AutoGen Quickstart 🤖 Build POWERFUL AI Applications in MINUTES
Summary
TLDRThis video tutorial introduces Autogen, an open-source framework by Microsoft for creating AI applications with multiple agents. It guides viewers through setting up Autogen locally, including installing Python 3.8, creating an OpenAI API key, and using a code editor like VS Code. The process involves creating a virtual environment, installing the Autogen package, and configuring AI models and API keys. The tutorial demonstrates creating a user proxy agent for interaction and an assistant agent for tasks, culminating in a demo where the agents collaborate to plot stock price changes for Nvidia and Tesla, showcasing Autogen's potential for complex problem-solving.
Takeaways
- 🌐 Autogen is an open-source framework by Microsoft for creating AI applications with multiple agents.
- 💻 To set up Autogen locally, you need Python 3.8 or higher, an OpenAI API key, and a code editor like VS Code.
- 🛠️ It's recommended to create a virtual environment for Autogen to manage dependencies specific to the project.
- 🔑 The OpenAI API key is stored in a file named 'oaioreconfig.json' in the project's root folder.
- 📝 The 'oaioreconfig.json' file specifies AI models available to the application, with 'GPT-4' used as an example.
- 🤖 Autogen applications require at least one user proxy agent for user interaction and one assistant agent to perform tasks.
- 💬 The user proxy agent serves as a middleman, receiving user input and directing it to other agents, and can execute code.
- 🔧 In the demo, the 'coder' assistant agent is tasked with generating code to plot stock price changes for Nvidia and Tesla.
- 📊 The coder agent proposes using Python libraries to generate the required code, which is then executed by the user proxy agent.
- 🔄 The process involves iterative communication between agents, with opportunities for user feedback or termination of the session.
Q & A
What is Autogen and who created it?
-Autogen is an open-source framework created by Microsoft that simplifies the creation of AI applications using multiple agents.
How does Autogen facilitate problem-solving?
-Autogen allows adding agents to a chat room where they collaborate to solve complex problems, either with or without user input, by assigning unique roles to the agents.
What are the minimum requirements to set up Autogen locally?
-To set up Autogen locally, you need Python 3.8 or greater, an OpenAI API key, and a code editor like VS Code.
Why is it recommended to create a virtual environment before installing Autogen?
-Creating a virtual environment ensures that all dependencies are installed only for the project and not globally on the machine, avoiding conflicts.
How do you create a virtual environment for Autogen in VS Code?
-In the integrated terminal of VS Code, you type 'python -m venv .venv' on Mac or Linux, then activate it by running '.venv/bin/activate'.
What is the purpose of the 'oaoreconfig.json' file in an Autogen project?
-The 'oaoreconfig.json' file is used to store a list of AI models that will be made available to the application, along with their API keys.
What is a user proxy agent in the context of Autogen?
-A user proxy agent is a middleman between the user and other agents, receiving user input and passing it to the agents, and possibly asking for additional feedback based on agent responses.
How does the user proxy agent execute code on behalf of the user?
-The user proxy agent receives coding instructions from the user, hands them over to an assistant agent to generate the code, and then executes the code in a designated folder.
What is the role of the 'initiate chat' method in Autogen applications?
-The 'initiate chat' method is used to start a conversation with the application, specifying the recipient (an assistant agent) and the message containing the task instruction.
What happens if the user proxy agent encounters an error during code execution?
-If an error occurs, such as a missing module, the assistant agent will inform the user proxy agent and suggest the necessary dependencies to install to proceed.
How can users provide feedback or make changes to the solution in an Autogen application?
-Users can provide feedback or request changes by typing new instructions, which the user proxy agent passes to the assistant agent for adjustments.
Outlines
💻 Setting Up Autogen for AI Applications
The video introduces Autogen, an open-source framework by Microsoft, designed to simplify the creation of AI applications using multiple agents. It allows users to integrate agents into a chat room to collaboratively solve complex problems, either autonomously or with user input. The setup process involves installing Python 3.8 or higher, creating an OpenAI API key, and using a code editor like VS Code. The tutorial guides viewers through creating a virtual environment, installing the Autogen package, and storing the API key in a project file. It also covers the creation of a configuration file for AI models and the fundamental concepts of user proxy and assistant agents in Autogen applications.
👨💻 Creating User Proxy and Assistant Agents
This segment of the video script details the creation of a user proxy agent and an assistant agent named 'coder' within the Autogen framework. The user proxy agent serves as an intermediary between the user and other agents, capable of executing code on the user's behalf. The assistant agent, in this case, is tasked with generating code. The video demonstrates how to import these agents, set their attributes, and specify the AI models they can access. It also explains how to set up the environment for code execution and how the agents interact to fulfill a user's request to plot a chart of stock price changes.
