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Sentence Transformer Embeddings

Embeddings for text data​

Sentence Transformer Embeddings are used for computing the vectors used in similarity retrieval. These embeddings are an essential component for each HybridStore, empowering you to fetch text data efficiently.

Usage

from hybridagi import SentenceTransformerEmbeddings

embeddings = SentenceTransformerEmbeddings(
    model_name_or_path = "sentence-transformers/all-MiniLM-L6-v2", # The name of the model to use
    dim = 384, # The dimension of the embeddings vector
    max_gpu_devices = 1, # The maximum number of GPU to use (default to 1)
    batch_size = 256, # The maximum of embeddings to compute in one batch (default to 256)
    max_seq_length = 256, # The maximum number of input tokens for the embeddings (default to 256) 
    normalize_embeddings = True, # Whether or not to normalize the embeddings (default to True)
)