---
title: '⚡ Quickstart'
description: '💡 Create an AI app on your own data in a minute'
---

## Installation

First install the NodeJs package:

```bash
npm i @llm-tools/embedjs
```

Once you have installed the package, depending upon your preference you can either use:

<CardGroup cols={2}>
    <Card title="Open Source Models" icon="osi" href="#open-source-models">
        This includes Open source LLMs like Mistral, Llama, etc.
        <br />
        Free to use, and runs locally on your machine.
    </Card>
    <Card title="Paid Models" icon="dollar-sign" href="#paid-models" color="#4A154B">
        This includes paid LLMs like GPT 4, Claude, etc.
        <br />
        Cost money and are accessible via an API.
    </Card>
</CardGroup>

## Open Source Models

This section provides a quickstart example of using `Llama3.2` as the Open source LLM and `nomic-embed-text` as the Open source embedding model.

These models are free and can be run locally using Ollama.

<CodeGroup>
```ts ollama_demo.ts
import { RAGApplicationBuilder } from '@llm-tools/embedjs';
import { OllamaEmbeddings, Ollama } from '@llm-tools/embedjs-ollama';
import { WebLoader } from '@llm-tools/embedjs-loader-web';
import { HNSWDb } from '@llm-tools/embedjs-hnswlib';

const ragApplication = await new RAGApplicationBuilder()
.setModel(new Ollama({ modelName: "llama3.2", baseUrl: 'http://localhost:11434' }))
.setEmbeddingModel(new OllamaEmbeddings({ model: 'nomic-embed-text', baseUrl: 'http://localhost:11434' }))
.setVectorDatabase(new HNSWDb())
.build();

await ragApplication.addLoader({ urlOrContent: 'https://www.forbes.com/profile/elon-musk' })
await ragApplication.addLoader({ urlOrContent: 'https://en.wikipedia.org/wiki/Elon_Musk' })

await ragApplication.query('What is the net worth of Elon Musk today?')
//Answer: The net worth of Elon Musk today is $258.7 billion.
```
</CodeGroup>

<Note>
You can also run these models using Huggingface. Read more about using Huggingface embeddings and LLMs with EmbedJs [here](/components/embeddings/huggingface) and [here](/components/llms/huggingface).
</Note>

## Paid Models

In this section, we will use both an LLM and embedding model from OpenAI.

<CodeGroup>
```ts openai_demo.ts
import { RAGApplicationBuilder, SIMPLE_MODELS } from '@llm-tools/embedjs';
import { OpenAiEmbeddings } from '@llm-tools/embedjs-openai';
import { WebLoader } from '@llm-tools/embedjs-loader-web';
import { HNSWDb } from '@llm-tools/embedjs-hnswlib';

//Replace this with your OpenAI key
process.env.OPENAI_API_KEY = "sk-xxxx"

const ragApplication = await new RAGApplicationBuilder()
.setModel(SIMPLE_MODELS.OPENAI_GPT4_O)
.setEmbeddingModel(new OpenAiEmbeddings())
.setVectorDatabase(new HNSWDb())
.build();

await ragApplication.addLoader(new WebLoader({ urlOrContent: 'https://www.forbes.com/profile/elon-musk' }));
await ragApplication.addLoader(new WebLoader({ urlOrContent: 'https://en.wikipedia.org/wiki/Elon_Musk' }));

await ragApplication.query('What is the net worth of Elon Musk today?')
//Answer: The net worth of Elon Musk today is $258.7 billion.
```
</CodeGroup>

# Next Steps

Now that you have created your first app, you can follow any of the links:

-   [Introduction](/get-started/introduction)
-   [Customization](/components/introduction)
-   [Use cases](/use-cases/introduction)
-   [Deployment](/get-started/deployment)
