import logging from random import random from typing import List import numpy as np import pytest import pandas as pd import responses from responses import matchers import _pytest from ..conftest import fail_at_version, haystack_version from haystack.errors import OpenAIRateLimitError from haystack.schema import Answer, Document, Span, Label from haystack.utils.deepsetcloud import DeepsetCloud, DeepsetCloudExperiments from haystack.utils.labels import aggregate_labels from haystack.utils.preprocessing import convert_files_to_docs, tika_convert_files_to_docs from haystack.utils.cleaning import clean_wiki_text from haystack.utils.reflection import retry_with_exponential_backoff from haystack.utils.context_matching import calculate_context_similarity, match_context, match_contexts from ..conftest import ( DC_API_ENDPOINT, DC_API_KEY, MOCK_DC, SAMPLES_PATH, deepset_cloud_fixture, fail_at_version, haystack_version, ) TEST_CONTEXT = context = """Der Merkantilismus förderte Handel und Verkehr mit teils marktkonformen, teils dirigistischen Maßnahmen. An der Schwelle zum 19. Jahrhundert entstand ein neuer Typus des Nationalstaats, der die Säkularisation durchsetzte, moderne Bildungssysteme etablierte und die Industrialisierung vorantrieb.\n Beim Begriff der Aufklärung geht es auch um die Prozesse zwischen diesen frühneuzeitlichen Eckpunkten. Man versucht die fortschrittlichen Faktoren zu definieren, die in das 19. Jahrhundert führten. Widerstände gegen diesen Fortschritt werden anti-aufklärerischen Kräften oder unreflektierten Traditionen zugeordnet. Die Epochendefinition rückt vor allem publizistisch tätige Gruppen in den gesellschaftlichen Fokus, die zunächst selten einen bürgerlichen Hintergrund aufwiesen, sondern weitaus häufiger der Geistlichkeit oder Aristokratie angehörten: Wissenschaftler, Journalisten, Autoren, sogar Regenten, die Traditionen der Kritik unterzogen, indem sie sich auf die Vernunftperspektive beriefen.""" TEST_CONTEXT_2 = """Beer is one of the oldest[1][2][3] and most widely consumed[4] alcoholic drinks in the world, and the third most popular drink overall after water and tea.[5] It is produced by the brewing and fermentation of starches, mainly derived from cereal grains—most commonly from malted barley, though wheat, maize (corn), rice, and oats are also used. During the brewing process, fermentation of the starch sugars in the wort produces ethanol and carbonation in the resulting beer.[6] Most modern beer is brewed with hops, which add bitterness and other flavours and act as a natural preservative and stabilizing agent. Other flavouring agents such as gruit, herbs, or fruits may be included or used instead of hops. In commercial brewing, the natural carbonation effect is often removed during processing and replaced with forced carbonation.[7] Some of humanity's earliest known writings refer to the production and distribution of beer: the Code of Hammurabi included laws regulating beer and beer parlours,[8] and "The Hymn to Ninkasi", a prayer to the Mesopotamian goddess of beer, served as both a prayer and as a method of remembering the recipe for beer in a culture with few literate people.[9][10] Beer is distributed in bottles and cans and is also commonly available on draught, particularly in pubs and bars. The brewing industry is a global business, consisting of several dominant multinational companies and many thousands of smaller producers ranging from brewpubs to regional breweries. The strength of modern beer is usually around 4% to 6% alcohol by volume (ABV), although it may vary between 0.5% and 20%, with some breweries creating examples of 40% ABV and above.[11] Beer forms part of the culture of many nations and is associated with social traditions such as beer festivals, as well as a rich pub culture involving activities like pub crawling, pub quizzes and pub games. When beer is distilled, the resulting liquor is a form of whisky.[12] """ def test_deprecation_fixture(): current_major, current_minor = tuple(int(num) for num in haystack_version.split(".")[:2]) @fail_at_version(0, 1) def test_previous_major_and_minor(): assert True with pytest.raises(_pytest.outcomes.Failed): test_previous_major_and_minor() @fail_at_version(0, current_minor) def test_previous_major_and_same_minor(): assert True with pytest.raises(_pytest.outcomes.Failed): test_previous_major_and_same_minor() @fail_at_version(0, 1000) def test_previous_major_and_later_minor(): assert True with pytest.raises(_pytest.outcomes.Failed): test_previous_major_and_later_minor() @fail_at_version(current_major, 1) def test_same_major_and_previous_minor(): assert True with pytest.raises(_pytest.outcomes.Failed): test_same_major_and_previous_minor() @fail_at_version(current_major, current_minor) def test_same_version(): assert True with pytest.raises(_pytest.outcomes.Failed): test_same_version() @fail_at_version(current_major, 1000) def test_same_major_and_later_minor(): assert True test_same_major_and_later_minor() @fail_at_version(1000, 1) def test_later_major_and_previous_minor(): assert True test_later_major_and_previous_minor() @fail_at_version(1000, current_minor) def test_later_major_and_same_minor(): assert True test_later_major_and_previous_minor() @fail_at_version(1000, 1000) def test_later_major_and_later_minor(): assert True test_later_major_and_later_minor() def test_convert_files_to_docs(): documents = convert_files_to_docs( dir_path=(SAMPLES_PATH).absolute(), clean_func=clean_wiki_text, split_paragraphs=True ) assert documents and len(documents) > 0 @pytest.mark.tika def test_tika_convert_files_to_docs(): documents = tika_convert_files_to_docs(dir_path=SAMPLES_PATH, clean_func=clean_wiki_text, split_paragraphs=True) assert documents and len(documents) > 0 def test_calculate_context_similarity_on_parts_of_whole_document(): whole_document = TEST_CONTEXT min_length = 100 margin = 5 context_size = min_length + margin for i in range(len(whole_document) - context_size): partial_context = whole_document[i : i + context_size] score = calculate_context_similarity(partial_context, whole_document, min_length=min_length) assert score == 100.0 def test_calculate_context_similarity_on_parts_of_whole_document_different_case(): whole_document = TEST_CONTEXT min_length = 100 margin = 5 context_size = min_length + margin for i in range(len(whole_document) - context_size): partial_context = whole_document[i : i + context_size].lower() score = calculate_context_similarity(partial_context, whole_document, min_length=min_length) assert score == 100.0 def test_calculate_context_similarity_on_parts_of_whole_document_different_whitesapce(): whole_document = TEST_CONTEXT words = whole_document.split() min_length = 100 context_word_size = 20 for i in range(len(words) - context_word_size): partial_context = "\n\t\t\t".join(words[i : i + context_word_size]) score = calculate_context_similarity(partial_context, whole_document, min_length=min_length) assert score == 100.0 def test_calculate_context_similarity_min_length(): whole_document = TEST_CONTEXT min_length = 100 context_size = min_length - 1 for i in range(len(whole_document) - context_size): partial_context = whole_document[i : i + context_size] score = calculate_context_similarity(partial_context, whole_document, min_length=min_length) assert score == 0.0 def test_calculate_context_similarity_on_partially_overlapping_contexts(): whole_document = TEST_CONTEXT min_length = 100 margin = 5 context_size = min_length + margin stride = context_size // 2 for i in range(len(whole_document) - context_size - stride): partial_context_1 = whole_document[i : i + context_size] partial_context_2 = whole_document[i + stride : i + stride + context_size] score = calculate_context_similarity(partial_context_1, partial_context_2, min_length=min_length) assert score >= 65.0 def test_calculate_context_similarity_on_non_matching_contexts(): whole_document = TEST_CONTEXT min_length = 100 margin = 5 context_size = min_length + margin scores = [] for i in range(len(whole_document) - context_size): partial_context = whole_document[i : i + context_size // 2] + _get_random_chars(context_size // 2) score = calculate_context_similarity(partial_context, whole_document, min_length=min_length) scores.append(score) for i in range(len(whole_document) - context_size): partial_context = ( _get_random_chars(context_size // 2) + whole_document[i + context_size // 2 : i + context_size] ) score = calculate_context_similarity(partial_context, whole_document, min_length=min_length) scores.append(score) accuracy = np.where(np.array(scores) < 65, 1, 0).mean() assert accuracy > 0.99 def test_calculate_context_similarity_on_parts_of_whole_document_with_noise(): whole_document = TEST_CONTEXT min_length = 100 margin = 5 context_size = min_length + margin for i in range(len(whole_document) - context_size): partial_context = _insert_noise(whole_document[i : i + context_size], 0.1) score = calculate_context_similarity(partial_context, whole_document, min_length=min_length) assert score >= 85.0 def test_calculate_context_similarity_on_partially_overlapping_contexts_with_noise(): whole_document = TEST_CONTEXT min_length = 100 margin = 5 context_size = min_length + margin stride = context_size // 2 scores = [] for i in range(len(whole_document) - context_size - stride): partial_context_1 = whole_document[i : i + context_size] partial_context_2 = _insert_noise(whole_document[i + stride : i + stride + context_size], 0.1) score = calculate_context_similarity(partial_context_1, partial_context_2, min_length=min_length) scores.append(score) accuracy = np.where(np.array(scores) >= 65, 1, 0).mean() assert accuracy > 0.99 def test_match_context_multi_process(): whole_document = TEST_CONTEXT[:300] min_length = 100 margin = 5 context_size = min_length + margin for i in range(len(whole_document) - context_size): partial_context = whole_document[i : i + context_size] candidates = ((str(i), TEST_CONTEXT if i == 0 else TEST_CONTEXT_2) for i in range(1000)) results = match_context(partial_context, candidates, min_length=min_length, num_processes=2) assert len(results) == 1 id, score = results[0] assert id == "0" assert score == 100.0 def test_match_context_single_process(): whole_document = TEST_CONTEXT min_length = 100 margin = 5 context_size = min_length + margin for i in range(len(whole_document) - context_size): partial_context = whole_document[i : i + context_size] candidates = ((str(i), TEST_CONTEXT if i == 0 else TEST_CONTEXT_2) for i in range(10)) results = match_context(partial_context, candidates, min_length=min_length, num_processes=1) assert len(results) == 1 id, score = results[0] assert id == "0" assert score == 100.0 def test_match_contexts_multi_process(): whole_document = TEST_CONTEXT min_length = 100 margin = 5 context_size = min_length + margin candidates = ((str(i), TEST_CONTEXT if i == 0 else TEST_CONTEXT_2) for i in range(10)) partial_contexts = [whole_document[i : i + context_size] for i in range(len(whole_document) - context_size)] result_list = match_contexts(partial_contexts, candidates, min_length=min_length, num_processes=2) assert len(result_list) == len(partial_contexts) for results in result_list: assert len(results) == 1 id, score = results[0] assert id == "0" assert score == 100.0 def _get_random_chars(size: int): chars = np.random.choice( list("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZß?