LlamaStackChatGenerator

Generate text using models available on Llama Stack server.

Key Features

Connects to a Llama Stack Server supporting multiple inference providers, including Ollama, Together AI, vLLM, and other cloud providers.
Compatible with the OpenAI chat completion API for generation kwargs.
Supports streaming responses and tool calling for agentic workflows.
Configurable timeout and retry settings.
Uses ChatMessage format for both input and output.

Configuration

Drag the LlamaStackChatGenerator component onto the canvas from the Component Library.
Click the component to open the configuration panel.
On the General tab:
1. Enter the model name, such as ollama/llama3.2:3b. The model must be available on your Llama Stack Server instance.
Go to the Advanced tab to configure the API base URL, timeout, and generation kwargs.

Connections

LlamaStackChatGenerator receives a messages list of ChatMessage objects from ChatPromptBuilder. It outputs replies (a list of ChatMessage objects). Connect its replies output to OutputAdapter before passing to AnswerBuilder or DeepsetAnswerBuilder.

Usage Example

This is an example RAG pipeline with LlamaStackChatGenerator 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

  LlamaStackChatGenerator:
    type: haystack_integrations.components.generators.llama_stack.chat.chat_generator.LlamaStackChatGenerator
    init_parameters:
      model: ollama/llama3.2:3b
      api_base_url: http://localhost:8321/v1/openai/v1
      streaming_callback:
      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: LlamaStackChatGenerator.messages
- sender: LlamaStackChatGenerator.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

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 model 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.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	str		The name of the model to use for chat completion. This depends on the inference provider used for the Llama Stack Server.
api_base_url	str	http://localhost:8321/v1/openai/v1	The Llama Stack API base URL. If not specified, localhost is used with the default port 8321.
organization	Optional[str]	None	Your organization ID.
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.
generation_kwargs	Optional[Dict[str, Any]]	None	Other parameters to use for the model. These parameters are sent directly to the Llama Stack endpoint. See the Llama Stack API documentation for more details. Supported parameters include: `max_tokens`, `temperature`, `top_p`, `stream`, `safe_prompt`, `random_seed`, `response_format`.
timeout	Optional[int]	None	Timeout for client calls. If not set, it defaults to either the `OPENAI_TIMEOUT` environment variable or 30 seconds.
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.
tools_strict	bool	False	Whether to enable strict schema adherence for tool calls. If set to `True`, the model follows exactly the schema provided in the `parameters` field of the tool definition, but this may increase latency.
max_retries	Optional[int]	None	Maximum number of retries to contact the server 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. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

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.

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Key Features​

Configuration​

Connections​

Usage Example​

Parameters​

Inputs​

Outputs​

Init Parameters​

Run Method Parameters​