Release (#2217)

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>

.cjs.swcrc

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

.eslintrc.js

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

.gitattributes

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

.github/ISSUE_TEMPLATE/bug_report.md

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

.github/ISSUE_TEMPLATE/bugfix.yml

@@ -1,7 +1,6 @@
 name: Bugfix
-title: "Sweep: "
-description: Write something like "We notice ... behavior when ... happens instead of ...""
-labels: sweep
+title: ""
+description: Write something like "We notice ... behavior when ... happens instead of ..." If you would like to use sweep.dev prefix with "Sweep:"
 body:
   - type: textarea
     id: description

.github/ISSUE_TEMPLATE/feature.yml

@@ -1,11 +1,10 @@
 name: Feature Request
-title: "Sweep: "
-description: Write something like "Write an api endpoint that does "..." in the "..." file"
-labels: sweep
+title: ""
+description: Write something like "Write an api endpoint that does "..." in the "..." file". If you would like to use sweep.dev prefix with "Sweep:"
 body:
   - type: textarea
     id: description
     attributes:
       label: Details
-      description: More details for Sweep
+      description: More details
       placeholder: The new endpoint should use the ... class from ... file because it contains ... logic

.github/ISSUE_TEMPLATE/refactor.yml

@@ -1,11 +1,10 @@
 name: Refactor
-title: "Sweep: "
-description: Write something like "Modify the ... api endpoint to use ... version and ... framework"
-labels: sweep
+title: ""
+description: Write something like "Modify the ... api endpoint to use ... version and ... framework" If you would like to use sweep.dev prefix with "Sweep:"
 body:
   - type: textarea
     id: description
     attributes:
       label: Details
-      description: More details for Sweep
+      description: More details
       placeholder: We are migrating this function to ... version because ...

.github/workflows/deploy-developer-hub.yml

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

.github/workflows/lint_on_push_or_pull.yml

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

.github/workflows/preview.yml

@@ -0,0 +1,33 @@
+name: Publish Preview
+on: [pull_request]
+
+env:
+  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
+  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
+  TURBO_REMOTE_ONLY: true
+
+jobs:
+  pre_release:
+    name: Pre Release
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout Repo
+        uses: actions/checkout@v4
+
+      - uses: pnpm/action-setup@v4
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: ".nvmrc"
+          cache: "pnpm"
+
+      - name: Install dependencies
+        run: pnpm install
+
+      - name: Build
+        run: pnpm run build
+
+      - name: Pre Release
+        run: pnpx pkg-pr-new publish --pnpm ./packages/* ./packages/providers/* ./packages/providers/storage/*

.github/workflows/publish_github.yml

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

@@ -0,0 +1,70 @@
+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

@@ -1,24 +1,217 @@
 name: Run Tests
 
-on: [push, pull_request]
+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
 
 jobs:
+  e2e:
+    strategy:
+      fail-fast: false
+      matrix:
+        node-version: [20.x, 22.x, 23.x]
+    name: E2E on Node.js ${{ matrix.node-version }}
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: ankane/setup-postgres@v1
+        with:
+          database: llamaindex_node_test
+          dev-files: true
+      - run: |
+          cd /tmp
+          git clone --branch v0.7.0 https://github.com/pgvector/pgvector.git
+          cd pgvector
+          make
+          sudo make install
+      - uses: pnpm/action-setup@v4
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: ${{ matrix.node-version }}
+          cache: "pnpm"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Run E2E Tests
+        run: pnpm run e2e
+
   test:
+    strategy:
+      fail-fast: false
+      matrix:
+        node-version: [20.x, 22.x, 23.x]
+    name: Test on Node.js ${{ matrix.node-version }}
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: pnpm/action-setup@v4
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: ${{ matrix.node-version }}
+          cache: "pnpm"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Run tests
+        run: pnpm run test
+  typecheck:
     runs-on: ubuntu-latest
 
     steps:
-      - name: Checkout code
-        uses: actions/checkout@v2
-
+      - uses: actions/checkout@v4
+      - uses: pnpm/action-setup@v4
       - name: Setup Node.js
-        uses: actions/setup-node@v2
+        uses: actions/setup-node@v4
         with:
-          node-version: "18"
-
+          node-version-file: ".nvmrc"
+          cache: "pnpm"
       - name: Install dependencies
+        run: pnpm install
+      - name: Build
+        run: pnpm run build
+      - name: Run Type Check
+        run: pnpm run type-check
+      - name: Run Circular Dependency Check
+        run: pnpm run circular-check
+  e2e-npm:
+    runs-on: ubuntu-latest
+    name: Test using packages with npm
+    steps:
+      - uses: actions/checkout@v4
+      - uses: pnpm/action-setup@v4
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: ".nvmrc"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Build packages
+        run: pnpm run build
+      - name: Pack packages
         run: |
-          npm i -g pnpm
-          pnpm install
-
+          pnpm pack --pack-destination ${{ runner.temp }} -C packages/llamaindex
+          pnpm pack --pack-destination ${{ runner.temp }} -C packages/workflow
+          pnpm pack --pack-destination ${{ runner.temp }} -C packages/core
+      - name: Install packed packages
+        run: npm add ${{ runner.temp }}/*.tgz
+        working-directory: e2e/npm
       - name: Run tests
-        run: pnpm run test
+        run: npm test
+        working-directory: e2e/npm
+  e2e-llamaindex-examples:
+    strategy:
+      fail-fast: false
+      matrix:
+        packages:
+          - cloudflare-worker-agent
+          - nextjs-agent
+          - nextjs-edge-runtime
+          - nextjs-node-runtime
+          - waku-query-engine
+          - vite-import-llamaindex
+    runs-on: ubuntu-latest
+    name: Build LlamaIndex Example (${{ matrix.packages }})
+    steps:
+      - uses: actions/checkout@v4
+      - uses: pnpm/action-setup@v4
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: ".nvmrc"
+          cache: "pnpm"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Build llamaindex
+        run: pnpm run build
+      - name: Build ${{ matrix.packages }}
+        run: pnpm run build
+        working-directory: e2e/examples/${{ matrix.packages }}
+
+  size-limit:
+    runs-on: ubuntu-latest
+    if: github.event_name == 'pull_request'
+    name: Size Limit
+    steps:
+      - uses: actions/checkout@v4
+      - uses: pnpm/action-setup@v4
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: ".nvmrc"
+          cache: "pnpm"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Build llamaindex
+        run: pnpm run build
+      - uses: andresz1/size-limit-action@94bc357df29c36c8f8d50ea497c3e225c3c95d1d
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          directory: e2e/examples/vite-import-llamaindex
+          skip_step: "install"
+          build_script: ci-build
+          package_manager: pnpm
+
+  typecheck-examples:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+      - uses: pnpm/action-setup@v4
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: ".nvmrc"
+          cache: "pnpm"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Build
+        run: pnpm run build
+      - name: Copy examples
+        run: rsync -rv --exclude=node_modules ./examples ${{ runner.temp }}
+      - name: Pack packages
+        run: |
+          for dir in packages/*; do
+            if [ -d "$dir" ] && [ -f "$dir/package.json" ] && [[ ! "$dir" =~ autotool ]]; then
+              echo "Packing $dir"
+              pnpm pack --pack-destination ${{ runner.temp }} -C $dir
+            else
+              echo "Skipping $dir, no package.json found"
+            fi
+          done
+      - name: Pack provider packages
+        run: |
+          for dir in packages/providers/* packages/providers/storage/*; do
+            if [ -d "$dir" ] && [ -f "$dir/package.json" ]; then
+              echo "Packing $dir"
+              pnpm pack --pack-destination ${{ runner.temp }} -C $dir
+            else
+              echo "Skipping $dir, no package.json found"
+            fi
+          done
+      - name: Install
+        run: npm add ${{ runner.temp }}/*.tgz --legacy-peer-deps
+        working-directory: ${{ runner.temp }}/examples
+      - name: Run Type Check
+        run: npx tsc --project ./tsconfig.json
+        working-directory: ${{ runner.temp }}/examples
+      - uses: actions/upload-artifact@v4
+        if: failure()
+        with:
+          name: build-dist
+          path: |
+            ${{ runner.temp }}/*.tgz
+          if-no-files-found: error

.gitignore

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

.husky/pre-commit

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

.husky/pre-push

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

.npmrc

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

.nvmrc

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

.prettierignore

@@ -0,0 +1,11 @@
+apps/docs/i18n
+apps/docs/docs/api
+pnpm-lock.yaml
+lib/
+dist/
+docs/
+.docusaurus/
+.source/
+# prttier doesn't support mdx3 we are using
+*.mdx
+packages/server/server/

.swcrc

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

.vscode/launch.json

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

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

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

.vscode/settings.json

@@ -4,5 +4,16 @@
   "editor.defaultFormatter": "esbenp.prettier-vscode",
   "[xml]": {
     "editor.defaultFormatter": "redhat.vscode-xml"
-  }
+  },
+  "[python]": {
+    "editor.defaultFormatter": "ms-python.black-formatter"
+  },
+  "[jsonc]": {
+    "editor.defaultFormatter": "esbenp.prettier-vscode"
+  },
+  "[json]": {
+    "editor.defaultFormatter": "esbenp.prettier-vscode"
+  },
+  "prettier.prettierPath": "./node_modules/prettier",
+  "prettier.configPath": "prettier.config.mjs"
 }

CLAUDE.md

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

CONTRIBUTING.md

@@ -2,79 +2,164 @@
 
 ## Structure
 
-This is a monorepo built with Turborepo
+LlamaIndex.TS uses pnpm monorepo.
 
-Right now there are two packages of importance:
+We recommend you to understand the basics of Node.js, TypeScript, pnpm, and of course, LLM before contributing.
 
-packages/core which is the main NPM library llamaindex
+There are some important folders in the repository:
 
-apps/simple is where the demo code lives
-
-### Turborepo docs
-
-You can checkout how Turborepo works using the default [README-turborepo.md](/README-turborepo.md)
+- `packages/*`: Contains the source code of the packages. Each package is a separate npm package.
+  - `llamaindex`: The starter package for LlamaIndex.TS, which contains the all sub-packages.
+  - `core`: The core package of LlamaIndex.TS, which contains the abstract classes and interfaces. It is designed for
+    all JS runtime environments.
+  - `env`: The environment package of LlamaIndex.TS, which contains the environment-specific classes and interfaces. It
+    includes compatibility layers for Node.js, Deno, Vercel Edge Runtime, Cloudflare Workers...
+  - `providers/*`: The providers package of LlamaIndex.TS, which contains the providers for LLM and other services.
+- `apps/*`: The applications based on LlamaIndex.TS.
+  - `next`: Our documentation website based on Next.js.
+- `examples`: The code examples of LlamaIndex.TS using Node.js.
 
 ## Getting Started
 
-Install NodeJS. Preferably v18 using nvm or n.
-
-Inside the LlamaIndexTS directory:
+Make sure you have Node.js LTS (Long-term Support) installed. You can check your Node.js version by running:
 
+```shell
+node -v
+# v22.x.x
 ```
-npm i -g pnpm ts-node
+
+### Use pnpm
+
+```shell
+npm install -g pnpm
+```
+
+### Install dependencies
+
+```shell
 pnpm install
+pnpm install -g tsx
 ```
 
-Note: we use pnpm in this repo, which has a lot of the same functionality and CLI options as npm but it does do some things better in a monorepo, like centralizing dependencies and caching.
+### Build the packages
 
-PNPM's has documentation on its [workspace feature](https://pnpm.io/workspaces) and Turborepo had some [useful documentation also](https://turbo.build/repo/docs/core-concepts/monorepos/running-tasks).
+To build all packages, run:
 
-### Running Typescript
-
-When we publish to NPM we will have a tsc compiled version of the library in JS. For now, the easiest thing to do is use ts-node.
-
-### Test cases
-
-To run them, run
-
-```
-pnpm run test
+```shell
+pnpm build
 ```
 
-To write new test cases write them in [packages/core/src/tests](/packages/core/src/tests)
+### Start Developing
 
-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
+You can launch the package in dev-mode by running:
 
-### Demo applications
-
-There is an existing ["simple"](/apps/simple/README.md) demos folder with mainly NodeJS scripts. Feel free to add additional demos to that folder. If you would like to try out your changes in the core package with a new demo, you need to run the build command in the README.
-
-You can create new demo applications in the apps folder. Just run pnpm init in the folder after you create it to create its own package.json
-
-### Installing packages
-
-To install packages for a specific package or demo application, run
-
-```
-pnpm add [NPM Package] --filter [package or application i.e. core or simple]
+```shell
+pnpm dev
 ```
 
-To install packages for every package or application run
+This will use turbo to run all packages in watch-mode. This means you can make changes and have them automatically built.
 
