Cohere PlatformQuickstart

Text Generation

Cohere’s Command family of LLMs are available via the Chat endpoint. This endpoint enables you to build generative AI applications and facilitates a conversational interface for building chatbots.

This quickstart guide shows you how to perform text generation with the Chat endpoint.

1

Setup

First, install the Cohere Python SDK with the following command.

pip install -U cohere

Next, import the library and create a client.

PYTHON
import cohere
co = cohere.Client(
    "COHERE_API_KEY"
)  # Get your free API key here: https://dashboard.cohere.com/api-keys
2

Basic Text Generation

To perform a basic text generation, call the Chat endpoint by passing the message parameter containing the user message.

PYTHON
response = co.chat(
    model="command-a-plus-05-2026",
    message="I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates?",
)
print(response.text)
"Excited to be part of the Co1t team, I'm [Your Name], a [Your Role], passionate about [Your Area of Expertise] and looking forward to contributing to the company's success."

State Management

To maintain the state of a conversation, such as for building chatbots, append a sequence of user and chatbot messages to the chat_history list. You can also include a preamble parameter, which will act as a system message to set the context of the conversation.

PYTHON
response = co.chat(
    model="command-a-plus-05-2026",
    preamble="You respond in concise sentences.",
    chat_history=[
        {"role": "user", "message": "Hello"},
        {
            "role": "chatbot",
            "message": "Hi, how can I help you today?",
        },
    ],
    message="I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates?",
)
print(response.text)
"Excited to join the team at Co1t, looking forward to contributing my skills and collaborating with everyone!"

Streaming

To stream the generated text, call the Chat endpoint using chat_stream instead of chat. This returns a generator that yields chunk objects, which you can access the generated text from.

PYTHON
message = "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates."
response = co.chat_stream(
    model="command-a-plus-05-2026", message=message
)
for chunk in response:
    if chunk.event_type == "text-generation":
        print(chunk.text, end="")
"Excited to be part of the Co1t team, I'm [Your Name], a [Your Role/Position], looking forward to contributing my skills and collaborating with this talented group to drive innovation and success."

Further Resources