changeset

.changeset/config.json

@@ -1,7 +1,7 @@
 {
   "$schema": "https://unpkg.com/@changesets/config@2.3.1/schema.json",
   "changelog": "@changesets/cli/changelog",
-  "commit": false,
+  "commit": true,
   "fixed": [],
   "linked": [],
   "access": "public",

.changeset/fuzzy-snails-applaud.md

@@ -0,0 +1,5 @@
+---
+"llamaindex": patch
+---
+
+feat: add filtering of metadata to PGVectorStore

.changeset/olive-wolves-own.md

@@ -0,0 +1,5 @@
+---
+"llamaindex": patch
+---
+
+feat(reranker): cohere reranker

.changeset/tame-ways-applaud.md

@@ -0,0 +1,5 @@
+---
+"llamaindex": patch
+---
+
+feat: use batching in vector store index

.changeset/ten-pears-study.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+update fastapi for CVE-2024-24762

.changeset/violet-cows-fix.md

@@ -0,0 +1,5 @@
+---
+"llamaindex": patch
+---
+
+Add reader for LlamaParse

.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 +1,14 @@
+module.exports = {
+  root: true,
+  // This tells ESLint to load the config from the package `eslint-config-custom`
+  extends: ["custom"],
+  settings: {
+    next: {
+      rootDir: ["apps/*/"],
+    },
+  },
+  rules: {
+    "max-params": ["error", 4],
+  },
+  ignorePatterns: ["dist/"],
+};

.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/workflows/e2e.yml

@@ -0,0 +1,68 @@
+name: E2E Tests
+on:
+  push:
+    branches: [main]
+  pull_request:
+    paths:
+      - "packages/create-llama/**"
+      - ".github/workflows/e2e.yml"
+    branches: [main]
+
+env:
+  POETRY_VERSION: "1.6.1"
+
+jobs:
+  e2e:
+    name: create-llama
+    timeout-minutes: 60
+    strategy:
+      fail-fast: true
+      matrix:
+        node-version: [18, 20]
+        python-version: ["3.11"]
+        os: [macos-latest, windows-latest]
+    defaults:
+      run:
+        shell: bash
+    runs-on: ${{ matrix.os }}
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install Poetry
+        uses: snok/install-poetry@v1
+        with:
+          version: ${{ env.POETRY_VERSION }}
+      - uses: pnpm/action-setup@v2
+      - name: Setup Node.js ${{ matrix.node-version }}
+        uses: actions/setup-node@v4
+        with:
+          node-version: ${{ matrix.node-version }}
+          cache: "pnpm"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Install Playwright Browsers
+        run: pnpm exec playwright install --with-deps
+        working-directory: ./packages/create-llama
+      - name: Build create-llama
+        run: pnpm run build
+        working-directory: ./packages/create-llama
+      - name: Pack
+        run: pnpm pack --pack-destination ./output
+        working-directory: ./packages/create-llama
+      - name: Extract Pack
+        run: tar -xvzf ./output/*.tgz -C ./output
+        working-directory: ./packages/create-llama
+      - name: Run Playwright tests
+        run: pnpm exec playwright test
+        env:
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+        working-directory: ./packages/create-llama
+      - uses: actions/upload-artifact@v3
+        if: always()
+        with:
+          name: playwright-report
+          path: ./packages/create-llama/playwright-report/
+          retention-days: 30

.github/workflows/lint_on_push_or_pull.yml

@@ -13,7 +13,9 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@v4
+      - uses: pnpm/action-setup@v2
+        with:
+          version: latest
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:

.github/workflows/preview.yml

@@ -1,28 +0,0 @@
-name: Publish Preview
-on: [pull_request]
-
-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

@@ -1,68 +1,18 @@
 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: [18.x, 20.x, 22.x, 23.x]
-    name: E2E on Node.js ${{ matrix.node-version }}
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: 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: [18.x, 20.x, 22.x, 23.x]
-    name: Test on Node.js ${{ matrix.node-version }}
     runs-on: ubuntu-latest
+
     steps:
       - uses: actions/checkout@v4
-      - uses: pnpm/action-setup@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
@@ -73,7 +23,7 @@ jobs:
 
     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:
@@ -82,46 +32,24 @@ jobs:
       - name: Install dependencies
         run: pnpm install
       - name: Build
-        run: pnpm run build
+        run: pnpm run build --filter llamaindex
       - name: Run Type Check
         run: pnpm run type-check
       - name: Run Circular Dependency Check
         run: pnpm run circular-check
-  e2e-llamaindex-examples:
-    strategy:
-      fail-fast: false
-      matrix:
-        packages:
-          - cloudflare-worker-agent
-          - nextjs-agent
-          - nextjs-edge-runtime
-          - nextjs-node-runtime
-          - waku-query-engine
-          - llama-parse-browser
-    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
+        working-directory: ./packages/core
+      - uses: actions/upload-artifact@v3
+        if: failure()
         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 }}
-
+          name: typecheck-build-dist
+          path: ./packages/core/dist
+          if-no-files-found: error
   typecheck-examples:
     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:
@@ -130,39 +58,15 @@ jobs:
       - name: Install dependencies
         run: pnpm install
       - name: Build
-        run: pnpm run build
+        run: pnpm run build --filter llamaindex
       - 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
+      - name: Pack
+        run: pnpm pack --pack-destination ${{ runner.temp }}
+        working-directory: packages/core
+      - name: Install llamaindex
         run: npm add ${{ runner.temp }}/*.tgz
         working-directory: ${{ runner.temp }}/examples
       - name: Run Type Check
         run: npx tsc --project ./tsconfig.json
         working-directory: ${{ runner.temp }}/examples
-      - uses: actions/upload-artifact@v4
-        if: failure()
-        with:
-          name: build-dist
-          path: |
-            ${{ runner.temp }}/*.tgz
-          if-no-files-found: error

.gitignore

@@ -44,10 +44,6 @@ test-results/
 playwright-report/
 blob-report/
 playwright/.cache/
-.tsbuildinfo
 
 # intellij
 **/.idea
-
-# generated API
-packages/cloud/src/client

.husky/pre-commit

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

.husky/pre-push

@@ -0,0 +1 @@
+pnpm test

.npmrc

@@ -1,5 +1,2 @@
 auto-install-peers = true
 enable-pre-post-scripts = true
-prefer-workspace-packages = true
-save-workspace-protocol = true
-link-workspace-packages = true

.nvmrc

@@ -1 +1 @@
-20
+18

.prettierignore

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

.swcrc

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

.vscode/launch.json

@@ -10,9 +10,8 @@
       "name": "Debug Example",
       "skipFiles": ["<node_internals>/**"],
       "runtimeExecutable": "pnpm",
-      "console": "integratedTerminal",
       "cwd": "${workspaceFolder}/examples",
-      "runtimeArgs": ["npx", "tsx", "${file}"]
+      "runtimeArgs": ["ts-node", "${fileBasename}"]
     }
   ]
 }

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

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

.vscode/settings.json

@@ -5,6 +5,7 @@
   "[xml]": {
     "editor.defaultFormatter": "redhat.vscode-xml"
   },
+  "jest.rootPath": "./packages/core",
   "[python]": {
     "editor.defaultFormatter": "ms-python.black-formatter"
   },
@@ -13,7 +14,5 @@
   },
   "[json]": {
     "editor.defaultFormatter": "esbenp.prettier-vscode"
-  },
-  "prettier.prettierPath": "./node_modules/prettier",
-  "prettier.configPath": "prettier.config.mjs"
+  }
 }

CONTRIBUTING.md

@@ -2,71 +2,91 @@
 
 ## 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...
-- `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.
+examples 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 LIS (Long-term Support) installed. You can check your Node.js version by running:
+Install NodeJS. Preferably v18 using nvm or n.
+
+Inside the LlamaIndexTS directory:
 
-```shell
-node -v
-# v20.x.x
 ```
-
-### Use pnpm
-
-```shell
-corepack enable
-```
-
-### Install dependencies
-
-```shell
+npm i -g pnpm ts-node
 pnpm install
 ```
 
-### Build the packages
+Note: we use pnpm in this repo, which has a lot of the same functionality and CLI options as npm but it does do some things better in a monorepo, like centralizing dependencies and caching.
 
-```shell
-# Build all packages
-turbo build --filter "./packages/*"
+PNPM's has documentation on its [workspace feature](https://pnpm.io/workspaces) and Turborepo had some [useful documentation also](https://turbo.build/repo/docs/core-concepts/monorepos/running-tasks).
+
+### Running Typescript
+
+When we publish to NPM we will have a tsc compiled version of the library in JS. For now, the easiest thing to do is use ts-node.
+
+### Test cases
+
+To run them, run
+
+```
+pnpm run test
+```
+
+To write new test cases write them in [packages/core/src/tests](/packages/core/src/tests)
+
+We use Jest https://jestjs.io/ to write our test cases. Jest comes with a bunch of built in assertions using the expect function: https://jestjs.io/docs/expect
+
+### Demo applications
+
+There is an existing ["example"](/examples/README.md) demos folder with mainly NodeJS scripts. Feel free to add additional demos to that folder. If you would like to try out your changes in the core package with a new demo, you need to run the build command in the README.
+
+You can create new demo applications in the apps folder. Just run pnpm init in the folder after you create it to create its own package.json
+
+### Installing packages
+
+To install packages for a specific package or demo application, run
+
+```
+pnpm add [NPM Package] --filter [package or application i.e. core or docs]
+```
+
+To install packages for every package or application run
+
+```
+pnpm add -w [NPM Package]
 ```
 
 ### Docs
 
-See the [docs](./apps/next/README.md) for more information.
+To contribute to the docs, go to the docs website folder and run the Docusaurus instance.
 
-## Changeset
-
-We use [changesets](https://github.com/changesets/changesets) for managing versions and changelogs. To create a new
-changeset, run in the root folder:
-
-```
-pnpm changeset
+```bash
+cd apps/docs
+pnpm install
+pnpm start
 ```
 
-Please send a descriptive changeset for each PR.
+That should start a webserver which will serve the docs on https://localhost:3000
 
-## Publishing (maintainers only)
+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.
 
