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
)embedding.EmbeddingCohere
Creates an embedding function provider backed by Cohere’s embedding models.
Usage
embedding.EmbeddingCohere()Implements the EmbeddingProvider 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: Optional[str] = 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.