Tutorials#
This section contains a collection of flow samples and step-by-step tutorials.
Category |
Sample |
Description |
---|---|---|
Tracing |
Prompt flow provides the tracing feature to capture and visualize the internal execution details for all flows |
|
Tracing |
Tracing LLM application |
|
Tracing |
Tracing LLM calls in autogen group chat application |
|
Tracing |
Tracing LLM calls in langchain application |
|
Tracing |
A tutorial on how to levarage custom OTLP collector. |
|
Prompty |
A quickstart tutorial to run a prompty and evaluate it. |
|
Prompty |
A quickstart tutorial to run a chat prompty and evaluate it. |
|
Prompty |
||
Flow |
A quickstart tutorial to run a flex flow and evaluate it. |
|
Flow |
A quickstart tutorial to run a class based flex flow and evaluate it. |
|
Flow |
A quickstart tutorial to run a class based flex flow in stream mode and evaluate it. |
|
Flow |
A quickstart tutorial to run a class based flex flow in stream mode and evaluate it. |
|
Flow |
A quickstart tutorial to run a flow and evaluate it. |
|
Flow |
This guide will walk you through the main scenarios of executing flow as a function. |
|
Flow |
Create pipeline using components to run a distributed job with tensorflow |
|
Flow |
Flow run management in Azure AI |
|
Flow |
Flow run management |
|
Flow |
A tutorial to converting LangChain criteria evaluator application to flex flow. |
|
Flow |
A quickstart tutorial to run a flex flow and evaluate it in Azure. |
|
Flow |
A quickstart tutorial to run a class based flex flow and evaluate it in azure. |
|
Flow |
A quickstart tutorial to run a flow in Azure AI and evaluate it. |
|
Deployment |
This example shows how to create a simple service with flow |
|
Deployment |
This example demos how to deploy flow as a docker app |
|
Deployment |
This example demos how to package flow as a executable app |
|
Deployment |
This example demos how to deploy a flow using Azure App Service |
|
Deployment |
This example demos how to deploy flow as a Kubernetes app |
|
Rag |
Retrieval Augmented Generation (or RAG) has become a prevalent pattern to build intelligent application with Large Language Models (or LLMs) since it can infuse external knowledge into the model, which is not trained with those up-to-date or proprietary information |
|
Rag |
This tutorial is designed to enhance your understanding of improving flow quality through prompt tuning and evaluation |
|
Rag |
A tutorial of chat-with-pdf flow that allows user ask questions about the content of a PDF file and get answers |
|
Rag |
In this tutorial, we will provide a detailed walkthrough on creating a RAG-based copilot using the Azure Machine Learning promptflow toolkit |
|
Rag |
In this doc, you will learn how to generate test data based on your documents for RAG app |
|
Rag |
A tutorial of chat-with-pdf flow that executes in Azure AI |
Learn more: Try out more promptflow examples.