-			social: [
-				{
-					icon: 'twitter',
-					label: 'Twitter',
-					href: 'https://x.com/llama_index'
-				},
-				{
-					icon: 'linkedin',
-					label: 'LinkedIn',
-					href: 'https://www.linkedin.com/company/llamaindex'
-				},
-				{
-					icon: 'blueSky',
-					label: 'Bluesky',
-					href: 'https://bsky.app/profile/llamaindex.bsky.social'
-				},
-				{
-					icon: 'github',
-					label: 'GitHub',
-					href: 'https://github.com/run-llama/LlamaIndexTS'
-				}
-			],
-			logo: {
-				light: './src/assets/llamaindex-dark.svg',
-				dark: './src/assets/llamaindex-light.svg',
-				replacesTitle: true,
-			},
-			favicon: '/logo-dark.png',
-			components: {
-				SiteTitle: './src/components/SiteTitle.astro',
-				Header: './src/components/Header.astro',
-			},
-			sidebar: [
-				{
-					label: 'Framework',
-					autogenerate: { directory: 'framework', collapsed: true },
-				},
-				{
-					label: 'Framework API Reference 🔗',
-					link: '/framework-api-reference/'
-				},
-			],
-		}),
-		AutoImport({
-			imports: [
-				{
-					'@icons-pack/react-simple-icons': ['SiBun', 'SiCloudflareworkers', 'SiDeno', 'SiNodedotjs', 'SiTypescript', 'SiVite', 'SiNextdotjs', 'SiDiscord', 'SiGithub', 'SiNpm', 'SiX'],
-					'@astrojs/starlight/components': ['Card', 'CardGrid', 'LinkCard', 'Icon', 'Tabs', 'TabItem', 'Aside']
-				},
-			],
-		}),
-		react(),
-	],
-	image: {
-		service: passthroughImageService(),
-	}
-});

docs/package.json

@@ -1,39 +0,0 @@
-{
-  "name": "",
-  "type": "module",
-  "version": "0.0.1",
-  "scripts": {
-    "dev": "astro dev",
-    "start": "astro dev",
-    "build": "astro build",
-    "preview": "astro preview",
-    "astro": "astro"
-  },
-  "dependencies": {
-    "@astrojs/mdx": "^4.2.6",
-    "@astrojs/react": "^4.2.7",
-    "@astrojs/starlight": "^0.34.3",
-    "@icons-pack/react-simple-icons": "^12.8.0",
-    "@types/react": "^19.1.4",
-    "@types/react-dom": "^19.1.5",
-    "astro": "^5.6.1",
-    "astro-auto-import": "^0.4.4",
-    "astro-icon": "^1.1.5",
-    "cross-env": "^7.0.3",
-    "dotenv": "^16.4.5",
-    "fs-extra": "^11.3.0",
-    "fumadocs-typescript": "^4.0.6",
-    "fumadocs-ui": "^15.3.3",
-    "glob": "^11.0.2",
-    "js-yaml": "^4.1.0",
-    "marked": "^15.0.11",
-    "react": "^19.1.0",
-    "react-dom": "^19.1.0",
-    "sharp": "^0.32.5",
-    "starlight-auto-sidebar": "^0.1.2",
-    "typedoc": "^0.28.4",
-    "typedoc-plugin-markdown": "^4.6.3",
-    "typedoc-plugin-merge-modules": "^7.0.0"
-  },
-  "packageManager": "pnpm@10.8.1"
-}

docs/src/assets/llamaindex-dark.svg

@@ -1,22 +0,0 @@
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-<linearGradient id="paint0_linear_3054_4082" x1="26.4432" y1="19.2646" x2="89.8582" y2="72.4099" gradientUnits="userSpaceOnUse">
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-<stop offset="0.325677" stop-color="#FFA5EA"/>
-<stop offset="0.589257" stop-color="#45DFF8"/>
-<stop offset="1" stop-color="#BC8DEB"/>
-</linearGradient>
-</defs>
-</svg>

docs/src/components/Header.astro

@@ -1,97 +0,0 @@
----
-import config from 'virtual:starlight/user-config';
-
-import LanguageSelect from 'virtual:starlight/components/LanguageSelect';
-import Search from 'virtual:starlight/components/Search';
-import SiteTitle from 'virtual:starlight/components/SiteTitle';
-import SocialIcons from 'virtual:starlight/components/SocialIcons';
-import ThemeSelect from 'virtual:starlight/components/ThemeSelect';
-
-/**
- * Render the `Search` component if Pagefind is enabled or the default search component has been overridden.
- */
-const shouldRenderSearch =
-	config.pagefind || config.components.Search !== '@astrojs/starlight/components/Search.astro';
----
-
-<div class="header">
-	<div class="title-wrapper sl-flex">
-		<SiteTitle />
-	</div>
-	<div class="sl-flex print:hidden">
-		{shouldRenderSearch && <Search />}
-	</div>
-	<div class="sl-hidden md:sl-flex print:hidden right-group">
-        <div class="sl-flex">
-            <a href="/python/framework/">Python</a>
-        </div>
-		<div class="sl-flex social-icons">
-			<SocialIcons />
-		</div>
-		<ThemeSelect />
-		<LanguageSelect />
-	</div>
-</div>
-
-<style>
-	@layer starlight.core {
-		.header {
-			display: flex;
-			gap: var(--sl-nav-gap);
-			justify-content: space-between;
-			align-items: center;
-			height: 100%;
-		}
-
-		.title-wrapper {
-			/* Prevent long titles overflowing and covering the search and menu buttons on narrow viewports. */
-			overflow: clip;
-			/* Avoid clipping focus ring around link inside title wrapper. */
-			padding: 0.25rem;
-			margin: -0.25rem;
-			min-width: 0;
-		}
-
-		.right-group,
-		.social-icons {
-			gap: 1rem;
-			align-items: center;
-		}
-		.social-icons::after {
-			content: '';
-			height: 2rem;
-			border-inline-end: 1px solid var(--sl-color-gray-5);
-		}
-
-		@media (min-width: 50rem) {
-			:global(:root[data-has-sidebar]) {
-				--__sidebar-pad: calc(2 * var(--sl-nav-pad-x));
-			}
-			:global(:root:not([data-has-toc])) {
-				--__toc-width: 0rem;
-			}
-			.header {
-				--__sidebar-width: max(0rem, var(--sl-content-inline-start, 0rem) - var(--sl-nav-pad-x));
-				--__main-column-fr: calc(
-					(
-							100% + var(--__sidebar-pad, 0rem) - var(--__toc-width, var(--sl-sidebar-width)) -
-								(2 * var(--__toc-width, var(--sl-nav-pad-x))) - var(--sl-content-inline-start, 0rem) -
-								var(--sl-content-width)
-						) / 2
-				);
-				display: grid;
-				grid-template-columns:
-        /* 1 (site title): runs up until the main content column’s left edge or the width of the title, whichever is the largest  */
-					minmax(
-						calc(var(--__sidebar-width) + max(0rem, var(--__main-column-fr) - var(--sl-nav-gap))),
-						auto
-					)
-					/* 2 (search box): all free space that is available. */
-					1fr
-					/* 3 (right items): use the space that these need. */
-					auto;
-				align-content: center;
-			}
-		}
-	}
-</style>

