Coze | How to use Workflows

Summary

このビデオでは、ワークフローを使ってAIチャットボットをカスタマイズする方法を紹介しています。NBAの最新情報や統計、スコアをリアルタイムで提供するNBAボットを作成する例を通じて、マルチステップタスクを自動化する方法を説明します。ワークフローはノードを用いてタスクを分割し、最終結果を得るためのステップを定義します。プラグインやコードノードを活用してAPIからデータを取得し、大規模言語モデルを用いてユーザーにわかりやすい形で情報を提供します。ワークフローの実装前後でのボットの応答の違いも比較され、カスタマイズの重要性が強調されています。

Takeaways

• 🤖 AIチャットボットを作成するためにコードを使用し、プロンプト、追加スキル、プラグインを使用してボットにパーソナを与え、ナレッジベースを使用してインテリジェンスを提供している。
• 🛠️ ワークフローを使用して、マルチステップのタスクを完了する方法を紹介し、ボットをさらにカスタマイズする方法を説明している。
• 🏀 NBAボットの例を使用して、シーズン中の最新情報、統計、スコアを提供するだけでなく、過去のゲームやリアルタイムのゲームも確認できるようにしている。
• 📅 ワークフローの追加前と追加後のボットの応答の違いを比較し、データの正確性と一貫性を高めるためにワークフローが重要な役割を果たしていることを示している。
• 🔗 ワークフローはノードから構成されており、それらはステップとして機能し、最終結果を得るために互いに接続している。
• 🔧 コードノードを使用して、入力変数を処理し、特定の値を生成することができるが、ワークフローを作成する際にはコーディングの知識は必要ないが役立つ。
• 🔍 ナレッジノードはナレッジベースを使用して、問い合わせと入力に基づいて情報を照合し、情報提供を行う。
• 📝 if条件ノードと変数ノードはロジックを処理し、特定の条件に基づいて意思決定を行い、値を読み書きして保存することができる。
• 🔌 プラグインはワークフローのノードとして機能し、特定の情報源にアクセスするために使用される。
• 📈 ワークフローのテスト実行を通じて、ボットがAPIから受け取ったデータを処理し、ユーザーに必要な情報のみを提供するように調整している。
• 📝 最後に、ワークフローを公開し、ボットにワークフローを追加して、より良い応答を提供するように調整している。

Q & A

コードを使用してAIチャットボットを作成することとはどういう意味ですか？

AIチャットボットを作成するためにコードを使用することは、プロンプトを使用してボットのパーソナを与えること、プラグインやスキルを追加してボットの機能を強化し、ナレッジベースを通じて知能を提供することであり、特定のニーズに応じてさらにカスタマイズすることが可能です。

ワークフローとは何で、どのようにしてマルチステップのタスクを完了させるのに役立つか説明してください。

ワークフローは、タスクを完了させるためのステップバイステップのプロセスを定義するツールです。マルチステップのタスクを完了させるためには、ワークフロー内の各ノードを通じて入力を処理し、最終的な結果を得るための出力を生成します。

このビデオではどのような種類のAIチャットボットを作成していますか？

ビデオではNBAボットを作成しており、その目的は最新の統計情報、スコア、シーズン中のすべてのNBAゲームに関する情報を提供することです。また、過去のシーズンやリアルタイムでのゲームも確認できます。

ワークフローを使わずにボットが生成するレスポンスと、ワークフローを使用した場合の違いは何ですか？

ワークフローを使わずにボットが生成するレスポンスは、不正確なデータや不要な情報を含んでいる可能性があります。一方、ワークフローを使用することで、最も正確なデータを取得し、ユーザーに一貫した方法で提供できます。

ノードとは何で、ワークフロー内で何の役割を果たしますか？

ノードはワークフローを構成する基本単位であり、特定の結果を得るために一つのステップを表します。ノードは互いに接続され、ユーザーからの入力を処理し、特定の答えを得るために必要な情報を提供します。

ワークフロー内で使用されるコードノードとは何であり、何のために使われますか？

コードノードはワークフロー内で使用され、入力変数を処理し、戻り値を生成します。これは、APIやプラグインから特定の結果を得る必要がある場合に、コーディング知識が非常に役立ちますが、ワークフローを作成するためにはコーディングの知識は必須ではありません。