-The [Release Github Action](.github/workflows/release.yml) is automatically generating and updating a
-PR called "Release {version}".
+## Publishing
 
-This PR will update the `package.json` and `CHANGELOG.md` files of each package according to
-the current changesets in the [.changeset](.changeset) folder.
+To publish a new version of the library, run
 
-If this PR is merged it will automatically add version tags to the repository and publish the updated packages to NPM.
+```shell
+pnpm new-llamaindex
+pnpm new-create-llama
+pnpm release
+git push # push to the main branch
+git push --tags
+```

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

@@ -1,17 +1,13 @@
-<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://www.npmjs.com/package/llamaindex)
 [![NPM Downloads](https://img.shields.io/npm/dm/llamaindex)](https://www.npmjs.com/package/llamaindex)
 [![Discord](https://img.shields.io/discord/1059199217496772688)](https://discord.com/invite/eN6D2HQ4aX)
 
-Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in JS runtime environments with TypeScript support.
+LlamaIndex is a data framework for your LLM application.
+
+Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.
 
 Documentation: https://ts.llamaindex.ai/
 
@@ -23,58 +19,58 @@ Try examples online:
 
 LlamaIndex.TS aims to be a lightweight, easy to use set of libraries to help you integrate large language models into your applications with your own data.
 
-## Compatibility
+## Getting started with an example:
 