+If you want to customize what packages are built/watched, you can run turbo directly and adjust the filter:
+
+```shell
+pnpm turbo run dev --filter="./packages/core" --concurrency=100
 ```
-pnpm add -w [NPM Package]
+
+In another terminal, you can write and run any script needed to quickly test your changes. For example:
+
+```typescript
+import { createMemory, staticBlock } from "@llamaindex/core/memory";
+
+// Create memory with predefined context
+const memory = createMemory({
+  memoryBlocks: [
+    staticBlock({
+      content:
+        "The user is a software engineer who loves TypeScript and LlamaIndex.",
+      messageRole: "system",
+    }),
+  ],
+});
+
+async function main() {
+  const result = await memory.getLLM();
+  console.log(result);
+}
+
+void main().catch(console.error);
 ```
 
+And run it with:
+
+```shell
+pnpm exec tsx my_script.ts
+```
+
+This flow allows you to easily test your changes without having to build the entire project.
+
+Once you are happy with your changes, be sure to add tests (and confirm existing tests are passing!).
+
+### Run tests
+
+#### Unit tests
+
+After build, to run all unit tests, call:
+
+```shell
+pnpm test
+```
+
+Unit tests are located in the `tests` folder of each package. They are using their own package (e.g. `@llamaindex/core-tests` for `@llamaindex/core`). The tests are importing the package under test and the test package is not published.
+
+#### E2E tests
+
+To run all E2E tests, call:
+
+```shell
+pnpm e2e
+```
+
+All E2E tests are in the `e2e` folder.
+
 ### Docs
 
-To contribute to the docs, go to the docs website folder and run the Docusaurus instance.
+See the [docs](./apps/next/README.md) for more information.
 
-```bash
-cd apps/docs
-pnpm install
-pnpm start
+## Adding a new package
+
+Please follow these steps to add a new package:
+
+1. Only add new packages to the `packages/providers` folder.
+2. Use the `package.json` and `tsconfig.json` of an existing packages as template.
+3. Reference your new package in the root `tsconfig.json` file
+4. Add your package to the `examples/package.json` file if you add a new example.
+
+## Before sending a PR
+
+Before sending a PR, make sure of the following:
+
+1. Tests are all running and you added meaningful tests for your change.
+2. If you have a new feature, document it in the `apps/next` docs folder.
+3. If you have a new feature, add a new example in the `examples` folder.
+4. You have a descriptive changeset for each PR:
+
+### Bumping the versions of packages you've modified
+
+We use [changesets](https://github.com/changesets/changesets) for managing versions and changelogs. To create a new
+changeset, run in the root folder:
+
+```shell
+pnpm changeset
 ```
 
-That should start a webserver which will serve the docs on https://localhost:3000
+You will be prompted to choose what packages need their versions bumped, and what kind of bump (major, minor or patch) is needed. Once you carry out this operation, the bumping will be automatic after the PR is merged.
 
-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.
+## Publishing (maintainers only)
+
+The [Release Github Action](.github/workflows/release.yml) is automatically generating and updating a
+PR called "Release {version}".
+
+This PR will update the `package.json` and `CHANGELOG.md` files of each package according to
+the current changesets in the [.changeset](.changeset) folder.
+
+If this PR is merged it will automatically add version tags to the repository and publish the updated packages to NPM.

README-turborepo.md

@@ -1,81 +0,0 @@
-# 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

@@ -1,67 +1,81 @@
-# LlamaIndex.TS
+<p align="center">
+  <img height="100" width="100" alt="LlamaIndex logo" src="https://ts.llamaindex.ai/square.svg" />
+</p>
+<h1 align="center">LlamaIndex.TS</h1>
+<h3 align="center">
+  Data framework for your LLM application.
+</h3>
 
-LlamaIndex is a data framework for your LLM application.
+[![NPM Version](https://img.shields.io/npm/v/llamaindex)](https://www.npmjs.com/package/llamaindex)
+[![NPM License](https://img.shields.io/npm/l/llamaindex)](https://github.com/run-llama/LlamaIndexTS/blob/main/LICENSE)
+[![NPM Downloads](https://img.shields.io/npm/dm/llamaindex)](https://www.npmjs.com/package/llamaindex)
+[![Discord](https://img.shields.io/discord/1059199217496772688)](https://discord.com/invite/eN6D2HQ4aX)
+[![Twitter](https://img.shields.io/twitter/follow/llama_index)](https://x.com/llama_index)
 
-Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.
+Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in JS runtime environments with TypeScript support.
 
 Documentation: https://ts.llamaindex.ai/
 
+Try examples online:
+
+[![Open in Stackblitz](https://developer.stackblitz.com/img/open_in_stackblitz.svg)](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples)
+
 ## What is LlamaIndex.TS?
 
 LlamaIndex.TS aims to be a lightweight, easy to use set of libraries to help you integrate large language models into your applications with your own data.
 
-## Getting started with an example:
+## Compatibility
 
-LlamaIndex.TS requries Node v18 or higher. You can download it from https://nodejs.org or use https://nvm.sh (our preferred option).
+### Multiple JS Environment Support
 
-In a new folder:
+LlamaIndex.TS supports multiple JS environments, including:
 
-```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
+- 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
 pnpm install llamaindex
-pnpm install @types/node
+yarn add llamaindex
 ```
 
-Create the file example.ts
+### Setup in Node.js, Deno, Bun, TypeScript...?
 
-```ts
-// example.ts
-import fs from "fs/promises";
-import { Document, VectorStoreIndex } from "llamaindex";
+See our official document: https://ts.llamaindex.ai/docs/llamaindex/getting_started
 
-async function main() {
-  // Load essay from abramov.txt in Node
-  const essay = await fs.readFile(
-    "node_modules/llamaindex/examples/abramov.txt",
-    "utf-8",
-  );
+### Adding provider packages
 
-  // Create Document object with essay
-  const document = new Document({ text: essay });
+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.
 
-  // Split text and create embeddings. Store them in a VectorStoreIndex
-  const index = await VectorStoreIndex.fromDocuments([document]);
+For example, to use the OpenAI LLM, you would install the following package:
 
