fix: paths in pgvector store example

docs: broken link on starter.md

.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": []
+}

.github/ISSUE_TEMPLATE/bugfix.yml

@@ -0,0 +1,10 @@
+name: Bugfix
+title: ""
+description: Write something like "We notice ... behavior when ... happens instead of ...""
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Details
+      description: More details about the bug
+      placeholder: The bug might be in ... file

.github/ISSUE_TEMPLATE/feature.yml

@@ -0,0 +1,10 @@
+name: Feature Request
+title: ""
+description: Write something like "Write an api endpoint that does "..." in the "..." file". If you would like to use sweep.dev prefix with "Sweep:"
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Details
+      description: More details
+      placeholder: The new endpoint should use the ... class from ... file because it contains ... logic

.github/ISSUE_TEMPLATE/refactor.yml

@@ -0,0 +1,10 @@
+name: Refactor
+title: ""
+description: Write something like "Modify the ... api endpoint to use ... version and ... framework" If you would like to use sweep.dev prefix with "Sweep:"
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Details
+      description: More details
+      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:
+      - uses: actions/checkout@v4
+      - uses: pnpm/action-setup@v2
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: '.nvmrc'
+          cache: "pnpm"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Run lint
+        run: pnpm run lint

.github/workflows/test.yml

@@ -0,0 +1,20 @@
+name: Run Tests
+
+on: [push, pull_request]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+      - uses: pnpm/action-setup@v2
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: '.nvmrc'
+          cache: "pnpm"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Run tests
+        run: pnpm run test

.gitignore

@@ -3,6 +3,7 @@
 # dependencies
 node_modules
 .pnp
+.pnpm-store
 .pnp.js
 
 # testing
@@ -35,4 +36,7 @@ yarn-error.log*
 # vercel
 .vercel
 
-storage/
+dist/
+
+# vs code
+.vscode/launch.json

.husky/pre-commit

@@ -2,3 +2,4 @@
 . "$(dirname -- "$0")/_/husky.sh"
 
 pnpm lint
+npx lint-staged

.nvmrc

@@ -0,0 +1 @@
+18

.vscode/settings.json

@@ -1,5 +1,9 @@
 {
   "editor.tabSize": 2,
   "editor.formatOnSave": true,
-  "editor.defaultFormatter": "esbenp.prettier-vscode"
-}
+  "editor.defaultFormatter": "esbenp.prettier-vscode",
+  "[xml]": {
+    "editor.defaultFormatter": "redhat.vscode-xml"
+  },
+  "jest.rootPath": "./packages/core"
+}

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

apps/docs/docs/environments.md

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

apps/docs/docs/installation.mdx

@@ -0,0 +1,63 @@
+---
+sidebar_position: 1
+---
+
+# Installation and Setup
+
+Make sure you have NodeJS v18 or higher.
+
+## Using create-llama
+
+The easiest way to get started with LlamaIndex is by using `create-llama`. This CLI tool enables you to quickly start building a new LlamaIndex application, with everything set up for you.
+
+Just run
+
+<Tabs>
+<TabItem value="1" label="npm" default>
+
+```bash
+npx create-llama@latest
+```
+
+</TabItem>
+<TabItem value="2" label="Yarn">
+
+```bash
+yarn create llama
+```
+
+</TabItem>
+<TabItem value="3" label="pnpm">
+
+```bash
+pnpm create llama@latest
+```
+
+</TabItem>
+</Tabs>
+
+to get started. Once your app is generated, run
+
+```bash npm2yarn
+npm run dev
+```
+
+to start the development server. You can then visit [http://localhost:3000](http://localhost:3000) to see your app
+
+## Installation from NPM
+
+```bash npm2yarn
+npm install llamaindex
+```
+
+### Environment variables
+
+Our examples use OpenAI by default. You'll need to set up your Open AI key like so:
+
+```bash
+export OPENAI_API_KEY="sk-......" # Replace with your key from https://platform.openai.com/account/api-keys
+```
+
+If you want to have it automatically loaded every time, add it to your .zshrc/.bashrc.
+
+WARNING: do not check in your OpenAI key into version control.

