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Last active December 19, 2023 23:46
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AI resources

Artificial Intelligent Resources.

The gist below is a list of useful resources for .NET developers getting started with the new wave of AI applications.

Sample Summary
A .NET introduction to RAG (Retrieval Augmented Generation) This blog explains how to use AI to create text, images, audio, or anything else, and how to build a simple chat app with .NET and Azure OpenAI.
A .NET introduction to Chat GPT Plugins This is a quickstart for sample for creating ChatGPT Plugin using GitHub Codespaces, Visual Studio or VS Code, and Azure. The sample includes templates to deploy the plugin to Azure Container Apps using the Azure Developer CLI.
Generative AI Native Sample: ChatGPT + Enterprise data with Azure OpenAI and Cognitive Search This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo), and Azure Cognitive Search for data indexing and retrieval.
EShop with AI This our "Golden App"(brownfield) sample that shows developers how to add Azure Open AI.
Polyglot Notebooks Open AI Cookbooks in .NET
Azure AI Reference Templates Templates that use Azure OpenAI and other cognitive services to build intelligent and interactive applications, such as chatbots, recommendation engines, and information search and discovery systems.
Semantic Kernel Documentation Semantic Kernel is an open-source SDK that lets you easily combine AI services like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C# and Python. By doing so, you can create AI apps that combine the best of both worlds.
Milvus C# SDK Milvus is an open-source vector database that is highly flexible, reliable, and blazing fast. It supports adding, deleting, updating, and near real-time search of vectors on a trillion-byte scale. Repo
What is a vector database They are specialized databases for handling vector embeddings, which are data representations that carry semantic information for AI applications.1 They offer optimized storage, querying, and filtering capabilities for embeddings.

Please note: More samples and learning materials will be added over the next few weeks.

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