What is LangChain and why should you care?

The worlds fastest growing language model application framework with LLM tools and extensive agent support

What is LangChain and why should you care?
Play this article

Langchain 🦜 has quickly captured the market in the open-source world, experiencing exponential growth. One of the major reasons behind this surge is the recent interest in Language Model Integrations (LLMs).

Let me break it down for you in simple terms. Langchain helps developers like us connect data to language models, like GPT models from OpenAI and various others all through their API. It also supports agent workflows. What are those, you ask? Well, they're like smart, automated processes that make our lives easier. Imagine having tasks automated and complex stuff streamlined. It's a total game-changer!

So if you're curious about how Langchain can help you scale your development work, stick around. It's gonna be a wild ride! πŸš€

Why do we need LangChain?

Guess what's challenging for developers these days while working with language models? Since the whole ecosystem is still evolving we often lack the right tools to smoothly deploy language models in real-world scenarios.

But here's where Langchain comes in to save the day! It's like a superhero framework equipped with all the cool features we need. Imagine getting prompt chaining, logging, callbacks, persistent memory, and efficient connections to multiple data sources right out of the box. No more struggling to set these things up manually!

And that's not all – Langchain provides a model-agnostic toolset that lets both companies and developers explore multiple LLM offerings. So, you can easily test which one works best for your specific use cases. The best part is that you can do all of this within a single interface. No more crazy scaling of code bases just to support different providers!

If you're tired of dealing with the rough edges of language models and crave a simpler development experience, Langchain somewhere starts to become the go-to solution.

LangChain Community

When it comes to checking out a tool, one of the big factors is the awesome community supporting it. And for open-source projects like Langchain this is even more crucial. You want to know you're not flying solo, right?

Langchain has got a massive fan base! As of today, it's got over 51k stars on GitHub, which is a pretty solid way to gauge its popularity in the open-source world. And guess what? It's racking up a million downloads every single month! That's some serious love from the developer community.

The community also maintains an active Discord channel. You know when there's a bustling chat going on, people are seriously into the project. So, if you ever hit a snag or just want to geek out with fellow community members you've got a place to hang out.

LangChain Community: Click Here

LangChain Documentation: Click Here

Bottom line: with that kind of following and engagement, you can bet Langchain is doing something right. Let's now try to understand what exactly all of this hype is about? πŸŽ‰

What does LangChain really do?

LangChain is all about giving developers a framework to build applications fueled by Language Model Integrations (LLMs). It lets developers connect their language model to other data sources, which means it can tap into vast amount of additional information. And that's not all – this framework allows the language model to interact with its surroundings, making it super responsive and dynamic!

Picture this: you can integrate AI chatbots like ChatGPT into your apps, and then hook them up to external sources like Wikipedia and Google Drive. This means you can create apps that are seriously powerful and language-driven! They can churn out personalized content based on what users input and the data they fetch from different sources.

In a nutshell, LangChain opens up a whole new world of possibilities for developers. It's like adding a touch of magic to your projects, making them smarter and more user-centric! πŸš€

Langchain has got these neat APIs that let us seamlessly work with the powerful LLMs for all kinds of cool use cases. The best part is they use Python libraries, which makes working with AI models very easy. We can chain all these components together, creating a seamless flow in our projects with the support of popular AI platforms like OpenAI and Hugging Face, making integration even more efficient.

LangChain's fundamental concept is its ability to β€œchain” together different components, also known as β€œchaining”, which allows for the development of advanced use cases that utilize LLMs. With this powerful concept, it becomes a playground for developers to create next-generation applications with advanced language processing capabilities.

How to get started with using LangChain?

If you're curious to try out LangChain, you're in luck! The project offers a comprehensive documentation guide that takes you through the entire process step by step.

To get started, you'll set up your development environment. Don't worry, they've got clear instructions to help you along the way. Once that's done, you'll dive into integrating those powerful AI models into your applications. It's some pretty cool stuff, I must say!

But here's where it gets even better – LangChain gives you the freedom to be creative. You can build your own modular components and tailor your app to your required content. Talk about flexibility!

Once you're up and running, the real fun begins. You can use LangChain for all sorts of awesome stuff like text summarization, generative question answering (GQA), and chatbots. By utilizing the power of LLMs in your applications, you’ll be able to create accurate summaries, provide relevant answers to user queries, and create engaging conversational experiences with the help of LangChain.

To understand why LangChain is so handy, let's first dive into how Language Model Integrations (LLMs) work. Under the hood, LLMs are statistical models that can predict the next set of text chunks based on the initial ones you feed them. These initial chunks are called "Prompts," and crafting the right prompts is an art known as "Prompt Engineering."

At its core, it's got three powerful capabilities:

  1. Abstraction Layer: With LangChain, developers can interact with various LLM providers using a standardized set of commands. No more dealing with different interfaces for each provider!

  2. Simplified Prompt Engineering: LangChain provides tools that formalize the prompt engineering process, guiding developers with best practices. Say goodbye to the guesswork – now we can fine-tune LLM results like pros!

  3. Chaining for Complexity: LangChain lets us chain its components together, unlocking the potential of complex interactions. It's like setting off a chain reaction of language magic!