apps/docs/docs/introduction.md

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

apps/docs/docs/modules/_category_.yml

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

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

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

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

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

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

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

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

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

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

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

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

@@ -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 [examples](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/subquestion.ts).
+
+```bash
+npx ts-node subquestion.ts
+```
+
+### Tools
+
+SubQuestionQueryEngine is implemented with Tools. The basic idea of Tools is that they are executable options for the large language model. In this case, our SubQuestionQueryEngine relies on QueryEngineTool, which as you guessed it is a tool to run queries on a QueryEngine. This allows us to give the model an option to query different documents for different questions for example. You could also imagine that the SubQuestionQueryEngine could use a Tool that searches for something on the web or gets an answer using Wolfram Alpha.
+
+You can learn more about Tools by taking a look at the LlamaIndex Python documentation https://gpt-index.readthedocs.io/en/latest/core_modules/agent_modules/tools/root.html
+
+## API Reference
+
+- [RetrieverQueryEngine](../../api/classes/RetrieverQueryEngine.md)
+- [SubQuestionQueryEngine](../../api/classes/SubQuestionQueryEngine.md)
+- [QueryEngineTool](../../api/interfaces/QueryEngineTool.md)

apps/docs/docs/modules/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) 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"/>
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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/mongodb/1_import.ts

@@ -0,0 +1,34 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import * as dotenv from "dotenv";
+import * as fs from "fs";
+import { MongoClient } from "mongodb";
+
+// Load environment variables from local .env file
+dotenv.config();
+
+const jsonFile = "tinytweets.json";
+const mongoUri = process.env.MONGODB_URI!;
+const databaseName = process.env.MONGODB_DATABASE!;
+const collectionName = process.env.MONGODB_COLLECTION!;
+
+async function importJsonToMongo() {
+  // Load the tweets from a local file
+  const tweets = JSON.parse(fs.readFileSync(jsonFile, "utf-8"));
+
+  // Create a new client and connect to the server
+  const client = new MongoClient(mongoUri);
+
+  const db = client.db(databaseName);
+  const collection = db.collection(collectionName);
+
+  // Insert the tweets into mongo
+  await collection.insertMany(tweets);
+
+  console.log(
+    `Data imported successfully to the MongoDB collection ${collectionName}.`,
+  );
+  await client.close();
+}
+
+// Run the import function
+importJsonToMongo();

apps/mongodb/2_load_and_index.ts

@@ -0,0 +1,50 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import * as dotenv from "dotenv";
+import {
+  MongoDBAtlasVectorSearch,
+  SimpleMongoReader,
+  storageContextFromDefaults,
+  VectorStoreIndex,
+} from "llamaindex";
+import { MongoClient } from "mongodb";
+
+// Load environment variables from local .env file
+dotenv.config();
+
+const mongoUri = process.env.MONGODB_URI!;
+const databaseName = process.env.MONGODB_DATABASE!;
+const collectionName = process.env.MONGODB_COLLECTION!;
+const vectorCollectionName = process.env.MONGODB_VECTORS!;
+const indexName = process.env.MONGODB_VECTOR_INDEX!;
+
+async function loadAndIndex() {
+  // Create a new client and connect to the server
+  const client = new MongoClient(mongoUri);
+  // load objects from mongo and convert them into LlamaIndex Document objects
+  // llamaindex has a special class that does this for you
+  // it pulls every object in a given collection
+  const reader = new SimpleMongoReader(client);
+  const documents = await reader.loadData(databaseName, collectionName, [
+    "full_text",
+  ]);
+
+  // create Atlas as a vector store
+  const vectorStore = new MongoDBAtlasVectorSearch({
+    mongodbClient: client,
+    dbName: databaseName,
+    collectionName: vectorCollectionName, // this is where your embeddings will be stored
+    indexName: indexName, // this is the name of the index you will need to create
+  });
+
+  // now create an index from all the Documents and store them in Atlas
+  const storageContext = await storageContextFromDefaults({ vectorStore });
+  await VectorStoreIndex.fromDocuments(documents, { storageContext });
+  console.log(
+    `Successfully created embeddings in the MongoDB collection ${vectorCollectionName}.`,
+  );
+  await client.close();
+}
+
+loadAndIndex();
+
+// you can't query your index yet because you need to create a vector search index in mongodb's UI now

