CohereChatGenerator

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

Parameters Overview

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

• The api_key init parameter
• 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.

Streaming

CohereChatGenerator 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:

pip install cohere-haystack

On its own

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

generator = CohereChatGenerator(api_key="<YOUR_COHERE_KEY>")
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.

from haystack import Pipeline
from haystack.components.builders import DynamicChatPromptBuilder
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", DynamicChatPromptBuilder())
pipe.add_component("llm", CohereChatGenerator(Secret.from_token(COHERE_API_KEY)))
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}, "prompt_source": messages}})
print(res)