Cohere

API KEYS

import os 
os.environ["COHERE_API_KEY"] = ""

Usage

from litellm import completion

## set ENV variables
os.environ["COHERE_API_KEY"] = "cohere key"

# cohere call
response = completion(
    model="command-r", 
    messages = [{ "content": "Hello, how are you?","role": "user"}]
)

Usage - Streaming

from litellm import completion

## set ENV variables
os.environ["COHERE_API_KEY"] = "cohere key"

# cohere call
response = completion(
    model="command-r", 
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    stream=True
)

for chunk in response:
    print(chunk)

Supported Models

Model Name	Function Call
command-r	completion('command-r', messages)
command-light	completion('command-light', messages)
command-r-plus	completion('command-r-plus', messages)
command-medium	completion('command-medium', messages)
command-medium-beta	completion('command-medium-beta', messages)
command-xlarge-nightly	completion('command-xlarge-nightly', messages)
command-nightly	completion('command-nightly', messages)

Embedding

from litellm import embedding
os.environ["COHERE_API_KEY"] = "cohere key"

# cohere call
response = embedding(
    model="embed-english-v3.0", 
    input=["good morning from litellm", "this is another item"], 
)

Setting - Input Type for v3 models

v3 Models have a required parameter: input_type, it can be one of the following four values:

• input_type="search_document": (default) Use this for texts (documents) you want to store in your vector database
• input_type="search_query": Use this for search queries to find the most relevant documents in your vector database
• input_type="classification": Use this if you use the embeddings as an input for a classification system
• input_type="clustering": Use this if you use the embeddings for text clustering

https://txt.cohere.com/introducing-embed-v3/

from litellm import embedding
os.environ["COHERE_API_KEY"] = "cohere key"

# cohere call
response = embedding(
    model="embed-english-v3.0", 
    input=["good morning from litellm", "this is another item"], 
    input_type="search_document" 
)

Supported Embedding Models

Model Name	Function Call
embed-english-v3.0	embedding(model="embed-english-v3.0", input=["good morning from litellm", "this is another item"])
embed-english-light-v3.0	embedding(model="embed-english-light-v3.0", input=["good morning from litellm", "this is another item"])
embed-multilingual-v3.0	embedding(model="embed-multilingual-v3.0", input=["good morning from litellm", "this is another item"])
embed-multilingual-light-v3.0	embedding(model="embed-multilingual-light-v3.0", input=["good morning from litellm", "this is another item"])
embed-english-v2.0	embedding(model="embed-english-v2.0", input=["good morning from litellm", "this is another item"])
embed-english-light-v2.0	embedding(model="embed-english-light-v2.0", input=["good morning from litellm", "this is another item"])
embed-multilingual-v2.0	embedding(model="embed-multilingual-v2.0", input=["good morning from litellm", "this is another item"])