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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 App | |
import your_loader | |
from my_module import CustomLoader | |
from my_module import CustomChunker | |
app = App() | |
loader = CustomLoader() | |
chunker = CustomChunker() | |
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 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. | |
``` | |