-  // Query the index
-  const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query(
-    "What did the author do in college?",
-  );
-
-  // Output response
-  console.log(response.toString());
-}
-
-main();
-```
-
-Then you can run it using
-
-```bash
-pnpx ts-node example.ts
+```shell
+npm install @llamaindex/openai
+pnpm install @llamaindex/openai
+yarn add @llamaindex/openai
 ```
 
 ## Playground
@@ -70,50 +84,13 @@ Check out our NextJS playground at https://llama-playground.vercel.app/. The sou
 
 ## Core concepts for getting started:
 
-- [Document](/packages/core/src/Node.ts): A document represents a text file, PDF file or other contiguous piece of data.
-
-- [Node](/packages/core/src/Node.ts): The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
-
-- [Embedding](/packages/core/src/Embedding.ts): Embeddings are sets of floating point numbers which represent the data in a Node. By comparing the similarity of embeddings, we can derive an understanding of the similarity of two pieces of data. One use case is to compare the embedding of a question with the embeddings of our Nodes to see which Nodes may contain the data needed to answer that quesiton.
-
-- [Indices](/packages/core/src/indices/): Indices store the Nodes and the embeddings of those nodes. QueryEngines retrieve Nodes from these Indices using embedding similarity.
-
-- [QueryEngine](/packages/core/src/QueryEngine.ts): Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected Nodes from your Index to give the LLM the context it needs to answer your query.
-
-- [ChatEngine](/packages/core/src/ChatEngine.ts): A ChatEngine helps you build a chatbot that will interact with your Indices.
-
-- [SimplePrompt](/packages/core/src/Prompt.ts): A simple standardized function call definition that takes in inputs and formats them in a template literal. SimplePrompts can be specialized using currying and combined using other SimplePrompt functions.
-
-## Note: NextJS:
-
-If you're using NextJS App Router, you'll need to use the NodeJS runtime (default) and add the follow config to your next.config.js to have it use imports/exports in the same way Node does.
-
-```js
-export const runtime = "nodejs"; // default
-```
-
-```js
-// next.config.js
-/** @type {import('next').NextConfig} */
-const nextConfig = {
-  experimental: {
-    serverComponentsExternalPackages: ["pdf-parse"], // Puts pdf-parse in actual NodeJS mode with NextJS App Router
-  },
-};
-
-module.exports = nextConfig;
-```
-
-## Supported LLMs:
-
-- OpenAI GPT-3.5-turbo and GPT-4
-- Anthropic Claude Instant and Claude 2
-- Llama2 Chat LLMs (70B, 13B, and 7B parameters)
+See our documentation: https://ts.llamaindex.ai/docs/llamaindex/getting_started/concepts
 
 ## Contributing:
 
-We are in the very early days of LlamaIndex.TS. If you’re interested in hacking on it with us check out our [contributing guide](/CONTRIBUTING.md)
+Please see our [contributing guide](CONTRIBUTING.md) for more information.
+You are highly encouraged to contribute to LlamaIndex.TS!
 
-## Bugs? Questions?
+## Community
 
 Please join our Discord! https://discord.com/invite/eN6D2HQ4aX

apps/docs/.gitignore

@@ -1,20 +0,0 @@
-# Dependencies
-/node_modules
-
-# Production
-/build
-
-# Generated files
-.docusaurus
-.cache-loader
-
-# Misc
-.DS_Store
-.env.local
-.env.development.local
-.env.test.local
-.env.production.local
-
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*

apps/docs/README-docusaurus.md

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

apps/docs/babel.config.js

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

apps/docs/docs/.gitignore

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

apps/docs/docs/end_to_end.md

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

apps/docs/docs/environments.md

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

apps/docs/docs/installation.md

@@ -1,25 +0,0 @@
----
-sidebar_position: 1
----
-
-# Installation and Setup
-
-## Installation from NPM
-
-Make sure you have NodeJS v18 or higher.
-
-```bash npm2yarn
-npm install llamaindex
-```
-
-## Environment variables
-
-Our examples use OpenAI by default. You'll need to set up your Open AI key like so:
-
-```bash
-export OPENAI_API_KEY="sk-......" # Replace with your key from https://platform.openai.com/account/api-keys
-```
-
-If you want to have it automatically loaded every time, add it to your .zshrc/.bashrc.
-
-WARNING: do not check in your OpenAI key into version control.

apps/docs/docs/introduction.md

@@ -1,56 +0,0 @@
----
-sidebar_position: 0
-slug: /
----
-
-# What is LlamaIndex.TS?
-
-LlamaIndex.TS is a data framework for LLM applications to ingest, structure, and access private or domain-specific data. While a python package is also available (see [here](https://gpt-index.readthedocs.io/en/latest/)), LlamaIndex.TS offers core features in a simple package, optimized for usage with TypeScript.
-
-## 🚀 Why LlamaIndex.TS?
-
-At their core, LLMs offer a natural language interface between humans and inferred data. Widely available models come pre-trained on huge amounts of publicly available data, from Wikipedia and mailing lists to textbooks and source code.
-
-Applications built on top of LLMs often require augmenting these models with private or domain-specific data. Unfortunately, that data can be distributed across siloed applications and data stores. It's behind APIs, in SQL databases, or trapped in PDFs and slide decks.
-
-That's where **LlamaIndex.TS** comes in.
-
-## 🦙 How can LlamaIndex.TS help?
-
-LlamaIndex.TS provides the following tools:
-
-- **Data loading** ingest your existing `.txt`, `.pdf`, `.csv`, `.md` and `.docx` data directly
-- **Data indexes** structure your data in intermediate representations that are easy and performant for LLMs to consume.
-- **Engines** provide natural language access to your data. For example:
-  - Query engines are powerful retrieval interfaces for knowledge-augmented output.
-  - Chat engines are conversational interfaces for multi-message, "back and forth" interactions with your data.
-
-## 👨‍👩‍👧‍👦 Who is LlamaIndex for?
-
-LlamaIndex.TS provides a core set of tools, essential for anyone building LLM apps with JavaScript and TypeScript.
-
-Our high-level API allows beginner users to use LlamaIndex.TS to ingest and query their data.
-
-For more complex applications, our lower-level APIs allow advanced users to customize and extend any module—data connectors, indices, retrievers, and query engines, to fit their needs.
-
-## Getting Started
-
-`npm install llamaindex`
-
-Our documentation includes [Installation Instructions](./installation.md) and a [Starter Tutorial](./starter.md) to build your first application.
-
-Once you're up and running, [High-Level Concepts](./concepts.md) has an overview of LlamaIndex's modular architecture. For more hands-on practical examples, look through our [End-to-End Tutorials](./end_to_end.md).
-
-## 🗺️ Ecosystem
-
-To download or contribute, find LlamaIndex on:
-
-- Github: https://github.com/run-llama/LlamaIndexTS
-- NPM: https://www.npmjs.com/package/llamaindex
-
-## Community
-
-Need help? Have a feature suggestion? Join the LlamaIndex community:
-
-- Twitter: https://twitter.com/llama_index
-- Discord https://discord.gg/dGcwcsnxhU

apps/docs/docs/modules/_category_.yml

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

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

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

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

@@ -1,20 +0,0 @@
----
-sidebar_position: 4
----
-
-# ChatEngine
-
-The chat engine is a quick and simple way to chat with the data in your index.
-
-```typescript
-const retriever = index.asRetriever();
-const chatEngine = new ContextChatEngine({ retriever });
-
-// start chatting
-const response = await chatEngine.chat(query);
-```
-
-## Api References
-
-- [ContextChatEngine](../../api/classes/ContextChatEngine.md)
-- [CondenseQuestionChatEngine](../../api/classes/ContextChatEngine.md)

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

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

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

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

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

@@ -1,18 +0,0 @@
----
-sidebar_position: 0
----
-
-# Documents and Nodes
-
-`Document`s and `Node`s are the basic building blocks of any index. While the API for these objects is similar, `Document` objects represent entire files, while `Node`s are smaller pieces of that original document, that are suitable for an LLM and Q&A.
-
-```typescript
-import { Document } from "llamaindex";
-
-document = new Document({ text: "text", metadata: { key: "val" } });
-```
-
-## API Reference
-
-- [Document](../../api/classes/Document.md)
-- [TextNode](../../api/classes/TextNode.md)

apps/docs/docs/modules/index.md

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

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

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

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

@@ -1,22 +0,0 @@
----
-sidebar_position: 1
----
-
-# Embedding
-
-The embedding model in LlamaIndex is responsible for creating numerical representations of text. By default, LlamaIndex will use the `text-embedding-ada-002` model from OpenAI.
-
-This can be explicitly set in the `ServiceContext` object.
-
-```typescript
-import { OpenAIEmbedding, serviceContextFromDefaults } from "llamaindex";
-
-const openaiEmbeds = new OpenAIEmbedding();
-
-const serviceContext = serviceContextFromDefaults({ embedModel: openaiEmbeds });
-```
-
-## API Reference
-
-- [OpenAIEmbedding](../../api/classes/OpenAIEmbedding.md)
-- [ServiceContext](../../api/interfaces/ServiceContext.md)

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

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

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

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

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

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

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

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

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

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

apps/docs/docs/starter.md

@@ -1,56 +0,0 @@
----
-sidebar_position: 2
----
-
-# Starter Tutorial
-
