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Version: 3.3

STACKIT

haystack_integrations.components.embedders.stackit.document_embedder​

STACKITDocumentEmbedder​

Bases: OpenAIDocumentEmbedder

A component for computing Document embeddings using STACKIT as model provider.

The embedding of each Document is stored in the embedding field of the Document.

Usage example:

python
from haystack import Document
from haystack_integrations.components.embedders.stackit import STACKITDocumentEmbedder

doc = Document(content="I love pizza!")

document_embedder = STACKITDocumentEmbedder()

result = document_embedder.run([doc])
print(result['documents'][0].embedding)

# [0.017020374536514282, -0.023255806416273117, ...]

SUPPORTED_MODELS​

python
SUPPORTED_MODELS: list[str] = [
    "intfloat/e5-mistral-7b-instruct",
    "Qwen/Qwen3-VL-Embedding-8B",
]

A non-exhaustive list of embedding models supported by this component. See https://docs.stackit.cloud/products/data-and-ai/ai-model-serving/basics/available-shared-models for the full list.

init​

python
__init__(
    model: str,
    api_key: Secret = Secret.from_env_var("STACKIT_API_KEY"),
    api_base_url: (
        str | None
    ) = "https://api.openai-compat.model-serving.eu01.onstackit.cloud/v1",
    prefix: str = "",
    suffix: str = "",
    batch_size: int = 32,
    progress_bar: bool = True,
    meta_fields_to_embed: list[str] | None = None,
    embedding_separator: str = "\n",
    *,
    dimensions: int | None = None,
    timeout: float | None = None,
    max_retries: int | None = None,
    http_client_kwargs: dict[str, Any] | None = None
) -> None

Creates a STACKITDocumentEmbedder component.

Parameters:

  • api_key (Secret) – The STACKIT API key.
  • model (str) – The name of the model to use.
  • api_base_url (str | None) – The STACKIT API Base url. For more details, see STACKIT docs.
  • prefix (str) – A string to add to the beginning of each text.
  • suffix (str) – A string to add to the end of each text.
  • batch_size (int) – Number of Documents to encode at once.
  • progress_bar (bool) – Whether to show a progress bar or not. Can be helpful to disable in production deployments to keep the logs clean.
  • meta_fields_to_embed (list[str] | None) – List of meta fields that should be embedded along with the Document text.
  • embedding_separator (str) – Separator used to concatenate the meta fields to the Document text.
  • dimensions (int | None) – The number of dimensions of the resulting embeddings. Only supported by some models - check the STACKIT model card for the model you are using to see if this parameter is supported.
  • timeout (float | None) – Timeout for STACKIT client calls. If not set, it defaults to either the OPENAI_TIMEOUT environment variable, or 30 seconds.
  • max_retries (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 (dict[str, Any] | None) – A dictionary of keyword arguments to configure a custom httpx.Clientor httpx.AsyncClient. For more information, see the HTTPX documentation.

to_dict​

python
to_dict() -> dict[str, Any]

Serializes the component to a dictionary.

Returns:

  • dict[str, Any] – Dictionary with serialized data.

haystack_integrations.components.embedders.stackit.text_embedder​

STACKITTextEmbedder​

Bases: OpenAITextEmbedder

A component for embedding strings using STACKIT as model provider.

Usage example:

python
from haystack_integrations.components.embedders.stackit import STACKITTextEmbedder

text_to_embed = "I love pizza!"
text_embedder = STACKITTextEmbedder()
print(text_embedder.run(text_to_embed))

SUPPORTED_MODELS​

python
SUPPORTED_MODELS: list[str] = [
    "intfloat/e5-mistral-7b-instruct",
    "Qwen/Qwen3-VL-Embedding-8B",
]

A non-exhaustive list of embedding models supported by this component. See https://docs.stackit.cloud/products/data-and-ai/ai-model-serving/basics/available-shared-models for the full list.

init​

python
__init__(
    model: str,
    api_key: Secret = Secret.from_env_var("STACKIT_API_KEY"),
    api_base_url: (
        str | None
    ) = "https://api.openai-compat.model-serving.eu01.onstackit.cloud/v1",
    prefix: str = "",
    suffix: str = "",
    *,
    dimensions: int | None = None,
    timeout: float | None = None,
    max_retries: int | None = None,
    http_client_kwargs: dict[str, Any] | None = None
) -> None

Creates a STACKITTextEmbedder component.

