Getting Started with LangChain: Building Applications with GPT

As AI technology advances rapidly, LangChain enables easy development of custom GPT-based NLP applications using Python libraries. In this article, we introduce how to create a simple question-and-answer app using LangChain.

What is LangChain?

LangChain is a Python library created by Harrison Chase that supports building NLP applications using LLM (Large Language Model). This enables the development of applications that can connect with various data sources and computational resources, as well as perform NLP tasks using domain-specific data sources and private repositories.

What can you do with LangChain?

With LangChain, you can develop applications such as:

• Chatbots
• Summaries and question-answers for specific domains
• Apps that fetch and process information from databases
• Agents that solve mathematical and logical puzzles

Overview of LangChain's Modules

LangChain includes modules such as LLM, Chains, Prompts, Document Loaders, Utils, and Agents. You can combine these modules to develop applications.

How to Create a Q&A App with LangChain

We introduce how to create a question-and-answer app using LangChain in five steps.

Step 1: Setting Up the Development Environment

First, ensure Python is installed and install the LangChain library using pip.

pip install langchain

Next, install the OpenAI SDK.

pip install openai

Step 2: Setting the OPENAI_API_KEY as an Environment Variable

Log in to your OpenAI account and obtain an API key. Set the API key as an environment variable using the os module within your Python script.

import os os.environ["OPENAI_API_KEY"] = "your-api-key-here"

Step 3: Making a Simple LLM Callwith LangChain

Make a simple LLM call using LangChain. In this example, we use OpenAI's text-davinci-003 model.

from langchain.llms import OpenAI llm = OpenAI(model_name="text-davinci-003")

Define a question and generate an answer.

question = "What is the best programming language to learn in 2023?" print(llm(question))

Step 4: Creating a Prompt Template

Let's try another question, like "What are the top 4 resources for learning Golang in 2023?"

question = "What are the top 4 resources for learning Golang in 2023?" print(llm(question))

This is sufficient for beginners, but when creating resources for learning multiple programming languages or technology stacks, repetition occurs. That's where prompt templates come in handy. You can create a template that can be formatted using one or more input variables.

from langchain import PromptTemplate template = "What are the top {k} resources for learning {this} in 2023?" prompt = PromptTemplate(template=template, input_variables=['k', 'this'])

Step 5: Running the First LLM Chain

Now that you have an LLM and prompt template, you can execute multiple LLM calls.

from langchain import LLMChain chain = LLMChain(llm=llm, prompt=prompt)

Pass the input as a dictionary and run the LLM chain.

input = {'k': 3, 'this': 'Rust'} print(chain.run(input))
You can access the official LangChain website here.

Conclusion

Now, you have created a simple Q&A app using LangChain. Your understanding of LangChain's features should have deepened. As the next step, try developing more interesting applications using LangChain. Happy coding!