docs/src/components/ProtectedContent.jsx

@@ -1,148 +0,0 @@
-import { useEffect, useState } from "react";
-
-const SELF_HOSTING_PASSWORD = "llamacloud-self-host-2025";
-const STORAGE_KEY = "llamacloud-self-hosting-auth";
-
-export default function ProtectedContent({ children }) {
-  const [isAuthenticated, setIsAuthenticated] = useState(false);
-  const [password, setPassword] = useState("");
-  const [error, setError] = useState("");
-  const [isLoading, setIsLoading] = useState(true);
-
-  useEffect(() => {
-    // Check if user is already authenticated
-    const savedAuth = localStorage.getItem(STORAGE_KEY);
-    if (savedAuth === "true") {
-      setIsAuthenticated(true);
-    }
-    setIsLoading(false);
-  }, []);
-
-  const handleSubmit = (e) => {
-    e.preventDefault();
-    if (password === SELF_HOSTING_PASSWORD) {
-      setIsAuthenticated(true);
-      localStorage.setItem(STORAGE_KEY, "true");
-      setError("");
-    } else {
-      setError("Incorrect password. Please try again.");
-      setPassword("");
-    }
-  };
-
-  const handleLogout = () => {
-    setIsAuthenticated(false);
-    localStorage.removeItem(STORAGE_KEY);
-    setPassword("");
-  };
-
-  if (isLoading) {
-    return <div>Loading...</div>;
-  }
-
-  if (!isAuthenticated) {
-    return (
-      <div
-        style={{
-          maxWidth: "400px",
-          margin: "2rem auto",
-          padding: "2rem",
-          border: "1px solid #e0e0e0",
-          borderRadius: "8px",
-          backgroundColor: "#f9f9f9",
-        }}
-      >
-        <h2 style={{ textAlign: "center", marginBottom: "1.5rem" }}>
-          Self-Hosting Documentation Access
-        </h2>
-        <p
-          style={{ textAlign: "center", marginBottom: "1.5rem", color: "#666" }}
-        >
-          This section requires a password to access. Interested in
-          self-hosting?{" "}
-          <a href="https://www.llamaindex.ai/contact">Contact sales</a> to learn
-          more.
-        </p>
-        <form onSubmit={handleSubmit}>
-          <div style={{ marginBottom: "1rem" }}>
-            <label
-              htmlFor="password"
-              style={{ display: "block", marginBottom: "0.5rem" }}
-            >
-              Password:
-            </label>
-            <input
-              type="password"
-              id="password"
-              value={password}
-              onChange={(e) => setPassword(e.target.value)}
-              style={{
-                width: "100%",
-                padding: "0.5rem",
-                border: "1px solid #ccc",
-                borderRadius: "4px",
-                fontSize: "1rem",
-              }}
-              required
-            />
-          </div>
-          {error && (
-            <p
-              style={{ color: "red", fontSize: "0.9rem", marginBottom: "1rem" }}
-            >
-              {error}
-            </p>
-          )}
-          <button
-            type="submit"
-            style={{
-              width: "100%",
-              padding: "0.75rem",
-              backgroundColor: "#007cba",
-              color: "white",
-              border: "none",
-              borderRadius: "4px",
-              fontSize: "1rem",
-              cursor: "pointer",
-            }}
-          >
-            Access Documentation
-          </button>
-        </form>
-      </div>
-    );
-  }
-
-  return (
-    <div>
-      <div
-        style={{
-          textAlign: "right",
-          marginBottom: "1rem",
-          padding: "0.5rem",
-          backgroundColor: "#f0f0f0",
-          borderRadius: "4px",
-        }}
-      >
-        <small style={{ marginRight: "1rem", color: "#666" }}>
-          Self-Hosting Documentation Access Granted
-        </small>
-        <button
-          onClick={handleLogout}
-          style={{
-            padding: "0.25rem 0.5rem",
-            backgroundColor: "#dc3545",
-            color: "white",
-            border: "none",
-            borderRadius: "3px",
-            fontSize: "0.8rem",
-            cursor: "pointer",
-          }}
-        >
-          Logout
-        </button>
-      </div>
-      {children}
-    </div>
-  );
-}

docs/src/components/SiteTitle.astro

@@ -1,59 +0,0 @@
----
-import { logos } from 'virtual:starlight/user-images';
-import config from 'virtual:starlight/user-config';
-const { siteTitle, siteTitleHref } = Astro.locals.starlightRoute;
----
-
-<a href="/" class="site-title sl-flex">
-	{
-		config.logo && logos.dark && (
-			<>
-				<img
-					class:list={{ 'light:sl-hidden print:hidden': !('src' in config.logo) }}
-					alt={config.logo.alt}
-					src={logos.dark.src}
-					width={logos.dark.width}
-					height={logos.dark.height}
-				/>
-				{/* Show light alternate if a user configure both light and dark logos. */}
-				{!('src' in config.logo) && (
-					<img
-						class="dark:sl-hidden print:block"
-						alt={config.logo.alt}
-						src={logos.light?.src}
-						width={logos.light?.width}
-						height={logos.light?.height}
-					/>
-				)}
-			</>
-		)
-	}
-	<span class:list={{ 'sr-only': config.logo?.replacesTitle }} translate="no">
-		{siteTitle}
-	</span>
-</a>
-
-<style>
-	@layer starlight.core {
-		.site-title {
-			align-items: center;
-			gap: var(--sl-nav-gap);
-			font-size: var(--sl-text-h4);
-			font-weight: 600;
-			color: var(--sl-color-text-accent);
-			text-decoration: none;
-			white-space: nowrap;
-			min-width: 0;
-		}
-		span {
-			overflow: hidden;
-		}
-		img {
-			height: calc(var(--sl-nav-height) - 2 * var(--sl-nav-pad-y));
-			width: auto;
-			max-width: 100%;
-			object-fit: contain;
-			object-position: 0 50%;
-		}
-	}
-</style>

docs/src/components/llamaExtract.js

@@ -1,71 +0,0 @@
-export const LEPython = ({ children }) => (
-  <>
-    <span>
-      <small>In Python:</small>
-    </span>
-    <pre>
-      extractor = LlamaExtract(
-      <br />
-      &nbsp;&nbsp;{children}
-      <br />)
-    </pre>
-  </>
-);
-
-export const LEAPI = ({
-  children,
-  endpoint = "",
-  isUpload = false,
-  outputFile = false,
-}) => {
-  if (!endpoint) endpoint = "parsing/upload";
-  let outputLine = <></>;
-  if (outputFile)
-    outputLine = (
-      <>
-        &nbsp;\
-        <br />
-        &nbsp;&nbsp;--output "file.png"
-      </>
-    );
-  let uploadLine = <></>;
-  if (isUpload)
-    uploadLine = (
-      <>
-        &nbsp;\
-        <br />
-        &nbsp;&nbsp;-F 'file=@/path/to/your/file.pdf;type=application/pdf'
-      </>
-    );
-  let paramList = <></>;
-  if (typeof children == "string") {
-    let entries = children.split("|");
-    paramList = entries.map((line) => {
-      return (
-        <>
-          &nbsp;\
-          <br />
-          &nbsp;&nbsp;--form '{line}'
-        </>
-      );
-    });
-  }
-  return (
-    <>
-      <span>
-        <small>Using the API:</small>
-      </span>
-      <pre>
-        curl -X 'POST' \<br />
-        &nbsp;&nbsp;'https://api.cloud.llamaindex.ai/api/{endpoint}' &nbsp;\
-        <br />
-        &nbsp;&nbsp;-H 'accept: application/json' \<br />
-        &nbsp;&nbsp;-H 'Content-Type: multipart/form-data' \<br />
-        &nbsp;&nbsp;-H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
-        {paramList}
-        {uploadLine}
-        {outputLine}
-      </pre>
-    </>
-  );
-};

docs/src/components/llamaParse.js

@@ -1,93 +0,0 @@
-import React from "react";
-
-export const LPPython = ({ children }) => (
-  <>
-    <span>
-      <small>In Python:</small>
-    </span>
-    <pre>
-      parser = LlamaParse(
-      <br />
-      &nbsp;&nbsp;{children}
-      <br />)
-    </pre>
-  </>
-);
-
-export const LPAPI = ({
-  children,
-  endpoint = "",
-  isUpload = false,
-  outputFile = false,
-}) => {
-  if (!endpoint) endpoint = "upload";
-  let outputLine = <></>;
-  if (outputFile)
-    outputLine = (
-      <>
-        &nbsp;\
-        <br />
-        &nbsp;&nbsp;--output "file.png"
-      </>
-    );
-  let uploadLine = <></>;
-  if (isUpload)
-    uploadLine = (
-      <>
-        &nbsp;\
-        <br />
-        &nbsp;&nbsp;-F 'file=@/path/to/your/file.pdf;type=application/pdf'
-      </>
-    );
-  let paramList = <></>;
-  if (typeof children !== "undefined") {
-    // Get the raw text content of children
-    let rawContent = "";
-    if (typeof children === "string") {
-      rawContent = children;
-    } else {
-      // For React elements, get their text content
-      // this is because React interprets key-value pairs with URLs as prop values
-      rawContent = React.Children.toArray(children)
-        .map((child) => {
-          if (typeof child === "string") return child;
-          if (React.isValidElement(child)) return child.props.children || "";
-          return "";
-        })
-        .join("");
-    }
-
-    // Split by | if multiple parameters
-    const entries = rawContent.split("|");
-
-    paramList = entries.map((line) => {
-      return (
-        <>
-          &nbsp;\
-          <br />
-          &nbsp;&nbsp;--form '{line.trim()}'
-        </>
-      );
-    });
-  }
-
-  return (
-    <>
-      <span>
-        <small>Using the API:</small>
-      </span>
-      <pre>
-        curl -X 'POST' \<br />
-        &nbsp;&nbsp;'https://api.cloud.llamaindex.ai/api/v1/parsing/{endpoint}'
-        &nbsp;\
-        <br />
-        &nbsp;&nbsp;-H 'accept: application/json' \<br />
-        &nbsp;&nbsp;-H 'Content-Type: multipart/form-data' \<br />
-        &nbsp;&nbsp;-H "Authorization: Bearer $LLAMA_CLOUD_API_KEY"
-        {paramList}
-        {uploadLine}
-        {outputLine}
-      </pre>
-    </>
-  );
-};

