in Blog

December 20, 2023

What is LangChain? Advancing Beyond Traditional Language Models

Author:




Edwin Lisowski

CSO & Co-Founder


Reading time:




9 minutes


The advent of large language models overturned most business and personal use-case scenarios and applications. [1] Suddenly, people could now generate text, images, and other content, including code.

However, the latter has proven much harder to accomplish with large language models. Besides requiring the technical know-how to use the right prompts effectively, developers also need external applications to combine and test-run code for applications. There’s also the issue of increased demand for dynamic, data-responsive applications that can generate up-to-date responses based on information available online.

For that, developers need a specialized modular framework for simplifying the development of applications powered by LLMs. That’s where LangChain comes in. With LangChain, developers get access to a wide variety of tools and abstractions that make it easier to connect language models to other sources, interact with them within their native environment, and build complex applications.

This guide will provide an in-depth, comprehensive analysis of Langchain, detailing everything from what it is, how it works, integration capabilities, and the benefits it has to offer.

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Understanding LangChain’s functionality

LangChain is an open-source framework that’s specially designed for developing applications powered by language models. [2] Essentially, LangChain allows developers to link large language models like GPT-3.5 and similar models to a variety of external data sources in order to create and reap the benefits of Natural Language Processing (NLP) applications.

Its simplistic and rather straightforward approach to application development allows any developer experienced in Python, TypeScript, or JavaScript programming languages to make use of various LangChain packages offered in those languages.

Read also more about LangChain Vs. LlamaIndex: Main Differences

What is LangChain? Unraveling the Working Mechanism

As a product development tool, LangChain can only work when paired with a suitable language model. In that regard, you can choose to either go for open-source models like GPT-3.5 or train your own model from scratch.

Once you have a preferable language model in place, you can start building applications with LangChain. LangChain provides an array of tools and APIs that make it easy to connect models to other data sources, build complex applications, and interact with the models’ environment.

As the name suggests, LangChain works by ‘chaining’ together various components called links to provide a flow. Each link within the chain performs a specific task. This may include the following:

  • Formatting user input
  • Accessing the data source
  • Calling the language model
  • Processing the language model’s output

Each link in the chain is connected to other links to form a sequence. The output of each link is passed onto the next link as an input, thus allowing the chain to perform complex tasks by chaining simple tasks together.

The links in a specific chain can be customized to perform different tasks. Alternatively, developers can change their order to create different workflows, thus adding to the framework’s flexibility.

Key elements of LangChain

LangChain has a few basic components that make up the entirety of the NLP pipeline. These components have unique features and characteristics that enable developers to tailor workflows based on their needs.

The various components of LangChain include:

Key elements of LangChain

Large language models

LangChain is designed to work with large language models. This makes LLMs an integral part of the framework’s mechanism. LLMs are trained on massive datasets of training data and can be used to perform various tasks, including text generation, language translation, and question-answering.

When used together with LangChain, developers can significantly expand LLM capabilities to create and develop complex applications.

Prompt templates

Prompt templates are used to convert prompts into machine-understandable form. They can also be used to specify the task that the language model should perform or add context to the user’s input. [3]

For instance, a prompt template for a chatbot might include task-specific information like the user’s name and their question.

Indexes

To function effectively, LangChain needs seamless access to the language model’s data. This data is stored in the form of indexes and may include everything from the text in the model’s training data, their metadata, and the relationships between the data. When used effectively, indexes can seamlessly find information that’s relevant to the user’s query.

Retrievers

Retrievers serve as a conduit for searching and retrieving the index for specific information. These algorithms can be used to find various documents related to the user’s query or identify similar documents to a given document. This unique mode of operation significantly improves the speed and accuracy of the language models’ responses.

Output pacers

As the name suggests, output pacers are used to format the information generated by language models. They can remove unwanted content from the LLM’s response or change the format of the response. This helps ensure the model’s output is easy to use and understand.

Agents

Agents are intelligent software programs that can ‘understand’ a problem and break it down into smaller sub-tasks. [4] This makes them especially suitable for controlling the chain’s flow and making decisions about which tasks to perform.

For instance, an agent can help determine whether a user’s question is best answered by the language model or human agent.

Vector stores

As the name suggests, vector stores are databases that store vectors. Vectors are mathematical representations of words or phrases. When stored within a vector store, it’s much easier to find similar words or phrases, which comes in pretty handy in NLP tasks like question answering and summarization.