📈 Testing the Autogen Application and Iterating on Agent Responses
The final paragraph of the script describes the process of testing the Autogen application. It outlines how the user proxy agent communicates with the assistant agent to execute a task, such as plotting a stock price chart. The video shows the interaction between agents, the user's opportunity to provide feedback or terminate the session, and the handling of errors like missing dependencies. It also demonstrates how to install necessary modules and rerun the code successfully. The script concludes with a user request to modify the task, which the assistant agent accommodates, and a teaser for the next video, which will explore adding multiple agents to create an AI workforce.
Mindmap
Keywords
💡Autogen
💡Agents
💡User Proxy Agent
💡Assistant Agent
💡Open AI API Key
💡Virtual Environment
💡Dependencies
💡Code Execution
💡Digital Workforce
💡Configuration File
Highlights
Autogen is an open-source framework by Microsoft for creating AI applications with multiple agents.
Agents in Autogen can solve complex problems with or without user input by assuming unique roles.
Setting up Autogen locally requires Python 3.8 or greater, an OpenAI API key, and a code editor like VS Code.
Creating a virtual environment for Autogen installation is recommended to manage dependencies.
Autogen can be installed using pip by typing 'pip install py autogen'.
An OpenAI API key should be stored in a project file named 'oaioreconfig.json' for secure access.
The 'oaioreconfig.json' file specifies AI models available to the application.
Autogen applications require at least one user proxy agent for user interaction.
User proxy agents can execute code on behalf of the user, facilitating tasks like writing code.
Assistant agents in Autogen perform tasks assigned by the user proxy agent, such as generating code.
The 'initiate chat' method allows the user proxy agent to interact with assistant agents.
Autogen applications can dynamically adjust solutions based on user feedback during interactions.
Dependencies for code execution generated by Autogen can be installed using provided instructions.
Autogen allows for the execution of generated code, showcasing its capability to perform practical tasks.
The coder agent in Autogen can modify its solution based on user requests, like plotting percentage changes.
Autogen supports the creation of a digital workforce by integrating multiple agents with distinct roles.
Future videos will explore adding multiple agents to Autogen applications for complex problem-solving.
Transcripts
autogen is an open- Source framework
created by Microsoft that simplifies the
creation of AI applications using
multiple agents basically autogen allows
you to add agents to a chat room where
they will then work together to solve
complex problems this can be done with
or without the user's input by assigning
unique roles to the agents you can
easily create your very own digital
Workforce in this video we will set up
autogen locally and you will learn the
fundamentals of creating your very first
autogen applications setting up autogen
locally is quite straightforward all you
need is to install python 3.8 or greater
you also need to create an open AI API
key and you will need a code editor like
vs code open up a new folder in VSS code
and then open the integrated terminal by
clicking on Terminal and then new
terminal before installing the autogen
package it is recommended to First
create a virtual environment this will
ensure that all dependencies are
installed for this project only and not
globally on our machines creating a
virtual environment is quite easy simply
type python if you're using Mac or Linux
type Python 3 followed by dasm then
VV space
VV again press enter this will now
create this VV folder in order to
activate our virtual environment we can
simply type V EnV tab scripts and within
scripts activate and then press enter if
everything was done correctly you should
see the virtual environment name at the
start of this command we can now go
ahead and install autogen by typing pep
install py autogen gen and this will now
install the auto genen package we are
nearly done with the setup there's only
one more step now we need to store our
open AI API key somewhere in the project
so after copying your open AI API key go
back to the project and in the root
folder create a new file and call it o
aore config uncore list in this file we
will specify a list of AI models that we
will make available to our application
in this example we will only make use of
one model but if you want you can add
multiple models to this list to add a
model we will create a dictionary using
curly braces and this dictionary will
accept two key value pairs the first
value is model followed by a value which
is the name of the model that we'd like
to use in our example that model will be
GPT 4 if you want you can also replace
this with something like GPT 3.5 turbo
but for this example I will use GPT 4 as
this will provide better results the
second value is API key with the value
of the open AI API key if you want you
can add additional models to this list
simply by typing comma and then adding
another dictionary but we will only use
one model this is all the setup we need
to create our autogen application let's
close this file and then in the root of
this folder let's create our first demo
let's create a new file and let's call
it demo 1. piy the first thing we need
to do is to pull in this list of AI
models into our application to do that
we'll type the following from Auto Jen
import config list and in the auto
complete we can see the different
functions that are available for
importing this config we will use the
fromjson function then we will create a
new variable called config list which we
will set equal to the config list from
Json function and this function takes an
argument called EnV or file which we
will set equal to the name of our config
list file which was O aior config uncore
list now in order to create autogen
applications we need at least the
following first we need a user proxy
agent and then second we need at least
one assistant agent let me explain what
these are the user proxy agent allows us
the users to interact with our
application this agent is sort of the
middleman between the user and the
agents and it plays quite an important
role the user proxy agent will receive
input from the user and then pass that
input to the agents and depending on the
responses from the agents this proxy
agent might ask the user for additional
feedback another important note about
proxy agents is that they are able to
execute code on the user's behalf so
let's go ahead and create this proxy
agent from autogen we will now also
import the user proxy agent agent and
