Module haystack_integrations.components.embedders.mistral.document_embedder

MistralDocumentEmbedder

A component for computing Document embeddings using Mistral models. 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.mistral import MistralDocumentEmbedder

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

document_embedder = MistralDocumentEmbedder()

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

# [0.017020374536514282, -0.023255806416273117, ...]

MistralDocumentEmbedder.__init__

def __init__(api_key: Secret = Secret.from_env_var("MISTRAL_API_KEY"),
             model: str = "mistral-embed",
             api_base_url: Optional[str] = "https://api.mistral.ai/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 MistralDocumentEmbedder component.

Arguments:

• api_key: The Mistral API key.
• model: The name of the model to use.
• api_base_url: The Mistral API Base url. For more details, see Mistral 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.mistral.text_embedder

MistralTextEmbedder

A component for embedding strings using Mistral models.

Usage example:

from haystack_integrations.components.embedders.mistral.text_embedder import MistralTextEmbedder

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

__output:__

__{'embedding': [0.017020374536514282, -0.023255806416273117, ...],__

__'meta': {'model': 'mistral-embed',__

__         'usage': {'prompt_tokens': 4, 'total_tokens': 4}}}__

MistralTextEmbedder.__init__

def __init__(api_key: Secret = Secret.from_env_var("MISTRAL_API_KEY"),
             model: str = "mistral-embed",
             api_base_url: Optional[str] = "https://api.mistral.ai/v1",
             prefix: str = "",
             suffix: str = "")

Creates an MistralTextEmbedder component.

Arguments:

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

Module haystack_integrations.components.generators.mistral.chat.chat_generator

MistralChatGenerator

Enables text generation using Mistral AI generative models. For supported models, see Mistral AI docs.

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

Key Features and Compatibility:

• Primary Compatibility: Designed to work seamlessly with the Mistral API Chat Completion endpoint.
• Streaming Support: Supports streaming responses from the Mistral API Chat Completion endpoint.
• Customizability: Supports all parameters supported by the Mistral API Chat Completion endpoint.

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

For more details on the parameters supported by the Mistral API, refer to the Mistral API Docs.

Usage example:

from haystack_integrations.components.generators.mistral import MistralChatGenerator
from haystack.dataclasses import ChatMessage

messages = [ChatMessage.from_user("What's Natural Language Processing?")]

client = MistralChatGenerator()
response = client.run(messages)
print(response)

>>{'replies': [ChatMessage(content='Natural Language Processing (NLP) is a branch of artificial intelligence
>>that focuses on enabling computers to understand, interpret, and generate human language in a way that is
>>meaningful and useful.', role=<ChatRole.ASSISTANT: 'assistant'>, name=None,
>>meta={'model': 'mistral-tiny', 'index': 0, 'finish_reason': 'stop',
>>'usage': {'prompt_tokens': 15, 'completion_tokens': 36, 'total_tokens': 51}})]}

MistralChatGenerator.__init__

def __init__(api_key: Secret = Secret.from_env_var("MISTRAL_API_KEY"),
             model: str = "mistral-tiny",
             streaming_callback: Optional[Callable[[StreamingChunk],
                                                   None]] = None,
             api_base_url: Optional[str] = "https://api.mistral.ai/v1",
             generation_kwargs: Optional[Dict[str, Any]] = None)

Creates an instance of MistralChatGenerator. Unless specified otherwise in the model, this is for Mistral's

mistral-tiny model.

Arguments:

• api_key: The Mistral API key.
• model: The name of the Mistral chat completion model to use.
• 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 Mistral API Base url. For more details, see Mistral docs.
• generation_kwargs: Other parameters to use for the model. These parameters are all sent directly to the Mistral endpoint. See Mistral API docs for more details. 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.