Vector based topics tagging for videos

learning_resources/etl/loaders_test.py

@@ -1465,9 +1465,14 @@ def test_load_video(mocker, video_exists, is_published, pass_topics):
         assert getattr(result, key) == value, f"Property {key} should equal {value}"
 
 
-def test_load_videos():
+def test_load_videos(mocker):
     """Verify that load_videos loads a list of videos"""
     assert Video.objects.count() == 0
+
+    mocker.patch(
+        "learning_resources_search.plugins.get_similar_topics_qdrant",
+        return_value=["topic1", "topic2"],
+    )
     video_resources = [video.learning_resource for video in VideoFactory.build_batch(5)]
     videos_data = [
         {
@@ -1493,6 +1498,10 @@ def test_load_playlist(mocker, playlist_exists):
         LearningResourceTopicFactory.create(name=topic["name"])
         for topic in expected_topics
     ]
+    mocker.patch(
+        "learning_resources_search.plugins.get_similar_topics_qdrant",
+        return_value=["topic1", "topic2"],
+    )
     mock_most_common_topics = mocker.patch(
         "learning_resources.etl.loaders.most_common_topics",
         return_value=expected_topics,
@@ -1905,6 +1914,10 @@ def test_course_with_unpublished_force_ingest_is_test_mode():
 
 @pytest.mark.django_db
 def test_load_articles(mocker, climate_platform):
+    mocker.patch(
+        "learning_resources_search.plugins.get_similar_topics_qdrant",
+        return_value=["topic1", "topic2"],
+    )
     articles_data = [
         {
             "title": "test",

learning_resources_search/api.py

@@ -35,7 +35,10 @@
     adjust_search_for_percolator,
     document_percolated_actions,
 )
-from vector_search.constants import RESOURCES_COLLECTION_NAME
+from vector_search.constants import (
+    RESOURCES_COLLECTION_NAME,
+    TOPICS_COLLECTION_NAME,
+)
 
 log = logging.getLogger(__name__)
 
@@ -830,6 +833,52 @@ def user_subscribed_to_query(
     )
 
 
+def get_similar_topics_qdrant(
+    resource: LearningResource, value_doc: dict, num_topics: int
+) -> list[str]:
+    from vector_search.encoders.utils import dense_encoder
+    from vector_search.utils import qdrant_client, vector_point_id
+
+    """
+    Get a list of similar topics based on vector similarity
+
+    Args:
+        value_doc (dict):
+            a document representing the data fields we want to search with
+        num_topics (int):
+            number of topics to return
+    Returns:
+        list of str:
+            list of topic values
+    """
+    encoder = dense_encoder()
+    client = qdrant_client()
+
+    response = client.retrieve(
+        collection_name=RESOURCES_COLLECTION_NAME,
+        ids=[vector_point_id(resource.readable_id)],
+        with_vectors=True,
+    )
+
+    embedding_context = "\n".join(
+        [value_doc[key] for key in value_doc if value_doc[key] is not None]
+    )
+    if response and len(response) > 0:
+        embeddings = response[0].vector.get(encoder.model_short_name())
+    else:
+        embeddings = encoder.embed(embedding_context)
+
+    return [
+        hit["name"]
+        for hit in _qdrant_similar_results(
+            input_query=embeddings,
+            num_resources=num_topics,
+            collection_name=TOPICS_COLLECTION_NAME,
+            score_threshold=0.2,
+        )
+    ]
+
+
 def get_similar_topics(
     value_doc: dict, num_topics: int, min_term_freq: int, min_doc_freq: int
 ) -> list[str]:
@@ -909,7 +958,12 @@ def get_similar_resources(
     )
 
 
-def _qdrant_similar_results(doc, num_resources):
+def _qdrant_similar_results(
+    input_query,
+    num_resources=6,
+    collection_name=RESOURCES_COLLECTION_NAME,
+    score_threshold=0,
+):
     """
     Get similar resources from qdrant
 
@@ -926,18 +980,18 @@ def _qdrant_similar_results(doc, num_resources):
     from vector_search.utils import (
         dense_encoder,
         qdrant_client,
-        vector_point_id,
     )
 
     encoder = dense_encoder()
     client = qdrant_client()
     return [
         hit.payload
         for hit in client.query_points(
-            collection_name=RESOURCES_COLLECTION_NAME,
-            query=vector_point_id(doc["readable_id"]),
+            collection_name=collection_name,
+            query=input_query,
             limit=num_resources,
             using=encoder.model_short_name(),
+            score_threshold=score_threshold,
         ).points
     ]
 
