Artificial Intelligence - Learn LangChain with Simple Examples

Artificial Intelligence - Learn LangChain with Simple Examples

How to Install LangChain and a Simple LLM Example Code

To use LangChain, you first need to install the LangChain library in your Python environment. You can do this using the pip package manager.

Installation Command:

pip install langchain

Additionally, if you want to use an LLM (Large Language Model) like OpenAI, you need to install the corresponding API client. For example, to use OpenAI, you can install it as follows:

pip install openai

Simple Example of Using LangChain

Below is an example of setting up and running a simple chain using LangChain. This example creates a chain that generates answers to input questions using OpenAI’s GPT-3.

Basic Setup:

First, you need to set your OpenAI API key, which you can obtain from OpenAI.

import os
from langchain import OpenAI, LLMChain

# Set OpenAI API key
os.environ["OPENAI_API_KEY"] = "your-openai-api-key-here"

Creating a Simple Chain:

In this example, we create a chain that takes a question from the user and generates an answer using OpenAI’s GPT-3 model.

from langchain.prompts import PromptTemplate

# Define a prompt template
prompt_template = PromptTemplate(
    input_variables=["question"],
    template="Q: {question}\nA:"
)

# Set up OpenAI LLM
llm = OpenAI(temperature=0.7)

# Create LLMChain
chain = LLMChain(llm=llm, prompt=prompt_template)

# Input question
question = "What is the capital of France?"

# Run the chain and print the output
answer = chain.run(question)
print(f"Question: {question}")
print(f"Answer: {answer}")

Execution Result:

When you run the above code, the OpenAI GPT-3 model generates an answer to the question. For example, if the input question is “What is the capital of France?”, the output would be:

Question: What is the capital of France?
Answer: The capital of France is Paris.

Summary

• Installation: Install LangChain with the command pip install langchain.
• Set API Key: Set the OpenAI API key as an environment variable.
• Create a Simple Chain: Use LLMChain and PromptTemplate to create a chain that answers questions.
• Execution: Run the chain to output the model’s response.

Through this simple example, you can see how easy it is to develop LLM-based applications using LangChain. LangChain also provides various other features to support complex AI workflows.