How to use Microsoft Azure AI Studio and Azure OpenAI models

Summary

本视频介绍了微软Azure AI Studio，一个集成了多种AI服务和工具的平台。视频分为五部分，包括Azure AI Studio概览、在Azure Open AI上构建模型、从CSV或数据库导入数据、配置和部署模型以及API的使用和访问。通过实际演示，展示了如何使用GPT-4模型创建项目、设置系统消息、添加变量、测试提示、使用提示流、添加数据源、部署模型以及如何通过API在项目中使用模型。视频内容丰富，适合初学者和专业开发者。

Takeaways

• 🌐 视频介绍了Azure AI Studio，这是一个集成了微软多种AI服务的工具平台。
• 🔧 视频中讲解了如何在Azure AI Studio上构建模型，并使用Azure Open AI服务。
• 📊 演示了如何从CSV或数据库导入数据到模型中。
• 🚀 展示了如何配置和部署AI模型。
• 🔗 讨论了API的工作原理以及如何使用和访问模型以用于项目。
• 🤖 强调了Azure AI Studio支持自定义数据以改善提示功能。
• 🎨 介绍了如何使用prompt flow功能来创建和定制模型的工作流程。
• 🔍 说明了如何整合内容安全过滤器以减少潜在的问题。
• 📚 展示了如何将多个AI能力结合起来，以产生更先进的生成性AI解决方案。
• 📈 提供了Azure AI Studio网站上不同模型的预览，并演示了如何选择合适的模型。
• 🔑 演示了如何在Azure AI Studio中添加数据源，并展示了如何上传文件以供模型查询。
• 🛠️ 讨论了如何在Azure AI Studio中进行模型部署，并如何通过API在项目中使用模型。

Q & A

Azure AI Studio 是用来做什么的？

Azure AI Studio 是一个集成了微软多种人工智能服务的工具平台，包括Azure OpenAI、机器学习和语音、视觉服务等。它可以帮助开发者构建更复杂的AI应用，无论是初学者还是专业开发者都可以使用。

视频中提到了哪些Azure AI Studio的主要功能？

Azure AI Studio的主要功能包括：部署模型、测试模型、导入数据（如CSV文件或数据库）、配置和部署模型以及使用API访问模型。此外，还可以定义提示流程、集成内容安全过滤器以及结合多种AI能力来创建高级的生成性AI解决方案。

如何在Azure AI Studio中创建新项目？

在Azure AI Studio中，用户可以登录后返回到工作室仪表板，选择创建新项目，并为项目命名，例如命名为'Azure AI Studio demo example'。然后，用户可以选择一个模型来使用，如GPT模型，并进行部署。

Azure AI Studio支持哪些类型的AI模型？

Azure AI Studio支持多种类型的AI模型，包括来自Meta和微软自家的模型，以及Azure OpenAI的模型。用户可以在Azure AI Studio网站上预览所有可用的模型。

如何在Azure AI Studio中添加数据源？

用户可以在Azure AI Studio的'添加数据'部分选择添加数据源，可以选择Azure AI搜索、Azure Blob存储或上传文件。完成这一步需要选择Azure订阅、Azure Blob和搜索资源，然后上传文件，文件上传并索引后即可用于模型。

Azure AI Studio中的提示流程是什么？

提示流程是Azure AI Studio的一个特色功能，它允许用户通过可视化的方式展示复杂的提示操作。用户可以创建自定义的提示流程，并在流程中添加步骤，以便在输入生成前或后进行定制化处理。

如何在Azure AI Studio中部署AI模型？

用户可以在Azure AI Studio的部署选项卡中查看已部署的AI模型实例，并创建新的部署。选择创建实时端点，选择所需的模型，确认后即可部署模型。如果需要访问特定模型，可能需要向Azure OpenAI服务团队申请访问权限。

如何使用Azure AI Studio的API？

用户可以在Azure AI Studio的部署部分获取模型的端点URL和密钥，然后在编程项目中使用这些信息来调用API。例如，可以使用Python、JavaScript或其他语言的代码模板来实现与API的交互。

视频中提到了哪些编程语言和工具？

视频中提到了JavaScript作为编程语言，并使用了Visual Studio Code作为代码编辑器。此外，还提到了Azure OpenAI服务、Azure AI Studio和Azure Blob存储等微软的AI服务和工具。

