change ksize in RAG

RAG/rag_DocumentSearcher.py

@@ -29,13 +29,29 @@ def query_(awsauth,inputs, session_id,search_types):
             "processed_element_embedding_bedrock-multimodal","processed_element_embedding_sparse","image_encoding","processed_element_embedding"
         ]
         },
-        "query":  {
-            "knn": {
-                "processed_element_embedding_bedrock-multimodal": {
-                    "vector": embedding, 
-                    "k": k}
+        "query": { # exact knn search
+                "script_score": {
+                    "query": {
+                    "match_all": {}
+                    },
+                    "script": {
+                    "source": "knn_score",
+                    "lang": "knn",
+                    "params": {
+                        "field": "processed_element_embedding_bedrock-multimodal",
+                        "query_value": embedding,
+                        "space_type": "cosinesimil"
+                    }
+                    }
                 }
-        }
+                } 
+        #         {   #approximate knn search
+        #     "knn": {
+        #         "processed_element_embedding_bedrock-multimodal": {
+        #             "vector": embedding, 
+        #             "k": k}
+        #         }
+        # }
     }
 
     path = st.session_state.input_index+"_mm/_search"
@@ -141,14 +157,29 @@ def query_(awsauth,inputs, session_id,search_types):
     if('Vector Search' in search_types):
         
         embedding  = invoke_models.invoke_model(question)
-        
-        vector_payload = {
-            "knn": {
-                 "processed_element_embedding": {
-                     "vector": embedding, 
-                     "k": 2}
-                 }
-                        }
+        vector_payload = {   # exact knn search
+                "script_score": {
+                    "query": {
+                    "match_all": {}
+                    },
+                    "script": {
+                    "source": "knn_score",
+                    "lang": "knn",
+                    "params": {
+                        "field": "processed_element_embedding",
+                        "query_value": embedding,
+                        "space_type": "cosinesimil"
+                    }
+                    }
+                }
+                }
+        # vector_payload = {   # aproximate knn search
+        #     "knn": {
+        #          "processed_element_embedding": {
+        #              "vector": embedding, 
+        #              "k": 2}
+        #          }
+        #                 }
                 
         hybrid_payload["query"]["hybrid"]["queries"].append(vector_payload)