-### Multiple JS Environment Support
+LlamaIndex.TS requires 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({
+    query: "What did the author do in college?",
+  });
+
+  // Output response
+  console.log(response.toString());
+}
+
+main();
+```
+
+Then you can run it using
+
+```bash
+pnpm dlx ts-node example.ts
 ```
 
 ## Playground
@@ -83,25 +79,59 @@ Check out our NextJS playground at https://llama-playground.vercel.app/. The sou
 
 ## Core concepts for getting started:
 
-- [Document](/packages/llamaindex/src/Node.ts): A document represents a text file, PDF file or other contiguous piece of data.
+- [Document](/packages/core/src/Node.ts): A document represents a text file, PDF file or other contiguous piece of data.
 
-- [Node](/packages/llamaindex/src/Node.ts): The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
+- [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/llamaindex/src/embeddings/OpenAIEmbedding.ts): Embeddings are sets of floating point numbers which represent the data in a Node. By comparing the similarity of embeddings, we can derive an understanding of the similarity of two pieces of data. One use case is to compare the embedding of a question with the embeddings of our Nodes to see which Nodes may contain the data needed to answer that question. Because the default service context is OpenAI, the default embedding is `OpenAIEmbedding`. If using different models, say through Ollama, use this [Embedding](/packages/llamaindex/src/embeddings/OllamaEmbedding.ts) (see all [here](/packages/llamaindex/src/embeddings)).
+- [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/llamaindex/src/indices/): Indices store the Nodes and the embeddings of those nodes. QueryEngines retrieve Nodes from these Indices using embedding similarity.
+- [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/llamaindex/src/engines/query/RetrieverQueryEngine.ts): Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected Nodes from your Index to give the LLM the context it needs to answer your query. To build a query engine from your Index (recommended), use the [`asQueryEngine`](/packages/llamaindex/src/indices/BaseIndex.ts) method on your Index. See all query engines [here](/packages/llamaindex/src/engines/query).
+- [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/llamaindex/src/engines/chat/SimpleChatEngine.ts): A ChatEngine helps you build a chatbot that will interact with your Indices. See all chat engines [here](/packages/llamaindex/src/engines/chat).
+- [ChatEngine](/packages/core/src/ChatEngine.ts): A ChatEngine helps you build a chatbot that will interact with your Indices.
 
-- [SimplePrompt](/packages/llamaindex/src/Prompt.ts): A simple standardized function call definition that takes in inputs and formats them in a template literal. SimplePrompts can be specialized using currying and combined using other SimplePrompt functions.
+- [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 following 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: ["pdf2json"],
+  },
+  webpack: (config) => {
+    config.resolve.alias = {
+      ...config.resolve.alias,
+      sharp$: false,
+      "onnxruntime-node$": false,
+    };
+    return config;
+  },
+};
+
+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)
+- MistralAI Chat LLMs
 
 ## 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 +1,21 @@
+# Dependencies
+/node_modules
+
+# Production
+/build
+
+# Generated files
+.docusaurus
+.cache-loader
+lib
+
+# Misc
+.DS_Store
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*

apps/docs/CHANGELOG.md

@@ -0,0 +1,13 @@
+# docs
+
+## 0.0.2
+
+### Patch Changes
+
+- 0f64084: docs: update API references
+
+## 0.0.1
+
+### Patch Changes
+
+- 3154f52: chore: add qdrant readme

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/examples/_category_.yml

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

apps/docs/docs/examples/agent.mdx

@@ -0,0 +1,85 @@
+# Agents
+
+A built-in agent that can take decisions and reasoning based on the tools provided to it.
+
+## OpenAI Agent
+
+```ts
+import { FunctionTool, OpenAIAgent } from "llamaindex";
+
+// Define a function to sum two numbers
+function sumNumbers({ a, b }: { a: number; b: number }): number {
+  return a + b;
+}
+
+// Define a function to divide two numbers
+function divideNumbers({ a, b }: { a: number; b: number }): number {
+  return a / b;
+}
+
+// Define the parameters of the sum function as a JSON schema
+const sumJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The first number",
+    },
+    b: {
+      type: "number",
+      description: "The second number",
+    },
+  },
+  required: ["a", "b"],
+};
+
+// Define the parameters of the divide function as a JSON schema
+const divideJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The dividend to divide",
+    },
+    b: {
+      type: "number",
+      description: "The divisor to divide by",
+    },
+  },
+  required: ["a", "b"],
+};
+
+async function main() {
+  // Create a function tool from the sum function
+  const sumFunctionTool = new FunctionTool(sumNumbers, {
+    name: "sumNumbers",
+    description: "Use this function to sum two numbers",
+    parameters: sumJSON,
+  });
+
+  // Create a function tool from the divide function
+  const divideFunctionTool = new FunctionTool(divideNumbers, {
+    name: "divideNumbers",
+    description: "Use this function to divide two numbers"
+    parameters: divideJSON,
+  });
+
+  // Create an OpenAIAgent with the function tools
+  const agent = new OpenAIAgent({
+    tools: [sumFunctionTool, divideFunctionTool],
+    verbose: true,
+  });
+
+  // Chat with the agent
+  const response = await agent.chat({
+    message: "How much is 5 + 5? then divide by 2",
+  });
+
+  // Print the response
+  console.log(String(response));
+}
+
+main().then(() => {
+  console.log("Done");
+});
+```

apps/docs/docs/examples/chat_engine.mdx

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

apps/docs/docs/examples/more_examples.mdx

@@ -0,0 +1,7 @@
+---
+sidebar_position: 5
+---
+
+# More examples
+
+You can check out more examples in the [examples](https://github.com/run-llama/LlamaIndexTS/tree/main/examples) folder of the repository.

apps/docs/docs/examples/save_load_index.mdx

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

apps/docs/docs/examples/summary_index.mdx

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

apps/docs/docs/examples/vector_index.mdx

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

apps/docs/docs/getting_started/_category_.yml

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

apps/docs/docs/getting_started/concepts.md

@@ -0,0 +1,78 @@
+---
+sidebar_position: 3
+---
+
+# 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:
+
+- how an LLM can answer questions using your own data.
+- key concepts and modules in LlamaIndex.TS for composing your own query pipeline.
+
+## Answering Questions Across Your Data
+
+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)
+
+This process is also known as Retrieval Augmented Generation (RAG).
+
+LlamaIndex.TS provides the essential toolkit for making both steps super easy.
+
+Let's explore each stage in detail.
+
+### Indexing Stage
+
+LlamaIndex.TS help you prepare the knowledge base with a suite of data connectors and indexes.
+
+![](../_static/concepts/indexing.jpg)
+
+[**Data Loaders**](../modules/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**](../modules/documents_and_nodes/index.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**](../modules/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.
+
+### Querying Stage
+
+In the querying stage, the query pipeline retrieves the most relevant context given a user query,
+and pass that to the LLM (along with the query) to synthesize a response.
+
+This gives the LLM up-to-date knowledge that is not in its original training data,
+(also reducing hallucination).
+
+The key challenge in the querying stage is retrieval, orchestration, and reasoning over (potentially many) knowledge bases.
+
+LlamaIndex provides composable modules that help you build and integrate RAG pipelines for Q&A (query engine), chatbot (chat engine), or as part of an agent.
+
+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)
+
+#### Building Blocks
+
+[**Retrievers**](../modules/retriever.md):
+A retriever defines how to efficiently retrieve relevant context from a knowledge base (i.e. index) when given a query.
+The specific retrieval logic differs for difference indices, the most popular being dense retrieval against a vector index.
+
+[**Response Synthesizers**](../modules/response_synthesizer.md):
+A response synthesizer generates a response from an LLM, using a user query and a given set of retrieved text chunks.
+
+#### Pipelines
+
+[**Query Engines**](../modules/query_engines):
+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**](../modules/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/getting_started/environments.md

@@ -0,0 +1,15 @@
+---
+sidebar_position: 2
+---
+
+# 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
+```

apps/docs/docs/getting_started/installation.mdx

@@ -0,0 +1,63 @@
+---
+sidebar_position: 0
+---
+
+# 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/getting_started/starter.md

@@ -0,0 +1,56 @@
+---
+sidebar_position: 1
+---
+
+# 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({
+    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/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](./getting_started/installation.mdx) and a [Starter Tutorial](./getting_started/starter.md) to build your first application.
+
+Once you're up and running, [High-Level Concepts](./getting_started/concepts.md) has an overview of LlamaIndex's modular architecture. For more hands-on practical examples, look through our Examples section on the sidebar.
+
+## 🗺️ Ecosystem
+
+To download or contribute, find LlamaIndex on:
+
+- Github: https://github.com/run-llama/LlamaIndexTS
+- NPM: https://www.npmjs.com/package/llamaindex
+
+## Community
+
+Need help? Have a feature suggestion? Join the LlamaIndex community:
+
+- Twitter: https://twitter.com/llama_index
+- Discord https://discord.gg/dGcwcsnxhU

apps/docs/docs/modules/_category_.yml

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

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

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

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

@@ -0,0 +1,14 @@
+# Agents
+
+An “agent” is an automated reasoning and decision engine. It takes in a user input/query and can make internal decisions for executing that query in order to return the correct result. The key agent components can include, but are not limited to:
+
+- Breaking down a complex question into smaller ones
+- Choosing an external Tool to use + coming up with parameters for calling the Tool
+- Planning out a set of tasks
+- Storing previously completed tasks in a memory module
+
+## Getting Started
+
+LlamaIndex.TS comes with a few built-in agents, but you can also create your own. The built-in agents include:
+
+- [OpenAI Agent](./openai.mdx)

apps/docs/docs/modules/agent/openai.mdx

@@ -0,0 +1,183 @@
+# OpenAI Agent
+
+OpenAI API that supports function calling, it’s never been easier to build your own agent!
+
+In this notebook tutorial, we showcase how to write your own OpenAI agent
+
+## Setup
+
+First, you need to install the `llamaindex` package. You can do this by running the following command in your terminal:
+
+```bash
+pnpm i llamaindex
+```
+
+Then we can define a function to sum two numbers and another function to divide two numbers.
+
+```ts
+function sumNumbers({ a, b }: { a: number; b: number }): number {
