On this technical article, we’ll discover learn how to leverage the ChatGPT 4 API together with Google Meet, Google Drive, and Google Docs APIs to robotically generate assembly minutes.

Taking minutes throughout a gathering is usually a time-consuming process, and it’s typically troublesome to seize all the pieces that’s mentioned. With using synthetic intelligence, the method could be streamlined to make sure that nothing is missed.

As Microsoft Groups or Zoom, Google Meet has the power to report conferences. As soon as the recording is activated, the transcript of the assembly is generated in a Google Doc format and is saved on an outlined Google Drive shared folder. Google Meet transcript file is used right here however related transcript textual content extract may be finished with Groups or Zoom recording.

For this, a easy internet utility will probably be used as a central level to handle the person interplay in addition to the totally different API calls. The aim is to show an inventory of those assembly transcript paperwork saved on a predefined Google Drive folder. The person will have the ability to choose one among them then press a button to generate a abstract of the assembly minutes in addition to the motion objects with due dates. Additionally, these two new sections will probably be inserted in the identical Google Doc with Google Docs API, containing the outcomes from ChatGPT API.

This text will stroll you thru the steps required to arrange the required configuration and to know the Sprint/Python utility code used to handle the ChatGPT, Google Drive & Docs APIs.

A hyperlink to my GitLab containing the complete Python/Sprint supply code can also be obtainable on the following sections.

By the top of this text, I’ll additionally share my ideas on some limitations and enhancements that could possibly be finished on this utility. I hope it can can help you discover new concepts on learn how to keep centered on extra useful duties than taking assembly minutes.

So let’s dive in!

The online utility seems to be just like the display screen beneath. The higher part shows an inventory of transcript paperwork current on the person’s shared Google Drive folder. Such paperwork are robotically generated within the ‘Meet Recordings’ folder when the person triggers the Google Meet recording button.

The person can choose a doc within the checklist. The chosen doc is displayed within the central half. Lastly, the person can press the button to generate the assembly minutes.

As soon as the button pressed, the Assembly minutes are robotically inserted in 2 new sections:

The ‘Assembly Abstract’ part is a brief description of the assembly based mostly on the assembly transcript. It’s going to keep artificial regardless of the length of the assembly.

The ‘Assembly Motion Gadgets’ part is a numbered motion objects checkbox checklist which can also be based mostly on the transcript. When recognized, a due date can also be inserted.

Every numbered assembly motion merchandise comprises a checkbox that’s natively supported by Google Docs. They could possibly be used in a while by your groups to comply with up the motion checklist and to test them as soon as they’re finished.

The next will can help you edit and run the code current on my GitLab. Earlier than doing that, you’ll must register at OpenAI to get your API key. Additionally, Google Drive and Docs APIs should be activated on the Google console, in addition to making a Google Service Account.

• Go to the OpenAI web site and signal as much as get your API Key
• Go to my GitLab venture named Assembly Minutes technology with ChatGPT
• Edit the Jupyter Python pocket book with Google Colab and reserve it by yourself Colab folder
• Substitute the ‘OPENAI_API_KEY’ worth within the code with your individual api key
• Use the next hyperlink to activate the Google Drive & the Google Doc APIs
• Use the next hyperlink to create a Google Service Account
• Obtain and save the Google Service Account Key (JSon File) in your Colab folder. Identify it ‘credentials_serviceaccount.json’ (or change the worth within the code)
• Share your ‘Meet Recordings’ Google Drive folder with the Google Service Account created beforehand (with ‘Editor’ permission)
• Attend a Google Meet assembly. Document it with the transcript. The video file and the transcript doc will robotically be generated in your ‘Meet Recordings’ Google Drive folder
• Within the code, change the ‘GOOGLE_MEET_RECORDING_FOLDER’ worth with the ID of your ‘Meet Recordings’ Google Drive folder shared beforehand
• Choose ‘Run All’ within the ‘Execution’ menu
• A WebApp ought to begin in a number of seconds. Click on on the URL generated within the backside of the Colab pocket book to show it

The applying ought to seem like the primary screenshot within the earlier part.

As of at the moment, the ChatGPT 4 API continues to be in beta. The used model within the code is ‘gpt-4–0314’ snapshot. It may also be switched to the present model, ‘gpt-3.5-turbo’.

I’ll focus solely on a very powerful items of the code.

4.1. Google Drive integration / API

The primary two traces of code are used to mount your Google Drive root folder. The primary utilization is to retrieve the Google Service Account credential key (JSon file) generated throughout the Fast Begin part.

The code of the following part retrieves a file checklist of all transcript paperwork saved within the Google Meet Recording folder. The checklist will probably be used later to show these paperwork on the internet utility.

