27 lines
895 B
Python
27 lines
895 B
Python
from sentence_transformers import SentenceTransformer
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import requests
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import torch
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from config.base import BATCH_SIZE
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class TextEmbedderGemma:
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def __init__(self, model_path):
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self.model = SentenceTransformer(model_path, trust_remote_code=True, local_files_only=True).to(device="cuda:0")
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def embed_texts(self, texts:list[str], query:bool = False)->list[list[float]]:
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"""
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Embed texts using the model.
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"""
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all_embeddings = []
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for i in range(0, len(texts), BATCH_SIZE):
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batch_texts = texts[i:i+BATCH_SIZE]
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if query:
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embeddings = self.model.encode_query(batch_texts)
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else:
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embeddings = self.model.encode_document(batch_texts)
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all_embeddings.extend(embeddings)
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torch.cuda.empty_cache()
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return all_embeddings |