Parameters:

  • api_key (Secret) – The STACKIT API key.
  • model (str) – The name of the STACKIT embedding model to be used.
  • api_base_url (str | None) – The STACKIT API Base url. For more details, see STACKIT docs.
  • prefix (str) – A string to add to the beginning of each text.
  • suffix (str) – A string to add to the end of each text.
  • dimensions (int | None) – The number of dimensions of the resulting embeddings. Only supported by some models - check the STACKIT model card for the model you are using to see if this parameter is supported.
  • timeout (float | None) – Timeout for STACKIT client calls. If not set, it defaults to either the OPENAI_TIMEOUT environment variable, or 30 seconds.
  • max_retries (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 (dict[str, Any] | None) – A dictionary of keyword arguments to configure a custom httpx.Clientor httpx.AsyncClient. For more information, see the HTTPX documentation.

to_dict​

python
to_dict() -> dict[str, Any]

Serializes the component to a dictionary.

Returns:

  • dict[str, Any] – Dictionary with serialized data.

haystack_integrations.components.generators.stackit.chat.chat_generator​

STACKITChatGenerator​

Bases: VLLMChatGenerator

Enables text generation using STACKIT generative models through their model serving service.

Users can pass any text generation parameters valid for the STACKIT Chat Completion API directly to this component using the generation_kwargs parameter in __init__ or the generation_kwargs parameter in run method.

This component uses the ChatMessage format for structuring both input and output, ensuring coherent and contextually relevant responses in chat-based text generation scenarios. Details on the ChatMessage format can be found in the Haystack docs

Usage example​

python
from haystack_integrations.components.generators.stackit import STACKITChatGenerator
from haystack.dataclasses import ChatMessage

generator = STACKITChatGenerator(model="cortecs/Llama-3.3-70B-Instruct-FP8-Dynamic")

result = generator.run([ChatMessage.from_user("Tell me a joke.")])
print(result)

SUPPORTED_MODELS​

python
SUPPORTED_MODELS: list[str] = [
    "Qwen/Qwen3-VL-235B-A22B-Instruct-FP8",
    "Qwen/Qwen3.6-27B",
    "cortecs/Llama-3.3-70B-Instruct-FP8-Dynamic",
    "openai/gpt-oss-120b",
    "google/gemma-3-27b-it",
    "openai/gpt-oss-20b",
]

A non-exhaustive list of chat models supported by this component. See https://docs.stackit.cloud/products/data-and-ai/ai-model-serving/basics/available-shared-models for the full list.

init​

python
__init__(
    model: str,
    api_key: Secret = Secret.from_env_var("STACKIT_API_KEY"),
    streaming_callback: StreamingCallbackT | None = None,
    api_base_url: str = "https://api.openai-compat.model-serving.eu01.onstackit.cloud/v1",
    generation_kwargs: dict[str, Any] | None = None,
    *,
    timeout: float | None = None,
    max_retries: int | None = None,
    http_client_kwargs: dict[str, Any] | None = None,
    tools: ToolsType | None = None
) -> None

Creates an instance of STACKITChatGenerator class.

Parameters:

  • model (str) – The name of the chat completion model to use.
  • api_key (Secret) – The STACKIT API key.
  • streaming_callback (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 (str) – The STACKIT API Base url.
  • generation_kwargs (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.
  • response_format: A response format dictionary containing a JSON schema. For Pydantic models, use model_json_schema() to build the schema; passing the model class directly is not supported.
  • timeout (float | None) – Timeout for STACKIT client calls. If not set, uses the OpenAI SDK default.
  • max_retries (int | None) – Maximum number of retries to contact STACKIT after an internal error. If not set, uses the OpenAI SDK default.
  • http_client_kwargs (dict[str, Any] | None) – A dictionary of keyword arguments to configure a custom httpx.Clientor httpx.AsyncClient. For more information, see the HTTPX documentation.
  • tools (ToolsType | None) – A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls. Each tool should have a unique name. Not all models support tools.

from_dict​

python
from_dict(data: dict[str, Any]) -> STACKITChatGenerator

Deserialize this component from a dictionary.

Parameters:

  • data (dict[str, Any]) – The dictionary representation of this component.

Returns:

  • STACKITChatGenerator – The deserialized component instance.