プラグインとは何で、ワークフロー内でどのように役立つのですか？

プラグインは外部サービスやAPIと接続し、特定のデータを取得したり機能を提供したりするツールです。ワークフロー内でプラグインは、特定のタスクを自動化したり、必要な情報を取得したりするのに役立ちます。

このビデオではどのようにして独自のプラグインを作成し、それをワークフローに統合しましたか？

ビデオでは独自のプラグインを作成し、NBA APIに接続して必要なデータを取得できるようにしました。そして、そのプラグインをワークフローにドラッグアンドドロップして、必要な情報を処理し、ユーザーに提供するように統合しました。

ワークフローの最終ステップであるエンドノードはどのような役割を持っていますか？

エンドノードはワークフローの最終ステップで、ユーザーが求める答えを生成し、フォーマットして提供します。エンドノードは、ワークフロー内のすべてのステップを通じて得られた情報を元に、ユーザーにわかりやすい形で結果を提示します。

ワークフローを使用することでボットのレスポンス品質はどのように向上するのですか？

ワークフローを使用することで、ボットはより正確な情報を取得し、必要のない情報を除外してユーザーに提供できます。これにより、ボットのレスポンス品質は向上し、ユーザーが求める情報をより正確かつ効果的に提供できます。

このビデオの最後にどのようにしてワークフローをボットに適用するか説明されていますか？

ビデオの最後に、ワークフローを作成し、それをボットの空間に追加する手順が説明されています。そして、ワークフローを適用することで、ボットのレスポンスがどのように向上するのかを比較することができます。

Outlines

00:00

🤖 AIチャットボットのワークフローの紹介

この段落では、AIチャットボットを作成し、その個性を作り出し、知識ベースを通じて知能を与える方法が説明されています。さらに、ワークフローを使ってボットが特定のニーズに合わせて複数ステップのタスクを完了する方法も紹介されています。ワークフローは、ノードと呼ばれる基本単位からなり、それらが相互に接続して最終結果を得る仕組みです。ノードには、大規模言語モデルノード、プロンプト、コードノード、知識ノード、条件分岐、変数ノードなどがあります。また、プラグインや他のワークフローもノードとして使用できると示されています。

05:02

🏀 NBAゲーム情報のためのプラグインの作成とワークフローの構築

この段落では、NBAのスコアと統計情報を取得するためのカスタムプラグインを作成し、それをワークフローに組み込む方法が説明されています。プラグインはNDA APIに接続し、過去のゲームやリアルタイムのゲーム情報を含むデータを取得します。ワークフローでは、開始ノードからプラグインにデータを渡し、コードノードを使って必要な情報だけを抽出します。その後、大規模言語モデルノードを使って情報をユーザーにとって理解しやすい形に変換し、応答コンテンツを使って最終的な結果を整形します。

10:04

📊 NBAゲーム結果の詳細な情報抽出と整形

この段落では、コードノードを使ってAPIから取得したデータを加工し、必要な情報のみを抽出する方法が説明されています。次に、大規模言語モデルノードを使って、抽出された情報をユーザーに伝えるためのプロンプトを作成し、応答コンテンツを使って結果を整形します。これにより、ボットからの応答はより正確で詳細になり、ユーザーが求める情報だけを効率的に提供できます。

15:04

📝 ワークフローの実装前後でのボット応答の比較

最後の段落では、ワークフローを実装する前後のボットの応答を比較しています。ワークフローを実装する前は、ボットからの応答が不正確で、望まないゲームの結果も含まれていることが示されています。しかし、ワークフローを実装した後、ボットはAPIとコードノード、大規模言語モデルを通じて正確な情報を提供し、ユーザーの質問に適切に答えることができます。ワークフローの実装の重要性とその効果が明確に示されています。

Keywords

💡AI chatbot

AI chatbotとは、人工知能を活用して会話が可能なボットのことです。このビデオでは、AI chatbotを作成し、パーソナリティやスキル、プラグインを用いてカスタマイズする方法を紹介しています。AI chatbotは、ユーザーとの対話を通じて情報を提供したり、タスクを完了させることが可能です。