apps/mongodb/3_query.ts

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

apps/mongodb/CHANGELOG.md

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

apps/mongodb/README.md

@@ -0,0 +1,127 @@
+# LlamaIndexTS retrieval augmented generation with MongoDB
+
+### Prepare Environment
+
+Make sure to run `pnpm install` and set your OpenAI environment variable before running these examples.
+
+```
+pnpm install
+export OPENAI_API_KEY="sk-..."
+```
+
+### Sign up for MongoDB Atlas
+
+We'll be using MongoDB's hosted database service, [MongoDB Atlas](https://www.mongodb.com/cloud/atlas/register). You can sign up for free and get a small hosted cluster for free:
+
+![MongoDB Atlas signup](./docs/1_signup.png)
+
+The signup process will walk you through the process of creating your cluster and ensuring it's configured for you to access. Once the cluster is created, choose "Connect" and then "Connect to your application". Choose Python, and you'll be presented with a connection string that looks like this:
+
+![MongoDB Atlas connection string](./docs/2_connection_string.png)
+
+### Set up environment variables
+
+Copy the connection string (make sure you include your password) and put it into a file called `.env` in the root of this repo. It should look like this:
+
+```
+MONGODB_URI=mongodb+srv://seldo:xxxxxxxxxxx@llamaindexdemocluster.xfrdhpz.mongodb.net/?retryWrites=true&w=majority
+```
+
+You will also need to choose a name for your database, and the collection where we will store the tweets, and also include them in .env. They can be any string, but this is what we used:
+
+```
+MONGODB_DATABASE=tiny_tweets_db
+MONGODB_COLLECTION=tiny_tweets_collection
+```
+
+### Import tweets into MongoDB
+
+You are now ready to import our ready-made data set into Mongo. This is the file `tinytweets.json`, a selection of approximately 1000 tweets from @seldo on Twitter in mid-2019. With your environment set up you can do this by running
+
+```
+pnpm ts-node 1_import.ts
+```
+
+If you don't want to use tweets, you can replace `json_file` with any other array of JSON objects, but you will need to modify some code later to make sure the correct field gets indexed. There is no LlamaIndex-specific code here; you can load your data into Mongo any way you want to.
+
+### Load and index your data
+
+Now we're ready to index our data. To do this, LlamaIndex will pull your text out of Mongo, split it into chunks, and then send those chunks to OpenAI to be turned into [vector embeddings](https://docs.llamaindex.ai/en/stable/understanding/indexing/indexing.html#what-is-an-embedding). The embeddings will then be stored in a new collection in Mongo. This will take a while depending how much text you have, but the good news is that once it's done you will be able to query quickly without needing to re-index.
+
+We'll be using OpenAI to do the embedding, so now is when you need to [generate an OpenAI API key](https://platform.openai.com/account/api-keys) if you haven't already and add it to your `.env` file like this:
+
+```
+OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
+```
+
+You'll also need to pick a name for the new collection where the embeddings will be stored, and add it to `.env`, along with the name of a vector search index (we'll be creating this in the next step, after you've indexed your data):
+
+```
+MONGODB_VECTORS=tiny_tweets_vectors
+MONGODB_VECTOR_INDEX=tiny_tweets_vector_index
+```
+
+If the data you're indexing is the tweets we gave you, you're ready to go:
+
+```bash
+pnpm ts-node 2_load_and_index.ts
+```
+
+> Note: this script is running a couple of minutes and currently doesn't show any progress.
+
+What you're doing here is creating a Reader which loads the data out of Mongo in the collection and database specified. It looks for text in a set of specific keys in each object. In this case we've given it just one key, "full_text".
+
+Now you're creating a vector search client for Mongo. In addition to a MongoDB client object, you again tell it what database everything is in. This time you give it the name of the collection where you'll store the vector embeddings, and the name of the vector search index you'll create in the next step.
+
+### Create a vector search index
+
+Now if all has gone well you should be able to log in to the Mongo Atlas UI and see two collections in your database: the original data in `tiny_tweets_collection`, and the vector embeddings in `tiny_tweets_vectors`.
+
+![MongoDB Atlas collections](./docs/3_vectors_in_db.png)
+
+Now it's time to create the vector search index so that you can query the data.
+It's not yet possible to programmatically create a vector search index using the [`createIndex`](https://www.mongodb.com/docs/manual/reference/method/db.collection.createIndex/) function, therefore we have to create one manually in the UI.
+To do so, first, click the Search tab, and then click "Create Search Index":
+
+![MongoDB Atlas create search index](./docs/4_search_tab.png)
+
+We have to use the JSON editor, as the Visual Editor does not yet support to create a vector search index:
+
+![MongoDB Atlas JSON editor](./docs/5_json_editor.png)
+
+Now under "database and collection" select `tiny_tweets_db` and within that select `tiny_tweets_vectors`. Then under "Index name" enter `tiny_tweets_vector_index` (or whatever value you put for MONGODB_VECTOR_INDEX in `.env`). Under that, you'll want to enter this JSON object:
+
+```json
+{
+  "mappings": {
+    "dynamic": true,
+    "fields": {
+      "embedding": {
+        "dimensions": 1536,
+        "similarity": "cosine",
+        "type": "knnVector"
+      }
+    }
+  }
+}
+```
+
+This tells Mongo that the `embedding` field in each document (in the `tiny_tweets_vectors` collection) is a vector of 1536 dimensions (this is the size of embeddings used by OpenAI), and that we want to use cosine similarity to compare vectors. You don't need to worry too much about these values unless you want to use a different LLM to OpenAI entirely.
+
+The UI will ask you to review and confirm your choices, then you need to wait a minute or two while it generates the index. If all goes well, you should see something like this screen:
+
+![MongoDB Atlas index created](./docs/7_index_created.png)
+
+Now you're ready to query your data!
+
+### Run a test query
+
+You can do this by running
+
+```bash
+pnpm ts-node 3_query.ts
+```
+
+This sets up a connection to Atlas just like `2_load_and_index.ts` did, then it creates a [query engine](https://docs.llamaindex.ai/en/stable/understanding/querying/querying.html#getting-started) and runs a query against it.
+
+If all is well, you should get a nuanced opinion about web frameworks.

