changeset

Add create-llama CLI tool

.changeset/README.md

@@ -0,0 +1,8 @@
+# Changesets
+
+Hello and welcome! This folder has been automatically generated by `@changesets/cli`, a build tool that works
+with multi-package repos, or single-package repos to help you version and publish your code. You can
+find the full documentation for it [in our repository](https://github.com/changesets/changesets)
+
+We have a quick list of common questions to get you started engaging with this project in
+[our documentation](https://github.com/changesets/changesets/blob/main/docs/common-questions.md)

.changeset/config.json

@@ -0,0 +1,11 @@
+{
+  "$schema": "https://unpkg.com/@changesets/config@2.3.1/schema.json",
+  "changelog": "@changesets/cli/changelog",
+  "commit": false,
+  "fixed": [],
+  "linked": [],
+  "access": "public",
+  "baseBranch": "main",
+  "updateInternalDependencies": "patch",
+  "ignore": []
+}

.changeset/eight-dolphins-pump.md

@@ -0,0 +1,5 @@
+---
+"llamaindex": patch
+---
+
+Add multimodal support (thanks Marcus)

.changeset/sixty-ladybugs-change.md

@@ -0,0 +1,5 @@
+---
+"create-llama": patch
+---
+
+Hello Create Llama (thanks Marcus!)

.github/ISSUE_TEMPLATE/sweep-bugfix.yml

@@ -0,0 +1,11 @@
+name: Bugfix
+title: "Sweep: "
+description: Write something like "We notice ... behavior when ... happens instead of ...""
+labels: sweep
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Details
+      description: More details about the bug
+      placeholder: The bug might be in ... file

.github/ISSUE_TEMPLATE/sweep-feature.yml

@@ -0,0 +1,11 @@
+name: Feature Request
+title: "Sweep: "
+description: Write something like "Write an api endpoint that does "..." in the "..." file"
+labels: sweep
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Details
+      description: More details for Sweep
+      placeholder: The new endpoint should use the ... class from ... file because it contains ... logic

.github/ISSUE_TEMPLATE/sweep-refactor.yml

@@ -0,0 +1,11 @@
+name: Refactor
+title: "Sweep: "
+description: Write something like "Modify the ... api endpoint to use ... version and ... framework"
+labels: sweep
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Details
+      description: More details for Sweep
+      placeholder: We are migrating this function to ... version because ...

.github/workflows/lint_on_push_or_pull.yml

@@ -0,0 +1,25 @@
+name: Lint on push or pull request
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Install pnpm
+        run: npm install -g pnpm
+
+      - name: Install dependencies
+        run: pnpm install
+
+      - name: Run lint
+        run: pnpm run lint

.github/workflows/test.yml

@@ -0,0 +1,24 @@
+name: Run Tests
+
+on: [push, pull_request]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v2
+        with:
+          node-version: "18"
+
+      - name: Install dependencies
+        run: |
+          npm i -g pnpm
+          pnpm install
+
+      - name: Run tests
+        run: pnpm run test

.gitignore

@@ -3,6 +3,7 @@
 # dependencies
 node_modules
 .pnp
+.pnpm-store
 .pnp.js
 
 # testing
@@ -34,3 +35,8 @@ yarn-error.log*
 
 # vercel
 .vercel
+
+dist/
+
+# vs code
+.vscode/launch.json

.vscode/settings.json

@@ -1,5 +1,8 @@
 {
   "editor.tabSize": 2,
   "editor.formatOnSave": true,
-  "editor.defaultFormatter": "esbenp.prettier-vscode"
+  "editor.defaultFormatter": "esbenp.prettier-vscode",
+  "[xml]": {
+    "editor.defaultFormatter": "redhat.vscode-xml"
+  }
 }

CONTRIBUTING.md

@@ -0,0 +1,80 @@
+# Contributing
+
+## Structure
+
+This is a monorepo built with Turborepo
+
+Right now there are two packages of importance:
+
+packages/core which is the main NPM library llamaindex
+
+apps/simple is where the demo code lives
+
+### Turborepo docs
+
+You can checkout how Turborepo works using the default [README-turborepo.md](/README-turborepo.md)
+
+## Getting Started
+
+Install NodeJS. Preferably v18 using nvm or n.
+
+Inside the LlamaIndexTS directory:
+
+```
+npm i -g pnpm ts-node
+pnpm install
+```
+
+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.
+
+PNPM's has documentation on its [workspace feature](https://pnpm.io/workspaces) and Turborepo had some [useful documentation also](https://turbo.build/repo/docs/core-concepts/monorepos/running-tasks).
+
+### Running Typescript
+
+When we publish to NPM we will have a tsc compiled version of the library in JS. For now, the easiest thing to do is use ts-node.
+
+### Test cases
+
+To run them, run
+
+```
+pnpm run test
+```
+
+To write new test cases write them in [packages/core/src/tests](/packages/core/src/tests)
+
+We use Jest https://jestjs.io/ to write our test cases. Jest comes with a bunch of built in assertions using the expect function: https://jestjs.io/docs/expect
+
+### Demo applications
+
+There is an existing ["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]
+```
+
+To install packages for every package or application run
+
+```
+pnpm add -w [NPM Package]
+```
+
+### Docs
+
+To contribute to the docs, go to the docs website folder and run the Docusaurus instance.
+
+```bash
+cd apps/docs
+pnpm install
+pnpm start
+```
+
+That should start a webserver which will serve the docs on https://localhost:3000
+
+Any changes you make should be reflected in the browser. If you need to regenerate the API docs and find that your TSDoc isn't getting the updates, feel free to remove apps/docs/api. It will automatically regenerate itself when you run pnpm start again.

