47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
import os
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from typing import Optional, Union
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from chromadb import EmbeddingFunction, Embeddings
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from embedchain.config import BaseEmbedderConfig
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from embedchain.embedder.base import BaseEmbedder
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from embedchain.models import VectorDimensions
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class MistralAIEmbeddingFunction(EmbeddingFunction):
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def __init__(self, config: BaseEmbedderConfig) -> None:
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super().__init__()
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try:
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from langchain_mistralai import MistralAIEmbeddings
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except ModuleNotFoundError:
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raise ModuleNotFoundError(
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"The required dependencies for MistralAI are not installed."
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'Please install with `pip install --upgrade "embedchain[mistralai]"`'
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) from None
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self.config = config
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api_key = self.config.api_key or os.getenv("MISTRAL_API_KEY")
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self.client = MistralAIEmbeddings(mistral_api_key=api_key)
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self.client.model = self.config.model
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def __call__(self, input: Union[list[str], str]) -> Embeddings:
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if isinstance(input, str):
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input_ = [input]
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else:
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input_ = input
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response = self.client.embed_documents(input_)
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return response
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class MistralAIEmbedder(BaseEmbedder):
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def __init__(self, config: Optional[BaseEmbedderConfig] = None):
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super().__init__(config)
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if self.config.model is None:
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self.config.model = "mistral-embed"
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embedding_fn = MistralAIEmbeddingFunction(config=self.config)
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self.set_embedding_fn(embedding_fn=embedding_fn)
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vector_dimension = self.config.vector_dimension or VectorDimensions.MISTRAL_AI.value
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self.set_vector_dimension(vector_dimension=vector_dimension)
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