# embedding.EmbeddingProvider


Interface for embedding function providers.


Usage

``` python
embedding.EmbeddingProvider()
```


To create a custom embedding provider:

1.  Subclass [EmbeddingProvider](embedding.EmbeddingProvider.md#raghilda.embedding.EmbeddingProvider) and implement [embed()](embedding.EmbeddingProvider.md#raghilda.embedding.EmbeddingProvider.embed), [get_config()](embedding.EmbeddingProvider.md#raghilda.embedding.EmbeddingProvider.get_config), and [from_config()](embedding.EmbeddingProvider.md#raghilda.embedding.EmbeddingProvider.from_config)
2.  Register it with `@register_embedding_provider("MyProvider")`

Registered providers are automatically restored when connecting to a database that was created with that provider.


## Examples


``` python
from raghilda.embedding import EmbeddingProvider, register_embedding_provider

@register_embedding_provider("MyCustomEmbedding")
class MyCustomEmbedding(EmbeddingProvider):
    def __init__(self, model: str = "default", api_key: str | None = None):
        self.model = model
        self.api_key = api_key
        # Initialize your embedding client here

    def embed(self, x, input_type=None):
        # Return list of embedding vectors
        ...

    def get_config(self):
        # Return config dict (exclude sensitive values like api_key)
        return {"type": "MyCustomEmbedding", "model": self.model}

    @classmethod
    def from_config(cls, config):
        return cls(model=config.get("model", "default"))
```


## Methods

| Name | Description |
|----|----|
| [embed()](#embed) | Generate embeddings for a sequence of texts. |
| [from_config()](#from_config) | Create a provider instance from a configuration dict. |
| [get_config()](#get_config) | Get the configuration dict for this provider. |

------------------------------------------------------------------------


### embed()


Generate embeddings for a sequence of texts.


Usage

``` python
embed(x, input_type=EmbedInputType.DOCUMENT)
```


#### Parameters


`x: Sequence[str]`  
A sequence of texts to generate embeddings for.

`input_type: EmbedInputType = EmbedInputType.DOCUMENT`  
The type of input being embedded. Some models (e.g., Cohere) produce different embeddings for queries vs documents. Default is DOCUMENT.


#### Returns


`Sequence[Sequence[float]]`  
A sequence of embeddings (the same length as `x`), where each embedding is a sequence of floats.


------------------------------------------------------------------------


### from_config()


Create a provider instance from a configuration dict.


Usage

``` python
from_config(config)
```


#### Parameters


`config: dict[str, Any]`  
Configuration dict from [get_config()](embedding.EmbeddingProvider.md#raghilda.embedding.EmbeddingProvider.get_config).


#### Returns


`EmbeddingProvider`  
A new instance of the provider.


------------------------------------------------------------------------


### get_config()


Get the configuration dict for this provider.


Usage

``` python
get_config()
```


The config should contain all parameters needed to recreate the provider, except for sensitive values like API keys. It must include a "type" key with the registered name of the provider.


#### Returns


`dict`  
Configuration dict that can be passed to [from_config()](embedding.EmbeddingProvider.md#raghilda.embedding.EmbeddingProvider.from_config).