Intro to LangChain

If you’ve used an LLM like those from OpenAI, you know they are very powerful tools. Using an interface like ChatGPT is impressive, but, what if you want to build your own applications that use LLMs?

That’s where tooling like LangChain comes into the picture. In this article I want to introduce the core modules in LangChain to give you an idea of what you can build with it.

What is LangChain?

LangChain is a robust framework developed to aid developers in building applications that leverage Language Models (LLMs). Whether you're looking to create a simple chatbot or a complex system that amalgamates multiple components, LangChain offers the tools and interfaces to make the job easier.

LangChain is composed of six modules. I’m going to break down each of the modules in LangChain to give you an idea of what you can do with it.

Model I/O

The heart of every LLM application is the model itself. LangChain's Model I/O provides the essential building blocks to seamlessly interact with any language model. This module has three main capabilities:

• Prompts: These are your tools to manage model inputs. They allow you to create templates and dynamically select the right model input for every scenario.
• Language models: Interface with language models in a standardized manner, irrespective of the model you're using.
• Output parsers: Post-processing tools to help you extract specific information from the responses provided by the model.

More Info: https://python.langchain.com/docs/modules/model_io/

Retrieval

If you want to build some variant of “chat with your own data”, this is an important module for you.

For applications requiring specific data not present in the model's training set, LangChain offers Retrieval Augmented Generation (RAG) tooling. This process fetches external data, which is then supplied to the LLM during the response generation phase.

LangChain has support for loading documents of different types, such as PDFs, transforming those documents into chunks, creating text embeddings that can be stored in a vector database, and working with those databases.

More Info: https://python.langchain.com/docs/modules/data_connection/

Chains

LangChain introduces the Chain interface to compose and chain various LLM components, or even other chains, allowing for a modular and easily manageable application structure. With this approach, building complex applications becomes less daunting, and maintenance is easier.

Why do we need chains?

Chains form the structural backbone of advanced LLM applications. By using chains, you can:

• Streamline Processes: Craft a chain that receives user input, structures it using a PromptTemplate, and then forwards the formatted data to an LLM.
• Architect Complex Systems: Develop intricate applications by fusing multiple chains or integrating chains with varied components.

Simply put, chains offer a blueprint, allowing developers to visualize and construct unified, efficient applications from discrete components.

More Info: https://python.langchain.com/docs/modules/chains/

Memory

Think of memory as the system's ability to recall past interactions, essential for conversational applications. Memory is how applications like ChatGPT can keep an ongoing conversation with a user. LangChain offers utilities for equipping your system with memory, ensuring a fluid conversational experience.

LangChain supports two types of memory: in-memory lists, and persisting to a database. If you’re building an interface like ChatGPT, where a user can access past chats, then you’ll need to persist to a database. If you’re building something simpler, you can stick with the in-memory list.

More Info: https://python.langchain.com/docs/modules/memory/

Agents

Where chains have hardcoded sequences, agents employ LLMs as reasoning engines, determining the series of actions to take based on LLM responses. In effect, the LLM can command the agent to perform tasks.

You are in full control what the tasks to be taken are. This makes it possible to build powerful, dynamic workflows that involve LLMs and your own code.

Internet research is a perfect use case for agents. You could have the agent take a user prompt, perform an internet search, and then use a LLM to generate a research report.

More Info: https://python.langchain.com/docs/modules/agents/

Callbacks

LangChain's callback system lets you monitor your LLM application's various stages. Ideal for tasks like logging, streaming, or monitoring, these callbacks can be integrated easily, ensuring you're always in the loop about your application's inner workings. More about the built-in callback integrations with third-party tools can be found here.

What Can You Build with LangChain?

From simple chatbots to complex LLM applications that interact with other software components or even databases, LangChain is very versatile. Whether you're just beginning your LLM journey or looking for a robust tool to facilitate your existing projects, LangChain is an invaluable asset for every developer.

They have a great Use Cases site that gives you some ideas of what can be built: https://python.langchain.com/docs/use_cases/question_answering/

Conclusion

In conclusion, LangChain is not just another tool; it's a transformative platform poised to redefine how developers approach LLM applications. With its versatile modules and user-friendly interfaces, your generative AI journey just got a whole lot more exciting. Dive into LangChain today and unlock a world of possibilities! Until next time, keep exploring and happy coding!