From 3586b9e058ca53ee4d143d195237cc7bb2e5525c Mon Sep 17 00:00:00 2001 From: Anastasia Beglova Date: Wed, 19 Nov 2025 19:15:41 -0500 Subject: [PATCH] use qdrant for hybrid search --- learning_resources_search/api.py | 78 ++-- learning_resources_search/api_test.py | 42 +- learning_resources_search/connection.py | 30 -- learning_resources_search/constants.py | 29 +- learning_resources_search/indexing_api.py | 142 ++---- .../indexing_api_test.py | 83 ++++ .../management/commands/recreate_index.py | 6 +- learning_resources_search/tasks.py | 21 +- learning_resources_search/tasks_test.py | 155 +++---- main/settings.py | 10 - poetry.lock | 431 +----------------- pyproject.toml | 1 - vector_search/utils.py | 3 + 13 files changed, 252 insertions(+), 779 deletions(-) diff --git a/learning_resources_search/api.py b/learning_resources_search/api.py index 55929e0077..12a4c69fa4 100644 --- a/learning_resources_search/api.py +++ b/learning_resources_search/api.py @@ -13,14 +13,13 @@ from learning_resources.models import LearningResource from learning_resources_search.connection import ( get_default_alias_name, - get_vector_model_id, ) from learning_resources_search.constants import ( - COMBINED_INDEX, CONTENT_FILE_TYPE, COURSE_QUERY_FIELDS, COURSE_TYPE, DEPARTMENT_QUERY_FIELDS, + HYBRID_COMBINED_INDEX, HYBRID_SEARCH_MODE, LEARNING_RESOURCE, LEARNING_RESOURCE_QUERY_FIELDS, @@ -55,6 +54,23 @@ "-created_on", ] +HYBRID_SEARCH_KNN_K_VALUE = 5 +HYBRID_SEARCH_PAGINATION_DEPTH = 10 +HYBRID_SEARCH_POST_PROCESSOR = { + "description": "Post processor for hybrid search", + "phase_results_processors": [ + { + "normalization-processor": { + "normalization": {"technique": "min_max"}, + "combination": { + "technique": "arithmetic_mean", + "parameters": {"weights": [0.8, 0.2]}, + }, + } + } + ], +} + def gen_content_file_id(content_file_id): """ @@ -86,7 +102,7 @@ def relevant_indexes(resource_types, aggregations, endpoint, use_hybrid_search): if endpoint == CONTENT_FILE_TYPE: return [get_default_alias_name(COURSE_TYPE)] elif use_hybrid_search: - return [get_default_alias_name(COMBINED_INDEX)] + return [get_default_alias_name(HYBRID_COMBINED_INDEX)] if aggregations and "resource_type" in aggregations: return map(get_default_alias_name, LEARNING_RESOURCE_TYPES) @@ -652,41 +668,22 @@ def add_text_query_to_search( text_query = {"bool": {"must": [text_query], "filter": query_type_query}} if use_hybrid_search: - vector_model_id = get_vector_model_id() - if not vector_model_id: - log.error("Vector model not found. Cannot perform hybrid search.") - error_message = "Vector model not found." - raise ValueError(error_message) - - vector_query_description = { - "neural": { - "description_embedding": { - "query_text": text, - "model_id": vector_model_id, - "min_score": 0.015, - }, - } - } - - vector_query_title = { - "neural": { - "title_embedding": { - "query_text": text, - "model_id": vector_model_id, - "min_score": 0.015, - }, + encoder = dense_encoder() + query_vector = encoder.embed_query(text) + vector_query = { + "knn": { + "vector_embedding": { + "vector": query_vector, + "k": HYBRID_SEARCH_KNN_K_VALUE, + } } } search = search.extra( query={ "hybrid": { - "pagination_depth": 10, - "queries": [ - text_query, - vector_query_description, - vector_query_title, - ], + "pagination_depth": HYBRID_SEARCH_PAGINATION_DEPTH, + "queries": [text_query, vector_query], } } ) @@ -803,22 +800,7 @@ def execute_learn_search(search_params): search = construct_search(search_params) if search_params.get("search_mode") == HYBRID_SEARCH_MODE: - search = search.extra( - search_pipeline={ - "description": "Post