import numpy as np import pandas as pd import pytest from buster.documents import DocumentsDB, DocumentsPickle @pytest.mark.parametrize("documents_manager, extension", [(DocumentsDB, "db"), (DocumentsPickle, "tar.gz")]) def test_write_read(tmp_path, documents_manager, extension): db = documents_manager(tmp_path / f"test.{extension}") data = pd.DataFrame.from_dict( { "title": ["test"], "url": ["http://url.com"], "content": ["cool text"], "embedding": [np.arange(10, dtype=np.float32) - 0.3], "n_tokens": [10], } ) db.add(source="test", df=data) db_data = db.get_documents("test") assert db_data["title"].iloc[0] == data["title"].iloc[0] assert db_data["url"].iloc[0] == data["url"].iloc[0] assert db_data["content"].iloc[0] == data["content"].iloc[0] assert np.allclose(db_data["embedding"].iloc[0], data["embedding"].iloc[0]) assert db_data["n_tokens"].iloc[0] == data["n_tokens"].iloc[0] @pytest.mark.parametrize("documents_manager, extension", [(DocumentsDB, "db"), (DocumentsPickle, "tar.gz")]) def test_write_write_read(tmp_path, documents_manager, extension): db = documents_manager(tmp_path / f"test.{extension}") data_1 = pd.DataFrame.from_dict( { "title": ["test"], "url": ["http://url.com"], "content": ["cool text"], "embedding": [np.arange(10, dtype=np.float32) - 0.3], "n_tokens": [10], } ) db.add(source="test", df=data_1) data_2 = pd.DataFrame.from_dict( { "title": ["other"], "url": ["http://url.com/page.html"], "content": ["lorem ipsum"], "embedding": [np.arange(20, dtype=np.float32) / 10 - 2.3], "n_tokens": [20], } ) db.add(source="test", df=data_2) db_data = db.get_documents("test") assert len(db_data) == len(data_2) assert db_data["title"].iloc[0] == data_2["title"].iloc[0] assert db_data["url"].iloc[0] == data_2["url"].iloc[0] assert db_data["content"].iloc[0] == data_2["content"].iloc[0] assert np.allclose(db_data["embedding"].iloc[0], data_2["embedding"].iloc[0]) assert db_data["n_tokens"].iloc[0] == data_2["n_tokens"].iloc[0]