have you ever found yourself on the
verge of making a controversial purchase
just as you're about to click on that
buy button an unexpected thought
suddenly crosses your mind wait a minute
they look a little bit like soy cheese
don't they no no no no no they're
absolutely beautiful and Kanye West
loves them he wears them all the time
but if I like things that Kanye likes is
that really a good thing okay I need to
relax everything is fine and buying
these makes me a Visionary a trend
Setter do these holes exist for
ventilation purposes oh okay time for a
break I need to urgently distress from
all this thinking with some Pringles
wait is you think this like really
unhealthy so that inner dialogue you
just witness is what Daniel conman
author of Book Thinking Fast and Slow
calls system to thinking it's a slow
conscious type of thinking that requires
deliberate effort and time the opposite
of that is system one or fast thinking
system one is subconscious and automatic
for example when you effortlessly
recognize a familiar face in a a crowd
but why am I talking about this in a
video about AI assistance well in order
to understand that I have to mention an
amazing YouTube video posted by Andre
karpati a great engineer at open AI in
that video Andre clarifies that right
now all large language models are only
capable of system one thinking they're
like Auto predict on steroids none of
the current llms can take let's say 40
minutes to process a request think about
a problem from various angles
and then offer a very rational solution
to a complex problem and this rational
or system to thinking is what we
ultimately want from AI but some smart
people found a way to work around this
limitation actually they came up with
two different methods the first and
simpler way to simulate this type of
rational thinking is with tree of
thought prompting you might have heard
of it so this involves forcing the llm
to consider an issue from multiple
perspectives or from perspectives of
various experts these experts then make
a final decision together by respecting
everyone's contribution the second
method utilizes platforms like crew aai
and agent systems crei allows anyone
literally anyone even non-programmers to
build their own custom agents or experts
that can collaborate with each other
thereby solving complex tasks you can
tap into any model that has an API or
run local models through AMA another
very cool platform
and in this video I want to show you how
to assemble your own team of smart AI
agents to solve tricky complex problems
and I'll also demonstrate how to make
them even more intelligent by giving
them access to real world data like
emails or redit conversations and
finally I'll explain how to avoid paying
fees to companies and exposing your
private info by running models locally
instead and speaking of local models
I've actually made some really
surprising discoveries and I'm going to
talk about it a little bit later so
let's build an agent team I'll guide you
through getting started in a way that's
simple to follow along even if you're
not a programmer in my first example
I'll set up three agents to analyze and
refine my startup concept okay so let's
begin first open vs code and open a new
terminal I've already created and
activated my virtual environment and I
recommend you do the same and once
that's done you can actually install
crew AI by typing the following in the
terminal Next Step will be to import
necessary modules and packages and
you're going to need an open API key so
in this case I'm going to need the
standard module and I need to import
agent task processing crew from crew AI
you can set the open AI as the
environmental variable so by default
crew AI is going to use GPT 4 and if you
want to use use GPT 3.5 you have to
actually specify that but I don't think
that you're going to get amazing results
with 3.5 I actually recommend use GPT 4
now let's define three agents that are
going to help me with my startup there's
no actual coding here this is just good
old prompting so let's instantiate three
agents like this each agent must have a
specific role and I want one of my
agents to be a market researcher expert
so I'm going to assign it or this
specific role also each agent should
have a clearly defined goal in my case I
want this research expert agent to help
me understand if there is a substantial
need for my products and provide
guidance on how to reach the widest
possible target audience and finally I
need a backstory for my agent something
that's going to additionally explain to
the agent what this role what this role
is about lastly you can set verbos to
True which will enable agents to create
detailed outputs and by setting this
parameter to true I'm allowing my agents
to collaborate with each other so I will
save this agent as a marketer and I'm
going to do the same for two other
agents so overall I I'll have a marketer
a technologist and a business
development expert on my team of AI
agents so once this part is done it's
time to Define tasks tasks are always
specific and results um in this case it
can be let's say a detailed business
plan or market analysis for example
agents should be defined as Blueprints
and they should be reused for different
goals but tasks should always be defined
as specific results that you want to get
in the end and tasks should have a
deion always something that
describes what the task is about
and they should also always have an
agent that's going to be assigned to
every specific test so in my case I want
to have three specific tasks my business
idea is to create elegant looking plugs
for Crocs so this iconic Footwear looks
less like Swiss chees I will assign the
first task to a marketer agent and this
agent will analyze the potential demand
for these super cool plugs in advis on
how to reach the largest possible
customer base another task is going to
