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PaulchWorks's Projects

aicentral icon aicentral

An AI Control Centre for monitoring, authenticating, and providing resilient access to multiple Open AI services.

appservice-landing-zone-accelerator icon appservice-landing-zone-accelerator

The Azure App Service landing zone accelerator is an open-source collection of architectural guidance and reference implementation to accelerate deployment of Azure App Service at scale.

azure-search-openai-demo icon azure-search-openai-demo

A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.

azurechat icon azurechat

🤖 💼 Azure Chat Solution Accelerator powered by Azure Open AI Service

azurechatgpt icon azurechatgpt

🤖 Azure ChatGPT: Private & secure ChatGPT for internal enterprise use 💼

bert icon bert

TensorFlow code and pre-trained models for BERT

botbuilder-samples icon botbuilder-samples

Welcome to the Bot Framework samples repository. Here you will find task-focused samples in C#, JavaScript and TypeScript to help you get started with the Bot Framework SDK!

chat-with-your-data-solution-accelerator icon chat-with-your-data-solution-accelerator

A Solution Accelerator for the RAG pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. This includes most common requirements and best practices.

cirq icon cirq

A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.

container-apps-openai icon container-apps-openai

This sample shows how to create two Azure Container Apps that use OpenAI, LangChain, ChromaDB, and Chainlit using Terraform.

cosmosdb-chatgpt icon cosmosdb-chatgpt

Sample application that combines Azure Cosmos DB with Azure OpenAI ChatGPT service

deep-language-networks icon deep-language-networks

We view Large Language Models as stochastic language layers in a network, where the learnable parameters are the natural language prompts at each layer. We stack two such layers, feeding the output of one layer to the next. We call the stacked architecture a Deep Language Network - DLN

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