@@ -956,7 +1010,12 @@ def get_similar_resources_qdrant(value_doc: dict, num_resources: int):
         list of str:
             list of learning resources
     """
-    hits = _qdrant_similar_results(value_doc, num_resources)
+    from vector_search.utils import vector_point_id
+
+    hits = _qdrant_similar_results(
+        input_query=vector_point_id(value_doc["readable_id"]),
+        num_resources=num_resources,
+    )
     return (
         LearningResource.objects.for_search_serialization()
         .filter(

learning_resources_search/api_test.py

@@ -1,6 +1,6 @@
 """Search API function tests"""
 
-from unittest.mock import Mock
+from unittest.mock import MagicMock, Mock
 
 import pytest
 from freezegun import freeze_time
@@ -21,6 +21,7 @@
     generate_sort_clause,
     generate_suggest_clause,
     get_similar_topics,
+    get_similar_topics_qdrant,
     percolate_matches_for_document,
     relevant_indexes,
 )
@@ -3266,3 +3267,39 @@ def test_dev_mode(dev_mode):
         assert construct_search(search_params).to_dict().get("explain")
     else:
         assert construct_search(search_params).to_dict().get("explain") is None
+
+
+@pytest.mark.django_db
+def test_get_similar_topics_qdrant_uses_cached_embedding(mocker):
+    """
+    Test that get_similar_topics_qdrant uses a cached embedding when available
+    """
+    resource = MagicMock()
+    resource.readable_id = "test-resource"
+    value_doc = {"title": "Test Title", "description": "Test Description"}
+    num_topics = 3
+
+    mock_encoder = mocker.patch("vector_search.encoders.utils.dense_encoder")
+    encoder_instance = mock_encoder.return_value
+    encoder_instance.model_short_name.return_value = "test-model"
+    encoder_instance.embed.return_value = [0.1, 0.2, 0.3]
+
+    mock_client = mocker.patch("vector_search.utils.qdrant_client")
+    client_instance = mock_client.return_value
+
+    # Simulate a cached embedding in the response
+    client_instance.retrieve.return_value = [
+        MagicMock(vector={"test-model": [0.9, 0.8, 0.7]})
+    ]
+
+    mocker.patch(
+        "learning_resources_search.api._qdrant_similar_results",
+        return_value=[{"name": "topic1"}, {"name": "topic2"}],
+    )
+
+    result = get_similar_topics_qdrant(resource, value_doc, num_topics)
+
+    # Assert that embed was NOT called (cached embedding used)
+    encoder_instance.embed.assert_not_called()
+    # Assert that the result is as expected
+    assert result == ["topic1", "topic2"]

learning_resources_search/plugins.py

@@ -7,7 +7,7 @@
 from django.conf import settings as django_settings
 
 from learning_resources_search import tasks
-from learning_resources_search.api import get_similar_topics
+from learning_resources_search.api import get_similar_topics_qdrant
 from learning_resources_search.constants import (
     COURSE_TYPE,
     PERCOLATE_INDEX_TYPE,
@@ -125,11 +125,10 @@ def resource_similar_topics(self, resource) -> list[dict]:
             "full_description": resource.full_description,
         }
 
-        topic_names = get_similar_topics(
+        topic_names = get_similar_topics_qdrant(
+            resource,
             text_doc,
             settings.OPEN_VIDEO_MAX_TOPICS,
-            settings.OPEN_VIDEO_MIN_TERM_FREQ,
-            settings.OPEN_VIDEO_MIN_DOC_FREQ,
         )
         return [{"name": topic_name} for topic_name in topic_names]
 

learning_resources_search/plugins_test.py

@@ -128,19 +128,18 @@ def test_resource_similar_topics(mocker, settings):
     """The plugin function should return expected topics for a resource"""
     expected_topics = ["topic1", "topic2"]
     mock_similar_topics = mocker.patch(
-        "learning_resources_search.plugins.get_similar_topics",
+        "learning_resources_search.plugins.get_similar_topics_qdrant",
         return_value=expected_topics,
     )
     resource = LearningResourceFactory.create()
     topics = SearchIndexPlugin().resource_similar_topics(resource)
     assert topics == [{"name": topic} for topic in expected_topics]
     mock_similar_topics.assert_called_once_with(
+        resource,
         {
             "title": resource.title,
             "description": resource.description,
             "full_description": resource.full_description,
         },
         settings.OPEN_VIDEO_MAX_TOPICS,
-        settings.OPEN_VIDEO_MIN_TERM_FREQ,
-        settings.OPEN_VIDEO_MIN_DOC_FREQ,
     )

