What is LangChain? AI App Development Framework Explained
LangChain is a framework written on TypeScript for developing applications powered by large language models (LLMs), which can be written in Python and JavaScript. As of October 2023, LangChain can be used in Node.js (ESM and CommonJS) — 18.x, 19.x, 20.x, Cloudflare Workers, Vercel / Next.js (Browser, Serverless and Edge functions), Supabase Edge Functions, Browser, Deno and Bun.
Harrison Chase, based out of San Francisco, launched LangChain back in October 2022 as an open-source project. The initial version of LangChain was also launched the same year. The basic concept of LangChain is that it provides a set of tools and abstractions that make it easier to connect LLMs to other data sources, interact with their environment, and build complex applications. Simply put, LangChain can help you build AI-powered applications that not only provide responses through LLMs such as Open AI’s GPT-3 or GPT-4 but also let you connect an external database to provide data to the user.
In April 2023, LangChain announced a seed investment of $10 Million from Benchmark, and just a week later they announced that they raised another $20 Million in funding at a valuation of roughly $200 Million from Sequoia Capital.
What you’ll learn in this guide:
- Important Features of LangChain?
- What are the Components of LangChain?
- What are the Advantages of LangChain?
- What are the Applications of LangChain?
- What is the Procedure to Utilize LangChain in Applications?
- Conclusion
- FAQ
Important features of LangChain
Model Interaction
Model Interaction, also called model I/O, is a module that lets LangChain interact with any language model. It is also capable of performing tasks such as managing inputs to the model and extracting information from its outputs.
Customization
LangChain is highly customizable, allowing developers to develop applications to their specific needs. For example, developers can customize the prompts that are fed to LLMs, as well as the way that responses are processed and generated. This means that your application can respond in a particular tone or even track specific information.
Memory
LangChain enables LLMs to retain information across interactions by supporting both, short-term and long-term memory. This means you could feed a piece of information into your AI application and it could retain that information for a long time so that you don’t have to feed the information again and again. This is particularly useful for building chatbots that can provide context-aware responses.
What are the Components of LangChain?
To build a powerful AI-powered application, you need strong building blocks and components are just that. Essentially they are modular building blocks that are ready to be used to build powerful applications. There are a total of 7 components in LangChain, they are:
Schema
Schema in LangChain is a set of rules that define the structure and format of the data that can be used with the platform. It is used to ensure that all data is consistent and can be easily parsed and processed by LangChain’s tools and services. Basically Schema in LangChain is a piece of unstructured data.
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