Welcome to the future of AI, where GPT models autonomously define and
execute tasks to achieve your objectives. In today's video I'll guide you
through setting up the number one trending GitHub project AutoGPT, an AGI
experiment that's transforming the game. With capabilities like Google search, long-term
memory, local file storage and text-to-speech, AutoGPT easily helped me develop these two
brand concepts with minimal human interaction. Get ready to experience AGI as we dive into this groundbreaking technology that's
redefining the boundaries of AI. Hi I'm Appy Dave and my goal is to build
an app a day using the power of AI. Today I explored autonomous GPT through
AutoGPT and I used this automatic bot to create a product that combined yoga mats
with meditation music and incense candles. And from there I was able to develop concepts
for Pure Bliss Essentials and Tranquility Heaven. Let me walk you through how I set this all up. Here we have a GitHub application called
AutoGPT. This is essentially an automation bot that uses Python to interact with
GPT-4, Pinecone text embeddings, Elevenlabs, text-to-speech and Google search and
is extendable to use other services. What this means is that it can learn from
itself, it can speak out loud the responses it has, it can automatically create documents
and spreadsheets, it can get smarter over time through human interactions and it can search
the internet for up-to-date information. And I'm sure it won't be long before you can speak to
it. Now I'm going to show you how to set it up. But rather than follow the instructions directly, I'm going to let ChatGPT read the
instructions and tell us how to install it. So first, we're going to find
the README file and click on it. When that's open, you can click on the
RAW button so that we can copy RAW text. Now, move down to the table of contents and then
just select all the text from that point to the bottom of the page and copy it. From here, you can
open ChatGPT. I like to select the GPT-4 model. I then paste the README file directly into
the chat. And then I tell GPT to read it and say ‘yay’ if it understands. I tell Chat
that I want it to help me set up AutoGPT. I also mention the type of computer
and the version of Python I am running. I got instructions on how to open my terminal,
how to clone this particular repository onto my computer, how to change directories into the
new repo, and how to use Python to install this application. It then gave some additional
information about renaming the environment template so that I could configure OpenAI, Eleven
Labs, Pinecone, and Google with access tokens. Now when I access Python or the Python package
manager on my computer, I use Python 3 or pip3, and I just wanted to mention this to the chat
so it can give me more accurate commands. And now it's just rewriting the
commands with the minor change. I'm installed, so I now ask it what to do next.
It tells me to cd into the folder and run python main.py as the main entry file. One of the
things I noticed is that I'm using Python 3 and it did not get that right. It is also a little
bit premature in asking me to run the app straight away. And the reason for that is we haven't
done any configuration. So I go back up through the file and look for the configurations for
ElevenLabs, OpenAI, Pinecone and Google Search. I then just asked GPT where the steps were for
configuration. It told me that I needed to copy the.env.template file into a file called.env.
I also asked what the difference was between Pinecone and Google in this context. I was
aware that Pinecone was a vector database, but I wasn't sure whether Google was playing a
similar role. It told me that Google was being used for the live search on the internet. Here I'm
creating my .env file. I follow the instructions and go to the OpenAI account information, click
on API keys and I create myself a new API key just for using this application. Then I paste
it against the OpenAI API environment variable. ElevenLabs is very similar. Go to the profile,
display the API key by clicking the show button, paste it against the environment variable
ElevelLabs API key. To set up Pinecone I asked chat how to do this. It gave me a link to the
Pinecone website. It also gave me the environment variables to alter. I signed up for a new Pinecone
account and followed the instructions to go to the default project. Copied the environment over
to pinecone underscore env then clicked the show API key, copied it and pasted it into the
pinecone API underscore key. Lastly I asked chat gpt how to set up the google search API key.
