# Workshop Content

| Notebook | Details
| --- | ---
| [0_SetupandConfig](./Workshop/0_SetupandConfig.ipynb) | Introduction to Azure OpenAI, Concepts, Setup and some basic understanding of the Azure OpenAI platform
| [1_LoadData](./Workshop/1_LoadData.ipynb) | Notebook that covers the concept on how to load data, create chunk on the large document and persist that data into Vector Store (Azure Cognitive Search)
| [2_AskQuestion](./Workshop/2_AskQuestion.ipynb) | With the data loaded into the vector store, notebook covers the concept on asking question over your document and different chain type techniques for token limitations
| [3_Summarization](./Workshop/3_Summarization.ipynb) | When you are looking to summarize your document, this notebook covers the concept on how to do that without loading the document into the vector store
| [4_ChatWithHistory](./Workshop/4_ChatWithHistory.ipynb) | Notebook covers the concept on how to use the history of the conversation to improve and interact using the conversational nature of asking questions.
| [5_SummaryAndQa](./Workshop/5_SummaryAndQa.ipynb) | Generating sample questions from the document is the focus for this notebook.
| [6_AskSql](./Workshop/6_AskSql.ipynb) | Notebook covers the concept on how to talk to your relation database.  In this example, we cover different options on how to ask questions to your database, using both langchain and not using langchain.
| [7_AskTabularData](./Workshop/7_AskTabularData.ipynb) | Showcase how to ask question on tabular data (CSV, excel) using the agent concept.
| [8_Agent](./Workshop/8_Agent.ipynb) | Experimental ideas on how to create Agents that can interact and talk to everything we covered in the workshop.  Ask question to your database, ask question to your document, ask question to your tabular data and ask question to your knowledge base.
