set top_n, top_k tp 1

app.py

@@ -80,7 +80,7 @@ recursive_index_faiss = VectorStoreIndex(nodes=base_nodes+objects, storage_conte
 # 1. Initalize our reranker using `FlagEmbeddingReranker` powered by the `BAAI/bge-reranker-large`.
 # 2. Set up our recursive query engine!
 reranker = FlagEmbeddingReranker(
-    top_n=5,
+    top_n=1,
     model="BAAI/bge-reranker-large",
 )
 
@@ -96,7 +96,7 @@ user_template = """ Think through your response step by step."""
 #user_query = "Who are the E-VP, Operations - and how old are they?"
 def resursive_fn(reranker):
     recursive_query_engine = recursive_index_faiss.as_query_engine(
-        similarity_top_k=5,
+        similarity_top_k=1,
         node_postprocessors=[reranker],
         verbose=True
     )