What Is LangChain and Why Does It Matter?
Discover what LangChain is and how it enables LLMs to do more.
As we know, at the core of this generative AI magic are large language models, or LLMs. These state-of-the-art models have been trained on massive amounts of text data, enabling them to do tasks like writing, summarizing, and translating. They’re powerful!
Now, here’s the thing: LLMs, on their own, can be tricky to use. They don’t always know how to solve complex problems on their own or connect to other information sources. That’s where LangChain comes in!
What is LangChain?
LangChain is like a “toolkit” for building applications with LLMs. It provides a way to use these powerful AI “brains” effectively. Think of it as a set of instructions that helps you guide and connect with an LLM to build something bigger and more useful.
LangChain helps in a few key ways. Firstly, it allows us to chain or connect multiple LLM actions together, enabling us to perform complex tasks. Secondly, it lets LLMs access and use information from outside of their built-in knowledge. This is essential when working with things like current news or specific data. Overall, LangChain turns the abstract potential of LLMs into something we can use to make awesome things.
LangChain is open-source and available in Python and JavaScript. Its open-source nature encourages collaboration among the developer community, resulting in innovative applications and enhanced capabilities quickly. One outcome of this collaboration is the quick integration of different service providers.
Why should you learn LangChain?
Now that we understand how LangChain simplifies working with LLMs, let’s explore why learning it can be so valuable. Well, it dramatically simplifies the process of building AI applications. Instead of wrestling with the raw complexity of LLMs, LangChain lets you focus on building your vision. It makes things easier even for beginners!
With LangChain, you can create all sorts of cool things. Chatbots with human-like conversations, AI tools that can help you write or research, or data analysis tools that can provide powerful insights. The possibilities are truly vast.
Plus, as AI becomes increasingly important, knowing how to use tools like Langhain is becoming a valuable skill. The demand for people who understand this technology is only going to grow.
What do you need to get started?
This is a beginner friendly course. If you're comfortable with basic Python programming, you should be all set. No prior experience with AI/ML libraries is required. What we do require is an interest in AI and a willingness to learn, we will take care of the rest!
Educative removes the complexity of setting up a local development environment for you. All of your code will work directly on the Educative platform. No installations needed!
Free LLMs with the Groq API
This course will mainly utilize the Meta’s Llama 3 language model. This can be easily done by using an inference provider such as Groq. The great thing about Groq is that it provides very generous access to most models in the free tier!
Note: While Groq offers generous free access, other platforms like HuggingFace also provide similar free tiers. We choose Groq for its extensive support of different models and its exceptionally fast inference times, currently the fastest available.
Let’s go through the following steps to set up an access key for Groq’s API:
We’ll start by first visiting Groq's playground.
Click the “API Keys” on the left navigation bar.
Now, click the “Create API Key” button to generate the new key.
Remember to copy the key because you won’t be able to view this key again once you click the “Done” button.
Save the key in the widget below to use it throughout the course by following the instructions below:
Click the “Edit” button in the following widget.
Enter your API key in the
GROQ_API_KEYfield.Click the “Save” button.
The API key has been set as an environment variable for all of the code widgets in this course. This allows the relevant libraries to access this when needed. Click the “Run” button to test the API key.
Do not worry about the code for now, we will dive into the details in the upcoming lessons.
from langchain_groq import ChatGroqllm = ChatGroq(model="llama-3.3-70b-versatile")messages=[{"role": "system", "content": "You are a helpful assistant."}]user_message = {"role": "user", "content": "Hi, my name is Alex."}response = llm.invoke(messages)print(response.content)
The widget above sends a message to our LLM using the Groq API key. We’re leveraging the ChatGroq class on line 3 directly from the langchain_groq library we imported on line 1, which is part of LangChain’s ecosystem. While it might look like we’re just calling the LLM, under the hood this ChatGroq model is a LangChain chat model that coordinates how the prompt is passed to the underlying LLM, how the messages are structured, and how the response is retrieved.
LangChain allows us to easily interact with LLMs. Feel free to modify the message sent to the LLM by modifying the content section on line 7 of the code above.
How does LangChain work?
Imagine you want to cook a complex recipe. You wouldn’t just dump all the ingredients together and hope for the best, right? You’d likely follow a recipe with specific steps, where each step builds on the previous one. LangChain does something similar with LLMs.
Instead of directly asking an LLM to do everything at once, LangChain allows you to:
Define a “Chain” of actions: You can create a sequence of actions that the LLM will execute. Each action might be a different task, like summarizing text, translating content, or answering questions based on a specific source of information.
Connect different tools: LangChain can hook into various external tools, like:
LLMs: To generate text, analyze, translate, etc.
Data Sources: To load information from text files, databases, or APIs.
Other Utilities: Like web search or even math tools
Manage the flow of information: LangChain acts as the orchestrator, ensuring that the output from one action becomes the input for the next action in the chain. This flow allows for complex operations that are beyond what a single LLM call could achieve.
The word “chain” is central because it represents the core way LangChain structures its work. Imagine a physical chain with multiple connected links. Each link represents one step in your process, whether it's using an LLM for a specific task, transforming some data, retrieving information, etc. By “chaining” these actions, you create more complex and powerful workflows.
A simple LangChain process is shown in the image above, where an “Input Query” (the user’s question or data) is first processed by a “Prompt Template.” The Prompt Template is a structured way of crafting the prompt so that the user’s input is clearly contextualized and formatted for the LLM (the core AI). After the LLM generates a response, that output goes through an “Output Parser,” which cleans up and structures the response—ensuring any important information is extracted and presented in a user-friendly way. Finally, the refined answer is returned to the user as the final “Response.” This chain highlights how LangChain handles input, manages interactions with the LLM, and formats the output, allowing for more complex and usable interactions than directly prompting an LLM.
This is just a quick introduction, but we hope it has sparked your curiosity! Now is a great time to get involved in the world of AI, and learning about LangChain is a fantastic first step. We encourage you to start exploring and see what you can create.