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| """ | |
| File: /client_small.py | |
| Created Date: Wednesday December 31st 2025 | |
| Author: Christian Nonis <alch.infoemail@gmail.com> | |
| ----- | |
| Last Modified: Wednesday December 31st 2025 9:55:46 am | |
| Modified By: Christian Nonis <alch.infoemail@gmail.com> | |
| ----- | |
| """ | |
| from __future__ import annotations | |
| from typing import TYPE_CHECKING | |
| from src.adapters.interfaces.embeddings import EmbeddingsClient | |
| from src.config import config | |
| if TYPE_CHECKING: | |
| from sentence_transformers import SentenceTransformer | |
| _model: SentenceTransformer | None = None | |
| def _get_model() -> SentenceTransformer: | |
| global _model | |
| if _model is None: | |
| from sentence_transformers import SentenceTransformer | |
| _model = SentenceTransformer(config.embeddings.small_model) | |
| return _model | |
| class EmbeddingsClientSmall(EmbeddingsClient): | |
| def embed_text(self, text: str) -> list[float]: | |
| """ | |
| Return the embedding vector for the given text using the configured model. | |
| Parameters: | |
| text (str): Input text to encode. | |
| Returns: | |
| list[float]: Dense embedding vector representing the input text. | |
| """ | |
| embedding = _get_model().encode(text, convert_to_numpy=True) | |
| return embedding.tolist() | |
| def embed_texts(self, texts: list[str]) -> list[list[float]]: | |
| """ | |
| Return the embedding vectors for the given texts using the configured model. | |
| Parameters: | |
| texts (list[str]): Input texts to encode. | |
| Returns: | |
| list[list[float]]: Dense embedding vectors representing the input texts. | |
| """ | |
| embeddings = _get_model().encode(texts, convert_to_numpy=True) | |
| return embeddings.tolist() | |
| _embeddings_small_client = EmbeddingsClientSmall() | |