💡Workflows

Workflowsとは、タスクを完了させるためのステップバイステップのプロセスを定義した仕組みです。ビデオでは、ワークフローを使ってAI chatbotがマルチステップのタスクをこなす方法を説明しています。これは、特定のニーズに合わせてボットをカスタマイズする上で非常に重要な要素です。

💡Persona

Personaとは、キャラクターや個々の特性を持つボットのパーソナリティを指します。ビデオでは、ボットにパーソナリティを与えることで、ユーザーとの対話をより自然で魅力的に行う方法が紹介されています。

💡Skills

Skillsは、AI chatbotが持っている特定の能力や機能を指します。ビデオでは、スキルを追加することでボットの能力を拡張し、より複雑なタスクをこなす方法が説明されています。

💡Plugins

Pluginsとは、AI chatbotに追加の機能を提供する外部のソフトウェア部品です。ビデオでは、プラグインを使ってボットを拡張し、特定の情報源にアクセスする方法が紹介されています。

💡Knowledge bases

Knowledge basesとは、AI chatbotが情報を検索し回答するためのデータベースです。ビデオでは、ナレッジベースを使ってボットがより正確な情報を提供する仕組みを説明しています。

💡Nodes

Nodesとは、ワークフロー内のステップを表す基本単位です。ビデオでは、ノードがワークフローを構成し、タスクを完了させるプロセスを制御する方法について説明しています。

💡Large language model

Large language modelとは、大量のデータを学習し、自然言語を理解し生成するAIモデルです。ビデオでは、ラージランゲージモデルがボットの応答を生成し、ユーザーとの対話を促進する役割を果たしていると説明されています。

💡API

APIとは、Application Programming Interfaceの略で、ソフトウェア間でデータをやり取りするためのインターフェースです。ビデオでは、APIを通じてリアルタイムのNBAのスコアや統計情報を取得する方法が紹介されています。

💡Customization

Customizationとは、ユーザーの特定のニーズに合わせてソフトウェアやサービスをカスタマイズするプロセスです。ビデオでは、ワークフローを使ってAI chatbotをカスタマイズし、ユーザーに合わせたサービスを提供する方法が説明されています。

Highlights

Creating an AI chatbot involves giving the bot a persona using prompts, extra skills or plugins, and knowledge bases.

Customization of the bot can be enhanced using workflows to complete multi-step tasks.

The example project is an NBA bot designed to provide the latest information, statistics, and scores of NBA games, including past, present, and real-time games.

Workflows help ensure accurate data by setting up multi-step tasks and providing uniform information to the user.

The starting node takes user input, and the end node produces the output in workflows.

Basic nodes include large language models, prompts, code nodes, knowledge nodes, if conditions, and variable nodes.

Plugins can also be used as nodes in workflows, and custom plugins can be created for specific needs.

An example custom plugin connects to an NBA API to fetch game data, scores, stats, and other relevant information.

Nodes in workflows connect in a sequence to achieve the desired result, with each node performing a specific task.

Code nodes allow processing of input variables and generation of return values, enhancing the workflow's capabilities.

Knowledge nodes use knowledge bases to match information based on inputs, aiding in accurate data retrieval.

If conditions and variable nodes help with logic, making decisions based on conditions and storing values for later use.

The example workflow involves nodes for starting input, NBA data retrieval, code processing, and output formatting.

The code node processes the NBA API data to extract only the necessary information, making the response concise.

The large language model node formats the extracted information into a readable format for the user.

Workflows significantly improve the accuracy and presentation of information provided by the bot.

The final output is tailored to present the number of games, teams, and scores for a specific date as per the workflow's design.