-Once you have [installed LlamaIndex.TS using NPM](installation.md) and set up your OpenAI key, you're ready to start your first app:
-
-In a new folder:
-
-```bash npm2yarn
-npm install typescript
-npm install @types/node
-npx tsc --init # if needed
-```
-
-Create the file `example.ts`. This code will load some example data, create a document, index it (which creates embeddings using OpenAI), and then creates query engine to answer questions about the data.
-
-```ts
-// example.ts
-import fs from "fs/promises";
-import { Document, VectorStoreIndex } from "llamaindex";
-
-async function main() {
-  // Load essay from abramov.txt in Node
-  const essay = await fs.readFile(
-    "node_modules/llamaindex/examples/abramov.txt",
-    "utf-8",
-  );
-
-  // Create Document object with essay
-  const document = new Document({ text: essay });
-
-  // Split text and create embeddings. Store them in a VectorStoreIndex
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  // Query the index
-  const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query(
-    "What did the author do in college?",
-  );
-
-  // Output response
-  console.log(response.toString());
-}
-
-main();
-```
-
-Then you can run it using
-
-```bash
-npx ts-node example.ts
-```
-
-Ready to learn more? Check out our NextJS playground at https://llama-playground.vercel.app/. The source is available at https://github.com/run-llama/ts-playground

apps/docs/docusaurus.config.js

@@ -1,152 +0,0 @@
-// @ts-check
-// Note: type annotations allow type checking and IDEs autocompletion
-
-const lightCodeTheme = require("prism-react-renderer/themes/github");
-const darkCodeTheme = require("prism-react-renderer/themes/dracula");
-
-/** @type {import('@docusaurus/types').Config} */
-const config = {
-  title: "LlamaIndex.TS",
-  tagline: "Unleash the power of LLMs over your data in TypeScript",
-  favicon: "img/favicon.png",
-
-  // Set the production url of your site here
-  url: "https://ts.llamaindex.ai",
-  // Set the /<baseUrl>/ pathname under which your site is served
-  // For GitHub pages deployment, it is often '/<projectName>/'
-  baseUrl: "/",
-
-  // GitHub pages deployment config.
-  // If you aren't using GitHub pages, you don't need these.
-  organizationName: "run-llama", // Usually your GitHub org/user name.
-  projectName: "LlamaIndex.TS", // Usually your repo name.
-
-  onBrokenLinks: "warn",
-  onBrokenMarkdownLinks: "warn",
-
-  // Even if you don't use internalization, you can use this field to set useful
-  // metadata like html lang. For example, if your site is Chinese, you may want
-  // to replace "en" with "zh-Hans".
-  i18n: {
-    defaultLocale: "en",
-    locales: ["en"],
-  },
-
-  presets: [
-    [
-      "classic",
-      /** @type {import('@docusaurus/preset-classic').Options} */
-      ({
-        docs: {
-          routeBasePath: "/",
-          sidebarPath: require.resolve("./sidebars.js"),
-          // Please change this to your repo.
-          // Remove this to remove the "edit this page" links.
-          // editUrl:
-          //   "https://github.com/facebook/docusaurus/tree/main/packages/create-docusaurus/templates/shared/",
-          remarkPlugins: [
-            [require("@docusaurus/remark-plugin-npm2yarn"), { sync: true }],
-          ],
-        },
-        blog: false,
-      }),
-    ],
-  ],
-
-  themeConfig:
-    /** @type {import('@docusaurus/preset-classic').ThemeConfig} */
-    ({
-      // Replace with your project's social card
-      image: "img/favicon.png", // TODO change this
-      navbar: {
-        title: "LlamaIndex.TS",
-        logo: {
-          alt: "LlamaIndex.TS",
-          src: "img/favicon.png",
-        },
-        items: [
-          {
-            type: "docSidebar",
-            sidebarId: "mySidebar",
-            position: "left",
-            label: "Docs",
-          },
-          {
-            href: "https://github.com/run-llama/LlamaIndexTS",
-            label: "GitHub",
-            position: "right",
-          },
-        ],
-      },
-      footer: {
-        style: "dark",
-        links: [
-          {
-            title: "Docs",
-            items: [
-              {
-                label: "API",
-                to: "/api",
-              },
-            ],
-          },
-          {
-            title: "Community",
-            items: [
-              {
-                label: "Discord",
-                href: "https://discord.com/invite/eN6D2HQ4aX",
-              },
-              {
-                label: "Twitter",
-                href: "https://twitter.com/LlamaIndex",
-              },
-            ],
-          },
-          {
-            title: "More",
-            items: [
-              {
-                label: "GitHub",
-                href: "https://github.com/run-llama/LlamaIndexTS",
-              },
-            ],
-          },
-        ],
-        copyright: `Copyright © ${new Date().getFullYear()} LlamaIndex. Built with Docusaurus.`,
-      },
-      prism: {
-        theme: lightCodeTheme,
-        darkTheme: darkCodeTheme,
-      },
-      algolia: {
-        // The application ID provided by Algolia
-        appId: "DYKPM6G4CX",
-
-        // Public API key: it is safe to commit it
-        apiKey: "c4ff3789f20bb72a5d735082aef17719",
-
-        indexName: "ts-llamaindex",
-
-        // Optional: see doc section below
-        contextualSearch: true,
-      },
-    }),
-  plugins: [
-    [
-      "docusaurus-plugin-typedoc",
-      {
-        entryPoints: ["../../packages/core/src/index.ts"],
-        tsconfig: "../../packages/core/tsconfig.json",
-        readme: "none",
-        sourceLinkTemplate:
-          "https://github.com/run-llama/LlamaIndexTS/blob/{gitRevision}/{path}#L{line}",
-        sidebar: {
-          position: 6,
-        },
-      },
-    ],
-  ],
-};
-
-module.exports = config;

apps/docs/package.json

@@ -1,53 +0,0 @@
-{
-  "name": "docs",
-  "version": "0.0.0",
-  "private": true,
-  "scripts": {
-    "docusaurus": "docusaurus",
-    "start": "docusaurus start",
-    "build": "docusaurus build",
-    "swizzle": "docusaurus swizzle",
-    "deploy": "docusaurus deploy",
-    "clear": "docusaurus clear",
-    "serve": "docusaurus serve",
-    "write-translations": "docusaurus write-translations",
-    "write-heading-ids": "docusaurus write-heading-ids",
-    "typecheck": "tsc"
-  },
-  "dependencies": {
-    "@docusaurus/core": "2.4.3",
-    "@docusaurus/preset-classic": "2.4.3",
-    "@docusaurus/remark-plugin-npm2yarn": "^2.4.3",
-    "@mdx-js/react": "^1.6.22",
-    "clsx": "^1.2.1",
-    "postcss": "^8.4.31",
-    "prism-react-renderer": "^1.3.5",
-    "raw-loader": "^4.0.2",
-    "react": "^17.0.2",
-    "react-dom": "^17.0.2"
-  },
-  "devDependencies": {
-    "@docusaurus/module-type-aliases": "2.4.3",
-    "@docusaurus/types": "^2.4.3",
-    "@tsconfig/docusaurus": "^2.0.1",
-    "docusaurus-plugin-typedoc": "^0.19.2",
-    "typedoc": "^0.24.8",
-    "typedoc-plugin-markdown": "^3.16.0",
-    "typescript": "^4.9.5"
-  },
-  "browserslist": {
-    "production": [
-      ">0.5%",
-      "not dead",
-      "not op_mini all"
-    ],
-    "development": [
-      "last 1 chrome version",
-      "last 1 firefox version",
-      "last 1 safari version"
-    ]
-  },
-  "engines": {
-    "node": ">=16.14"
-  }
-}

apps/docs/sidebars.js

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

apps/docs/src/css/custom.css

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

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

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

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----
-title: Markdown page example
----
-
-# Markdown page example
-
-You don't need React to write simple standalone pages.

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apps/docs/tsconfig.json

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

apps/simple/CHANGELOG.md

@@ -1,271 +0,0 @@
-# simple
-
-## 0.0.33
-
-### Patch Changes
-
-- Updated dependencies [63f2108]
-  - llamaindex@0.0.35
-
-## 0.0.32
-
-### Patch Changes
-
-- Updated dependencies [2a27e21]
-  - llamaindex@0.0.34
-
-## 0.0.31
-
-### Patch Changes
-
-- Updated dependencies [5e2e92c]
-  - llamaindex@0.0.33
-
-## 0.0.30
-
-### Patch Changes
-
-- Updated dependencies [90c0b83]
-- Updated dependencies [dfd22aa]
-  - llamaindex@0.0.32
-
-## 0.0.29
-
-### Patch Changes
-
-- Updated dependencies [6c55b2d]
-- Updated dependencies [8aa8c65]
-- Updated dependencies [6c55b2d]
-  - llamaindex@0.0.31
-
-## 0.0.28
-
-### Patch Changes
-
-- Updated dependencies [139abad]
-- Updated dependencies [139abad]
-- Updated dependencies [eb0e994]
-- Updated dependencies [eb0e994]
-- Updated dependencies [139abad]
-  - llamaindex@0.0.30
-
-## 0.0.27
-
-### Patch Changes
-
-- Updated dependencies [a52143b]
-- Updated dependencies [1b7fd95]
-- Updated dependencies [0db3f41]
-  - llamaindex@0.0.29
-
-## 0.0.26
-
-### Patch Changes
-
-- Updated dependencies [96bb657]
-- Updated dependencies [96bb657]
-- Updated dependencies [837854d]
-  - llamaindex@0.0.28
-
-## 0.0.25
-
-### Patch Changes
-
-- Updated dependencies [4a5591b]
-- Updated dependencies [4a5591b]
-- Updated dependencies [4a5591b]
-  - llamaindex@0.0.27
-
-## 0.0.24
-
-### Patch Changes
-
-- Updated dependencies [5bb55bc]
-  - llamaindex@0.0.26
-
-## 0.0.23
-
-### Patch Changes
-
-- Updated dependencies [e21eca2]
-- Updated dependencies [40a8f07]
-- Updated dependencies [40a8f07]
-  - llamaindex@0.0.25
-
-## 0.0.22
-
-### Patch Changes
-
-- Updated dependencies [e4af7b3]
-- Updated dependencies [259fe63]
-  - llamaindex@0.0.24
-
-## 0.0.21
-
-### Patch Changes
-
-- Updated dependencies
-- Updated dependencies [9d6b2ed]
-  - llamaindex@0.0.23
-
-## 0.0.20
-
-### Patch Changes
-
-- Updated dependencies [454f3f8]
-- Updated dependencies [454f3f8]
-- Updated dependencies [454f3f8]
-- Updated dependencies [454f3f8]
-- Updated dependencies [99df58f]
-  - llamaindex@0.0.22
-
-## 0.0.19
-
-### Patch Changes
-
-- Updated dependencies [f7a57ca]
-- Updated dependencies [0a09de2]
-- Updated dependencies [f7a57ca]
-  - llamaindex@0.0.21
-
-## 0.0.18
-
-### Patch Changes
-
-- Updated dependencies [b526a2d]
-- Updated dependencies [b526a2d]
-- Updated dependencies [b526a2d]
-  - llamaindex@0.0.20
-
-## 0.0.17
-
-### Patch Changes
-
-- Updated dependencies [a747f28]
-- Updated dependencies [355910b]
-- Updated dependencies [355910b]
-  - llamaindex@0.0.19
-
-## 0.0.16
-
-### Patch Changes
