> ## Documentation Index
> Fetch the complete documentation index at: https://llm-tools.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# ❓ Question Answering

Utilizing large language models (LLMs) for question answering is a transformative application, bringing significant benefits to various real-world situations. EmbedJs extensively supports tasks related to question answering, including summarization, content creation, language translation, and data analysis. The versatility of question answering with LLMs enables solutions for numerous practical applications such as:

* **Educational Aid**: Enhancing learning experiences and aiding with homework
* **Customer Support**: Addressing and resolving customer queries efficiently
* **Research Assistance**: Facilitating academic and professional research endeavors
* **Healthcare Information**: Providing fundamental medical knowledge
* **Technical Support**: Resolving technology-related inquiries
* **Legal Information**: Offering basic legal advice and information
* **Business Insights**: Delivering market analysis and strategic business advice
* **Language Learning** Assistance: Aiding in understanding and translating languages
* **Travel Guidance**: Supplying information on travel and hospitality
* **Content Development**: Assisting authors and creators with research and idea generation

## Example: Build a Q\&A System with EmbedJs for Next.JS

Quickly create a RAG pipeline to answer queries about the [Next.JS Framework](https://nextjs.org/) using EmbedJs tools.

### Step 1: Set Up Your RAG Pipeline

First, let's create your RAG pipeline. Open your NodeJs application and add the following code:

```ts Create pipeline theme={null}
import { RAGApplicationBuilder } from '@llm-tools/embedjs';
import { OpenAi } from '@llm-tools/embedjs-openai';
import { HNSWDb } from '@llm-tools/embedjs-hnswlib';

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

This initializes your application.

### Step 2: Populate Your Pipeline with Data

Now, let's add data to your pipeline. We'll include the Next.JS website and its documentation:

```ts Ingest data sources theme={null}
import { SitemapLoader } from '@llm-tools/embedjs-loader-sitemap';

//Add Next.JS Website and docs
app.addLoader(new SitemapLoader({ url: "https://nextjs.org/sitemap.xml" }))

//Add Next.JS Forum data
app.addLoader(new SitemapLoader({ url: "https://nextjs-forum.com/sitemap.xml" }))
```

This step incorporates over **15K pages** from the Next.JS website and forum into your pipeline. For more data source options, check the [EmbedJs data sources overview](/components/data-sources/overview).

### Step 3: Local Testing of Your Pipeline

Test the pipeline on your local machine:

```ts Query App theme={null}
app.query("Summarize the features of Next.js 14?")
```

Run this query to see how your pipeline responds with information about Next.js 14.

## Need help?

If you are looking to configure the RAG pipeline further, feel free to checkout the [API reference](/api-reference/pipeline/query).

In case you run into issues, feel free to contact us via any of the following methods:

<Snippet file="get-help.mdx" />