Solutions that leverage artificial intelligence (AI) are on the rise, but many companies may struggle with developing these enhanced products fast enough. According to IBM’s Global AI Adoption Index, the top obstacles businesses face when implementing AI include lack of AI skills and knowledge (34%), lack of tools and platforms for model development (25%) and high project complexity that makes them difficult to scale or integrate (24%). However, engineering teams are gaining access to more and more tools that can boost AI-enabled software development. This article will focus on one of these tools – LangChain. Read on to find out what LangChain is, how it works and why it has been met with so much enthusiasm from software developers.
What is LangChain?
LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). Its use cases largely overlap with those of language models in general, including document analysis and summarization, chatbots and code analysis. Launched in October 2022 by Harrison Chase, this groundbreaking software development framework has quickly caught the attention of the tech industry, with a burgeoning community of developers and a substantial influx of venture capital investment.
The LangChain framework simplifies the creation of applications that leverage LLMs. LLMs have broad use cases across industries and can be implemented in various business areas including document analysis and summarization, chatbot creation and code analysis. They can improve customer experiences, boost operational efficiency and reduce costs. LangChain offers a streamlined way to integrate these capabilities into various applications. Some of the clearest examples of LangChain in practice are personal assistants, which effectively combine the core principles of this framework: integrating personalized data to trigger an action.
LangChain – A rising star in the tech scene
LangChain’s rapid ascension has been nothing short of remarkable. The framework started as an open-source project when Chase was working at Robust Intelligence, a machine learning startup. The project quickly gained traction and attracted an active community of developers who engage with the project on its Discord server, lead discussions on Twitter, report improvements on GitHub and create a plethora of YouTube tutorials. This momentum culminated in the startup raising over $20 million in funding from venture capital firm Sequoia Capital, a week after announcing a $10 million seed investment from Benchmark.
Impressive range of integrations contributes to LangChain’s popularity
LangChain’s power lies in its versatility and integrations. As of March 2023, LangChain boasts integrations with a range of systems, from major cloud storage platforms like Amazon, Google, and Microsoft Azure, to API wrappers that provide the latest news, weather forecasts or film information. It supports multiple web scraping subsystems to generate in-context learning prompts, find “to-do” tasks in code and sum them up. LangChain also integrates with Google Drive documents, spreadsheets and presentations so that an application can extract information, as well as create or summarize documents. Additionally, LangChain has a built-in search functionality for Google Search and Microsoft Bing. It supports OpenAI, Anthropic and Hugging Face language models, and can search and summarize iFixit repair guides and wikis.
The framework’s capabilities don’t end there. LangChain can also read from more than 50 document types and data sources, extending its reach to a myriad of applications. Its potential is vast, and it’s exciting to think about the ways it will shape the future of software development and LLM application creation.
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How LangChain works
LangChain operates as a framework for developing applications that are powered by LLMs. It is based on the premise that applications that will make the most impact and stand out in the market won’t merely call out to a language model via an API. Instead, these apps will also:
1. Be data-aware: By connecting a language model to other sources of data, they enrich the context and increase the utility of the model
2. Be agentic: They enable a language model to interact with its environment, expanding the range of tasks it can automate and the complexity of problems it can solve
LangChain offers two advantages for engineers who want to facilitate the development of advanced apps:
Components: In LangChain, the components you need to work with language models come with modular abstractions. The framework also provides a range of implementations for these abstractions. The components are meant to be easy to use for everyone, even if you’re not going to use other parts of the framework.
Use case-specific chains: You can interpret chains as a process of putting components together so that you can create a particular use case in an optimal way. Chains serve as a higher-level interface which developers use to quickly start working on a specific use case. It’s worth noting that chains are customizable, enabling developers to tailor the framework to their specific needs and objectives.
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LLM-powered applications help companies gain a competitive edge
With LangChain, developing LLM-based apps becomes faster and relatively easier. By combining LLMs with other information sources, LangChain enables developers to build products that will make waves with highly personalized user experiences. Just like LLMs, the framework can be applied to solutions across different industries, empowering companies to take the lead in their sectors by leveraging AI and machine learning functionalities.
The recent rise of AI tools and their impact on the software industry has inspired businesses to boost their innovation efforts. To accelerate time to market in a highly competitive market, organizations often look to external software partners like Software Mind for AI expertise and additional engineering capabilities. Use the form to get in touch and our experts will discuss with you how we can help enhance your solutions and speed up software delivery life cycles.
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About the authorDamian Mazurek
Chief Innovation Officer
A certified cloud architect and AI expert with over 15 years’ experience in the software industry, Damian has spent the last several years as a cloud and AI consultant. In his current role he oversees the technology strategy and operations, while working with clients to design and implement scalable and effective cloud solutions and AI tools. Damian’s cloud, data and machine learning expertise has enabled him to help numerous organizations leverage these technologies to improve operations and drive business growth.