apps/mongodb/package.json

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

apps/simple/listIndex.ts

@@ -1,17 +0,0 @@
-import { Document } from "@llamaindex/core/src/Node";
-import { ListIndex, ListRetrieverMode } from "@llamaindex/core/src/index/list";
-import essay from "./essay";
-
-async function main() {
-  const document = new Document({ text: essay });
-  const index = await ListIndex.fromDocuments([document]);
-  const queryEngine = index.asQueryEngine(ListRetrieverMode.LLM);
-  const response = await queryEngine.aquery(
-    "What did the author do growing up?"
-  );
-  console.log(response.toString());
-}
-
-main().catch((e: Error) => {
-  console.error(e, e.stack);
-});

apps/simple/openai.ts

@@ -1,19 +0,0 @@
-// @ts-ignore
-import process from "node:process";
-import { Configuration, OpenAIWrapper } from "@llamaindex/core/src/openai";
-
-(async () => {
-  const configuration = new Configuration({
-    apiKey: process.env.OPENAI_API_KEY,
-  });
-
-  const openai = new OpenAIWrapper(configuration);
-
-  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);
-})();

apps/simple/package.json

@@ -1,12 +0,0 @@
-{
-  "name": "simple",
-  "dependencies": {
-    "@llamaindex/core": "workspace:*"
-  },
-  "devDependencies": {
-    "@types/node": "^18"
-  },
-  "scripts": {
-    "lint": "eslint ."
-  }
-}

apps/simple/subquestion.ts

@@ -1,60 +0,0 @@
-// from llama_index import SimpleDirectoryReader, VectorStoreIndex
-// from llama_index.query_engine import SubQuestionQueryEngine
-// from llama_index.tools import QueryEngineTool, ToolMetadata
-
-// # load data
-// pg_essay = SimpleDirectoryReader(
-//     input_dir="docs/examples/data/paul_graham/"
-// ).load_data()
-
-// # build index and query engine
-// query_engine = VectorStoreIndex.from_documents(pg_essay).as_query_engine()
-
-// # setup base query engine as tool
-// query_engine_tools = [
-//     QueryEngineTool(
-//         query_engine=query_engine,
-//         metadata=ToolMetadata(
-//             name="pg_essay", description="Paul Graham essay on What I Worked On"
-//         ),
-//     )
-// ]
-
-// query_engine = SubQuestionQueryEngine.from_defaults(
-//     query_engine_tools=query_engine_tools
-// )
-
-// response = query_engine.query(
-//     "How was Paul Grahams life different before and after YC?"
-// )
-
-// print(response)
-
-import { Document } from "@llamaindex/core/src/Node";
-import { VectorStoreIndex } from "@llamaindex/core/src/BaseIndex";
-import { SubQuestionQueryEngine } from "@llamaindex/core/src/QueryEngine";
-
-import essay from "./essay";
-
-(async () => {
-  const document = new Document({ text: 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.aquery(
-    "How was Paul Grahams life different before and after YC?"
-  );
-
-  console.log(response);
-})();