-
-- Updated dependencies [824c13c]
-- Updated dependencies [18b8915]
-- Updated dependencies [ade9d8f]
-- Updated dependencies [824c13c]
-  - llamaindex@0.0.18
-
-## 0.0.15
-
-### Patch Changes
-
-- Updated dependencies [f80b062]
-- Updated dependencies [b3fec86]
-- Updated dependencies [b3fec86]
-  - llamaindex@0.0.17
-
-## 0.0.14
-
-### Patch Changes
-
-- Updated dependencies [ec12633]
-- Updated dependencies [9214b06]
-- Updated dependencies [3316c6b]
-- Updated dependencies [3316c6b]
-  - llamaindex@0.0.16
-
-## 0.0.13
-
-### Patch Changes
-
-- Updated dependencies [b501eb5]
-- Updated dependencies [f9d1a6e]
-  - llamaindex@0.0.15
-
-## 0.0.12
-
-### Patch Changes
-
-- Updated dependencies [4ef334a]
-- Updated dependencies [0af7773]
-- Updated dependencies [bea4af9]
-- Updated dependencies [4ef334a]
-  - llamaindex@0.0.14
-
-## 0.0.11
-
-### Patch Changes
-
-- Updated dependencies [4f6f245]
-  - llamaindex@0.0.13
-
-## 0.0.10
-
-### Patch Changes
-
-- Updated dependencies [68bdaaa]
-  - llamaindex@0.0.12
-
-## 0.0.9
-
-### Patch Changes
-
-- Updated dependencies [fb7fb76]
-  - llamaindex@0.0.11
-
-## 0.0.8
-
-### Patch Changes
-
-- Updated dependencies [6f2cb31]
-  - llamaindex@0.0.10
-
-## 0.0.7
-
-### Patch Changes
-
-- Updated dependencies [02d9bb0]
-  - llamaindex@0.0.9
-
-## 0.0.6
-
-### Patch Changes
-
-- Updated dependencies [ea5038e]
-  - llamaindex@0.0.8
-
-## 0.0.5
-
-### Patch Changes
-
-- Updated dependencies [9fa6d4a]
-  - llamaindex@0.0.7
-
-## 0.0.4
-
-### Patch Changes
-
-- Updated dependencies
-  - llamaindex@0.0.6
-
-## 0.0.3
-
-### Patch Changes
-
-- Updated dependencies [5a765aa]
-  - llamaindex@0.0.5
-
-## 0.0.2
-
-### Patch Changes
-
-- Updated dependencies [c65d671]
-  - llamaindex@0.0.4
-
-## 0.0.1
-
-### Patch Changes
-
-- Updated dependencies [ca9410f]
-  - llamaindex@0.0.3

apps/simple/README.md

@@ -1,10 +0,0 @@
-# Simple Examples
-
-Due to packaging, you will need to run these commands to get started.
-
-```bash
-pnpm install
-pnpm --filter llamaindex build
-```
-
-Then run the examples with `ts-node`, for example `npx ts-node vectorIndex.ts`

apps/simple/anthropic.ts

@@ -1,16 +0,0 @@
-import { Anthropic } from "llamaindex";
-
-(async () => {
-  const anthropic = new Anthropic();
-  const result = await anthropic.chat([
-    { content: "You want to talk in rhymes.", role: "system" },
-    { content: "Hello, world!", role: "user" },
-    { content: "Hello!", role: "assistant" },
-    {
-      content:
-        "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
-      role: "user",
-    },
-  ]);
-  console.log(result);
-})();

apps/simple/chatEngine.ts

@@ -1,33 +0,0 @@
-import { stdin as input, stdout as output } from "node:process";
-// readline/promises is still experimental so not in @types/node yet
-// @ts-ignore
-import readline from "node:readline/promises";
-
-import {
-  ContextChatEngine,
-  Document,
-  serviceContextFromDefaults,
-  VectorStoreIndex,
-} from "llamaindex";
-
-import essay from "./essay";
-
-async function main() {
-  const document = new Document({ text: essay });
-  const serviceContext = serviceContextFromDefaults({ chunkSize: 512 });
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
-  const retriever = index.asRetriever();
-  retriever.similarityTopK = 5;
-  const chatEngine = new ContextChatEngine({ retriever });
-  const rl = readline.createInterface({ input, output });
-
-  while (true) {
-    const query = await rl.question("Query: ");
-    const response = await chatEngine.chat(query);
-    console.log(response.toString());
-  }
-}
-
-main().catch(console.error);

apps/simple/csv.ts

@@ -1,49 +0,0 @@
-import {
-  CompactAndRefine,
-  OpenAI,
-  PapaCSVReader,
-  ResponseSynthesizer,
-  serviceContextFromDefaults,
-  VectorStoreIndex,
-} from "llamaindex";
-
-async function main() {
-  // Load CSV
-  const reader = new PapaCSVReader();
-  const path = "data/titanic_train.csv";
-  const documents = await reader.loadData(path);
-
-  const serviceContext = serviceContextFromDefaults({
-    llm: new OpenAI({ model: "gpt-4" }),
-  });
-
-  // Split text and create embeddings. Store them in a VectorStoreIndex
-  const index = await VectorStoreIndex.fromDocuments(documents, {
-    serviceContext,
-  });
-
-  const csvPrompt = ({ context = "", query = "" }) => {
-    return `The following CSV file is loaded from ${path}
-\`\`\`csv
-${context}
-\`\`\`
-Given the CSV file, generate me Typescript code to answer the question: ${query}. You can use built in NodeJS functions but avoid using third party libraries.
-`;
-  };
-
-  const responseSynthesizer = new ResponseSynthesizer({
-    responseBuilder: new CompactAndRefine(serviceContext, csvPrompt),
-  });
-
-  const queryEngine = index.asQueryEngine({ responseSynthesizer });
-
-  // Query the index
-  const response = await queryEngine.query(
-    "What is the correlation between survival and age?",
-  );
-
-  // Output response
-  console.log(response.toString());
-}
-
-main().catch(console.error);

apps/simple/data/titanic_train.csv

@@ -1,715 +0,0 @@
-survived,pclass,name,sex,age,sibsp,parch,ticket,fare,cabin,embarked
-0,3,Braund,male,22,1,0,A/5 21171,7.25,,S
-1,1,Cumings,female,38,1,0,PC 17599,71.2833,C85,C
-1,3,Heikkinen,female,26,0,0,STON/O2. 3101282,7.925,,S
-1,1,Futrelle,female,35,1,0,113803,53.1,C123,S
-0,3,Allen,male,35,0,0,373450,8.05,,S
-0,1,McCarthy,male,54,0,0,17463,51.8625,E46,S
-0,3,Palsson,male,2,3,1,349909,21.075,,S
-1,3,Johnson,female,27,0,2,347742,11.1333,,S
-1,2,Nasser,female,14,1,0,237736,30.0708,,C
-1,3,Sandstrom,female,4,1,1,PP 9549,16.7,G6,S
-1,1,Bonnell,female,58,0,0,113783,26.55,C103,S
-0,3,Saundercock,male,20,0,0,A/5. 2151,8.05,,S
-0,3,Andersson,male,39,1,5,347082,31.275,,S
-0,3,Vestrom,female,14,0,0,350406,7.8542,,S
-1,2,Hewlett,female,55,0,0,248706,16,,S
-0,3,Rice,male,2,4,1,382652,29.125,,Q
-0,3,Vander,female,31,1,0,345763,18,,S
-0,2,Fynney,male,35,0,0,239865,26,,S
-1,2,Beesley,male,34,0,0,248698,13,D56,S
-1,3,McGowan,female,15,0,0,330923,8.0292,,Q
-1,1,Sloper,male,28,0,0,113788,35.5,A6,S
-0,3,Palsson,female,8,3,1,349909,21.075,,S
-1,3,Asplund,female,38,1,5,347077,31.3875,,S
-0,1,Fortune,male,19,3,2,19950,263,C23 C25 C27,S
-0,1,Uruchurtu,male,40,0,0,PC 17601,27.7208,,C
-0,2,Wheadon,male,66,0,0,C.A. 24579,10.5,,S
-0,1,Meyer,male,28,1,0,PC 17604,82.1708,,C
-0,1,Holverson,male,42,1,0,113789,52,,S
-0,3,Cann,male,21,0,0,A./5. 2152,8.05,,S
-0,3,Vander,female,18,2,0,345764,18,,S
-1,3,Nicola-Yarred,female,14,1,0,2651,11.2417,,C
-0,3,Ahlin,female,40,1,0,7546,9.475,,S
-0,2,Turpin,female,27,1,0,11668,21,,S
-1,2,Laroche,female,3,1,2,SC/Paris 2123,41.5792,,C
-1,3,Devaney,female,19,0,0,330958,7.8792,,Q
-0,3,Arnold-Franchi,female,18,1,0,349237,17.8,,S
-0,3,Panula,male,7,4,1,3101295,39.6875,,S
-0,3,Nosworthy,male,21,0,0,A/4. 39886,7.8,,S
-1,1,Harper,female,49,1,0,PC 17572,76.7292,D33,C
-1,2,Faunthorpe,female,29,1,0,2926,26,,S
-0,1,Ostby,male,65,0,1,113509,61.9792,B30,C
-1,2,Rugg,female,21,0,0,C.A. 31026,10.5,,S
-0,3,Novel,male,28.5,0,0,2697,7.2292,,C
-1,2,West,female,5,1,2,C.A. 34651,27.75,,S
-0,3,Goodwin,male,11,5,2,CA 2144,46.9,,S
-0,3,Sirayanian,male,22,0,0,2669,7.2292,,C
-1,1,Icard,female,38,0,0,113572,80,B28,
-0,1,Harris,male,45,1,0,36973,83.475,C83,S
-0,3,Skoog,male,4,3,2,347088,27.9,,S
-1,2,Nye,female,29,0,0,C.A. 29395,10.5,F33,S
-0,3,Crease,male,19,0,0,S.P. 3464,8.1583,,S
-1,3,Andersson,female,17,4,2,3101281,7.925,,S
-0,3,Kink,male,26,2,0,315151,8.6625,,S
-0,2,Jenkin,male,32,0,0,C.A. 33111,10.5,,S
-0,3,Goodwin,female,16,5,2,CA 2144,46.9,,S
-0,2,Hood,male,21,0,0,S.O.C. 14879,73.5,,S
-0,3,Chronopoulos,male,26,1,0,2680,14.4542,,C
-1,3,Bing,male,32,0,0,1601,56.4958,,S
-0,3,Moen,male,25,0,0,348123,7.65,F G73,S
-1,2,Caldwell,male,0.83,0,2,248738,29,,S
-1,3,Dowdell,female,30,0,0,364516,12.475,,S
-0,3,Waelens,male,22,0,0,345767,9,,S
-1,3,Sheerlinck,male,29,0,0,345779,9.5,,S
-0,1,Carrau,male,28,0,0,113059,47.1,,S
-1,2,Ilett,female,17,0,0,SO/C 14885,10.5,,S
-1,3,Backstrom,female,33,3,0,3101278,15.85,,S
-0,3,Ford,male,16,1,3,W./C. 6608,34.375,,S
-1,1,Fortune,female,23,3,2,19950,263,C23 C25 C27,S
-0,3,Celotti,male,24,0,0,343275,8.05,,S
-0,3,Christmann,male,29,0,0,343276,8.05,,S
-0,3,Andreasson,male,20,0,0,347466,7.8542,,S
-0,1,Chaffee,male,46,1,0,W.E.P. 5734,61.175,E31,S
-0,3,Dean,male,26,1,2,C.A. 2315,20.575,,S
-0,3,Coxon,male,59,0,0,364500,7.25,,S
-0,1,Goldschmidt,male,71,0,0,PC 17754,34.6542,A5,C
-1,1,Greenfield,male,23,0,1,PC 17759,63.3583,D10 D12,C
-1,2,Doling,female,34,0,1,231919,23,,S
-0,2,Kantor,male,34,1,0,244367,26,,S
-0,3,Petranec,female,28,0,0,349245,7.8958,,S
-0,1,White,male,21,0,1,35281,77.2875,D26,S
-0,3,Johansson,male,33,0,0,7540,8.6542,,S
-0,3,Gustafsson,male,37,2,0,3101276,7.925,,S
-0,3,Mionoff,male,28,0,0,349207,7.8958,,S
-1,3,Salkjelsvik,female,21,0,0,343120,7.65,,S
-0,3,Rekic,male,38,0,0,349249,7.8958,,S
-0,1,Porter,male,47,0,0,110465,52,C110,S
-0,3,Zabour,female,14.5,1,0,2665,14.4542,,C
-0,3,Barton,male,22,0,0,324669,8.05,,S
-0,3,Jussila,female,20,1,0,4136,9.825,,S
-0,3,Attalah,female,17,0,0,2627,14.4583,,C
-0,3,Pekoniemi,male,21,0,0,STON/O 2. 3101294,7.925,,S
-0,3,Connors,male,70.5,0,0,370369,7.75,,Q
-0,2,Turpin,male,29,1,0,11668,21,,S
-0,1,Baxter,male,24,0,1,PC 17558,247.5208,B58 B60,C
-0,3,Andersson,female,2,4,2,347082,31.275,,S
-0,2,Hickman,male,21,2,0,S.O.C. 14879,73.5,,S
-0,2,Nasser,male,32.5,1,0,237736,30.0708,,C
-1,2,Webber,female,32.5,0,0,27267,13,E101,S
-0,1,White,male,54,0,1,35281,77.2875,D26,S
-1,3,Nicola-Yarred,male,12,1,0,2651,11.2417,,C
-1,3,Madsen,male,24,0,0,C 17369,7.1417,,S
-0,3,Ekstrom,male,45,0,0,347061,6.975,,S
-0,3,Drazenoic,male,33,0,0,349241,7.8958,,C
-0,3,Coelho,male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
-0,3,Robins,female,47,1,0,A/5. 3337,14.5,,S
-1,2,Weisz,female,29,1,0,228414,26,,S
-0,2,Sobey,male,25,0,0,C.A. 29178,13,,S
-0,2,Richard,male,23,0,0,SC/PARIS 2133,15.0458,,C
-1,1,Newsom,female,19,0,2,11752,26.2833,D47,S
-0,1,Futrelle,male,37,1,0,113803,53.1,C123,S
-0,3,Osen,male,16,0,0,7534,9.2167,,S
-0,1,Giglio,male,24,0,0,PC 17593,79.2,B86,C
-1,3,Nysten,female,22,0,0,347081,7.75,,S
-1,3,Hakkarainen,female,24,1,0,STON/O2. 3101279,15.85,,S
-0,3,Burke,male,19,0,0,365222,6.75,,Q
-0,2,Andrew,male,18,0,0,231945,11.5,,S
-0,2,Nicholls,male,19,1,1,C.A. 33112,36.75,,S
-1,3,Andersson,male,27,0,0,350043,7.7958,,S
-0,3,Ford,female,9,2,2,W./C. 6608,34.375,,S
-0,2,Navratil,male,36.5,0,2,230080,26,F2,S
-0,2,Byles,male,42,0,0,244310,13,,S
-0,2,Bateman,male,51,0,0,S.O.P. 1166,12.525,,S
-1,1,Pears,female,22,1,0,113776,66.6,C2,S
