pip install cohere. Cohere embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
You also need to set the COHERE_API_KEY environment variable to use the Cohere API.
Supported models are:
- embed-english-v3.0
- embed-multilingual-v3.0
- embed-english-light-v3.0
- embed-multilingual-light-v3.0
- embed-english-v2.0
- embed-english-light-v2.0
- embed-multilingual-v2.0
create method) are:
| Parameter | Type | Default Value | Description |
|---|---|---|---|
name | str | "embed-english-v2.0" | The model ID of the cohere model to use. Supported base models for Text Embeddings: embed-english-v3.0, embed-multilingual-v3.0, embed-english-light-v3.0, embed-multilingual-light-v3.0, embed-english-v2.0, embed-english-light-v2.0, embed-multilingual-v2.0 |
source_input_type | str | "search_document" | The type of input data to be used for the source column. |
query_input_type | str | "search_query" | The type of input data to be used for the query. |
| Input Type | Description |
|---|---|
”search_document” | Used for embeddings stored in a vector |
| database for search use-cases. | |
”search_query” | Used for embeddings of search queries |
| run against a vector DB | |
”semantic_similarity” | Specifies the given text will be used |
| for Semantic Textual Similarity (STS) | |
“classification” | Used for embeddings passed through a |
| text classifier. | |
”clustering” | Used for the embeddings run through a |
| clustering algorithm |