如何在JavaScript项目中使用Azure AI Studio的API？

在JavaScript项目中，首先需要安装`openai`包，然后通过环境变量传递API密钥和端点URL。接着，可以编写一个异步函数来初始化OpenAI客户端，并通过客户端调用`getChatCompletion`方法来获取AI生成的文本。

视频中提到的'变量'在Azure AI Studio中的作用是什么？

在Azure AI Studio中，'变量'可以在系统提示和聊天提示中被调用，用于构建和定制化AI模型的功能。例如，可以创建一个名为'languages'的变量，并在系统提示中使用双波浪括号引用该变量，以便在AI会话中根据变量值进行相应的操作。

Azure AI Studio中的'内容安全过滤器'有什么作用？

Azure AI Studio中的'内容安全过滤器'可以帮助开发者减少生成的AI内容中可能存在的问题，如不当内容。通过集成这一功能，可以根据构建解决方案的类型来减轻潜在的有害问题。

Outlines

00:00

🚀 介绍Azure AI Studio及其功能

本段落介绍了Azure AI Studio，这是一个集成了微软多种AI服务的工具平台，如Azure OpenAI机器学习和语音、视觉服务。视频将分为五部分，包括Azure AI Studio概览、在Azure OpenAI上构建模型、从CSV或数据库导入数据、配置和部署模型以及API的使用。感谢微软赞助，视频将主要基于微软Azure服务构建。Azure AI Studio允许用户部署模型、测试、添加自定义数据以改善提示、定义工作流程似的提示流、集成内容安全过滤器以及结合多种AI能力来生成更高级的AI解决方案。

05:00

📚 如何使用Azure AI Studio添加和管理数据

在这一部分中，讲解了如何在Azure AI Studio中添加和管理数据。首先，用户可以选择添加数据源，如Azure AI搜索、Azure Blob存储或上传文件。上传文件后，文件将被索引并可用于模型。此外，用户可以在数据部分下查看和管理已上传的数据。通过上传的文件，模型可以引用文档内容来更准确地回答查询。还展示了如何通过提示流使用自定义数据源，以及如何在Azure AI Studio中部署AI模型。

10:01

🛠️ 使用Azure AI Studio部署模型和访问API

这部分内容讲解了如何在Azure AI Studio中部署模型以及如何通过API访问模型。首先，用户需要请求Azure OpenAI服务团队的访问权限。一旦获得批准，用户可以选择不同的模型进行部署，并立即使用。视频还展示了如何在playground中切换模型版本，以及如何启用增强功能。接下来，讲解了如何使用API进行项目开发，包括获取目标URL和密钥、在代码中使用预设提示、以及如何在Visual Studio Code中设置和使用环境密钥和端点URL。最后，通过一个简单的JavaScript示例，展示了如何使用Azure AI Studio生成代码提示。

15:02

🔧 编写代码以使用Azure AI Studio API

本段落详细介绍了如何编写代码以使用Azure AI Studio的API。首先，需要在Visual Studio Code中安装Azure OpenAI包，并设置环境密钥和端点URL。然后，创建一个新的JavaScript文件，初始化OpenAI客户端，并定义一个提示。通过调用客户端的getChatCompletion方法，传入模型和提示，即可获取AI生成的回复。最后，通过循环遍历结果并打印输出，展示了如何使用API完成句子。视频还提供了相关项目的链接，供用户进一步学习和探索。

Keywords

💡Azure AI Studio

Azure AI Studio 是微软提供的一个集成工具平台，它结合了Azure OpenAI、机器学习和语音、视觉等AI服务，使用户能够在一个中央位置部署模型、进行测试和定制。在视频中，Azure AI Studio 被用来展示如何构建更复杂的AI解决方案，包括导入数据、配置和部署模型等。

💡模型构建

模型构建是指在AI领域中创建和训练模型的过程，这些模型能够基于输入数据进行预测或生成输出。在视频中，讲解了如何在Azure AI Studio上构建模型，包括使用Azure OpenAI服务和导入数据来训练和优化模型。

