Update app.py

app.py

@@ -88,13 +88,20 @@ st.sidebar.caption(f"Session ID: {genuuid()}")
 
 
 # Main chat interface
-st.markdown("""Query translation in a Retrieval-Augmented Generation (RAG) architecture is the process where an LLM acts as a translator between the user's natural language input and the underlying retrieval system. When a user sends a message, the query translator takes this input and:
+st.markdown("""## Query Translation in RAG Architecture
 
-** Adds Context: Enriches the user's input with relevant context (e.g., expanding vague questions or specifying details) to make it more precise.
-** Converts to Concise Query: Reformulates the input into a succinct and effective query optimized for the retrieval system's semantic search capabilities.
+Query translation in a Retrieval-Augmented Generation (RAG) architecture is the process where an LLM acts as a translator between the user's natural language input and the retrieval system. 
 
-This ensures that the retrieval system gets a clear and focused query, increasing the relevance of the information it retrieves. The query translator essentially bridges the gap between human conversational language and the technical requirements of a semantic retrieval system.""")
+### Key Functions of Query Translation:
+1. **Adds Context**  
+   The LLM enriches the user's input with relevant context (e.g., expanding vague questions or specifying details) to make it more precise.
+   
+2. **Converts to Concise Query**  
+   The LLM reformulates the input into a succinct and effective query optimized for the retrieval system's semantic search capabilities.
 
+### Purpose
+This ensures that the retrieval system receives a clear and focused query, increasing the relevance of the information it retrieves. The query translator acts as a bridge between human conversational language and the technical requirements of a semantic retrieval system.
+"""
 # User ID Input
 user_id = st.text_input("Experiment ID:", key="user_id")