+  return a + b;
+}
+
+// Define a function to divide two numbers
+function divideNumbers({ a, b }: { a: number; b: number }): number {
+  return a / b;
+}
+```
+
+## Create a function tool
+
+Now we can create a function tool from the sum function and another function tool from the divide function.
+
+For the parameters of the sum function, we can define a JSON schema.
+
+### JSON Schema
+
+```ts
+const sumJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The first number",
+    },
+    b: {
+      type: "number",
+      description: "The second number",
+    },
+  },
+  required: ["a", "b"],
+};
+
+const divideJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The dividend a to divide",
+    },
+    b: {
+      type: "number",
+      description: "The divisor b to divide by",
+    },
+  },
+  required: ["a", "b"],
+};
+
+const sumFunctionTool = new FunctionTool(sumNumbers, {
+  name: "sumNumbers",
+  description: "Use this function to sum two numbers",
+  parameters: sumJSON,
+});
+
+const divideFunctionTool = new FunctionTool(divideNumbers, {
+  name: "divideNumbers",
+  description: "Use this function to divide two numbers",
+  parameters: divideJSON,
+});
+```
+
+## Create an OpenAIAgent
+
+Now we can create an OpenAIAgent with the function tools.
+
+```ts
+const worker = new OpenAIAgent({
+  tools: [sumFunctionTool, divideFunctionTool],
+  verbose: true,
+});
+```
+
+## Chat with the agent
+
+Now we can chat with the agent.
+
+```ts
+const response = await worker.chat({
+  message: "How much is 5 + 5? then divide by 2",
+});
+
+console.log(String(response));
+```
+
+## Full code
+
+```ts
+import { FunctionTool, OpenAIAgent } from "llamaindex";
+
+// Define a function to sum two numbers
+function sumNumbers({ a, b }: { a: number; b: number }): number {
+  return a + b;
+}
+
+// Define a function to divide two numbers
+function divideNumbers({ a, b }: { a: number; b: number }): number {
+  return a / b;
+}
+
+// Define the parameters of the sum function as a JSON schema
+const sumJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The first number",
+    },
+    b: {
+      type: "number",
+      description: "The second number",
+    },
+  },
+  required: ["a", "b"],
+};
+
+// Define the parameters of the divide function as a JSON schema
+const divideJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The argument a to divide",
+    },
+    b: {
+      type: "number",
+      description: "The argument b to divide",
+    },
+  },
+  required: ["a", "b"],
+};
+
+async function main() {
+  // Create a function tool from the sum function
+  const sumFunctionTool = new FunctionTool(sumNumbers, {
+    name: "sumNumbers",
+    description: "Use this function to sum two numbers",
+    parameters: sumJSON,
+  });
+
+  // Create a function tool from the divide function
+  const divideFunctionTool = new FunctionTool(divideNumbers, {
+    name: "divideNumbers",
+    description: "Use this function to divide two numbers",
+    parameters: divideJSON,
+  });
+
+  // Create an OpenAIAgent with the function tools
+  const agent = new OpenAIAgent({
+    tools: [sumFunctionTool, divideFunctionTool],
+    verbose: true,
+  });
+
+  // Chat with the agent
+  const response = await agent.chat({
+    message: "How much is 5 + 5? then divide by 2",
+  });
+
+  // Print the response
+  console.log(String(response));
+}
+
+main().then(() => {
+  console.log("Done");
+});
+```

apps/docs/docs/modules/agent/query_engine_tool.mdx

@@ -0,0 +1,128 @@
+# OpenAI Agent + QueryEngineTool
+
+QueryEngineTool is a tool that allows you to query a vector index. In this example, we will create a vector index from a set of documents and then create a QueryEngineTool from the vector index. We will then create an OpenAIAgent with the QueryEngineTool and chat with the agent.
+
+## Setup
+
+First, you need to install the `llamaindex` package. You can do this by running the following command in your terminal:
+
+```bash
+pnpm i llamaindex
+```
+
+Then you can import the necessary classes and functions.
+
+```ts
+import {
+  OpenAIAgent,
+  SimpleDirectoryReader,
+  VectorStoreIndex,
+  QueryEngineTool,
+} from "llamaindex";
+```
+
+## Create a vector index
+
+Now we can create a vector index from a set of documents.
+
+```ts
+// Load the documents
+const documents = await new SimpleDirectoryReader().loadData({
+  directoryPath: "node_modules/llamaindex/examples/",
+});
+
+// Create a vector index from the documents
+const vectorIndex = await VectorStoreIndex.fromDocuments(documents);
+```
+
+## Create a QueryEngineTool
+
+Now we can create a QueryEngineTool from the vector index.
+
+```ts
+// Create a query engine from the vector index
+const abramovQueryEngine = vectorIndex.asQueryEngine();
+
+// Create a QueryEngineTool with the query engine
+const queryEngineTool = new QueryEngineTool({
+  queryEngine: abramovQueryEngine,
+  metadata: {
+    name: "abramov_query_engine",
+    description: "A query engine for the Abramov documents",
+  },
+});
+```
+
+## Create an OpenAIAgent
+
+```ts
+// Create an OpenAIAgent with the query engine tool tools
+
+const agent = new OpenAIAgent({
+  tools: [queryEngineTool],
+  verbose: true,
+});
+```
+
+## Chat with the agent
+
+Now we can chat with the agent.
+
+```ts
+const response = await agent.chat({
+  message: "What was his salary?",
+});
+
+console.log(String(response));
+```
+
+## Full code
+
+```ts
+import {
+  OpenAIAgent,
+  SimpleDirectoryReader,
+  VectorStoreIndex,
+  QueryEngineTool,
+} from "llamaindex";
+
+async function main() {
+  // Load the documents
+  const documents = await new SimpleDirectoryReader().loadData({
+    directoryPath: "node_modules/llamaindex/examples/",
+  });
+
+  // Create a vector index from the documents
+  const vectorIndex = await VectorStoreIndex.fromDocuments(documents);
+
+  // Create a query engine from the vector index
+  const abramovQueryEngine = vectorIndex.asQueryEngine();
+
+  // Create a QueryEngineTool with the query engine
+  const queryEngineTool = new QueryEngineTool({
+    queryEngine: abramovQueryEngine,
+    metadata: {
+      name: "abramov_query_engine",
+      description: "A query engine for the Abramov documents",
+    },
+  });
+
+  // Create an OpenAIAgent with the function tools
+  const agent = new OpenAIAgent({
+    tools: [queryEngineTool],
+    verbose: true,
+  });
+
+  // Chat with the agent
+  const response = await agent.chat({
+    message: "What was his salary?",
+  });
+
+  // Print the response
+  console.log(String(response));
+}
+
+main().then(() => {
+  console.log("Done");
+});
+```

apps/docs/docs/modules/chat_engine.md

@@ -0,0 +1,29 @@
+---
+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({ message: query });
+```
+
+The `chat` function also supports streaming, just add `stream: true` as an option:
+
+```typescript
+const stream = await chatEngine.chat({ message: query, stream: true });
+for await (const chunk of stream) {
+  process.stdout.write(chunk.response);
+}
+```
+
+## Api References
+
+- [ContextChatEngine](../api/classes/ContextChatEngine.md)
+- [CondenseQuestionChatEngine](../api/classes/ContextChatEngine.md)

apps/docs/docs/modules/data_index.md

@@ -0,0 +1,23 @@
+---
+sidebar_position: 4
+---
+
+# 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/data_loader.mdx

@@ -0,0 +1,35 @@
+---
+sidebar_position: 4
+---
+
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../examples/readers/src/simple-directory-reader";
+import CodeSource2 from "!raw-loader!../../../../examples/readers/src/custom-simple-directory-reader";
+
+# Loader
+
+Before you can start indexing your documents, you need to load them into memory.
+
+### SimpleDirectoryReader
+
+[![Open in StackBlitz](https://developer.stackblitz.com/img/open_in_stackblitz.svg)](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples/readers?file=src/simple-directory-reader.ts&title=Simple%20Directory%20Reader)
+
+LlamaIndex.TS supports easy loading of files from folders using the `SimpleDirectoryReader` class.
+
+It is a simple reader that reads all files from a directory and its subdirectories.
+
+<CodeBlock language="ts">{CodeSource}</CodeBlock>
+
+Currently, it supports reading `.csv`, `.docx`, `.html`, `.md` and `.pdf` files,
+but support for other file types is planned.
+
+Also, you can provide a `defaultReader` as a fallback for files with unsupported extensions.
+Or pass new readers for `fileExtToReader` to support more file types.
+
+<CodeBlock language="ts" showLineNumbers metastring="{8-12,17-21}">
+  {CodeSource2}
+</CodeBlock>
+
+## API Reference
+
+- [SimpleDirectoryReader](../api/classes/SimpleDirectoryReader.md)

apps/docs/docs/modules/documents_and_nodes/_category_.yml

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

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

@@ -0,0 +1,18 @@
+---
+sidebar_position: 1
+---
+
+# Documents and Nodes
+
+`Document`s and `Node`s are the basic building blocks of any index. While the API for these objects is similar, `Document` objects represent entire files, while `Node`s are smaller pieces of that original document, that are suitable for an LLM and Q&A.
+
+```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/documents_and_nodes/metadata_extraction.md

@@ -0,0 +1,45 @@
+# Metadata Extraction Usage Pattern
+
+You can use LLMs to automate metadata extraction with our `Metadata Extractor` modules.
+
+Our metadata extractor modules include the following "feature extractors":
+
+- `SummaryExtractor` - automatically extracts a summary over a set of Nodes
+- `QuestionsAnsweredExtractor` - extracts a set of questions that each Node can answer
+- `TitleExtractor` - extracts a title over the context of each Node by document and combine them
+- `KeywordExtractor` - extracts keywords over the context of each Node
+
+Then you can chain the `Metadata Extractors` with the `IngestionPipeline` to extract metadata from a set of documents.
+
+```ts
+import {
+  IngestionPipeline,
+  TitleExtractor,
+  QuestionsAnsweredExtractor,
+  Document,
+  OpenAI,
+} from "llamaindex";
+
+async function main() {
+  const pipeline = new IngestionPipeline({
+    transformations: [
+      new TitleExtractor(),
+      new QuestionsAnsweredExtractor({
+        questions: 5,
+      }),
+    ],
+  });
+
+  const nodes = await pipeline.run({
+    documents: [
+      new Document({ text: "I am 10 years old. John is 20 years old." }),
+    ],
+  });
+
+  for (const node of nodes) {
+    console.log(node.metadata);
+  }
+}
+
+main().then(() => console.log("done"));
+```

apps/docs/docs/modules/embedding.md

@@ -0,0 +1,22 @@
+---
+sidebar_position: 4
+---
+
+# 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/ingestion_pipeline/_category_.yml

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

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

@@ -0,0 +1,99 @@
+# Ingestion Pipeline
+
+An `IngestionPipeline` uses a concept of `Transformations` that are applied to input data.
+These `Transformations` are applied to your input data, and the resulting nodes are either returned or inserted into a vector database (if given).
+
+## Usage Pattern
+
+The simplest usage is to instantiate an IngestionPipeline like so:
+
+```ts
+import fs from "node:fs/promises";
+
+import {
+  Document,
+  IngestionPipeline,
+  MetadataMode,
+  OpenAIEmbedding,
+  TitleExtractor,
+  SimpleNodeParser,
+} from "llamaindex";
+
+async function main() {
+  // Load essay from abramov.txt in Node
+  const path = "node_modules/llamaindex/examples/abramov.txt";
+
+  const essay = await fs.readFile(path, "utf-8");
+
+  // Create Document object with essay
+  const document = new Document({ text: essay, id_: path });
+  const pipeline = new IngestionPipeline({
+    transformations: [