Transcripts

so you've been using codes to create an

AI chatbot that means that you've given

your Bot a Persona using prompts some

extra skills or plugins and then also

intelligence using knowledge bases

however you want to customize it even

more so that your Bot knows how to

really complete task tailor towards your

specific needs so in this video I'm

going to show you how to use workflows

so that your Bot knows how to complete

multi-step task let's head over to the

CO's workspace so I can show you how

workflows can really enhance your B

[Music]

all right so I'm creating an NBA bot and

the purpose of this spot is to give me

the latest information the statistics

and the scores of all the NBA games that

are happening this season and not only

that I'll be able to look at games that

happened in the past and previous

seasons and also games that are

happening in real time so the spot is

pretty powerful and workflows are going

to help us get there so there's actually

no need for me to really even look at

ESPN anymore because I have my own

personal assistant so if we look at our

Persona impr prompts I have a character

set with some skills and constraints and

over time I'll be able to add more to

this to enhance our bot even further so

if we look at the skills here we don't

have any plugins or workflows right now

so the reason why is because we haven't

created it yet and also because I want

to show you the difference between how

this operates without a workflow versus

towards the end of the video how it

operates when we do add the workflow so

let's ask this bot what the scores were

for January 20th 2024 and we'll see the

difference later on so let's see how

this bot generates a

response so I do remember watching games

on this day and I don't remember any of

these games happening the Warriors never

played the Rockets and the Lakers did

not play the Milwaukee Bucks on this

date so this is where workflows are

going to come into play we're not sure

where this data came from however with

workflows we'll be able to set up a

multi-step task in order for us to get

the most accurate data and deliver it to

our user in a uniform way so what we'll

do now is we'll go over here to add

workflows and we'll create our

workflow and we'll name our workflow

MBA workflow for

now and the description box is a place

that you can describe your workflow of

course however it uses a large language

model to help our workflow understand

how it needs to be invoked so we can

just say get the latest

NBA

scores okay so the first thing to

understand about workflows are nodes and

nodes are the basic unit of what make up

workflow and nodes connect to one

another in order to get an end result so

think of a node as a step that it takes

in order to give our user the specific

answer that we want so we have our

starting node here that it comes with

and this starting node is where the user

puts an input or the question that

they're asking and then the other node

that uh the workflow gives us is the end

node here and the end node is what

produces our output and Returns the

value that we're looking for here so

that would be our

answer now there are a few other nodes

that go in between the start node and

the end node and if we bring our

attention here to the left side we'll

see that we have basic nodes so we have

our large language model node which

invokes a large language model and it

can gener generate a response based on

the input that we give it and then we

also have a prompt that we can have to

specify our answer even more and I'll

show you how to use that too we also

have our code node here which allows you

to process an input variable and it will

generate a return value so with work

flows just keep in mind you don't need

to know how to code however it does help

a lot if you do have this knowledge

because being able to get these specific

answers from things like plugins or apis

you're going to be required to know how

to code or at least understand what's

going on to get the results that you

want now the next node that we have here

are the knowledge nodes and this is a

node that uses the knowledge bases that

you create and matches information based

on what you're asking here and what your

inputs are then we also have our if

condition and our variable nodes here

which are a little bit more related to

coding however these are to help with

logic so our if condition here will

allow us to um actually make some type

of decision if something's happening

then do this and our variable node here

is to help us read and write values so

that we can store things and pass them

on when we need them to be um so not

only do we have these basic nodes we

also have plugins that can be nodes and

workflows that can also be nodes so you

can also use another workflow that you

you created as a node itself but in this

video we're not going to cover that but

uh we're actually going to talk about

plugins here so with plugins we have

plenty of plugins like Reddit Microsoft

Outlook slack Google search you name in

however these plugins right here aren't

really going to specifically give me

what I want so I took my time to create

my own plug-in and this plugin connects

to an NDA API that I'm able to grab the

data that I want so this data consists

of games that happened in the past or

even games that are happening right now

in real time so I'm able to grab the

scores and stats and also even see who

even officiated the game now I might not

need all that information to create this

workflow however we're going to use bits

and pieces of it all right so let's add

our MBA node here from our plugins and

we'll just drag it here to the middle

now what we'll do is actually name this

starting input that we have here and

we're just going to name the starting

input date because this is where the

user is going to be asking their

question um so this question is based

off of the date that we're going to be

sending to this NBA API node that we

have here as well so our description

we'll just set this simply as what this

is going to do it's just going to be

taking a date in by a format and the

