Update app.py

app.py

@@ -259,43 +259,57 @@ def aisearch():
 
 @app.route('/agent/chat/suggestion', methods=['POST'])
 def process_json():
-    print("RQST")
     print(f"\n{'*' * 100}\n")
     print("Request Received >>>>>>>>>>>>>>>>>>", datetime.now().strftime("%H:%M:%S"))
     content_type = request.headers.get('Content-Type')
-    if (content_type == 'application/json'):
+    if content_type == 'application/json':
         requestQuery = request.get_json()
         print(type(requestQuery))
-        custDetailsPresent=False
-        customerName=""
-        customerDistrict=""
-        if("custDetails" in requestQuery):
+        custDetailsPresent = False
+        customerName = ""
+        customerDistrict = ""
+        if "custDetails" in requestQuery:
             custDetailsPresent = True
-            customerName=requestQuery['custDetails']['cName']
-            customerDistrict=requestQuery['custDetails']['cDistrict']
+            customerName = requestQuery['custDetails']['cName']
+            customerDistrict = requestQuery['custDetails']['cDistrict']
 
+        selectedLLMID=defaultLLMID
+        if "llmID" in requestQuery:
+            selectedLLMID=(int) (requestQuery['llmID'])
         print("chain initiation")
-        chainRAG=getRAGChain(customerName, customerDistrict, custDetailsPresent,vectordb)
+        chainRAG = getRAGChain(customerName, customerDistrict, custDetailsPresent, vectordb,selectedLLMID)
         print("chain created")
         suggestionArray = []
+        searchResultArray = []
 
         for index, query in enumerate(requestQuery['message']):
-            #message = answering(query)
-            relevantDoc = vectordb.similarity_search_with_score(query)
-            for doc in relevantDoc:
+            # message = answering(query)
+
+            relevantDoc = vectordb.similarity_search_with_score(query, distance_metric="cos", k=3)
+            print("Printing Retriever Docs")
+            for doc in getRetriever(vectordb).get_relevant_documents(query):
+                searchResult = {}
                 print(f"\n{'-' * 100}\n")
-                print("Document Source>>>>>>  " + doc[len(doc) - 2].metadata['source'] + "\n\n")
-                print("Page Content>>>>>> " + doc[len(doc) - 2].page_content + "\n\n")
-                print("Similarity Score>>>> " + str(doc[len(doc) - 1]))
+                searchResult['documentSource'] = doc.metadata['source']
+                searchResult['pageContent'] = doc.page_content
+                print(doc)
+                print("Document Source>>>>>>  " + searchResult['documentSource'] + "\n\n")
+                print("Page Content>>>>>> " + searchResult['pageContent'] + "\n\n")
                 print(f"\n{'-' * 100}\n")
+                searchResultArray.append(searchResult)
+            print("Printing Retriever Docs Ended")
+
+            print("Chain Run Started >>>>>>>>>>>>>>>>>>", datetime.now().strftime("%H:%M:%S"))
             message = chainRAG.run({"query": query})
-            print("query:",query)
+            print("Chain Run Completed >>>>>>>>>>>>>>>>>>", datetime.now().strftime("%H:%M:%S"))
+            print("query:", query)
             print("Response:", message)
             if "I don't know" in message:
-                 message = "Dear Sir/ Ma'am, Could you please ask questions relevant to Jio?"
-            responseJSON={"message":message,"id":index}
+                message = "Dear Sir/ Ma'am, Could you please ask questions relevant to Jio?"
+            responseJSON = {"message": message, "id": index}
             suggestionArray.append(responseJSON)
-        return jsonify(suggestions=suggestionArray)
+        print("Response Sent >>>>>>>>>>>>>>>>>>", datetime.now().strftime("%H:%M:%S"))
+        return jsonify(suggestions=suggestionArray, searchResult=searchResultArray)
     else:
         return 'Content-Type not supported!'