processor for hybrid search", - "phase_results_processors": [ - { - "normalization-processor": { - "normalization": {"technique": "min_max"}, - "combination": { - "technique": "arithmetic_mean", - "parameters": {"weights": [0.6, 0.2, 0.2]}, - }, - } - } - ], - } - ) + search = search.extra(search_pipeline=HYBRID_SEARCH_POST_PROCESSOR) results = search.execute().to_dict() if results.get("_shards", {}).get("failures"): diff --git a/learning_resources_search/api_test.py b/learning_resources_search/api_test.py index 4adb5feef5..77dbd67e42 100644 --- a/learning_resources_search/api_test.py +++ b/learning_resources_search/api_test.py @@ -1946,8 +1946,7 @@ def test_execute_learn_search_for_learning_resource_query(opensearch): "content", "summary", "flashcards", - "description_embedding", - "title_embedding", + "vector_embedding", ] }, } @@ -2395,8 +2394,7 @@ def test_execute_learn_search_with_script_score( "content", "summary", "flashcards", - "description_embedding", - "title_embedding", + "vector_embedding", ] }, } @@ -2417,10 +2415,8 @@ def test_execute_learn_search_with_hybrid_search(mocker, settings, opensearch): settings.DEFAULT_SEARCH_MODE = "best_fields" - mocker.patch( - "learning_resources_search.api.get_vector_model_id", - return_value="vector_model_id", - ) + mock_encoder = mocker.patch("learning_resources_search.api.dense_encoder")() + mock_encoder.embed_query.return_value = [0.1, 0.2, 0.3] search_params = { "aggregations": ["offered_by"], @@ -2727,24 +2723,7 @@ def test_execute_learn_search_with_hybrid_search(mocker, settings, opensearch): "filter": {"exists": {"field": "resource_type"}}, } }, - { - "neural": { - "description_embedding": { - "query_text": "math", - "model_id": "vector_model_id", - "min_score": 0.015, - } - } - }, - { - "neural": { - "title_embedding": { - "query_text": "math", - "model_id": "vector_model_id", - "min_score": 0.015, - } - } - }, + {"knn": {"vector_embedding": {"vector": [0.1, 0.2, 0.3], "k": 5}}}, ], } }, @@ -2805,7 +2784,7 @@ def test_execute_learn_search_with_hybrid_search(mocker, settings, opensearch): "normalization": {"technique": "min_max"}, "combination": { "technique": "arithmetic_mean", - "parameters": {"weights": [0.6, 0.2, 0.2]}, + "parameters": {"weights": [0.8, 0.2]}, }, } } @@ -2824,8 +2803,7 @@ def test_execute_learn_search_with_hybrid_search(mocker, settings, opensearch): "content", "summary", "flashcards", - "description_embedding", - "title_embedding", + "vector_embedding", ] }, } @@ -3217,8 +3195,7 @@ def test_execute_learn_search_with_min_score(mocker, settings, opensearch): "content", "summary", "flashcards", - "description_embedding", - "title_embedding", + "vector_embedding", ] }, } @@ -3396,8 +3373,7 @@ def test_execute_learn_search_for_content_file_query(opensearch): "content", "summary", "flashcards", - "description_embedding", - "title_embedding", + "vector_embedding", ] }, } diff --git a/learning_resources_search/connection.py b/learning_resources_search/connection.py index a8c1b45f63..8fb5fd893d 100644 --- a/learning_resources_search/connection.py +++ b/learning_resources_search/connection.py @@ -135,33 +135,3 @@ def refresh_index(index): """ conn = get_conn() conn.indices.refresh(index) - - -def get_vector_model_id(): - """ - Get the model ID for the currently loaded vector model - """ - conn = get_conn() - model_name = settings.OPENSEARCH_VECTOR_MODEL_NAME - body = {"query": {"term": {"name.keyword": model_name}}} - models = conn.transport.perform_request( - "GET", "/_plugins/_ml/models/_search", body=body - ) - - if len(models.get("hits", {}).get("hits", [])) > 0: - return