What Is LangChain?

LangChain is a framework utilized to develop applications driven by large language models (LLMs). It’s available in Python and Javascript libraries, and you can use it to aid in the development of applications such as chatbots and virtual agents. 

According to IBM, “LangChain enjoyed a meteoric rise to prominence: as of June 2023, it was the single fastest-growing open source project on GitHub. Coinciding with the momentous launch of OpenAI’s ChatGPT the following month, LangChain has played a significant role in making generative AI more accessible to enthusiasts in the wake of its widespread popularity” [1].

Explore LangChain’s framework, discover its key features, and learn how to install and configure the tool for your own projects. 

What is LangChain used for?

LangChain simplifies the development, production, and deployment of LLM applications by enforcing standard interfaces. This framework allows developers who are working with artificial intelligence (AI) and machine learning (ML) applications to integrate LLMs with external frameworks. 

Understanding the LangChain framework

LangChain’s framework includes various components, including LLMs, agents, memory systems, and prompt templates. 

• LLMs: Before using LangChain, you must develop a language model. You can use a publicly available language model such as GPT3 or train your own model. 

• Agents: Using LangChain, you can develop chains for complex applications, such as an agent. An agent is a chain that prompts the LLM to implement the optimal sequence to produce the best output in response to a user’s question or prompt. 

• Memory systems: LangChain enables you to implement various memory capabilities, including simple memory systems that can recall recent conversations with users and intricate memory structures that can recall and analyze historical data. 

• Prompt templates: You can develop prompt templates using LangChain to format queries for AI models and chatbots. 

Key features of LangChain

LangChain provides features such as seamless integration with popular language models and continuous data retrieval 

Integration with language models

LangChain seamlessly integrates with various language models, including OpenAI, Google Generative AI, and Amazon Web Services (AWS), among others. Since LangChain is not a standalone tool, it must integrate with an LLM to fulfill its intended function. For instance, ChatGPT is a chatbot application that utilizes one of the GPT language models. The GPT model processes the input and generates a natural language response, and the application provides the user experience (UX) interface for the user to interact with the chatbot. 

Data retrieval 

Each LLM is trained on a set of data. For instance, GPT’s training data consists of the entirety of the internet. LangChain implements retrieval-augmented generation (RAG), which is an application design that compares input data to more recently retrieved data, enabling the LLM to provide a more accurate answer to a given query. This data might come from different sources, necessitating a conversion into a consistent format in order to continue processing it. LangChain converts the disparate data into this format internally, enabling data to seamlessly pass between various chains. 

Who uses LangChain?

Various organizations and professionals utilize LangChain: 

• Microsoft: Developers from Microsoft utilize LangChain to build AI applications, aid in natural language experiences, and enhance existing features within their product offering. 

• Elastic: Elastic, a widely used search analytics company, utilizes LangChain to build AI chatbot applications like the Elastic AI Assistant. They use this chatbot to optimize security-related workflows. 

• Ally Financial: Ally Financial, the US’s largest digital bank, utilizes LangChain to implement a coding module called the PII Masking module, which safeguards personally identifiable information (PII). 

Pros and cons of using LangChain

This framework offers a powerful tool with a rich community and ample resources. Still, it also poses some potential challenges. Explore both in more detail. 

Some advantages of using LangChain include: 

• Open-source access: LangChain is an open-source framework that enables collaboration with peers and ready access to tutorials, resources, and documentation from the LangChain community. 

• Optimizes workflows: Developers can easily switch between various LLMs, minimizing integration complexity and streamlining workflows.

Some disadvantages of using LangChain include: 

• Abstraction design: Since LangChain is built around multiple emerging technologies such as AI and LLMs, designing abstractions can be difficult and may make code harder to interpret. 

• Steep learning curve: Beginners may struggle with understanding and debugging LangChain due to its complexity. 

Getting started with LangChain

Learn how to install and set up LangChain and discover various capabilities within the tool, including chatbots, data enhancements, and coding assistants. 

Installation and setup

LangChain’s website provides a tutorial on how to set up and install LangChain to build LLM-powered applications. It also explains how to utilize language models and prompt templates, and how to debug and trace your application with LangChain. 

LangChain’s website also provides a tutorial on how to build a LLM-driven chatbot. Users can create a chatbot that streamlines data from external data sources (conversational RAG), build actionable chatbots that can complete various actions (agents), or develop chatbots that utilize LLMs to have a conversation with the user. If you’re interested in learning more about various aspects of chatbot development, LangChain’s site provides tutorials on how to manage and add message history and more. 

Basic examples and implementation

Some examples of LangChain’s capabilities include chatbots, data enhancements, and coding assistants. 

• Chatbots: LangChain could help you develop chatbots that can process complex questions and have sophisticated user interactions, similar to ChatGPT. 

• Data enhancement: LangChain can generate new data based on the data it’s been trained on, which can contribute to ML training and data set development. 

• Coding assistants: The tool helps users create coding assistants utilizing LangChain and OpenAI’s API. Developers can use these assistants to augment their coding skills and enhance productivity. 

Learning about LangChain on Coursera

LanChain is a widely used framework that aids in the development, production, and deployment of LLM-powered applications. Consider deepening your understanding of AI, machine learning, and other emerging technologies with online courses like the IBM Machine Learning Professional Certificate or steps to build your own chatbot and implement prompt engineering using LangChain on Coursera with DeepLearning.AI’s LangChain Chat with Your Data.