LangChain is a powerful framework designed to streamline the development of applications that utilize language models. One of its standout features is the ability to build "chains," which are sequences of calls that can be combined to accomplish complex tasks.
Chains allow developers to create workflows by connecting multiple components in a logical sequence. For instance, a typical use case might involve fetching some data, processing it, and then generating a response using a language model—all in one streamlined process.
Here's a simple code snippet that demonstrates how to create a chain using LangChain:
from langchain import LLMChain
from langchain.prompts import PromptTemplate
# Define a prompt template
prompt_template = PromptTemplate(
input_variables=["topic"],
template="Write a short article about {topic}."
)
# Create an LLM chain
text_chain = LLMChain(prompt_template=prompt_template)
# Run the chain
output = text_chain.run(topic="The Benefits of Meditation")
print(output)
With LangChain, creating sophisticated workflows with language models becomes an intuitive and organized process. Whether you're building conversational agents, data processing scripts, or creative writing tools, the chain feature provides the versatility and power needed to bring your ideas to life.