GitHub Meets GPT-4: Crafting a Repo Analyzer - Langchain Tutorial | Part 1 🌐

in the dynamic landscape of AI there's always something new around the corner and today I've got something that's had me on the edge of my seat for a while now allow me to introduce a new tool that marries the capabilities of open AI GPT with the intricate world of GitHub repositories ever sifted through lines of code and wished you could just ask the repo directly now's your chance simply pose your questions and watch as the AI deciphers the broader scope of repositories for you whether you're trying to grasp the overall purpose or dive into understanding specific functions this tool is your new best friend as we embark on building this together we'll unravel the magic of embeddings and Vector stores the backbone of our ai's understanding and with just a Sprinkle of Express and a dash of Tailwind CSS will whip up a userfriendly interface in no time ready for some live coding Magic let's dive in and remember as always the complete code base is available on GitHub check the description below for the link all right time to kick things off let's begin with a fresh new project and initialize it using npm in it next up we'll add the type module to our package.json this Nifty Edition lets us use es6 import statements making our code cleaner and more modular always a good move when setting the foundation let's create a new JavaScript file named GitHub do loader. JS this will serve as our main hub for loading and interacting with GitHub data first things first we're going to import the GitHub loader from Lang chain this powerful utility will be our bridge to seamlessly interact with GitHub let's get it set up and see it in action ah but there's a small catch before we can harness the power of the GitHub loader we need to install it a quick npm install Lang chain should do the trick let's get that dependency in and move forward with the installation complete we're all set to bring it into our project let's go ahead and import the GitHub loader from Lang chain document loaders web all right let's dive into the core functionality we're setting up an asnc function named analyze repo that takes in a repo URL and a repo query in side we're initializing our GitHub repo loader with the provided URL and some configuration options notice the recursive true this means we're diving deep exploring all layers of the repository we're also targeting the main branch and setting a limit on concurrency to ensure smooth operations with everything set we'll call the load method to fetch our data and there you have it a neat function ready to retrieve and analyze any GitHub repository now let's put our analyze repo function to the test we're targeting the repository our query a simple request please generate a brief summary about the repository with the await keyword we'll ensure our function has all the time it needs to fetch and process the data a quick correction the correct name of the module we're using is GitHub repol loader it's crucial to get these names right as they are the gateways to the functionalities we want to tap into as we check our console the result unfolds we're presented with a structured summary of the business knowledge repository it encapsulates key details from the main objective I and core functionalities to notable files and contributors now we're bringing in the big guns we're initializing an instance of open AI specifically targeting the gp4 model this will be our Powerhouse the brain behind the operations capable of understanding and generating humanlike text with llm set up we're poised to Leverage the impressive capabilities of GPT 4 next we're diving straight into the action we're using the predict method of our llm instance to pose a dynamic request by embedding our Reaper query and the fetched data into our question we're asking gp4 to sift through the repositories details and provide a meaningful answer it's showtime let's run our code and see gp4 in action Transforming Our repository data into insightful answers fingers crossed and let's try it out whoops seems we hit a snag when passing our data to GPT 4 it's seeing it as object object which is not what we intended it's a common hicup when directly passing objects into Strings we'll need to format our data properly so GPT or can make sense of it all right diving a bit deeper into our data I noticed the documents have an attribute named page content this seems to be the gold mine of information we're after let's leverage this by mapping out the page content from our documents this should give us a cleaner more digestable format for gp4 to understand let's get to it okay with our data now mapped and defined it's time for round two let's feed this adjusted data into GPT 4 and see if we hit the mark this time ready let's try it again ah a classic hurdle we've exceeded the rate limit and here's another twist gp4 has a 4K context limit and with GitHub repos size can be unpredictable we're essentially trying to fit an ocean into a bottle we'll need a strategy to manage and condense our data ensuring it fits within the model's constraints let's brainstorm and Tackle this challenge head on all right let's pivot one immediate solution switching to a model that can handle more context let's give the GPT 3.5 turbo a shot which boasts a 16k context limit this should provide us with more wiggle room to work with those expansive GitHub repos well it seems even with the beefier GPT 3.5 turbo we're still overshooting our data resulted in a whopping 48 through 339 tokens far exceeding the 16385 token limit it's clear that simply switching models won't cut it we'll need to be more strategic with our data processing time to rethink and refine our approach to fit Within These constraints you know what this isn't the first time we've wrestled with token limitations in fact there's an entire playlist titled overcoming token limitations surpassing gp4s constraints for Gigantic web content that dives deep into these challenges if you're as intrigued as I am about bypassing these barriers that playlist is a gold mine but for now we're going to leave you hanging just a bit what happens next how do we tackle this Beast of a challenge well you'll have to tune in to the next episode to find out if you've enjoyed this journey so far do hit that like button and subscribe you won't want to miss what comes next until then keep coding keep exploring and I'll see you soon

Channel: Sebastian Schlaak

Views: 16

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Id: vWYTu6DXJXU

Length: 8min 19sec (499 seconds)

Published: Wed Oct 04 2023