TogetherAIChatGenerator

This component enables chat completion using models hosted on Together AI.

Most common position in a pipeline	After a ChatPromptBuilder
Mandatory init variables	"api_key": A Together API key. Can be set with TOGETHER_API_KEY env var.
Mandatory run variables	"messages": A list of ChatMessage objects
Output variables	"replies": A list of ChatMessage objects
API reference	TogetherAI
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/togetherai

Overview

TogetherAIChatGenerator supports models hosted on Together AI, such as meta-llama/Llama-3.3-70B-Instruct-Turbo. For the full list of supported models, see Together AI documentation.

This 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.

You can pass any text generation parameters valid for the Together AI chat completion API directly to this component using the generation_kwargs parameter in __init__ or the generation_kwargs parameter in run method. For more details on the parameters supported by the Together AI API, see Together AI API documentation.

To use this integration, you need to have an active TogetherAI subscription with sufficient credits and an API key. You can provide it with:

• The TOGETHER_API_KEY environment variable (recommended)
• The api_key init parameter and Haystack Secret API: Secret.from_token("your-api-key-here")

By default, the component uses Together AI's OpenAI-compatible base URL https://api.together.xyz/v1, which you can override with api_base_url if needed.

Tool Support

TogetherAIChatGenerator 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.togetherai import TogetherAIChatGenerator

# 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 = TogetherAIChatGenerator(
    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

TogetherAIChatGenerator supports streaming responses from the LLM, allowing tokens to be emitted as they are generated. To enable streaming, pass a callable to the streaming_callback parameter during initialization.

Usage

Install the togetherai-haystack package to use the TogetherAIChatGenerator:

shell

pip install togetherai-haystack

On its own

Basic usage:

python

from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.togetherai import TogetherAIChatGenerator

client = TogetherAIChatGenerator()
response = client.run(
    [ChatMessage.from_user("What are Agentic Pipelines? Be brief.")]
)
print(response["replies"][0].text)

With streaming:

python

from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.togetherai import TogetherAIChatGenerator

client = TogetherAIChatGenerator(
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    streaming_callback=lambda chunk: print(chunk.content, end="", flush=True),
)

response = client.run(
    [ChatMessage.from_user("What are Agentic Pipelines? Be brief.")]
)

# check the model used for the response
print("\n\nModel used:", response["replies"][0].meta.get("model"))

In a Pipeline

python

from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.togetherai import TogetherAIChatGenerator

prompt_builder = ChatPromptBuilder()
llm = TogetherAIChatGenerator(model="meta-llama/Llama-3.3-70B-Instruct-Turbo")

pipe = Pipeline()
pipe.add_component("builder", prompt_builder)
pipe.add_component("llm", llm)
pipe.connect("builder.prompt", "llm.messages")

messages = [
    ChatMessage.from_system("Give brief answers."),
    ChatMessage.from_user("Tell me about {{city}}"),
]

response = pipe.run(
    data={"builder": {"template": messages,
                      "template_variables": {"city": "Berlin"}}}
)
print(response)