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This project demonstrates a small-scale proof-of-concept deployment of an enterprise chatbot leveraging the power of Azure OpenAI and Azure AI Search, built and deployed using Streamlit. Utilizing the Retrieval-Augmented Generation (RAG) pattern.

License: MIT License

Bicep 35.45% Jupyter Notebook 21.24% Dockerfile 3.00% Python 40.31%
azure azure-ai-search azure-openai chat-application streamlit

aoai-rag-example-scenario's Introduction

Example Scenarios: Generative AI Applications with Azure OpenAI

๐Ÿ“š Overview

This repository demonstrates small-scale proof-of-concept deployments of Generative AI applications with Azure OpenAI. The repository showcases the capabilities of Azure OpenAI and provides a foundation for enterprises seeking to implement Generative AI applications with Azure OpenAI.

๐Ÿ’ป Getting Started

This repository will highlight different areas of interest when implementing Generative AI applications with Azure OpenAI. Each example scenario demonstrates a small-scale proof-of-concept that can be used as a foundation for enterprises seeking to implement Generative AI applications with Azure OpenAI.

More information on the example scenarios is outlined in the Getting Started section of this repository.

Setup

Detailed instructions for deploying these proofs-of-concept are outlined in the Step-by-Step Setup section of this repository. The example scenarios will illustrate concepts such as:

  • Building a search index using Azure AI Search that can be used to retrieve relevant information.
  • Implementing the Retrieval-Augmented Generation (RAG) pattern.
  • Developing a lightweight web interface for a chatbot using Streamlit.
  • Deploying the Generative AI application using Streamlit.
  • Deploy the application using Azure Container Apps.

Example Scenarios

This proof-of-concept will cover the following example scenarios:

Example Scenario Description
Contoso Trek Product Info Chatbot (Custom) Small-scale proof-of-concept demonstrating a chatbot leveraging built and deployed using Streamlit and Azure Container Apps. The chatbot uses a custom RAG implementation and Azure AI Search for pull-based data ingestion.

โš–๏ธ License

Details on licensing for the project can be found in the LICENSE file.

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