Update app.py

app.py

@@ -32,12 +32,8 @@ def retrieve_thoughts(query, n, db):
 
     # print(db.similarity_search_with_score(query = query, k = k, fetch_k = k*10))
     #filter = {'Product Name': prod}
-    if yt:
-        docs_with_score = db.similarity_search_with_score(query = query, filter = {'categories': None},k = len(db.index_to_docstore_id.values()), fetch_k = len(db.index_to_docstore_id.values()))
-    else:
-        # Make it include only news + people or just create a whole new db with articles having their common key, and yt/podcasts having their own.
-        
-        docs_with_score = db.similarity_search_with_score(query = query, k = len(db.index_to_docstore_id.values()), fetch_k = len(db.index_to_docstore_id.values()))
+    
+    docs_with_score = db.similarity_search_with_score(query = query, k = len(db.index_to_docstore_id.values()), fetch_k = len(db.index_to_docstore_id.values()))
     
     df = pd.DataFrame([dict(doc[0])['metadata'] for doc in docs_with_score], )
     df = pd.concat((df, pd.DataFrame([dict(doc[0])['page_content'] for doc in docs_with_score], columns = ['page_content'])), axis = 1)
@@ -58,8 +54,8 @@ def retrieve_thoughts(query, n, db):
     tier_1_adjusted['score'] = score
     tier_1_adjusted.sort_values("score", inplace = True)
 
-    if n:
-      tier_1_adjusted = tier_1_adjusted[:min(len(tier_1_adjusted), n)]
+    
+    tier_1_adjusted = tier_1_adjusted[:min(len(tier_1_adjusted), 50)]
 
     return {'tier 1':tier_1_adjusted, }