Unlocking the Power of AI Development

Intern Thomas spent his time at Codit building an AI chatbot capable of summarizing large documents. Using the LangChain framework, Thomas developed a sophisticated solution that shows the capabilities of AI and large language models (LLMs). In this blog series, we'll follow Thomas's step-by-step journey, from initial setup to the final product.

Author: Codit
Data Driven Solutions

LangChain is a state-of-the-art, freely available tool created to help app developers utilize large language models (LLMs) in the creation of AI powered applications. Do you want to create an application like this easily? Read on!


Key Features

Its main power is in its ability to manage vast amounts of data efficiently, which is key for building applications like advanced AI chatbots, question-answering systems, and language summarization tools. It can handle it all with ease.

One of the main strengths of LangChain is that it uses python. This makes it relatively easy and flexible to use. Want to create an AI powered app but don’t know any of the fitting programming languages? Python is very readable and in no time you’ll be creating AI apps. The setup is very easy too, requiring no large setups to get started. You can create any python file and start writing your application.


Enhanced LLM Capabilities

LangChain is unique in its ability to boost the capabilities of LLMs. It helps in creating agents that can think about problems and break them down into smaller tasks. By creating intermediary stages and chaining complex commands, LangChain adds context and memory to tasks.


Memory and Context Enhancement

The tool’s appeal is its ability to enhance existing LLMs with memory (chat history) and context, leading to more complex tasks being completed with greater precision. For the user, there is no difficulty. They just ask a question and LangChain handles the rest.


Compatibility with Various Models

LangChain works with many language models, including OpenAI models, Hugging Face, and Azure specific models. It provides a toolkit and APIs to make it easier to link language models to external data sources, simplifying the development of complex applications. Every type of LLM has its own function to call and from there LangChain takes over.


Concept of Chains

LangChain uses the concept of Chains. Chains are mechanisms that link  together one or several LLMs to process and transform input data. They form the fundamental structure that allows for the flow of information through the system. The chains are designed to integrate different elements coherently, enabling the system to efficiently handle complex language processing tasks. They play a crucial role in managing the flow and transformation of data, thereby improving the system’s overall functionality and efficiency.


Ease of Use and Open Source

LangChain is easy to start using, with its source code available on GitHub and installation as simple as a pip command in Python. With LangChain, developers can create a variety of applications, such as content creation tools, chatbots, and data analysis software without being tied to a single company. This is because LangChain is open source, meaning it is free to use and developers can download the source code from GitHub to create their own applications. LangChain also provides pre-trained models.


Future Prospects

In the future, LangChain’s main application will be chat-based applications on top of LLMs, particularly ChatGPT, also known as « chat interfaces. » It is important to note that there have been talks to add new elements to it. The world of AI is still new, and it is hard to predict what the future will bring. As AI continues to evolve and play an integral role in big data and daily life, it will be fascinating to see how LangChain adapts and grows. Only time will tell.

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