/.,;:-#äöüÄÖÜ+*~1234567890$€%&!§ "), size=size ) return "".join(list(chars)) def _insert_noise(input: str, ratio): size = int(ratio * len(input)) insert_idxs = sorted(np.random.choice(range(len(input)), size=size, replace=False), reverse=True) insert_chars = np.random.choice( list("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZß?/.,;:-#äöüÄÖÜ+*~1234567890$€%&!§"), size=size ) for idx, char in zip(insert_idxs, insert_chars): input = input[:idx] + char + input[idx:] return input @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_upload_file_to_deepset_cloud(caplog): if MOCK_DC: responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/files", json={"file_id": "abc"}, status=200, ) responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/files", json={"file_id": "def"}, status=200, ) responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/files", json={"file_id": "def"}, status=200, ) client = DeepsetCloud.get_file_client(api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY) file_paths = [ SAMPLES_PATH / "docx/sample_docx.docx", SAMPLES_PATH / "pdf/sample_pdf_1.pdf", SAMPLES_PATH / "docs/doc_1.txt", ] metas = [{"file_id": "sample_docx.docx"}, {"file_id": "sample_pdf_1.pdf"}, {"file_id": "doc_1.txt"}] with caplog.at_level(logging.INFO): client.upload_files(file_paths=file_paths, metas=metas) assert f"Successfully uploaded {len(file_paths)} files." in caplog.text @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_upload_file_to_deepset_cloud_file_fails(caplog): if MOCK_DC: responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/files", json={"file_id": "abc"}, status=200, ) responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/files", json={"error": "my-error"}, status=500, ) responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/files", json={"file_id": "def"}, status=200, ) client = DeepsetCloud.get_file_client(api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY) file_paths = [ SAMPLES_PATH / "docx/sample_docx.docx", SAMPLES_PATH / "pdf/sample_pdf_1.pdf", SAMPLES_PATH / "docs/doc_1.txt", ] metas = [{"file_id": "sample_docx.docx"}, {"file_id": "sample_pdf_1.pdf"}, {"file_id": "doc_1.txt"}] with caplog.at_level(logging.INFO): client.upload_files(file_paths=file_paths, metas=metas) assert f"Successfully uploaded 2 files." in caplog.text assert f"Error uploading file" in caplog.text assert f"my-error" in caplog.text @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_delete_file_to_deepset_cloud(): if MOCK_DC: responses.add(method=responses.DELETE, url=f"{DC_API_ENDPOINT}/workspaces/default/files/abc", status=200) client = DeepsetCloud.get_file_client(api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY) client.delete_file(file_id="abc") @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_delete_all_file_to_deepset_cloud(): if MOCK_DC: responses.add(method=responses.DELETE, url=f"{DC_API_ENDPOINT}/workspaces/default/files", status=200) client = DeepsetCloud.get_file_client(api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY) client.delete_all_files() @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_list_files_on_deepset_cloud(): if MOCK_DC: responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/files", json={ "data": [ { "characters": -1, "created_at": "2022-05-19T15:40:07.538162+00:00", "file_id": "b6cdd48b-3db5-488b-a44d-4240c12a96d5", "languages": [], "meta": {}, "name": "sample_pdf_1.pdf", "params": {"id_hash_keys": ["content", "meta"]}, "size": 44524, "url": "/api/v1/workspaces/e282219f-19b2-41ff-927e-bda4e6e67418/files/b6cdd48b-3db5-488b-a44d-4240c12a96d5", }, { "characters": -1, "created_at": "2022-05-23T12:39:53.393716+00:00", "file_id": "51e9c2af-5676-453d-9b71-db9a560ae266", "languages": [], "meta": {"file_id": "sample_pdf_2.pdf"}, "name": "sample_pdf_2.pdf", "params": {"id_hash_keys": ["content", "meta"]}, "size": 26093, "url": "/api/v1/workspaces/e282219f-19b2-41ff-927e-bda4e6e67418/files/51e9c2af-5676-453d-9b71-db9a560ae266", }, ], "has_more": False, "total": 2, }, status=200, ) client = DeepsetCloud.get_file_client(api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY) files = [f for f in client.list_files()] assert len(files) == 2 assert files[0]["name"] == "sample_pdf_1.pdf" assert files[1]["name"] == "sample_pdf_2.pdf" @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_create_eval_run(): if MOCK_DC: responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs", json={"data": {"eval_run_name": "my-eval-run-1"}}, status=200, match=[ matchers.json_params_matcher( { "name": "my-eval-run-1", "pipeline_name": "my-pipeline-1", "evaluation_set_name": "my-eval-set-1", "eval_mode": 0, "comment": "this is my first run", "debug": False, "tags": ["my-experiment-1"], } ) ], ) responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs", json={ "data": [ { "created_at": "2022-05-24T12:13:16.445857+00:00", "eval_mode": 0, "eval_run_id": "17875c63-7c07-42d8-bb01-4fcd95ce113c", "name": "my-eval-run-1", "comment": "this is my first run", "tags": ["my-experiment-1"], "eval_run_labels": [], "logs": {}, "metrics": { "integrated_exact_match": None, "integrated_f1": None, "integrated_sas": None, "isolated_exact_match": None, "isolated_f1": None, "isolated_sas": None, "mean_average_precision": None, "mean_reciprocal_rank": None, "normal_discounted_cummulative_gain": None, "precision": None, "recall_multi_hit": None, "recall_single_hit": None, }, "parameters": { "debug": False, "eval_mode": 