LangChain is an open-source framework designed to simplify the creation of applications using large language models (LLMs). It provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications. It allows AI developers to develop applications based on the combined Large Language Models (LLMs) such as GPT-4 with external sources of computation and data. This framework comes with a package for both Python and JavaScript.
LangChain follows a general pipeline where a user asks a question to the language model where the vector representation of the question is used to do a similarity search in the vector database and the relevant information is fetched from the vector database and the response is later fed to the language model. further, the language model generates an answer or takes an action.
Applications of LangChain
LangChain is a powerful tool that can be used to build a wide range of LLM-powered applications. It is simple to use and has a large user and contributor community.
- Document analysis and summarization
- Chatbots: LangChain can be used to build chatbots that interact with users naturally. For example, LangChain can be used to build a chatbot that can answer client questions, provide customer assistance, and even arrange appointments.
- Code analysis: LangChain can be used to analyse code and find potential bugs or security flaws.
- Answering questions using sources: LangChain can be used to answer questions using a variety of sources, including text, code, and data. For example, LangChain can be used to answer questions about a specific topic by searching through a variety of sources, such as Wikipedia, news articles, and code repositories.
- Data augmentation: LangChain can be used to augment data by generating new data that is similar to existing data. For example, LangChain can be used to generate new text data that is similar to existing text data. This can be useful for training machine learning models or for creating new datasets.
- Text classification: LangChain can be used for text classifications and sentiment analysis with the text input data
- Text summarization: LangChain can be used to summarize the text in the specified number of words or sentences.
- Machine translation: LangChain can be used to translate the input text data into different languages.
LangChain Key Concepts:
The main properties of LangChain Framework are :
- Components: Components are modular building blocks that are ready and easy to use to build powerful applications. Components include LLM Wrappers, Prompt Template and Indexes for relevant information retrieval.
- Chains: Chains allow us to combine multiple components together to solve a specific task. Chains make it easy for the implementation of complex applications by making it more modular and simple to debug and maintain.
- Agents: Agents allow LLMs to interact with their environment. For example, using an external API to perform a specific action.
Setting up the environment
Installation of langchain is very simple and similar as you install other libraries using the pip command.
!pip install langchain
There are various LLMs that you can use with LangChain. In this article, I will be using OpenAI. Let us install Openai using the following command:
!pip install openai
I am also installing the dotenv library to store the API key in an environmental variable. Install it using the command:
!pip install python-dotenv
You can generate your own API key by signing up to the openai platform. Next, we create a .env file and store our API key in it as follows:
Python
OPENAI_KEY='your_api_key' |
Now, I am creating a new file named ‘lang.py’ where I will be using the LangChain framework to generate responses. Let us start by importing the required libraries as follows:
Python
import osimport openai,langchainfrom dotenv import load_dotenvload_dotenv()api_key=os.getenv("OPENAI_KEY",None) |
That was the initial setup required to use the LangChain framework with OpenAI LLM.
Building an Application
As this is an introductory article, let us start by generating a simple answer for a simple question such as “Suggest me a skill that is in demand?”.
We start by importing long-chain and initializing an LLM as follows:
Python
from langchain.llms import OpenAIllm = OpenAI(temperature=0.9,openai_api_key=api_key) |
We are initializing it with a high temperature which means that the results will be random and less accurate. For it to be more accurate you can give a temperature as 0.4 or lesser. We are then assigning openai_api_key as api_key which we have loaded previously from .env file.
The next step would be to predict by passing in the text as follows:
Python
response=llm.predict("Suggest me a skill that is in demand?")print(response) |
That is it! The response generated is as follows:
One skill in demand right now is software/web development, which includes everything from coding to content management systems to web design. Other skills in demand include cloud computing, machine learning and artificial intelligence, digital marketing, cybersecurity, data analysis, and project management.
Conclusion:
That was the basic introduction to langchain framework. I hope you have understood the usage and there are a lot more concepts such as prompt templates, chains and agents to learn. The LangChain framework is a great interface to develop interesting AI-powered applications and from personal assistants to prompt management as well as automating tasks. So, Keep learning and keep developing powerful applications.
