STACKITChatGenerator

Generate text using STACKIT generative models through their model serving service.

Basic Information

Type: haystack_integrations.components.generators.stackit.chat.chat_generator.STACKITChatGenerator
Components it can connect with:
- ChatPromptBuilder: STACKITChatGenerator receives a rendered prompt from ChatPromptBuilder.
- DeepsetAnswerBuilder: STACKITChatGenerator 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 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.

Overview

Use STACKITChatGenerator to generate text using models through STACKIT API. For a list of supported models, see STACKIT documentation.

Authorization

You need an STACKIT API key to use this component. Connect Haystack Platform to your STACKIT account by creating a secret called STACKIT_API_KEY. For more information about secrets, see Secrets.

Usage Example

Using the Component in a Pipeline

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

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.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 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.

Was this page helpful?

Basic Information​

Inputs​

Outputs​

Overview​

Authorization​

Usage Example​

Using the Component in a Pipeline​

Parameters​

Init Parameters​

Run Method Parameters​