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You are currently on a page documenting the use of Cohere models as text completion models. Many popular Cohere models are chat completion models.You may be looking for this page instead.
Cohere is a Canadian startup that provides natural language processing models that help companies improve human-machine interactions.
Head to the API reference for detailed documentation of all attributes and methods.

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Setup

The integration lives in the langchain-community package. We also need to install the cohere package itself. We can install these with:
The langchain-community package is no longer maintained. Examples that import from langchain_community may be outdated or broken. Use with caution.

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Credentials

We’ll need to get a Cohere API key and set the COHERE_API_KEY environment variable: 
import getpass
import os

if "COHERE_API_KEY" not in os.environ:
    os.environ["COHERE_API_KEY"] = getpass.getpass()

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Installation

pip install -U langchain-community langchain-cohere
It’s also helpful (but not needed) to set up LangSmith for best-in-class observability 
os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass()

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Invocation

Cohere supports all LLM functionality: 
from langchain_cohere import Cohere
from langchain.messages import HumanMessage

model = Cohere(max_tokens=256, temperature=0.75)

message = "Knock knock"
model.invoke(message)

" Who's there?"

await model.ainvoke(message)

" Who's there?"

stream = model.stream_events(message, version="v3")
for token in stream.text:
    print(token, end="", flush=True)

 Who's there?

model.batch([message])

[" Who's there?"]

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API reference

For detailed documentation of all Cohere llm features and configurations head to the API reference
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