Added Azure Overview section

Added reasoning as to why use Azure Cosmos DB / MongoDB vCore as the vector store.

Author: codingbandit

00_Azure_Overview/README.md

@@ -0,0 +1 @@
+# Azure Overview

08_Vector_Search_Cosmos_DB/README.md

@@ -20,6 +20,10 @@ In this example, assume textual data is vectorized and stored within an Azure Co
 
 ![A typical vector search request in a RAG scenario depicts an incoming message getting vectorized and used as input to a vector store index search. Multiple results of the vector search are used to build a prompt fed to the LLM. The LLM returns a response to the requestor.](media/vector_search_flow.png)
 
+## Why use Azure Cosmos DB for MongoDB vCore as a vector store?
+
+It is common practice to store vectorized data in a dedicated vector store as vector search indexing is not a common capability of most databases. However, this introduces additional complexity to the solution as the data must be stored in two different locations. Azure Cosmos DB for MongoDB vCore supports vector search indexing, which means that the vectorized data can be stored in the same document as the original data. This reduces the complexity of the solution and allows for a single database to be used for both the vector store and the original data.
+
 ## Lab 3 - Use vector search on embeddings in Azure Cosmos DB for MongoDB vCore
 
 In this lab, a notebook demonstrates how to add an embedding field to a document, create a vector search index, and perform a vector search query. The notebook ends with a demonstration of utilizing vector search with an LLM in a RAG scenario.

11_Prompt_Engineering/README.md

@@ -10,15 +10,7 @@
 
 [LangChain](https://www.langchain.com/) is an open source framework designed to simplify the creation of applications using large language models (LLMs).
 
-A chain in LangChain is an end-to-end wrapper around multiple individual components executed in a defined order. Chains allow you to go beyond just a single API call to a language model and instead chain together multiple calls in a logical sequence. Chains can perform various natural language processing tasks, such as content generation, summarization, question answering, and more. There are three types of chains in LangChain: LLMChain, Sequential Chain, and Router Chain. Each type has its own structure, functionality, and use cases.
-
-Source: Conversation with Bing, 12/22/2023
-(1) Chaining the Future: An In-depth Dive into LangChain - Comet. https://www.comet.com/site/blog/chaining-the-future-an-in-depth-dive-into-langchain/.
-(2) A Comprehensive Guide to Using Chains in Langchain. https://www.analyticsvidhya.com/blog/2023/10/a-comprehensive-guide-to-using-chains-in-langchain/.
-(3) Intro to Chains in the Langchain & Its Types - Lancer Ninja. https://lancerninja.com/demystifying-chains-in-langchain/.
-(4) Explained: What is LangChain? How to Use LangChain Chains? – EcoAGI. https://ecoagi.ai/articles/langchain-chains-what-is-langchain.
-
-It provides a standard interface for chains, the ability to integrate with external tools and systems, and end-to-end chains for common applications. LangChain can facilitate most use cases for LLMs and natural language processing (NLP), like chatbots, intelligent search, question-answering, summarization services or even virtual agents capable of robotic process automation.
+LangChain provides a syntax for chains, the ability to integrate with external tools and systems, and end-to-end agents for common applications. LangChain can facilitate most use cases for LLMs and natural language processing (NLP), like chatbots, intelligent search, question-answering, summarization services or even virtual agents.
 
 ### Diagram RAG using Azure Cosmos DB for MongoDB vCore as a retriever