README.md

@@ -1,64 +1,119 @@
-# LlamaScript: LlamaIndex for TS/JS
+# LlamaIndex.TS
 
-## Structure
+LlamaIndex is a data framework for your LLM application.
 
-This is a monorepo built with Turborepo
+Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.
 
-Right now there are two packages of importance:
+Documentation: https://ts.llamaindex.ai/
 
-packages/core which is the main NPM library @llamaindex/core
+## What is LlamaIndex.TS?
 
-apps/simple is where the demo code lives
+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.
 
-### Turborepo docs
+## Getting started with an example:
 
-You can checkout how Turborepo works using the built in [README-turborepo.md](README-turborepo.md)
+LlamaIndex.TS requries Node v18 or higher. You can download it from https://nodejs.org or use https://nvm.sh (our preferred option).
 
-## Getting Started
+In a new folder:
 
-Install NodeJS. Preferably v18 using nvm or n.
-
-Inside the llamascript directory:
-
-```
-npm i -g pnpm ts-node
-pnpm install
+```bash
+export OPENAI_API_KEY="sk-......" # Replace with your key from https://platform.openai.com/account/api-keys
+pnpm init
+pnpm install typescript
+pnpm exec tsc --init # if needed
+pnpm install llamaindex
+pnpm install @types/node
 ```
 
-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.
+Create the file example.ts
 
-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).
+```ts
+// example.ts
+import fs from "fs/promises";
+import { Document, VectorStoreIndex } from "llamaindex";
 
-### Running Typescript
+async function main() {
+  // Load essay from abramov.txt in Node
+  const essay = await fs.readFile(
+    "node_modules/llamaindex/examples/abramov.txt",
+    "utf-8",
+  );
 
-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.
+  // Create Document object with essay
+  const document = new Document({ text: essay });
 
-### Test cases
+  // Split text and create embeddings. Store them in a VectorStoreIndex
+  const index = await VectorStoreIndex.fromDocuments([document]);
 
-To run them, run
+  // Query the index
+  const queryEngine = index.asQueryEngine();
+  const response = await queryEngine.query(
+    "What did the author do in college?",
+  );
 
-```
-pnpm run test
+  // Output response
+  console.log(response.toString());
+}
+
+main();
 ```
 
-To write new test cases write them in packages/core/src/tests
+Then you can run it using
 
