Contoso-Chat

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contoso-chat is a sample application demonstrating the end-to-end process of creating a RAG application with Prompty and Azure AI Foundry. It includes GPT-4 LLM application code and evaluations.

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At a glance

Pricing

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Free tier

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API

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Skill level

Technical

About

What is contoso-chat?

contoso-chat is a sample application designed to showcase the complete workflow of building a Retrieval Augmented Generation (RAG) application using Prompty and Azure AI Foundry. It provides GPT-4 LLM application code along with evaluation capabilities. The tool also features deployment automation, leveraging the AZD CLI and GitHub Actions for streamlined operations. Additionally, it supports intent mapping, enabling the mapping of multiple LLM tasks.

Best used for

Demonstrating and learning the end-to-end process of creating RAG applications with Prompty and Azure AI Foundry.

Common actions

Build RAG applications

Evaluate LLM performance

Automate deployments

Learn Azure AI Foundry

Develop with GPT-4

automated workflowopen-sourceworkflowscollaborationdeepfakelow-code/no-code"AI Agents"face swappinggithub copilot

Capabilities

Key features

RAG application sample
GPT-4 LLM code
Deployment automation
Intent mapping
Azure AI Foundry

Target Audience

DevelopersAI EngineersResearchersData Scientists

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

Does contoso-chat provide a cost estimate or pricing model for deploying its RAG application components on Azure?

As a sample application, contoso-chat itself doesn't have a direct pricing model. However, deploying its components will incur costs based on the Azure services consumed, such as Azure AI Foundry, GPT-4 LLM usage, and other associated resources. Users should refer to Azure's pricing for these services.

Can contoso-chat be adapted to use LLMs other than GPT-4, or is it exclusively tied to the OpenAI model?

contoso-chat is designed to showcase GPT-4 integration within Azure AI Foundry. While the core RAG workflow is adaptable, modifying it to use other LLMs would require changes to the LLM application code and potentially the Prompty configurations to support different model APIs and prompt formats.

What level of technical expertise is required to effectively deploy and customize contoso-chat using AZD CLI and GitHub Actions?

Deploying and customizing contoso-chat requires advanced technical expertise. Users should be familiar with Azure services, command-line interfaces (AZD CLI), GitHub Actions for CI/CD, and have a good understanding of LLM concepts and RAG architectures.

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