0, "evaluation_set_name": "my-eval-set-1", "pipeline_name": "my-pipeline-1", }, "status": 1, } ], "has_more": False, "total": 1, }, status=200, ) responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs/my-eval-run-1", json={ "created_at": "2022-05-24T12:13:16.445857+00:00", "eval_mode": 0, "eval_run_id": "17875c63-7c07-42d8-bb01-4fcd95ce113c", "name": "my-eval-run-1", "comment": "this is my first run", "tags": ["my-experiment-1"], "eval_run_labels": [], "logs": {}, "metrics": { "integrated_exact_match": None, "integrated_f1": None, "integrated_sas": None, "isolated_exact_match": None, "isolated_f1": None, "isolated_sas": None, "mean_average_precision": None, "mean_reciprocal_rank": None, "normal_discounted_cummulative_gain": None, "precision": None, "recall_multi_hit": None, "recall_single_hit": None, }, "parameters": { "debug": False, "eval_mode": 0, "evaluation_set_name": "my-eval-set-1", "pipeline_name": "my-pipeline-1", }, "status": 1, }, status=200, ) client = DeepsetCloud.get_eval_run_client(api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY) client.create_eval_run( eval_run_name="my-eval-run-1", pipeline_config_name="my-pipeline-1", evaluation_set="my-eval-set-1", eval_mode="integrated", comment="this is my first run", tags=["my-experiment-1"], ) runs = client.get_eval_runs() assert len(runs) == 1 assert runs[0]["name"] == "my-eval-run-1" assert runs[0]["tags"] == ["my-experiment-1"] assert runs[0]["comment"] == "this is my first run" assert runs[0]["parameters"]["pipeline_name"] == "my-pipeline-1" assert runs[0]["parameters"]["evaluation_set_name"] == "my-eval-set-1" run = client.get_eval_run("my-eval-run-1") assert run["name"] == "my-eval-run-1" assert run["tags"] == ["my-experiment-1"] assert run["comment"] == "this is my first run" assert run["parameters"]["pipeline_name"] == "my-pipeline-1" assert run["parameters"]["evaluation_set_name"] == "my-eval-set-1" @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_update_eval_run(): if MOCK_DC: responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs", json={"data": {"eval_run_name": "my-eval-run-1"}}, status=200, match=[ matchers.json_params_matcher( { "name": "my-eval-run-1", "pipeline_name": "my-pipeline-1", "evaluation_set_name": "my-eval-set-1", "eval_mode": 0, "comment": "this is my first run", "debug": False, "tags": ["my-experiment-1"], } ) ], ) responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs/my-eval-run-1", json={ "created_at": "2022-05-24T12:13:16.445857+00:00", "eval_mode": 0, "eval_run_id": "17875c63-7c07-42d8-bb01-4fcd95ce113c", "name": "my-eval-run-1", "comment": "this is my first run", "tags": ["my-experiment-1"], "eval_run_labels": [], "logs": {}, "metrics": { "integrated_exact_match": None, "integrated_f1": None, "integrated_sas": None, "isolated_exact_match": None, "isolated_f1": None, "isolated_sas": None, "mean_average_precision": None, "mean_reciprocal_rank": None, "normal_discounted_cummulative_gain": None, "precision": None, "recall_multi_hit": None, "recall_single_hit": None, }, "parameters": { "debug": False, "eval_mode": 0, "evaluation_set_name": "my-eval-set-1", "pipeline_name": "my-pipeline-1", }, "status": "CREATED", }, status=200, ) responses.add( method=responses.PATCH, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs/my-eval-run-1", json={"data": {"eval_run_name": "my-eval-run-1"}}, status=200, match=[ matchers.json_params_matcher( {"pipeline_name": "my-pipeline-2", "comment": "this is my first run with second pipeline"} ) ], ) responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs/my-eval-run-1", json={ "created_at": "2022-05-24T12:13:16.445857+00:00", "eval_mode": 0, "eval_run_id": "17875c63-7c07-42d8-bb01-4fcd95ce113c", "name": "my-eval-run-1", "comment": "this is my first run with second pipeline", "tags": ["my-experiment-1"], "eval_run_labels": [], "logs": {}, "metrics": { "integrated_exact_match": None, "integrated_f1": None, "integrated_sas": None, "isolated_exact_match": None, "isolated_f1": None, "isolated_sas": None, "mean_average_precision": None, "mean_reciprocal_rank": None, "normal_discounted_cummulative_gain": None, "precision": None, "recall_multi_hit": None, "recall_single_hit": None, }, "parameters": { "debug": False, "eval_mode": 0, "evaluation_set_name": "my-eval-set-1", "pipeline_name": "my-pipeline-2", }, "status": "CREATED", }, status=200, ) client = DeepsetCloud.get_eval_run_client(api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY) client.create_eval_run( eval_run_name="my-eval-run-1", pipeline_config_name="my-pipeline-1", evaluation_set="my-eval-set-1", eval_mode="integrated", comment="this is my first run", tags=["my-experiment-1"], ) run = client.get_eval_run("my-eval-run-1") assert run["name"] == "my-eval-run-1" assert run["tags"] == ["my-experiment-1"] assert run["comment"] == "this is my first run" assert run["parameters"]["pipeline_name"] == "my-pipeline-1" assert run["parameters"]["evaluation_set_name"] == "my-eval-set-1" client.update_eval_run( eval_run_name="my-eval-run-1", pipeline_config_name="my-pipeline-2", comment="this is my first run with second pipeline", ) run = client.get_eval_run("my-eval-run-1") assert run["name"] == "my-eval-run-1" assert run["tags"] == ["my-experiment-1"] assert run["comment"] == "this is my first run with second pipeline" assert run["parameters"]["pipeline_name"] == "my-pipeline-2" assert run["parameters"]["evaluation_set_name"] == "my-eval-set-1" @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_start_eval_run(): if MOCK_DC: responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs", json={"data": {"eval_run_name": "my-eval-run-1"}}, status=200, match=[ matchers.json_params_matcher( { "name": "my-eval-run-1", "pipeline_name": "my-pipeline-1", "evaluation_set_name": "my-eval-set-1", "eval_mode": 0, "comment": "this is my first run", "debug": False, "tags": ["my-experiment-1"], } ) ], ) responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs/my-eval-run-1", json={ "created_at": "2022-05-24T12:13:16.445857+00:00", "eval_mode": 0, "eval_run_id": "17875c63-7c07-42d8-bb01-4fcd95ce113c", "name": "my-eval-run-1", "comment": "this is my first run", "tags": ["my-experiment-1"], "eval_run_labels": [], "logs": {}, "metrics": { "integrated_exact_match": None, "integrated_f1": None, "integrated_sas": None, "isolated_exact_match": None, "isolated_f1": None, "isolated_sas": None, "mean_average_precision": None, "mean_reciprocal_rank": None, "normal_discounted_cummulative_gain": None, "precision": None, "recall_multi_hit": None, "recall_single_hit": None, }, "parameters": { "debug": False, "eval_mode": 0, "evaluation_set_name": "my-eval-set-1", "pipeline_name": "my-pipeline-1", }, "status": "CREATED", }, status=200, ) responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs/my-eval-run-1/start", json={}, status=200, ) responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs/my-eval-run-1", json={ "created_at": "2022-05-24T12:13:16.445857+00:00", "eval_mode": 0, "eval_run_id": "17875c63-7c07-42d8-bb01-4fcd95ce113c", "name": "my-eval-run-1", "comment": "this is my first run", "tags": ["my-experiment-1"], "eval_run_labels": [], "logs": {}, "metrics": { "integrated_exact_match": None, "integrated_f1": None, "integrated_sas": None, "isolated_exact_match": None, "isolated_f1": None, "isolated_sas": None, "mean_average_precision": None, "mean_reciprocal_rank": None, "normal_discounted_cummulative_gain": None, "precision": None, "recall_multi_hit": None, "recall_single_hit": None, }, "parameters": { "debug": False, "eval_mode": 0, "evaluation_set_name": "my-eval-set-1", "pipeline_name": "my-pipeline-1", }, "status": "STARTED", }, status=200, ) client = DeepsetCloud.get_eval_run_client(api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY) client.create_eval_run( eval_run_name="my-eval-run-1", pipeline_config_name="my-pipeline-1", evaluation_set="my-eval-set-1", eval_mode="integrated", comment="this is my first run", tags=["my-experiment-1"], ) run = client.get_eval_run("my-eval-run-1") assert run["name"] == "my-eval-run-1" assert run["tags"] == ["my-experiment-1"] assert run["comment"] == "this is my first run" assert run["parameters"]["pipeline_name"] == "my-pipeline-1" assert run["parameters"]["evaluation_set_name"] == "my-eval-set-1" assert run["status"] == "CREATED" client.start_eval_run(eval_run_name="my-eval-run-1") run = client.get_eval_run("my-eval-run-1") assert run["name"] == "my-eval-run-1" assert run["tags"] == ["my-experiment-1"] assert run["comment"] == "this is my first run" assert run["parameters"]["pipeline_name"] == "my-pipeline-1" assert run["parameters"]["evaluation_set_name"] == "my-eval-set-1" assert run["status"] == "STARTED" @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_fetch_predictions_for_node(): mock_prediction = {} if MOCK_DC: responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs", json={"data": {"eval_run_name": "my-eval-run-1"}}, status=200, match=[ matchers.json_params_matcher( { "name": "my-eval-run-1", "pipeline_name": "my-pipeline-1", "evaluation_set_name": "my-eval-set-1", "eval_mode": 0, "comment": "this is my first run", "debug": False, "tags": ["my-experiment-1"], } ) ], ) responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs", json={ "data": [ { "created_at": "2022-05-24T12:13:16.445857+00:00", "eval_mode": 0, "eval_run_id": "17875c63-7c07-42d8-bb01-4fcd95ce113c", "name": "my-eval-run-1", "comment": "this is my first run", "tags": ["my-experiment-1"], "eval_run_labels": [], "logs": {}, "eval_results": [ { "node_name": "AnswerNode", "node_type": "answer_node", "isolated_exact_match": 1.0, "isolated_f1": 1.0, "integrated_exact_match": 0, "integrated_f1": 0, } ], "parameters": { "debug": False, "eval_mode": 0, "evaluation_set_name": "my-eval-set-1", "pipeline_name": "my-pipeline-1", }, "status": 1, } ], "has_more": False, "total": 1, }, status=200, ) mock_prediction = { "prediction_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", "created_at": "2022-08-03T13:42:58.968Z", "updated_at": "2022-08-03T13:42:58.968Z", "eval_node_result_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", "eval_mode": "Evaluation mode", "query": "What?", "context": "This", "rank": 0, "document_id": "0", "filters": [{}], "labels": [ { "label_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", "query": "What?", "answer": "This", "answer_start": 0, "answer_end": 3, "meta": {}, "context": "This", "external_file_name": "this_file.txt", "file_id": "3fa85f64-5717-4562-b3fc-2c963f66afa7", "state": "MATCHED", "candidates": None, "answer_exact_match": True, "f1": 1.0, "document_id_match": True, "answer_match": "Answer match", "context_similarity": "Context similarity", } ], "prediction_type": "answer", "answer": "This", "exact_match": True, "f1": 1.0, "exact_match_context_scope": True, "f1_document_id_scope": 0, "exact_match_document_id_and_context_scope": True, "f1_context_scope": 0, "f1_document_id_and_context_scope": 0, "answer_start": "Answer start", "answer_end": "Answer end", } responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs/my-eval-run-1/nodes/AnswerNode/predictions?page_number=1", json={"data": [mock_prediction], "has_more": False, "total": 1}, status=200, ) client = DeepsetCloud.get_eval_run_client(api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY) client.create_eval_run( eval_run_name="my-eval-run-1", pipeline_config_name="my-pipeline-1", evaluation_set="my-eval-set-1", eval_mode="integrated", comment="this is my first run", tags=["my-experiment-1"], ) predictions = client.get_eval_run_predictions(eval_run_name="my-eval-run-1", node_name="AnswerNode") assert len(predictions) == 1 assert predictions[0] == mock_prediction @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_delete_eval_run(): if MOCK_DC: responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs", json={"data": {"eval_run_name": "my-eval-run-1"}}, status=200, match=[ matchers.json_params_matcher( { "name": "my-eval-run-1", "pipeline_name": "my-pipeline-1", "evaluation_set_name": "my-eval-set-1", "eval_mode": 0, "comment": "this is my first run", "debug": False, "tags": ["my-experiment-1"], } ) ], ) responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs", json={ "data": [ { "created_at": "2022-05-24T12:13:16.445857+00:00", "eval_mode": 0, "eval_run_id": "17875c63-7c07-42d8-bb01-4fcd95ce113c", "name": "my-eval-run-1", "comment": "this is my first run", "tags": ["my-experiment-1"], "eval_run_labels": [], "logs": {}, "metrics": { "integrated_exact_match": None, "integrated_f1": None, "integrated_sas": None, "isolated_exact_match": None, "isolated_f1": None, "isolated_sas": None, "mean_average_precision": None, "mean_reciprocal_rank": None, "normal_discounted_cummulative_gain": None, "precision": None, "recall_multi_hit": None, "recall_single_hit": None, }, "parameters": { "debug": False, "eval_mode": 0, "evaluation_set_name": "my-eval-set-1", "pipeline_name": "my-pipeline-1", }, "status": 1, } ], "has_more": False, "total": 1, }, status=200, ) responses.add( method=responses.DELETE, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs/my-eval-run-1", status=204 ) responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs", json={"data": [], "has_more": False, "total": 0}, status=200, ) client = DeepsetCloud.get_eval_run_client(api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY) client.create_eval_run( eval_run_name="my-eval-run-1", pipeline_config_name="my-pipeline-1", evaluation_set="my-eval-set-1", eval_mode="integrated", comment="this is my first run", tags=["my-experiment-1"], ) runs = client.get_eval_runs() assert len(runs) == 1 run = client.delete_eval_run("my-eval-run-1") runs = client.get_eval_runs() assert len(runs) == 0 @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_upload_eval_set(caplog): if MOCK_DC: responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/evaluation_sets/import", json={"evaluation_set_id": "c2d06025-2c00-43b5-8f73-b81b12e63afc"}, status=200, ) client = DeepsetCloud.get_evaluation_set_client(api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY) with caplog.at_level(logging.INFO): client.upload_evaluation_set(file_path=SAMPLES_PATH / "dc/matching_test_1.csv") assert f"Successfully uploaded evaluation set file" in caplog.text assert f"You can access it now under evaluation set 'matching_test_1.csv'." in caplog.text @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_upload_existing_eval_set(caplog): if MOCK_DC: responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/evaluation_sets/import", json={"errors": ["Evaluation set with the same name already exists."]}, status=409, ) client = DeepsetCloud.get_evaluation_set_client(api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY) with caplog.at_level(logging.INFO): client.upload_evaluation_set(file_path=SAMPLES_PATH / "dc/matching_test_1.csv") assert f"Successfully uploaded evaluation set file" not in caplog.text assert f"You can access it now under evaluation set 'matching_test_1.csv'." not in caplog.text assert "Evaluation set with the same name already exists." in caplog.text @pytest.mark.usefixtures(deepset_cloud_fixture.__name__) @responses.activate def test_get_eval_run_results(): if MOCK_DC: responses.add( method=responses.POST, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs", json={"data": {"eval_run_name": "my-eval-run-1"}}, status=200, match=[ matchers.json_params_matcher( { "name": "my-eval-run-1", "pipeline_name": "my-pipeline-1", "evaluation_set_name": "my-eval-set-1", "eval_mode": 0, "comment": "this is my first run", "debug": False, "tags": ["my-experiment-1"], } ) ], ) responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs/my-eval-run-1", json={ "created_at": "2022-05-24T12:13:16.445857+00:00", "eval_mode": 0, "eval_run_id": "17875c63-7c07-42d8-bb01-4fcd95ce113c", "name": "my-eval-run-1", "comment": "this is my first run", "tags": ["my-experiment-1"], "eval_run_labels": [], "logs": {}, "eval_results": [ { "node_name": "AnswerNode", "node_type": "answer_node", "isolated_exact_match": 1.0, "isolated_f1": 1.0, "integrated_exact_match": 0, "integrated_f1": 0, } ], "parameters": { "debug": False, "eval_mode": 0, "evaluation_set_name": "my-eval-set-1", "pipeline_name": "my-pipeline-1", }, "status": 1, }, status=200, ) mock_prediction = { "prediction_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", "created_at": "2022-08-03T13:42:58.968Z", "updated_at": "2022-08-03T13:42:58.968Z", "eval_node_result_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", "eval_mode": "Evaluation mode", "query": "What?", "context": "This", "rank": 0, "document_id": "0", "filters": [{}], "labels": [ { "label_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6", "query": "What?", "answer": "This", "answer_start": 0, "answer_end": 