top of page
  • Writer's pictureInderpreet Singh

Intro to LangChain: Building Apps with Language Models

Introduction to large language models

Language models have revolutionized the field of natural language processing (NLP) and have become vital components in various applications. These models are designed to understand and generate human language, enabling machines to communicate effectively with humans. One of the most prominent advancements in this field is the development of large language models. In this article, we will delve into the world of large language models, specifically focusing on the LangChain framework.

Language models are powerful tools that can generate natural language texts, answer questions, summarize documents, and much more. However, building applications with language models can be challenging, as they require integrating different components, such as data sources, APIs, user interfaces, and logic. Furthermore, language models continue to evolve and getting better so developers must stay up to date on the newest breakthroughs and best practices.

AI Robot

Introduction to LangChain: A Framework for Language Model-Powered Applications

LangChain is a framework for developing applications powered by language models. It simplifies the process of creating and deploying applications that use language models in various ways. LangChain provides modular abstractions for the components necessary to work with language models, such as data connectors, wrappers, generators, and evaluators. LangChain also has collections of implementations for all these abstractions, so you can easily use them in your projects.This is a very basic Introduction to LangChain

Creating Use-Case Specific Chains with LangChain

LangChain also enables you to create use-case specific chains, which are combinations of components that accomplish a particular task or goal. Imagine creating a sequence where one step generates questions from a document, the next step answers those questions, and the final step evaluates the response. This can be achieved by building a chain that uses a language model for each task. You can then connect these chains together to create a complete question answering system.

Flexibility and Customization: LangChain for Tailored Language Model Development

LangChain is designed to be flexible and customizable. You can use any language model you want, from OpenAI’s GPT-3 to Anthropic’s AI21 to Hugging Face’s Transformers. You can also connect your language model to any data source you want, such as cloud storage, web scraping, or databases. You can also use any API you want, such as Google Search, Wolfram Alpha, or iFixit. You can also create your own components and chains if you need something more specific or advanced.

LangChain is also designed to be easy to use and learn. You can use LangChain in Python or JavaScript, depending on your preference and needs. You can also use LangChain with Streamlit, a framework for creating interactive web apps with Python. LangChain has extensive documentation and tutorials to help you get started and learn more.


If you are interested in building applications with language models, LangChain is a framework you should definitely check out. It will save you time and effort, while allowing you to create powerful and differentiated applications. You can find more information about LangChain on its website:

This article is also available on Medium

17 views0 comments


bottom of page