docs/src/components/mdx_components.jsx

@@ -1,33 +0,0 @@
-import TabItem from "@theme/TabItem";
-import Tabs from "@theme/Tabs";
-import { Children, cloneElement } from "react";
-
-export const StyledTitle = ({ title, subtitle }) => {
-  return (
-    <div style={{ marginBottom: "1rem" }}>
-      <h3 style={{ marginBottom: "0.25rem" }}>{title}</h3>
-      {subtitle && (
-        <span style={{ fontSize: "0.875rem", opacity: 0.6 }}>{subtitle}</span>
-      )}
-    </div>
-  );
-};
-export const StyledTab = ({ children }) => {
-  return (
-    <Tabs>
-      {Children.map(children, (child) => {
-        if (child.type !== TabItem) return child;
-
-        return cloneElement(child, {
-          children: (
-            <div style={{ fontSize: "0.875rem" }}>{child.props.children}</div>
-          ),
-        });
-      })}
-    </Tabs>
-  );
-};
-
-export const ImageSizer = ({ children, width = "350px" }) => {
-  return <div style={{ width, margin: "0" }}>{children}</div>;
-};

docs/src/content.config.ts

@@ -1,13 +0,0 @@
-import { docsLoader } from "@astrojs/starlight/loaders";
-import { docsSchema } from "@astrojs/starlight/schema";
-import { defineCollection } from "astro:content";
-import { autoSidebarLoader } from "starlight-auto-sidebar/loader";
-import { autoSidebarSchema } from "starlight-auto-sidebar/schema";
-
-export const collections = {
-  docs: defineCollection({ loader: docsLoader(), schema: docsSchema() }),
-  autoSidebar: defineCollection({
-    loader: autoSidebarLoader(),
-    schema: autoSidebarSchema(),
-  }),
-};

docs/src/content/docs/framework/getting_started/_meta.yml

@@ -1,2 +0,0 @@
-label: Getting Started
-collapsed: true

docs/src/content/docs/framework/getting_started/concepts.mdx

@@ -1,60 +0,0 @@
----
-title: High-Level Concepts
----
-
-This is a quick guide to the high-level concepts you'll encounter frequently when building LLM applications.
-
-## Large Language Models (LLMs)
-
-LLMs are the fundamental innovation that launched LlamaIndex. They are an artificial intelligence (AI) computer system that can understand, generate, and manipulate natural language, including answering questions based on their training data or data provided to them at query time. 
-
-## Agentic Applications
-
-When an LLM is used within an application, it is often used to make decisions, take actions, and/or interact with the world. This is the core definition of an **agentic application**.
-
-While the definition of an agentic application is broad, there are several key characteristics that define an agentic application:
-
-- **LLM Augmentation**: The LLM is augmented with tools (i.e. arbitrary callable functions in code), memory, and/or dynamic prompts.
-- **Prompt Chaining**: Several LLM calls are used that build on each other, with the output of one LLM call being used as the input to the next.
-- **Routing**: The LLM is used to route the application to the next appropriate step or state in the application.
-- **Parallelism**: The application can perform multiple steps or actions in parallel.
-- **Orchestration**: A hierarchical structure of LLMs is used to orchestrate lower-level actions and LLMs.
-- **Reflection**: The LLM is used to reflect and validate outputs of previous steps or LLM calls, which can be used to guide the application to the next appropriate step or state.
-
-In LlamaIndex, you can build agentic applications by using the workflows to orchestrate a sequence of steps and LLMs. You can [learn more about workflows](/typescript/framework/tutorials/workflows).
-
-## Agents
-
-We define an agent as a specific instance of an "agentic application". An agent is a piece of software that semi-autonomously performs tasks by combining LLMs with other tools and memory, orchestrated in a reasoning loop that decides which tool to use next (if any).
-
-What this means in practice, is something like:
-- An agent receives a user message
-- The agent uses an LLM to determine the next appropriate action to take using the previous chat history, tools, and the latest user message
-- The agent may invoke one or more tools to assist in the users request
-- If tools are used, the agent will then interpret the tool outputs and use them to inform the next action
-- Once the agent stops taking actions, it returns the final output to the user
-
-You can [learn more about agents](/typescript/framework/tutorials/basic_agent).
-
-## Retrieval Augmented Generation (RAG)
-
-Retrieval-Augmented Generation (RAG) is a core technique for building data-backed LLM applications with LlamaIndex. It allows LLMs to answer questions about your private data by providing it to the LLM at query time, rather than training the LLM on your data. To avoid sending **all** of your data to the LLM every time, RAG indexes your data and selectively sends only the relevant parts along with your query. You can [learn more about RAG](/typescript/framework/tutorials/rag).
-
-## Use cases
-
-There are endless use cases for data-backed LLM applications but they can be roughly grouped into four categories:
-
-[**Agents**](/typescript/framework/tutorials/basic_agent):
-An agent is an automated decision-maker powered by an LLM that interacts with the world via a set of [tools](/typescript/framework/modules/agents/tool). Agents can take an arbitrary number of steps to complete a given task, dynamically deciding on the best course of action rather than following pre-determined steps. This gives it additional flexibility to tackle more complex tasks.
-
-[**Workflows**](/typescript/framework/tutorials/workflows):
-A Workflow in LlamaIndex is a specific event-driven abstraction that allows you to orchestrate a sequence of steps and LLMs calls. Workflows can be used to implement any agentic application, and are a core component of LlamaIndex.
-
-[**Structured Data Extraction**](/typescript/framework/tutorials/structured_data_extraction):
-Pydantic extractors allow you to specify a precise data structure to extract from your data and use LLMs to fill in the missing pieces in a type-safe way. This is useful for extracting structured data from unstructured sources like PDFs, websites, and more, and is key to automating workflows.
-
-[**Query Engines**](/typescript/framework/modules/rag/query_engines):
-A query engine is an end-to-end flow that allows you to ask questions 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**](/typescript/framework/modules/rag/chat_engine):
-A chat engine is an end-to-end flow for having a conversation with your data (multiple back-and-forth instead of a single question-and-answer).

docs/src/content/docs/framework/getting_started/create_llama.mdx

@@ -1,26 +0,0 @@
----
-title: Create-Llama
----
-
-`create-llama` is a powerful but easy to use command-line tool that generates a working, full-stack web application that allows you to chat with your data. You can learn more about it on [the `create-llama` README page](https://www.npmjs.com/package/create-llama).
-
-Run it once and it will ask you a series of questions about the kind of application you want to generate. Then you can customize your application to suit your use-case. To get started, run:
-
-```bash npm2yarn
-npx create-llama@latest
-```
-
-Once your app is generated, `cd` into your app directory and run
-
-```bash npm2yarn
-npm run dev
-```
-
-to start the development server. You can then visit [http://localhost:3000](http://localhost:3000) to see your app, which should look something like this:
-
-![create-llama interface](/images/create_llama.png)
-
-## Learn more
-
-- [Learn more about `create-llama`](https://github.com/run-llama/create-llama)
-- [Want to use the same UI components? You can use our React components](https://ui.llamaindex.ai/)

docs/src/content/docs/framework/getting_started/examples.mdx

@@ -1,33 +0,0 @@
----
-title: Code examples
----
-
-Our GitHub repository has a wealth of examples to explore and try out. You can check out our [examples folder](https://github.com/run-llama/LlamaIndexTS/tree/main/examples) to see them all at once, or browse the pages in this section for some selected highlights.
-
-## Use examples locally
-
-It may be useful to check out all the examples at once so you can try them out locally. To do this into a folder called `my-new-project`, run these commands:
-
-```bash npm2yarn
-npx degit run-llama/LlamaIndexTS/examples my-new-project
-cd my-new-project
-npm i
-```
-
-Then you can run any example in the folder with `tsx`, e.g.:
-
-```bash npm2yarn
-export OPENAI_API_KEY=your-api-key
-npx tsx ./agents/agent/openai.ts
-```
-
-## Try examples online
-
-You can also try the examples online using StackBlitz:
-
-<iframe
-  className="w-full h-[440px]"
-  aria-label="LlamaIndex.TS Examples"
-  aria-description="This is a list of examples for LlamaIndex.TS."
-  src="https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples?file=README.md"
-/>

