CohereChatGenerator

CohereChatGenerator enables chat completions using Cohere's large language models (LLMs).

Most common position in a pipeline	After a ChatPromptBuilder
Mandatory init variables	"api_key": The Cohere API key. Can be set with COHERE_API_KEY or CO_API_KEY env var.
Mandatory run variables	“messages” A list of ChatMessage objects
Output variables	"replies": A list of ChatMessage objects ”meta”: A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and so on
API reference	Cohere
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/cohere

This integration supports Cohere chat models such as command,command-r and comman-r-plus. Check out the most recent full list in Cohere documentation.

Overview

CohereChatGenerator needs a Cohere API key to work. You can set this key in:

• The api_key init parameter using Secret API
• The COHERE_API_KEY environment variable (recommended)

Then, the component needs a prompt to operate, but you can pass any text generation parameters valid for the Co.chat method directly to this component using the generation_kwargs parameter, both at initialization and to run() method. For more details on the parameters supported by the Cohere API, refer to the Cohere documentation.

Finally, the component needs a list of ChatMessage objects to operate. ChatMessage is a data class that contains a message, a role (who generated the message, such as user, assistant, system, function), and optional metadata.

Tool Support

CohereChatGenerator supports function calling through the tools parameter, which accepts flexible tool configurations:

• A list of Tool objects: Pass individual tools as a list
• A single Toolset: Pass an entire Toolset directly
• Mixed Tools and Toolsets: Combine multiple Toolsets with standalone tools in a single list

This allows you to organize related tools into logical groups while also including standalone tools as needed.

python

from haystack.tools import Tool, Toolset
from haystack_integrations.components.generators.cohere import CohereChatGenerator

# Create individual tools
weather_tool = Tool(name="weather", description="Get weather info", ...)
news_tool = Tool(name="news", description="Get latest news", ...)

# Group related tools into a toolset
math_toolset = Toolset([add_tool, subtract_tool, multiply_tool])

# Pass mixed tools and toolsets to the generator
generator = CohereChatGenerator(
    tools=[math_toolset, weather_tool, news_tool]  # Mix of Toolset and Tool objects
)

For more details on working with tools, see the Tool and Toolset documentation.

Streaming

This Generator supports streaming the tokens from the LLM directly in output. To do so, pass a function to the streaming_callback init parameter.

Usage

You need to install cohere-haystack package to use the CohereChatGenerator:

shell

pip install cohere-haystack

On its own

python

from haystack_integrations.components.generators.cohere import CohereChatGenerator
from haystack.dataclasses import ChatMessage

generator = CohereChatGenerator()
message = ChatMessage.from_user("What's Natural Language Processing? Be brief.")
print(generator.run([message]))

In a Pipeline

You can also use CohereChatGenerator to use cohere chat models in your pipeline.

python

from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.cohere import CohereChatGenerator
from haystack.utils import Secret

pipe = Pipeline()
pipe.add_component("prompt_builder", ChatPromptBuilder())
pipe.add_component("llm", CohereChatGenerator())
pipe.connect("prompt_builder", "llm")

country = "Germany"
system_message = ChatMessage.from_system("You are an assistant giving out valuable information to language learners.")
messages = [system_message, ChatMessage.from_user("What's the official language of {{ country }}?")]

res = pipe.run(data={"prompt_builder": {"template_variables": {"country": country}, "template": messages}})
print(res)