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

# Cohere

export const PyEmbeddingCohereUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\ncohere = (\n    EmbeddingFunctionRegistry.get_instance()\n    .get(\"cohere\")\n    .create(name=\"embed-multilingual-v2.0\")\n)\n\nclass TextModel(LanceModel):\n    text: str = cohere.SourceField()\n    vector: Vector(cohere.ndims()) = cohere.VectorField()\n\ndata = [{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}]\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"cohere-demo\"))\ntbl = db.create_table(\"test\", schema=TextModel, mode=\"overwrite\")\ntbl.add(data)\n";

Using cohere API requires cohere package, which can be installed using `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

Supported parameters (to be passed in `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.                                                                                                                                                                                                                |

Cohere supports following input types:

| 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                   |

Usage Example:

<CodeGroup>
  <CodeBlock filename="Python" language="Python" icon="python">
    {PyEmbeddingCohereUsage}
  </CodeBlock>
</CodeGroup>