-We use Jest https://jestjs.io/ to write our test cases. Jest comes with a bunch of built in assertions using the expect function: https://jestjs.io/docs/expect
-
-### Demo applications
-
-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]
+```bash
+pnpx ts-node example.ts
 ```
 
-To install packages for every package or application run
+## Playground
 
+Check out our NextJS playground at https://llama-playground.vercel.app/. The source is available at https://github.com/run-llama/ts-playground
+
+## Core concepts for getting started:
+
+- [Document](/packages/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
 ```
-pnpm add -w [NPM Package]
+
+```js
+// next.config.js
+/** @type {import('next').NextConfig} */
+const nextConfig = {
+  experimental: {
+    serverComponentsExternalPackages: ["pdf-parse"], // Puts pdf-parse in actual NodeJS mode with NextJS App Router
+  },
+};
+
+module.exports = nextConfig;
 ```
+
+## Supported LLMs:
+
+- OpenAI GPT-3.5-turbo and GPT-4
+- Anthropic Claude Instant and Claude 2
+- Llama2 Chat LLMs (70B, 13B, and 7B parameters)
+
+## Contributing:
+
+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)
+
+## Bugs? Questions?
+
+Please join our Discord! https://discord.com/invite/eN6D2HQ4aX

apps/docs/.eslintrc.js

@@ -1,4 +0,0 @@
-module.exports = {
-  root: true,
-  extends: ["custom"],
-};

apps/docs/.gitignore

@@ -1,34 +1,20 @@
-# See https://help.github.com/articles/ignoring-files/ for more about ignoring files.
-
-# dependencies
+# Dependencies
 /node_modules
-/.pnp
-.pnp.js
 
-# testing
-/coverage
-
-# next.js
-/.next/
-/out/
-
-# production
+# Production
 /build
 
-# misc
+# Generated files
+.docusaurus
+.cache-loader
+
+# Misc
 .DS_Store
-*.pem
-
-# debug
-npm-debug.log*
-yarn-debug.log*
-yarn-error.log*
-
-# local env files
 .env.local
 .env.development.local
 .env.test.local
 .env.production.local
 
-# vercel
-.vercel
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*

apps/docs/README-docusaurus.md

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

apps/docs/README.md

@@ -1,30 +0,0 @@
-## Getting Started
-
-First, run the development server:
-
-```bash
-yarn dev
-```
-
-Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
-
-You can start editing the page by modifying `pages/index.js`. The page auto-updates as you edit the file.
-
-[API routes](https://nextjs.org/docs/api-routes/introduction) can be accessed on [http://localhost:3000/api/hello](http://localhost:3000/api/hello). This endpoint can be edited in `pages/api/hello.js`.
-
-The `pages/api` directory is mapped to `/api/*`. Files in this directory are treated as [API routes](https://nextjs.org/docs/api-routes/introduction) instead of React pages.
-
-## Learn More
-
-To learn more about Next.js, take a look at the following resources:
-
-- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
-- [Learn Next.js](https://nextjs.org/learn/foundations/about-nextjs) - an interactive Next.js tutorial.
-
-You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome!
-
-## Deploy on Vercel
-
-The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_source=github.com&utm_medium=referral&utm_campaign=turborepo-readme) from the creators of Next.js.
-
-Check out our [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.

apps/docs/app/layout.tsx

@@ -1,11 +0,0 @@
-export default function RootLayout({
-  children,
-}: {
-  children: React.ReactNode;
-}) {
-  return (
-    <html lang="en">
-      <body>{children}</body>
-    </html>
-  );
-}

apps/docs/app/page.tsx

@@ -1,10 +0,0 @@
-import { Button, Header } from "ui";
-
-export default function Page() {
-  return (
-    <>
-      <Header text="Docs" />
-      <Button />
-    </>
-  );
-}

apps/docs/babel.config.js

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

apps/docs/docs/.gitignore

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

apps/docs/docs/concepts.md

@@ -0,0 +1,78 @@
+---
+sidebar_position: 3
+---
+
+# High-Level Concepts
+
+LlamaIndex.TS helps you build LLM-powered applications (e.g. Q&A, chatbot) over custom data.
+
+In this high-level concepts guide, you will learn:
+
+- how an LLM can answer questions using your own data.
+- key concepts and modules in LlamaIndex.TS for composing your own query pipeline.
+
+## Answering Questions Across Your Data
+
+LlamaIndex uses a two stage method when using an LLM with your data:
+
+1. **indexing stage**: preparing a knowledge base, and
+2. **querying stage**: retrieving relevant context from the knowledge to assist the LLM in responding to a question
+
+![](./_static/concepts/rag.jpg)
+
+This process is also known as Retrieval Augmented Generation (RAG).
+
+LlamaIndex.TS provides the essential toolkit for making both steps super easy.
+
+Let's explore each stage in detail.
+
+### Indexing Stage
+
+LlamaIndex.TS help you prepare the knowledge base with a suite of data connectors and indexes.
+
+![](./_static/concepts/indexing.jpg)
+
+[**Data Loaders**](./modules/high_level/data_loader.md):
+A data connector (i.e. `Reader`) ingest data from different data sources and data formats into a simple `Document` representation (text and simple metadata).
+
+[**Documents / Nodes**](./modules/high_level/documents_and_nodes.md): A `Document` is a generic container around any data source - for instance, a PDF, an API output, or retrieved data from a database. A `Node` is the atomic unit of data in LlamaIndex and represents a "chunk" of a source `Document`. It's a rich representation that includes metadata and relationships (to other nodes) to enable accurate and expressive retrieval operations.