now we can create a new variable called
user proxy but you are welcome to call
this whatever you want we will set this
equal to the user proxy agent class and
this class takes in a couple of
attributes the first one being the name
of this agent which will call user proxy
but you are welcome to call this agent
whatever you want then we will specify
another attribute called code uncore
execution underscore config and this
argument takes a dictionary as input
with a key value of work uncore dur
which we can give any value that we want
I'll just call it coding as I mentioned
earlier these user proxy agents are able
to execute code on the user's behalf so
in this demo we'll actually ask our
agent to write code for us and the proxy
agent will hand that coding instruction
over to an assistant agent which will
then generate the code that code will
then be saved in a folder called coding
and the user proxy agent will then
execute the code in that coding folder
so with that said let's go ahead and
create our coding agent we can do that
by creating an assistant agent so from
autogen let's also import an assistant
agent then under assistant agent let's
create a new variable called coder you
are welcome to call this whatever you
want if this was a tester or a document
writer or a project manager you will
call this variable whatever you want we
can set this equal to the assistant
agent class which also takes a couple of
attributes like the name of the agent
which will simply call coder for these
assistant agents we need to specify the
llm models that it has access to so as a
second argument we need to specify the
llm config argument which will set equal
to a dictionary with a key value of
config list with a value of the config
list variable that we created up here so
now we have an assistant with a name
coder which has access to the models
specified in this file we will look at
this later in this video but you are
also able to specify other attributes
like the system message which you can
use to Prime this agent but we will have
a look at that a bit later one in
announcement I do recommend you do is to
remove this dictionary and instead
create a new variable called llm config
which is equal to that dictionary so
when we create a new agent we simply
pass in the llm config variable and this
makes sense for autogen applications
since the whole purpose of using autogen
is to have multiple assistant agents in
your application and the just simplif
things if you can reuse the same
variable so now that we have a proxy
agent that we as the users can interact
with and we have one assistant agent
that will perform a task we can now
finally test out our application in
order to chat with our application we
can call user proxy and on user proxy
there's a method called initiate chat
the initiate chat method takes in an
argument of recipient and recipient is
this assistant agent that it needs to
interact with so let's simply add coder
to this argument so now that the proxy
agent knows which agent it's talking to
we now need to tell it what the message
is by specifying an argument called
message and the string value containing
our task instruction let's go with this
example where we say Plott a chart of
Nvidia and Tesla's stock price change
year to date let's say this file and
then in the terminal let's just expand
this a bit so in the terminal we can go
ahead and run this file by typing py
demo 1. py in the terminal we can see a
few important things first you will
notice this message is from the user
proxy and in Brackets it's saying which
agent it's talking to and in this
example it's talking to the coder agent
and it's passing our instruction to the
the coder agent after a few seconds we
can now see a message from the coder
agent and it is passing the message to
the user proxy and this is the message
from the coder basically it's saying
that it will use these python libraries
to write the code to visualize this
output and we can also see the code that
it's written also at the bottom of the
terminal we now have an opportunity to
change the solution or to provide
feedback or we can type X it to just
terminate this session alternatively we
can just press enter and that will allow
the agents to continue by themselves so
after pressing enter we can see this
message saying that no human input was
received and therefore the agents just
kept going the user proxy agent tried to
execute the code generated by the coder
agent we can also see that the user
proxy agent tried to execute the code
but the execution of the code failed and
the reason reason for the failure was no
module named why Finance so the coder
then replied back to user proxy saying
that in order for this code to work we
need to install a couple of dependencies
and it also tells us how to install
those dependencies so let's do that we
can simply copy this code I'll open up a
new terminal I'll paste in that code and
just run this after installing these
dependencies I'll just go back to this
terminal and let's let just press enter
so now the user proxy is telling the
coder that those packages have already
been installed because we just installed
them ourselves and we can now see the
coder responding to the user proxy
saying great now that you have these
modules installed we can now proceed to
execute this code and it's giving us the
code again so what I'll do now is just
press enter and the user proxy agent
just executed that code and now we can
see this graph being returned what we
can also see in our project folder is
that within the coding folder we can see
the code that was generated by the coder
agent and if we expand this we can see
all the code and this is the code that
our user proxy agent executed after
closing the popup we can see the user
proxy telling the coder that the
execution was successful and we can see
the coder respond back to the user proxy
saying it's glad the code execution was
successful and now we have another
opportunity to make changes to the
solution or to terminate the session
let's try making a simple change let's
type something like plot the percentage
change instead we can see the user proxy
agent passing our instruction back to
the coder the coder is now politely
apologizing to the user proxy agent and
it's explaining the changes that it will
make to the solution let just press
enter and now we are seeing a different
graph for the percentage change in the
stock price for in video and Tesla in
the next video we will have a look at
adding multiple agents to our
application in order to create our very
own AI Workforce if you enjoyed this
video please hit the like button and
consider subscribing to my channel I'll
see you in the next one bye-bye
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