💡数据导入

数据导入是指将数据从一个系统或格式转移到另一个系统或模型中，以便模型可以使用这些数据进行训练或提供更准确的输出。在视频中，讲解了如何将CSV文件、数据库或文档等数据导入到Azure AI Studio中的模型，以便模型可以利用这些数据提供更有针对性的回答。

💡配置和部署

配置和部署是指设置和激活AI模型的过程，使其能够在实际应用中运行和使用。在视频中，讲解了如何在Azure AI Studio中配置模型的参数，并将其部署到云端，使其可以通过API被访问和使用。

💡API访问

API访问是指通过应用程序编程接口（API）来请求和接收服务或数据的过程。在视频中，讲解了如何使用Azure AI Studio中的API来访问和使用部署的AI模型，以便在外部项目中集成和利用这些模型的功能。

💡Prompt Flow

Prompt Flow 是Azure AI Studio中的一个功能，它允许用户通过可视化的方式创建和管理复杂的提示流程。这个功能可以帮助用户定义模型如何处理输入、生成输出，并在过程中执行特定的步骤或操作。

💡内容安全过滤器

内容安全过滤器是指用于检测和过滤潜在有害内容的机制。在Azure AI Studio中，用户可以集成这样的过滤器来减轻模型可能生成的不当内容，确保生成的文本或响应符合特定的安全标准。

💡自定义提示

自定义提示是指用户根据特定需求和场景为AI模型设定的输入文本。通过自定义提示，用户可以引导AI模型按照特定的方式回答问题或执行任务。在视频中，讲解了如何在Azure AI Studio中定义和使用自定义提示来改善模型的输出。

💡变量

变量在编程和AI模型中是一种存储信息的容器，可以在不同的上下文中被引用和使用。在Azure AI Studio中，用户可以创建变量并在系统提示或聊天提示中调用它们，以便构建更复杂和动态的AI对话。

💡响应温度

响应温度是指在AI模型中调整输出的随机性或创造性的一种参数。较高的温度可能导致更多样化和创造性的输出，而较低的温度可能导致更保守和确定性的回答。在Azure AI Studio中，用户可以根据需要调整这个参数来定制模型的响应。

💡代码提示

代码提示是指AI模型根据用户的输入提供编程代码建议或解决方案的功能。在Azure AI Studio中，用户可以利用模型的代码提示能力来解决编程问题或生成代码片段。

