LangChain: A Necessary Tool For Working With Large Language Models
LangChain is one of the best frameworks to build AI apps powered by LLMs. Read to find out why it’s important and what exciting features it has.
Key Takeaways
LangChain is an open-source framework for building robust LLM-powered applications – including chatbots like ChatGPT and other custom apps.
LangChain simplifies LLM application development by allowing users to quickly chain together different components for more advanced use cases, call out to models via API, streamline prompt optimization, and more.
It comprises seven components: schema, models, prompts, indexes, memory, chains, and agents.
LLMs lack human-like reasoning capabilities. LangChain injects LLMs with artificial reasoning ability. This helps language models make better decisions regarding choosing the right tools and using certain functions.
LangChain also provides a standard memory interface to models, helping them remember previous responses and generate more human-like conversations.
LangChain introduces composability to the app-development process.
For data scientists, LangChain can simplify interacting with multiple libraries and making inferences from data output rather than the code itself. The framework also simplifies code orchestration.
Some of the key use cases of LangChain include building chatbot-like apps for question answering, code understanding, API interactions, data extraction, and analysis.
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ChatGPT’s launch in 2022 was a revolutionary moment in the development of generative AI. But the most endearing aspect of ChatGPT is that it made GenAI accessible to the common man like never before.
Now imagine a similar tool for LLM app developers: something that makes building these apps more accessible. That’s LangChain.
In this article, we will dive deeper into what LangChain is, how it works, and most importantly, why it is so exciting and unique over existing LLM app frameworks.
What is LangChain?
Launched in October 2022, LangChain is an open-source framework for building robust LLM-powered applications – including chatbots like ChatGPT and other custom apps.
It contains tools that help users chain prompts and develop LLM-powered apps. Let’s use an analogy to understand how LangChain works.
Imagine you’re building a house and all you currently have is a bunch of bricks. In order to transform them into a livable house, you need to know which brick to place where.
As long as you have a little bit of know-how, you could probably do this — mainly thanks to your uniquely human reasoning abilities. In that case, you’re the connector or the chain that transformed the bricks into a house.
Large language models, however, do not have the same abilities, and that’s where LangChain comes in.
LangChain acts as a glue between different LLMs and data sources, giving them memory and context.
It also simplifies LLM application development by allowing users to quickly chain together different components for more advanced use cases, call out to models via API, streamline prompt optimization, and more.
On top of that, the framework also lets users create data-aware and agentic apps by equipping LLMs with previously-unavailable functionalities.
Data-aware – Developers can use LangChain to make models more data-aware by connecting them with external data sources, such as the Internet or private databases. That way, the models can fetch the information they need and generate more accurate, up-to-date, and personalized responses.
Agentic – LangChain also gives users access to agents, functions that allow models to interact with their environments, or, more precisely, to make decisions on which actions to take.
Since its launch, LangChain has garnered several improvements from developers on GitHub, and it just announced a $10M seed round. Thousands of developers have already used it to build intelligent AI applications. Let’s explore why.
How Does LangChain Work?
LangChain works by providing developers access to seven components that enhance the powers of LLMs, namely schema, models, prompts, indexes, memory, chains, and agents.
Models
LangChain doesn’t exactly provide models itself, but it does allow users to integrate the models from other LLM providers. Currently, it supports three types of models:
LLMs: This documentation shows how users can integrate LangChain with various different LLM providers, including OpenAI, AI21, Cohere, and others.
Chat Models: Unlike LLMs, chat models take chat messages as inputs and return them as outputs. Users can integrate LangChain with chat models from Azure, Anthropic, and other LLM providers.
Text Embedding Models: Text embedding models take text as input and return a list of floats. The embedding class offers a standard interface for different text embedding model providers, like OpenAI, Cohere, and others.
Prompts
LangChain also helps users streamline prompt management by offering tools for prompt optimization and serialization. In other words, the prompts help users to seamlessly optimize their prompts to get the best results from their language models.
The prompt module in LangChain leads to improved application performance while making it simple to create unique and powerful applications powered by LLMs.
Chains
Chains can help developers customize models for chatbot-like interactions, data extraction, evaluation, and many other complex applications.
That way, LangChain enables developers to build LLM-powered apps in less time, with less effort, and with significantly less expertise. Plus, end-to-end chains extend the capabilities of LLMs by connecting the models to external data sources.
Indexes
Indexes help developers combine text data with language models to enable better interactions with LLMs.
Some common index classes are:
Loaders: Loads documents from various sources.