main/settings_course_etl.py

@@ -96,7 +96,7 @@
 # course catalog video etl settings
 OPEN_VIDEO_DATA_BRANCH = get_string("OPEN_VIDEO_DATA_BRANCH", "master")
 OPEN_VIDEO_USER_LIST_OWNER = get_string("OPEN_VIDEO_USER_LIST_OWNER", None)
-OPEN_VIDEO_MAX_TOPICS = get_int("OPEN_VIDEO_MAX_TOPICS", 3)
+OPEN_VIDEO_MAX_TOPICS = get_int("OPEN_VIDEO_MAX_TOPICS", 2)
 OPEN_VIDEO_MIN_TERM_FREQ = get_int("OPEN_VIDEO_MIN_TERM_FREQ", 1)
 OPEN_VIDEO_MIN_DOC_FREQ = get_int("OPEN_VIDEO_MIN_DOC_FREQ", 15)
 

vector_search/constants.py

@@ -3,6 +3,7 @@
 
 RESOURCES_COLLECTION_NAME = f"{settings.QDRANT_BASE_COLLECTION_NAME}.resources"
 CONTENT_FILES_COLLECTION_NAME = f"{settings.QDRANT_BASE_COLLECTION_NAME}.content_files"
+TOPICS_COLLECTION_NAME = f"{settings.QDRANT_BASE_COLLECTION_NAME}.topics"
 
 QDRANT_CONTENT_FILE_PARAM_MAP = {
     "key": "key",
@@ -43,6 +44,10 @@
 }
 
 
+QDRANT_TOPICS_PARAM_MAP = {
+    "name": "name",
+}
+
 QDRANT_LEARNING_RESOURCE_INDEXES = {
     "readable_id": models.PayloadSchemaType.KEYWORD,
     "resource_type": models.PayloadSchemaType.KEYWORD,
@@ -82,3 +87,7 @@
     "edx_block_id": models.PayloadSchemaType.KEYWORD,
     "url": models.PayloadSchemaType.KEYWORD,
 }
+
+QDRANT_TOPIC_INDEXES = {
+    "name": models.PayloadSchemaType.KEYWORD,
+}

vector_search/management/commands/sync_topic_embeddings.py

@@ -0,0 +1,27 @@
+"""Management command to update or create the topics collection in Qdrant"""
+
+from django.core.management.base import BaseCommand, CommandError
+
+from main.utils import clear_search_cache, now_in_utc
+from vector_search.tasks import sync_topics
+
+
+class Command(BaseCommand):
+    """Syncs embeddings for topics in Qdrant"""
+
+    help = "update or create the topics collection in Qdrant"
+
+    def handle(self, *args, **options):  # noqa: ARG002
+        """Sync the topics collection"""
+        task = sync_topics.apply()
+        self.stdout.write("Waiting on task...")
+        start = now_in_utc()
+        error = task.get()
+        if error:
+            msg = f"Geenerate embeddings errored: {error}"
+            raise CommandError(msg)
+        clear_search_cache()
+        total_seconds = (now_in_utc() - start).total_seconds()
+        self.stdout.write(
+            f"Embeddings generated and stored, took {total_seconds} seconds"
+        )

vector_search/tasks.py

@@ -32,7 +32,11 @@
     chunks,
     now_in_utc,
 )
-from vector_search.utils import embed_learning_resources, remove_qdrant_records
+from vector_search.utils import (
+    embed_learning_resources,
+    embed_topics,
+    remove_qdrant_records,
+)
 
 log = logging.getLogger(__name__)
 
@@ -362,3 +366,11 @@ def remove_run_content_files(run_id):
             for ids in chunks(content_file_ids, chunk_size=settings.QDRANT_CHUNK_SIZE)
         ]
     )
+
+
+@app.task
+def sync_topics():
+    """
+    Sync topics to the Qdrant collection
+    """
+    embed_topics()