models["hits"]["hits"][0]["_source"]["model_id"] - - return None - - -def get_vector_model_info(): - """ - Get information about the currently loaded vector model - """ - - conn = get_conn() - model_id = get_vector_model_id() - if not model_id: - return None - - return conn.transport.perform_request("GET", f"/_plugins/_ml/models/{model_id}") diff --git a/learning_resources_search/constants.py b/learning_resources_search/constants.py index 7ceaf6548a..c90695dce4 100644 --- a/learning_resources_search/constants.py +++ b/learning_resources_search/constants.py @@ -22,8 +22,7 @@ CURRENT_INDEX = "current_index" REINDEXING_INDEX = "reindexing_index" BOTH_INDEXES = "all_indexes" -COMBINED_INDEX = "combined_hybrid" - +HYBRID_COMBINED_INDEX = "combined_hybrid" LEARNING_RESOURCE = "learning_resource" HYBRID_SEARCH_MODE = "hybrid" @@ -49,7 +48,7 @@ class IndexestoUpdate(Enum): ) -BASE_INDEXES = (PERCOLATE_INDEX_TYPE, COMBINED_INDEX) +BASE_INDEXES = (PERCOLATE_INDEX_TYPE, HYBRID_COMBINED_INDEX) ALL_INDEX_TYPES = BASE_INDEXES + LEARNING_RESOURCE_TYPES @@ -323,26 +322,7 @@ class FilterConfig: "max_weekly_hours": {"type": "integer"}, } -EMBEDDING_FIELDS = { - "title_embedding": { - "type": "knn_vector", - "method": { - "engine": "lucene", - "space_type": "l2", - "name": "hnsw", - "parameters": {}, - }, - }, - "description_embedding": { - "type": "knn_vector", - "method": { - "engine": "lucene", - "space_type": "l2", - "name": "hnsw", - "parameters": {}, - }, - }, -} +EMBEDDING_FIELDS = {"vector_embedding": {"type": "knn_vector"}} CONTENT_FILE_MAP = { @@ -471,8 +451,7 @@ class FilterConfig: "content", "summary", "flashcards", - "description_embedding", - "title_embedding", + "vector_embedding", ] LEARNING_RESOURCE_SEARCH_SORTBY_OPTIONS = { diff --git a/learning_resources_search/indexing_api.py b/learning_resources_search/indexing_api.py index 2f4eb5aa85..cfa2629154 100644 --- a/learning_resources_search/indexing_api.py +++ b/learning_resources_search/indexing_api.py @@ -8,7 +8,6 @@ from django.conf import settings from django.contrib.auth import get_user_model -from opensearch_py_ml.ml_commons import MLCommonClient from opensearchpy.exceptions import ConflictError, NotFoundError from opensearchpy.helpers import BulkIndexError, bulk @@ -18,17 +17,15 @@ get_conn, get_default_alias_name, get_reindexing_alias_name, - get_vector_model_id, - get_vector_model_info, make_backing_index_name, refresh_index, ) from learning_resources_search.constants import ( ALIAS_ALL_INDICES, ALL_INDEX_TYPES, - COMBINED_INDEX, COURSE_TYPE, EMBEDDING_FIELDS, + HYBRID_COMBINED_INDEX, LEARNING_RESOURCE_MAP, MAPPING, PERCOLATE_INDEX_TYPE, @@ -45,6 +42,7 @@ serialize_content_file_for_bulk_deletion, ) from main.utils import chunks +from vector_search.utils import dense_encoder, retrieve_points_matching_params log = logging.getLogger(__name__) User = get_user_model() @@ -191,24 +189,16 @@ def clear_and_create_index(*, index_name=None, skip_mapping=False, object_type=N } } - if object_type == COMBINED_INDEX: - model_info = get_vector_model_info() - if not model_info: - return - index_create_data["settings"]["index.knn"] = True - index_create_data["settings"]["default_pipeline"] = "vector_ingest_pipeline" - - embedding_dimension = model_info["model_config"]["embedding_dimension"] - embedding_mapping = EMBEDDING_FIELDS - for field in embedding_mapping.values(): - field["dimension"] = embedding_dimension + if object_type == HYBRID_COMBINED_INDEX: + vector_map = EMBEDDING_FIELDS + vector_map["vector_embedding"]["dimension"] = dense_encoder().dim() index_create_data["mappings"] = { - "properties": (LEARNING_RESOURCE_MAP | embedding_mapping) + "properties": (LEARNING_RESOURCE_MAP | vector_map) } + index_create_data["settings"]["index.knn"] = True elif not skip_mapping: index_create_data["mappings"] = {"properties": MAPPING[object_type]} - # from https://www.elastic.co/guide/en/elasticsearch/guide/current/asciifolding-token-filter.html conn.indices.create(index_name, body=index_create_data) @@ -334,12 +324,44 @@ def index_learning_resources(ids, base_index_name, index_types): Args: ids(list of int): List of learning resource id's - base_index_name: The resource type of the resources + base_index_name: The resource type of the resources or HYBRID_COMBINED_INDEX index_types (string): one of the values IndexestoUpdate. Whether the default index, the reindexing index or both need to be updated """ - index_items(serialize_bulk_learning_resources(ids), base_index_name, index_types) + if base_index_name == HYBRID_COMBINED_INDEX: + index_items( + serialize_bulk_learning_resources_with_embeddings(ids), + HYBRID_COMBINED_INDEX, + index_types, + ) + else: + index_items( + serialize_bulk_learning_resources(ids), base_index_name, index_types + ) + + +def serialize_bulk_learning_resources_with_embeddings(ids): + """ + Serialize learning resources including vector embeddings for bulk indexing + + Args: + ids(list of int): List of learning resource id's + """ + serialized_resources = serialize_bulk_learning_resources(ids) + for resource in serialized_resources: + vector_result = list( + retrieve_points_matching_params( + {"readable_id": resource["readable_id"]}, with_vectors=True + ) + ) + + if len(vector_result) > 0: + resource["vector_embedding"] = vector_result[0].vector.get( + dense_encoder().model_short_name() + ) + + yield resource def deindex_learning_resources(ids, base_index_name): @@ -650,85 +672,3 @@ def get_existing_reindexing_indexes(obj_types): break return reindexing_indexes - - -def update_local_index_settings_for_hybrid_search(): - """ - Update local OpenSearch cluster settings for hybrid search. - On production only_run_on_ml_node should be set to false and - there should be dedicated ML nodes. - """ - settings_body = { - "persistent": { - "archived.plugins.index_state_management.metadata_migration.status": None, - "archived.plugins.index_state_management.template_migration.control": None, - } - } - conn = get_conn() - conn.cluster.put_settings(body=settings_body) - settings_body = { - "persistent": { - "plugins": { - "ml_commons": { - "only_run_on_ml_node": "false", - "native_memory_threshold": "99", - } - } - } - } - conn = get_conn() - conn.cluster.put_settings(body=settings_body) - - -def get_ml_client(): - """ - Get an MLCommonClient for the OpenSearch - """ - conn = get_conn() - return MLCommonClient(conn) - - -def register_model(): - """ - Register the vector OpenSearch model if it does not already exist . - """ - model_id = get_vector_model_id() - if model_id: - log.error("Model already registered.") - return - - ml_client = get_ml_client() - ml_client.register_pretrained_model( - model_name=settings.OPENSEARCH_VECTOR_MODEL_NAME, - model_version=settings.OPENSEARCH_VECTOR_MODEL_NAME_VERSION, - model_format=settings.OPENSEARCH_VECTOR_MODEL_FORMAT, - deploy_model=True, - ) - - -def create_ingest_pipeline(): - """ - Create an ingest pipeline for the combined_vector index for text embedding. - """ - conn = get_conn() - model_id = get_vector_model_id() - if not model_id: - log.error("Model not found. Cannot create ingest pipeline.") - return - - pipeline = { - "description": "An NLP ingest pipeline", - "processors": [ - { - "text_embedding": { - "model_id": model_id, - "field_map": { - "description": "description_embedding", - "title": "title_embedding", - }, - } - } - ], - } - - conn.ingest.put_pipeline("vector_ingest_pipeline", pipeline) diff --git a/learning_resources_search/indexing_api_test.py b/learning_resources_search/indexing_api_test.py index ebab1879c8..598fb65585 100644 --- a/learning_resources_search/indexing_api_test.py +++ b/learning_resources_search/indexing_api_test.py @@ -22,6 +22,7 @@ from learning_resources_search.constants import ( ALIAS_ALL_INDICES, COURSE_TYPE, + HYBRID_COMBINED_INDEX, PROGRAM_TYPE, IndexestoUpdate, ) @@ -265,6 +266,88 @@ def test_index_learning_resources( ) +@pytest.mark.parametrize("errors", [(), "error"]) +@pytest.mark.parametrize( + "index_types", + [ + IndexestoUpdate.current_index.value, + IndexestoUpdate.reindexing_index.value, + IndexestoUpdate.all_indexes.value, + ], +) +def test_index_learning_resources_for_hybrid_index( + mocked_es, + mocker, + settings, + errors, + index_types, +): + """ + Index functions should call bulk with correct arguments + """ + settings.OPENSEARCH_INDEXING_CHUNK_SIZE = 3 + documents = [ + {"readable_id": "doc1"}, + {"readable_id": "doc2"}, + {"readable_id": "doc3"}, + {"readable_id": "doc4"}, + {"readable_id": "doc5"}, + ] + + documents_with_vector = [{**doc, "vector_embedding": [2, 3]} for doc in documents] + mock_get_aliases = mocker.patch( + "learning_resources_search.indexing_api.get_active_aliases", + autospec=True, + return_value=["a", "b"], + ) + mocker.patch( + "learning_resources_search.indexing_api.serialize_bulk_learning_resources", + autospec=True, + return_value=(doc for doc in documents), + ) + + mock_encoder = mocker.patch( + "learning_resources_search.indexing_api.dense_encoder" + )() + mock_encoder.model_short_name.return_value = "test-encoder-model" + + qdrant_point = mocker.Mock() + qdrant_point.vector = {"test-encoder-model": [2, 3]} + + mocker.patch( + "learning_resources_search.indexing_api.retrieve_points_matching_params", + return_value=[qdrant_point], + ) + bulk_mock = mocker.patch( + "learning_resources_search.indexing_api.bulk", + autospec=True, + return_value=(0, errors), + ) + + if errors: + with pytest.raises(ReindexError): + index_learning_resources([1, 2, 3], HYBRID_COMBINED_INDEX, index_types) + else: + index_learning_resources([1, 2, 3], HYBRID_COMBINED_INDEX, index_types) + mock_get_aliases.assert_called_with( + mocked_es.conn, + object_types=[HYBRID_COMBINED_INDEX], + index_types=index_types, + ) + + for alias in mock_get_aliases.return_value: + for chunk in chunks( + documents_with_vector, + chunk_size=settings.OPENSEARCH_INDEXING_CHUNK_SIZE, + ): + bulk_mock.assert_any_call( + mocked_es.conn, + chunk, + index=alias, + chunk_size=settings.OPENSEARCH_INDEXING_CHUNK_SIZE, + ) + + @pytest.mark.parametrize("errors", [(), "error"]) def test_deindex_learning_resources(mocked_es, mocker, settings, errors): """ diff --git a/learning_resources_search/management/commands/recreate_index.py b/learning_resources_search/management/commands/recreate_index.py index 6bf13d1c1e..8ea87ea306 100644 --- a/learning_resources_search/management/commands/recreate_index.py +++ b/learning_resources_search/management/commands/recreate_index.py @@ -2,7 +2,7 @@ from django.core.management.base import BaseCommand, CommandError -from learning_resources_search.constants import ALL_INDEX_TYPES, COMBINED_INDEX +from learning_resources_search.constants import ALL_INDEX_TYPES, HYBRID_COMBINED_INDEX from learning_resources_search.indexing_api import get_existing_reindexing_indexes from learning_resources_search.tasks