> ## Documentation Index
> Fetch the complete documentation index at: https://docs.chonkie.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Create your own custom embeddings handler

Chonkie allows you to use your own embeddings handler by creating a child class of the `BaseEmbeddings` class, and implementing the necessary methods. It's quite simple!

## Example

First, we create a child class of the `BaseEmbeddings` class, and implement the necessary methods.

```python theme={"system"}
from chonkie.embeddings import BaseEmbeddings

class CustomEmbeddings(BaseEmbeddings):
    
    @property
    def dimension(self) -> int:
        ...

    def embed(self, text: str) -> "np.ndarray":
        ...

    def embed_batch(self, texts: list[str]) -> list["np.ndarray"]:
        ...

    def count_tokens(self, text: str) -> int:
        ...

    def count_tokens_batch(self, texts: list[str]) -> list[int]:
        ...

    def get_tokenizer(self):
        ...

    @classmethod
    def is_available(cls) -> bool:
        ...

    def __repr__(self) -> str:
        ...
```

At this point, we have a custom embeddings handler, we can use it like this:

```python theme={"system"}
embeddings = CustomEmbeddings()
```

But let's say we want to use this together with the `AutoEmbeddings` class, for the sake of convenience. We can do this by registering it with the `EmbeddingsRegistry`.

```python theme={"system"}
from chonkie.embeddings import EmbeddingsRegistry

# Register with the embeddings registry
EmbeddingsRegistry.register(
    "custom",
    CustomEmbeddings,
    pattern=r"^custom/|^model-name", 
    valid_types=["CustomEmbeddings"]
)
```

Now we can use our custom embeddings handler with the `AutoEmbeddings` class.

```python theme={"system"}
embeddings = AutoEmbeddings.get_embeddings("custom/my-custom-embeddings")
```

Finally, we can use our custom embeddings handler in the same way we would use any other embeddings handler.

```python theme={"system"}
chunker = SemanticChunker(embedding_model=embeddings, threshold=0.7)
chunks = chunker(text)
```