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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.

    Bash
    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
    PYTHON
    import cohere

co = cohere.Client(
    api_key="",  # Leave this blank
    base_url="<YOUR_DEPLOYMENT_URL>",
)
    PYTHON
    import cohere

co = cohere.BedrockClient(
    aws_region="AWS_REGION",
    aws_access_key="AWS_ACCESS_KEY_ID",
    aws_secret_key="AWS_SECRET_ACCESS_KEY",
    aws_session_token="AWS_SESSION_TOKEN",
)

# Get the model name: https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html
    PYTHON
    import cohere

co = cohere.SagemakerClient(
    aws_region="AWS_REGION",
    aws_access_key="AWS_ACCESS_KEY_ID",
    aws_secret_key="AWS_SECRET_ACCESS_KEY",
    aws_session_token="AWS_SESSION_TOKEN",
)
    PYTHON
    import cohere

co = cohere.Client(
    api_key="AZURE_API_KEY",
    base_url="AZURE_ENDPOINT",  # example: "https://cohere-command-r-plus-08-2024-xyz.eastus.models.ai.azure.com/"
)

  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)
    PYTHON
    response = co.chat(
    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)
    PYTHON
    response = co.chat(
    model="YOUR_MODEL_NAME",
    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)
    PYTHON
    response = co.chat(
    model="YOUR_ENDPOINT_NAME",
    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)
    PYTHON
    response = co.chat(
    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)
    wordWrap
    "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."

  3. 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)
    PYTHON
    response = co.chat(
    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)
    PYTHON
    response = co.chat(
    model="YOUR_MODEL_NAME",
    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)
    PYTHON
    response = co.chat(
    model="YOUR_ENDPOINT_NAME",
    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)
    PYTHON
    response = co.chat(
    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)
    wordWrap
    "Excited to join the team at Co1t, looking forward to contributing my skills and collaborating with everyone!"

  4. 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="")
    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(message=message)

for chunk in response:
    if chunk.event_type == "text-generation":
        print(chunk.text, end="")
    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="YOUR_MODEL_NAME", message=message)

for chunk in response:
    if chunk.event_type == "text-generation":
        print(chunk.text, end="")
    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="YOUR_ENDPOINT_NAME", message=message)

for chunk in response:
    if chunk.event_type == "text-generation":
        print(chunk.text, end="")
    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(message=message)

for chunk in response:
    if chunk.event_type == "text-generation":
        print(chunk.text, end="")
    wordWrap
    "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."

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