haystack/docs-website/reference_versioned_docs/version-2.17/integrations-api/mistral.md

title	id	description	slug
Mistral	integrations-mistral	Mistral integration for Haystack	/integrations-mistral

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",
             *,
             timeout: Optional[float] = None,
             max_retries: Optional[int] = None,
             http_client_kwargs: Optional[Dict[str, Any]] = None)

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.
• timeout: Timeout for Mistral client calls. If not set, it defaults to either the OPENAI_TIMEOUT environment variable, or 30 seconds.
• max_retries: Maximum number of retries to contact Mistral after an internal error. If not set, it defaults to either the OPENAI_MAX_RETRIES environment variable, or set to 5.
• http_client_kwargs: A dictionary of keyword arguments to configure a custom httpx.Clientor httpx.AsyncClient. For more information, see the HTTPX documentation.

MistralDocumentEmbedder.to_dict

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

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

MistralDocumentEmbedder.from_dict

@classmethod
def from_dict(cls, data: dict[str, Any]) -> "OpenAIDocumentEmbedder"

Deserializes the component from a dictionary.

Arguments:

• data: Dictionary to deserialize from.

Returns:

Deserialized component.

MistralDocumentEmbedder.run

@component.output_types(documents=list[Document], meta=dict[str, Any])
def run(documents: list[Document])

Embeds a list of documents.

Arguments:

• documents: A list of documents to embed.

Returns:

A dictionary with the following keys:

• documents: A list of documents with embeddings.
• meta: Information about the usage of the model.

MistralDocumentEmbedder.run_async

@component.output_types(documents=list[Document], meta=dict[str, Any])
async def run_async(documents: list[Document])

Embeds a list of documents asynchronously.

Arguments:

• documents: A list of documents to embed.

Returns:

A dictionary with the following keys:

• documents: A list of documents with embeddings.
• meta: Information about the usage of the model.

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 = "",
             *,
             timeout: Optional[float] = None,
             max_retries: Optional[int] = None,
             http_client_kwargs: Optional[Dict[str, Any]] = None)

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.
• timeout: Timeout for Mistral client calls. If not set, it defaults to either the OPENAI_TIMEOUT environment variable, or 30 seconds.
• max_retries: Maximum number of retries to contact Mistral after an internal error. If not set, it defaults to either the OPENAI_MAX_RETRIES environment variable, or set to 5.
• http_client_kwargs: A dictionary of keyword arguments to configure a custom httpx.Clientor httpx.AsyncClient. For more information, see the HTTPX documentation.

MistralTextEmbedder.to_dict

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

Serializes the component to a dictionary.

Returns:

Dictionary with serialized data.

MistralTextEmbedder.from_dict

@classmethod
def from_dict(cls, data: dict[str, Any]) -> "OpenAITextEmbedder"

Deserializes the component from a dictionary.

Arguments:

• data: Dictionary to deserialize from.

Returns:

Deserialized component.

MistralTextEmbedder.run

@component.output_types(embedding=list[float], meta=dict[str, Any])
def run(text: str)

Embeds a single string.

Arguments:

• text: Text to embed.

Returns:

A dictionary with the following keys:

• embedding: The embedding of the input text.
• meta: Information about the usage of the model.

MistralTextEmbedder.run_async

@component.output_types(embedding=list[float], meta=dict[str, Any])
async def run_async(text: str)

Asynchronously embed a single string.

This is the asynchronous version of the run method. It has the same parameters and return values but can be used with await in async code.

Arguments:

• text: Text to embed.

Returns:

A dictionary with the following keys:

• embedding: The embedding of the input text.
• meta: Information about the usage of the model.

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(_role=<ChatRole.ASSISTANT: 'assistant'>, _content=[TextContent(text=
>> "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.")], _name=None,
>> _meta={'model': 'mistral-small-latest', '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-small-latest",
             streaming_callback: Optional[StreamingCallbackT] = None,
             api_base_url: Optional[str] = "https://api.mistral.ai/v1",
             generation_kwargs: Optional[Dict[str, Any]] = None,
             tools: Optional[Union[List[Tool], Toolset]] = None,
             *,
             timeout: Optional[float] = None,
             max_retries: Optional[int] = None,
             http_client_kwargs: Optional[Dict[str, Any]] = None)

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

mistral-small-latest 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.
• tools: A list of tools or a Toolset for which the model can prepare calls. This parameter can accept either a list of Tool objects or a Toolset instance.
• timeout: The timeout for the Mistral API call. If not set, it defaults to either the OPENAI_TIMEOUT environment variable, or 30 seconds.
• max_retries: Maximum number of retries to contact OpenAI after an internal error. If not set, it defaults to either the OPENAI_MAX_RETRIES environment variable, or set to 5.
• http_client_kwargs: A dictionary of keyword arguments to configure a custom httpx.Clientor httpx.AsyncClient. For more information, see the HTTPX documentation.

MistralChatGenerator.to_dict

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

Serialize this component to a dictionary.

Returns:

The serialized component as a dictionary.

MistralChatGenerator.from_dict

@classmethod
def from_dict(cls, data: dict[str, Any]) -> "OpenAIChatGenerator"

Deserialize this component from a dictionary.

Arguments:

• data: The dictionary representation of this component.

Returns:

The deserialized component instance.

MistralChatGenerator.run

@component.output_types(replies=list[ChatMessage])
def run(messages: list[ChatMessage],
        streaming_callback: Optional[StreamingCallbackT] = None,
        generation_kwargs: Optional[dict[str, Any]] = None,
        *,
        tools: Optional[ToolsType] = None,
        tools_strict: Optional[bool] = None)

Invokes chat completion based on the provided messages and generation parameters.

Arguments:

• messages: A list of ChatMessage instances representing the input messages.
• streaming_callback: A callback function that is called when a new token is received from the stream.
• generation_kwargs: Additional keyword arguments for text generation. These parameters will override the parameters passed during component initialization. For details on OpenAI API parameters, see OpenAI documentation.
• tools: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls. If set, it will override the tools parameter provided during initialization.
• tools_strict: Whether to enable strict schema adherence for tool calls. If set to True, the model will follow exactly the schema provided in the parameters field of the tool definition, but this may increase latency. If set, it will override the tools_strict parameter set during component initialization.

Returns:

A dictionary with the following key:

• replies: A list containing the generated responses as ChatMessage instances.

MistralChatGenerator.run_async

@component.output_types(replies=list[ChatMessage])
async def run_async(messages: list[ChatMessage],
                    streaming_callback: Optional[StreamingCallbackT] = None,
                    generation_kwargs: Optional[dict[str, Any]] = None,
                    *,
                    tools: Optional[ToolsType] = None,
                    tools_strict: Optional[bool] = None)

Asynchronously invokes chat completion based on the provided messages and generation parameters.

This is the asynchronous version of the run method. It has the same parameters and return values but can be used with await in async code.

Arguments:

• messages: A list of ChatMessage instances representing the input messages.
• streaming_callback: A callback function that is called when a new token is received from the stream. Must be a coroutine.
• generation_kwargs: Additional keyword arguments for text generation. These parameters will override the parameters passed during component initialization. For details on OpenAI API parameters, see OpenAI documentation.
• tools: A list of Tool and/or Toolset objects, or a single Toolset for which the model can prepare calls. If set, it will override the tools parameter provided during initialization.
• tools_strict: Whether to enable strict schema adherence for tool calls. If set to True, the model will follow exactly the schema provided in the parameters field of the tool definition, but this may increase latency. If set, it will override the tools_strict parameter set during component initialization.

Returns:

A dictionary with the following key:

• replies: A list containing the generated responses as ChatMessage instances.