Introduction to LangChain LLM: A Beginner’s Guide
With the introduction of large language models (LLMs), Natural Language Processing has been the talk of the internet. New applications are being developed daily due to LLMs like ChatGPT and LangChain.
LangChain is an open-source Python framework enabling developers to develop applications powered by large language models. Its applications are chatbots, summarization, generative questioning and answering, and many more.
This article will provide an introduction to LangChain LLM. It will cover the basic concepts, how it compares to other language models, and how to get started with it.
Understanding the LangChain LLM
Before explaining how LangChain works, first, you need to understandhow large language models work. A large language model is a type of artificial intelligence (AI) thatuses deep learningto train the machine learning models on big data consisting of textual, numerical, and code data.
The vast amount of data enables the model to learn the existing patterns and relationships between words, figures, and symbols. This feature allows the model to perform an array of tasks, such as:
LLMs’ most significant limitation is that the models are very general. This feature means that despite their ability to perform several tasks effectively, they may sometimes provide general answers to questions or prompts requiring expertise and deep domain knowledge instead of specific answers.
Developed by Harrison Chase in late 2022, the LangChain framework offers an innovative approach to LLMs. The process starts by preprocessing the dataset texts by breaking it down into smaller parts or summaries. The summaries are then embedded in a vector space. The model receives a question, searches the summaries, and provides the appropriate response.
LangChain’s preprocessing method is a critical feature that is unavoidable as LLMs become more powerful and data-intensive. This method is mainly used in code and semantic search cases because it provides real-time collection and interaction with the LLMs.
LangChain LLM vs. Other Language Models
The following comparative overview aims to highlight the unique features and capabilities that set LangChain LLM apart from other existing language models in the market:
Getting Started With LangChain LLM
Now you will learn how to implement LangChain in a real use-case scenario to understand how it works. Before starting the development, you need to set up the development environment.
Setting Up Your Development Environment
First,create a virtual environmentand install the dependencies below:
Using pip, run the command below to install the dependencies:
The command above installs the packages and creates a virtual environment.
Import the Installed Dependencies
First, import the necessary classes such asLLMChain,OpenAI,ConversationChain, andPromptTemplatefrom thelangchainpackage.
The LangChain classes outline and execute the language model chains.
Access OpenAI API Key
Next, get the OpenAI API key. To access OpenAI’s API key, you must have an OpenAI account, then move to theOpenAI API platform.
On the dashboard, click on the Profile icon. Then, click theView API keysbutton.
Next, click theCreate new secret keybutton to get a new API key.
Enter the requested name of the API key.
You will receive asecret keyprompt.
Copy and store the API key in a safe place for future use.
Developing an Application Using LangChain LLM
You will now proceed to develop a simple chat application as follows:
Next, you will load the ChatGPT chain using the API key you stored earlier.
This code loads the LLM chain with the OpenAI API key and the prompt template. User input is then provided, and its output is displayed.
Above is the expected output.
The Rising Influence of LLMs
LLMs consumption is growing rapidly and changing how humans interact with knowledge machines. Frameworks like LangChain are at the forefront of providing developers with a smooth and simple way to serve the LLMs to applications. Generative AI models like ChatGPT, Bard, and Hugging Face are also not left behind in advancing LLM applications.
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