# embedding.EmbeddingCohere


Creates an embedding function provider backed by Cohere's embedding models.


Usage

``` python
embedding.EmbeddingCohere(
    model="embed-english-v3.0", api_key=None, batch_size=96
)
```


Implements the [EmbeddingProvider](%60raghilda.EmbeddingProvider%60) interface.

Cohere's embedding models produce different embeddings for queries vs documents to optimize retrieval performance. Use `input_type=EmbedInputType.QUERY` when embedding search queries and `input_type=EmbedInputType.DOCUMENT` (default) when embedding documents for indexing.


## Parameters


`model: str = ``"embed-english-v3.0"`  
The Cohere embedding model to use. Default is "embed-english-v3.0".

`api_key: str | None = None`  
The API key for authenticating with Cohere. If None, it will use the CO_API_KEY environment variable if set.

`batch_size: int = ``96`  
The number of texts to process in each batch when calling the API. Cohere supports up to 96 texts per request.


## Examples


``` python
from raghilda.embedding import EmbeddingCohere, EmbedInputType

provider = EmbeddingCohere(model="embed-english-v3.0")

# Embed documents for indexing
doc_embeddings = provider.embed(
    ["Hello world", "Testing embeddings"],
    input_type=EmbedInputType.DOCUMENT
)

# Embed a query for search
query_embedding = provider.embed(
    ["How do I test embeddings?"],
    input_type=EmbedInputType.QUERY
)
```