format doesn't really matter because

it's going to be running through an llm

anyways so it's going to be able to

determine the date based on how we

rewrite it now the next step is we're

going to connect our starting node to

our NBA Daily data node that has our NBA

plugin in it right so this plugin is

already created and it's already looking

for this game date here so we're using

the game date to determine where uh what

games are being played now this game

date we're going to reference this to

the input that we have from the start

node on the date so we're connecting

this date this question of what games

happen on this date to this NBA node

that's actually a plugin that connects

to an API and this API has all of the

data that we need for games that are

happening in real time or games that

happened in the past based on the date

that we ask now when we use this plug-in

here we have this payload with all this

data and so for the next step what I'm

going to do is I'm going to add a code

node here so you can see what it looks

like when we connect all this to code

and we start to parse out this

information the code node requires the

most amount of complexity here but it's

not required for you to make a

workflow I'm still going to show you how

to do it anyways because it is important

to know the power that this code node

can actually bring to your workflow that

will then be presented in your Bot so

we'll go ahead and we'll connect this

daily data node to our code node and

I'll use a block of code that I've

already created and I'll explain it and

go down each

line but before we do that let's take a

look at these input puts I want to make

sure that I am getting the inputs that I

need from this NBA Daily data node so

I'm going to need a few things let's

look through this uh payload that we get

so we look through here through date and

I definitely want some games all this

data from who was the away team to the

profiles of the players all the way to

the game count as well so let's take

this input and we're going to name it

games and then we're going to have this

reference our NBA Daily data payload and

we'll look for here in date and we'll

get games and then I'm going to add

another input here for the games count

so I want to be able to tell my user how

many games happened on that day as well

because every day is different so I'll

take this I'll go to NBA Daily data and

then I'll also go to my payload my dat

and game count and it's just great

because I can go through all these

different fields here and customize my

response how I want it to look so now

that I have these inputs here let's take

a look at this code block a bit now this

code block all it's telling me is to go

through the uh list of games that we

have inside of our payload so it'll tell

me all the games that are happening

create a new array of these games and

bring me back the profile the box score

who the home team was and who was the

away team and here all I'm saying is

bring me back the city of the home team

and bring me back the name of the home

team and the same thing for the away

team as well so I'm just going through

all this information that's being taken

from this node and pass to the code and

I'm just splitting it into little tiny

chunks so I can only get the information

that I'm actually looking for and not a

bunch of other things that are not

necessary now for this output what I'll

do now as well is I'll set up my out

outut so that my things that I'm getting

as my input from here are going to be

passed on to the next node which would

be my large language model so I want

these same inputs these answers for what

were the games and how many games

happened and then within that same uh

information we're getting from this node

we'll get the profile the box score who

the home team was and who the way team

was and so forth so let's go here and

add our

games and we'll keep this as a type

string right because it's just a text

and then we're going to add another one

here for game

count and we'll change this to a number

because just the number of games that

we're going to be presenting to the user

so the next step is to create a large

language model node and put it here in

order to connect to our code node

because this large language model node

is going to be able to take this

information that we have in this

unreadable format and make it more

legible for our user to understand so

what I'll do now is I'll take this large

language model prompt that I've already

created as well and I'll put this inside

of our large language model node so

let's move things over to the

side and we'll connect our nodes

together right and let's connect this to

the end node here and what I'll do now

is change my GPT model to GPT 4 and I

can keep the temperature the same but

the things that are different is I'm

going to this input right is actually

going to be referencing our input of the

games so I'm going to put games here and

then I'm going to reference what I'm

getting from this code node it's being

passed down so this input that I put

here for the games that's getting from

the plugin and then it's spitting back

out here through this output I want to

pass this output all the way to this

large language model so let's go here

and take from our code I want the games

now I have a prompt that I've already

written as well and this prompt is just

describing what I want this workflow to

do with the data that I'm passing to it

from the code node now this is just

going to be able to tell our uh large

language model exactly what we want and

it's going to be able to Output inside

of our end result a lot better than how

it would without this larger language

model so for this output I'm just going

to custom make this to game results and

I'll just do game res for short and I'll

keep this as a string because this is

just going to be a text again that's

going to hold all the information that I

want and I'll just say that this is the

results of the

games okay great so now I have

everything ready to go and it's

connected to my end node here now my end

node is pretty much how I want my result