be given to a technologist and this
agent will provide the analysis and
suggestions for how to make these plugs
and the final task will be given to a
business cons consultant who's going to
take into consideration everyone's
reports and write a business plan now
that I have defined all the agents and
all the tasks as a final step I'm going
to instantiate the crew or the team of
Agents I'm going to include all the
agents and tasks and I'm going to define
a
process process defines how these agents
work together and right now it's only
possible to have a sequential process
which means output of the first agent
will be the input for the second agent
and then that's going to be the input
for the third agent and now I'm going to
make my crew work with this final line
of code I also want to to see all the
results printed in the console so that's
the most basic possible example and it's
the best way to understand actually how
crew AI works and I expect these results
to be far from impressive I actually
believe that the results are going to be
just a little bit better than just
asking Char with to write a business
plan but let's
see okay so now I have the results I
have business plan with 10 build points
I have five business goals and a time
schedule and so I should have a 3D
printing technology and injection molds
laser Cuts apply machine learning
algorithms to analyze custom preferences
and predict future buying Behavior so I
guess this agent really took very
seriously my business idea and I even
have sustainable or recycled materials
that's great so there you go so how to
make a team of Agents even smarter
making agents smarter is very easy and
straightforward with tools by adding
these tools you're giving agents access
to real world realtime data and there
are two ways to go about this first and
easier option is to add built-in tools
that are part of L train and I will
include a link to a complete list of
Lang chain tools but some of my personal
favorites are 11 Labs text to speech
which generates the most realistic AI
voices then there are tools that allow
access to YouTube and all kinds of
Google data and Wikipedia so now I'll
change my crew and in this next example
I'll have three agents researcher
technical writer and writing critic
everyone will have their own task but in
the end I want to have a detailed report
in form of a blog or a newsletter about
the latest Ai and machine learning
Innovation the blog must absolutely have
10 paragraphs it has to have all the
names of all the projects tools written
in bold and every paragraph has to have
a link to the project I'll use Lang
chain Google seral tool which will fetch
Google search results but first I'll
send it for free API key through serer
Dev I'm going to include the link to all
the code and all the prompts in the
deion box as usual so let's begin
by importing necessary modules and let's
initialize Sur API tool with API key so
I'll instantiate the tool I'll name the
tool a Google scraper tool and I'll give
it a functionality which is to execute
search queries and along with
deion to indicate the use case as
a last step before running the I
should assign this tool to my agent
that's going to run first and once I run
the I can see all the scrape data
in blue letters green letters show agent
processing this information and white
letters are going to be the final output
of each agent so this is what my
newsletter looks like right now and I
have 10 paragraphs as requested each
paragraph has a link and around two to
three sentences so the form it is fine
it's exactly what I was looking for but
there is a big problem so the quality of
information in the newsletter is not
really the best none of these projects
are really in the news at this moment
and my newsletter is only as good as the
information that goes into it so let's
fix that how do I improve the quality of
the newsletter
well it's actually quite simple I just
need to find a better source of
information and that brings me to custom
made tools but before I dive into that
it's worth mentioning that there is one
more cool and very useful pre-built tool
that people might Overlook and that is
human inal lope this tool will ask you
for input if it runs into conflicting
information okay so back to fixing the
newsletter my favorite way to get
information is local llama subreddit the
community is amazing and they Shir an
incredible amount of cool exciting
projects and I just don't have enough
time to sit and read through all of it
so instead I'm going to write a custom
tool that scrapes latest 10 hot posts as
well as five comments per each post
there is a preil tool through length
chain a Reddit scraper but I don't
really like using it my own custom tool
gives me a lot more control and
flexibility here's a quick look at the
code so import Pro and Tool from link
chain and I'm going to create a new
class that's called browser tools which
is how I'm going to create this Custom
Tool then I'm going to need a decorator
and a single line dog string that
describes what the tool is for the
scrape Reddit method starts by
initializing the pro rdit with
client ID client secret and user agent
it then selects the subreddit local
llama to scrape data from then the
method iterates through 12 hotest posts
on the Reddit extracting the post title
URL and up to seven top level comments
it handles API exceptions by pausing the
scraping process for 60 seconds before
continuing and the scrape data is
compiled into a list of dictionaries
each containing details of a post and
its comments which is returned in the
end and the rest of the code is the same
so I'm just going to copy it from the
previous tool with the exception of this
time I'm going to assign a Custom Tool
uh from the browser tool class and this
is the result that I'm getting with jp4
I'm just going to copypaste the output
