Update app.py

app.py

@@ -104,25 +104,27 @@ async def chat(query,history,sources,reports,subtype,year):
 
     ##------------------------decide which collection to fetch------------------------------
     if len(reports) == 0:
-        vectorstore = get_local_qdrant(client,sources)
+        print("done")
+        #vectorstore = get_local_qdrant(client,sources)
     else:
-        vectorstore = get_local_qdrant(client,"allreports")
+        print("done")
+        #vectorstore = get_local_qdrant(client,"allreports")
 
     ##------------------------------get context----------------------------------------------------   
-    context_retrieved_lst = []
-    question_lst= [query]
-    for question in question_lst:
-        retriever = vectorstore.as_retriever(
-          search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.6, "k": 3})
-
-        context_retrieved = retriever.invoke(question)
-
-        def format_docs(docs):
-            return "\n\n".join(doc.page_content for doc in docs)
-
-        context_retrieved_formatted = format_docs(context_retrieved)
-        context_retrieved_lst.append(context_retrieved_formatted)
-    print(context_retrieved_lst)
+    #context_retrieved_lst = []
+    #question_lst= [query]
+    #for question in question_lst:
+    #    retriever = vectorstore.as_retriever(
+    #      search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.6, "k": 3})
+    #    
+    #    context_retrieved = retriever.invoke(question)
+    #
+    #    def format_docs(docs):
+    #        return "\n\n".join(doc.page_content for doc in docs)
+    
+    #    context_retrieved_formatted = format_docs(context_retrieved)
+    #    context_retrieved_lst.append(context_retrieved_formatted)
+    # print(context_retrieved_lst)