Highlights

视频将介绍Azure AI Studio，这是一个对于初学者或专业开发者构建复杂项目都非常有用的工具。

视频分为五个部分，涵盖Azure AI Studio的介绍、在Azure Open AI上构建模型、导入数据、配置和部署模型以及API的使用。

感谢Microsoft对视频的赞助，我们将主要基于Microsoft Azure服务进行操作。

Azure AI Studio集成了Microsoft的多种工具，如Azure Open AI、机器学习和语音视觉服务等。

用户可以在Azure Open AI服务上部署模型，添加自定义数据以优化提示，如使用数据库或文件。

Azure AI Studio支持定义提示流程，类似于流程图，允许用户创建和定制模型及其功能。

可以集成内容安全过滤器，帮助减轻解决方案构建过程中的潜在问题。

Azure AI Studio支持结合多种AI能力，以产生更先进的生成性AI解决方案。

Azure AI Studio网站提供了多种不同的模型，包括来自Meta和Microsoft的模型。

用户可以选择使用GPT-4模型，但需要先登录账户。

在Azure AI Studio的主控制面板中，用户可以创建新项目并选择模型进行使用。

Azure AI Studio的独特之处在于其提示流程功能，允许用户可视化地展示复杂的提示过程。

用户可以通过添加数据源来向模型中添加数据，支持Azure AI搜索、Azure Blob存储和文件上传。

上传文件后，用户可以在数据部分中管理这些数据，并对其进行标签化或上传新版本。

Azure AI Studio支持在Azure上进行模型部署，用户可以创建新的实时端点进行模型部署。

用户可以通过API将Azure AI Studio集成到实际项目中，支持多种编程语言和格式。

视频提供了如何使用Azure AI Studio进行项目开发的详细步骤和示例。

Transcripts

in this video I'm going to cover Azure

AI Studio which is useful if you're

looking to build something a bit more

complex as either a beginner or a

professional developer at a company

there'll be five parts to this video

first I want to take a look at Azure AI

Studio second we'll take a look at how

to build models on top of azure open AI

Third how to import data to a model from

like a CSV or a database fourth how to

configure and then deploy that model and

then fifth how the API works and how to

use and access that model for a project

now I'd also like to thank Microsoft for

sponsoring this video a lot of what we

do today will be built on top of

Microsoft Azure service so if you want

to learn a little bit more about that

I'll add links in the description below

Azure AI studio is a combination of

tools from Microsoft such as Azure open

AI machine learning and other AI

services such as speech and vision all

in a central place with it I'm able to

do things like deploy models and test

them on the aure openai service I can

add custom data for better prompting

such as using a database or a file or a

document or even a web address I can

Define prompts to work almost like a

flowchart this means that the prompt

flow feature allows you to create and

customize the models and how they

function I can also integrate content

safety filters this helps me mitigate

problems like harm depending on the type

of solution I'm building I can also

combine multiple AI capabilities to

produce a much more advanced generative

AI solution here is the Azure AI Studio

website I'm going to link it in the

description below what's pretty

interesting is that there are quite a

few different models here ones from meta

as well as ones from Microsoft

themselves and the ones here from Azure

open AI which we'll be looking at today

if you want to take a look at all the

models and the catalog then you can

select here to preview all of them

there's quite a few including Nvidia the

Microsoft research program and Desi AI

and many more for this example I'm going

to select Azure open Ai and I'm going to

select to use the GPT 4 model but I'm

going to need to sign in first so I'm

going to sign in with my user account

once done I'm taking back to the studio

dashboard and here I'm going to have the

option to create a new project as well

as select a model to use I'm going to

create a project called Azure AI Studio

demo example then I'm going to scroll

down and select to use the GPT model

over here so that I can get this

deployed and start using the studio

properly to deploy it I just give it a

deployment name and connect it to one of

my projects this takes me into the main

dashboard for Azure AI Studio the main

part here is the playground if you've

used AI playgrounds in the past this is

very similar you have your AI settings

for the system on the left hand side the

chat dialogue in the middle and

additional parameter configurations on

the right what makes Azure AI Studio

unique is a few other things such as the

prompt flow which which will'll create

later as well as being able to manage

your data here in this data section

heading back to the playground let me

set up a basic environment that we can

utilize so I can show you how some of

these things work the system message is

what initializes the context for the AI

and its chat session you can write

whatever you want here and there is no

limit but be aware that it does count

towards your token limit for this

example I'll say that this is a coding

assistant AI helping me solve problems

now there's also Al something called

variables I'm going to add one right now

called languages and in programming

there's lots of different programming

languages so I'm going to create a

language that we're currently using

which will be JavaScript this variable

can now be called inside of system

prompts as well as chat prompts and this

can be useful if you're building out an

application I can reference this

variable inside of my system prompt

using the double squiggly bracket I'll

select apply changes and continue to

make sure that this model is now updated

I'm going to test all of this out by

adding a simple prompt here hello what

are you and the response back here from

the AI is that they're a coding

assistant helping me explain problems in

JavaScript on the top menu here we have

playground settings if you select it you

can select which language the AI uses

what subscription you're utilizing and

if you want speech you can add a speech

resource too I'm going to select a save

for this and on the right hand side we

have parameters you've probably seen

this before where you can set the

response the temperature top PE but most

of these things is useful to keep on

default unless you're specifically

customizing it for an application I'll

have chat history and most of my chats

will continue on in the form that the

system message presents which is in

JavaScript with an explanation of what's

happening on the top right here I can

change the mode from chat to completions

