| """Local embeddings via Sentence-Transformers (no inference provider).""" |
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|
| from __future__ import annotations |
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| from functools import lru_cache |
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|
| from langchain_core.embeddings import Embeddings |
| from sentence_transformers import SentenceTransformer |
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|
| from src.config import settings |
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|
| @lru_cache(maxsize=1) |
| def _model() -> SentenceTransformer: |
| return SentenceTransformer(settings.embedding_model) |
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| class LocalSentenceTransformerEmbeddings(Embeddings): |
| def embed_documents(self, texts: list[str]) -> list[list[float]]: |
| vecs = _model().encode(texts, normalize_embeddings=True) |
| tolist = getattr(vecs, "tolist", None) |
| return tolist() if callable(tolist) else [list(map(float, v)) for v in vecs] |
|
|
| def embed_query(self, text: str) -> list[float]: |
| vec = _model().encode([text], normalize_embeddings=True)[0] |
| tolist = getattr(vec, "tolist", None) |
| return tolist() if callable(tolist) else [float(x) for x in vec] |
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| def get_embeddings() -> Embeddings: |
| return LocalSentenceTransformerEmbeddings() |
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