Feature Spotlight: Chaining Language Models
One of the standout features of LangChain is the ability to chain multiple language models together to enhance the overall functionality and performance. This allows developers to create complex applications that can perform a variety of tasks by breaking them down into manageable steps.
For instance, you can use one model to generate a summary of a text, and then pass that summary to another model to generate relevant questions. Here’s a simple example of how you can implement this chaining feature in LangChain:
from langchain import Chain, LLM
# Create two language models
summarizer = LLM(model_name="summarization-model")
question_generator = LLM(model_name="question-generation-model")
# Define a chain that summarizes text and generates questions
chain = Chain(steps=[
summarizer,
question_generator
])
# Input text to be summarized
input_text = "LangChain is an innovative framework for building applications with language models."
# Execute the chain
output = chain.run(input_text)
print(output)