docs/src/content/docs/framework/getting_started/installation/_meta.yml

@@ -1,2 +0,0 @@
-label: Installation
-collapsed: true

docs/src/content/docs/framework/getting_started/installation/index.mdx

@@ -1,177 +0,0 @@
----
-title: Installation
-description: How to install and set up LlamaIndex.TS for your project.
----
-
-## Quick Start
-
-Install the core package:
-
-```package-install
-npm i llamaindex
-```
-
-In most cases, you'll also need an LLM provider and the Workflow package:
-
-```package-install
-npm i @llamaindex/openai @llamaindex/workflow
-```
-
-## Environment Setup
-
-### API Keys
-
-Most LLM providers require API keys. Set your OpenAI key (or other provider):
-
-```bash
-export OPENAI_API_KEY=your-api-key
-```
-
-Or use a `.env` file:
-
-```bash
-echo "OPENAI_API_KEY=your-api-key" > .env
-```
-
-<Aside type="caution">Never commit API keys to your repository.</Aside>
-
-### Loading Environment Variables
-
-For Node.js applications:
-
-```bash
-node --env-file .env your-script.js
-```
-
-For other environments, see the deployment-specific guides below.
-
-## TypeScript Configuration
-
-LlamaIndex.TS is built with TypeScript and provides excellent type safety. Add these settings to your `tsconfig.json`:
-
-```json5
-{
-  "compilerOptions": {
-    // Essential for module resolution
-    "moduleResolution": "bundler", // or "nodenext" | "node16" | "node"
-    
-    // Required for Web Stream API support
-    "lib": ["DOM.AsyncIterable"],
-    
-    // Recommended for better compatibility
-    "target": "es2020",
-    "module": "esnext"
-  }
-}
-```
-
-## Running your first agent
-
-### Set up
-
-If you don't already have a project, you can create a new one in a new folder:
-
-```package-install
-npm init
-npm i -D typescript @types/node
-npm i @llamaindex/openai @llamaindex/workflow llamaindex zod
-```
-
-### Run the agent
-
-Create the file `example.ts`. This code will:
-
-- Create two tools for use by the agent:
-  - A `sumNumbers` tool that adds two numbers
-  - A `divideNumbers` tool that divides numbers
-- Give an example of the data structure we wish to generate
-- Prompt the LLM with instructions and the example, plus a sample transcript
-
-<include cwd>../../examples/agents/agent/openai.ts</include>
-
-To run the code:
-
-```package-install
-npx tsx example.ts
-```
-
-You should expect output something like:
-
-```
-{
-  result: '5 + 5 is 10. Then, 10 divided by 2 is 5.',
-  state: {
-    memory: Memory {
-      messages: [Array],
-      tokenLimit: 30000,
-      shortTermTokenLimitRatio: 0.7,
-      memoryBlocks: [],
-      memoryCursor: 0,
-      adapters: [Object]
-    },
-    scratchpad: [],
-    currentAgentName: 'Agent',
-    agents: [ 'Agent' ],
-    nextAgentName: null
-  }
-}
-Done
-```
-
-## Performance Optimization
-
-### Tokenization Speed
-
-Install `gpt-tokenizer` for 60x faster tokenization (Node.js environments only):
-
-```package-install
-npm i gpt-tokenizer
-```
-
-LlamaIndex will automatically use this when available.
-
-## Deployment Guides
-
-Choose your deployment target:
-
-<CardGrid>
-  <Card 
-    title="Server APIs & Backends" 
-    description="Express, Fastify, Koa, standalone Node.js servers"
-    href="/typescript/framework/getting_started/installation/server-apis" 
-  />
-  <Card 
-    title="Serverless Functions" 
-    description="Vercel, Netlify, AWS Lambda, Cloudflare Workers"
-    href="/typescript/framework/getting_started/installation/serverless" 
-  />
-  <Card 
-    title="Next.js Applications" 
-    description="API routes, server components, edge runtime"
-    href="/typescript/framework/getting_started/installation/nextjs" 
-  />
-  <Card 
-    title="Troubleshooting" 
-    description="Common issues, bundle optimization, compatibility"
-    href="/typescript/framework/getting_started/installation/troubleshooting" 
-  />
-</CardGrid>
-
-## LLM/Embedding Providers
-
-Go to [LLM APIs](/typescript/framework/modules/models/llms) and [Embedding APIs](/typescript/framework/modules/models/embeddings) to find out how to use different LLM and embedding providers beyond OpenAI.
-
-## What's Next?
-
-<CardGrid>
-  <Card
-    title="Learn LlamaIndex.TS"
-    description="Learn how to use LlamaIndex.TS by starting with one of our tutorials."
-    href="/typescript/framework/tutorials/basic_agent"
-  />
-  <Card
-    title="Show me code examples"
-    description="Explore code examples using LlamaIndex.TS."
-    href="/typescript/framework/getting_started/examples"
-  />
-</CardGrid>

docs/src/content/docs/framework/getting_started/installation/nextjs.mdx

@@ -1,405 +0,0 @@
----
-title: Next.js Applications
-description: Deploy LlamaIndex.TS in Next.js applications with API routes, server components, and edge runtime.
----
-
-This guide covers integrating LlamaIndex.TS agents with Next.js applications.
-
-## Essential Configuration
-
-### Next.js Config
-
-Use `withLlamaIndex` to ensure compatibility:
-
-```javascript
-// next.config.mjs
-import withLlamaIndex from "llamaindex/next";
-
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-  // Your existing config
-};
-
-export default withLlamaIndex(nextConfig);
-```
-
-## API Routes
-
-### App Router (Recommended)
-
-```typescript
-// app/api/chat/route.ts
-import { agent } from "@llamaindex/workflow";
-import { tool } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-import { z } from "zod";
-import { NextRequest, NextResponse } from "next/server";
-
-// Initialize agent once (consider using a singleton pattern)
-let myAgent: any = null;
-
-async function initializeAgent() {
-  if (myAgent) return myAgent;
-  
-  try {
-    const greetTool = tool({
-      name: "greet",
-      description: "Greets a user with their name",
-      parameters: z.object({
-        name: z.string(),
-      }),
-      execute: ({ name }) => `Hello, ${name}! How can I help you today?`,
-    });
-
-    myAgent = agent({
-      tools: [greetTool],
-      llm: openai({ model: "gpt-4o-mini" }),
-    });
-    
-    return myAgent;
-  } catch (error) {
-    console.error("Failed to initialize agent:", error);
-    throw error;
-  }
-}
-
-export async function POST(request: NextRequest) {
-  try {
-    const { message } = await request.json();
-    
-    if (!message || typeof message !== 'string') {
-      return NextResponse.json(
-        { error: "Message is required and must be a string" },
-        { status: 400 }
-      );
-    }
-    
-    const agent = await initializeAgent();
-    const result = await agent.run(message);
-    
-    return NextResponse.json({ response: result.data });
-  } catch (error) {
-    console.error("Chat error:", error);
-    return NextResponse.json(
-      { error: "Internal server error" },
-      { status: 500 }
-    );
-  }
-}
-```
-
-### Pages Router (Legacy)
-
-```typescript
-// pages/api/chat.ts
-import { agent } from "@llamaindex/workflow";
-import { tool } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-import { z } from "zod";
-import type { NextApiRequest, NextApiResponse } from "next";
-
-let myAgent: any = null;
-
-async function initializeAgent() {
-  if (myAgent) return myAgent;
-  
-  const timeTool = tool({
-    name: "getCurrentTime",
-    description: "Gets the current time",
-    parameters: z.object({}),
-    execute: () => new Date().toISOString(),
-  });
-
-  myAgent = agent({
-    tools: [timeTool],
-    llm: openai({ model: "gpt-4o-mini" }),
-  });
-  
-  return myAgent;
-}
-
-export default async function handler(
-  req: NextApiRequest,
-  res: NextApiResponse
-) {
-  if (req.method !== "POST") {
-    return res.status(405).json({ error: "Method not allowed" });
-  }
-  
-  try {
-    const { message } = req.body;
-    
-    const agent = await initializeAgent();
-    const result = await agent.run(message);
-    
-    res.json({ response: result.data });
-  } catch (error) {
-    console.error("Chat error:", error);
-    res.status(500).json({ error: "Internal server error" });
-  }
-}
-```
-
-## Server Components
-
-Initialize agents in server components:
-
-```typescript
-// app/chat/page.tsx
-import { agent } from "@llamaindex/workflow";
-import { tool } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-import { z } from "zod";
-
-async function initializeAgent() {
-  const helpTool = tool({
-    name: "getHelp",
-    description: "Provides help information",
-    parameters: z.object({
-      topic: z.string().optional(),
-    }),
-    execute: ({ topic }) => {
-      if (topic) {
-        return `Here's help for ${topic}: This is a helpful resource about ${topic}.`;
-      }
-      return "Available topics: general, troubleshooting, api, deployment";
-    },
-  });
-
-  return agent({
-    tools: [helpTool],
-    llm: openai({ model: "gpt-4o-mini" }),
-  });
-}
-
-export default async function ChatPage() {
-  const chatAgent = await initializeAgent();
-  
-  return (
-    <div>
-      <h1>Chat Interface</h1>
-      <p>Agent initialized and ready to help!</p>
-      {/* Your chat UI components */}
-    </div>
-  );
-}
-```
-
-## Edge Runtime
-
-The Edge Runtime has limited Node.js API access:
-
-```typescript
-// app/api/chat-edge/route.ts
-import { NextRequest, NextResponse } from "next/server";
-
-export const runtime = "edge";
-
-export async function POST(request: NextRequest) {
-  const { setEnvs } = await import("@llamaindex/env");
-  setEnvs(process.env);
-  
-  try {
-    const { message } = await request.json();
-    
-    const { agent } = await import("@llamaindex/workflow");
-    const { tool } = await import("llamaindex");
-    const { openai } = await import("@llamaindex/openai");
-    const { z } = await import("zod");
-
-    const timeTool = tool({
-      name: "time",
-      description: "Gets current time",
-      parameters: z.object({}),
-      execute: () => new Date().toISOString(),
-    });
-
-    const myAgent = agent({
-      tools: [timeTool],
-      llm: openai({ model: "gpt-4o-mini" }),
-    });
-
-    const result = await myAgent.run(message);
-    return NextResponse.json({ response: result.data });
-  } catch (error) {
-    return NextResponse.json({ error: error.message }, { status: 500 });
-  }
-}
-```
-
-## Streaming Responses
-
-Implement streaming for better user experience:
-
-```typescript
-// app/api/chat-stream/route.ts
-import { agent } from "@llamaindex/workflow";
-import { tool } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-import { agentStreamEvent } from "@llamaindex/workflow";
-import { NextRequest } from "next/server";
-import { z } from "zod";
-
-// Initialize agent once (consider using a singleton pattern)
-let myAgent: any = null;
-
-async function initializeAgent() {
-  if (myAgent) return myAgent;
-  
-  try {
-    const greetTool = tool({
-      name: "greet",
-      description: "Greets a user with their name",
-      parameters: z.object({
-        name: z.string(),
-      }),
-      execute: ({ name }) => `Hello, ${name}! How can I help you today?`,
-    });
-
-    myAgent = agent({
-      tools: [greetTool],
-      llm: openai({ model: "gpt-4o-mini" }),
-    });
-    
-    return myAgent;
-  } catch (error) {
-    console.error("Failed to initialize agent:", error);
-    throw error;
-  }
-}
-
-export async function POST(request: NextRequest) {
-  const { message } = await request.json();
-  
-  const stream = new ReadableStream({
-    async start(controller) {
-      try {
-        const agent = await initializeAgent();
-        const events = agent.runStream(message);
-        
-        for await (const event of events) {
-          if (agentStreamEvent.include(event)) {
-            controller.enqueue(new TextEncoder().encode(event.data.delta));
-          }
-        }
-        
-        controller.close();
-      } catch (error) {
-        controller.error(error);
-      }
-    },
-  });
-  
-  return new Response(stream, {
-    headers: {
-      "Content-Type": "text/plain",
-      "Transfer-Encoding": "chunked",
-    },
-  });
-}
-```
-
-## Client-side Integration
-
-### React Hook for API Calls
-
-```typescript
-// hooks/useAgentChat.ts
-import { useState } from "react";
-
-export function useAgentChat() {
-  const [loading, setLoading] = useState(false);
-  const [error, setError] = useState<string | null>(null);
-  const [response, setResponse] = useState<string | null>(null);
-  
-  const chat = async (message: string) => {
-    setLoading(true);
-    setError(null);
-    
-    try {
-      const res = await fetch("/api/chat", {
-        method: "POST",
-        headers: { "Content-Type": "application/json" },
-        body: JSON.stringify({ message }),
-      });
-      
-      if (!res.ok) {
-        throw new Error(`HTTP error! status: ${res.status}`);
-      }
-      
-      const data = await res.json();
-      setResponse(data.response);
-    } catch (err) {
-      setError(err instanceof Error ? err.message : "An error occurred");
-    } finally {
-      setLoading(false);
-    }
-  };
-  
-  return { chat, loading, error, response };
-}
-```
-
-### Chat Component
-
-```typescript
-// components/ChatInterface.tsx
-"use client";
-
-import { useState } from "react";
-import { useAgentChat } from "@/hooks/useAgentChat";
-
-export default function ChatInterface() {
-  const [message, setMessage] = useState("");
-  const { chat, loading, error, response } = useAgentChat();
-  
-  const handleSubmit = async (e: React.FormEvent) => {
-    e.preventDefault();
-    if (!message.trim()) return;
-    
-    await chat(message);
-    setMessage("");
-  };
-  
-  return (
-    <div className="max-w-2xl mx-auto p-4">
-      <form onSubmit={handleSubmit} className="mb-4">
-        <input
-          type="text"
-          value={message}
-          onChange={(e) => setMessage(e.target.value)}
-          placeholder="Send a message..."
-          className="w-full p-2 border rounded"
-          disabled={loading}
-        />
-        <button
-          type="submit"
-          disabled={loading || !message.trim()}
-          className="mt-2 px-4 py-2 bg-blue-500 text-white rounded disabled:opacity-50"
-        >
-          {loading ? "Thinking..." : "Send"}
-        </button>
-      </form>
-      
-      {error && (
-        <div className="p-3 mb-4 bg-red-100 border border-red-400 text-red-700 rounded">
-          Error: {error}
-        </div>
-      )}
-      
-      {response && (
-        <div className="p-3 bg-gray-100 border rounded">
-          <strong>Agent:</strong>
-          <p>{response}</p>
-        </div>
-      )}
-    </div>
-  );
-}
-```
-
-## Next Steps
-
-- Learn about [serverless deployment](/typescript/framework/getting_started/installation/serverless)
-- Explore [server APIs](/typescript/framework/getting_started/installation/server-apis)
-- Check [troubleshooting guide](/typescript/framework/getting_started/installation/troubleshooting) for common issues 

