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WEBVTT
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And welcome back to team PC and Team Mac as we come back together again for a quick video.
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In this one, I'm just going to quickly walk through what you do.
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If Anaconda has given you problems, you're not able to get that to work and you want to fall back to
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the simpler virtualenv approach.
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This is a quicker, simpler approach.
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It's less guaranteed to be compatible.
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It's less heavyweight and powerful than the Anaconda environment, but it's perfectly okay.
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And so by all means, use this if you would prefer.
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So I'm going to do it on my Mac.
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But I'll point out when things are different for the PC and it's basically the same, although I'll
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say the the instructions begin where it says alternative setup instructions in the Readme on on the,
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the, the repo.
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And one important difference between PC and Mac is that if you haven't, I imagine that that almost
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everyone on this has has installed Python before on their PC.
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But if you haven't, then you will need to do that.
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Python comes on Macs by default, but not on PCs, so I've got a link in here to go to the Python website.
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When you go to the Python download website, you can choose to download the latest version of Python,
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but it might be better to choose Python 3.11.9, which is the latest version 3.11 to be compatible,
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and you can come down here and do a windows installer to install on windows.
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And if you don't have this version and if you have the time to install this version on windows, that
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would be better.
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Okay.
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Once you have done that, then on a mac, bring up a terminal window on the PC.
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Bring up a normal PowerShell prompt, not the Anaconda prompt, just a regular prompt, and go into
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the projects directory that we created earlier and then into the LM Engineering directory where the
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project sits.
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And now you do a simple command.
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Let's first just quickly check what version of Python we're running Python minus minus version.
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And on my computer my default version is actually 3.12.3.
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It still works as far as I can tell for everything, but it's not as guaranteed to be compatible.
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But still, I'm going to push ahead with this.
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Uh, and what we now do is we type Python minus m.
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Venv venv.
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You might wonder what magic, what strange concoction that is.
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Python is saying I want to create a new virtual environment, and then the name of that virtual environment
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is going to be Venv again, which is something that people usually often use.
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And if I look in this directory, you'll see that there is now a folder called Venv virtual environment,
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which will contain all of the Python files for this virtual environment.
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And so far everything has been the same with a PC.
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What I'm going to do now is different between a mac and a PC.
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On a mac, what we're going to do is activate this virtual environment on a mac.
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You do that by typing source venv slash bin slash, activate.
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And again, all of this is of course I just did that.
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All of this is of course written in the Readme instructions.
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And the way we know we've activated the environment is that Venv appears over here on the left.
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You can see there's also this base here because I've got Anaconda installed.
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You may not see that if you haven't installed Anaconda and on a PC you won't, you won't see that.
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but that can be safely ignored.
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We are in the venv.
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Environment.
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And now what you do, which is something that probably most of you have done many times before, is
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we do pip install because we want to install Python packages.
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Minus R means we have a file of names of packages that we want to specify, and it's called requirements.txt.
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And then when I press enter it is now going to be installing these packages.
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But it's not installing them on my the python that comes with my box.
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It's installing them specifically on this virtual environment called Venv.
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So I'm isolating my environment from anything else that I might be doing on my computer with Python.
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And you can see that there's plenty going on here as it does all of these things.
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It will be slower for you because you can see it's saying it's used cached.
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Because I've done this before, it won't be cached for you presumably.
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So there'll be a lot of downloading going on, but it's still a lot quicker than setting up the Anaconda,
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uh, window.
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And then the business happens at the end.
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Right now it is at the moment, now going ahead and installing all of that.
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And you can see a lot of different packages here.
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Uh, of course, only some of these are the packages we're actually going to use on this course.
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Many of them are things on which they depend.
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So it's installed both the packages we need and all of their dependencies up there.
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Um, but you'll see that there's things like SQL alchemy, and that's because we're going to be using
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an object database that's going to depend on that.
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Um, and you can see other things going on here.
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Chroma is the the vector data store, a wonderful open source vector data store that we'll be using
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Tokenizers part of our Huggingface code.
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So lots of things you can take your time while it's doing this, to look around and see the names of
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some of the packages that we will be using.
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All right.
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That has now happened.
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And it didn't take very long at all.
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And at the end of this, all I have to now do is again, uh oh, I might just quickly mention that the
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the command for a PC to activate your environment is in fact venv and then backslash scripts.
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Backslash activate.
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So you would type that first and then you would type the pip install r requirements.txt.
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And of course that's all specified in the readme.
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But whether you're on a mac or on a PC at this point all you would do is type the simple JupyterLab.
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And with any luck, when I press enter, it is now going to launch the JupyterLab environment.
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Here it comes.
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And this is going to be our home for the next few weeks.
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The next eight weeks.
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Folders for each of the eight weeks and the usual setup.
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The only difference being now we have this Venv folder which contains our virtual environment.
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So that's the fallback plan.
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I hope that makes sense.
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If you have any problems at all with the fallback plan, then please, please, please reach out.
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I will fix it.
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We'll get you on your way quickly.
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All right, on to the next video when we're going to be doing setting up API keys with OpenAI.
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See you there.