+      new SimpleNodeParser({ chunkSize: 1024, chunkOverlap: 20 }),
+      new TitleExtractor(),
+      new OpenAIEmbedding(),
+    ],
+  });
+
+  // run the pipeline
+  const nodes = await pipeline.run({ documents: [document] });
+
+  // print out the result of the pipeline run
+  for (const node of nodes) {
+    console.log(node.getContent(MetadataMode.NONE));
+  }
+}
+
+main().catch(console.error);
+```
+
+## Connecting to Vector Databases
+
+When running an ingestion pipeline, you can also chose to automatically insert the resulting nodes into a remote vector store.
+
+Then, you can construct an index from that vector store later on.
+
+```ts
+import fs from "node:fs/promises";
+
+import {
+  Document,
+  IngestionPipeline,
+  MetadataMode,
+  OpenAIEmbedding,
+  TitleExtractor,
+  SimpleNodeParser,
+  QdrantVectorStore,
+  VectorStoreIndex,
+} from "llamaindex";
+
+async function main() {
+  // Load essay from abramov.txt in Node
+  const path = "node_modules/llamaindex/examples/abramov.txt";
+
+  const essay = await fs.readFile(path, "utf-8");
+
+  const vectorStore = new QdrantVectorStore({
+    host: "http://localhost:6333",
+  });
+
+  // Create Document object with essay
+  const document = new Document({ text: essay, id_: path });
+  const pipeline = new IngestionPipeline({
+    transformations: [
+      new SimpleNodeParser({ chunkSize: 1024, chunkOverlap: 20 }),
+      new TitleExtractor(),
+      new OpenAIEmbedding(),
+    ],
+    vectorStore,
+  });
+
+  // run the pipeline
+  const nodes = await pipeline.run({ documents: [document] });
+
+  // create an index
+  const index = VectorStoreIndex.fromVectorStore(vectorStore);
+}
+
+main().catch(console.error);
+```

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

@@ -0,0 +1,77 @@
+# Transformations
+
+A transformation is something that takes a list of nodes as an input, and returns a list of nodes. Each component that implements the Transformatio class has both a `transform` definition responsible for transforming the nodes
+
+Currently, the following components are Transformation objects:
+
+- [SimpleNodeParser](../api/classes/SimpleNodeParser.md)
+- [MetadataExtractor](../documents_and_nodes/metadata_extraction.md)
+- Embeddings
+
+## Usage Pattern
+
+While transformations are best used with with an IngestionPipeline, they can also be used directly.
+
+```ts
+import { SimpleNodeParser, TitleExtractor, Document } from "llamaindex";
+
+async function main() {
+  let nodes = new SimpleNodeParser().getNodesFromDocuments([
+    new Document({ text: "I am 10 years old. John is 20 years old." }),
+  ]);
+
+  const titleExtractor = new TitleExtractor();
+
+  nodes = await titleExtractor.transform(nodes);
+
+  for (const node of nodes) {
+    console.log(node.getContent(MetadataMode.NONE));
+  }
+}
+
+main().catch(console.error);
+```
+
+## Custom Transformations
+
+You can implement any transformation yourself by implementing the `TransformerComponent`.
+
+The following custom transformation will remove any special characters or punctutaion in text.
+
+```ts
+import { TransformerComponent, Node } from "llamaindex";
+
+class RemoveSpecialCharacters extends TransformerComponent {
+  async transform(nodes: Node[]): Promise<Node[]> {
+    for (const node of nodes) {
+      node.text = node.text.replace(/[^\w\s]/gi, "");
+    }
+
+    return nodes;
+  }
+}
+```
+
+These can then be used directly or in any IngestionPipeline.
+
+```ts
+import { IngestionPipeline, Document } from "llamaindex";
+
+async function main() {
+  const pipeline = new IngestionPipeline({
+    transformations: [new RemoveSpecialCharacters()],
+  });
+
+  const nodes = await pipeline.run({
+    documents: [
+      new Document({ text: "I am 10 years old. John is 20 years old." }),
+    ],
+  });
+
+  for (const node of nodes) {
+    console.log(node.getContent(MetadataMode.NONE));
+  }
+}
+
+main().catch(console.error);
+```

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

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

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

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

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

@@ -0,0 +1,80 @@
+# Anthropic
+
+## Usage
+
+```ts
+import { Anthropic, serviceContextFromDefaults } from "llamaindex";
+
+const anthropicLLM = new Anthropic({
+  apiKey: "<YOUR_API_KEY>",
+});
+
+const serviceContext = serviceContextFromDefaults({ llm: anthropicLLM });
+```
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+```
+
+## Query
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+## Full Example
+
+```ts
+import {
+  Anthropic,
+  Document,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  // Create an instance of the Anthropic LLM
+  const anthropicLLM = new Anthropic({
+    apiKey: "<YOUR_API_KEY>",
+  });
+
+  // Create a service context
+  const serviceContext = serviceContextFromDefaults({ llm: anthropicLLM });
+
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    serviceContext,
+  });
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,88 @@
+# Azure OpenAI
+
+To use Azure OpenAI, you only need to set a few environment variables together with the `OpenAI` class.
+
+For example:
+
+## Environment Variables
+
+```
+export AZURE_OPENAI_KEY="<YOUR KEY HERE>"
+export AZURE_OPENAI_ENDPOINT="<YOUR ENDPOINT, see https://learn.microsoft.com/en-us/azure/ai-services/openai/quickstart?tabs=command-line%2Cpython&pivots=rest-api>"
+export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name
+```
+
+## Usage
+
+```ts
+import { OpenAI, serviceContextFromDefaults } from "llamaindex";
+
+const azureOpenaiLLM = new OpenAI({ model: "gpt-4", temperature: 0 });
+
+const serviceContext = serviceContextFromDefaults({ llm: azureOpenaiLLM });
+```
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+```
+
+## Query
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+## Full Example
+
+```ts
+import {
+  Anthropic,
+  Document,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  // Create an instance of the LLM
+  const azureOpenaiLLM = new OpenAI({ model: "gpt-4", temperature: 0 });
+
+  // Create a service context
+  const serviceContext = serviceContextFromDefaults({ llm: azureOpenaiLLM });
+
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    serviceContext,
+  });
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

apps/docs/docs/modules/llms/available_llms/llama2.md

@@ -0,0 +1,97 @@
+# LLama2
+
+## Usage
+
+```ts
+import { Ollama, serviceContextFromDefaults } from "llamaindex";
+
+const llama2LLM = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
+
+const serviceContext = serviceContextFromDefaults({ llm: llama2LLM });
+```
+
+## Usage with Replication
+
+```ts
+import {
+  Ollama,
+  ReplicateSession,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+const replicateSession = new ReplicateSession({
+  replicateKey,
+});
+
+const llama2LLM = new LlamaDeuce({
+  chatStrategy: DeuceChatStrategy.META,
+  replicateSession,
+});
+
+const serviceContext = serviceContextFromDefaults({ llm: llama2LLM });
+```
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+```
+
+## Query
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+## Full Example
+
+```ts
+import {
+  Anthropic,
+  Document,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  // Create an instance of the LLM
+  const llama2LLM = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
+
+  // Create a service context
+  const serviceContext = serviceContextFromDefaults({ llm: mistralLLM });
+
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    serviceContext,
+  });
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,79 @@
+# Mistral
+
+## Usage
+
+```ts
+import { Ollama, serviceContextFromDefaults } from "llamaindex";
+
+const mistralLLM = new MistralAI({
+  model: "mistral-tiny",
+  apiKey: "<YOUR_API_KEY>",
+});
+
+const serviceContext = serviceContextFromDefaults({ llm: mistralLLM });
+```
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+```
+
+## Query
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+## Full Example
+
+```ts
+import {
+  Anthropic,
+  Document,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  // Create an instance of the LLM
+  const mistralLLM = new MistralAI({ model: "mistral-tiny" });
+
+  // Create a service context
+  const serviceContext = serviceContextFromDefaults({ llm: mistralLLM });
+
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    serviceContext,
+  });
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,76 @@
+# Ollama
+
+## Usage
+
+```ts
+import { Ollama, serviceContextFromDefaults } from "llamaindex";
+
+const ollamaLLM = new Ollama({ model: "llama2", temperature: 0.75 });
+
+const serviceContext = serviceContextFromDefaults({ llm: ollamaLLM });
+```
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+```
+
+## Query
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+## Full Example
+
+```ts
+import {
+  Anthropic,
+  Document,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  // Create an instance of the LLM
+  const ollamaLLM = new Ollama({ model: "llama2", temperature: 0.75 });
+
+  // Create a service context
+  const serviceContext = serviceContextFromDefaults({ llm: ollamaLLM });
+
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    serviceContext,
+  });
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,80 @@
+# OpenAI
+
+```ts
+import { OpenAI, serviceContextFromDefaults } from "llamaindex";
+
+const openaiLLM = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0, apiKey: <YOUR_API_KEY> });
+
+const serviceContext = serviceContextFromDefaults({ llm: openaiLLM });
+```
+
+You can setup the apiKey on the environment variables, like:
+
+```bash
+export OPENAI_API_KEY="<YOUR_API_KEY>"
+```
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+```
+
+## Query
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+## Full Example
+
+```ts
+import {
+  Anthropic,
+  Document,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  // Create an instance of the LLM
+  const openaiLLM = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
+
+  // Create a service context
+  const serviceContext = serviceContextFromDefaults({ llm: openaiLLM });
+
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    serviceContext,
+  });
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,80 @@
+# Portkey LLM
+
+## Usage
+
+```ts
+import { Portkey, serviceContextFromDefaults } from "llamaindex";
+
+const portkeyLLM = new Portkey({
+  apiKey: "<YOUR_API_KEY>",
+});
+
+const serviceContext = serviceContextFromDefaults({ llm: portkeyLLM });
+```
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+```
+
+## Query
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+## Full Example
+
+```ts
+import {
+  Anthropic,
+  Document,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  // Create an instance of the LLM
+  const portkeyLLM = new Portkey({
+    apiKey: "<YOUR_API_KEY>",
+  });
+
+  // Create a service context
+  const serviceContext = serviceContextFromDefaults({ llm: portkeyLLM });
+
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    serviceContext,
+  });
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,80 @@
+# Together LLM
+
+## Usage
+
+```ts
+import { TogetherLLM, serviceContextFromDefaults } from "llamaindex";
+
+const togetherLLM = new TogetherLLM({
+  apiKey: "<YOUR_API_KEY>",
+});
+
+const serviceContext = serviceContextFromDefaults({ llm: togetherLLM });
+```
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+const document = new Document({ text: essay, id_: "essay" });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+```
+
+## Query
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const query = "What is the meaning of life?";
