Update README.md

README.md

@@ -9,32 +9,30 @@ pinned: false
 
 ## Semantic Kernel Chatbot with Cosmos DB
 A sophisticated AI-powered chatbot built with Microsoft Semantic Kernel that intelligently queries databases using both predefined SQL templates and dynamic query generation. The system includes RAG capabilities, analytics dashboards, and semantic query clustering.
-Overview
+### Overview
 This project implements an intelligent database query assistant that leverages Large Language Models (LLMs) to interact with data stored in Azure Cosmos DB. The chatbot can understand natural language queries and either use predefined SQL templates or generate custom queries on the fly.
 
 ## Key Features
 ### Intelligent Query System
 
-Semantic Kernel Integration: Built on Microsoft Semantic Kernel framework for orchestrating AI workflows
-Dual Query Modes:
-
-Template-based: Predefined SQL queries with parameter filling by the LLM
-Dynamic Generation: LLM generates custom SQL queries for complex or novel requests
-Cosmos DB Backend: All data stored and managed in Azure Cosmos DB
-Query History Storage: All queries stored in Cosmos DB for continuous learning and semantic clustering
+- Semantic Kernel Integration: Built on Microsoft Semantic Kernel framework for orchestrating AI workflows
+- Template-based: Predefined SQL queries with parameter filling by the LLM
+- Dynamic Generation: LLM generates custom SQL queries for complex or novel requests
+- Cosmos DB Backend: All data stored and managed in Azure Cosmos DB
+- Query History Storage: All queries stored in Cosmos DB for continuous learning and semantic clustering
 
 ### RAG Implementation
 
-Local Ollama Integration: Run models locally for enhanced privacy and reduced costs
-Retrieval-Augmented Generation: Improves response accuracy by retrieving relevant context
+- Local Ollama Integration: Run models locally for enhanced privacy and reduced costs
+- Retrieval-Augmented Generation: Improves response accuracy by retrieving relevant context
 
 
 ### Analytics Dashboard
 
-Query Monitoring: Track all queries made to the system
-Error Tracking: Comprehensive error monitoring and logging
-Semantic Clustering: Queries are semantically clustered using RAG to identify patterns and common use cases
-Usage Insights: Understand how users interact with the chatbot
+- Query Monitoring: Track all queries made to the system
+- Error Tracking: Comprehensive error monitoring and logging
+- Semantic Clustering: Queries are semantically clustered using RAG to identify patterns and common use cases
+- Usage Insights: Understand how users interact with the chatbot
 
 ## Architecture
 **Semantic Kernel Chatbot**