The session will be on how to harness Azure's powerful capabilities to build an innovative and efficient tool for analysis across multiple documents.

1. Introduction to Multi-Document RAG (5 minutes):

Overview of Multi-Document RAG and its significance in research.
Explanation of how RAG models combine retrieval and generation for contextually rich responses.

2.Understanding Azure Services (5 minutes):

Overview of Azure services including Azure Cognitive Search, Azure Functions, and Azure Web Apps.
Explanation of how Azure facilitates scalable, reliable, and secure application development.

3.Data Preparation and Ingestion (5 minutes):

Discussion on preparing documents for ingestion, including formatting and preprocessing.
Demonstration of ingesting multiple documents into Azure Cognitive Search.

4.Building the Retrieval Component (10 minutes):

Explanation of how to leverage Azure Cognitive Search for document retrieval based on user queries.
Walkthrough of implementing search queries and filtering criteria to retrieve relevant documents.

5.Implementing the RAG Model (10 minutes):

Overview of integrating the OpenAI API for RAG model inference.
Demonstration of invoking the RAG model within Azure Functions to generate contextually relevant responses based on retrieved documents.

6.User Interface Development (5 minutes):

Introduction to building a user-friendly interface using Azure Web Apps.
Demonstration of designing a responsive web interface for querying research topics and displaying RAG-generated responses.

7.Scalability and Performance Considerations (3 minutes):

Discussion on optimizing application performance and scalability using Azure's auto-scaling capabilities.
Explanation of best practices for deploying and monitoring the application to ensure optimal performance.