Update hybrid search weights

chat/src/handlers/opensearch_neural_search.py

@@ -28,19 +28,19 @@ def __init__(
         self.text_field = text_field
 
     def similarity_search(
-        self, query: str, k: int = 10, subquery: Any = None, **kwargs: Any
+        self, query: str, k: int = 10, **kwargs: Any
     ) -> List[Document]:
         """Return docs most similar to the embedding vector."""
         docs_with_scores = self.similarity_search_with_score(
-            query, k, subquery, **kwargs
+            query, k, **kwargs
         )
         return [doc[0] for doc in docs_with_scores]
 
     def similarity_search_with_score(
-        self, query: str, k: int = 10, subquery: Any = None, **kwargs: Any
+        self, query: str, k: int = 10, **kwargs: Any
     ) -> List[Tuple[Document, float]]:
         """Return docs most similar to query."""
-        dsl = hybrid_query(query=query, model_id=self.model_id, vector_field=self.vector_field, k=k, subquery=subquery, **kwargs)
+        dsl = hybrid_query(query=query, model_id=self.model_id, vector_field=self.vector_field, k=k, **kwargs)
         response = self.client.search(index=self.index, body=dsl, params={"search_pipeline": self.search_pipeline} if self.search_pipeline else None)
         documents_with_scores = [
             (

chat/src/helpers/hybrid_query.py

@@ -11,22 +11,18 @@ def filter(query: dict):
         }
     }
 
-def hybrid_query(query: str, model_id: str, vector_field: str = "embedding", k: int = 10, subquery: Any = None, **kwargs: Any):
-    if subquery:
-        weights = [0.5, 0.3, 0.2]
-    else:
-        weights = [0.7, 0.3]
-
+def hybrid_query(query: str, model_id: str, vector_field: str = "embedding", k: int = 10, **kwargs: Any):
     result = {
         "size": k,
         "query": {
             "hybrid": {
                 "queries": [
                     filter({
                         "query_string": {
-                            "default_operator": "AND", 
-                            "fields": ["title^5", "all_controlled_labels", "all_ids^5"], 
-                            "query": query
+                            "default_operator": "AND",
+                            "fields": ["all_titles^5", "all_controlled_labels", "all_ids^5"], 
+                            "query": query,
+                            "analyzer": "english"
                         }
                     }),
                     filter({
@@ -47,7 +43,7 @@ def hybrid_query(query: str, model_id: str, vector_field: str = "embedding", k:
                     "normalization-processor": {
                         "combination": {
                             "parameters": {
-                                "weights": weights
+                                "weights": [0.25, 0.75]
                             },
                             "technique": "arithmetic_mean"
                         },
@@ -60,12 +56,7 @@ def hybrid_query(query: str, model_id: str, vector_field: str = "embedding", k:
         }
     }
 
-    if subquery:
-        result["query"]["hybrid"]["queries"].append(filter(subquery))
-
     for key, value in kwargs.items():
         result[key] = value
-        
+
     return result
-
-

chat/src/helpers/response.py

@@ -39,16 +39,7 @@ def get_and_send_original_question(docs):
 
     def prepare_response(self):
         try:
-            subquery = { 
-                "match": {
-                    "all_titles":  {
-                        "query": self.config.question, 
-                        "operator": "AND",
-                        "analyzer": "english"
-                    }
-                }
-            }
-            retriever = self.config.opensearch.as_retriever(search_type="similarity", search_kwargs={"k": self.config.k, "subquery": subquery, "_source": {"excludes": ["embedding"]}})
+            retriever = self.config.opensearch.as_retriever(search_type="similarity", search_kwargs={"k": self.config.k, "_source": {"excludes": ["embedding"]}})
             chain = (
                 {"context": retriever, "question": RunnablePassthrough()}
                 | self.original_question_passthrough()

chat/test/helpers/test_hybrid_query.py

@@ -6,19 +6,17 @@
 
 class TestFunction(TestCase):
     def test_hybrid_query(self):
-        subquery = { "term": { "title": { "value": "The Title" } } }
-        dsl = hybrid_query("Question?", "MODEL_ID", k=10, subquery=subquery)
+        dsl = hybrid_query("Question?", "MODEL_ID", k=10)
         subject = dsl["query"]["hybrid"]["queries"]
 
         checks = [
           (lambda x: x["query_string"]["query"], "Question?"),
-          (lambda x: x["neural"]["embedding"]["model_id"], "MODEL_ID"),
-          (lambda x: x["term"]["title"]["value"], "The Title")
+          (lambda x: x["neural"]["embedding"]["model_id"], "MODEL_ID")
         ]
 
-        self.assertEqual(len(subject), 3)
+        self.assertEqual(len(subject), 2)
 
-        for i in range(3):
+        for i in range(2):
           lookup, expected = checks[i]
           queries = subject[i]["bool"]["must"]
           self.assertEqual(lookup(queries[0]), expected)

template.yaml

@@ -1159,3 +1159,7 @@ Resources:
       AuthorizationType: NONE
       RouteKey: GET /docs/v2/{proxy+}
       Target: !Sub "integrations/${docsIntegration}"
+Outputs:
+  Endpoint:
+    Description: "The base API endpoint for the stack"
+    Value: !Sub "https://${CustomDomainHost}.${CustomDomainZone}/api/v2"