khoj/tests/conftest.py
2022-09-03 22:24:10 +03:00

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2.6 KiB
Python

# Standard Packages
import pytest
# Internal Packages
from src.search_type import image_search, text_search
from src.utils.config import SearchType
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 = "sentence-transformers/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_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)
# 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, SearchType.Org, regenerate=False)
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 = 1,
use_xmp_metadata = False)
return content_config