to be formatted here right so I'm

already have all the information

information that I need and now I'm just

being able to present it to the user the

way that I want to with this end node

and I'm going to take this game result

that we

have and I'm going to have that as an

input but before I do that let's add an

answer with a direct answer content as

well and this is where we're going to be

able to format our response the way we

want it to be so we'll take game results

let's do game res and I'm going to

reference that to what we're getting

from our large link model all right I'll

also take my date that I'm going to be

taking from the user from the beginning

right so I have to reference my date

from the start and then I also am going

to take my game count right so we have

our game count because we want to have

our amount of games that we're going to

present to our user as well and we have

our game count is coming from our uh our

code all right so now all we need to do

is create some type of answer content

and we're going to customize how this

answer is going to look so um we're

going to use these same input fields in

order to do that and I've already have

this written out here so that when it

does print out to the user and they ask

hey tell me the scores for this specific

date it's going to list it in a way that

we have this formatted here so the bot's

going to

say there were blank amount of games on

a certain date here were the results so

now we have all these notes connected

and let's give it a test run and see how

it

works so we have this date here we'll

submit for the 20th of January and we'll

submit it and let's zoom out a bit and

we're going to see how this workflow is

being used here so we have our starting

node is already having a success and our

start node again is just taking in our

input so we can display our result here

and you'll see this just taking in the

date right and then we also have our NBA

Daily data node and this here is taking

in our our game date and it's giving us

back this payload and it's letting us

know we had eight games on that day

here's some highlights here's the away

team and all this other information that

we're going to need but if you look at

how much data is in here there's a lot

and we really don't need all of this now

this then gets passed to our code node

and this is where we take care of

truncating that data and making it a lot

smaller because we're only asking for

the profile the box score who the home

team was who the away team was and the

amount of games that did happen on that

day so we have our display result here

and we'll see that we have this input

given from this API and look how much

longer this is from this input that

we're getting from the API or this node

and how much shorter it is now when we

run it through this code because we're

only taking out the information that we

want now this is being passed to our

large language model node now this large

language model node is taking this

information from this output and running

it through this prompt to make it more

readable for us

and we show this display result here and

we take this input this same output that

we get from here it turns into an input

and an outputs here are the game results

uh the NBA games that happen Milwaukee

Bucks Detroit Pistons it shows all these

games that happen on this date and

notice how this list is a lot shorter

now it's only showing these eight games

so when we pass this large language

model node inputs and outputs to our

successful end node we're going to only

take out this information that we do

want to present to our user the game

results the game date and the game count

and if we display our results here

you'll see that we have it custom made

on how we've written here in our answer

content and it says there were x amount

of games here on this date and it's

going to present it in the way that we

wanted to so let's go right here and

publish our workflow after a successful

run that we just

have and we can go over here to are bot

currently there's no workflows or

plugins in this bot it's just powered by

a Persona impr prompt at the moment so

what I'm going to do now is just compare

how this bot responds without a workflow

versus how it will respond when we do

add the workflow so let's just ask this

spot the same question we had before

what happens on January 20th tell me the

scores and let's

see so right now I can already tell you

off the bat this Warriors versus Lakers

game did not happen on this date

and either did this Heat versus 76ers

game so I'm not sure where this

information is coming from and it

doesn't mean that these Bots are not

intelligent it's just that it's not

tailored for our needs yet and that's

where the workflow is come into play so

let's see how this looks when we add a

workflow now if I go to I created and

add my workflow to the

space I'll ask this bot the same

question again and then we're going to

see how this response is completely

different

now so as you you can see here it's

going through the workflow just remember

all those nodes that the inputs are

being passed from one node to the next

all the way to get to the end result and

as we look at how this response is we'll

see that this is tailored to how we

wrote it in our workflow remember we

wrote in our workflow that there were

going to be a certain amount of games on

this date that's how we formatted the

text to be and now this workflow is

going through the API and the also the

code node the large language model all

the way to the end to give us the

accurate information and the accurate

scores that happen on the state so

that's the power of workflows right I

can specify how I want things to look

and tailor make it for my users all

right so that's how you use workflows

with codes now it's the most advanced

feature when it comes to using the

platform however you can really see the

difference in the quality of the answer

your Bot gives when you implement a

workflow now if you want to learn more

check out our documentation and also

join us on Discord keep a lookout for

more videos and I'll see you next time