-0,3,Meo,male,55.5,0,0,A.5. 11206,8.05,,S
-0,3,van,male,40.5,0,2,A/5. 851,14.5,,S
-0,1,Williams,male,51,0,1,PC 17597,61.3792,,C
-1,3,Gilnagh,female,16,0,0,35851,7.7333,,Q
-0,3,Corn,male,30,0,0,SOTON/OQ 392090,8.05,,S
-0,3,Cribb,male,44,0,1,371362,16.1,,S
-1,2,Watt,female,40,0,0,C.A. 33595,15.75,,S
-0,3,Bengtsson,male,26,0,0,347068,7.775,,S
-0,3,Calic,male,17,0,0,315093,8.6625,,S
-0,3,Panula,male,1,4,1,3101295,39.6875,,S
-1,3,Goldsmith,male,9,0,2,363291,20.525,,S
-0,3,Skoog,female,45,1,4,347088,27.9,,S
-0,3,Ling,male,28,0,0,1601,56.4958,,S
-0,1,Van,male,61,0,0,111240,33.5,B19,S
-0,3,Rice,male,4,4,1,382652,29.125,,Q
-1,3,Johnson,female,1,1,1,347742,11.1333,,S
-0,3,Sivola,male,21,0,0,STON/O 2. 3101280,7.925,,S
-0,1,Smith,male,56,0,0,17764,30.6958,A7,C
-0,3,Klasen,male,18,1,1,350404,7.8542,,S
-0,1,Isham,female,50,0,0,PC 17595,28.7125,C49,C
-0,2,Hale,male,30,0,0,250653,13,,S
-0,3,Leonard,male,36,0,0,LINE,0,,S
-0,3,Asplund,male,9,4,2,347077,31.3875,,S
-1,2,Becker,male,1,2,1,230136,39,F4,S
-1,3,Kink-Heilmann,female,4,0,2,315153,22.025,,S
-1,1,Romaine,male,45,0,0,111428,26.55,,S
-0,3,Bourke,male,40,1,1,364849,15.5,,Q
-0,3,Turcin,male,36,0,0,349247,7.8958,,S
-1,2,Pinsky,female,32,0,0,234604,13,,S
-0,2,Carbines,male,19,0,0,28424,13,,S
-1,3,Andersen-Jensen,female,19,1,0,350046,7.8542,,S
-1,2,Navratil,male,3,1,1,230080,26,F2,S
-1,1,Brown,female,44,0,0,PC 17610,27.7208,B4,C
-1,1,Lurette,female,58,0,0,PC 17569,146.5208,B80,C
-0,3,Olsen,male,42,0,1,4579,8.4042,,S
-0,2,Yrois,female,24,0,0,248747,13,,S
-0,3,Vande,male,28,0,0,345770,9.5,,S
-0,3,Johanson,male,34,0,0,3101264,6.4958,,S
-0,3,Youseff,male,45.5,0,0,2628,7.225,,C
-1,3,Cohen,male,18,0,0,A/5 3540,8.05,,S
-0,3,Strom,female,2,0,1,347054,10.4625,G6,S
-0,3,Backstrom,male,32,1,0,3101278,15.85,,S
-1,3,Albimona,male,26,0,0,2699,18.7875,,C
-1,3,Carr,female,16,0,0,367231,7.75,,Q
-1,1,Blank,male,40,0,0,112277,31,A31,C
-0,3,Ali,male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
-1,2,Cameron,female,35,0,0,F.C.C. 13528,21,,S
-0,3,Perkin,male,22,0,0,A/5 21174,7.25,,S
-0,2,Givard,male,30,0,0,250646,13,,S
-1,1,Newell,female,31,1,0,35273,113.275,D36,C
-1,3,Honkanen,female,27,0,0,STON/O2. 3101283,7.925,,S
-0,2,Jacobsohn,male,42,1,0,243847,27,,S
-1,1,Bazzani,female,32,0,0,11813,76.2917,D15,C
-0,2,Harris,male,30,0,0,W/C 14208,10.5,,S
-1,3,Sunderland,male,16,0,0,SOTON/OQ 392089,8.05,,S
-0,2,Bracken,male,27,0,0,220367,13,,S
-0,3,Green,male,51,0,0,21440,8.05,,S
-1,1,Hoyt,male,38,1,0,19943,90,C93,S
-0,3,Berglund,male,22,0,0,PP 4348,9.35,,S
-1,2,Mellors,male,19,0,0,SW/PP 751,10.5,,S
-0,3,Lovell,male,20.5,0,0,A/5 21173,7.25,,S
-0,2,Fahlstrom,male,18,0,0,236171,13,,S
-1,1,Harris,female,35,1,0,36973,83.475,C83,S
-0,3,Larsson,male,29,0,0,347067,7.775,,S
-0,2,Sjostedt,male,59,0,0,237442,13.5,,S
-1,3,Asplund,female,5,4,2,347077,31.3875,,S
-0,2,Leyson,male,24,0,0,C.A. 29566,10.5,,S
-0,2,Hold,male,44,1,0,26707,26,,S
-1,2,Collyer,female,8,0,2,C.A. 31921,26.25,,S
-0,2,Pengelly,male,19,0,0,28665,10.5,,S
-0,2,Hunt,male,33,0,0,SCO/W 1585,12.275,,S
-0,2,Coleridge,male,29,0,0,W./C. 14263,10.5,,S
-0,3,Maenpaa,male,22,0,0,STON/O 2. 3101275,7.125,,S
-0,3,Attalah,male,30,0,0,2694,7.225,,C
-0,1,Minahan,male,44,2,0,19928,90,C78,Q
-0,3,Lindahl,female,25,0,0,347071,7.775,,S
-1,2,Hamalainen,female,24,0,2,250649,14.5,,S
-1,1,Beckwith,male,37,1,1,11751,52.5542,D35,S
-0,2,Carter,male,54,1,0,244252,26,,S
-0,3,Strom,female,29,1,1,347054,10.4625,G6,S
-0,1,Stead,male,62,0,0,113514,26.55,C87,S
-0,3,Lobb,male,30,1,0,A/5. 3336,16.1,,S
-0,3,Rosblom,female,41,0,2,370129,20.2125,,S
-1,3,Touma,female,29,0,2,2650,15.2458,,C
-1,1,Cherry,female,30,0,0,110152,86.5,B77,S
-1,1,Ward,female,35,0,0,PC 17755,512.3292,,C
-1,2,Parrish,female,50,0,1,230433,26,,S
-1,3,Asplund,male,3,4,2,347077,31.3875,,S
-0,1,Taussig,male,52,1,1,110413,79.65,E67,S
-0,1,Harrison,male,40,0,0,112059,0,B94,S
-0,2,Reeves,male,36,0,0,C.A. 17248,10.5,,S
-0,3,Panula,male,16,4,1,3101295,39.6875,,S
-1,3,Persson,male,25,1,0,347083,7.775,,S
-1,1,Graham,female,58,0,1,PC 17582,153.4625,C125,S
-1,1,Bissette,female,35,0,0,PC 17760,135.6333,C99,S
-1,3,Tornquist,male,25,0,0,LINE,0,,S
-1,2,Mellinger,female,41,0,1,250644,19.5,,S
-0,1,Natsch,male,37,0,1,PC 17596,29.7,C118,C
-1,1,Andrews,female,63,1,0,13502,77.9583,D7,S
-0,3,Lindblom,female,45,0,0,347073,7.75,,S
-0,3,Rice,male,7,4,1,382652,29.125,,Q
-1,3,Abbott,female,35,1,1,C.A. 2673,20.25,,S
-0,3,Duane,male,65,0,0,336439,7.75,,Q
-0,3,Olsson,male,28,0,0,347464,7.8542,,S
-0,3,de,male,16,0,0,345778,9.5,,S
-1,3,Dorking,male,19,0,0,A/5. 10482,8.05,,S
-0,3,Stankovic,male,33,0,0,349239,8.6625,,C
-1,3,de,male,30,0,0,345774,9.5,,S
-0,3,Naidenoff,male,22,0,0,349206,7.8958,,S
-1,2,Hosono,male,42,0,0,237798,13,,S
-1,3,Connolly,female,22,0,0,370373,7.75,,Q
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-0,3,Hegarty,female,18,0,0,365226,6.75,,Q
-0,2,Hickman,male,24,2,0,S.O.C. 14879,73.5,,S
-0,3,Bourke,female,32,1,1,364849,15.5,,Q
-0,2,Eitemiller,male,23,0,0,29751,13,,S
-0,1,Newell,male,58,0,2,35273,113.275,D48,C
-1,1,Frauenthal,male,50,2,0,PC 17611,133.65,,S
-0,3,Badt,male,40,0,0,2623,7.225,,C
-0,1,Colley,male,47,0,0,5727,25.5875,E58,S
-0,3,Coleff,male,36,0,0,349210,7.4958,,S
-1,3,Lindqvist,male,20,1,0,STON/O 2. 3101285,7.925,,S
-0,2,Hickman,male,32,2,0,S.O.C. 14879,73.5,,S
-0,2,Butler,male,25,0,0,234686,13,,S
-0,3,Cook,male,43,0,0,A/5 3536,8.05,,S
-1,2,Brown,female,40,1,1,29750,39,,S
-0,1,Davidson,male,31,1,0,F.C. 12750,52,B71,S
-0,2,Mitchell,male,70,0,0,C.A. 24580,10.5,,S
-1,2,Wilhelms,male,31,0,0,244270,13,,S
-0,3,Edvardsson,male,18,0,0,349912,7.775,,S
-0,3,Sawyer,male,24.5,0,0,342826,8.05,,S
-1,3,Turja,female,18,0,0,4138,9.8417,,S
-0,3,Goodwin,female,43,1,6,CA 2144,46.9,,S
-1,1,Cardeza,male,36,0,1,PC 17755,512.3292,B51 B53 B55,C
-1,1,Hassab,male,27,0,0,PC 17572,76.7292,D49,C
-0,3,Olsvigen,male,20,0,0,6563,9.225,,S
-0,3,Goodwin,male,14,5,2,CA 2144,46.9,,S
-0,2,Brown,male,60,1,1,29750,39,,S
-0,2,Laroche,male,25,1,2,SC/Paris 2123,41.5792,,C
-0,3,Panula,male,14,4,1,3101295,39.6875,,S
-0,3,Dakic,male,19,0,0,349228,10.1708,,S
-0,3,Fischer,male,18,0,0,350036,7.7958,,S
-1,1,Madill,female,15,0,1,24160,211.3375,B5,S
-1,1,Dick,male,31,1,0,17474,57,B20,S
-1,3,Karun,female,4,0,1,349256,13.4167,,C
-0,3,Saad,male,25,0,0,2672,7.225,,C
-0,1,Weir,male,60,0,0,113800,26.55,,S
-0,2,Chapman,male,52,0,0,248731,13.5,,S
-0,3,Kelly,male,44,0,0,363592,8.05,,S
-0,1,Thayer,male,49,1,1,17421,110.8833,C68,C
-0,3,Humblen,male,42,0,0,348121,7.65,F G63,S
-1,1,Astor,female,18,1,0,PC 17757,227.525,C62 C64,C
-1,1,Silverthorne,male,35,0,0,PC 17475,26.2875,E24,S
-0,3,Barbara,female,18,0,1,2691,14.4542,,C
-0,3,Gallagher,male,25,0,0,36864,7.7417,,Q
-0,3,Hansen,male,26,1,0,350025,7.8542,,S
-0,2,Morley,male,39,0,0,250655,26,,S
-1,2,Kelly,female,45,0,0,223596,13.5,,S
-1,1,Calderhead,male,42,0,0,PC 17476,26.2875,E24,S
-1,1,Cleaver,female,22,0,0,113781,151.55,,S
-1,1,Mayne,female,24,0,0,PC 17482,49.5042,C90,C
-1,1,Taylor,male,48,1,0,19996,52,C126,S
-0,3,Larsson,male,29,0,0,7545,9.4833,,S
-0,2,Greenberg,male,52,0,0,250647,13,,S
-0,3,Soholt,male,19,0,0,348124,7.65,F G73,S
-1,1,Endres,female,38,0,0,PC 17757,227.525,C45,C
-1,2,Troutt,female,27,0,0,34218,10.5,E101,S
-0,3,Johnson,male,33,0,0,347062,7.775,,S
-1,2,Harper,female,6,0,1,248727,33,,S
-0,3,Jensen,male,17,1,0,350048,7.0542,,S
-0,2,Gillespie,male,34,0,0,12233,13,,S
-0,2,Hodges,male,50,0,0,250643,13,,S
-1,1,Chambers,male,27,1,0,113806,53.1,E8,S
-0,3,Oreskovic,male,20,0,0,315094,8.6625,,S
-1,2,Renouf,female,30,3,0,31027,21,,S
-0,2,Bryhl,male,25,1,0,236853,26,,S
-0,3,Ilmakangas,female,25,1,0,STON/O2. 3101271,7.925,,S
-1,1,Allen,female,29,0,0,24160,211.3375,B5,S
-0,3,Hassan,male,11,0,0,2699,18.7875,,C
-0,2,Berriman,male,23,0,0,28425,13,,S
-0,2,Troupiansky,male,23,0,0,233639,13,,S
-0,3,Williams,male,28.5,0,0,54636,16.1,,S
-0,3,Ford,female,48,1,3,W./C. 6608,34.375,,S
-1,1,Lesurer,male,35,0,0,PC 17755,512.3292,B101,C
-0,1,Cavendish,male,36,1,0,19877,78.85,C46,S
-1,1,Ryerson,female,21,2,2,PC 17608,262.375,B57 B59 B63 B66,C
-0,3,McNamee,male,24,1,0,376566,16.1,,S
-1,3,Stranden,male,31,0,0,STON/O 2. 3101288,7.925,,S
-0,1,Crosby,male,70,1,1,WE/P 5735,71,B22,S
-0,3,Abbott,male,16,1,1,C.A. 2673,20.25,,S
-1,2,Sinkkonen,female,30,0,0,250648,13,,S
-0,1,Marvin,male,19,1,0,113773,53.1,D30,S
-0,3,Connaghton,male,31,0,0,335097,7.75,,Q
-1,2,Wells,female,4,1,1,29103,23,,S
-1,3,Moor,male,6,0,1,392096,12.475,E121,S
-0,3,Vande,male,33,0,0,345780,9.5,,S
-0,3,Jonkoff,male,23,0,0,349204,7.8958,,S
-1,2,Herman,female,48,1,2,220845,65,,S
-1,2,Hamalainen,male,0.67,1,1,250649,14.5,,S
-0,3,Carlsson,male,28,0,0,350042,7.7958,,S
-0,2,Bailey,male,18,0,0,29108,11.5,,S
-0,3,Theobald,male,34,0,0,363294,8.05,,S
-1,1,Rothes,female,33,0,0,110152,86.5,B77,S
-0,3,Nirva,male,41,0,0,SOTON/O2 3101272,7.125,,S
-1,3,Barah,male,20,0,0,2663,7.2292,,C
-1,1,Carter,female,36,1,2,113760,120,B96 B98,S
-0,3,Eklund,male,16,0,0,347074,7.775,,S
-1,1,Hogeboom,female,51,1,0,13502,77.9583,D11,S
-0,3,Mangan,female,30.5,0,0,364850,7.75,,Q
-0,3,Gronnestad,male,32,0,0,8471,8.3625,,S
-0,3,Lievens,male,24,0,0,345781,9.5,,S
-0,3,Jensen,male,48,0,0,350047,7.8542,,S
-0,2,Mack,female,57,0,0,S.O./P.P. 3,10.5,E77,S
-1,2,Hocking,female,54,1,3,29105,23,,S
-0,3,Myhrman,male,18,0,0,347078,7.75,,S
-1,3,Emanuel,female,5,0,0,364516,12.475,,S
-1,1,Robert,female,43,0,1,24160,211.3375,B3,S
-1,3,Ayoub,female,13,0,0,2687,7.2292,,C
-1,1,Dick,female,17,1,0,17474,57,B20,S
-0,1,Long,male,29,0,0,113501,30,D6,S
-0,3,Ali,male,25,0,0,SOTON/O.Q. 3101312,7.05,,S
-0,3,Harmer,male,25,0,0,374887,7.25,,S
-1,3,Sjoblom,female,18,0,0,3101265,7.4958,,S
-0,3,Rice,male,8,4,1,382652,29.125,,Q
-1,3,Dean,male,1,1,2,C.A. 2315,20.575,,S
-0,1,Guggenheim,male,46,0,0,PC 17593,79.2,B82 B84,C
-0,2,Gaskell,male,16,0,0,239865,26,,S
-0,3,Dantcheff,male,25,0,0,349203,7.8958,,S
-0,2,Otter,male,39,0,0,28213,13,,S
-1,1,Leader,female,49,0,0,17465,25.9292,D17,S
-1,3,Osman,female,31,0,0,349244,8.6833,,S
-0,3,Ibrahim,male,30,0,0,2685,7.2292,,C
-0,3,Van,female,30,1,1,345773,24.15,,S
-0,2,Ponesell,male,34,0,0,250647,13,,S
-1,2,Collyer,female,31,1,1,C.A. 31921,26.25,,S
-1,1,Carter,male,11,1,2,113760,120,B96 B98,S
-1,3,Thomas,male,0.42,0,1,2625,8.5167,,C
-1,3,Hedman,male,27,0,0,347089,6.975,,S
-0,3,Johansson,male,31,0,0,347063,7.775,,S
-0,1,Andrews,male,39,0,0,112050,0,A36,S
-0,3,Pettersson,female,18,0,0,347087,7.775,,S
-0,2,Meyer,male,39,0,0,248723,13,,S
-1,1,Chambers,female,33,1,0,113806,53.1,E8,S
-0,3,Alexander,male,26,0,0,3474,7.8875,,S
-0,3,Lester,male,39,0,0,A/4 48871,24.15,,S
-0,2,Slemen,male,35,0,0,28206,10.5,,S