apps/simple/subquestionCustomize.ts

@@ -1,50 +0,0 @@
-
-
-import { Document } from "@llamaindex/core/src/Node";
-import { VectorStoreIndex } from "@llamaindex/core/src/BaseIndex";
-import { SubQuestionQueryEngine } from "@llamaindex/core/src/QueryEngine";
-
-import essay from "./essay";
-import { LLMQuestionGenerator } from "@llamaindex/core/src/QuestionGenerator";
-import { defaultSubQuestionPrompt } from "@llamaindex/core/src/Prompt";
-import { Refine, ResponseSynthesizer } from "@llamaindex/core/src/ResponseSynthesizer";
-import { serviceContextFromDefaults } from "@llamaindex/core/src/ServiceContext";
-
-// Customize the prompt for the question generator
-async function main() {
-  const document = new Document({ text: essay });
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  
-  // TODO: hard to modify the existing prompts since it's a function
-  const questionGen = new LLMQuestionGenerator({
-    prompt : defaultSubQuestionPrompt,
-  });
-
-  const serviceContext = serviceContextFromDefaults();
-
-  const builder = new Refine(serviceContext);
-  const synthesizer = new ResponseSynthesizer(builder);
-
-  const queryEngine = SubQuestionQueryEngine.fromDefaults({
-    questionGen: questionGen,
-    responseSynthesizer: synthesizer,
-    queryEngineTools: [
-      {
-        queryEngine: index.asQueryEngine(),
-        metadata: {
-          name: "pg_essay",
-          description: "Paul Graham essay on What I Worked On",
-        },
-      },
-    ],
-  });
-
-  const response = await queryEngine.aquery(
-    "How was Paul Grahams life different before and after YC?"
-  );
-
-  console.log(response);
-}
-
-main().catch(console.error);

apps/simple/vectorIndex.ts

@@ -1,15 +0,0 @@
-import { Document } from "@llamaindex/core/src/Node";
-import { VectorStoreIndex } from "@llamaindex/core/src/BaseIndex";
-import essay from "./essay";
-
-async function main() {
-  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());
-}
-
-main().catch(console.error);

apps/simple/vectorIndexCustomize.ts

@@ -1,20 +0,0 @@
-import { Document } from "@llamaindex/core/src/Node";
-import { VectorStoreIndex } from "@llamaindex/core/src/BaseIndex";
-import essay from "./essay";
-import { RetrieverQueryEngine } from "@llamaindex/core/src/QueryEngine";
-
-// Customize retrieval and query args 
-async function main() {
-  const document = new Document({ text: essay });
-  const index = await VectorStoreIndex.fromDocuments([document]);
-  const retriever = index.asRetriever();
-  // TODO: using similarityTopK > 2 causes an error
-  retriever.similarityTopK = 3;
-  // TODO: cannot pass responseSynthesizer into retriever query engine
-  const queryEngine =  new RetrieverQueryEngine(retriever);
-  const response = await queryEngine.aquery('Who is the author of the essay?');
-  console.log(response.response);
-
-}
-
-main().catch(console.error);

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>
-  );
-}

apps/web/app/page.tsx

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

apps/web/next-env.d.ts

@@ -1,5 +0,0 @@
-/// <reference types="next" />
-/// <reference types="next/image-types/global" />
-
-// NOTE: This file should not be edited
-// see https://nextjs.org/docs/basic-features/typescript for more information.

