Exploring Chain Composition in LangChain

LangChain is a powerful framework for developing applications powered by language models. One of its standout features is the ability to compose various chains seamlessly. This allows developers to create complex workflows by chaining together different components, enhancing functionality and keeping the code organized.

Example of a Simple Chain

Below is a code snippet that demonstrates how to create a simple chain using LangChain to handle a basic input-output operation:

from langchain import Chain, OpenAI

# Create a simple chain that translates text
class TranslateChain(Chain):
    def __init__(self):
        super().__init__(llm=OpenAI())

    def run(self, text):
        return self.llm.generate(f"Translate the following text to French: {text}")

# Using the chain
translator = TranslateChain()
result = translator.run("Hello, how are you?")
print(result)

    

This code sets up a translation chain that utilizes the capabilities of an OpenAI model to translate English text into French. By leveraging chains in LangChain, developers can streamline the development process and focus on higher-order functions.

Start exploring LangChain today and streamline your language model applications!