or images and this is useful if you're

using other models right now since I'm

using GPT 3.5 turbo it doesn't have

images or completions but I can swap the

models around by selecting a different

deployment one toggle I like is so Json

which allows you to see the raw inputs

and outputs that are being sent to and

from the API these include the system

message as well as messages from the

user and the AI assistant and it's

perfect if you want to copy and paste

these into just some programming code

prompt flows is another feature from

Azure AI Studio which has the ability to

visually showcase exactly what's

happening in a more complex prompt I'll

create one here using the custom prompt

flow option and it's going to generate

this in the prompt flow area it's a

visual on the right hand side of an

input a chat and an output on the left

hand side I see some of the raw code of

what's actually happening in the

background and this is where I can

customize it whether I want to add more

steps in this process before or after an

input gets generated heading back to the

prompt flow dashboard let me create one

from scratch there's a few different

types you can select from standard to

chat flow to eval and there's 's also

some pre-existing ones you can use as a

demo to get a better idea of how they

function let me show you this one with

chat with Wikipedia since it's doing a

few interesting things like using python

adding a few steps and accessing the

internet once it's loaded here on the

right hand side I can see exactly what's

happening which is it's extracting the

query from a question grabbing the URL

from Wikipedia then searching through

the results of that URL and then then

processing those search results to send

back to the AI while this might seem a

bit complex and you'll need to know a

little bit of python and other things to

be able to code this this is more or

less just an example of the scalability

that you can build out if you want to

learn how to utilize this properly now

I'm going to show how to add data to

your model here on the left select add

your data then select to add a data

source this is probably one of the more

useful features here in add data you can

select from the data sources being Azure

AI search Azure blob storage and upload

a file which I'll be showing in this

example to complete this step you'll

need to select your Azure subscription

you may need to also select your Azure

blob and search resource but once you're

done select the name of the index select

acknowledge and select next this will

bring you to the upload file section you

can drag and drop any kind of file that

is readable such as text or documents or

PDFs I've got this nice handbook from

Flavio CZ which I'm going to be using

which is kind of like a handbook on

nextjs if you have internal company

documentation PDFs or resources you want

to query the model this is the perfect

place to upload it I'll drag and drop it

and upload it just here selecting next I

can select the search type to be keyword

or semantic be aware that using semantic

search will incur a cost using Azure you

can always fall back to keyword

processing here in the background it

will upload the file and once it's done

it'll index it and it'll be available to

use mine's just finished so let me try

out a prompt since the file was about

nextjs I'm going to ask it what's the

best way to run a server side action on

a file the response I get back is an

example of a bit of code with some

information about what this piece of

code is doing and if you have a look

closely here at the bottom you'll notice

that it is referencing the document that

I've actually uploaded earlier I can see

the citations it's using as context to

be able to answer this query properly

this can be customized further in the

advanc section

with how similar or strict content is

when you do upload files or data you can

head over to the data section under

components to see it I've got one here

for index.js and another one here I

created for search index I can select

these and I can see the current version

on one which is version one I can add

tags to them or I can even upload a new

version if I head back to the playground

and go to add your data I can also

remove the data source if I'm no longer

using that specific one or even if I

wanted to change it what's pretty

interesting is that if you load up the

prompt flow with a custom data source

you get to see a little bit of what's

Happening behind the scenes here you'll

see that inputs have a determined intent

which extracts the intent and retrieves

the document then it formulates a reply

and sends that reply to the user under

the hood there is actually quite a few

complex prompts happening here which is

useful if you want to learn some prompt

engineering now I want to show how to do

some deployments on Azure AI studio and

while you can deploy a web app we might

take a look at that a little bit later

when I select the deployments tab I've

got a few examples of instances of the

AI model that I've already deployed and

I can also create new ones if I want

these are currently on Chad GPT 3.5

turbo I might want to deploy a model on

Chad GPT version 4 by selecting create

and selecting realtime endpoint I've got

a number of options for models here

these actually are all the models

available and I can select GPT 4 right

here selecting confirm I'm taking to one

more step to deploy the model but I do

have an error here to get access to this

model I do need to put in a request to

the Azure openi service team here in the

documentation I can select apply now and

this takes me to the form to request

access I filled this out earlier passing

in my subscription ID and it was quite

quickly approved heading back to

deployments I can select GPT 4 now and

I'm going to select the model that comes

with vision I'm going to ignore this

message because I do know that I have