4.2. Google Meet transcript doc textual content extract

These features are used to extract textual content components from an outlined Google Doc ID. Google Meet generates a paragraph named ‘Transcript’. The placement of the ‘Transcript’ part is recognized and will probably be used later as a place to begin to insert the assembly minutes. The 2 sections inserted by the appliance will probably be situated simply earlier than this ‘Transcript’ part. (and proper after the ‘Attendees’ part)

4.3. ChatGPT preparation: break down of the transcript textual content into chunks

ChatGPT API fashions have a restricted variety of tokens per request. In an effort to keep appropriate with the ‘gpt-3.5-turbo’ mannequin, the max worth used within the code is 4096 tokens per request. However remember the fact that the ‘gpt-4’ mannequin can deal with rather more. A 8k or a 32k fashions are additionally obtainable, they can be utilized to considerably enhance the assembly minutes’ high quality for lengthy conferences.

As a consequence, the Google Meet Transcript doc textual content must be damaged down into chunks of 4000 tokens with an overlap of 100 tokens.

These features will put together and return an inventory of chunks that will probably be used later by the ChatGPT API.

4.4. ChatGPT API utilization

This operate generates the assembly abstract and motion objects in a number of steps. A ChatGPT API name is finished for every of them:

• Step 1: Summarize the assembly transcript textual content. The operate iterates over the chunk checklist generated beforehand. The content material despatched to ChatGPT is predicated on the recorded dialog between the attendees. The ChatGPT API is known as for every chunk with the next request: ‘Summarize this assembly transcript: <chunk>’
• Step 2: Consolidate the response (Assembly abstract) from Step 1. The ChatGPT API is known as with the next request: ‘Consolidate these assembly summaries: <ChatGPT responses from Step 1>’
• Step 3: Get motion objects with due dates from the transcript. The operate iterates over the chunk checklist generated beforehand. The ChatGPT API is known as for every chunk with the next request: ‘Present an inventory of motion objects with a due date from the supplied assembly transcript textual content: <chunk>’
• Step 4: Consolidate the assembly motion objects from Step 3 in a concise numbered checklist. The ChatGPT API is known as with the next request: ‘Consolidate these assembly motion objects with a concise numbered checklist: <ChatGPT responses from Step 3>’

Every ChatGPT API used parameter (i.e. ‘temperature’) is documented within the code.

4.5. Google Docs API administration utilization to insert the ultimate assembly minutes

The target of this operate is to insert the assembly minutes into the Google Doc chosen by the person. The textual content is inserted earlier than the ‘Transcript’ paragraph. The beginning index recognized within the earlier features is used right here as a place to begin.

Two sections are inserted right here: ‘Assembly Abstract’ and ‘Assembly Motion objects’.

Every part’s insertion is finished with the next steps:

• the part’s title is inserted (as a textual content i.e. ‘Assembly Abstract’)
• its type is ready to ‘HEADING_1’, its textual content type is ready to ‘daring’, its font dimension is ready to ‘14’
• the part’s content material is inserted (this comes from the ChatGPT API end result)
• its type is ready to ‘NORMAL’. A bullet level can also be inserted with an arrow for the ‘Assembly Abstract’ part and a checkbox for the ‘Assembly Motion objects’ part

Some ‘tabulation’ and ‘new line’ characters are additionally inserted to right the textual content returned from the ChatGPT API.

Tip: Please observe that the ‘ar’ desk is iterated in a reversed method to make sure the beginning index place stays at all times updated following every textual content insertion.

4.6. The primary Python Sprint Net Utility

This half is used to construct a easy internet utility on which the person can work together. Principally, it shows an inventory of paperwork saved on a Google Drive shared folder. The person can choose one among them which is displayed within the central a part of the display screen. As soon as the button is pressed, the assembly minutes are inserted into this doc. The up to date doc is refreshed with the outcomes.

This code is constructed on high of the Sprint framework. It even works inside a Google Colab pocket book.

Every doc is displayed inside a devoted iFrame. The doc’s hyperlink is predicated on ‘embedLink’ worth, beforehand retrieved by the Google Drive API.

Additionally, a progress bar is displayed throughout the ChatGPT API calls and the Google Doc assembly minutes’ insertion steps.

The primary problem utilizing ChatGPT inside your organization is to have a leak of delicate info your organization is engaged on. This occurred not too long ago at Samsung the place staff have by chance leaked firm secrets and techniques with ChatGPT.

One of many enhancements of this code could possibly be the execution of knowledge masking earlier than calling the ChatGPT API. Not less than the attendee names and extra tagged fields containing delicate info must be masked. The assembly title may additionally include some tags for knowledge masking. I.e ‘Assembly with <Microsoft>’ the place ‘Microsoft’ will probably be masked on your complete transcript doc knowledge extract. As soon as the response is acquired from ChatGPT API, the reverse must be finished. Every masked info must be unmasked earlier than calling the Google Docs API.

For this, a reference desk must be used to retailer every area ID with its clear and its masked worth. So these fields could possibly be masked earlier than calling the ChatGPT API, then unmasked when inserting the assembly minutes’ sections with Google Docs API.

Thanks for studying my article to the top, hope you loved it!

As you’ll be able to see, ChatGPT 4 API mixed with Google Drive/Docs APIs are very highly effective and might contribute considerably to bettering your day-to-day work.

You’ll find your complete supply code on my GitLab: Assembly Minutes technology with ChatGPT