Update app.py

app.py

@@ -16,7 +16,7 @@ import pinecone
 from results import results_agent
 from filter import filter_agent
 from reranker import reranker
-from utils import build_filter
+from utils import build_filter, clean_pinecone
 from router import routing_agent
 
 OPENAI_API = st.secrets["OPENAI_API"]
@@ -44,8 +44,7 @@ class_time = st.slider(
 
 units = st.slider(
     "Number of units",
-    1, 4,
-    value = (1, 4)
+    1, 4, 4
 )
 
 days = st.multiselect("What days are you free?",
@@ -57,6 +56,8 @@ days = st.multiselect("What days are you free?",
 assistant = st.chat_message("assistant")
 initial_message = "How can I help you today?"
 
+
+
 def get_rag_results(prompt):
     '''
 1. Remove filters from the prompt to optimize success of the RAG-based step.
@@ -86,13 +87,15 @@ def get_rag_results(prompt):
   ## Query the pinecone database
     response = index.query(
         vector = embeddings.embed_query(query),
-        top_k = 25,
+        top_k = 45,
         filter = query_filter,
         include_metadata = True
     )
+
+    response, additional_metadata = clean_pinecone(response)
     response = reranker(query, response) # BERT cross encoder for ranking
 
-    return response
+    return response, additional_metadata
 
     
 
@@ -120,8 +123,8 @@ if prompt := st.chat_input("What kind of class are you looking for?"):
 
         if route == "1":
         ## Option for accessing Vector DB
-            rag_response = get_rag_results(prompt)
-            result_query = 'Original Query:' + prompt + 'Query Results:' + str(rag_response)
+            rag_response, additional_metadata = get_rag_results(prompt)
+            result_query = 'Original Query:' + prompt + 'Query Results:' + str(rag_response) + '\n Additional Class Times:' + str(additional_metadata)
             assistant_response = results_agent(result_query, OPENAI_API)
         else:    
         ## Option if not accessing Database