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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. LiteLLM defaults to search_document. 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"])

Rerank​

Usage​

from litellm import rerank
import os

os.environ["COHERE_API_KEY"] = "sk-.."

query = "What is the capital of the United States?"
documents = [
    "Carson City is the capital city of the American state of Nevada.",
    "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean. Its capital is Saipan.",
    "Washington, D.C. is the capital of the United States.",
    "Capital punishment has existed in the United States since before it was a country.",
]

response = rerank(
    model="cohere/rerank-english-v3.0",
    query=query,
    documents=documents,
    top_n=3,
)
print(response)