---
title: '⚙️ Custom'
---

When we say "custom", we mean that you can customize the loader and chunker to your needs. This is done by passing a custom loader and chunker to the `add` method.

```python
from embedchain import Pipeline as App
import your_loader
import your_chunker

app = App()
loader = your_loader()
chunker = your_chunker()

app.add("source", data_type="custom", loader=loader, chunker=chunker)
```

<Note>
    The custom loader and chunker must be a class that inherits from the [`BaseLoader`](https://github.com/embedchain/embedchain/blob/main/embedchain/loaders/base_loader.py) and [`BaseChunker`](https://github.com/embedchain/embedchain/blob/main/embedchain/chunkers/base_chunker.py) classes respectively.
</Note>

<Note>
    If the `data_type` is not a valid data type, the `add` method will fallback to the `custom` data type and expect a custom loader and chunker to be passed by the user.
</Note>

Example:

```python
from embedchain import Pipeline as App
from embedchain.loaders.github import GithubLoader

app = App()

loader = GithubLoader(config={"token": "ghp_xxx"})

app.add("repo:embedchain/embedchain type:repo", data_type="github", loader=loader)

app.query("What is Embedchain?")
# Answer: Embedchain is a Data Platform for Large Language Models (LLMs). It allows users to seamlessly load, index, retrieve, and sync unstructured data in order to build dynamic, LLM-powered applications. There is also a JavaScript implementation called embedchain-js available on GitHub.
```
