![]() ![]() These snippets should allow you to get all set up and ready to use LangChain.įirst, let's get our environment set up. He provides great tutorials on this topic. I'll be explaining everything with short code snippets from Rabbitmetrics ( Github). Moving forward, you should be able to apply the concepts to start to craft your own use-cases and create your own apps. ![]() I am going to give you an overview of each, so that you can get a high-level understanding of how LangChain works. How to Get Started with LangChainĪ LangChain application consists of 5 main components: I will cover proper build tutorials in future articles, so stay tuned for that. Social Media Content Creation AssistantĪnd the list goes on.Custom Company Customer Service Chatbots.There are many possible use-cases for this – here are just a few off the top of my head: Check out AgentGPT, a great example of this. LangChain also allows you to create apps that can take actions – such as surf the web, send emails, and complete other API-related tasks. Once the relevant information is retrieved, we use that in conjunction with the prompt to feed to the LLM to generate our answer. Think of it as a mini-Google for your document. When we insert a prompt into our new chatbot, LangChain will query the Vector Store for relevant information. Now that we have vectorized representations of the large document, we can use this in conjunction with the LLM to retrieve only the information we need to be referenced when creating a prompt-completion pair. Simple Diagram of creating a Vector Store It works by taking a big source of data, take for example a 50-page PDF, and breaking it down into "chunks" which are then embedded into a Vector Store. In short, LangChain just composes large amounts of data that can easily be referenced by a LLM with as little computation power as possible. While you may be thinking that LangChain sounds pretty complicated, it's actually quite approachable. This was thanks to its versatility and the many possibilities it opens up when paired with a powerful LLM. Recently, LangChain has experienced a significant surge in popularity, especially after the launch of GPT-4 in March. So you can now have your GPT models access up-to-date data in the form of reports, documents, and website info. That's exactly what LangChain does.Įssentially, it allows your LLM to reference entire databases when coming up with its answers. And while these models' general knowledge is great, being able to connect them to custom data and computations opens up many doors. It's offered in Python or JavaScript (TypeScript) packages.Īs you may know, GPT models have been trained on data up until 2021, which can be a significant limitation. LangChain is an open source framework that allows AI developers to combine Large Language Models (LLMs) like GPT-4 with external data. Hopefully, I'll inspire one of you to come up with one of your own. In this article, I'm going to introduce you to LangChain and show you how it's being used in combination with OpenAI's API to create these game-changing tools. Instead, you can rely on AI to do the heavy lifting.īut how exactly are all these developers creating and using these tools? Well, many of them are using an open source framework called LangChain. Gone are the days where you need to scroll through a 50-page document just to find a simple answer. ![]() You may have even started using some of them.ĪI tools such as ChatPDF and CustomGPT AI have become very useful to people – and for good reason. You may have read about the large number of AI apps that have been released over the last couple of months.
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