mem0/docs/pipelines/quickstart.mdx

---
title: '🚀 Pipelines'
description: '💡 Start building LLM powered data pipelines in 1 minute'
---

Embedchain lets you build data pipelines on your own data sources and deploy it in production in less than a minute. It can load, index, retrieve, and sync any unstructured data.

Install embedchain python package:

```bash
pip install embedchain
```

Creating a pipeline involves 3 steps:

<Steps>
  <Step title="⚙️ Import pipeline instance">
```python
from embedchain import Pipeline
p = Pipeline(name="Elon Musk")
```
  </Step>

  <Step title="🗃️ Add data sources">
```python
# Add different data sources
p.add("https://en.wikipedia.org/wiki/Elon_Musk")
p.add("https://www.forbes.com/profile/elon-musk")
# You can also add local data sources such as pdf, csv files etc.
# p.add("/path/to/file.pdf")
```
  </Step>
  <Step title="💬 Deploy your pipeline to Embedchain platform">
```python
p.deploy()
```
  </Step>
</Steps>

That's it. Now, head to the [Embedchain platform](https://app.embedchain.ai) and your pipeline is available there. Make sure to set the `OPENAI_API_KEY` 🔑 environment variable in the code.

After you deploy your pipeline to Embedchain platform, you can still add more data sources and update the pipeline multiple times.

Here is a Google Colab notebook for you to get started: [![Open in Colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/drive/1YVXaBO4yqlHZY4ho67GCJ6aD4CHNiScD?usp=sharing)