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Text generation - quickstart

About text generation

Section titled “About 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.ClientV2(
    "COHERE_API_KEY"
)  # Get your free API key here: https://dashboard.cohere.com/api-keys
    PYTHON
    import cohere

co = cohere.ClientV2(
    api_key="",  # Leave this blank
    base_url="<YOUR_DEPLOYMENT_URL>",
)
    PYTHON
    import cohere

co = cohere.BedrockClientV2(
    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.SagemakerClientV2(
    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.ClientV2(
    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 messages parameter containing the user message.

    With reasoning models such as Command A+, the response content list can include a thinking block before the final text block. Iterate over the content items and check each item's type instead of assuming content[0] is text. For more information, see the Reasoning page.

    PYTHON
    response = co.chat(
    model="command-a-plus-05-2026",
    messages=[
        {
            "role": "user",
            "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
        }
    ],
)

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print("text:", content_item.text)
    PYTHON
    response = co.chat(
    model="command-a-plus-05-2026",
    messages=[
        {
            "role": "user",
            "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
        }
    ],
)

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print("text:", content_item.text)
    PYTHON
    response = co.chat(
    model="YOUR_MODEL_NAME",
    messages=[
        {
            "role": "user",
            "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
        }
    ],
)

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print("text:", content_item.text)
    PYTHON
    response = co.chat(
    model="YOUR_ENDPOINT_NAME",
    messages=[
        {
            "role": "user",
            "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
        }
    ],
)

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print("text:", content_item.text)
    PYTHON
    response = co.chat(
    model="model",  # Pass a dummy string
    messages=[
        {
            "role": "user",
            "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
        }
    ],
)

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print("text:", content_item.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 assistant messages to the messages list. You can also include a system message at the start of the list to set the context of the conversation.

    PYTHON
    messages = [
    {
        "role": "system",
        "content": "You respond in concise sentences.",
    },
    {"role": "user", "content": "Hello"},
]

# User sends a message

response = co.chat(
    model="command-a-plus-05-2026",
    messages=messages,
)

# The model responds

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print(
            "text:", content_item.text
        )  # Hi, how can I help you today?

# Append the model's response to the messages

messages.append(response.message)

# append another user message to the messages

messages.append(
    {
        "role": "user",
        "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
    }
)

# get the model's second response

response = co.chat(
    model="command-a-plus-05-2026",
    messages=messages,
)

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print("text:", content_item.text)
    PYTHON
    messages = [
    {
        "role": "system",
        "content": "You respond in concise sentences.",
    },
    {"role": "user", "content": "Hello"},
]

# User sends a message
response = co.chat(
    model="command-a-plus-05-2026",
    messages=messages,
)

# The model responds
for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print(
            "text:", content_item.text
        )  # Hi, how can I help you today?

# Append the model's response to the messages
messages.append(response.message)

# append another user message to the messages
messages.append(
    {
        "role": "user",
        "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
    }
)

# get the model's second response
response = co.chat(
    model="command-a-plus-05-2026",
    messages=messages,
)

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print("text:", content_item.text)
    PYTHON
    messages = [
    {
        "role": "system",
        "content": "You respond in concise sentences.",
    },
    {"role": "user", "content": "Hello"},
]

# User sends a message

response = co.chat(
    model="YOUR_MODEL_NAME",
    messages=messages,
)

# The model responds

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print(
            "text:", content_item.text
        )  # Hi, how can I help you today?

# Append the model's response to the messages

messages.append(response.message)

# append another user message to the messages

messages.append(
    {
        "role": "user",
        "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
    }
)

# get the model's second response

response = co.chat(
    model="YOUR_MODEL_NAME",
    messages=messages,
)

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print("text:", content_item.text)
    PYTHON
    messages = [
    {
        "role": "system",
        "content": "You respond in concise sentences.",
    },
    {"role": "user", "content": "Hello"},
]

# User sends a message
response = co.chat(
    model="YOUR_ENDPOINT_NAME",
    messages=messages,
)

# The model responds
for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print(
            "text:", content_item.text
        )  # Hi, how can I help you today?

# Append the model's response to the messages
messages.append(response.message)

# append another user message to the messages
messages.append(
    {
        "role": "user",
        "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
    }
)

# get the model's second response
response = co.chat(
    model="YOUR_ENDPOINT_NAME",
    messages=messages,
)

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print("text:", content_item.text)
    PYTHON
    messages = [
    {
        "role": "system",
        "content": "You respond in concise sentences.",
    },
    {"role": "user", "content": "Hello"},
]

# User sends a message

response = co.chat(
    model="model",  # Pass a dummy string
    messages=messages,
)

# The model responds

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print(
            "text:", content_item.text
        )  # Hi, how can I help you today?

# Append the model's response to the messages

messages.append(response.message)

# append another user message to the messages

messages.append(
    {
        "role": "user",
        "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
    }
)

# get the model's second response

response = co.chat(
    model="model",  # Pass a dummy string
    messages=messages,
)

for content_item in response.message.content:
    if content_item.type == "thinking":
        print("thinking:", content_item.thinking)
    if content_item.type == "text":
        print("text:", content_item.text)
    wordWrap
    "Excited to join the team at Co1t, looking forward to contributing my skills and collaborating with everyone!"

  4. Streaming

    To stream text generation, 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
    res = co.chat_stream(
    model="command-a-plus-05-2026",
    messages=[
        {
            "role": "user",
            "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
        }
    ],
)

for chunk in res:
    if chunk.type == "content-delta":
        if chunk.delta.message.content.thinking:
            print(chunk.delta.message.content.thinking, end="")
        if chunk.delta.message.content.text:
            print(chunk.delta.message.content.text, end="")
    PYTHON
    res = co.chat_stream(
    model="command-a-plus-05-2026",
    messages=[
        {
            "role": "user",
            "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
        }
    ],
)

for chunk in res:
    if chunk.type == "content-delta":
        if chunk.delta.message.content.thinking:
            print(chunk.delta.message.content.thinking, end="")
        if chunk.delta.message.content.text:
            print(chunk.delta.message.content.text, end="")
    PYTHON
    res = co.chat_stream(
    model="YOUR_MODEL_NAME",
    messages=[
        {
            "role": "user",
            "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
        }
    ],
)

for chunk in res:
    if chunk.type == "content-delta":
        if chunk.delta.message.content.thinking:
            print(chunk.delta.message.content.thinking, end="")
        if chunk.delta.message.content.text:
            print(chunk.delta.message.content.text, end="")
    PYTHON
    res = co.chat_stream(
    model="YOUR_ENDPOINT_NAME",
    messages=[
        {
            "role": "user",
            "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
        }
    ],
)

for chunk in res:
    if chunk.type == "content-delta":
        if chunk.delta.message.content.thinking:
            print(chunk.delta.message.content.thinking, end="")
        if chunk.delta.message.content.text:
            print(chunk.delta.message.content.text, end="")
    PYTHON
    res = co.chat_stream(
    model="model",  # Pass a dummy string
    messages=[
        {
            "role": "user",
            "content": "I'm joining a new startup called Co1t today. Could you help me write a one-sentence introduction message to my teammates.",
        }
    ],
)

for chunk in res:
    if chunk.type == "content-delta":
        if chunk.delta.message.content.thinking:
            print(chunk.delta.message.content.thinking, end="")
        if chunk.delta.message.content.text:
            print(chunk.delta.message.content.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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