Merge branch 'main' of https://github.com/solliancenet/cosmos-db-openai-python-dev-guide

Author: codingbandit

02_Overview_Azure_OpenAI/README.md

@@ -1 +1,8 @@
 # Overview of Azure OpenAI
+
+Azure OpenAI is a collaboration between Microsoft Azure and OpenAI, a leading research organization in artificial intelligence. It is a cloud-based platform that enables developers and data scientists to build and deploy AI models quickly and easily. With Azure OpenAI, users can access a wide range of AI tools and technologies to create intelligent applications, including natural language processing, computer vision, and deep learning.
+
+Azure OpenAI is designed to accelerate the development of AI applications, allowing users to focus on creating innovative solutions that deliver value to their organizations and customers.
+
+https://microsoft.github.io/PartnerResources/azure/data-analytics-ai/openai
+

03_Overview_AI_Concepts/README.md

@@ -1,7 +1,35 @@
 # Overview of the following concepts (summaries with reference links):
 
-- Large Language Models (LLM)
-- Standard patterns (RAG, CoT, ReAct, etc.)
-- Vectorization and vector search
-  - What are you trying to solve with finding relevant data through vector search?
-- Prompt engineering
+
+## Large Language Models (LLM)
+
+## Standard Patterns
+
+### RAG
+
+Retrieval Augmentation Generation (RAG) is an architecture that augments the capabilities of a Large Language Model (LLM) like ChatGPT by adding an information retrieval system that provides grounding data. Adding an information retrieval system gives you control over grounding data used by an LLM when it formulates a response. For an enterprise solution, RAG architecture means that you can constrain generative AI to your enterprise content sourced from vectorized documents, images, audio, and video.
+
+https://learn.microsoft.com/en-us/azure/search/retrieval-augmented-generation-overview
+
+### CoT
+
+Instead of splitting a task into smaller steps, with Chain of Though (CoT) the model response is instructed to proceed step-by-step and present all the steps involved. Doing so reduces the possibility of inaccuracy of outcomes and makes assessing the model response easier.
+
+https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/advanced-prompt-engineering?pivots=programming-language-chat-completions#chain-of-thought-prompting
+
+### ReAct
+
+### Others?
+
+https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/advanced-prompt-engineering?pivots=programming-language-chat-completions#provide-grounding-context
+
+
+## Vectorization and Vector Search
+
+What are you trying to solve with finding relevant data through vector?
+
+## Prompt Engineering
+
+https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/prompt-engineering
+
+https://learn.microsoft.com/en-us/semantic-kernel/prompt-engineering/

06_Explore_OpenAI_models/README.md

@@ -1,8 +1,17 @@
 # Explore the Azure OpenAI models and endpoints (console app)
 
-- Selecting an LLM
-- Explore and use models from code
-    - Completions
-    - Chat completions
-- Do I use an out-of-the-box model or a fine-tuned model?
-- Use the embeddings model to vectorize data
+## Selecting an LLM
+
+https://learn.microsoft.com/en-us/semantic-kernel/prompt-engineering/llm-models
+
+
+## Explore and use models from code
+
+- Completions
+- Chat completions
+
+
+
+## Do I use an out-of-the-box model or a fine-tuned model?
+
+## Use the embeddings model to vectorize data

09_Chatbot_API/README.md

@@ -1,5 +1,8 @@
 # Complete the chatbot API
 
-- Select an orchestration framework
-- Update the starter project to implement the Azure Cosmos DB for MongoDB database for chat history
-- Implement Azure OpenAI for embeddings and completions
+
+## Select an orchestration framework
+
+## Update the starter project to implement the Azure Cosmos DB for MongoDB database for chat history
+
+## Implement Azure OpenAI for embeddings and completions