# Standard Packages import pytest import torch # Internal Packages from src.search_type import image_search, text_search from src.utils.rawconfig import ContentConfig, TextContentConfig, ImageContentConfig, SearchConfig, TextSearchConfig, ImageSearchConfig from src.processor.org_mode.org_to_jsonl import org_to_jsonl @pytest.fixture(scope='session') def search_config(tmp_path_factory): model_dir = tmp_path_factory.mktemp('data') search_config = SearchConfig() search_config.symmetric = TextSearchConfig( encoder = "sentence-transformers/all-MiniLM-L6-v2", cross_encoder = "cross-encoder/ms-marco-MiniLM-L-6-v2", model_directory = model_dir ) search_config.asymmetric = TextSearchConfig( encoder = "sentence-transformers/multi-qa-MiniLM-L6-cos-v1", cross_encoder = "cross-encoder/ms-marco-MiniLM-L-6-v2", model_directory = model_dir ) search_config.image = ImageSearchConfig( encoder = "clip-ViT-B-32", model_directory = model_dir ) return search_config @pytest.fixture(scope='session') def model_dir(search_config): model_dir = search_config.asymmetric.model_directory device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") # Generate Image Embeddings from Test Images content_config = ContentConfig() content_config.image = ImageContentConfig( input_directories = ['tests/data/images'], embeddings_file = model_dir.joinpath('image_embeddings.pt'), batch_size = 10, use_xmp_metadata = False) image_search.setup(content_config.image, search_config.image, regenerate=False, verbose=True) # Generate Notes Embeddings from Test Notes content_config.org = TextContentConfig( input_files = None, input_filter = 'tests/data/org/*.org', compressed_jsonl = model_dir.joinpath('notes.jsonl.gz'), embeddings_file = model_dir.joinpath('note_embeddings.pt')) text_search.setup(org_to_jsonl, content_config.org, search_config.asymmetric, regenerate=False, device=device, verbose=True) return model_dir @pytest.fixture(scope='session') def content_config(model_dir): content_config = ContentConfig() content_config.org = TextContentConfig( input_files = None, input_filter = 'tests/data/org/*.org', compressed_jsonl = model_dir.joinpath('notes.jsonl.gz'), embeddings_file = model_dir.joinpath('note_embeddings.pt')) content_config.image = ImageContentConfig( input_directories = ['tests/data/images'], embeddings_file = model_dir.joinpath('image_embeddings.pt'), batch_size = 10, use_xmp_metadata = False) return content_config