A quick introduction to Langchain, an open-source framework that revolutionizes AI development by connecting large language models to external data sources and APIs.

Introduction

Langchain is an open-source framework that enables developers to combine large language models, such as GPT-4, with external sources of computation and data. Offered as a Python or a JavaScript package, the popularity of this framework has skyrocketed after the introduction of GPT-4 in March 2023. In this blog post, we will dive into the core concepts and practical applications of Langchain.

Langchain in Action: A Practical Example

Imagine you want to extract information from your own document or database, and then use GPT-4 to help you take an action, such as sending an email containing specific data. Langchain makes this possible by connecting GPT-4 to your own data sources and external APIs.

Core Concepts of Langchain

Langchain’s main value proposition is centered around three core concepts:

1. LLM wrappers: These wrappers allow developers to connect to large language models like GPT-4 and interact with them.
2. Prompt templates: These templates enable dynamic input to the LLMs, avoiding hard-coded text.
3. Chains: Chains combine multiple components to solve specific tasks and build entire LLM applications, making it possible to create sequential workflows for complex tasks.

In addition to these core concepts, Langchain also employs embeddings and vector stores (indexes) to store and extract relevant information for the LLMs. Finally, agents enable the LLM to interact with external APIs.

Getting Started with Langchain

To start using Langchain, you will need to install the necessary libraries and obtain API keys for both OpenAI and Pine Cone or Supabase. We use Pinecone or Supabase to store our vector embeddings. Then, you can import Langchain’s tools to interact with LLMs, create prompt templates, build chains, and work with embeddings and vector stores. With a few lines of code, you can create powerful AI applications that combine GPT-4 with your own data sources and external APIs

Example applications

Dynamic prompting:

Most of the times the prompts are dynamic and prompt templates in Langchain. This allows to inject user input to the prompt and then feed that to the language model

Chains:
It is a like a composite of several chains or functions or tools. Below is an example of simple sequential chaining where it takes the output of the first chain as an input to the second chain

Conclusion

Langchain is a powerful framework that revolutionizes the way developers work with large language models like GPT-4. By enabling the connection to external data sources and APIs, Langchain opens up a world of possibilities for AI development. Whether you are interested in personal assistance, or data science, Langchain offers a practical solution for harnessing the power of AI.

Check out this guide if you’re looking for a comprehensive non-technical background of LangChain!