Update app.py

Add metadata filters for pinecone

app.py

@@ -48,6 +48,11 @@ units = st.slider(
     value = (1, 4)
 )
 
+days = st.checkbox("What days are you free?",
+               options = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat"],
+               default = None,
+               placeholder = "Any day"
+                )
 
 assistant = st.chat_message("assistant")
 initial_message = "How can I help you today?"
@@ -58,23 +63,39 @@ def get_rag_results(prompt):
 2. Query the Pinecone DB and return the top 25 results based on cosine similarity
 3. Rerank the results from vector DB using a BERT-based cross encoder
     '''
-    query = prompt
-    response = filter_agent(prompt, OPENAI_API)
+    query = filter_agent(prompt, OPENAI_API)
+
+  ##Get metadata filters  
+    days_filter = list()
     query_filter = {
-        "Units": str(int(units)) + ".0 units",
-        "start": ${"gte": str(class_time[0])},
-        "end": ${"lte": str(class_time[1])}
+        "start": {"$gte": str(class_time[0])},
+        "end": {"$lte": str(class_time[1])}
     }
+
+    if units != "any":
+        query_filter["units"] = str(int(units)) + ".0 units"
+
+    if len(days) > 0:
+        for i in range(len(days)):
+            days_filter.append(days[i])
+            for j in range(i+1, len(days)):
+                two_day = days[i] + ", " +  days[j]
+                days_filter.append(two_day)
+        query_filter["days"] = {"$in": days_filter}
+
+  ## Query the pinecone database
     response = index.query(
         vector = embeddings.embed_query(query),
         top_k = 25,
-        # filter = query_filter,
+        filter = query_filter,
         include_metadata = True
     )
     response = reranker(query, response) # BERT cross encoder for ranking
 
     return response
 
+    
+
 if "messages" not in st.session_state:
     st.session_state.messages = []
     with st.chat_message("assistant"):