+
+[**Data Indexes**](./modules/high_level/data_index.md):
+Once you've ingested your data, LlamaIndex helps you index data into a format that's easy to retrieve.
+
+Under the hood, LlamaIndex parses the raw documents into intermediate representations, calculates vector embeddings, and stores your data in-memory or to disk.
+
+### Querying Stage
+
+In the querying stage, the query pipeline retrieves the most relevant context given a user query,
+and pass that to the LLM (along with the query) to synthesize a response.
+
+This gives the LLM up-to-date knowledge that is not in its original training data,
+(also reducing hallucination).
+
+The key challenge in the querying stage is retrieval, orchestration, and reasoning over (potentially many) knowledge bases.
+
+LlamaIndex provides composable modules that help you build and integrate RAG pipelines for Q&A (query engine), chatbot (chat engine), or as part of an agent.
+
+These building blocks can be customized to reflect ranking preferences, as well as composed to reason over multiple knowledge bases in a structured way.
+
+![](./_static/concepts/querying.jpg)
+
+#### Building Blocks
+
+[**Retrievers**](./modules/low_level/retriever.md):
+A retriever defines how to efficiently retrieve relevant context from a knowledge base (i.e. index) when given a query.
+The specific retrieval logic differs for difference indices, the most popular being dense retrieval against a vector index.
+
+[**Response Synthesizers**](./modules/low_level/response_synthesizer.md):
+A response synthesizer generates a response from an LLM, using a user query and a given set of retrieved text chunks.
+
+#### Pipelines
+
+[**Query Engines**](./modules/high_level/query_engine.md):
+A query engine is an end-to-end pipeline that allow you to ask question over your data.
+It takes in a natural language query, and returns a response, along with reference context retrieved and passed to the LLM.
+
+[**Chat Engines**](./modules/high_level/chat_engine.md):
+A chat engine is an end-to-end pipeline for having a conversation with your data
+(multiple back-and-forth instead of a single question & answer).

apps/docs/docs/end_to_end.md

@@ -0,0 +1,43 @@
+---
+sidebar_position: 4
+---
+
+# End to End Examples
+
+We include several end-to-end examples using LlamaIndex.TS in the repository
+
+## [Chat Engine](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/chatEngine.ts)
+
+Read a file and chat about it with the LLM.
+
+## [Vector Index](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/vectorIndex.ts)
+
+Create a vector index and query it. The vector index will use embeddings to fetch the top k most relevant nodes. By default, the top k is 2.
+
+## [Summary Index](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/summaryIndex.ts)
+
+Create a list index and query it. This example also use the `LLMRetriever`, which will use the LLM to select the best nodes to use when generating answer.
+
+## [Save / Load an Index](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/storageContext.ts)
+
+Create and load a vector index. Persistance to disk in LlamaIndex.TS happens automatically once a storage context object is created.
+
+## [Customized Vector Index](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/vectorIndexCustomize.ts)
+
+Create a vector index and query it, while also configuring the the `LLM`, the `ServiceContext`, and the `similarity_top_k`.
+
+## [OpenAI LLM](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/openai.ts)
+
+Create an OpenAI LLM and directly use it for chat.
+
+## [Llama2 DeuceLLM](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/llamadeuce.ts)
+
+Create a Llama-2 LLM and directly use it for chat.
+
+## [SubQuestionQueryEngine](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/subquestion.ts)
+
+Uses the `SubQuestionQueryEngine`, which breaks complex queries into multiple questions, and then aggreates a response across the answers to all sub-questions.
+
+## [Low Level Modules](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/lowlevel.ts)
+
+This example uses several low-level components, which removes the need for an actual query engine. These components can be used anywhere, in any application, or customized and sub-classed to meet your own needs.

apps/docs/docs/environments.md

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

apps/docs/docs/installation.md

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

apps/docs/docs/introduction.md

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

apps/docs/docs/modules/_category_.yml

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

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

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

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

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

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

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

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

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

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

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

apps/docs/docs/modules/high_level/query_engine.md

@@ -0,0 +1,36 @@
+---
+sidebar_position: 3
+---
+
+# QueryEngine
+
+A query engine wraps a `Retriever` and a `ResponseSynthesizer` into a pipeline, that will use the query string to fetech nodes and then send them to the LLM to generate a response.