Text Separation: Helps in dividing texts into smaller parts.
VectorStores: VectorStores focuses and relies on embeddings.
Retrievers: Helps fetch relevant documents per the queries and combines them with the language models.
Agents
Agents are modules that have access to a range of tools, and depending on the user input, they can decide which tools to call. Agents are mainly used for applications that require an unknown chain apart from a predetermined chain of calls to LLMs.
LangChain currently has two main types of agents, which users can use independently or together with one another:
Action Agents decide which action to take based on user input and take that action one step at a time. They’re more suitable for smaller tasks that require less planning.
Plan-and-Execute Agents make a plan of actions to take and execute those actions one at a time.
Agents use tools to interact with the world. LangChain users can define their own custom tools, or use readily available tools such as Wikipedia, Bing Search, or Wolfram Alpha.
Memory
LangChain enhances a model’s language and recall capabilities by empowering it with standard memory interfaces.
LLMs like GPT-3 and Bard are good at executing a single prompt at a time. If you asked an LLM to build you a house, it would give you a house. But these models do not have memory and cannot generate sequential, context-aware responses.
If you asked the same LLM to give you the steps for building a house and go back to a step or provide the next step based on the previous one, it will not be able to do so.
Since LangChain can provide memory and artificial reasoning capabilities to LLMs, it solves the above problem.
LangChain Use Cases
Some of LangChain’s primary use cases include:
Virtual personal assistants: LangChain can be used to build virtual personal assistants with long-term memory. Such agents would be able to remember interactions, access and analyze your data, and customize results.
Q&A: Q&A apps that work with PDFs are another great application for LangChain. Chaining LLMs and command prompts together can help you build a ChatGPT-like tool for all kinds of docs including PDFs, Databases, etc.
Code Understanding: LangChain can help developers query source codes from GitHub.
API Interactions: LangChain enables LLMs to interact with APIs with real-time data and updates. This somewhat solves the data recency problem.
Extraction and Analysis: LangChain can not only analyze data from SQL databases and CSV files but also extract data from unstructured documents.
Besides these, LangChain is great for building chatbots, agent simulations, and evaluating LLMs.
Why is LangChain Exciting?
LangChain provides developers with more comprehensive support than other existing tools when it comes to building AI applications. Here are some of the main reasons why it’s so useful and unique.
Reasoning
LLMs lack human-like reasoning capabilities. LangChain injects them with artificial reasoning ability that helps them decide whether to use tools and interactions, which one, and how.
With the help of reasoning traces, LLMs can make better decisions when executing tasks like information extraction or action planning.
Memory
Memory is another one of LangChain’s most exciting features. We’ll use ChatGPT as an example to demonstrate why.
Imagine you’ve asked ChatGPT a question and received an answer that inspired you to ask another one. ChatGPT would be able to answer this and other subsequent questions because it would remember its previous answer.
LLMs, on the other hand, do not have this ability. They wouldn’t have enough context for any subsequent questions and wouldn’t be able to answer them correctly.
This can be a huge problem — especially for developers.
Developers are often forced to repeatedly give the same context to the model after every step, which can be super ineffective as the development becomes more complex.
LangChain solves this issue by equipping LLMs with memory implementations and context. These add-ons enable LLMs to act as agents capable of retaining information and generating context-aware responses.
This essentially means that developers can provide LangChain with a set of functions and ask it to create a schema, even introducing intermediary steps wherever needed. LangChain introduces composability to the app-development process, making it indispensable. It also allows developers to communicate naturally across models and remembers the context of the conversation.
Application in Data Science
LangChain can also be extremely helpful in data science. It can help:
simplify interactions with multiple libraries,
make inferences from data output rather than the code itself, and
simplify code orchestration for developers by chaining together pre-existing components.
Drawbacks of LangChain
Despite being arguably better than other such open-source frameworks, LangChain does have some drawbacks.
New
LangChain is a new library, so there’s a possibility of the risk of bugs and issues may be somewhat high.
Text-Only
Langchain has only been trained on text, so it cannot work with audiovisual datasets.
No Multi-Task Learning
LangChain does not support multi-task learning, but there’s a possibility this feature might be introduced in the future.
Conclusion
LangChain makes developing sophisticated LLM applications and user interfaces easier and more effective. Its unique design allows developers to chain different language models together and equip LLMs to generate composable results. Moreover, it introduces memory and context to LLMs, enhancing their capabilities.
With frameworks like this one, large language models can be used to design truly revolutionary apps in the future. Moreover, they would also be moving more towards artificial general intelligence.