-0,3,Andersson,female,6,4,2,347082,31.275,,S
-0,3,Tomlin,male,30.5,0,0,364499,8.05,,S
-0,3,Heininen,female,23,0,0,STON/O2. 3101290,7.925,,S
-0,2,Mallet,male,31,1,1,S.C./PARIS 2079,37.0042,,C
-0,3,Holm,male,43,0,0,C 7075,6.45,,S
-0,3,Skoog,male,10,3,2,347088,27.9,,S
-1,1,Hays,female,52,1,1,12749,93.5,B69,S
-1,3,Lulic,male,27,0,0,315098,8.6625,,S
-0,1,Reuchlin,male,38,0,0,19972,0,,S
-1,3,Moor,female,27,0,1,392096,12.475,E121,S
-0,3,Panula,male,2,4,1,3101295,39.6875,,S
-1,2,Mallet,male,1,0,2,S.C./PARIS 2079,37.0042,,C
-1,1,Stone,female,62,0,0,113572,80,B28,
-1,3,Yasbeck,female,15,1,0,2659,14.4542,,C
-1,2,Richards,male,0.83,1,1,29106,18.75,,S
-0,3,Augustsson,male,23,0,0,347468,7.8542,,S
-0,3,Allum,male,18,0,0,2223,8.3,,S
-1,1,Compton,female,39,1,1,PC 17756,83.1583,E49,C
-0,3,Pasic,male,21,0,0,315097,8.6625,,S
-1,3,Chip,male,32,0,0,1601,56.4958,,S
-0,3,Alhomaki,male,20,0,0,SOTON/O2 3101287,7.925,,S
-0,2,Mudd,male,16,0,0,S.O./P.P. 3,10.5,,S
-1,1,Serepeca,female,30,0,0,113798,31,,C
-0,3,Lemberopolous,male,34.5,0,0,2683,6.4375,,C
-0,3,Culumovic,male,17,0,0,315090,8.6625,,S
-0,3,Abbing,male,42,0,0,C.A. 5547,7.55,,S
-0,3,Markoff,male,35,0,0,349213,7.8958,,C
-0,2,Harper,male,28,0,1,248727,33,,S
-0,3,Andersson,male,4,4,2,347082,31.275,,S
-0,3,Svensson,male,74,0,0,347060,7.775,,S
-0,3,Boulos,female,9,1,1,2678,15.2458,,C
-1,1,Lines,female,16,0,1,PC 17592,39.4,D28,S
-0,2,Carter,female,44,1,0,244252,26,,S
-1,3,Aks,female,18,0,1,392091,9.35,,S
-1,1,Wick,female,45,1,1,36928,164.8667,,S
-1,1,Daly,male,51,0,0,113055,26.55,E17,S
-1,3,Baclini,female,24,0,3,2666,19.2583,,C
-0,3,Hansen,male,41,2,0,350026,14.1083,,S
-0,2,Giles,male,21,1,0,28134,11.5,,S
-1,1,Swift,female,48,0,0,17466,25.9292,D17,S
-0,2,Gill,male,24,0,0,233866,13,,S
-1,2,Bystrom,female,42,0,0,236852,13,,S
-1,2,Duran,female,27,1,0,SC/PARIS 2149,13.8583,,C
-0,1,Roebling,male,31,0,0,PC 17590,50.4958,A24,S
-1,3,Johnson,male,4,1,1,347742,11.1333,,S
-0,3,Balkic,male,26,0,0,349248,7.8958,,S
-1,1,Beckwith,female,47,1,1,11751,52.5542,D35,S
-0,1,Carlsson,male,33,0,0,695,5,B51 B53 B55,S
-0,3,Vander,male,47,0,0,345765,9,,S
-1,2,Abelson,female,28,1,0,P/PP 3381,24,,C
-1,3,Najib,female,15,0,0,2667,7.225,,C
-0,3,Gustafsson,male,20,0,0,7534,9.8458,,S
-0,3,Petroff,male,19,0,0,349212,7.8958,,S
-1,1,Potter,female,56,0,1,11767,83.1583,C50,C
-1,2,Shelley,female,25,0,1,230433,26,,S
-0,3,Markun,male,33,0,0,349257,7.8958,,S
-0,3,Dahlberg,female,22,0,0,7552,10.5167,,S
-0,2,Banfield,male,28,0,0,C.A./SOTON 34068,10.5,,S
-0,3,Sutehall,male,25,0,0,SOTON/OQ 392076,7.05,,S
-0,3,Rice,female,39,0,5,382652,29.125,,Q
-0,2,Montvila,male,27,0,0,211536,13,,S
-1,1,Graham,female,19,0,0,112053,30,B42,S
-1,1,Behr,male,26,0,0,111369,30,C148,C
-0,3,Dooley,male,32,0,0,370376,7.75,,Q

apps/simple/directory.ts

@@ -1,24 +0,0 @@
-import { SimpleDirectoryReader } from "llamaindex";
-
-function callback(
-  category: string,
-  name: string,
-  status: any,
-  message?: string,
-): boolean {
-  console.log(category, name, status, message);
-  if (name.endsWith(".pdf")) {
-    console.log("I DON'T WANT PDF FILES!");
-    return false;
-  }
-  return true;
-}
-
-async function main() {
-  // Load page
-  const reader = new SimpleDirectoryReader(callback);
-  const params = { directoryPath: "./data" };
-  await reader.loadData(params);
-}
-
-main().catch(console.error);

apps/simple/gptllama.ts

@@ -1,40 +0,0 @@
-import { stdin as input, stdout as output } from "node:process";
-// readline/promises is still experimental so not in @types/node yet
-// @ts-ignore
-import readline from "node:readline/promises";
-
-import { ChatMessage, LlamaDeuce, OpenAI } from "llamaindex";
-
-(async () => {
-  const gpt4 = new OpenAI({ model: "gpt-4", temperature: 0.9 });
-  const l2 = new LlamaDeuce({
-    model: "Llama-2-70b-chat-4bit",
-    temperature: 0.9,
-  });
-
-  const rl = readline.createInterface({ input, output });
-  const start = await rl.question("Start: ");
-  const history: ChatMessage[] = [
-    {
-      content:
-        "Prefer shorter answers. Keep your response to 100 words or less.",
-      role: "system",
-    },
-    { content: start, role: "user" },
-  ];
-
-  while (true) {
-    const next = history.length % 2 === 1 ? gpt4 : l2;
-    const r = await next.chat(
-      history.map(({ content, role }) => ({
-        content,
-        role: next === l2 ? role : role === "user" ? "assistant" : "user",
-      })),
-    );
-    history.push({
-      content: r.message.content,
-      role: next === l2 ? "assistant" : "user",
-    });
-    await rl.question((next === l2 ? "Llama: " : "GPT: ") + r.message.content);
-  }
-})();

apps/simple/html.ts

@@ -1,21 +0,0 @@
-import { HTMLReader, VectorStoreIndex } from "llamaindex";
-
-async function main() {
-  // Load page
-  const reader = new HTMLReader();
-  const documents = await reader.loadData("data/18-1_Changelog.html");
-
-  // Split text and create embeddings. Store them in a VectorStoreIndex
-  const index = await VectorStoreIndex.fromDocuments(documents);
-
-  // Query the index
-  const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query(
-    "What were the notable changes in 18.1?",
-  );
-
-  // Output response
-  console.log(response.toString());
-}
-
-main().catch(console.error);

apps/simple/llamadeuce.ts

@@ -1,7 +0,0 @@
-import { DeuceChatStrategy, LlamaDeuce } from "llamaindex";
-
-(async () => {
-  const deuce = new LlamaDeuce({ chatStrategy: DeuceChatStrategy.META });
-  const result = await deuce.chat([{ content: "Hello, world!", role: "user" }]);
-  console.log(result);
-})();

apps/simple/llmStream.ts

@@ -1,47 +0,0 @@
-import { ChatMessage, SimpleChatEngine } from "llamaindex";
-import { stdin as input, stdout as output } from "node:process";
-import readline from "node:readline/promises";
-import { Anthropic } from "../../packages/core/src/llm/LLM";
-
-async function main() {
-  const query: string = `
-Where is Istanbul?
-  `;
-
-  // const llm = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0.1 });
-  const llm = new Anthropic();
-  const message: ChatMessage = { content: query, role: "user" };
-
-  //TODO: Add callbacks later
-
-  //Stream Complete
-  //Note: Setting streaming flag to true or false will auto-set your return type to
-  //either an AsyncGenerator or a Response.
-  // Omitting the streaming flag automatically sets streaming to false
-
-  const chatEngine: SimpleChatEngine = new SimpleChatEngine({
-    chatHistory: undefined,
-    llm: llm,
-  });
-
-  const rl = readline.createInterface({ input, output });
-  while (true) {
-    const query = await rl.question("Query: ");
-
-    if (!query) {
-      break;
-    }
-
-    //Case 1: .chat(query, undefined, true) => Stream
-    //Case 2: .chat(query, undefined, false) => Response object
-    //Case 3: .chat(query, undefined) => Response object
-    const chatStream = await chatEngine.chat(query, undefined, true);
-    var accumulated_result = "";
-    for await (const part of chatStream) {
-      accumulated_result += part;
-      process.stdout.write(part);
-    }
-  }
-}
-
-main();

apps/simple/notion.ts

@@ -1,89 +0,0 @@
-import { Client } from "@notionhq/client";
-import { program } from "commander";
-import { NotionReader, VectorStoreIndex } from "llamaindex";
-import { stdin as input, stdout as output } from "node:process";
-// readline/promises is still experimental so not in @types/node yet
-// @ts-ignore
-import readline from "node:readline/promises";
-
-program
-  .argument("[page]", "Notion page id (must be provided)")
-  .action(async (page, _options, command) => {
-    // Initializing a client
-
-    if (!process.env.NOTION_TOKEN) {
-      console.log(
-        "No NOTION_TOKEN found in environment variables. You will need to register an integration https://www.notion.com/my-integrations and put it in your NOTION_TOKEN environment variable.",
-      );
-      return;
-    }
-
-    const notion = new Client({
-      auth: process.env.NOTION_TOKEN,
-    });
-
-    if (!page) {
-      const response = await notion.search({
-        filter: {
-          value: "page",
-          property: "object",
-        },
-        sort: {
-          direction: "descending",
-          timestamp: "last_edited_time",
-        },
-      });
-
-      const { results } = response;
-
-      if (results.length === 0) {
-        console.log(
-          "No pages found. You will need to share it with your integration. (tap the three dots on the top right, find Add connections, and add your integration)",
-        );
-        return;
-      } else {
-        const pages = results
-          .map((result) => {
-            if (!("url" in result)) {
-              return null;
-            }
-
-            return {
-              id: result.id,
-              url: result.url,
-            };
-          })
-          .filter((page) => page !== null);
-        console.log("Found pages:");
-        console.table(pages);
-        console.log(`To run, run ts-node ${command.name()} [page id]`);
-        return;
-      }
-    }
-
-    const reader = new NotionReader({ client: notion });
-    const documents = await reader.loadData(page);
-    console.log(documents);
-
-    // Split text and create embeddings. Store them in a VectorStoreIndex
-    const index = await VectorStoreIndex.fromDocuments(documents);
-
-    // Create query engine
-    const queryEngine = index.asQueryEngine();
-
-    const rl = readline.createInterface({ input, output });
-    while (true) {
-      const query = await rl.question("Query: ");
-
-      if (!query) {
-        break;
-      }
-
-      const response = await queryEngine.query(query);
-
-      // Output response
-      console.log(response.toString());
-    }
-  });
-
-program.parse();

apps/simple/openai.ts

@@ -1,15 +0,0 @@
-import { OpenAI } from "llamaindex";
-
-(async () => {
-  const llm = new OpenAI({ model: "gpt-4-1106-preview", temperature: 0.1 });
-
-  // complete api
-  const response1 = await llm.complete("How are you?");
-  console.log(response1.message.content);
-
-  // chat api
-  const response2 = await llm.chat([
-    { content: "Tell me a joke.", role: "user" },
-  ]);
-  console.log(response2.message.content);
-})();

apps/simple/package.json

@@ -1,18 +0,0 @@
-{
-  "version": "0.0.33",
-  "private": true,
-  "name": "simple",
-  "dependencies": {
-    "@notionhq/client": "^2.2.13",
-    "@pinecone-database/pinecone": "^1.1.2",
-    "commander": "^11.1.0",
-    "llamaindex": "workspace:*"
-  },
-  "devDependencies": {