into my notion notebook so that you can
see it better I have to say that I'm
more than pleased with the results it
would take me at least an hour to read
latest posts on Lo and Lama then to
summarize them and take notes but CI
agents did all of this in less than a
minute this is a type of research that I
need to do a few times a day and also
this is the first time that I managed to
completely automate part of my work with
agents one thing that I noticed is that
sometimes even GPT 4 doesn't really
follow my instructions there are no
links to these projects in this output
and I asked for them but when I run the
yesterday the agent successfully
included all the links and these outputs
were made on the same day but they're
formatted differently so output varies
and agents can act a little bit flaky
from time to time I also test the Gemini
Pro which offer offers a free API key
you can request it through a link that
I'm going to include in the deion
box essentially you just need to import
special package from L chain you need to
load Gemini with this line and then
you're going to need to assign this llm
to every agent so Gemini output was a
little bit underwhelming the model
didn't understand the task instead it
wrote a bunch of generic text from its
training data which is really
unfortunate so let me know if you run
into different results I'm I'm really
curious and now let's talk about price I
rent The many times and as part
of my experiments but on this particular
day 11th of January I remember that I
ran the four times which means
that I paid around 30 cents every time I
ran it so as you can tell it adds up
pretty quickly and of course this is gp4
how to avoid paying for all these pricey
API calls and how to keep your team of
agents and conversation private yes
local model mod so let's talk about that
right now I've tested 13 open source
models in total and only one was able to
understand the Tas and completed in some
sense all the other models failed which
was a little bit surprising to me
because I expected a little bit more I
guess from these local models and I'll
reveal which ones perform the best and
the worst but first let me show you how
to run local models through all llama
the most important thing to keep in mind
is that you should have at least 8 GB of
RAM available to run models with 7
billion parameters 16 GB for 13 billion
and 32 GB to run 33 billion parameter
models having said that even though I
have a laptop with 16 GB of RAM I
couldn't run Falcon that only has 7
billion parameters and vuna with 13
billion parameters whenever I try to run
these two models my laptop would freeze
and crash so something to keep in mind
if you already installed a llama and you
downloaded a specific model you can very
easily instruct crew AI to use local
model instead of openi with this line
just import a llama from Lang chain and
set set the open source model that you
previously downloaded once you do that
you should also pass that model to all
the agents otherwise they're going to
default to CH GPD among 30 models that I
experimented with the worst performing
ones were llama 2 Series with seven b
parameters and another model that
performed poorly was 52 the smallest of
all of them latu was definitely
struggling to produce any type of
meaningful output and Fu was just losing
it it was painful to watch the best
performing model with seven bilder
parameters in my opinion was open chat
which produced an output that sounds
very newsletter the only downside was
that it didn't actually contain any data
from from local llama subreddit which
was the whole point obviously the model
didn't understand what the task is
similarly but with a lot more emojis
mistol produced a generic but fine
newsletter this is basically Mistro's
training data none of these projects are
companies were part of local subred
discussions which means that mistal
agents didn't understand what the task
is and open hermis and new hermis had a
similar output all of these outputs are
are the best attempts they were even
worst outputs since the results weren't
really that great I played with
different prompts variations of prompts
but that didn't really achieve anything
also I changed the model file that comes
with local models played with parameters
for each of the models and I added a
system PRT that specifically references
local llama but again no improvement my
agents still didn't understand what the
task is so the only remaining idea I had
was to run more modles with 13 billion
parameters which is the upper limit for
my laptop so I first ran llama 13
billion chat and text bottles not
quantized but full Precision models my
assumption was that these models are
going to be better at generating a
newsletter because they're bigger models
but I was wrong the output didn't look
better than let's say open chat or
mistro and the problem was still there
agents couldn't really understand what
the task is so I ended up with a bunch
of generic texts about self-driving cars
as usual again nothing even remotely
similar to actual Reddit conversations
on logal Lama so out of pure desperation
I tried a regular llama 13 billion
parameters model a model that is not
even fine-tuned for anything my
expectations were really low but to my
surprise it was the only model that
actually took into consideration this
great data from the subreddit it didn't
really sound like a newsletter or a Blog
but at least the names were there
together with some random free flung
thoughts which I found a little bit
surprising so there you have it you can
find my notes about which local modes to
avoid and which ones were okay together
with all the code on my GitHub which
I'll link down below and I'm curious
have you tried crew Ai and what were
your experiences like thank you for
watching and see you in the next one
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