docs/src/content/docs/framework/getting_started/installation/server-apis.mdx

@@ -1,211 +0,0 @@
----
-title: Server APIs & Backends
-description: Deploy LlamaIndex.TS in server environments like Express, Fastify, and standalone Node.js applications.
----
-
-This guide covers adding LlamaIndex.TS agents to traditional server environments where you have full Node.js runtime access.
-
-## Supported Runtimes
-
-LlamaIndex.TS works seamlessly with:
-
-- **Node.js** (v18+)
-- **Bun** (v1.0+)
-- **Deno** (v1.30+)
-
-## Common Server Frameworks
-
-### Express.js
-
-```typescript
-import express from 'express';
-import { agent } from '@llamaindex/workflow';
-import { tool } from 'llamaindex';
-import { openai } from '@llamaindex/openai';
-import { z } from 'zod';
-
-const app = express();
-app.use(express.json());
-
-// Initialize agent once at startup
-let myAgent: any;
-
-async function initializeAgent() {
-  // Create tools for the agent
-  const sumTool = tool({
-    name: "sum",
-    description: "Adds two numbers",
-    parameters: z.object({
-      a: z.number(),
-      b: z.number(),
-    }),
-    execute: ({ a, b }) => a + b,
-  });
-
-  const multiplyTool = tool({
-    name: "multiply",
-    description: "Multiplies two numbers",
-    parameters: z.object({
-      a: z.number(),
-      b: z.number(),
-    }),
-    execute: ({ a, b }) => a * b,
-  });
-
-  // Create the agent
-  myAgent = agent({
-    tools: [sumTool, multiplyTool],
-    llm: openai({ model: "gpt-4o-mini" }),
-  });
-}
-
-app.post('/api/chat', async (req, res) => {
-  try {
-    const { message } = req.body;
-    const result = await myAgent.run(message);
-    res.json({ response: result.data });
-  } catch (error) {
-    res.status(500).json({ error: 'Chat failed' });
-  }
-});
-
-// Initialize and start server
-initializeAgent().then(() => {
-  app.listen(3000, () => {
-    console.log('Server running on port 3000');
-  });
-});
-```
-
-### Fastify
-
-```typescript
-import Fastify from 'fastify';
-import { agent } from '@llamaindex/workflow';
-import { tool } from 'llamaindex';
-import { openai } from '@llamaindex/openai';
-import { z } from 'zod';
-
-const fastify = Fastify();
-let myAgent: any;
-
-async function initializeAgent() {
-  const sumTool = tool({
-    name: "sum",
-    description: "Adds two numbers",
-    parameters: z.object({
-      a: z.number(),
-      b: z.number(),
-    }),
-    execute: ({ a, b }) => a + b,
-  });
-
-  myAgent = agent({
-    tools: [sumTool],
-    llm: openai({ model: "gpt-4o-mini" }),
-  });
-}
-
-fastify.post('/api/chat', async (request, reply) => {
-  try {
-    const { message } = request.body as { message: string };
-    const result = await myAgent.run(message);
-    return { response: result.data };
-  } catch (error) {
-    reply.status(500).send({ error: 'Chat failed' });
-  }
-});
-
-const start = async () => {
-  await initializeAgent();
-  await fastify.listen({ port: 3000 });
-  console.log('Server running on port 3000');
-};
-
-start();
-```
-
-### Hono
-
-```typescript
-import { Hono } from "hono";
-import { agent } from "@llamaindex/workflow";
-import { tool } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-import { z } from "zod";
-
-type Bindings = {
-  OPENAI_API_KEY: string;
-};
-
-const app = new Hono<{ Bindings: Bindings }>();
-
-app.post("/api/chat", async (c) => {
-  const { setEnvs } = await import("@llamaindex/env");
-  setEnvs(c.env);
-  
-  const { message } = await c.req.json();
-  
-  const greetTool = tool({
-    name: "greet",
-    description: "Greets a user",
-    parameters: z.object({
-      name: z.string(),
-    }),
-    execute: ({ name }) => `Hello, ${name}!`,
-  });
-
-  const myAgent = agent({
-    tools: [greetTool],
-    llm: openai({ model: "gpt-4o-mini" }),
-  });
-  
-  try {
-    const result = await myAgent.run(message);
-    return c.json({ response: result.data });
-  } catch (error) {
-    return c.json({ error: error.message }, 500);
-  }
-});
-
-export default app;
-```
-
-## Streaming Responses
-
-For real-time agent responses:
-
-```typescript
-import { agentStreamEvent } from "@llamaindex/workflow";
-
-app.post('/api/chat-stream', async (req, res) => {
-  const { message } = req.body;
-  
-  res.writeHead(200, {
-    'Content-Type': 'text/plain',
-    'Transfer-Encoding': 'chunked',
-  });
-  
-  try {
-    const events = myAgent.runStream(message);
-    
-    for await (const event of events) {
-      if (agentStreamEvent.include(event)) {
-        res.write(event.data.delta);
-      }
-    }
-    
-    res.end();
-  } catch (error) {
-    res.write('Error: ' + error.message);
-    res.end();
-  }
-});
-```
-
-
-## Next Steps
-
-- Learn about [serverless deployment](/typescript/framework/getting_started/installation/serverless)
-- Explore [Next.js integration](/typescript/framework/getting_started/installation/nextjs)
-- Check [troubleshooting guide](/typescript/framework/getting_started/installation/troubleshooting) for common issues 