-    "@types/node": "^18.18.6",
-    "ts-node": "^10.9.1"
-  },
-  "scripts": {
-    "lint": "eslint ."
-  }
-}

apps/simple/split.ts

@@ -1,16 +0,0 @@
-import fs from "node:fs/promises";
-
-import { SentenceSplitter } from "llamaindex";
-
-async function main() {
-  const path = "node_modules/llamaindex/examples/abramov.txt";
-  const essay = await fs.readFile(path, "utf-8");
-
-  const textSplitter = new SentenceSplitter();
-
-  const chunks = textSplitter.splitTextWithOverlaps(essay);
-
-  console.log(chunks);
-}
-
-main();

apps/simple/storageContext.ts

@@ -1,44 +0,0 @@
-import {
-  Document,
-  storageContextFromDefaults,
-  VectorStoreIndex,
-} from "llamaindex";
-import essay from "./essay";
-
-async function main() {
-  // Create Document object with essay
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Split text and create embeddings. Store them in a VectorStoreIndex
-  // persist the vector store automatically with the storage context
-  const storageContext = await storageContextFromDefaults({
-    persistDir: "./storage",
-  });
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    storageContext,
-  });
-
-  // Query the index
-  const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query(
-    "What did the author do in college?",
-  );
-
-  // Output response
-  console.log(response.toString());
-
-  // load the index
-  const secondStorageContext = await storageContextFromDefaults({
-    persistDir: "./storage",
-  });
-  const loadedIndex = await VectorStoreIndex.init({
-    storageContext: secondStorageContext,
-  });
-  const loadedQueryEngine = loadedIndex.asQueryEngine();
-  const loadedResponse = await loadedQueryEngine.query(
-    "What did the author do growing up?",
-  );
-  console.log(loadedResponse.toString());
-}
-
-main().catch(console.error);

apps/simple/subquestion.ts

@@ -1,26 +0,0 @@
-import { Document, SubQuestionQueryEngine, VectorStoreIndex } from "llamaindex";
-
-import essay from "./essay";
-
-(async () => {
-  const document = new Document({ text: essay, id_: essay });
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  const queryEngine = SubQuestionQueryEngine.fromDefaults({
-    queryEngineTools: [
-      {
-        queryEngine: index.asQueryEngine(),
-        metadata: {
-          name: "pg_essay",
-          description: "Paul Graham essay on What I Worked On",
-        },
-      },
-    ],
-  });
-
-  const response = await queryEngine.query(
-    "How was Paul Grahams life different before and after YC?",
-  );
-
-  console.log(response.toString());
-})();

apps/simple/vectorIndexCustomize.ts

@@ -1,42 +0,0 @@
-import {
-  Document,
-  OpenAI,
-  RetrieverQueryEngine,
-  serviceContextFromDefaults,
-  SimilarityPostprocessor,
-  VectorStoreIndex,
-} from "llamaindex";
-import essay from "./essay";
-
-// Customize retrieval and query args
-async function main() {
-  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 retriever = index.asRetriever();
-  retriever.similarityTopK = 5;
-  const nodePostprocessor = new SimilarityPostprocessor({
-    similarityCutoff: 0.7,
-  });
-  // TODO: cannot pass responseSynthesizer into retriever query engine
-  const queryEngine = new RetrieverQueryEngine(
-    retriever,
-    undefined,
-    undefined,
-    [nodePostprocessor],
-  );
-
-  const response = await queryEngine.query(
-    "What did the author do growing up?",
-  );
-  console.log(response.response);
-}
-
-main().catch(console.error);

apps/simple/vectorIndexFromVectorStore.ts

@@ -1,197 +0,0 @@
-import {
-  OpenAI,
-  ResponseSynthesizer,
-  RetrieverQueryEngine,
-  serviceContextFromDefaults,
-  TextNode,
-  TreeSummarize,
-  VectorIndexRetriever,
-  VectorStore,
-  VectorStoreIndex,
-  VectorStoreQuery,
-  VectorStoreQueryResult,
-} from "llamaindex";
-
-import { Index, Pinecone, RecordMetadata } from "@pinecone-database/pinecone";
-
-/**
- * Please do not use this class in production; it's only for demonstration purposes.
- */
-class PineconeVectorStore<T extends RecordMetadata = RecordMetadata>
-  implements VectorStore
-{
-  storesText = true;
-  isEmbeddingQuery = false;
-
-  indexName!: string;
-  pineconeClient!: Pinecone;
-  index!: Index<T>;
-
-  constructor({ indexName, client }: { indexName: string; client: Pinecone }) {
-    this.indexName = indexName;
-    this.pineconeClient = client;
-    this.index = client.index<T>(indexName);
-  }
-
-  client() {
-    return this.pineconeClient;
-  }
-
-  async query(
-    query: VectorStoreQuery,
-    kwargs?: any,
-  ): Promise<VectorStoreQueryResult> {
-    let queryEmbedding: number[] = [];
-    if (query.queryEmbedding) {
-      if (typeof query.alpha === "number") {
-        const alpha = query.alpha;
-        queryEmbedding = query.queryEmbedding.map((v) => v * alpha);
-      } else {
-        queryEmbedding = query.queryEmbedding;
-      }
-    }
-
-    // Current LlamaIndexTS implementation only support exact match filter, so we use kwargs instead.
-    const filter = kwargs?.filter || {};
-
-    const response = await this.index.query({
-      filter,
-      vector: queryEmbedding,
-      topK: query.similarityTopK,
-      includeValues: true,
-      includeMetadata: true,
-    });
-
-    console.log(
-      `Numbers of vectors returned by Pinecone after preFilters are applied: ${
-        response?.matches?.length || 0
-      }.`,
-    );
-
-    const topKIds: string[] = [];
-    const topKNodes: TextNode[] = [];
-    const topKScores: number[] = [];
-
-    const metadataToNode = (metadata?: T): Partial<TextNode> => {
-      if (!metadata) {
-        throw new Error("metadata is undefined.");
-      }
-
-      const nodeContent = metadata["_node_content"];
-      if (!nodeContent) {
-        throw new Error("nodeContent is undefined.");
-      }
-
-      if (typeof nodeContent !== "string") {
-        throw new Error("nodeContent is not a string.");
-      }
-
-      return JSON.parse(nodeContent);
-    };
-
-    if (response.matches) {
-      for (const match of response.matches) {
-        const node = new TextNode({
-          ...metadataToNode(match.metadata),
-          embedding: match.values,
-        });
-
-        topKIds.push(match.id);
-        topKNodes.push(node);
-        topKScores.push(match.score ?? 0);
-      }
-    }
-
-    const result = {
-      ids: topKIds,
-      nodes: topKNodes,
-      similarities: topKScores,
-    };
-
-    return result;
-  }
-
-  add(): Promise<string[]> {
-    return Promise.resolve([]);
-  }
-
-  delete(): Promise<void> {
-    throw new Error("Method `delete` not implemented.");
-  }
-
-  persist(): Promise<void> {
-    throw new Error("Method `persist` not implemented.");
-  }
-}
-
-/**
- * The goal of this example is to show how to use Pinecone as a vector store
- * for LlamaIndexTS with(out) preFilters.
- *
- * It should not be used in production like that,
- * as you might want to find a proper PineconeVectorStore implementation.
- */
-async function main() {
-  process.env.PINECONE_API_KEY = "Your Pinecone API Key.";
-  process.env.PINECONE_ENVIRONMENT = "Your Pinecone Environment.";
-  process.env.PINECONE_PROJECT_ID = "Your Pinecone Project ID.";
-  process.env.PINECONE_INDEX_NAME = "Your Pinecone Index Name.";
-  process.env.OPENAI_API_KEY = "Your OpenAI API Key.";
-  process.env.OPENAI_API_ORGANIZATION = "Your OpenAI API Organization.";
-
-  const getPineconeVectorStore = async () => {
-    return new PineconeVectorStore({
-      indexName: process.env.PINECONE_INDEX_NAME || "index-name",
-      client: new Pinecone(),
-    });
-  };
-
-  const getServiceContext = () => {
-    const openAI = new OpenAI({
-      model: "gpt-4",
-      apiKey: process.env.OPENAI_API_KEY,
-    });
-
-    return serviceContextFromDefaults({
-      llm: openAI,
-    });
-  };
-
-  const getQueryEngine = async (filter: unknown) => {
-    const vectorStore = await getPineconeVectorStore();
-    const serviceContext = getServiceContext();
-
-    const vectorStoreIndex = await VectorStoreIndex.fromVectorStore(
-      vectorStore,
-      serviceContext,
-    );
-
-    const retriever = new VectorIndexRetriever({
-      index: vectorStoreIndex,
-      similarityTopK: 500,
-    });
-
-    const responseSynthesizer = new ResponseSynthesizer({
-      serviceContext,
-      responseBuilder: new TreeSummarize(serviceContext),
-    });
-
-    return new RetrieverQueryEngine(retriever, responseSynthesizer, {
-      filter,
-    });
-  };
-
-  // whatever is a key from your metadata
-  const queryEngine = await getQueryEngine({
-    whatever: {
-      $gte: 1,
-      $lte: 100,
-    },
-  });
-
-  const response = await queryEngine.query("How many results do you have?");
-
-  console.log(response.toString());
-}
-
-main().catch(console.error);

apps/simple/vectorIndexGPT4.ts

@@ -1,36 +0,0 @@
-import fs from "node:fs/promises";
-
-import {
-  Document,
-  OpenAI,
-  serviceContextFromDefaults,
-  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");
-
-  // Create Document object with essay
-  const document = new Document({ text: essay, id_: path });
-
-  // Split text and create embeddings. Store them in a VectorStoreIndex
-  const serviceContext = serviceContextFromDefaults({
-    llm: new OpenAI({ model: "gpt-4" }),
-  });
-  const index = await VectorStoreIndex.fromDocuments([document], {
-    serviceContext,
-  });
-
-  // Query the index
-  const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query(
-    "What did the author do in college?",
-  );
-
-  // Output response
-  console.log(response.toString());
-}
-
-main().catch(console.error);

apps/simple/vision.ts

@@ -1,15 +0,0 @@
-import { OpenAI } from "llamaindex";
-
-(async () => {
-  const llm = new OpenAI({ model: "gpt-4-vision-preview", temperature: 0.1 });
-
-  // complete api
-  const response1 = await llm.complete("How are you?");
-  console.log(response1.message.content);
-
-  // chat api
-  const response2 = await llm.chat([
-    { content: "Tell me a joke!", role: "user" },
-  ]);
-  console.log(response2.message.content);
-})();

assets/tslogo.svg

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

docs/.gitignore

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

docs/CLAUDE.md

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

docs/astro.config.mjs

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

docs/package.json

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

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