---
title: 'The easiest way to get started with LlamaIndex: create-llama'
url: 'https://mcavdar.com/blog/the-easiest-way-to-get-started-with-llamaindex-create-llama'
markdown: 'https://mcavdar.com/blog/the-easiest-way-to-get-started-with-llamaindex-create-llama.md'
date: '2026-07-28'
description: 'Building a Toy Project with LlamaIndex, Next.js, and Ollama In one of my previous blog posts, I introduced LlamaIndex—a powerful framework for building LLM applications. In this post, I’d like to take it a step further by creating a toy project using a Next.js backend paired with the Ollama provide…'
taxonomy:
  tag:
    - llamaindex
    - ollama
    - create-llama
    - llm
    - node.js
    - next.js
    - nvm
    - ts
    - nlp
---

## [The easiest way to get started with LlamaIndex: create-llama](https://mcavdar.com/blog/the-easiest-way-to-get-started-with-llamaindex-create-llama)

    28th Jul 2026   [llamaindex](https://mcavdar.com/tag:llamaindex#body-wrapper) [ollama](https://mcavdar.com/tag:ollama#body-wrapper) [create-llama](https://mcavdar.com/tag:create-llama#body-wrapper) [llm](https://mcavdar.com/tag:llm#body-wrapper) [node.js](https://mcavdar.com/tag:node.js#body-wrapper) [next.js](https://mcavdar.com/tag:next.js#body-wrapper) [nvm](https://mcavdar.com/tag:nvm#body-wrapper) [ts](https://mcavdar.com/tag:ts#body-wrapper) [nlp](https://mcavdar.com/tag:nlp#body-wrapper)  

## Building a Toy Project with LlamaIndex, Next.js, and Ollama

In one of my [previous blog posts](https://mcavdar.com/blog/llamaindex), I introduced **LlamaIndex**—a powerful framework for building LLM applications. In this post, I’d like to take it a step further by creating a **toy project** using a **Next.js backend** paired with the **Ollama** provider for running local LLMs.

Let’s get started.

---

### 🛠 Requirements

To follow along, make sure you have the following installed:

- **Node.js**
- **Ollama** (for running models locally)

---

### 💡 Tip: Use `nvm` for Node.js Version Management

I highly recommend using [nvm](https://github.com/nvm-sh/nvm) to manage multiple versions of Node.js easily. It’s a lifesaver for projects with varying version requirements.

---

### 🚀 Step 1: Install Ollama

Download and install Ollama from their official site:
👉 <https://ollama.com/download>

And pull these models:

```bash
ollama pull llama3.2
ollama pull nomic-embed-text
```

---

### 🧱 Step 2: Scaffold the Project

We’ll start by creating a LlamaIndex-powered app:

```bash
npx create-llama@latest
```

You will be asked for the name of your project, along with other configuration options, something like this:

```
npm create llama@latest
Need to install the following packages:
  create-llama@latest
Ok to proceed? (y) y
✔ What is your project named? … my-app
✔ What app do you want to build? › Agentic RAG
✔ What language do you want to use? › Next.js
✔ Do you want to use LlamaCloud services? … No / Yes
✔ Please provide your LlamaCloud API key (leave blank to skip): …
✔ Please provide your OpenAI API key (leave blank to skip): …
? How would you like to proceed? › - Use arrow-keys. Return to submit.
    Just generate code (~1 sec)
   Start in VSCode (~1 sec)
❯   Generate code and install dependencies (~2 min)
```

### 🔌 Step 3: Add Ollama Provider

To integrate Ollama, we need to install the official LlamaIndex Ollama bindings:

```bash
npm install @llamaindex/ollama
```

---

### ⚙️ Step 4: Update Your `.env` File

Edit your `.env` file to include the following:

```env
# The provider for the AI models to use.
MODEL_PROVIDER=ollama

# The name of LLM model to use.
MODEL=llama3.2:latest

# Name of the embedding model to use.
EMBEDDING_MODEL=nomic-embed-text:latest
```

Make sure the model names match those available in your local Ollama installation.

---

### 🧩 Step 5: Configure the Provider

Update `app/api/chat/engine/provider.ts` with the following code:

```ts
import { Ollama } from "@llamaindex/ollama";
import { OllamaEmbedding } from "@llamaindex/ollama";

export function setupProvider() {
  Settings.llm = new Ollama({
    model: process.env.MODEL ?? "gemini-2.5-pro-preview-03-25",
    maxTokens: process.env.LLM_MAX_TOKENS
      ? Number(process.env.LLM_MAX_TOKENS)
      : undefined,
  });

  Settings.embedModel = new OllamaEmbedding({
    model: process.env.EMBEDDING_MODEL,
    dimensions: process.env.EMBEDDING_DIM
      ? parseInt(process.env.EMBEDDING_DIM)
      : undefined,
  });
}
```

This code tells LlamaIndex to use Ollama both for LLM inference and embedding generation.

---

### 🧪 Step 6: Generate and Run the App

Now it’s time to build and run your app:

```bash
npm run generate
npm run dev
```

---

And that’s it! 🎉 You now have a toy project running LlamaIndex on a Next.js backend, powered by local LLMs via Ollama.

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---

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