Use cases and examples for LangChain

LLM-based applications developed using LangChain can be applied to various use cases across multiple industries and vertical markets. Some of the most notable use cases of LangChain-developed LLM-based applications include:

Customer service chatbots

Customer service chatbots are some of the most common LLM-based applications developed using LangChain. LangChain facilitates the development of advanced chat applications that can handle complex questions and summarization tasks. [5] One of the most beneficial features of these applications is their ability to understand and maintain a user’s context throughout the conversation.

Coding assistants

LangChain isn’t just helpful in developing LLM-based applications like chatbots. It can also be used to develop coding assistants to enhance developers’ coding skills and productivity.

Marketing and e-commerce

Businesses with e-commerce platforms can’t just rely on personalization to boost sales and engagement. With current technological advancements in LLM-based applications, businesses can effectively revolutionize their customer service and data collection capabilities.

For instance, besides developing responsive chatbots, businesses can also leverage LLM-based applications to collect and understand customer data like demographics and purchasing patterns. Additionally, businesses can use LangChain-developed LLM-based applications to generate compelling product descriptions and recommendations for potential customers, thus improving sales and engagement.

Healthcare

AI has made significant strides in the healthcare sector. [6] LLM-based applications are helping doctors make diagnoses and automate repetitive tasks such as scheduling patient appointments. When used appropriately, these applications can significantly boost productivity and allow workers to focus on more important tasks.

LangChain: Integration capabilities

LangChain enables developers to build applications through multiple integrations with LLM providers and external data sources. For instance, developers can use LangChain to build chatbots and question-answering systems by integrating a suitable LLM like those from OpenAI and Hugging Face with data sources and data stores like Google Search, Apify Actors, and Wikipedia.

Through these integrations, LangChain is capable of helping develop apps that can take user input, process it, and retrieve information from disparate external sources for better, up-to-date responses.

LangChain also integrates seamlessly with cloud storage platforms like Google Cloud, Microsoft Azure, and Amazon Web Services, as well as vector databases. The latter, vector databases, can store high volumes of high dimensional data like long-form text, images, and videos as mathematical representations, thus making it easier for applications to query and search for various data elements.

Why use LangChain?

Besides facilitating seamless LLM-based application development, LangChain also offers other notable benefits, including:

Flexibility

LangChain does not excerpt constraints on the number and types of applications that can be developed. Besides the more common chatbots, LangChain can also be used to develop analytical and automation software programs.

Easy to use

Its ease of use is one of LangChain’s biggest selling points. The framework comes equipped with a high-level API that makes it easy to connect LLMs to external data sources when building complex applications.

Scalability

LangChain’s unique architecture makes it incredibly scalable, both in terms of the size of language models it can work with and the scalable characteristics of the applications produced. Most LLM-based applications developed using LangChain can scale and handle vast amounts of data without sacrificing speed and efficiency.

Community support

Despite being a relatively new addition to LLM technology, LangChain has garnered a huge fanbase with various community forums full of developers who use the framework. LangChain community forums typically feature discussions on how to use it more effectively, as well as open-source programs meant to streamline various applications.

Final thoughts

LangChain is poised to revolutionize how applications are developed, particularly when it comes to developing LLM-based applications. LangChain’s unique approach to coding makes it fairly easy for any developer with sufficient coding skills in supported languages to build complex, responsive applications.

As the need for LLM-based applications increases, we’re poised to see greater utilization of the framework across numerous other industries. LangChain is also expected to see further improvements, especially when it comes to speed and integrations.

For further information on LangChain and its advantages,
reach out to Generative AI development company

 

References

[1] Bootcamp. UX design.cc. Understanding how LLMs Like Chatgpt Will Change Your Business. URL: https://bootcamp.uxdesign.cc/understanding-how-llms-like-chatgpt-will-change-your-business-c5e65532fbab. Accessed on December 14, 2023
[2] Python. Langchain.com. Introduction. URL: https://python.langchain.com/docs/get_started/introduction, Accessed on December 15, 2023
[3] Python. Langchain.com. Prompt Templates. URL: https://python.langchain.com/docs/modules/model_io/prompts/prompt_templates/, Accessed on December 15, 2023
[4] Python. Langchain.com. Agents. URL: https://bit.ly/48nOdC5. Accessed on December 15, 2023
[5] Towardsdatascience.com. Can a LLMA 2 Powered Chatbot be Trained on a CPU. URL: https://bit.ly/3tftIZK.
[6] Foreseemed.com. Healthcare LLMs. URL: https://bit.ly/4aqUkrg.  Accessed on December 16, 2023



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Generative AI