There was a link that it gave me. I went to the Google Cloud Console, found a little
drop down near the top left of the screen, where I was able to see a dialogue and select
new project. I named my project Autonomous GPT. After creation, I was able to click
APIs and Services under Quick Access. There was a little button
called Enable APIs and Services, and the name of the service I
wanted was called Custom Search. You can then create credentials by clicking
on the Credentials button on the left, then click the blue Create Credentials
link at the top of the page. A dialogue will appear, and you can copy the API key and paste it over to the
Google environment variable. ChatGPT also told me that I needed to set up
a Custom Search engine, and it gave me a link, so I clicked on Custom search engine and when I
saw all search engines I clicked the add button. I selected the “search the
entire web” radio button. I also had to give it a name
and fill in the capture card before creation. Now your custom search engine
will be created and there will be a little ID that you need to copy. Just paste that ID against
the custom search engine ID environment variable. I asked chat GPT if there was anything else
that I had to set up. It said I was okay but I did spend the next hour trying to debug the
library to fix one little problem that was easy to solve in retrospect. The issue
was that the open API base environment that GPT was giving me, api.openai.com
as an answer, was incorrect. The issue was around the
OPENAI_API_BASE environment variable GPT was giving me api.openai.com as an answer, but
the correct answer was actually api.openai.com/v1. After this final bit of configuration, I was
able to get AutoGPT running. But I will say it took three or four attempts before I could
get it to do a useful chat. There are still a number of issues within this project that need to
be addressed before it becomes production worthy. In the next section, I will talk about
one simple continuous chat that I did get, to come up with a product concept for a business. I gave the AI a name. I
called it Product Inventor. Next, I gave it a role, and its role was to take three products and come up with a
new product that blends the ideas. Finally, I gave it three products, scented
candles, yoga mats, and relaxing music. Now I can enter up to five but if you press enter
on an empty line it'll just kick off the bot. Now the first thing that the bot
does is it reflects on what its own thoughts are on the project.
The goal is to come up with a new product that blends the ideas of scented
candles, yoga mats and relaxation music. So it works out that it should research the
benefits that these products bring by finding the commonalities between them. It then reasons
for itself that it needs a deeper understanding of the products and how blending them can inform
its future decisions. It also understands that it needs to create a product that will be
revolutionary. So it comes up with a plan, conduct research on scented candles,
yoga mats and relaxation music, look for their commonalities and brainstorm
ideas for potential new products. The last piece, criticism, is really powerful
because Bots can have a problem with going down rabbit holes in a direction that the author
didn't intend. And so it tells us that it needs to ensure that it's taking the time to
thoroughly research each product and should not jump to any conclusions too quickly. It then
creates its next command and asks for guidance. So the command is a Google search on the benefits of
scented candles, yoga mats, and relaxation music. So now it's looking for input from the user. So
let's break this down. If you just want it to execute the next command, press yes to authorize.
If you want to steer the way it's thinking, then just give it some feedback in the
form of a prompt. If you are finished with all the commands that the bot has
run, you can press N to exit the program. You might've noticed that I skipped the continuous
command, which is yes followed by “ y -N””. The reason is that the chatbot will
actually run automatically and spawn a lot of requests to different services
such as the Google Search API, the Chat GPT API, the Pinecone API and you could rack up
a really hefty bill with some of these services if you are not paying attention. Now with
this understanding in place let's press Y. So we can see at this point it's
gone and researched some information on various websites like Lullify,
Studio Growth, Healthline and it's putting all this information into
its long-term memory in Pinecone. Now if you look at the inventor
thoughts, reasoning, plan and criticism, you'll notice that there's not a lot of
difference in this next action it's about to take and the previous action. So we'll
press Y and see what it does differently. And it looks like it's just
continuing its research. It's gone to Twitter, Amazon, Yogarascals
and other URLs to get information. It now looks like the bot has done enough research
on the products themselves and decided that what it needs to do now is, how can it blend these
products together in a unique and effective way. And now it's coming up with a lot of
information that it's committing to its long-term memory in Pinecone. And from this
information decides to research marketing strategies around wellness products, followed
by consumer preferences for wellness products. Now I let the bot run for a while, pressing
yes each time, until I get to this section. Because here it ran a new command called
write to file and you can see it's written out a file called product_pitch.test with
a whole lot of content. Now what's great is that it's not just in the Pinecone memory,
but it's now out in a file where I can use it. So the bot continues for a little while doing
different searches building up a knowledge base and then it gets to this section. It's about to
write a report for me in markdown format and this will be great because I'll be able to use it to
develop presentations and social media assets. Now the bot actually had an error and failed to write
out the report to the file but as you can see it is all here so I just copied this and put it into
my own markdown file and you can view it here. I then used a manual prompt asking it to give me a
hierarchical list of tasks that it had worked on. It failed with the command but it still gave the list and I was able to just move
this straight into a mind map. I pressed no to exit then I
proceeded to develop a bunch of assets in mid-journey and Canva
and create these two presentations. The interesting thing is that Canva has an API
so a lot of the presentation work could have been done automatically as well and while
Midjourney doesn't currently have an API, other platforms like Stable Diffusion, DALI
and the up-and-coming Firefly by Adobe will. If you are interested in writing your
own AGI then subscribe to the channel. Part 2 in this series will be about
reverse engineering autonomous bots.
