Module haystack_integrations.components.generators.stackit.chat.chat_generator

STACKITChatGenerator

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

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

generator = STACKITChatGenerator(model="neuralmagic/Meta-Llama-3.1-70B-Instruct-FP8")

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

STACKITChatGenerator.__init__

def __init__(
        model: str,
        api_key: Secret = Secret.from_env_var("STACKIT_API_KEY"),
        streaming_callback: Optional[Callable[[StreamingChunk], None]] = None,
        api_base_url:
    Optional[
        str] = "https://api.openai-compat.model-serving.eu01.onstackit.cloud/v1",
        generation_kwargs: Optional[Dict[str, Any]] = None)

Creates an instance of STACKITChatGenerator class.

Arguments:

• model: The name of the chat completion model to use.
• api_key: The STACKIT API key.
• streaming_callback: 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: The STACKIT API Base url.
• generation_kwargs: 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.

STACKITChatGenerator.to_dict

def to_dict() -> Dict[str, Any]

Serialize this component to a dictionary.

Returns:

The serialized component as a dictionary.

Module haystack_integrations.components.embedders.stackit.document_embedder

STACKITDocumentEmbedder

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:

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, ...]

STACKITDocumentEmbedder.__init__

def __init__(
        model: str,
        api_key: Secret = Secret.from_env_var("STACKIT_API_KEY"),
        api_base_url:
    Optional[
        str] = "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: Optional[List[str]] = None,
        embedding_separator: str = "\n")

Creates a STACKITDocumentEmbedder component.

Arguments:

• api_key: The STACKIT API key.
• model: The name of the model to use.
• api_base_url: The STACKIT API Base url. For more details, see STACKIT docs.
• prefix: A string to add to the beginning of each text.
• suffix: A string to add to the end of each text.
• batch_size: Number of Documents to encode at once.
• progress_bar: 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 of meta fields that should be embedded along with the Document text.
• embedding_separator: Separator used to concatenate the meta fields to the Document text.

Module haystack_integrations.components.embedders.stackit.text_embedder

STACKITTextEmbedder

A component for embedding strings using STACKIT as model provider.

Usage example:

from haystack_integrations.components.embedders.stackit import STACKITTextEmbedder

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

STACKITTextEmbedder.__init__

def __init__(
        model: str,
        api_key: Secret = Secret.from_env_var("STACKIT_API_KEY"),
        api_base_url:
    Optional[
        str] = "https://api.openai-compat.model-serving.eu01.onstackit.cloud/v1",
        prefix: str = "",
        suffix: str = "")

Creates a STACKITTextEmbedder component.

Arguments:

• api_key: The STACKIT API key.
• model: The name of the STACKIT embedding model to be used.
• api_base_url: The STACKIT API Base url. For more details, see STACKIT docs.
• prefix: A string to add to the beginning of each text.
• suffix: A string to add to the end of each text.