+
+```typescript
+const queryEngine = index.asQueryEngine();
+const response = await queryEngine.query("query string");
+```
+
+## Sub Question Query Engine
+
+The basic concept of the Sub Question Query Engine is that it splits a single query into multiple queries, gets an answer for each of those queries, and then combines those different answers into a single coherent response for the user. You can think of it as the "think this through step by step" prompt technique but iterating over your data sources!
+
+### Getting Started
+
+The easiest way to start trying the Sub Question Query Engine is running the subquestion.ts file in [apps/simple](https://github.com/run-llama/LlamaIndexTS/blob/main/apps/simple/subquestion.ts).
+
+```bash
+npx ts-node subquestion.ts
+```
+
+### Tools
+
+SubQuestionQueryEngine is implemented with Tools. The basic idea of Tools is that they are executable options for the large language model. In this case, our SubQuestionQueryEngine relies on QueryEngineTool, which as you guessed it is a tool to run queries on a QueryEngine. This allows us to give the model an option to query different documents for different questions for example. You could also imagine that the SubQuestionQueryEngine could use a Tool that searches for something on the web or gets an answer using Wolfram Alpha.
+
+You can learn more about Tools by taking a look at the LlamaIndex Python documentation https://gpt-index.readthedocs.io/en/latest/core_modules/agent_modules/tools/root.html
+
+## API Reference
+
+- [RetrieverQueryEngine](../../api/classes/RetrieverQueryEngine.md)
+- [SubQuestionQueryEngine](../../api/classes/SubQuestionQueryEngine.md)
+- [QueryEngineTool](../../api/interfaces/QueryEngineTool.md)

apps/docs/docs/modules/index.md

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

apps/docs/docs/starter.md

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

apps/docs/docusaurus.config.js

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

apps/docs/next.config.js

@@ -1,4 +0,0 @@
-module.exports = {
-  reactStrictMode: true,
-  transpilePackages: ["ui"],
-};

apps/docs/package.json

@@ -1,25 +1,53 @@
 {
   "name": "docs",
-  "version": "1.0.0",
+  "version": "0.0.0",
   "private": true,
   "scripts": {
-    "dev": "next dev --port 3001",
-    "build": "next build",
-    "start": "next start",
-    "lint": "next lint"
+    "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": {
-    "next": "^13.4.1",
-    "react": "^18.2.0",
-    "react-dom": "^18.2.0",
-    "ui": "workspace:*"
+    "@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": {
-    "@types/node": "^17.0.12",
-    "@types/react": "^18.0.22",
-    "@types/react-dom": "^18.0.7",
-    "eslint-config-custom": "workspace:*",
-    "tsconfig": "workspace:*",
-    "typescript": "^4.5.3"
+    "@docusaurus/module-type-aliases": "2.4.3",
+    "@docusaurus/types": "^2.4.3",
+    "@tsconfig/docusaurus": "^2.0.1",
+    "docusaurus-plugin-typedoc": "^0.19.2",
+    "typedoc": "^0.24.8",
+    "typedoc-plugin-markdown": "^3.16.0",
+    "typescript": "^4.9.5"
+  },
+  "browserslist": {
+    "production": [
+      ">0.5%",
+      "not dead",
+      "not op_mini all"
+    ],
+    "development": [
+      "last 1 chrome version",
+      "last 1 firefox version",
+      "last 1 safari version"
+    ]
+  },
+  "engines": {
+    "node": ">=16.14"
   }
 }

apps/docs/sidebars.js

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

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

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

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

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

apps/docs/src/css/custom.css

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

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

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

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

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

apps/docs/static/img/logo.svg

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

apps/docs/static/img/undraw_docusaurus_mountain.svg

@@ -0,0 +1,171 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="1088" height="687.962" viewBox="0 0 1088 687.962">
+  <title>Easy to Use</title>
+  <g id="Group_12" data-name="Group 12" transform="translate(-57 -56)">
+    <g id="Group_11" data-name="Group 11" transform="translate(57 56)">
+      <path id="Path_83" data-name="Path 83" d="M1017.81,560.461c-5.27,45.15-16.22,81.4-31.25,110.31-20,38.52-54.21,54.04-84.77,70.28a193.275,193.275,0,0,1-27.46,11.94c-55.61,19.3-117.85,14.18-166.74,3.99a657.282,657.282,0,0,0-104.09-13.16q-14.97-.675-29.97-.67c-15.42.02-293.07,5.29-360.67-131.57-16.69-33.76-28.13-75-32.24-125.27-11.63-142.12,52.29-235.46,134.74-296.47,155.97-115.41,369.76-110.57,523.43,7.88C941.15,276.621,1036.99,396.031,1017.81,560.461Z" transform="translate(-56 -106.019)" fill="#3f3d56"/>
+      <path id="Path_84" data-name="Path 84" d="M986.56,670.771c-20,38.52-47.21,64.04-77.77,80.28a193.272,193.272,0,0,1-27.46,11.94c-55.61,19.3-117.85,14.18-166.74,3.99a657.3,657.3,0,0,0-104.09-13.16q-14.97-.675-29.97-.67-23.13.03-46.25,1.72c-100.17,7.36-253.82-6.43-321.42-143.29L382,283.981,444.95,445.6l20.09,51.59,55.37-75.98L549,381.981l130.2,149.27,36.8-81.27L970.78,657.9l14.21,11.59Z" transform="translate(-56 -106.019)" fill="#f2f2f2"/>
+      <path id="Path_85" data-name="Path 85" d="M302,282.962l26-57,36,83-31-60Z" opacity="0.1"/>