3, "meta": {}, "context": "This", "external_file_name": "this_file.txt", "file_id": "3fa85f64-5717-4562-b3fc-2c963f66afa7", "state": "MATCHED", "candidates": None, "answer_exact_match": True, "f1": 1.0, "document_id_match": True, "answer_match": True, "context_similarity": 1.0, } ], "prediction_type": "answer", "answer": "This", "exact_match": True, "f1": 1.0, "exact_match_context_scope": True, "f1_document_id_scope": 0.0, "exact_match_document_id_and_context_scope": True, "f1_context_scope": 0.0, "f1_document_id_and_context_scope": 0.0, "answer_start": 1, "answer_end": 10, } responses.add( method=responses.GET, url=f"{DC_API_ENDPOINT}/workspaces/default/eval_runs/my-eval-run-1/nodes/AnswerNode/predictions?page_number=1", json={"data": [mock_prediction], "has_more": False, "total": 1}, status=200, ) experiments_client = DeepsetCloudExperiments() eval_run_results = experiments_client.get_run_result( eval_run_name="my-eval-run-1", api_endpoint=DC_API_ENDPOINT, api_key=DC_API_KEY ) assert "AnswerNode" in eval_run_results node_results = eval_run_results["AnswerNode"] assert isinstance(node_results, pd.DataFrame) first_result = node_results.iloc[0] assert first_result["exact_match"] == True assert first_result["answer"] == "This" def test_exponential_backoff(): # Test that the exponential backoff works as expected # should raise exception, check the exception contains the correct message with pytest.raises(Exception, match="retries \(2\)"): @retry_with_exponential_backoff(backoff_in_seconds=1, max_retries=2) def greet(name: str): if random() < 1.1: raise OpenAIRateLimitError("Too many requests") return f"Hello {name}" greet("John") # this should not raise exception and should print "Hello John" @retry_with_exponential_backoff(backoff_in_seconds=1, max_retries=1) def greet2(name: str): return f"Hello {name}" assert greet2("John") == "Hello John" class TestAggregateLabels: @pytest.fixture def standard_labels(self) -> List[Label]: return [ Label( id="standard", query="question", answer=Answer(answer="answer1", offsets_in_document=[Span(start=12, end=18)]), document=Document(content="some", id="123"), is_correct_answer=True, is_correct_document=True, origin="gold-label", ), # same label should be ignored Label( id="standard", query="question", answer=Answer(answer="answer1", offsets_in_document=[Span(start=12, end=18)]), document=Document(content="some", id="123"), is_correct_answer=True, is_correct_document=True, origin="gold-label", ), # different answer in same doc Label( id="diff-answer-same-doc", query="question", answer=Answer(answer="answer2", offsets_in_document=[Span(start=12, end=18)]), document=Document(content="some", id="123"), is_correct_answer=True, is_correct_document=True, origin="gold-label", ), # answer in different doc Label( id="diff-answer-diff-doc", query="question", answer=Answer(answer="answer3", offsets_in_document=[Span(start=12, end=18)]), document=Document(content="some other", id="333"), is_correct_answer=True, is_correct_document=True, origin="gold-label", ), # no_answer Label( id="no-answer", query="question", answer=Answer(answer="", offsets_in_document=[Span(start=0, end=0)]), document=Document(content="some", id="777"), is_correct_answer=True, is_correct_document=True, origin="gold-label", ), # no_answer in doc with other answer Label( id="no-answer-of-doc-with-other-answer", query="question", answer=Answer(answer="", offsets_in_document=[Span(start=0, end=0)]), document=Document(content="some", id="123"), is_correct_answer=True, is_correct_document=True, origin="gold-label", ), # negative label Label( id="negative", query="question", answer=Answer(answer="answer5", offsets_in_document=[Span(start=12, end=18)]), document=Document(content="some", id="123"), is_correct_answer=False, is_correct_document=True, origin="gold-label", ), ] @pytest.fixture def filter_meta_labels(self) -> List[Label]: return [ Label( id="standard", query="question", answer=Answer(answer="answer1", offsets_in_document=[Span(start=12, end=18)]), document=Document(content="some", id="123"), is_correct_answer=True, is_correct_document=True, origin="gold-label", filters={"from_filter": 123}, meta={"from_meta": ["123"]}, ), # different answer in same doc Label( id="diff-answer-same-doc", query="question", answer=Answer(answer="answer2", offsets_in_document=[Span(start=12, end=18)]), document=Document(content="some", id="123"), is_correct_answer=True, is_correct_document=True, origin="gold-label", filters={"from_filter": 123}, meta={"from_meta": ["123"]}, ), # answer in different doc Label( id="diff-answer-diff-doc", query="question", answer=Answer(answer="answer3", offsets_in_document=[Span(start=12, end=18)]), document=Document(content="some other", id="333"), is_correct_answer=True, is_correct_document=True, origin="gold-label", filters={"from_filter": 333}, meta={"from_meta": ["333"]}, ), # 'no answer' Label( id="no-answer", query="question", answer=Answer(answer="", offsets_in_document=[Span(start=0, end=0)]), document=Document(content="some", id="777"), is_correct_answer=True, is_correct_document=True, origin="gold-label", filters={"from_filter": 777}, meta={"from_meta": ["777"]}, ), # different id, meta, same filters Label( id="5-888", query="question", answer=Answer(answer="answer5", offsets_in_document=[Span(start=12, end=18)]), document=Document(content="some", id="123"), is_correct_answer=True, is_correct_document=True, origin="gold-label", filters={"from_filter": 123}, meta={"from_meta": ["888"]}, ), ] def test_label_aggregation(self, standard_labels: List[Label]): multi_labels = aggregate_labels(standard_labels) assert len(multi_labels) == 1 assert len(multi_labels[0].labels) == 6 assert