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| from sentence_transformers import SentenceTransformer | |
| import os | |
| class Embeddings: | |
| def __init__(self): | |
| model_path = ( | |
| os.getenv("EMBEDDER_MODEL_PATH") | |
| or "all-MiniLM-L6-v2" | |
| ) | |
| self.embedder = SentenceTransformer(model_path) | |
| def create_embedding(self, text: str): | |
| embedding = self.embedder.encode( | |
| text, | |
| convert_to_numpy=True, | |
| normalize_embeddings=True | |
| ) | |
| return embedding.tolist() | |
| def create_embeddings(self, texts: list): | |
| embeddings = self.embedder.encode( | |
| texts, | |
| convert_to_numpy=True, | |
| normalize_embeddings=True | |
| ) | |
| return embeddings.tolist() |