TogetherAIChatGenerator

Generate text using large language models hosted on Together AI.

Basic Information

• Type: haystack_integrations.components.generators.togetherai.chat.chat_generator.TogetherAIChatGenerator
• Components it can connect with:
  • ChatPromptBuilder: TogetherAIChatGenerator receives a rendered prompt from ChatPromptBuilder.
  • DeepsetAnswerBuilder: TogetherAIChatGenerator sends the generated replies to DeepsetAnswerBuilder through OutputAdapter.

Inputs

Parameter	Type	Default	Description
messages	List[ChatMessage]		A list of ChatMessage instances representing the input messages.
streaming_callback	Optional[StreamingCallbackT]	None	A callback function called when the LLM receives a new token from the stream.
generation_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for text generation. These parameters override the parameters in pipeline configuration.
tools	Optional[Union[List[Tool], Toolset]]	None	A list of tools or a Toolset for which the model can prepare calls. If set, it overrides the tools parameter set during component initialization.
tools_strict	Optional[bool]	None	Whether to enable strict schema adherence for tool calls.

Outputs

Parameter	Type	Default	Description
replies	List[ChatMessage]		A list containing the generated responses as ChatMessage instances.

Overview

Use TogetherAIChatGenerator to generate text with models hosted on Together AI. For supported models, see Together AI documentation.

You can pass any text generation parameters valid for the Together AI chat completion API directly to this component using the generation_kwargs parameter.

Key Features

• Primary Compatibility: Designed to work seamlessly with the Together AI chat completion endpoint.
• Streaming Support: Supports streaming responses from the Together AI chat completion endpoint.
• Customizability: Supports all parameters supported by the Together AI chat completion endpoint.

This component uses the ChatMessage format for structuring both input and output, ensuring coherent and contextually relevant responses in chat-based text generation scenarios.

Authorization

You need a Together AI API key to use this component. Connect deepset to your Together AI account on the Integrations page. For detailed instructions, see Use Together AI Models

Usage Example

This is an example RAG pipeline with TogetherAIChatGenerator and DeepsetAnswerBuilder connected through OutputAdapter:

components:
  bm25_retriever:
    type: haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: 'Standard-Index-English'
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      top_k: 20
      fuzziness: 0

  query_embedder:
    type: deepset_cloud_custom_nodes.embedders.nvidia.text_embedder.DeepsetNvidiaTextEmbedder
    init_parameters:
      normalize_embeddings: true
      model: intfloat/e5-base-v2

  embedding_retriever:
    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: 'Standard-Index-English'
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      top_k: 20

  document_joiner:
    type: haystack.components.joiners.document_joiner.DocumentJoiner
    init_parameters:
      join_mode: concatenate

  ranker:
    type: deepset_cloud_custom_nodes.rankers.nvidia.ranker.DeepsetNvidiaRanker
    init_parameters:
      model: intfloat/simlm-msmarco-reranker
      top_k: 8

  meta_field_grouping_ranker:
    type: haystack.components.rankers.meta_field_grouping_ranker.MetaFieldGroupingRanker
    init_parameters:
      group_by: file_id
      subgroup_by:
      sort_docs_by: split_id

  answer_builder:
    type: deepset_cloud_custom_nodes.augmenters.deepset_answer_builder.DeepsetAnswerBuilder
    init_parameters:
      reference_pattern: acm

  ChatPromptBuilder:
    type: haystack.components.builders.chat_prompt_builder.ChatPromptBuilder
    init_parameters:
      template:
      - _content:
        - text: "You are a helpful assistant answering the user's questions based on the provided documents.\nDo not use your own knowledge.\n"
        _role: system
      - _content:
        - text: "Provided documents:\n{% for document in documents %}\nDocument [{{ loop.index }}] :\n{{ document.content }}\n{% endfor %}\n\nQuestion: {{ query }}\n"
        _role: user
      required_variables:
      variables:

  OutputAdapter:
    type: haystack.components.converters.output_adapter.OutputAdapter
    init_parameters:
      template: '{{ replies[0] }}'
      output_type: List[str]
      custom_filters:
      unsafe: false