len(multi_labels[0].answers) == 4 assert len(multi_labels[0].document_ids) == 4 assert multi_labels[0].no_answer is False def test_label_aggregation_drop_negatives(self, standard_labels: List[Label]): multi_labels = aggregate_labels(standard_labels, drop_negative_labels=True) assert len(multi_labels) == 1 assert len(multi_labels[0].labels) == 5 assert len(multi_labels[0].answers) == 3 assert "5-negative" not in [l.id for l in multi_labels[0].labels] assert len(multi_labels[0].document_ids) == 3 assert multi_labels[0].no_answer is False def test_label_aggregation_drop_no_answers(self, standard_labels: List[Label]): multi_labels = aggregate_labels(standard_labels, drop_no_answers=True) assert len(multi_labels) == 1 assert len(multi_labels[0].labels) == 4 assert len(multi_labels[0].answers) == 4 assert len(multi_labels[0].document_ids) == 4 assert multi_labels[0].no_answer is False def test_label_aggregation_drop_negatives_and_no_answers(self, standard_labels: List[Label]): multi_labels = aggregate_labels(standard_labels, drop_negative_labels=True, drop_no_answers=True) assert len(multi_labels) == 1 assert len(multi_labels[0].labels) == 3 assert len(multi_labels[0].answers) == 3 assert len(multi_labels[0].document_ids) == 3 assert multi_labels[0].no_answer is False def test_label_aggregation_closed_domain(self, standard_labels: List[Label]): multi_labels = aggregate_labels(standard_labels, add_closed_domain_filter=True) assert len(multi_labels) == 3 label_counts = [len(ml.labels) for ml in multi_labels] assert label_counts == [4, 1, 1] assert len(multi_labels[0].answers) == len(multi_labels[0].document_ids) assert multi_labels[0].no_answer is False assert multi_labels[1].no_answer is False assert multi_labels[2].no_answer is True for ml in multi_labels: assert "_id" in ml.filters def test_label_aggregation_closed_domain_drop_negatives(self, standard_labels: List[Label]): multi_labels = aggregate_labels(standard_labels, add_closed_domain_filter=True, drop_negative_labels=True) assert len(multi_labels) == 3 label_counts = [len(ml.labels) for ml in multi_labels] assert label_counts == [3, 1, 1] assert len(multi_labels[0].answers) == len(multi_labels[0].document_ids) assert multi_labels[0].no_answer is False assert multi_labels[1].no_answer is False assert multi_labels[2].no_answer is True for ml in multi_labels: assert "_id" in ml.filters def test_aggregate_labels_filter_aggregations_with_no_sequence_values(self, filter_meta_labels: List[Label]): multi_labels = aggregate_labels(filter_meta_labels) assert len(multi_labels) == 3 label_counts = [len(ml.labels) for ml in multi_labels] assert label_counts == [3, 1, 1] assert len(multi_labels[0].answers) == len(multi_labels[0].document_ids) for multi_label in multi_labels: for l in multi_label.labels: assert "from_filter" in l.filters assert multi_label.filters == l.filters def test_aggregate_labels_filter_aggregations_with_string_values(self, filter_meta_labels: List[Label]): for label in filter_meta_labels: label.filters["from_filter"] = str(label.filters["from_filter"]) multi_labels = aggregate_labels(filter_meta_labels) assert len(multi_labels) == 3 label_counts = [len(ml.labels) for ml in multi_labels] assert label_counts == [3, 1, 1] assert len(multi_labels[0].answers) == len(multi_labels[0].document_ids) for multi_label in multi_labels: for l in multi_label.labels: assert "from_filter" in l.filters assert multi_label.filters == l.filters def test_aggregate_labels_filter_aggregations_with_list_values(self, filter_meta_labels: List[Label]): for label in filter_meta_labels: label.filters["from_filter"] = [label.filters["from_filter"], "some_other_value"] multi_labels = aggregate_labels(filter_meta_labels) assert len(multi_labels) == 3 label_counts = [len(ml.labels) for ml in multi_labels] assert label_counts == [3, 1, 1] assert len(multi_labels[0].answers) == len(multi_labels[0].document_ids) for multi_label in multi_labels: for l in multi_label.labels: assert "from_filter" in l.filters assert multi_label.filters == l.filters def test_aggregate_labels_filter_aggregations_with_no_sequence_values_closed_domain( self, filter_meta_labels: List[Label] ): multi_labels = aggregate_labels(filter_meta_labels, add_closed_domain_filter=True) assert len(multi_labels) == 3 label_counts = [len(ml.labels) for ml in multi_labels] assert label_counts == [3, 1, 1] assert len(multi_labels[0].answers) == len(multi_labels[0].document_ids) for multi_label in multi_labels: for l in multi_label.labels: assert "from_filter" in l.filters assert "_id" in l.filters assert multi_label.filters == l.filters def test_aggregate_labels_meta_aggregations(self, filter_meta_labels: List[Label]): multi_labels = aggregate_labels(filter_meta_labels, add_meta_filters="from_meta") assert len(multi_labels) == 4 label_counts = [len(ml.labels) for ml in multi_labels] assert label_counts == [2, 1, 1, 1] for multi_label in multi_labels: for l in multi_label.labels: assert "from_filter" in l.filters assert l.filters["from_meta"] == l.meta["from_meta"] assert multi_label.filters == l.filters def test_aggregate_labels_meta_aggregations_closed_domain(self, filter_meta_labels: List[Label]): multi_labels = aggregate_labels(filter_meta_labels, add_closed_domain_filter=True, add_meta_filters="from_meta") assert len(multi_labels) == 4 label_counts = [len(ml.labels) for ml in multi_labels] assert label_counts == [2, 1, 1, 1] for multi_label in multi_labels: for l in multi_label.labels: assert "from_filter" in l.filters assert l.filters["from_meta"] == l.meta["from_meta"] assert "_id" in l.filters assert multi_label.filters == l.filters