import start_recreate_index from main.utils import now_in_utc @@ -27,13 +27,13 @@ def add_arguments(self, parser): parser.add_argument( "--combined_hybrid", - dest=COMBINED_INDEX, + dest=HYBRID_COMBINED_INDEX, action="store_true", help="Recreate combined index for hybrid search", ) for object_type in sorted(ALL_INDEX_TYPES): - if object_type != COMBINED_INDEX: + if object_type != HYBRID_COMBINED_INDEX: parser.add_argument( f"--{object_type}s", dest=object_type, diff --git a/learning_resources_search/tasks.py b/learning_resources_search/tasks.py index 571766e118..0a8d1127ec 100644 --- a/learning_resources_search/tasks.py +++ b/learning_resources_search/tasks.py @@ -34,11 +34,10 @@ gen_content_file_id, percolate_matches_for_document, ) -from learning_resources_search.connection import get_vector_model_id from learning_resources_search.constants import ( - COMBINED_INDEX, CONTENT_FILE_TYPE, COURSE_TYPE, + HYBRID_COMBINED_INDEX, LEARNING_RESOURCE_TYPES, PERCOLATE_INDEX_TYPE, SEARCH_CONN_EXCEPTIONS, @@ -293,9 +292,9 @@ def index_learning_resources(ids, index_name, index_types): Args: ids(list of int): List of course id's + index_name (string): resource_type value or HYBRID_COMBINED_INDEX index_types (string): one of the values IndexestoUpdate. Whether the default index, the reindexing index or both need to be updated - resource_type (string): resource_type value for the learning resource objects """ try: @@ -540,7 +539,7 @@ def wrap_retry_exception(*exception_classes): @app.task(bind=True) -def start_recreate_index(self, indexes, remove_existing_reindexing_tags): # noqa: C901 +def start_recreate_index(self, indexes, remove_existing_reindexing_tags): """ Wipe and recreate index and mapping, and index all items. """ @@ -556,16 +555,6 @@ def start_recreate_index(self, indexes, remove_existing_reindexing_tags): # noq log.exception(error) return error - if COMBINED_INDEX in indexes: - vector_model_id = get_vector_model_id() - if not vector_model_id: - log.warning( - "No vector model is configured. Skipping hybrid index reindexing.", - ) - indexes.remove(COMBINED_INDEX) - if len(indexes) == 0: - return None - api.delete_orphaned_indexes( indexes, delete_reindexing_tags=remove_existing_reindexing_tags ) @@ -631,13 +620,13 @@ def start_recreate_index(self, indexes, remove_existing_reindexing_tags): # noq ) ] - if COMBINED_INDEX in indexes: + if HYBRID_COMBINED_INDEX in indexes: blocklisted_ids = load_course_blocklist() index_tasks = index_tasks + [ index_learning_resources.si( ids, - COMBINED_INDEX, + HYBRID_COMBINED_INDEX, index_types=IndexestoUpdate.reindexing_index.value, ) for ids in chunks( diff --git a/learning_resources_search/tasks_test.py b/learning_resources_search/tasks_test.py index 3e38c93c77..d7450b2af2 100644 --- a/learning_resources_search/tasks_test.py +++ b/learning_resources_search/tasks_test.py @@ -23,9 +23,9 @@ from learning_resources.views import FeaturedViewSet from learning_resources_search.api import gen_content_file_id from learning_resources_search.constants import ( - COMBINED_INDEX, CONTENT_FILE_TYPE, COURSE_TYPE, + HYBRID_COMBINED_INDEX, LEARNING_RESOURCE_TYPES, PROGRAM_TYPE, IndexestoUpdate, @@ -134,26 +134,20 @@ def test_system_exit_retry(mocker): @pytest.mark.parametrize( - ("indexes", "model_exists"), + "indexes", [ - (["course"], False), - (["program"], False), - (["combined_hybrid"], False), - (["combined_hybrid"], True), + ["course"], + ["program"], + ["combined_hybrid"], ], ) -def test_start_recreate_index(mocker, mocked_celery, user, indexes, model_exists): # noqa: PLR0912,PLR0915,C901 +def