+
+const results = await queryEngine.query({
+  query,
+});
+```
+
+## Full Example
+
+```ts
+import {
+  Anthropic,
+  Document,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  // Create an instance of the LLM
+  const togetherLLM = new TogetherLLM({
+    apiKey: "<YOUR_API_KEY>",
+  });
+
+  // Create a service context
+  const serviceContext = serviceContextFromDefaults({ llm: togetherLLM });
+
+  const document = new Document({ text: essay, id_: "essay" });
+
+  // Load and index documents
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    serviceContext,
+  });
+
+  // Create a query engine
+  const queryEngine = index.asQueryEngine({
+    retriever,
+  });
+
+  const query = "What is the meaning of life?";
+
+  // Query
+  const response = await queryEngine.query({
+    query,
+  });
+
+  // Log the response
+  console.log(response.response);
+}
+```

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

@@ -0,0 +1,34 @@
+---
+sidebar_position: 3
+---
+
+# Large Language Models (LLMs)
+
+The LLM is responsible for reading text and generating natural language responses to queries. By default, LlamaIndex.TS uses `gpt-3.5-turbo`.
+
+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 });
+```
+
+## Azure OpenAI
+
+To use Azure OpenAI, you only need to set a few environment variables.
+
+For example:
+
+```
+export AZURE_OPENAI_KEY="<YOUR KEY HERE>"
+export AZURE_OPENAI_ENDPOINT="<YOUR ENDPOINT, see https://learn.microsoft.com/en-us/azure/ai-services/openai/quickstart?tabs=command-line%2Cpython&pivots=rest-api>"
+export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name
+```
+
+## API Reference
+
+- [OpenAI](../api/classes/OpenAI.md)
+- [ServiceContext](../api/interfaces//ServiceContext.md)

apps/docs/docs/modules/node_parser.md

@@ -0,0 +1,33 @@
+---
+sidebar_position: 4
+---
+
+# NodeParser
+
+The `NodeParser` in LlamaIndex is responsible 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/node_postprocessors/_category_.yml

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

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

@@ -0,0 +1,71 @@
+# Cohere Reranker
+
+The Cohere Reranker is a postprocessor that uses the Cohere API to rerank the results of a search query.
+
+## Setup
+
+Firstly, you will need to install the `llamaindex` package.
+
+```bash
+pnpm install llamaindex
+```
+
+Now, you will need to sign up for an API key at [Cohere](https://cohere.ai/). Once you have your API key you can import the necessary modules and create a new instance of the `CohereRerank` class.
+
+```ts
+import {
+  CohereRerank,
+  Document,
+  OpenAI,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+```
+
+## Load and index documents
+
+For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
+
+```ts
+const document = new Document({ text: essay, id_: "essay" });
+
+const serviceContext = serviceContextFromDefaults({
+  llm: new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 }),
+});
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+```
+
+## Increase similarity topK to retrieve more results
+
+The default value for `similarityTopK` is 2. This means that only the most similar document will be returned. To retrieve more results, you can increase the value of `similarityTopK`.
+
+```ts
+const retriever = index.asRetriever();
+retriever.similarityTopK = 5;
+```
+
+## Create a new instance of the CohereRerank class
+
+Then you can create a new instance of the `CohereRerank` class and pass in your API key and the number of results you want to return.
+
+```ts
+const nodePostprocessor = new CohereRerank({
+  apiKey: "<COHERE_API_KEY>",
+  topN: 4,
+});
+```
+
+## Create a query engine with the retriever and node postprocessor
+
+```ts
+const queryEngine = index.asQueryEngine({
+  retriever,
+  nodePostprocessors: [nodePostprocessor],
+});
+
+// log the response
+const response = await queryEngine.query("Where did the author grown up?");
+```

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

@@ -0,0 +1,110 @@
+# Node Postprocessors
+
+## Concept
+
+Node postprocessors are a set of modules that take a set of nodes, and apply some kind of transformation or filtering before returning them.
+
+In LlamaIndex, node postprocessors are most commonly applied within a query engine, after the node retrieval step and before the response synthesis step.
+
+LlamaIndex offers several node postprocessors for immediate use, while also providing a simple API for adding your own custom postprocessors.
+
+## Usage Pattern
+
+An example of using a node postprocessors is below:
+
+```ts
+import {
+  Node,
+  NodeWithScore,
+  SimilarityPostprocessor,
+  CohereRerank,
+} from "llamaindex";
+
+const nodes: NodeWithScore[] = [
+  {
+    node: new TextNode({ text: "hello world" }),
+    score: 0.8,
+  },
+  {
+    node: new TextNode({ text: "LlamaIndex is the best" }),
+    score: 0.6,
+  },
+];
+
+// similarity postprocessor: filter nodes below 0.75 similarity score
+const processor = new SimilarityPostprocessor({
+  similarityCutoff: 0.7,
+});
+
+const filteredNodes = processor.postprocessNodes(nodes);
+
+// cohere rerank: rerank nodes given query using trained model
+const reranker = new CohereRerank({
+  apiKey: "<COHERE_API_KEY>",
+  topN: 2,
+});
+
+const rerankedNodes = await reranker.postprocessNodes(nodes, "<user_query>");
+
+console.log(filteredNodes, rerankedNodes);
+```
+
+Now you can use the `filteredNodes` and `rerankedNodes` in your application.
+
+## Using Node Postprocessors in LlamaIndex
+
+Most commonly, node-postprocessors will be used in a query engine, where they are applied to the nodes returned from a retriever, and before the response synthesis step.
+
+### Using Node Postprocessors in a Query Engine
+
+```ts
+import { Node, NodeWithScore, SimilarityPostprocessor, CohereRerank } from "llamaindex";
+
+const nodes: NodeWithScore[] = [
+  {
+    node: new TextNode({ text: "hello world" }),
+    score: 0.8,
+  },
+  {
+    node: new TextNode({ text: "LlamaIndex is the best" }),
+    score: 0.6,
+  }
+];
+
+// cohere rerank: rerank nodes given query using trained model
+const reranker = new CohereRerank({
+  apiKey: "<COHERE_API_KEY>,
+  topN: 2,
+})
+
+const document = new Document({ text: "essay", id_: "essay" });
+
+const serviceContext = serviceContextFromDefaults({
+  llm: new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 }),
+});
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  serviceContext,
+});
+
+const queryEngine = index.asQueryEngine({
+  nodePostprocessors: [processor, reranker],
+});
+
+// all node post-processors will be applied during each query
+const response = await queryEngine.query("<user_query>");
+```
+
+### Using with retrieved nodes
+
+```ts
+import { SimilarityPostprocessor } from "llamaindex";
+
+nodes = await index.asRetriever().retrieve("test query str");
+
+const processor = new SimilarityPostprocessor({
+  similarityCutoff: 0.7,
+});
+
+const filteredNodes = processor.postprocessNodes(nodes);
+```

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

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

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

@@ -0,0 +1,41 @@
+# 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);
+}
+```
+
+## Sub Question Query Engine
+
+The basic concept of the Sub Question Query Engine is that it splits a single query into multiple queries, gets an answer for each of those queries, and then combines those different answers into a single coherent response for the user. You can think of it as the "think this through step by step" prompt technique but iterating over your data sources!
+
+### Getting Started
+
+The easiest way to start trying the Sub Question Query Engine is running the subquestion.ts file in [examples](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/subquestion.ts).
+
+```bash
+npx ts-node subquestion.ts
+```
+
+### Tools
+
+SubQuestionQueryEngine is implemented with Tools. The basic idea of Tools is that they are executable options for the large language model. In this case, our SubQuestionQueryEngine relies on QueryEngineTool, which as you guessed it is a tool to run queries on a QueryEngine. This allows us to give the model an option to query different documents for different questions for example. You could also imagine that the SubQuestionQueryEngine could use a Tool that searches for something on the web or gets an answer using Wolfram Alpha.
+
+You can learn more about Tools by taking a look at the LlamaIndex Python documentation https://gpt-index.readthedocs.io/en/latest/core_modules/agent_modules/tools/root.html
+
+## API Reference
+
+- [RetrieverQueryEngine](../../api/classes/RetrieverQueryEngine.md)
+- [SubQuestionQueryEngine](../../api/classes/SubQuestionQueryEngine.md)
+- [QueryEngineTool](../../api/interfaces/QueryEngineTool.md)

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

@@ -0,0 +1,152 @@
+# Metadata Filtering
+
+Metadata filtering is a way to filter the documents that are returned by a query based on the metadata associated with the documents. This is useful when you want to filter the documents based on some metadata that is not part of the document text.
+
+You can also check our multi-tenancy blog post to see how metadata filtering can be used in a multi-tenant environment. [https://blog.llamaindex.ai/building-multi-tenancy-rag-system-with-llamaindex-0d6ab4e0c44b] (the article uses the Python version of LlamaIndex, but the concepts are the same).
+
+## Setup
+
+Firstly if you haven't already, you need to install the `llamaindex` package:
+
+```bash
+pnpm i llamaindex
+```
+
+Then you can import the necessary modules from `llamaindex`:
+
+```ts
+import {
+  ChromaVectorStore,
+  Document,
+  VectorStoreIndex,
+  storageContextFromDefaults,
+} from "llamaindex";
+
+const collectionName = "dog_colors";
+```
+
+## Creating documents with metadata
+
+You can create documents with metadata using the `Document` class:
+
+```ts
+const docs = [
+  new Document({
+    text: "The dog is brown",
+    metadata: {
+      color: "brown",
+      dogId: "1",
+    },
+  }),
+  new Document({
+    text: "The dog is red",
+    metadata: {
+      color: "red",
+      dogId: "2",
+    },
+  }),
+];
+```
+
+## Creating a ChromaDB vector store
+
+You can create a `ChromaVectorStore` to store the documents:
+
+```ts
+const chromaVS = new ChromaVectorStore({ collectionName });
+const serviceContext = await storageContextFromDefaults({
+  vectorStore: chromaVS,
+});
+
+const index = await VectorStoreIndex.fromDocuments(docs, {
+  storageContext: serviceContext,
+});
+```
+
+## Querying the index with metadata filtering
+
+Now you can query the index with metadata filtering using the `preFilters` option:
+
+```ts
+const queryEngine = index.asQueryEngine({
+  preFilters: {
+    filters: [
+      {
+        key: "dogId",
+        value: "2",
+        filterType: "ExactMatch",
+      },
+    ],
+  },
+});
+
+const response = await queryEngine.query({
+  query: "What is the color of the dog?",
+});
+
+console.log(response.toString());
+```
+
+## Full Code
+
+```ts
+import {
+  ChromaVectorStore,
+  Document,
+  VectorStoreIndex,
+  storageContextFromDefaults,
+} from "llamaindex";
+
+const collectionName = "dog_colors";
+
+async function main() {
+  try {
+    const docs = [
+      new Document({
+        text: "The dog is brown",
+        metadata: {
+          color: "brown",
+          dogId: "1",
+        },
+      }),
+      new Document({
+        text: "The dog is red",
+        metadata: {
+          color: "red",
+          dogId: "2",
+        },
+      }),
+    ];
+
+    console.log("Creating ChromaDB vector store");
+    const chromaVS = new ChromaVectorStore({ collectionName });
