# Standard Packages import pytest # Internal Packages from src.search_type import asymmetric, image_search from src.utils.rawconfig import ContentConfig, TextContentConfig, ImageContentConfig, SearchConfig, SymmetricSearchConfig, AsymmetricSearchConfig, ImageSearchConfig @pytest.fixture(scope='session') def search_config(tmp_path_factory): model_dir = tmp_path_factory.mktemp('data') search_config = SearchConfig() search_config.asymmetric = SymmetricSearchConfig( encoder = "sentence-transformers/paraphrase-MiniLM-L6-v2", cross_encoder = "cross-encoder/ms-marco-MiniLM-L-6-v2", model_directory = model_dir ) search_config.asymmetric = AsymmetricSearchConfig( encoder = "sentence-transformers/msmarco-MiniLM-L-6-v3", 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 # Generate Image Embeddings from Test Images content_config = ContentConfig() content_config.image = ImageContentConfig( input_directory = 'tests/data', 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 = ['tests/data/main_readme.org', 'tests/data/interface_emacs_readme.org'], input_filter = None, compressed_jsonl = model_dir.joinpath('.notes.jsonl.gz'), embeddings_file = model_dir.joinpath('.note_embeddings.pt')) asymmetric.setup(content_config.org, search_config.asymmetric, regenerate=False, verbose=True) return model_dir @pytest.fixture(scope='session') def content_config(model_dir): content_config = ContentConfig() content_config.org = TextContentConfig( input_files = ['tests/data/main_readme.org', 'tests/data/interface_emacs_readme.org'], input_filter = None, compressed_jsonl = model_dir.joinpath('.notes.jsonl.gz'), embeddings_file = model_dir.joinpath('.note_embeddings.pt')) content_config.image = ImageContentConfig( input_directory = 'tests/data', embeddings_file = model_dir.joinpath('.image_embeddings.pt'), batch_size = 10, use_xmp_metadata = False) return content_config