LangChain QuickStart with Llama 2

LangChain helps you to tackle a significant limitation of LLMs — utilizing external data and tools.

Venelin Valkov

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LangChain helps you to tackle a significant limitation of LLMs — utilizing external data and tools. This library enables you to take in data from various document types like PDFs, Excel files, and plain text files. It also facilitates the use of tools such as code interpreters and API calls. Additionally, LangChain provides an excellent interface for creating chatbots, whether you have external data or not. Getting started is a breeze. Let’s dive in!

While LangChain was originally developed to work well with ChatGPT/GPT-4, it’s compatible with virtually any LLM. In this tutorial, we’ll be using an open LLM provided by Meta AI — Llama 2.

In this tutorial, we will be using Jupyter Notebook to run the code. If you prefer to follow along, you can find the notebook on GitHub: GitHub Repository

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