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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 NameFunction Call
command-rcompletion('command-r', messages)
command-lightcompletion('command-light', messages)
command-r-pluscompletion('command-r-plus', messages)
command-mediumcompletion('command-medium', messages)
command-medium-betacompletion('command-medium-beta', messages)
command-xlarge-nightlycompletion('command-xlarge-nightly', messages)
command-nightlycompletion('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 NameFunction Call
embed-english-v3.0embedding(model="embed-english-v3.0", input=["good morning from litellm", "this is another item"])
embed-english-light-v3.0embedding(model="embed-english-light-v3.0", input=["good morning from litellm", "this is another item"])
embed-multilingual-v3.0embedding(model="embed-multilingual-v3.0", input=["good morning from litellm", "this is another item"])
embed-multilingual-light-v3.0embedding(model="embed-multilingual-light-v3.0", input=["good morning from litellm", "this is another item"])
embed-english-v2.0embedding(model="embed-english-v2.0", input=["good morning from litellm", "this is another item"])
embed-english-light-v2.0embedding(model="embed-english-light-v2.0", input=["good morning from litellm", "this is another item"])
embed-multilingual-v2.0embedding(model="embed-multilingual-v2.0", input=["good morning from litellm", "this is another item"])