Now, let's get our hands dirty with some code:

const model = new OpenAI();
import { PromptTemplate } from "langchain/prompts";

const prompt = PromptTemplate.fromTemplate(`Tell me a joke about {topic}`);
const chain = new LLMChain({ llm: model, prompt: prompt });
const response = await chain.call({ topic: "developers" });

With this snippet, we create a chain to ask LangChain for a joke about a specific topic (in this case, "developers"). But wait, there's more! We can level up with a more complex application and add a translation twist:

const translatePrompt = PromptTemplate.fromTemplate(`translate the following text to Spanish: {text}`);
const translateChain = new LLMChain({ llm: model, prompt: translatePrompt });
const overallChain = new SimpleSequentialChain({
    chains: [chain, translateChain],
    verbose: true,
const results = await overallChain.run("developers");

By using verbose: true in SimpleSequentialChain, we can peek into the generation process, which comes in handy for debugging.

And that's not all! LangChain goes beyond simple prompt chaining with two fantastic modules:

  1. Memory Module: This gem allows developers to store state across chains, whether in external databases like Redis or DynamoDB, or simply in memory. No more losing important data!

  2. Agents Module: With this module, chains can interact with external providers and take actions based on their responses. The possibilities are endless!

Keep in mind that LangChain is still in active development, so use it with a little caution in production. But with all these tools and possibilities, it's worth exploring and having some fun with it! πŸš€

Setting up LangChain

Now let's get started with LangChain! First things first, we need to set up everything properly to work with those cool LLMs. I'll walk you through importing the necessary libraries and dependencies, required for working with LLMs effectively.

import langchain
import openai
import os
import IPython
from langchain.llms import OpenAI
from dotenv import load_dotenv
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import LLMChain
from langchain.chains import RetrievalQA
from langchain import ConversationChain


openai.api_key = os.getenv("OPENAI_API_KEY")

Now that we have all the necessary imports and configurations set up, we're ready to dive into the fun part – interacting with LLMs using LangChain! This involves a series of steps that will let us tap into the incredible capabilities of pre-trained language models for text generation and understanding tasks. Let's go through each part with code examples:

Initializing an LLM

To get things rolling, we'll initialize an LLM in LangChain. It's as easy as importing the LangModel and specifying the language model we want to use. Check out this example:

from langchain import LangModel
model_name = 'gpt3'
llm = LangModel(model_name)

That's it! Now we're set up and ready to harness the power of LangChain and LLMs to do some amazing text generation and understanding tasks. Let's see what else we can do on this incredible journey! πŸš€

Inputting Prompts & Retrieving Generated Texts or responses

Once you've got your Language Model (LLM) up and running with LangChain, it's time to put it to work by inputting prompts. These prompts serve as the starting point for the LLM to generate text or provide responses.

For example, you can set a single prompt like "Once upon a time" to get the LLM going. Then, you use that prompt to generate some amazing text like a fairy tale or any creative stuff you can think of. It's like magic!

To retrieve the generated text or responses from the LLM, you simply print them out. The cool part is that the generated text or responses will be based on the context you provided with the prompts and the superpowers of the language model. So, you'll get some mind-blowing results!

Here's the code snippet to give you an idea:

prompt = "Once upon a time"
generated_text = llm.generate_text(prompt)
for response in responses:

What can be built with LangChain?

LangChain comes with a bunch of awesome features that developers can dive into and use in their applications. The key components include models, prompts, chains, indexes, and memory, with which we can discover the fantastic capabilities it brings to the table.

First and foremost, models are at the core of Langchain. They enable seamless integration with language models like OpenAI's GPT through APIs. This empowers developers to tap into the impressive capabilities of these language models and use them to enhance their applications.

Moving on to prompts, Langchain allows us to chain them together, creating dynamic and interactive conversations with the language models. This opens up avenues for more engaging and personalized interactions with users.

The power of Langchain doesn't stop there. With chains, developers can design agent workflows that automate tasks and streamline processes. This feature can significantly improve efficiency and productivity, making applications more sophisticated and user-friendly.

Indexes come into play to efficiently manage and organize data within Langchain. They offer easy access and retrieval of information, contributing to smoother and faster operations within applications.

Last but not least, Langchain provides persistent memory, allowing applications to retain information over time. This valuable feature ensures that crucial data is preserved, even after restarting the application.

LangChain with all these cool features is like having a powerful toolkit at our disposal, ready to be integrated with our applications to make them smarter, more efficient, and just plain awesome! πŸš€

Putting it all in a nutshell

I'm super pumped about LangChain, which is why I took the time to write this up. I genuinely believe it's the answer to many of the real problems developers and builders face while working with Language Model Integrations (LLMs). The best part is that LangChain unlocks a vast amount of possibilities when it comes to building applications using LLMs. I mean think about it – text completion, language translation, sentiment analysis, text summarization, named entity recognition – all of that becomes a reality with LangChain and powerful APIs. It's like futuristic sci-fi that brings your wildest ideas to life!

By tapping into the potential of LLMs through LangChain, you can create smart applications that not only understand human-like text but also generate it. It's like revolutionizing the way we interact with language, making it more natural and context-aware.

To sum it up, here are the key takeaways: LangChain is currently the best solution when it comes to developing applications that leverage the incredible capabilities of Language Models (LLMs) for understanding and generating human-like text. It makes the process pretty easy for developers allowing them to integrate LLMs into their projects by simply installing the LangChain SDK and using API credentials.

And the result? You'll create applications that offer users more natural and context-aware interactions, taking their experience to a whole new level and boosting engagement like never before!

Well if you're as excited as I am about the capabilities of LangChain hop on board with the LangChain community and make sure to check out the documentation to learn more about LangChain with all the helpful guides, tutorials, and examples that walk you through every step of building LLM powered applications using LangChain.

Did you find this article valuable?

Support Dhrubajyoti Chakraborty by becoming a sponsor. Any amount is appreciated!