+    const ctx = await storageContextFromDefaults({ vectorStore: chromaVS });
+
+    console.log("Embedding documents and adding to index");
+    const index = await VectorStoreIndex.fromDocuments(docs, {
+      storageContext: ctx,
+    });
+
+    console.log("Querying index");
+    const queryEngine = index.asQueryEngine({
+      preFilters: {
+        filters: [
+          {
+            key: "dogId",
+            value: "2",
+            filterType: "ExactMatch",
+          },
+        ],
+      },
+    });
+    const response = await queryEngine.query({
+      query: "What is the color of the dog?",
+    });
+    console.log(response.toString());
+  } catch (e) {
+    console.error(e);
+  }
+}
+
+main();
+```

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

@@ -0,0 +1,189 @@
+# Router Query Engine
+
+In this tutorial, we define a custom router query engine that selects one out of several candidate query engines to execute a query.
+
+## Setup
+
+First, we need to install import the necessary modules from `llamaindex`:
+
+```bash
+pnpm i lamaindex
+```
+
+```ts
+import {
+  OpenAI,
+  RouterQueryEngine,
+  SimpleDirectoryReader,
+  SimpleNodeParser,
+  SummaryIndex,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+```
+
+## Loading Data
+
+Next, we need to load some data. We will use the `SimpleDirectoryReader` to load documents from a directory:
+
+```ts
+const documents = await new SimpleDirectoryReader().loadData({
+  directoryPath: "node_modules/llamaindex/examples",
+});
+```
+
+## Service Context
+
+Next, we need to define some basic rules and parse the documents into nodes. We will use the `SimpleNodeParser` to parse the documents into nodes and `ServiceContext` to define the rules (eg. LLM API key, chunk size, etc.):
+
+```ts
+const nodeParser = new SimpleNodeParser({
+  chunkSize: 1024,
+});
+
+const serviceContext = serviceContextFromDefaults({
+  nodeParser,
+  llm: new OpenAI(),
+});
+```
+
+## Creating Indices
+
+Next, we need to create some indices. We will create a `VectorStoreIndex` and a `SummaryIndex`:
+
+```ts
+const vectorIndex = await VectorStoreIndex.fromDocuments(documents, {
+  serviceContext,
+});
+
+const summaryIndex = await SummaryIndex.fromDocuments(documents, {
+  serviceContext,
+});
+```
+
+## Creating Query Engines
+
+Next, we need to create some query engines. We will create a `VectorStoreQueryEngine` and a `SummaryQueryEngine`:
+
+```ts
+const vectorQueryEngine = vectorIndex.asQueryEngine();
+const summaryQueryEngine = summaryIndex.asQueryEngine();
+```
+
+## Creating a Router Query Engine
+
+Next, we need to create a router query engine. We will use the `RouterQueryEngine` to create a router query engine:
+
+We're defining two query engines, one for summarization and one for retrieving specific context. The router query engine will select the most appropriate query engine based on the query.
+
+```ts
+const queryEngine = RouterQueryEngine.fromDefaults({
+  queryEngineTools: [
+    {
+      queryEngine: vectorQueryEngine,
+      description: "Useful for summarization questions related to Abramov",
+    },
+    {
+      queryEngine: summaryQueryEngine,
+      description: "Useful for retrieving specific context from Abramov",
+    },
+  ],
+  serviceContext,
+});
+```
+
+## Querying the Router Query Engine
+
+Finally, we can query the router query engine:
+
+```ts
+const summaryResponse = await queryEngine.query({
+  query: "Give me a summary about his past experiences?",
+});
+
+console.log({
+  answer: summaryResponse.response,
+  metadata: summaryResponse?.metadata?.selectorResult,
+});
+```
+
+## Full code
+
+```ts
+import {
+  OpenAI,
+  RouterQueryEngine,
+  SimpleDirectoryReader,
+  SimpleNodeParser,
+  SummaryIndex,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  // Load documents from a directory
+  const documents = await new SimpleDirectoryReader().loadData({
+    directoryPath: "node_modules/llamaindex/examples",
+  });
+
+  // Parse the documents into nodes
+  const nodeParser = new SimpleNodeParser({
+    chunkSize: 1024,
+  });
+
+  // Create a service context
+  const serviceContext = serviceContextFromDefaults({
+    nodeParser,
+    llm: new OpenAI(),
+  });
+
+  // Create indices
+  const vectorIndex = await VectorStoreIndex.fromDocuments(documents, {
+    serviceContext,
+  });
+
+  const summaryIndex = await SummaryIndex.fromDocuments(documents, {
+    serviceContext,
+  });
+
+  // Create query engines
+  const vectorQueryEngine = vectorIndex.asQueryEngine();
+  const summaryQueryEngine = summaryIndex.asQueryEngine();
+
+  // Create a router query engine
+  const queryEngine = RouterQueryEngine.fromDefaults({
+    queryEngineTools: [
+      {
+        queryEngine: vectorQueryEngine,
+        description: "Useful for summarization questions related to Abramov",
+      },
+      {
+        queryEngine: summaryQueryEngine,
+        description: "Useful for retrieving specific context from Abramov",
+      },
+    ],
+    serviceContext,
+  });
+
+  // Query the router query engine
+  const summaryResponse = await queryEngine.query({
+    query: "Give me a summary about his past experiences?",
+  });
+
+  console.log({
+    answer: summaryResponse.response,
+    metadata: summaryResponse?.metadata?.selectorResult,
+  });
+
+  const specificResponse = await queryEngine.query({
+    query: "Tell me about abramov first job?",
+  });
+
+  console.log({
+    answer: specificResponse.response,
+    metadata: specificResponse.metadata.selectorResult,
+  });
+}
+
+main().then(() => console.log("Done"));
+```

apps/docs/docs/modules/response_synthesizer.md

@@ -0,0 +1,64 @@
+---
+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({
+  query: "What age am I?",
+  nodesWithScore,
+});
+console.log(response.response);
+```
+
+The `synthesize` function also supports streaming, just add `stream: true` as an option:
+
+```typescript
+const stream = await responseSynthesizer.synthesize({
+  query: "What age am I?",
+  nodesWithScore,
+  stream: true,
+});
+for await (const chunk of stream) {
+  process.stdout.write(chunk.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/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/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/modules/vector_stores/_category_.yml

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

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

@@ -0,0 +1,86 @@
+# Qdrant Vector Store
+
+To run this example, you need to have a Qdrant instance running. You can run it with Docker:
+
+```bash
+docker pull qdrant/qdrant
+docker run -p 6333:6333 qdrant/qdrant
+```
+
+## Importing the modules
+
+```ts
+import fs from "node:fs/promises";
+import { Document, VectorStoreIndex, QdrantVectorStore } from "llamaindex";
+```
+
+## Load the documents
+
+```ts
+const path = "node_modules/llamaindex/examples/abramov.txt";
+const essay = await fs.readFile(path, "utf-8");
+```
+
+## Setup Qdrant
+
+```ts
+const vectorStore = new QdrantVectorStore({
+  url: "http://localhost:6333",
+});
+```
+
+## Setup the index
+
+```ts
+const document = new Document({ text: essay, id_: path });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  vectorStore,
+});
+```
+
+## Query the index
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const response = await queryEngine.query({
+  query: "What did the author do in college?",
+});
+
+// Output response
+console.log(response.toString());
+```
+
+## Full code
+
+```ts
+import fs from "node:fs/promises";
+import { Document, VectorStoreIndex, QdrantVectorStore } from "llamaindex";
+
+async function main() {
+  const path = "node_modules/llamaindex/examples/abramov.txt";
+  const essay = await fs.readFile(path, "utf-8");
+
+  const vectorStore = new QdrantVectorStore({
+    url: "http://localhost:6333",
+  });
+
+  const document = new Document({ text: essay, id_: path });
+
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    vectorStore,
+  });
+
+  const queryEngine = index.asQueryEngine();
+
+  const response = await queryEngine.query({
+    query: "What did the author do in college?",
+  });
+
+  // Output response
+  console.log(response.toString());
+}
+
+main().catch(console.error);
+```

apps/docs/docs/observability/_category_.yml

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

apps/docs/docs/observability/index.md

@@ -0,0 +1,35 @@
+# Observability
+
+LlamaIndex provides **one-click observability** 🔭 to allow you to build principled LLM applications in a production setting.
+
+A key requirement for principled development of LLM applications over your data (RAG systems, agents) is being able to observe, debug, and evaluate
+your system - both as a whole and for each component.
+
+This feature allows you to seamlessly integrate the LlamaIndex library with powerful observability/evaluation tools offered by our partners.
+Configure a variable once, and you'll be able to do things like the following:
+
+- View LLM/prompt inputs/outputs
+- Ensure that the outputs of any component (LLMs, embeddings) are performing as expected
+- View call traces for both indexing and querying
+
+Each provider has similarities and differences. Take a look below for the full set of guides for each one!
+
+## OpenLLMetry
+
+[OpenLLMetry](https://github.com/traceloop/openllmetry-js) is an open-source project based on OpenTelemetry for tracing and monitoring
+LLM applications. It connects to [all major observability platforms](https://www.traceloop.com/docs/openllmetry/integrations/introduction) and installs in minutes.
+
+### Usage Pattern
+
+```bash
+npm install @traceloop/node-server-sdk
+```
+
+```js
+import * as traceloop from "@traceloop/node-server-sdk";
+
+traceloop.initialize({
+  apiKey: process.env.TRACELOOP_API_KEY,
+  disableBatch: true,
+});
+```

apps/docs/docusaurus.config.js

@@ -0,0 +1,181 @@
+// @ts-check
+// Note: type annotations allow type checking and IDEs autocompletion
+
+const renderer = require("prism-react-renderer");
+const lightCodeTheme = renderer.themes.github;
+const darkCodeTheme = renderer.themes.dracula;
+
+/** @type {import('@docusaurus/types').Config} */
+const config = {
+  title: "LlamaIndex.TS",
+  tagline: "Unleash the power of LLMs over your data in TypeScript",
+  favicon: "img/favicon.png",
+
+  // Set the production url of your site here
+  url: "https://ts.llamaindex.ai",
+  // Set the /<baseUrl>/ pathname under which your site is served
+  // For GitHub pages deployment, it is often '/<projectName>/'
+  baseUrl: "/",
+
+  // GitHub pages deployment config.
+  // If you aren't using GitHub pages, you don't need these.
+  organizationName: "run-llama", // Usually your GitHub org/user name.
+  projectName: "LlamaIndex.TS", // Usually your repo name.
+
+  onBrokenLinks: "warn",
+  onBrokenMarkdownLinks: "warn",
+
+  // 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",
+      "zh-Hans",
+      "es",
+      "fr",
+      "de",
+      "ja",
+      "ko",
+      "pt",
+      "ar",
+      "it",
+      "tr",
+      "pl",
+      "nl",
+      "vi",
+      "th",
+    ], // "fa", "ru", "ro", "sv", "hu", "cs", "el", "da", "fi", "he", "no", "hi", "in", "sl", "se", "sk", "uk", "bg", "hr", "lt", "lv", "et", "cat"
+  },
+
+  presets: [
+    [
+      "@docusaurus/preset-classic",
+      /** @type {import('@docusaurus/preset-classic').Options} */
+      ({
+        docs: {
+          path: "docs",
+          routeBasePath: "/",
+          sidebarPath: require.resolve("./sidebars.js"),
+          // Please change this to your repo.
+          // Remove this to remove the "edit this page" links.
+          // editUrl:
+          //   "https://github.com/facebook/docusaurus/tree/main/packages/create-docusaurus/templates/shared/",
+          remarkPlugins: [
