STACKITChatGenerator

Generates text using STACKIT AI models through their OpenAI-compatible model serving API.

Key Features

• Access to STACKIT's generative AI models through an OpenAI-compatible API.
• Customizable generation with generation_kwargs for temperature, token limits, and more.
• Tool calling support for agentic workflows.
• Streaming support for real-time token delivery.
• Configurable timeout and retry settings.

Configuration

Authentication

You need a STACKIT API key to use this component. Create a secret called STACKIT_API_KEY in your workspace. For more information, see Add Secrets.

1. Drag the STACKITChatGenerator component onto the canvas from the Component Library.
2. Click the component to open the configuration panel.
3. On the General tab:
   1. Enter the name of the STACKIT model to use. For available models, see STACKIT documentation.
4. Go to the Advanced tab to configure the API key, API base URL, timeout, generation parameters, and streaming callback.

Connections

STACKITChatGenerator accepts a list of ChatMessage objects as input. It outputs a list of ChatMessage objects containing the generated responses.

Connect a ChatPromptBuilder to its messages input. Connect its replies output through an OutputAdapter to a DeepsetAnswerBuilder.

Usage Example

This is an example RAG pipeline with STACKITChatGenerator and DeepsetAnswerBuilder:

components:
  bm25_retriever: # Selects the most similar documents from the document store
    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 # The number of results to return
      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: # Selects the most similar documents from the document store
    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 # The number of results to return

  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.\nIf the answer is not in the documents, rely on the web_search tool to find information.\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

  STACKITChatGenerator:
    type: haystack_integrations.components.generators.stackit.chat.chat_generator.STACKITChatGenerator
    init_parameters:
      model:
      api_key:
        type: env_var
        env_vars:
        - STACKIT_API_KEY
        strict: false
      api_base_url: https://api.openai-compat.model-serving.eu01.onstackit.cloud/v1
      generation_kwargs:
      streaming_callback:
      timeout:
      max_retries:
      http_client_kwargs:

connections:  # Defines how the components are connected
- 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: STACKITChatGenerator.messages
- sender: STACKITChatGenerator.replies
  receiver: OutputAdapter.replies

inputs:  # Define the inputs for your pipeline
  query:  # These components will receive the query as input
  - "bm25_retriever.query"
  - "query_embedder.text"
  - "ranker.query"
  - "answer_builder.query"
  - "ChatPromptBuilder.query"
  filters:  # These components will receive a potential query filter as input
  - "bm25_retriever.filters"
  - "embedding_retriever.filters"

outputs:  # Defines the output of your pipeline
  documents: "meta_field_grouping_ranker.documents"  # The output of the pipeline is the retrieved documents
  answers: "answer_builder.answers"  # The output of the pipeline is the generated 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 that is 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. For a list of supported parameters, see STACKIT documentation.
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 in pipeline configuration. This parameter can accept either a list of Tool objects or a Toolset instance.
tools_strict	Optional[bool]	None	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. If set, it overrides the tools_strict parameter in pipeline configuration.

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 chat completion model to use.
api_key	Secret	Secret.from_env_var('STACKIT_API_KEY')	The STACKIT API key.
streaming_callback	Optional[StreamingCallbackT]	None	A callback function that is 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.openai-compat.model-serving.eu01.onstackit.cloud/v1	The STACKIT API Base url.
generation_kwargs	Optional[Dict[str, Any]]	None	Other parameters to use for the model. These parameters are all sent directly to the STACKIT endpoint. Some of the supported parameters: - max_tokens: The maximum number of tokens the output text can have. - temperature: What sampling temperature to use. Higher values mean the model will take more risks. Try 0.9 for more creative applications and 0 (argmax sampling) for ones with a well-defined answer. - top_p: An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. - stream: Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message. - safe_prompt: Whether to inject a safety prompt before all conversations. - random_seed: The seed to use for random sampling.
timeout	Optional[float]	None	Timeout for STACKIT client calls. If not set, it defaults to either the OPENAI_TIMEOUT environment variable, or 30 seconds.
max_retries	Optional[int]	None	Maximum number of retries to contact STACKIT after an internal error. If not set, it defaults to either the OPENAI_MAX_RETRIES environment variable, or set to 5.
http_client_kwargs	Optional[Dict[str, Any]]	None	A dictionary of keyword arguments to configure a custom httpx.Clientor 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 that is 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. For a list of supported parameters, see STACKIT documentation.
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 in pipeline configuration. This parameter can accept either a list of Tool objects or a Toolset instance.
tools_strict	Optional[bool]	None	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. If set, it overrides the tools_strict parameter in pipeline configuration.