apps/web/next.config.js

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

apps/web/package.json

@@ -1,25 +0,0 @@
-{
-  "name": "web",
-  "version": "1.0.0",
-  "private": true,
-  "scripts": {
-    "dev": "next dev",
-    "build": "next build",
-    "start": "next start",
-    "lint": "next lint"
-  },
-  "dependencies": {
-    "next": "^13.4.1",
-    "react": "^18.2.0",
-    "react-dom": "^18.2.0",
-    "ui": "workspace:*"
-  },
-  "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"
-  }
-}

apps/web/tsconfig.json

@@ -1,8 +0,0 @@
-{
-  "extends": "tsconfig/nextjs.json",
-  "compilerOptions": {
-    "plugins": [{ "name": "next" }]
-  },
-  "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],
-  "exclude": ["node_modules"]
-}

assets/tslogo.svg

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

examples/.gitignore

@@ -0,0 +1,2 @@
+package-lock.json
+storage

examples/CHANGELOG.md

@@ -0,0 +1,22 @@
+# simple
+
+## 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

examples/README.md

@@ -0,0 +1,29 @@
+# Simple Examples
+
+Before running any of the examples, make sure to set your OpenAI environment variable:
+
+```bash
+export OPENAI_API_KEY="sk-..."
+```
+
+There are two ways to run the examples, using the latest published version of `llamaindex` or using a local build.
+
+## Using the latest published version
+
+Make sure to call `npm install` before running these examples:
+
+```bash
+npm install
+```
+
+Then run the examples with `ts-node`, for example `npx ts-node vectorIndex.ts`
+
+## Using the local build
+
+```bash
+pnpm install
+pnpm --filter llamaindex build
+pnpm link ../packages/core
+```
+
+Then run the examples with `ts-node`, for example `pnpx ts-node vectorIndex.ts`

examples/anthropic.ts

@@ -0,0 +1,14 @@
+import { Anthropic } from "llamaindex";
+
+(async () => {
+  const anthropic = new Anthropic();
+  const result = await anthropic.chat([
+    { content: "You want to talk in rhymes.", role: "system" },
+    {
+      content:
+        "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
+      role: "user",
+    },
+  ]);
+  console.log(result);
+})();

examples/chatEngine.ts

@@ -1,21 +1,23 @@
-// @ts-ignore
-import * as readline from "node:readline/promises";
-// @ts-ignore
 import { stdin as input, stdout as output } from "node:process";
-import { Document } from "@llamaindex/core/src/Node";
-import { VectorStoreIndex } from "@llamaindex/core/src/BaseIndex";
-import { ContextChatEngine } from "@llamaindex/core/src/ChatEngine";
+// 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";
-import { serviceContextFromDefaults } from "@llamaindex/core/src/ServiceContext";
 
 async function main() {
   const document = new Document({ text: essay });
   const serviceContext = serviceContextFromDefaults({ chunkSize: 512 });
-  const index = await VectorStoreIndex.fromDocuments(
-    [document],
-    undefined,
-    serviceContext
-  );
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    serviceContext,
+  });
   const retriever = index.asRetriever();
   retriever.similarityTopK = 5;
   const chatEngine = new ContextChatEngine({ retriever });
@@ -23,8 +25,8 @@ async function main() {
 
   while (true) {
     const query = await rl.question("Query: ");
-    const response = await chatEngine.achat(query);
-    console.log(response);
+    const response = await chatEngine.chat(query);
+    console.log(response.toString());
   }
 }
 

examples/clip.ts

@@ -0,0 +1,33 @@
+import { ClipEmbedding, similarity, SimilarityType } from "llamaindex";
+
+async function main() {
+  const clip = new ClipEmbedding();
+
+  // Get text embeddings
+  const text1 = "a car";
+  const textEmbedding1 = await clip.getTextEmbedding(text1);
+  const text2 = "a football match";
+  const textEmbedding2 = await clip.getTextEmbedding(text2);
+
+  // Get image embedding
+  const image =
+    "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/football-match.jpg";
+  const imageEmbedding = await clip.getImageEmbedding(image);
+
+  // Calc similarity
+  const sim1 = similarity(
+    textEmbedding1,
+    imageEmbedding,
+    SimilarityType.DEFAULT,
+  );
+  const sim2 = similarity(
+    textEmbedding2,
+    imageEmbedding,
+    SimilarityType.DEFAULT,
+  );
+
+  console.log(`Similarity between "${text1}" and the image is ${sim1}`);
+  console.log(`Similarity between "${text2}" and the image is ${sim2}`);
+}
+
+main();

examples/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);