docs/src/content/docs/framework/getting_started/installation/serverless.mdx

@@ -1,240 +0,0 @@
----
-title: Serverless Functions
-description: Deploy LlamaIndex.TS in serverless environments like Vercel, Netlify, AWS Lambda, and Cloudflare Workers.
----
-
-This guide covers adding LlamaIndex.TS agents to serverless environments where you have execution time and memory constraints.
-
-## Cloudflare Workers
-
-```typescript
-export default {
-  async fetch(request: Request, env: Env): Promise<Response> {
-    const { setEnvs } = await import("@llamaindex/env");
-    setEnvs(env);
-    
-    const { agent } = await import("@llamaindex/workflow");
-    const { openai } = await import("@llamaindex/openai");
-    const { tool } = await import("llamaindex");
-    const { z } = await import("zod");
-
-    const timeTool = tool({
-      name: "getCurrentTime",
-      description: "Gets the current time",
-      parameters: z.object({}),
-      execute: () => new Date().toISOString(),
-    });
-
-    const myAgent = agent({
-      tools: [timeTool],
-      llm: openai({ model: "gpt-4o-mini" }),
-    });
-
-    try {
-      const { message } = await request.json();
-      const result = await myAgent.run(message);
-      
-      return new Response(JSON.stringify({ response: result.data }), {
-        headers: { "Content-Type": "application/json" },
-      });
-    } catch (error) {
-      return new Response(JSON.stringify({ error: error.message }), {
-        status: 500,
-        headers: { "Content-Type": "application/json" },
-      });
-    }
-  },
-};
-```
-
-
-
-## Vercel Functions
-
-### Node.js Runtime
-
-```typescript
-// pages/api/chat.ts or app/api/chat/route.ts
-import { agent } from "@llamaindex/workflow";
-import { tool } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-import { z } from "zod";
-
-export default async function handler(req, res) {
-  if (req.method !== 'POST') {
-    return res.status(405).json({ error: 'Method not allowed' });
-  }
-  
-  const { message } = req.body;
-  
-  const weatherTool = tool({
-    name: "getWeather",
-    description: "Get weather information",
-    parameters: z.object({
-      city: z.string(),
-    }),
-    execute: ({ city }) => `Weather in ${city}: 72°F, sunny`,
-  });
-
-  const myAgent = agent({
-    tools: [weatherTool],
-    llm: openai({ model: "gpt-4o-mini" }),
-  });
-  
-  try {
-    const result = await myAgent.run(message);
-    res.json({ response: result.data });
-  } catch (error) {
-    res.status(500).json({ error: error.message });
-  }
-}
-```
-
-### Edge Runtime
-
-```typescript
-// app/api/chat/route.ts
-import { NextRequest, NextResponse } from "next/server";
-
-export const runtime = "edge";
-
-export async function POST(request: NextRequest) {
-  const { setEnvs } = await import("@llamaindex/env");
-  setEnvs(process.env);
-  
-  const { message } = await request.json();
-  
-  try {
-    // Use simpler tools for edge runtime
-    const { agent } = await import("@llamaindex/workflow");
-    const { tool } = await import("llamaindex");
-    const { openai } = await import("@llamaindex/openai");
-    const { z } = await import("zod");
-
-    const timeTool = tool({
-      name: "time",
-      description: "Gets current time",
-      parameters: z.object({}),
-      execute: () => new Date().toISOString(),
-    });
-
-    const myAgent = agent({
-      tools: [timeTool],
-      llm: openai({ model: "gpt-4o-mini" }),
-    });
-
-    const result = await myAgent.run(message);
-    return NextResponse.json({ response: result.data });
-  } catch (error) {
-    return NextResponse.json({ error: error.message }, { status: 500 });
-  }
-}
-```
-
-## AWS Lambda
-
-```typescript
-import { APIGatewayProxyHandler } from "aws-lambda";
-import { agent } from "@llamaindex/workflow";
-import { tool } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-import { z } from "zod";
-
-export const handler: APIGatewayProxyHandler = async (event, context) => {
-  const { message } = JSON.parse(event.body || "{}");
-  
-  const calculatorTool = tool({
-    name: "calculate",
-    description: "Performs basic math",
-    parameters: z.object({
-      expression: z.string(),
-    }),
-    execute: ({ expression }) => {
-      // Simple calculator implementation
-      try {
-        return `Result: ${eval(expression)}`;
-      } catch {
-        return "Invalid expression";
-      }
-    },
-  });
-
-  const myAgent = agent({
-    tools: [calculatorTool],
-    llm: openai({ model: "gpt-4o-mini" }),
-  });
-  
-  try {
-    const result = await myAgent.run(message);
-    
-    return {
-      statusCode: 200,
-      headers: {
-        "Content-Type": "application/json",
-        "Access-Control-Allow-Origin": "*",
-      },
-      body: JSON.stringify({ response: result.data }),
-    };
-  } catch (error) {
-    return {
-      statusCode: 500,
-      body: JSON.stringify({ error: error.message }),
-    };
-  }
-};
-```
-
-## Netlify Functions
-
-```typescript
-// netlify/functions/chat.ts
-import { Handler } from "@netlify/functions";
-import { agent } from "@llamaindex/workflow";
-import { tool } from "llamaindex";
-import { openai } from "@llamaindex/openai";
-import { z } from "zod";
-
-export const handler: Handler = async (event, context) => {
-  if (event.httpMethod !== "POST") {
-    return { statusCode: 405, body: "Method Not Allowed" };
-  }
-  
-  const { message } = JSON.parse(event.body || "{}");
-  
-  const helpTool = tool({
-    name: "help",
-    description: "Provides help information",
-    parameters: z.object({
-      topic: z.string().optional(),
-    }),
-    execute: ({ topic }) => {
-      return topic ? `Help for ${topic}` : "Available help topics";
-    },
-  });
-
-  const myAgent = agent({
-    tools: [helpTool],
-    llm: openai({ model: "gpt-4o-mini" }),
-  });
-  
-  try {
-    const result = await myAgent.run(message);
-    
-    return {
-      statusCode: 200,
-      body: JSON.stringify({ response: result.data }),
-    };
-  } catch (error) {
-    return {
-      statusCode: 500,
-      body: JSON.stringify({ error: error.message }),
-    };
-  }
-};
-```
-
-## Next Steps
-
-- Learn about [Next.js integration](/typescript/framework/getting_started/installation/nextjs)
-- Explore [server deployment](/typescript/framework/getting_started/installation/server-apis)
-- Check [troubleshooting guide](/typescript/framework/getting_started/installation/troubleshooting) for common issues 