requested and confirmed my approval

selecting create has deployed the model

and now it's available for me to use

immediately in the playground or with

the API which I think we'll take a look

at next inside of the playground on the

right hand side under deployments I can

select to swap it from GPT 3.5 to GPT 4

I can also enable enhancements which for

example allows me to add Vision to the

this model but you will need to enable

this aure service now the final part I

want to take a look at using the API for

an actual project that I might be coding

under deployments I've got my ader and

twarog chat GPT 3.5 turbo example and in

this example I have the target URL this

is kind of of like the endpoint as well

as the key that I can pass in as an

environmental key the other thing here

is the playground itself under

playground there's a useful Little Thing

Called view code if I select the this

button I actually get a preview of a

pre-made prompt that I could use inside

of some code this one's in Python and

it's got the python configuration for a

chat completion with the URL endpoint

and the key that I need I can also view

this in other languages like cop as well

as in just a Json format if I'm using a

different language like JavaScript or

typescript which I think I'll do next by

selecting a learn more I can actually go

to the documentation website and here

I'll have a lot more more info on how to

configure using it inside of a project

since I want to use JavaScript I'm going

to select the JavaScript option and the

main thing here that I need is the keys

as well as the endpoint URL inside of

the Microsoft Azure dashboard I can

search up Azure AI Studio these are all

the instances that I've created so far

what I'm going to do is select the

latest one and instead of selecting to

launch the AI Studio what I'm going to

do instead is scroll down and see the

keys as well as the endpoint URL which I

can now connect inside of a coding

project inside of vs code here's a brand

new empty project in vs code I'm going

to create a file called index.js in

order to query Azure open AI I need to

install the package so here I'm going to

install at Azure slopen aai next I need

to pass the environmental keys so I'm

going to create another file here called

EnV and the two keys I need is the API

key and the endpoint let me select to

copy these from the documentation and

paste these in let me delete the syntax

so it's properly assigned for JavaScript

here I'll add one more environmental key

the model itself which I'll set a little

bit later on for this example I'm going

to deploy out a new model I'm going to

select deploy a real-time model and this

model will be the GPT 3.5 turbo instruct

model once deployed I'll need to grab

the key as well as the URL that I'm

going to use for the endpoint so for the

URL I'm going to select to the

playground I'm going to go to the view

code section and I'm just going to pull

the open AI base URL from here I'm also

going to grab the model itself or the

engine as they put it here and this

engine will have the following name

gpt-3 dturbo d instruct finally I'll

head back to deployments and select the

deployment model once more and here I'm

going to copy out the key and paste this

in here now make sure you keep this

private and Anonymous and don't share

this out publicly because it is what

authorizes all of your requests

now back to the index.js file here I'm

going to require the constant values of

open AI client and the Azure key

credentials from at aure slopen aai now

I want some environmental keys to be

used our require. EnV which we installed

earlier calling config what I can do now

is call const endpoint is equal to

process. env. aure open AI endpoint and

this can save it to the value endpoint

I'll do the same for the API key

as well as for the model itself I also

want to define a prompt so I'm going to

do cons prompt is equal to and in an

array I'm going to add the string the

best way to do Hello World in JavaScript

is buy I've done it this way because

it's not a chat but a chat completion

I'll be doing next I want to run a

function but I just want it to

automatically run so inside brackets

I'll do Asing function as an arrow

function and I'll call it straight after

inside of this asnc function I'm going

to do console log and begin this chat

completion I'll initialize the client by

calling cons client is equal to new open

AI client here I'll pass in the endpoint

as well as the Azure key for the API I

also need to add the deployment ID so

I'll pass it in here calling the

environmental key that I had earlier set

to model finally I'm going to call this

chat completion I'll const out the

results here and call await client.

getet chat completion and then I'll pass

in the model through deployment ID as

well as the prompt I said earlier to

view the results I'll have to Loop

through them so I'll call for cons

choice of results. choices and here I'll

just console log out the choice. text

and that's basically it I can now open

up the terminal and run this function

and hopefully I'll get the result back

to run it I'll call

node. index.js and here I've got a

response back open your code editor of

choice and then and as you can see it

hasn't finished the final line because

this is a chat completion there's

probably a default token limit but it

gives you an example of how this API

completes the sentence of what you're

writing if you want to learn a little

bit more I've added a link in the

description which gives you access to

this repo of this project I've worked on

as well as the ability to sign up to

Azure AI studio and its documentation to

learn more I'll be covering a few more

videos on Microsoft Azure open a AI so

if you're interested in any specific

topics or projects then let me know in

the description below otherwise I hope

you guys enjoyed this video