+            [require("@docusaurus/remark-plugin-npm2yarn"), { sync: true }],
+          ],
+        },
+        blog: false,
+        gtag: {
+          trackingID: "G-NB9B8LW9W5",
+          anonymizeIP: true,
+        },
+      }),
+    ],
+  ],
+
+  themeConfig:
+    /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
+    ({
+      // Replace with your project's social card
+      image: "img/favicon.png", // TODO change this
+      navbar: {
+        title: "LlamaIndex.TS",
+        logo: {
+          alt: "LlamaIndex.TS",
+          src: "img/favicon.png",
+        },
+        items: [
+          {
+            type: "docSidebar",
+            sidebarId: "mySidebar",
+            position: "left",
+            label: "Docs",
+          },
+          {
+            type: "localeDropdown",
+            position: "left",
+          },
+          {
+            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,
+        },
+      },
+    ],
+  ],
+  markdown: {
+    format: "detect",
+  },
+};
+
+module.exports = config;

apps/docs/i18n/ar/docusaurus-plugin-content-docs/current/api

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

apps/docs/i18n/ar/docusaurus-plugin-content-docs/current/concepts.md

@@ -0,0 +1,86 @@
+---
+sidebar_position: 3
+---
+
+# المفاهيم على المستوى العالي
+
+`تمت ترجمة هذه الوثيقة تلقائيًا وقد تحتوي على أخطاء. لا تتردد في فتح طلب سحب لاقتراح تغييرات.`
+
+يساعدك LlamaIndex.TS في بناء تطبيقات قائمة على LLM (مثل Q&A و chatbot) على بيانات مخصصة.
+
+في هذا الدليل عن المفاهيم على المستوى العالي، ستتعلم:
+
+- كيف يمكن لـ LLM الإجابة على الأسئلة باستخدام بياناتك الخاصة.
+- المفاهيم الرئيسية والوحدات في LlamaIndex.TS لبناء خط أنابيب الاستعلام الخاص بك.
+
+## الإجابة على الأسئلة عبر بياناتك
+
+يستخدم LlamaIndex طريقة مكونة من مرحلتين عند استخدام LLM مع بياناتك:
+
+1. **مرحلة الفهرسة**: إعداد قاعدة المعرفة، و
+2. **مرحلة الاستعلام**: استرداد السياق ذي الصلة من المعرفة لمساعدة LLM في الاستجابة لسؤال
+
+![](./_static/concepts/rag.jpg)
+
+تُعرف هذه العملية أيضًا باسم "استرجاع معزز للتوليد" (RAG).
+
+يوفر LlamaIndex.TS أدوات أساسية لجعل كلا المرحلتين سهلتين للغاية.
+
+دعنا نستكشف كل مرحلة بالتفصيل.
+
+### مرحلة الفهرسة
+
+يساعدك LlamaIndex.TS في إعداد قاعدة المعرفة باستخدام مجموعة من موصلات البيانات والفهارس.
+
+![](./_static/concepts/indexing.jpg)
+
+[**محمّلات البيانات**](./modules/high_level/data_loader.md):
+موصل البيانات (أي `Reader`) يقوم بتجميع البيانات من مصادر بيانات مختلفة وتنسيقات بيانات مختلفة في تمثيل بسيط للـ `Document` (نص وبيانات تعريفية بسيطة).
+
+[**المستندات / العقد**](./modules/high_level/documents_and_nodes.md): المستند هو حاوية عامة حول أي مصدر بيانات - على سبيل المثال، ملف PDF، نتائج واجهة برمجة التطبيقات، أو بيانات استرداد من قاعدة بيانات. العقد هو الوحدة الذرية للبيانات في LlamaIndex ويمثل "قطعة" من المستند الأصلي. إنه تمثيل غني يتضمن بيانات تعريفية وعلاقات (مع عقد أخرى) لتمكين عمليات الاسترجاع الدقيقة والتعبيرية.
+
+[**فهارس البيانات**](./modules/high_level/data_index.md):
+بمجرد أن تقوم بتجميع بياناتك، يساعدك LlamaIndex في فهرسة البيانات في تنسيق سهل الاسترداد.
+
+تحت الغطاء، يقوم LlamaIndex بتحليل المستندات الخام إلى تمثيلات وسيطة، وحساب تضمينات الناقلات، وتخزين بياناتك في الذاكرة أو على القرص.
+
+"
+
+### مرحلة الاستعلام
+
+في مرحلة الاستعلام، يقوم خط الأنابيب للاستعلام بجلب السياق الأكثر صلة بناءً على استعلام المستخدم،
+ويمرر ذلك إلى LLM (جنبًا إلى جنب مع الاستعلام) لتوليد استجابة.
+
+يمنح ذلك LLM معرفة محدثة ليست موجودة في بيانات التدريب الأصلية لديه،
+(مما يقلل أيضًا من الهلوسة).
+
+التحدي الرئيسي في مرحلة الاستعلام هو الاسترجاع والتنسيق والاستدلال عبر قواعد المعرفة (المحتملة).
+
+يوفر LlamaIndex وحدات قابلة للتركيب تساعدك في بناء ودمج خطوط أنابيب RAG لـ Q&A (محرك الاستعلام)، chatbot (محرك الدردشة)، أو كجزء من وكيل.
+
+يمكن تخصيص هذه الكتل البنائية لتعكس تفضيلات التصنيف، وكذلك تركيبها للاستدلال عبر عدة قواعد معرفة بطريقة منظمة.
+
+![](./_static/concepts/querying.jpg)
+
+#### الكتل الأساسية
+
+[**مسترجعات**](./modules/low_level/retriever.md):
+يحدد المسترجع كيفية استرجاع السياق ذي الصلة بكفاءة من قاعدة المعرفة (أي الفهرس) عند إعطاء استعلام.
+تختلف منطق الاسترجاع المحددة حسب الفهارس المختلفة، والأكثر شيوعًا هو الاسترجاع الكثيف ضد فهرس الناقل.
+
+[**مركبات الاستجابة**](./modules/low_level/response_synthesizer.md):
+تقوم مركبة الاستجابة بتوليد استجابة من LLM باستخدام استعلام المستخدم ومجموعة معينة من أجزاء النص المسترجعة.
+
+"
+
+#### خطوط الأنابيب
+
+[**محركات الاستعلام**](./modules/high_level/query_engine.md):
+محرك الاستعلام هو خط أنابيب شامل يتيح لك طرح الأسئلة على بياناتك.
+يأخذ استعلامًا بلغة طبيعية ويعيد استجابة، جنبًا إلى جنب مع السياق المرجعي المسترجع والممرر إلى LLM.
+
+[**محركات الدردشة**](./modules/high_level/chat_engine.md):
+محرك الدردشة هو خط أنابيب شامل لإجراء محادثة مع بياناتك
+(عدة تفاعلات بدلاً من سؤال وجواب واحد).
+
+"

apps/docs/i18n/ar/docusaurus-plugin-content-docs/current/end_to_end.md

@@ -0,0 +1,61 @@
+---
+sidebar_position: 4
+---
+
+# أمثلة من البداية إلى النهاية
+
+`تمت ترجمة هذه الوثيقة تلقائيًا وقد تحتوي على أخطاء. لا تتردد في فتح طلب سحب لاقتراح تغييرات.`
+
+نقدم العديد من الأمثلة من البداية إلى النهاية باستخدام LlamaIndex.TS في المستودع
+
+تحقق من الأمثلة أدناه أو جربها وأكملها في دقائق مع دروس تفاعلية على Github Codespace المقدمة من Dev-Docs [هنا](https://codespaces.new/team-dev-docs/lits-dev-docs-playground?devcontainer_path=.devcontainer%2Fjavascript_ltsquickstart%2Fdevcontainer.json):
+
+## [محرك الدردشة](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/chatEngine.ts)
+
+اقرأ ملفًا وتحدث عنه مع LLM.
+
+## [فهرس الفيكتور](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/vectorIndex.ts)
+
+إنشاء فهرس فيكتور واستعلامه. سيستخدم فهرس الفيكتور التضمينات لاسترداد أعلى k عقد ذات صلة. بشكل افتراضي ، يكون k الأعلى هو 2.
+
+"
+
+## [مؤشر الملخص](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/summaryIndex.ts)
+
+إنشاء مؤشر قائمة واستعلامه. يستخدم هذا المثال أيضًا `LLMRetriever` ، الذي سيستخدم LLM لتحديد أفضل العقد لاستخدامها عند إنشاء الإجابة.
+
+"
+
+## [حفظ / تحميل فهرس](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/storageContext.ts)
+
+إنشاء وتحميل فهرس ناقل. يحدث التخزين المؤقت على القرص تلقائيًا في LlamaIndex.TS بمجرد إنشاء كائن سياق التخزين.
+
+"
+
+## [فهرس الناقل المخصص](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/vectorIndexCustomize.ts)
+
+إنشاء فهرس ناقل واستعلامه، مع تكوين `LLM` و `ServiceContext` و `similarity_top_k`.
+
+"
+
+## [OpenAI LLM](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/openai.ts)
+
+أنشئ OpenAI LLM واستخدمه مباشرة للدردشة.
+
+"
+
+## [Llama2 DeuceLLM](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/llamadeuce.ts)
+
+إنشاء Llama-2 LLM واستخدامه مباشرة للدردشة.
+
+"
+
+## [محرك استعلام الأسئلة الفرعية](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/subquestion.ts)
+
+يستخدم `محرك استعلام الأسئلة الفرعية` الذي يقسم الاستعلامات المعقدة إلى أسئلة فرعية متعددة، ثم يجمع الاستجابة عبر الإجابات على جميع الأسئلة الفرعية.
+
+"
+
+## [وحدات منخفضة المستوى](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/lowlevel.ts)
+
+يستخدم هذا المثال العديد من المكونات منخفضة المستوى، مما يزيل الحاجة إلى محرك استعلام فعلي. يمكن استخدام هذه المكونات في أي مكان، في أي تطبيق، أو تخصيصها وتصنيفها الفرعي لتلبية احتياجاتك الخاصة.

apps/docs/i18n/ar/docusaurus-plugin-content-docs/current/environments.md

@@ -0,0 +1,17 @@
+---
+sidebar_position: 5
+---
+
+# البيئات
+
+`تمت ترجمة هذه الوثيقة تلقائيًا وقد تحتوي على أخطاء. لا تتردد في فتح طلب سحب لاقتراح تغييرات.`
+
+يدعم LlamaIndex حاليًا رسميًا NodeJS 18 و NodeJS 20.
+
+## NextJS App Router
+
+إذا كنت تستخدم معالج الطرق / الوظائف الخادمة في NextJS App Router ، فستحتاج إلى استخدام وضع NodeJS:
+
+```js
+export const runtime = "nodejs"; // الافتراضي
+```

apps/docs/i18n/ar/docusaurus-plugin-content-docs/current/installation.mdx

@@ -0,0 +1,67 @@
+---
+sidebar_position: 1
+---
+
+# التثبيت والإعداد
+
+`تمت ترجمة هذه الوثيقة تلقائيًا وقد تحتوي على أخطاء. لا تتردد في فتح طلب سحب لاقتراح تغييرات.`
+
+تأكد من أن لديك NodeJS v18 أو أحدث.
+
+## باستخدام create-llama
+
+أسهل طريقة للبدء مع LlamaIndex هي باستخدام `create-llama`. هذه الأداة سطر الأوامر تمكنك من بدء بناء تطبيق LlamaIndex جديد بسرعة، مع كل شيء معد لك.
+
+ما عليك سوى تشغيل
+
+<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>
+
+للبدء. بمجرد إنشاء التطبيق الخاص بك، قم بتشغيل
+
+```bash npm2yarn
+npm run dev
+```
+
+لبدء خادم التطوير. يمكنك ثم زيارة [http://localhost:3000](http://localhost:3000) لرؤية تطبيقك.
+
+## التثبيت من NPM
+
+```bash npm2yarn
+npm install llamaindex
+```
+
+### المتغيرات البيئية
+
+تستخدم أمثلتنا OpenAI افتراضيًا. ستحتاج إلى إعداد مفتاح Open AI الخاص بك على النحو التالي:
+
+```bash
+export OPENAI_API_KEY="sk-......" # استبدله بالمفتاح الخاص بك من https://platform.openai.com/account/api-keys
+```
+
+إذا كنت ترغب في تحميله تلقائيًا في كل مرة، قم بإضافته إلى ملف .zshrc/.bashrc الخاص بك.
+
+تحذير: لا تقم بإضافة مفتاح OpenAI الخاص بك إلى نظام التحكم في الإصدارات.
+
+"

apps/docs/i18n/ar/docusaurus-plugin-content-docs/current/introduction.md

@@ -0,0 +1,60 @@
+---
+sidebar_position: 0
+slug: /
+---
+
+# ما هو LlamaIndex.TS؟
+
+`تمت ترجمة هذه الوثيقة تلقائيًا وقد تحتوي على أخطاء. لا تتردد في فتح طلب سحب لاقتراح تغييرات.`
+
+LlamaIndex.TS هو إطار بيانات لتطبيقات LLM لاستيعاب وتنظيم والوصول إلى البيانات الخاصة أو الخاصة بالمجال. في حين أن حزمة Python متاحة أيضًا (انظر [هنا](https://docs.llamaindex.ai/en/stable/)), يوفر LlamaIndex.TS ميزات أساسية في حزمة بسيطة ، محسنة للاستخدام مع TypeScript.
+
+## 🚀 لماذا LlamaIndex.TS؟
+
+في جوهرها ، توفر LLMs واجهة لغة طبيعية بين البشر والبيانات المستنتجة. تأتي النماذج المتاحة على نطاق واسع محملة مسبقًا بكميات هائلة من البيانات المتاحة للجمهور ، من ويكيبيديا وقوائم البريد الإلكتروني إلى الكتب المدرسية وشفرة المصدر.
+
+غالبًا ما تتطلب التطبيقات المبنية على LLMs تعزيز هذه النماذج بالبيانات الخاصة أو الخاصة بالمجال. للأسف ، يمكن توزيع هذه البيانات عبر تطبيقات ومخازن بيانات معزولة. إنها خلف واجهات برمجة التطبيقات ، في قواعد البيانات SQL ، أو محبوسة في ملفات PDF وعروض تقديمية.
+
+هنا يأتي دور **LlamaIndex.TS**.
+
+## 🦙 كيف يمكن أن يساعد LlamaIndex.TS؟
+
+يوفر LlamaIndex.TS الأدوات التالية:
+
+- **تحميل البيانات**: استيعاب البيانات الحالية الخاصة بك بتنسيقات `.txt`, `.pdf`, `.csv`, `.md` و `.docx` مباشرة.
+- **فهارس البيانات**: تنظيم البيانات الخاصة بك في تمثيلات وسيطة سهلة وفعالة للاستخدام من قبل LLMs.
+- **المحركات**: توفر واجهات الوصول إلى اللغة الطبيعية لبياناتك. على سبيل المثال:
+  - محركات الاستعلام هي واجهات استرجاع قوية للإخراج المعزز بالمعرفة.
+  - محركات الدردشة هي واجهات محادثة للتفاعلات "ذهابًا وإيابًا" متعددة الرسائل مع بياناتك.
+
+## 👨‍👩‍👧‍👦 من أجل من هو LlamaIndex؟
+
+يوفر LlamaIndex.TS مجموعة أدوات أساسية ، ضرورية لأي شخص يقوم ببناء تطبيقات LLM باستخدام JavaScript و TypeScript.
+
+يتيح لنا واجهة برمجة التطبيقات على مستوى عالي استخدام LlamaIndex.TS لاستيعاب واستعلام البيانات الخاصة بهم.
+
+بالنسبة للتطبيقات المعقدة أكثر ، تتيح لنا واجهات برمجة التطبيقات على مستوى أدنى للمستخدمين المتقدمين تخصيص وتوسيع أي وحدة - موصلات البيانات والفهارس وأجهزة الاسترجاع ومحركات الاستعلام - لتناسب احتياجاتهم.
+
+## البدء
+
+`npm install llamaindex`
+
+تتضمن وثائقنا [تعليمات التثبيت](./installation.mdx) و[دليل البداية](./starter.md) لبناء تطبيقك الأول.
+
+بمجرد أن تكون جاهزًا وتعمل ، يحتوي [مفاهيم عالية المستوى](./getting_started/concepts.md) على نظرة عامة على الهندسة المعمارية المتعددة المستويات لـ LlamaIndex. لمزيد من الأمثلة العملية التفصيلية ، يمكنك الاطلاع على [دروس النهاية إلى النهاية](./end_to_end.md).
+
+## 🗺️ النظام البيئي
+
+لتنزيل أو المساهمة ، ابحث عن LlamaIndex على:
+
+- Github: https://github.com/run-llama/LlamaIndexTS
+- NPM: https://www.npmjs.com/package/llamaindex
+
+"
+
+## المجتمع
+
+هل تحتاج إلى مساعدة؟ هل لديك اقتراح لميزة؟ انضم إلى مجتمع LlamaIndex:
+
+- تويتر: https://twitter.com/llama_index
+- ديسكورد: https://discord.gg/dGcwcsnxhU