  TogetherAIChatGenerator:
    type: haystack_integrations.components.generators.togetherai.chat.chat_generator.TogetherAIChatGenerator
    init_parameters:
      api_key:
        type: env_var
        env_vars:
        - TOGETHER_API_KEY
        strict: false
      model: meta-llama/Llama-3.3-70B-Instruct-Turbo
      streaming_callback:
      api_base_url: https://api.together.xyz/v1
      generation_kwargs:
      tools:
      timeout:
      max_retries:
      http_client_kwargs:

connections:
- sender: bm25_retriever.documents
  receiver: document_joiner.documents
- sender: query_embedder.embedding
  receiver: embedding_retriever.query_embedding
- sender: embedding_retriever.documents
  receiver: document_joiner.documents
- sender: document_joiner.documents
  receiver: ranker.documents
- sender: ranker.documents
  receiver: meta_field_grouping_ranker.documents
- sender: meta_field_grouping_ranker.documents
  receiver: answer_builder.documents
- sender: meta_field_grouping_ranker.documents
  receiver: ChatPromptBuilder.documents
- sender: OutputAdapter.output
  receiver: answer_builder.replies
- sender: ChatPromptBuilder.prompt
  receiver: TogetherAIChatGenerator.messages
- sender: TogetherAIChatGenerator.replies
  receiver: OutputAdapter.replies

inputs:
  query:
  - "bm25_retriever.query"
  - "query_embedder.text"
  - "ranker.query"
  - "answer_builder.query"
  - "ChatPromptBuilder.query"
  filters:
  - "bm25_retriever.filters"
  - "embedding_retriever.filters"

outputs:
  documents: "meta_field_grouping_ranker.documents"
  answers: "answer_builder.answers"

max_runs_per_component: 100

metadata: {}

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
api_key	Secret	Secret.from_env_var('TOGETHER_API_KEY')	The Together AI API key.
model	str	meta-llama/Llama-3.3-70B-Instruct-Turbo	The name of the Together AI chat completion model to use.
streaming_callback	Optional[StreamingCallbackT]	None	A callback function called when a new token is received from the stream. The callback function accepts StreamingChunk as an argument.
api_base_url	Optional[str]	https://api.together.xyz/v1	The Together AI API base URL. For more details, see Together AI documentation.
generation_kwargs	Optional[Dict[str, Any]]	None	Other parameters to use for the model. These parameters are sent directly to the Together AI endpoint. See Together AI API documentation for more details. Supported parameters include: max_tokens (maximum number of tokens the output text can have), temperature (sampling temperature for creativity control), top_p (nucleus sampling probability mass), stream (whether to stream back partial progress), safe_prompt (whether to inject a safety prompt before all conversations), random_seed (the seed to use for random sampling), response_format (a JSON schema or Pydantic model that enforces the structure of the model's response).
tools	Optional[Union[List[Tool], Toolset]]	None	A list of tools or a Toolset for which the model can prepare calls. Each tool should have a unique name.
timeout	Optional[float]	None	The timeout for the Together AI API call.
max_retries	Optional[int]	None	Maximum number of retries to contact Together AI after an internal error. If not set, it defaults to either the OPENAI_MAX_RETRIES environment variable or five.
http_client_kwargs	Optional[Dict[str, Any]]	None	A dictionary of keyword arguments to configure a custom httpx.Client or httpx.AsyncClient. For more information, see the HTTPX documentation.

Run Method Parameters

These are the parameters you can configure for the component's run() method. You can pass these parameters at query time through the API, in Playground, or when running a job.

Parameter	Type	Default	Description
messages	List[ChatMessage]		A list of ChatMessage instances representing the input messages.
streaming_callback	Optional[StreamingCallbackT]	None	A callback function called when a new token is received from the stream.
generation_kwargs	Optional[Dict[str, Any]]	None	Additional keyword arguments for text generation. These parameters override the parameters in pipeline configuration.
tools	Optional[Union[List[Tool], Toolset]]	None	A list of tools or a Toolset for which the model can prepare calls. If set, it overrides the tools parameter set during component initialization.
tools_strict	Optional[bool]	None	Whether to enable strict schema adherence for tool calls.