mem0/docs/get-started/quickstart.mdx

---
title: '🚀 Quickstart'
description: '💡 Start building LLM powered apps under 30 seconds'
---

Embedchain is a Data Platform for LLMs - load, index, retrieve, and sync any unstructured data. Using embedchain, you can easily create LLM powered apps over any data. 

Install embedchain python package:

```bash
pip install embedchain
```

Creating an app involves 3 steps:

<Steps>
  <Step title="⚙️ Import app instance">
```python
from embedchain import App
app = App()
```
  </Step>
  <Step title="🗃️ Add data sources">
```python
# Add different data sources
elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
elon_bot.add("https://www.forbes.com/profile/elon-musk")
# You can also add local data sources such as pdf, csv files etc.
# elon_bot.add("/path/to/file.pdf")
```
  </Step>
  <Step title="💬 Query or chat on your data and get answers">
```python
elon_bot.query("What is the net worth of Elon Musk today?")
# Answer: The net worth of Elon Musk today is $258.7 billion.
```
  </Step>
</Steps>

Putting it together, you can run your first app using the following code. Make sure to set the `OPENAI_API_KEY` 🔑 environment variable in the code.

```python
import os
from embedchain import App

os.environ["OPENAI_API_KEY"] = "xxx"
elon_bot = App()

# Add different data sources
elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
elon_bot.add("https://www.forbes.com/profile/elon-musk")
# You can also add local data sources such as pdf, csv files etc.
# elon_bot.add("/path/to/file.pdf")

response = elon_bot.query("What is the net worth of Elon Musk today?")
print(response)
# Answer: The net worth of Elon Musk today is $258.7 billion.
```