try hyde query

app.py

@@ -1,17 +1,22 @@
 from datasets import load_dataset
 from llama_index.core import VectorStoreIndex, Document
+from llama_index.core.indices.query.query_transform.base import (
+    HyDEQueryTransform,
+)
+from llama_index.core.query_engine import TransformQueryEngine
 import gradio as gr
 
 # dataset=load_dataset("davidr70/megillah_english_sugyot", split="train")
 dataset=load_dataset("davidr70/megilla_sugyot_merged", split="train")
 documents = [Document(text=item['content'], metadata=item['metadata']) for item in dataset]
 
-
+hyde = HyDEQueryTransform(include_original=True)
 #documents = SimpleDirectoryReader("data").load_data()
 index = VectorStoreIndex.from_documents(documents)
 retriever = index.as_retriever(
     similarity_top_k=7,  # Number of hits to return
-    vector_store_query_mode="default"  # Basic semantic search
+    vector_store_query_mode="default",  # Basic semantic search
+    query_transform=hyde
 )