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This sample has the full End2End process of creating RAG application with Prompty and AI Studio. It includes GPT 3.5 Turbo LLM application code, evaluations, deployment automation with AZD CLI, GitHub actions for evaluation and deployment and intent mapping for multiple LLM task mapping.
This repository contains various examples of how to use LangChain, a way to use natural language to interact with LLM, a large language model from Azure OpenAI Service.
This sample shows how to create two AKS-hosted chat applications that use OpenAI, LangChain, ChromaDB, and Chainlit using Python and deploy them to an AKS environment built in Terraform.
In this repository, you will find an example code for creating an interactive chat experience that allows you to ask questions about your CSV data with chart visualization capabilities.
This example shows how a multitenant service can distribute requests evenly among multiple Azure OpenAI Service instances and manage tokens per minute (TPM) for multiple tenants.
This example shows how a multitenant service can distribute requests evenly among multiple Azure OpenAI Service instances and manage tokens per minute (TPM) for multiple tenants.
In this repository, you will discover how Streamlit, can work seamlessly with Azure OpenAI Service's Embedding and GPT 3.5 models. These tools make it possible to create a user-friendly web application that enables users to ask questions in natural language about a PDF file they have uploaded.
Create 3D celestial models with Azure OpenAI, Python, and Blender! AzureOpenAI-SpaceGen-Py leverages LLM-powered prompts to generate Python scripts, utilizing Azure Compute for intensive rendering tasks. Explore space-themed modeling while learning Python, Azure Bicep, and Blender.