test_start_recreate_index(mocker, mocked_celery, user, indexes): # noqa: C901 """ recreate_index should recreate the OpenSearch index and reindex all data with it """ settings.OPENSEARCH_INDEXING_CHUNK_SIZE = 2 settings.OPENSEARCH_DOCUMENT_INDEXING_CHUNK_SIZE = 2 - model_value = "test_vector_model_id" if model_exists else None - mocker.patch( - "learning_resources_search.tasks.get_vector_model_id", return_value=model_value - ) - mock_blocklist = mocker.patch( "learning_resources_search.tasks.load_course_blocklist", return_value=[] ) @@ -208,96 +202,87 @@ def test_start_recreate_index(mocker, mocked_celery, user, indexes, model_exists finish_recreate_index_dict = {} - if not model_exists and COMBINED_INDEX in indexes: + with pytest.raises(mocked_celery.replace_exception_class): start_recreate_index.delay(indexes, remove_existing_reindexing_tags=False) - assert create_backing_index_mock.call_count == 0 - assert finish_recreate_index_mock.s.call_count == 0 - assert delete_orphaned_indexes_mock.call_count == 0 - else: - with pytest.raises(mocked_celery.replace_exception_class): - start_recreate_index.delay(indexes, remove_existing_reindexing_tags=False) - for doctype in [COURSE_TYPE, PROGRAM_TYPE, COMBINED_INDEX]: - if doctype in indexes: - finish_recreate_index_dict[doctype] = backing_index - create_backing_index_mock.assert_any_call(doctype) + for doctype in [COURSE_TYPE, PROGRAM_TYPE, HYBRID_COMBINED_INDEX]: + if doctype in indexes: + finish_recreate_index_dict[doctype] = backing_index + create_backing_index_mock.assert_any_call(doctype) - finish_recreate_index_mock.s.assert_called_once_with(finish_recreate_index_dict) + finish_recreate_index_mock.s.assert_called_once_with(finish_recreate_index_dict) - delete_orphaned_indexes_mock.assert_called_once_with( - indexes, delete_reindexing_tags=False - ) + delete_orphaned_indexes_mock.assert_called_once_with( + indexes, delete_reindexing_tags=False + ) - assert mocked_celery.group.call_count == 1 + assert mocked_celery.group.call_count == 1 - # Celery's 'group' function takes a generator as an argument. In order to make assertions about the items - # in that generator, 'list' is being called to force iteration through all of those items. - list(mocked_celery.group.call_args[0][0]) + # Celery's 'group' function takes a generator as an argument. In order to make assertions about the items + # in that generator, 'list' is being called to force iteration through all of those items. + list(mocked_celery.group.call_args[0][0]) - if COURSE_TYPE in indexes: - assert index_learning_resources_mock.si.call_count == 3 + if COURSE_TYPE in indexes: + assert index_learning_resources_mock.si.call_count == 3 - if PROGRAM_TYPE in indexes: - assert index_learning_resources_mock.si.call_count == 2 + if PROGRAM_TYPE in indexes: + assert index_learning_resources_mock.si.call_count == 2 - if COMBINED_INDEX in indexes and model_exists: - assert index_learning_resources_mock.si.call_count == 5 + if HYBRID_COMBINED_INDEX in indexes: + assert index_learning_resources_mock.si.call_count == 5 - if COMBINED_INDEX in indexes and not model_exists: - assert index_learning_resources_mock.si.call_count == 0 + if COURSE_TYPE in indexes or HYBRID_COMBINED_INDEX in indexes: + mock_blocklist.assert_called_once() + index_type = indexes[0] + index_learning_resources_mock.si.assert_any_call( + [courses[0].learning_resource_id, courses[1].learning_resource_id], + index_type, + index_types=IndexestoUpdate.reindexing_index.value, + ) + index_learning_resources_mock.si.assert_any_call( + [courses[2].learning_resource_id, courses[3].learning_resource_id], + index_type, + index_types=IndexestoUpdate.reindexing_index.value, + ) + index_learning_resources_mock.si.assert_any_call( + [courses[4].learning_resource_id, courses[5].learning_resource_id], + index_type, + index_types=IndexestoUpdate.reindexing_index.value, + ) - if