+      <path id="Path_86" data-name="Path 86" d="M610.5,753.821q-14.97-.675-29.97-.67L465.04,497.191Z" transform="translate(-56 -106.019)" opacity="0.1"/>
+      <path id="Path_87" data-name="Path 87" d="M464.411,315.191,493,292.962l130,150-132-128Z" opacity="0.1"/>
+      <path id="Path_88" data-name="Path 88" d="M908.79,751.051a193.265,193.265,0,0,1-27.46,11.94L679.2,531.251Z" transform="translate(-56 -106.019)" opacity="0.1"/>
+      <circle id="Ellipse_11" data-name="Ellipse 11" cx="3" cy="3" r="3" transform="translate(479 98.962)" fill="#f2f2f2"/>
+      <circle id="Ellipse_12" data-name="Ellipse 12" cx="3" cy="3" r="3" transform="translate(396 201.962)" fill="#f2f2f2"/>
+      <circle id="Ellipse_13" data-name="Ellipse 13" cx="2" cy="2" r="2" transform="translate(600 220.962)" fill="#f2f2f2"/>
+      <circle id="Ellipse_14" data-name="Ellipse 14" cx="2" cy="2" r="2" transform="translate(180 265.962)" fill="#f2f2f2"/>
+      <circle id="Ellipse_15" data-name="Ellipse 15" cx="2" cy="2" r="2" transform="translate(612 96.962)" fill="#f2f2f2"/>
+      <circle id="Ellipse_16" data-name="Ellipse 16" cx="2" cy="2" r="2" transform="translate(736 192.962)" fill="#f2f2f2"/>
+      <circle id="Ellipse_17" data-name="Ellipse 17" cx="2" cy="2" r="2" transform="translate(858 344.962)" fill="#f2f2f2"/>
+      <path id="Path_89" data-name="Path 89" d="M306,121.222h-2.76v-2.76h-1.48v2.76H299V122.7h2.76v2.759h1.48V122.7H306Z" fill="#f2f2f2"/>
+      <path id="Path_90" data-name="Path 90" d="M848,424.222h-2.76v-2.76h-1.48v2.76H841V425.7h2.76v2.759h1.48V425.7H848Z" fill="#f2f2f2"/>
+      <path id="Path_91" data-name="Path 91" d="M1144,719.981c0,16.569-243.557,74-544,74s-544-57.431-544-74,243.557,14,544,14S1144,703.413,1144,719.981Z" transform="translate(-56 -106.019)" fill="#3f3d56"/>
+      <path id="Path_92" data-name="Path 92" d="M1144,719.981c0,16.569-243.557,74-544,74s-544-57.431-544-74,243.557,14,544,14S1144,703.413,1144,719.981Z" transform="translate(-56 -106.019)" opacity="0.1"/>
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+  <path d="M410.74039,545.70532a3.31768,3.31768,0,1,0,0-6.63536,3.41133,3.41133,0,0,0-.42333.04247c-.02655-.09953-.04911-.19907-.077-.29859a3.319,3.319,0,0,0-1.278-6.37923,3.28174,3.28174,0,0,0-2.00122.68742q-.10947-.11346-.22294-.22295a3.282,3.282,0,0,0,.67149-1.98265,3.31768,3.31768,0,0,0-6.37-1.2992,13.27078,13.27078,0,1,0,0,25.54082,3.31768,3.31768,0,0,0,6.37-1.2992,3.282,3.282,0,0,0-.67149-1.98265q.11347-.10947.22294-.22294a3.28174,3.28174,0,0,0,2.00122.68742,3.31768,3.31768,0,0,0,1.278-6.37923c.02786-.0982.05042-.19907.077-.29859a3.41325,3.41325,0,0,0,.42333.04246" transform="translate(-35.5 -118.5)" fill="#44d860" fill-rule="evenodd" />
+  <path d="M424.01111,447.8338a3.60349,3.60349,0,0,1-.65028-.06636,3.34415,3.34415,0,0,1-.62372-.18579,3.44679,3.44679,0,0,1-.572-.30522,5.02708,5.02708,0,0,1-.50429-.4114,3.88726,3.88726,0,0,1-.41007-.50428,3.27532,3.27532,0,0,1-.55737-1.84463,3.60248,3.60248,0,0,1,.06636-.65027,3.82638,3.82638,0,0,1,.18447-.62373,3.48858,3.48858,0,0,1,.30656-.57064,3.197,3.197,0,0,1,.91436-.91568,3.44685,3.44685,0,0,1,.572-.30523,3.344,3.344,0,0,1,.62372-.18578,3.06907,3.06907,0,0,1,1.30053,0,3.22332,3.22332,0,0,1,1.19436.491,5.02835,5.02835,0,0,1,.50429.41139,4.8801,4.8801,0,0,1,.41139.50429,3.38246,3.38246,0,0,1,.30522.57064,3.47806,3.47806,0,0,1,.25215,1.274A3.36394,3.36394,0,0,1,426.36,446.865a5.02708,5.02708,0,0,1-.50429.4114,3.3057,3.3057,0,0,1-1.84463.55737m26.54143-1.65884a3.38754,3.38754,0,0,1-2.35024-.96877,5.04185,5.04185,0,0,1-.41007-.50428,3.27532,3.27532,0,0,1-.55737-1.84463,3.38659,3.38659,0,0,1,.96744-2.34892,5.02559,5.02559,0,0,1,.50429-.41139,3.44685,3.44685,0,0,1,.572-.30523,3.3432,3.3432,0,0,1,.62373-.18579,3.06952,3.06952,0,0,1,1.30052,0,3.22356,3.22356,0,0,1,1.19436.491,5.02559,5.02559,0,0,1,.50429.41139,3.38792,3.38792,0,0,1,.96876,2.34892,3.72635,3.72635,0,0,1-.06636.65026,3.37387,3.37387,0,0,1-.18579.62373,4.71469,4.71469,0,0,1-.30522.57064,4.8801,4.8801,0,0,1-.41139.50429,5.02559,5.02559,0,0,1-.50429.41139,3.30547,3.30547,0,0,1-1.84463.55737" transform="translate(-35.5 -118.5)" fill-rule="evenodd" />
+</svg>

apps/docs/tsconfig.json

@@ -1,8 +1,7 @@
 {
-  "extends": "tsconfig/nextjs.json",
+  // This file is not used in compilation. It is here just for a nice editor experience.
+  "extends": "@tsconfig/docusaurus/tsconfig.json",
   "compilerOptions": {
-    "plugins": [{ "name": "next" }]
-  },
-  "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
-  "exclude": ["node_modules"]
+    "baseUrl": "."
+  }
 }

apps/simple/CHANGELOG.md

@@ -0,0 +1,264 @@
+# simple
+
+## 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

@@ -0,0 +1,10 @@
+# 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

@@ -0,0 +1,16 @@
+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

@@ -0,0 +1,33 @@
+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

@@ -0,0 +1,49 @@
+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

@@ -0,0 +1,715 @@
+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
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+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

@@ -0,0 +1,24 @@
+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

@@ -0,0 +1,40 @@
+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-vision-preview", 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

@@ -0,0 +1,21 @@
+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/index.ts

@@ -1,13 +0,0 @@