docs/src/content/docs/framework/getting_started/installation/troubleshooting.mdx

@@ -1,501 +0,0 @@
----
-title: Troubleshooting
-description: Common issues and solutions when installing and deploying LlamaIndex.TS applications.
----
-
-This guide addresses common issues you might encounter when installing and deploying LlamaIndex.TS applications across different environments.
-
-## Installation Issues
-
-### Module Resolution Errors
-
-**Problem:** Import errors or module not found errors
-
-**Solution:** Ensure your `tsconfig.json` is properly configured:
-
-```json5
-{
-  "compilerOptions": {
-    "moduleResolution": "bundler", // or "nodenext" | "node16" | "node"
-    "lib": ["DOM.AsyncIterable"],
-    "target": "es2020",
-    "module": "esnext"
-  }
-}
-```
-
-**Alternative solution:** Try different module resolution strategies:
-
-```bash
-# Clear node_modules and reinstall
-rm -rf node_modules package-lock.json
-npm install
-
-# Or try with different package manager
-pnpm install
-# or
-yarn install
-```
-
-### TypeScript Errors
-
-**Problem:** TypeScript compilation errors with LlamaIndex imports
-
-**Solution:** Ensure you have the correct TypeScript configuration:
-
-```json5
-{
-  "compilerOptions": {
-    "strict": true,
-    "skipLibCheck": true, // Skip type checking of node_modules
-    "allowSyntheticDefaultImports": true,
-    "esModuleInterop": true
-  }
-}
-```
-
-### Package Compatibility Issues
-
-**Problem:** Some packages don't work in certain environments
-
-**Common incompatibilities:**
-- `@llamaindex/readers` - May not work in serverless environments
-- `@llamaindex/huggingface` - Limited browser/edge compatibility
-- File system readers - Don't work in browser/edge environments
-
-**Solution:** Use environment-specific alternatives:
-
-```typescript
-// Instead of file system readers in serverless
-// Use remote data sources
-async function loadDocumentsFromAPI() {
-  const response = await fetch('https://api.example.com/documents');
-  const data = await response.json();
-  return data.map(doc => new Document(doc.content));
-}
-```
-
-## Runtime Issues
-
-### Memory Errors
-
-**Problem:** Out of memory errors during index creation or querying
-
-**Solution:** Optimize memory usage:
-
-```typescript
-// Batch process large document sets
-async function batchProcessDocuments(documents: Document[], batchSize = 10) {
-  const results = [];
-  
-  for (let i = 0; i < documents.length; i += batchSize) {
-    const batch = documents.slice(i, i + batchSize);
-    const batchIndex = await VectorStoreIndex.fromDocuments(batch);
-    results.push(batchIndex);
-    
-    // Optional: Add delay between batches
-    await new Promise(resolve => setTimeout(resolve, 100));
-  }
-  
-  return results;
-}
-```
-
-**For serverless environments:**
-
-```typescript
-// Use external vector stores instead of in-memory
-// TODO: Example with Pinecone, Weaviate, etc.
-// const vectorStore = new PineconeVectorStore(/* config */);
-// const index = await VectorStoreIndex.fromVectorStore(vectorStore);
-```
-
-### API Rate Limiting
-
-**Problem:** Rate limiting errors from LLM providers
-
-**Solution:** Implement retry logic with exponential backoff:
-
-```typescript
-async function queryWithRetry(queryEngine: any, question: string, maxRetries = 3) {
-  for (let i = 0; i < maxRetries; i++) {
-    try {
-      return await queryEngine.query(question);
-    } catch (error) {
-      if (error.message.includes('rate limit') && i < maxRetries - 1) {
-        const delay = Math.pow(2, i) * 1000; // Exponential backoff
-        await new Promise(resolve => setTimeout(resolve, delay));
-        continue;
-      }
-      throw error;
-    }
-  }
-}
-```
-
-### Tokenization Performance
-
-**Problem:** Slow tokenization affecting performance
-
-**Solution:** Install faster tokenizer (Node.js only):
-
-```bash
-npm install gpt-tokenizer
-```
-
-LlamaIndex will automatically use this for 60x faster tokenization.
-
-## Bundling Issues
-
-### Bundle Size Too Large
-
-**Problem:** Large bundle sizes affecting performance
-
-**Solution:** Use dynamic imports and code splitting:
-
-```typescript
-// Lazy load LlamaIndex components
-const initializeLlamaIndex = async () => {
-  const { VectorStoreIndex, SimpleDirectoryReader } = await import("llamaindex");
-  return { VectorStoreIndex, SimpleDirectoryReader };
-};
-
-// In your API route
-export async function POST(request: NextRequest) {
-  const { VectorStoreIndex, SimpleDirectoryReader } = await initializeLlamaIndex();
-  // Use the imported modules
-}
-```
-
-### Webpack/Vite Bundling Issues
-
-**Problem:** Bundler compatibility issues
-
-**Solution for Next.js:**
-
-```javascript
-// next.config.mjs
-import withLlamaIndex from "llamaindex/next";
-
-const nextConfig = {
-  webpack: (config, { isServer }) => {
-    // Custom webpack configuration if needed
-    if (!isServer) {
-      config.resolve.fallback = {
-        ...config.resolve.fallback,
-        fs: false,
-        net: false,
-        tls: false,
-      };
-    }
-    return config;
-  },
-};
-
-export default withLlamaIndex(nextConfig);
-```
-
-**Solution for Vite:**
-
-```typescript
-// vite.config.ts
-import { defineConfig } from 'vite';
-
-export default defineConfig({
-  define: {
-    global: 'globalThis',
-  },
-  resolve: {
-    alias: {
-      // Add aliases for problematic modules
-    },
-  },
-  optimizeDeps: {
-    include: ['llamaindex'],
-  },
-});
-```
-
-## Environment-Specific Issues
-
-### Node.js Version Compatibility
-
-**Problem:** Node.js version compatibility issues
-
-**Solution:** Use supported Node.js versions:
-
-```json
-{
-  "engines": {
-    "node": ">=18.0.0"
-  }
-}
-```
-
-**Check your Node.js version:**
-
-```bash
-node --version
-```
-
-### Cloudflare Workers Issues
-
-**Problem:** Module not available in Cloudflare Workers
-
-**Solution:** Use `@llamaindex/env` for environment compatibility:
-
-```typescript
-export default {
-  async fetch(request: Request, env: Env): Promise<Response> {
-    const { setEnvs } = await import("@llamaindex/env");
-    setEnvs(env);
-    
-    // Your LlamaIndex code here
-  },
-};
-```
-
-### Vercel Edge Runtime Issues
-
-**Problem:** Limited Node.js API access in Edge Runtime
-
-**Solution:** Use standard runtime or adapt code:
-
-```typescript
-// Force standard runtime
-export const runtime = "nodejs";
-
-// Or adapt for edge
-export const runtime = "edge";
-
-export async function POST(request: NextRequest) {
-  // Use edge-compatible code only
-  const { setEnvs } = await import("@llamaindex/env");
-  setEnvs(process.env);
-  
-  // Avoid file system operations
-  // Use remote data sources
-}
-```
-
-## Performance Issues
-
-### Slow Query Responses
-
-**Problem:** Slow query performance
-
-**Solution:** Implement caching and optimization:
-
-```typescript
-import { LRUCache } from 'lru-cache';
-
-const queryCache = new LRUCache<string, string>({
-  max: 100,
-  ttl: 1000 * 60 * 10, // 10 minutes
-});
-
-export async function optimizedQuery(question: string, queryEngine: any) {
-  // Check cache first
-  const cached = queryCache.get(question);
-  if (cached) return cached;
-  
-  // Query and cache result
-  const result = await queryEngine.query(question);
-  queryCache.set(question, result);
-  
-  return result;
-}
-```
-
-### Cold Start Issues
-
-**Problem:** Slow cold starts in serverless environments
-
-**Solution:** Pre-warm your functions:
-
-```typescript
-// Pre-initialize outside handler
-let cachedQueryEngine: any = null;
-
-export async function handler(event: any) {
-  if (!cachedQueryEngine) {
-    cachedQueryEngine = await initializeQueryEngine();
-  }
-  
-  // Use cached engine
-  return await cachedQueryEngine.query(question);
-}
-```
-
-## Environment Variable Issues
-
-### Missing API Keys
-
-**Problem:** API key not found or invalid
-
-**Solution:** Verify environment variable setup:
-
-```typescript
-// Check if API key is available
-if (!process.env.OPENAI_API_KEY) {
-  throw new Error('OPENAI_API_KEY environment variable is required');
-}
-
-// For debugging (remove in production)
-console.log('API Key present:', !!process.env.OPENAI_API_KEY);
-```
-
-### Environment Variable Loading
-
-**Problem:** Environment variables not loading correctly
-
-**Solution:** Use proper loading mechanisms:
-
-```typescript
-// For Node.js
-import 'dotenv/config';
-
-// For Next.js - use .env.local
-// Variables are automatically loaded
-
-// For Cloudflare Workers
-export default {
-  async fetch(request: Request, env: Env): Promise<Response> {
-    // Use env parameter, not process.env
-    const apiKey = env.OPENAI_API_KEY;
-    // ...
-  },
-};
-```
-
-## Common Error Messages
-
-### "Cannot find module 'llamaindex'"
-
-**Cause:** Package not installed or module resolution issue
-
-**Solution:**
-```bash
-npm install llamaindex
-```
-
-### "Module not found: Can't resolve 'fs'"
-
-**Cause:** File system modules used in browser/edge environment
-
-**Solution:**
-```typescript
-// Use dynamic imports with fallbacks
-const loadDocuments = async () => {
-  if (typeof window !== 'undefined') {
-    // Browser environment - use alternative
-    return await loadDocumentsFromAPI();
-  } else {
-    // Node.js environment - use file system
-    const { SimpleDirectoryReader } = await import('llamaindex');
-    return await new SimpleDirectoryReader('data').loadData();
-  }
-};
-```
-
-### "ReferenceError: global is not defined"
-
-**Cause:** Global polyfill missing in browser environments
-
-**Solution:**
-```typescript
-// Add to your app entry point
-if (typeof global === 'undefined') {
-  global = globalThis;
-}
-```
-
-### "Cannot read properties of undefined (reading 'query')"
-
-**Cause:** Query engine not properly initialized
-
-**Solution:**
-```typescript
-// Always check initialization
-if (!queryEngine) {
-  throw new Error('Query engine not initialized');
-}
-
-// Or use optional chaining
-const response = await queryEngine?.query(question);
-```
-
-## Debugging Tips
-
-### Enable Debug Logging
-
-```typescript
-// Enable debug logging
-process.env.DEBUG = "llamaindex:*";
-
-// Or specific modules
-process.env.DEBUG = "llamaindex:vector-store";
-```
-
-### Check Package Versions
-
-```bash
-npm list llamaindex
-npm list @llamaindex/openai
-```
-
-### Test in Isolation
-
-```typescript
-// Create minimal test case
-import { VectorStoreIndex } from 'llamaindex';
-
-async function testBasic() {
-  try {
-    console.log('Testing basic import...');
-    const index = new VectorStoreIndex();
-    console.log('Success!');
-  } catch (error) {
-    console.error('Error:', error);
-  }
-}
-
-testBasic();
-```
-
-## Getting Help
-
-### Before Asking for Help
-
-1. **Check this troubleshooting guide**
-2. **Search existing GitHub issues**
-3. **Try minimal reproduction**
-4. **Check your environment configuration**
-
-### When Reporting Issues
-
-Include:
-- Node.js version (`node --version`)
-- Package versions (`npm list llamaindex`)
-- Environment (Node.js, Cloudflare Workers, Vercel, etc.)
-- Minimal code reproduction
-- Full error message and stack trace
-
-### Useful Resources
-
-- [GitHub Issues](https://github.com/run-llama/LlamaIndexTS/issues)
-- [Discord Community](https://discord.gg/dGcwcsnxhU)
-- [Documentation](https://docs.llamaindex.ai/)
-
-## Next Steps
-
-If you're still experiencing issues:
-
-1. **Check specific deployment guides:**
-   - [Server APIs](/typescript/framework/getting_started/installation/server-apis)
-   - [Serverless Functions](/typescript/framework/getting_started/installation/serverless)
-   - [Next.js Applications](/typescript/framework/getting_started/installation/nextjs)
-
-2. **Open an issue** on GitHub with a minimal reproduction
-
-3. **Join our Discord** for community support 