COURSE_TYPE in indexes or (COMBINED_INDEX in indexes and model_exists): - mock_blocklist.assert_called_once() - index_type = indexes[0] - index_learning_resources_mock.si.assert_any_call( - [courses[0].learning_resource_id, courses[1].learning_resource_id], - index_type, - index_types=IndexestoUpdate.reindexing_index.value, - ) - index_learning_resources_mock.si.assert_any_call( - [courses[2].learning_resource_id, courses[3].learning_resource_id], - index_type, - index_types=IndexestoUpdate.reindexing_index.value, + if COURSE_TYPE in indexes: + for course in ocw_courses: + content_file_ids = ( + course.learning_resource.runs.first() + .content_files.order_by("id") + .values_list("id", flat=True) ) - index_learning_resources_mock.si.assert_any_call( - [courses[4].learning_resource_id, courses[5].learning_resource_id], - index_type, + index_files_mock.si.assert_any_call( + [content_file_ids[0], content_file_ids[1]], + course.learning_resource_id, index_types=IndexestoUpdate.reindexing_index.value, ) - if COURSE_TYPE in indexes: - for course in ocw_courses: - content_file_ids = ( - course.learning_resource.runs.first() - .content_files.order_by("id") - .values_list("id", flat=True) - ) - index_files_mock.si.assert_any_call( - [content_file_ids[0], content_file_ids[1]], - course.learning_resource_id, - index_types=IndexestoUpdate.reindexing_index.value, - ) - - index_files_mock.si.assert_any_call( - [content_file_ids[2]], - course.learning_resource_id, - index_types=IndexestoUpdate.reindexing_index.value, - ) - - if PROGRAM_TYPE in indexes or (COMBINED_INDEX in indexes and model_exists): - index_type = indexes[0] - index_learning_resources_mock.si.assert_any_call( - [programs[0].learning_resource_id, programs[1].learning_resource_id], - index_type, - index_types=IndexestoUpdate.reindexing_index.value, - ) - index_learning_resources_mock.si.assert_any_call( - [programs[2].learning_resource_id, programs[3].learning_resource_id], - index_type, + index_files_mock.si.assert_any_call( + [content_file_ids[2]], + course.learning_resource_id, index_types=IndexestoUpdate.reindexing_index.value, ) - assert mocked_celery.replace.call_count == 1 - assert mocked_celery.replace.call_args[0][1] == mocked_celery.chain.return_value + + if PROGRAM_TYPE in indexes or HYBRID_COMBINED_INDEX in indexes: + index_type = indexes[0] + index_learning_resources_mock.si.assert_any_call( + [programs[0].learning_resource_id, programs[1].learning_resource_id], + index_type, + index_types=IndexestoUpdate.reindexing_index.value, + ) + index_learning_resources_mock.si.assert_any_call( + [programs[2].learning_resource_id, programs[3].learning_resource_id], + index_type, + index_types=IndexestoUpdate.reindexing_index.value, + ) + assert mocked_celery.replace.call_count == 1 + assert mocked_celery.replace.call_args[0][1] == mocked_celery.chain.return_value @pytest.mark.parametrize( diff --git a/main/settings.py b/main/settings.py index 16ae7d841e..02ecb219c3 100644 --- a/main/settings.py +++ b/main/settings.py @@ -604,16 +604,6 @@ OPENSEARCH_MAX_SUGGEST_HITS = get_int("OPENSEARCH_MAX_SUGGEST_HITS", 1) OPENSEARCH_MAX_SUGGEST_RESULTS = get_int("OPENSEARCH_MAX_SUGGEST_RESULTS", 1) OPENSEARCH_SHARD_COUNT = get_int("OPENSEARCH_SHARD_COUNT", 2) -OPENSEARCH_VECTOR_MODEL_NAME = get_string( - 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