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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.

The previous default, embed-multilingual-v2.0, produced 768-dimensional vectors. The new default, embed-multilingual-v3.0, produces 1024-dimensional vectors. 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	"embed-multilingual-v3.0"	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
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)