-import { Document } from "@llamaindex/core/src/Node";
-import { VectorStoreIndex } from "@llamaindex/core/src/BaseIndex";
-import essay from "./essay";
-
-(async () => {
-  const document = new Document({ text: essay });
-  const index = await VectorStoreIndex.fromDocuments([document]);
-  const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.aquery(
-    "What did the author do growing up?"
-  );
-  console.log(response.toString());
-})();

apps/simple/keywordIndex.ts

@@ -0,0 +1,32 @@
+import {
+  Document,
+  KeywordTableIndex,
+  KeywordTableRetrieverMode,
+} from "llamaindex";
+import essay from "./essay";
+
+async function main() {
+  const document = new Document({ text: essay, id_: "essay" });
+  const index = await KeywordTableIndex.fromDocuments([document]);
+
+  const allModes: KeywordTableRetrieverMode[] = [
+    KeywordTableRetrieverMode.DEFAULT,
+    KeywordTableRetrieverMode.SIMPLE,
+    KeywordTableRetrieverMode.RAKE,
+  ];
+  allModes.forEach(async (mode) => {
+    const queryEngine = index.asQueryEngine({
+      retriever: index.asRetriever({
+        mode,
+      }),
+    });
+    const response = await queryEngine.query(
+      "What did the author do growing up?",
+    );
+    console.log(response.toString());
+  });
+}
+
+main().catch((e: Error) => {
+  console.error(e, e.stack);
+});

apps/simple/llamadeuce.ts

@@ -0,0 +1,7 @@
+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

@@ -0,0 +1,47 @@
+import { ChatMessage, OpenAI, SimpleChatEngine } from "llamaindex";
+import {Anthropic} from "../../packages/core/src/llm/LLM";
+import { stdin as input, stdout as output } from "node:process";
+import readline from "node:readline/promises";
+
+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/lowlevel.ts

@@ -1,6 +1,10 @@
-import { Document, TextNode, NodeWithScore } from "@llamaindex/core/src/Node";
-import { ResponseSynthesizer } from "@llamaindex/core/src/ResponseSynthesizer";
-import { SimpleNodeParser } from "@llamaindex/core/src/NodeParser";
+import {
+  Document,
+  NodeWithScore,
+  ResponseSynthesizer,
+  SimpleNodeParser,
+  TextNode,
+} from "llamaindex";
 
 (async () => {
   const nodeParser = new SimpleNodeParser();
@@ -23,9 +27,9 @@ import { SimpleNodeParser } from "@llamaindex/core/src/NodeParser";
     },
   ];
 
-  const response = await responseSynthesizer.asynthesize(
+  const response = await responseSynthesizer.synthesize(
     "What age am I?",
-    nodesWithScore
+    nodesWithScore,
   );
   console.log(response.response);
 })();

apps/simple/markdown.ts

@@ -0,0 +1,20 @@
+import { MarkdownReader, VectorStoreIndex } from "llamaindex";
+
+async function main() {
+  // Load Markdown file
+  const reader = new MarkdownReader();
+  const documents = await reader.loadData("node_modules/llamaindex/README.md");
+
+  // 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 does the example code do?");
+
+  // Output response
+  console.log(response.toString());
+}
+
+main().catch(console.error);

apps/simple/mongo.ts

@@ -0,0 +1,68 @@
+import { MongoClient } from "mongodb";
+import { VectorStoreIndex } from "../../packages/core/src/indices";
+import { Document } from "../../packages/core/src/Node";
+import { SimpleMongoReader } from "../../packages/core/src/readers/SimpleMongoReader";
+
+import { stdin as input, stdout as output } from "node:process";
+import readline from "node:readline/promises";
+
+async function main() {
+  //Dummy test code
+  const query: object = { _id: "waldo" };
+  const options: object = {};
+  const projections: object = { embedding: 0 };
+  const limit: number = Infinity;
+  const uri: string = process.env.MONGODB_URI ?? "fake_uri";
+  const client: MongoClient = new MongoClient(uri);
+
+  //Where the real code starts
+  const MR = new SimpleMongoReader(client);
+  const documents: Document[] = await MR.loadData(
+    "data",
+    "posts",
+    1,
+    {},
+    options,
+    projections,
+  );
+
+  //
+  //If you need to look at low-level details of
+  // a queryEngine (for example, needing to check each individual node)
+  //
+
+  // Split text and create embeddings. Store them in a VectorStoreIndex
+  // var storageContext = await storageContextFromDefaults({});
+  // var serviceContext = serviceContextFromDefaults({});
+  // const docStore = storageContext.docStore;
+
+  // for (const doc of documents) {
+  //   docStore.setDocumentHash(doc.id_, doc.hash);
+  // }
+  // const nodes = serviceContext.nodeParser.getNodesFromDocuments(documents);
+  // console.log(nodes);
+
+  //
+  //Making Vector Store from documents
+  //
+
+  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());
+  }
+}
+
+main();

apps/simple/notion.ts

@@ -0,0 +1,89 @@
+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

@@ -1,18 +1,15 @@
-import process from "node:process";
-import { Configuration, OpenAIWrapper } from "@llamaindex/core/src/openai";
+import { OpenAI } from "llamaindex";
 
 (async () => {
-  const configuration = new Configuration({
-    apiKey: process.env.OPENAI_API_KEY,
-  });
+  const llm = new OpenAI({ model: "gpt-4-1106-preview", temperature: 0.1 });
 
-  const openai = new OpenAIWrapper(configuration);
+  // complete api
+  const response1 = await llm.complete("How are you?");
+  console.log(response1.message.content);
 
-  const { data } = await openai.createChatCompletion({
-    model: "gpt-3.5-turbo-0613",
-    messages: [{ role: "user", content: "Hello, world!" }],
-  });
-
-  console.log(data);
-  console.log(data.choices[0].message);
