import os from typing import TYPE_CHECKING, Dict, List, Optional from pydantic import ConfigDict from tenacity import retry, stop_after_attempt, wait_random_exponential from tenacity.retry import retry_if_not_exception_type if TYPE_CHECKING: from redisvl.extensions.cache.embeddings.embeddings import EmbeddingsCache from redisvl.utils.utils import deprecated_argument from redisvl.utils.vectorize.base import BaseVectorizer # ignore that mistralai isn't imported # mypy: disable-error-code="name-defined" class MistralAITextVectorizer(BaseVectorizer): """The MistralAITextVectorizer class utilizes MistralAI's API to generate embeddings for text data. This vectorizer is designed to interact with Mistral's embeddings API, requiring an API key for authentication. The key can be provided directly in the `api_config` dictionary or through the `MISTRAL_API_KEY` environment variable. Users must obtain an API key from Mistral's website (https://console.mistral.ai/). Additionally, the `mistralai` python client must be installed with `pip install mistralai`. The vectorizer supports both synchronous and asynchronous operations, allowing for batch processing of texts and flexibility in handling preprocessing tasks. You can optionally enable caching to improve performance when generating embeddings for repeated text inputs. .. code-block:: python # Basic usage vectorizer = MistralAITextVectorizer( model="mistral-embed", api_config={"api_key": "your_api_key"} # OR set MISTRAL_API_KEY in your env ) embedding = vectorizer.embed("Hello, world!") # With caching enabled from redisvl.extensions.cache.embeddings import EmbeddingsCache cache = EmbeddingsCache(name="mistral_embeddings_cache") vectorizer = MistralAITextVectorizer( model="mistral-embed", api_config={"api_key": "your_api_key"}, cache=cache ) # First call will compute and cache the embedding embedding1 = vectorizer.embed("Hello, world!") # Second call will retrieve from cache embedding2 = vectorizer.embed("Hello, world!") # Asynchronous batch embedding of multiple texts embeddings = await vectorizer.aembed_many( ["Hello, world!", "How are you?"], batch_size=2 ) """ model_config = ConfigDict(arbitrary_types_allowed=True) def __init__( self, model: str = "mistral-embed", api_config: Optional[Dict] = None, dtype: str = "float32", cache: Optional["EmbeddingsCache"] = None, **kwargs, ): """Initialize the MistralAI vectorizer. Args: model (str): Model to use for embedding. Defaults to 'mistral-embed'. api_config (Optional[Dict], optional): Dictionary containing the API key. Defaults to None. dtype (str): the default datatype to use when embedding text as byte arrays. Used when setting `as_buffer=True` in calls to embed() and embed_many(). Defaults to 'float32'. cache (Optional[EmbeddingsCache]): Optional EmbeddingsCache instance to cache embeddings for better performance with repeated texts. Defaults to None. Raises: ImportError: If the mistralai library is not installed. ValueError: If the Mistral API key is not provided. ValueError: If an invalid dtype is provided. """ super().__init__(model=model, dtype=dtype, cache=cache) # Initialize client and set up the model self._setup(api_config, **kwargs) def _setup(self, api_config: Optional[Dict], **kwargs): """Set up the MistralAI client and determine the embedding dimensions.""" # Initialize client self._initialize_client(api_config, **kwargs) # Set model dimensions after initialization self.dims = self._set_model_dims() def _initialize_client(self, api_config: Optional[Dict], **kwargs): """ Setup the Mistral client using the provided API key or an environment variable. Args: api_config: Dictionary with API configuration options **kwargs: Additional arguments to pass to MistralAI client Raises: ImportError: If the mistralai library is not installed ValueError: If no API key is provided """ if api_config is None: api_config = {} # Dynamic import of the mistralai module try: from mistralai import Mistral except ImportError: raise ImportError( "MistralAI vectorizer requires the mistralai library. " "Please install with `pip install mistralai`" ) # Fetch the API key from api_config or environment variable api_key = ( api_config.get("api_key") if api_config else os.getenv("MISTRAL_API_KEY") ) if not api_key: raise ValueError( "MISTRAL API key is required. " "Provide it in api_config or set the MISTRAL_API_KEY environment variable." ) # Store client as a regular attribute instead of PrivateAttr self._client = Mistral(api_key=api_key, **kwargs) def _set_model_dims(self) -> int: """ Determine the dimensionality of the embedding model by making a test call. Returns: int: Dimensionality of the embedding model Raises: ValueError: If embedding dimensions cannot be determined """ try: # Call the protected _embed method to avoid caching this test embedding embedding = self._embed("dimension check") return len(embedding) except (KeyError, IndexError) as ke: raise ValueError(f"Unexpected response from the MISTRAL API: {str(ke)}") except Exception as e: # pylint: disable=broad-except # fall back (TODO get more specific) raise ValueError(f"Error setting embedding model dimensions: {str(e)}") @deprecated_argument("text", "content") @retry( wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6), retry=retry_if_not_exception_type(TypeError), ) def _embed(self, content: str = "", text: str = "", **kwargs) -> List[float]: """ Generate a vector embedding for a single text using the MistralAI API. Args: content: Text to embed text: Text to embed (deprecated - use `content` instead) **kwargs: Additional parameters to pass to the MistralAI API Returns: List[float]: Vector embedding as a list of floats Raises: TypeError: If content is not a string ValueError: If embedding fails """ content = content or text if not isinstance(content, str): raise TypeError("Must pass in a str value to embed.") try: result = self._client.embeddings.create( model=self.model, inputs=[content], **kwargs ) return result.data[0].embedding # type: ignore except Exception as e: raise ValueError(f"Embedding text failed: {e}") @deprecated_argument("texts", "contents") @retry( wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6), retry=retry_if_not_exception_type(TypeError), ) def _embed_many( self, contents: Optional[List[str]] = None, texts: Optional[List[str]] = None, batch_size: int = 10, **kwargs, ) -> List[List[float]]: """ Generate vector embeddings for a batch of texts using the MistralAI API. Args: contents: List of texts to embed texts: List of texts to embed (deprecated - use `contents` instead) batch_size: Number of texts to process in each API call **kwargs: Additional parameters to pass to the MistralAI API Returns: List[List[float]]: List of vector embeddings as lists of floats Raises: TypeError: If contents is not a list of strings ValueError: If embedding fails """ contents = contents or texts if not isinstance(contents, list): raise TypeError("Must pass in a list of str values to embed.") if contents and not isinstance(contents[0], str): raise TypeError("Must pass in a list of str values to embed.") try: embeddings: List = [] for batch in self.batchify(contents, batch_size): response = self._client.embeddings.create( model=self.model, inputs=batch, **kwargs ) embeddings.extend([r.embedding for r in response.data]) return embeddings except Exception as e: raise ValueError(f"Embedding texts failed: {e}") @deprecated_argument("text", "content") @retry( wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6), retry=retry_if_not_exception_type(TypeError), ) async def _aembed(self, content: str = "", text: str = "", **kwargs) -> List[float]: """ Asynchronously generate a vector embedding for a single text using the MistralAI API. Args: content: Text to embed text: Text to embed (deprecated - use `content` instead) **kwargs: Additional parameters to pass to the MistralAI API Returns: List[float]: Vector embedding as a list of floats Raises: TypeError: If `content` is not a string ValueError: If embedding fails """ content = content or text if not isinstance(content, str): raise TypeError("Must pass in a str value to embed.") try: result = await self._client.embeddings.create_async( model=self.model, inputs=[content], **kwargs ) return result.data[0].embedding # type: ignore except Exception as e: raise ValueError(f"Embedding content failed: {e}") @deprecated_argument("texts", "contents") @retry( wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6), retry=retry_if_not_exception_type(TypeError), ) async def _aembed_many( self, contents: Optional[List[str]] = None, texts: Optional[List[str]] = None, batch_size: int = 10, **kwargs, ) -> List[List[float]]: """ Asynchronously generate vector embeddings for a batch of texts using the MistralAI API. Args: contents: List of texts to embed texts: List of texts to embed (deprecated - use `contents` instead) batch_size: Number of texts to process in each API call **kwargs: Additional parameters to pass to the MistralAI API Returns: List[List[float]]: List of vector embeddings as lists of floats Raises: TypeError: If texts is not a list of strings ValueError: If embedding fails """ contents = contents or texts if not isinstance(contents, list): raise TypeError("Must pass in a list of str values to embed.") if contents and not isinstance(contents[0], str): raise TypeError("Must pass in a list of str values to embed.") try: embeddings: List = [] for batch in self.batchify(contents, batch_size): response = await self._client.embeddings.create_async( model=self.model, inputs=batch, **kwargs ) embeddings.extend([r.embedding for r in response.data]) return embeddings except Exception as e: raise ValueError(f"Embedding contents failed: {e}") @property def type(self) -> str: return "mistral"