docs/src/content/docs/framework/index.mdx

@@ -1,118 +0,0 @@
----
-title: Welcome to LlamaIndex.TS
-description: LlamaIndex.TS is the leading framework for utilizing context engineering to build LLM applications in JavaScript and TypeScript.
----
-
-LlamaIndex.TS is a **framework for utilizing context engineering to build generative AI applications** with large language models. From rapid-prototyping RAG chatbots to deploying multi-agent workflows in production, LlamaIndex gives you everything you need — all in idiomatic TypeScript.
-
-Built for modern JavaScript runtimes like <SiNodedotjs className="inline" color="#5FA04E" /> **Node.js**, <SiDeno className="inline" color="#70FFAF" /> **Deno**, <SiBun className="inline" /> **Bun**, <SiCloudflareworkers className="inline" color="#F38020" /> **Cloudflare Workers**, and more.
-
-<div className="grid grid-cols-1 gap-4 sm:grid-cols-2 lg:grid-cols-3 my-6">
-  <a href="#introduction" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
-    <h3 className="mb-1 text-lg font-semibold underline">Introduction</h3>
-    <p className="text-sm text-gray-400 no-underline">Context engineering, agents &amp; workflows — what do they mean?</p>
-  </a>
-
-  <a href="#use-cases" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
-    <h3 className="mb-1 text-lg font-semibold underline">Use cases</h3>
-    <p className="text-sm text-gray-400 no-underline">See what you can build with LlamaIndex.TS.</p>
-  </a>
-
-  <a href="#getting-started" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
-    <h3 className="mb-1 text-lg font-semibold underline">Getting started</h3>
-    <p className="text-sm text-gray-400 no-underline">Your first app in 5 lines of code.</p>
-  </a>
-
-  <a href="https://docs.cloud.llamaindex.ai/" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline" target="_blank" rel="noopener noreferrer">
-    <h3 className="mb-1 text-lg font-semibold underline">LlamaCloud</h3>
-    <p className="text-sm text-gray-400 no-underline">Managed parsing, extraction &amp; retrieval pipelines.</p>
-  </a>
-
-  <a href="#community" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
-    <h3 className="mb-1 text-lg font-semibold underline">Community</h3>
-    <p className="text-sm text-gray-400 no-underline">Join thousands of builders on Discord, Twitter, and more.</p>
-  </a>
-
-  <a href="#related-projects" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
-    <h3 className="mb-1 text-lg font-semibold underline">Related projects</h3>
-    <p className="text-sm text-gray-400 no-underline">Connectors, demos &amp; starter kits.</p>
-  </a>
-</div>
-
-## Introduction
-
-### What are agents?
-
-[Agents](/typescript/framework/tutorials/agents/1_setup) are LLM-powered assistants that can reason, use external tools, and take actions to accomplish tasks such as research, data extraction, and automation. 
-LlamaIndex.TS provides foundational building blocks for creating and orchestrating these agents.
-
-### What are workflows?
-
-[Workflows](/typescript/framework/tutorials/workflows) are multi-step, event-driven processes that combine agents, data connectors, and other tools to solve complex problems. 
-With LlamaIndex.TS you can chain together retrieval, generation, and tool-calling steps and then deploy the entire pipeline as a microservice.
-
-### What is context engineering?
-
-LLMs come pre-trained on vast public corpora, but not on **your** private or domain-specific data. 
-Context engineering bridges that gap by injecting the right pieces of your data into the LLM prompt at the right time. 
-The most popular example is [Retrieval-Augmented Generation (RAG)](/typescript/framework/getting_started/concepts), but the same idea powers agent memory, evaluation, extraction, summarisation, and more.
-
-LlamaIndex.TS gives you:
-
-- **Data connectors** to ingest from APIs, files, SQL, and dozens more sources.
-- **Indexes & retrievers** to store and retrieve your data for LLM consumption.
-- **Agents and Engines** to query and use chat+reasoning interfaces over your data.
-- **Workflows** for fine-grained orchestration of your data and LLM-powered agents.
-- **Observability** integrations so you can iterate with confidence.
-
-You can learn more about these concepts in our [concepts guide](/typescript/framework/getting_started/concepts).
-
-## Use cases
-
-Popular scenarios include:
-
-- [LLM-Powered Agents](/typescript/framework/tutorials/agents/1_setup)
-- [Indexing and Retrieval](/typescript/framework/tutorials/rag)
-- [Extracting Structured Data](/typescript/framework/tutorials/structured_data_extraction)
-- [Custom Orchestration with Workflows](/typescript/framework/tutorials/workflows)
-
-## Getting started
-
-The fastest way to get started is in StackBlitz below — no local setup required:
-
-<iframe
-  className="w-full h-[440px]"
-  aria-label="LlamaIndex.TS Starter"
-  aria-description="Interactive starter for LlamaIndex.TS"
-  src="https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples?embed=1&file=starter.ts"
-/>
-
-Want to learn more? We have several tutorials to get you started:
-
-- [Installation + Runtime Guide](/typescript/framework/getting_started/installation)
-- [Create your first agent](/typescript/framework/tutorials/agents/1_setup)
-- [Learn how to index data and chat with it](/typescript/framework/tutorials/rag)
-- [Learn how to write your own workflows and agents](/typescript/framework/tutorials/workflows)
-
----
-
-## LlamaCloud
-
-Need an end-to-end managed pipeline? Check out **[LlamaCloud](https://cloud.llamaindex.ai/)**: best-in-class document parsing (LlamaParse), extraction (LlamaExtract), and indexing services with generous free tiers.
-
----
-
-## Community
-
-- [Twitter](https://twitter.com/llama_index)
-- [Discord](https://discord.gg/dGcwcsnxhU)
-- [LinkedIn](https://www.linkedin.com/company/llamaindex/)
-
-We 💜 contributors! View our [contributing guide](https://github.com/run-llama/LlamaIndexTS/blob/main/CONTRIBUTING.md) to get started.
-
-## Related projects
-
-- [Python framework GitHub](https://github.com/run-llama/llama_index)
-- [Python docs](https://docs.llamaindex.ai/)
-- [create-llama](https://www.npmjs.com/package/create-llama) — scaffold a new project in seconds!
-- [UI Components](https://ui.llamaindex.ai/) — build chat applications with our Next.js components.

docs/src/content/docs/framework/integration/_meta.yml

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-label: Integration
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