+  // chat api
+  const response2 = await llm.chat([
+    { content: "Tell me a joke.", role: "user" },
+  ]);
+  console.log(response2.message.content);
 })();

apps/simple/package.json

@@ -1,10 +1,16 @@
 {
+  "version": "0.0.32",
+  "private": true,
   "name": "simple",
   "dependencies": {
-    "@llamaindex/core": "workspace:*"
+    "@notionhq/client": "^2.2.13",
+    "@pinecone-database/pinecone": "^1.1.2",
+    "commander": "^11.1.0",
+    "llamaindex": "workspace:*"
   },
   "devDependencies": {
-    "@types/node": "^18"
+    "@types/node": "^18.18.6",
+    "ts-node": "^10.9.1"
   },
   "scripts": {
     "lint": "eslint ."

apps/simple/pdf.ts

@@ -0,0 +1,19 @@
+import { PDFReader, VectorStoreIndex } from "llamaindex";
+
+async function main() {
+  // Load PDF
+  const reader = new PDFReader();
+  const documents = await reader.loadData("data/brk-2022.pdf");
+
+  // 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 mistakes did they make?");
+
+  // Output response
+  console.log(response.toString());
+}
+
+main().catch(console.error);

apps/simple/portkey.ts

@@ -0,0 +1,23 @@
+import { Portkey } from "llamaindex";
+
+(async () => {
+  const llms = [{
+
+  }]
+  const portkey = new Portkey({
+    mode: "single",
+    llms: [{
+      provider:"anyscale",
+      virtual_key:"anyscale-3b3c04",
+      model: "meta-llama/Llama-2-13b-chat-hf",
+      max_tokens: 2000
+    }]
+  });
+  const result = portkey.stream_chat([
+    { role: "system", content: "You are a helpful assistant." },
+    { role: "user", content: "Tell me a joke." }
+  ]);
+  for await (const res of result) {
+    process.stdout.write(res)
+  }
+})();

apps/simple/simple.txt

@@ -1,9 +0,0 @@
-Simple flow:
-
-Get document list, in this case one document.
-Split each document into nodes, in this case sentences or lines.
-Embed each of the nodes and get vectors. Store them in memory for now.
-Embed query.
-Compare query with nodes and get the top n
-Put the top n nodes into the prompt.
-Execute prompt, get result.

apps/simple/split.ts

@@ -0,0 +1,16 @@
+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

@@ -0,0 +1,44 @@
+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

@@ -0,0 +1,26 @@
+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/summaryIndex.ts

@@ -0,0 +1,31 @@
+import {
+  Document,
+  SimpleNodeParser,
+  SummaryIndex,
+  SummaryRetrieverMode,
+  serviceContextFromDefaults,
+} from "llamaindex";
+import essay from "./essay";
+
+async function main() {
+  const serviceContext = serviceContextFromDefaults({
+    nodeParser: new SimpleNodeParser({
+      chunkSize: 40,
+    }),
+  });
+  const document = new Document({ text: essay, id_: "essay" });
+  const index = await SummaryIndex.fromDocuments([document], {
+    serviceContext,
+  });
+  const queryEngine = index.asQueryEngine({
+    retriever: index.asRetriever({ mode: SummaryRetrieverMode.LLM }),
+  });
+  const response = await queryEngine.query(
+    "What did the author do growing up?",
+  );
+  console.log(response.toString());
+}
+
+main().catch((e: Error) => {
+  console.error(e, e.stack);
+});

apps/simple/vectorIndex.ts

@@ -0,0 +1,27 @@
+import fs from "node:fs/promises";
+
+import { Document, 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 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().catch(console.error);

apps/simple/vectorIndexAnthropic.ts

@@ -0,0 +1,35 @@
+import fs from "node:fs/promises";
+
+import {
+  Anthropic,
+  Document,
+  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 Anthropic() });
+  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/vectorIndexCustomize.ts

@@ -0,0 +1,42 @@
+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

@@ -0,0 +1,197 @@
+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

@@ -0,0 +1,36 @@
+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

@@ -0,0 +1,15 @@
+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);
+})();

apps/web/.eslintrc.js

@@ -1,4 +0,0 @@
-module.exports = {
-  root: true,
-  extends: ["custom"],
-};

apps/web/README.md

@@ -1,30 +0,0 @@
-## Getting Started
-
-First, run the development server:
-
-```bash
-yarn dev
-```
-
-Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
-
-You can start editing the page by modifying `pages/index.js`. The page auto-updates as you edit the file.
-
-[API routes](https://nextjs.org/docs/api-routes/introduction) can be accessed on [http://localhost:3000/api/hello](http://localhost:3000/api/hello). This endpoint can be edited in `pages/api/hello.js`.
-
-The `pages/api` directory is mapped to `/api/*`. Files in this directory are treated as [API routes](https://nextjs.org/docs/api-routes/introduction) instead of React pages.
-
-## Learn More
-
-To learn more about Next.js, take a look at the following resources:
-
-- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
-- [Learn Next.js](https://nextjs.org/learn/foundations/about-nextjs) - an interactive Next.js tutorial.
-
-You can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome!
-
-## Deploy on Vercel
-
-The easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_source=github.com&utm_medium=referral&utm_campaign=turborepo-readme) from the creators of Next.js.
-
-Check out our [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.

apps/web/app/layout.tsx

@@ -1,11 +0,0 @@
-export default function RootLayout({
-  children,
-}: {
-  children: React.ReactNode;
-}) {
-  return (
-    <html lang="en">
-      <body>{children}</body>
-    </html>
-  );
-}