return HuggingFaceEmbedding(
model_name=config.llm_embedding_model
or "sentence-transformers/all-MiniLM-L6-v2",
+ cache_folder=str(settings.DATA_DIR / "hf_cache"),
)
case LLMEmbeddingBackend.OLLAMA:
from llama_index.embeddings.ollama import OllamaEmbedding
from unittest.mock import patch
import pytest
+from django.conf import settings
from documents.models import Document
from paperless.models import LLMEmbeddingBackend
model = get_embedding_model()
MockHuggingFaceEmbedding.assert_called_once_with(
model_name="sentence-transformers/all-MiniLM-L6-v2",
+ cache_folder=str(settings.DATA_DIR / "hf_cache"),
)
assert model == MockHuggingFaceEmbedding.return_value