# chat-llamaindex

**Repository Path**: HesenjanJava/chat-llamaindex

## Basic Information

- **Project Name**: chat-llamaindex
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-03-28
- **Last Updated**: 2024-03-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<br /><br />

<p align="center">
  <img src="./public/android-chrome-192x192.png" alt="LlamaIndex Chat Logo" width="70">
</p>

<h3 align="center"><b>LlamaIndex Chat</b></h3>
<p align="center"><b>Create chat bots that know your data</b></p>

<p>
      <img
        src="./public/screenshot.png"
        alt="LlamaIndex Chat Screen"
        width="100%"
      />
</p>

Welcome to [LlamaIndex Chat](https://github.com/run-llama/chat-llamaindex). You can create and share LLM chatbots that know your data (PDF or text documents).

Getting started with LlamaIndex Chat is a breeze. Visit https://chat-llamaindex.vercel.app - a hosted version of LlamaIndex Chat with no user authentication that provides an immediate start.

## 🚀 Features

LlamaIndex Chat is an example chatbot application for [LlamaIndexTS](https://github.com/run-llama/LlamaIndexTS).
You can:

- Create bots using prompt engineering and share them with other users.
- Modify the demo bots by using the UI or directly editing the [./app/bots/bot.data.ts](./app/bots/bot.data.ts) file.
- Integrate your data by uploading documents or generating new [data sources](#📀-data-sources).

## ⚡️ Quick start

### Local Development

Requirement: [NodeJS](https://nodejs.org) 18

- Clone the repository

```bash
git clone https://github.com/run-llama/chat-llamaindex
cd chat-llamaindex
```

- Set the environment variables

```bash
cp .env.template .env.development.local
```

Edit environment variables in `.env.development.local`.

- Run the dev server

```bash
pnpm install
pnpm dev
```

### Vercel Deployment

Deploying to Vercel is simple; click the button below and follow the instructions:

[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Frun-llama%2Fchat-llamaindex&env=OPENAI_API_KEY)

If you're deploying to a [Vercel Hobby](https://vercel.com/docs/accounts/plans#hobby) account, [change the running time](./app/api/llm/route.ts#L196) to 10 seconds, as this is the limit for the free plan.

If you want to use the [sharing](#🔄-sharing) functionality, then you need to create a Vercel KV store and connect it to your project.
Just follow [this step from the quickstart](https://vercel.com/docs/storage/vercel-kv/quickstart#create-a-kv-database). No further configuration is necessary as the app automatically uses a connected KV store.

## 🔄 Sharing

LlamaIndex Chat supports the sharing of bots via URLs. Demo bots are read-only and can't be shared. But you can create new bots (or clone and modify a demo bot) and call the share functionality in the context menu. It will create a unique URL that you can share with others. Opening the URL, users can directly use the shared bot.

## 📀 Data Sources

The app is using a [`ChatEngine`](https://ts.llamaindex.ai/modules/chat_engine) for each bot with a [`VectorStoreIndex`](https://ts.llamaindex.ai/examples/vector_index) attached.
The `cache` folder in the root directory is used as [Storage](https://ts.llamaindex.ai/modules/storage) for each `VectorStoreIndex`.

Each subfolder in the `cache` folder contains the data for one `VectorStoreIndex`. To set which `VectorStoreIndex` is used for a bot, use the subfolder's name as `datasource` attribute in the [bot's data](./app/bots/bot.data.ts).

> **Note**: To use the changed bots, you have to clear your local storage. Otherwise, the old bots are still used. You can clear your local storage by opening the developer tools and running `localStorage.clear()` in the console and reloading the page.

### Generate Data Sources

To generate a new data source, create a new subfolder in the `datasources` directory and add the data files (e.g., PDFs).
Then, create the `VectorStoreIndex`` for the data source by running the following command:

```bash
pnpm run generate <datasource-name>
```

Where `<datasource-name>` is the name of the subfolder with your data files.

## 🙏 Thanks

Thanks go to @Yidadaa for his [ChatGPT-Next-Web](https://github.com/Yidadaa/ChatGPT-Next-Web) project, which was used as a starter template for this project.