Cohere

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.

export COHERE_API_KEY="your-api-key"

Supported models are:

embed-english-v3.0
embed-multilingual-v3.0
embed-english-light-v3.0
embed-multilingual-light-v3.0

The v2.0 models (embed-english-v2.0, embed-english-light-v2.0 and embed-multilingual-v2.0) were retired by Cohere on April 4, 2026. Python 0.41.0 defaults to embed-multilingual-v2.0 (768 dimensions). Python 0.42.0b8 defaults to embed-multilingual-v3.0 (1024 dimensions). Set name="embed-multilingual-v3.0" explicitly, as in the example below, rather than relying on the client default. Existing tables containing vectors from a v2.0 model must be re-embedded with an active model into a table whose vector column has the matching dimension. Supported parameters (to be passed in create method) are:

Parameter	Type	Default Value	Description
`name`	`str`	v2.0 in Python 0.41.0; v3.0 in Python 0.42.0b8	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
`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 database.
`semantic_similarity`	Specifies that the text will be used for semantic textual similarity (STS).
`classification`	Used for embeddings passed through a text classifier.
`clustering`	Used for embeddings run through a clustering algorithm.

Usage Example:

import tempfile
from pathlib import Path

import lancedb
from lancedb.embeddings import EmbeddingFunctionRegistry
from lancedb.pydantic import LanceModel, Vector

cohere = (
    EmbeddingFunctionRegistry.get_instance()
    .get("cohere")
    .create(name="embed-multilingual-v3.0")
)

class TextModel(LanceModel):
    text: str = cohere.SourceField()
    vector: Vector(cohere.ndims()) = cohere.VectorField()

data = [{"text": "hello world"}, {"text": "goodbye world"}]

db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "cohere-demo"))
tbl = db.create_table("test", schema=TextModel, mode="overwrite")
tbl.add(data)

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