Add an onboarding topic-aware user embedder with various embedding sources and strategies

package.json

@@ -3,6 +3,6 @@
     "serverless-python-requirements": "^6.1.0"
   },
   "dependencies": {
-    "serverless": "^3.39.0"
+    "serverless": "^3.40.0"
   }
 }

scripts/local_req.py

@@ -0,0 +1,128 @@
+# Simulates a request to the recommender without requiring Serverless
+import warnings
+
+from poprox_concepts.api.recommendations import RecommendationResponse
+from poprox_recommender.handler import generate_recs
+from poprox_recommender.paths import project_root
+from poprox_recommender.topics import extract_general_topics
+
+warnings.filterwarnings("ignore")
+
+
+if __name__ == "__main__":
+    with open(project_root() / "tests/request_data/onboarding.json", "r") as req_file:
+        raw_json = req_file.read()
+
+    event_nrms = {
+        "body": raw_json,
+        "queryStringParameters": {"pipeline": "nrms"},
+        "isBase64Encoded": False,
+    }
+    event_static = {
+        "body": raw_json,
+        "queryStringParameters": {"pipeline": "nrms-topics-static"},
+        "isBase64Encoded": False,
+    }
+    event_candidate = {
+        "body": raw_json,
+        "queryStringParameters": {"pipeline": "nrms-topics-candidate"},
+        "isBase64Encoded": False,
+    }
+    event_clicked = {
+        "body": raw_json,
+        "queryStringParameters": {"pipeline": "nrms-topics-clicked"},
+        "isBase64Encoded": False,
+    }
+    event_hybrid = {
+        "body": raw_json,
+        "queryStringParameters": {"pipeline": "nrms-topics-hybrid"},
+        "isBase64Encoded": False,
+    }
+    event_rrf_static_candidate = {
+        "body": raw_json,
+        "queryStringParameters": {"pipeline": "nrms_rrf_static_candidate"},
+        "isBase64Encoded": False,
+    }
+    event_rrf_static_clicked = {
+        "body": raw_json,
+        "queryStringParameters": {"pipeline": "nrms_rrf_static_clicked"},
+        "isBase64Encoded": False,
+    }
+
+    response_nrms = generate_recs(event_nrms, {})
+    response_nrms = RecommendationResponse.model_validate_json(response_nrms["body"])
+
+    response_static = generate_recs(event_static, {})
+    response_static = RecommendationResponse.model_validate_json(response_static["body"])
+
+    response_candidate = generate_recs(event_candidate, {})
+    response_candidate = RecommendationResponse.model_validate_json(response_candidate["body"])
+
+    response_clicked = generate_recs(event_clicked, {})
+    response_clicked = RecommendationResponse.model_validate_json(response_clicked["body"])
+
+    response_hybrid = generate_recs(event_hybrid, {})
+    response_hybrid = RecommendationResponse.model_validate_json(response_hybrid["body"])
+
+    response_rrf_static_candidate = generate_recs(event_rrf_static_candidate, {})
+    response_rrf_static_candidate = RecommendationResponse.model_validate_json(response_rrf_static_candidate["body"])
+
+    response_rrf_static_clicked = generate_recs(event_rrf_static_clicked, {})
+    response_rrf_static_clicked = RecommendationResponse.model_validate_json(response_rrf_static_clicked["body"])
+
+    for profile_id, recs in response_nrms.recommendations.items():
+        print("\n")
+        print(f"Recs for {profile_id}:")
+        print(f"{event_nrms['queryStringParameters']['pipeline']}")
+
+        for idx, article in enumerate(recs):
+            article_topics = extract_general_topics(article)
+            print(f"{idx + 1}. {article.headline} {article_topics}")
+
+    for profile_id, recs in response_static.recommendations.items():
+        print("\n")
+        print(f"{event_static['queryStringParameters']['pipeline']}")
+
+        for idx, article in enumerate(recs):
+            article_topics = extract_general_topics(article)
+            print(f"{idx + 1}. {article.headline} {article_topics}")
+
+    for profile_id, recs in response_candidate.recommendations.items():
+        print("\n")
+        print(f"{event_candidate['queryStringParameters']['pipeline']}")
+
+        for idx, article in enumerate(recs):
+            article_topics = extract_general_topics(article)
+            print(f"{idx + 1}. {article.headline} {article_topics}")
+
+    for profile_id, recs in response_clicked.recommendations.items():
+        print("\n")
+        print(f"{event_clicked['queryStringParameters']['pipeline']}")
+
+        for idx, article in enumerate(recs):
+            article_topics = extract_general_topics(article)
+            print(f"{idx + 1}. {article.headline} {article_topics}")
+
+    for profile_id, recs in response_hybrid.recommendations.items():
+        print("\n")
+        print(f"{event_hybrid['queryStringParameters']['pipeline']}")
+
+        for idx, article in enumerate(recs):
+            article_topics = extract_general_topics(article)
+            print(f"{idx + 1}. {article.headline} {article_topics}")
+
+    for profile_id, recs in response_rrf_static_candidate.recommendations.items():
+        print("\n")
+        print(f"{event_rrf_static_candidate['queryStringParameters']['pipeline']}")
+
+        for idx, article in enumerate(recs):
+            article_topics = extract_general_topics(article)
+            print(f"{idx + 1}. {article.headline} {article_topics}")
+
+    for profile_id, recs in response_rrf_static_clicked.recommendations.items():
+        print("\n")
+        print(f"{event_rrf_static_clicked['queryStringParameters']['pipeline']}")
+
+        for idx, article in enumerate(recs):
+            article_topics = extract_general_topics(article)
+            print(f"{idx + 1}. {article.headline} {article_topics}")