One of the standout features of LangChain is its ability to chain various language model components together seamlessly. This allows developers to create more complex flows than simple input-output models. With LangChain, you can combine various types of prompts, tools, and memory systems to build intricate and contextual conversation pathways.
Below is a basic example of how to create a simple chatbot flow using LangChain:
from langchain import LLMChain, PromptTemplate
from langchain.llms import OpenAI
# Initialize the language model
llm = OpenAI(api_key='your-api-key')
# Define a prompt template
template = "You are a friendly chatbot. User: {user_input}\nChatbot:"
prompt = PromptTemplate(input_variables=["user_input"], template=template)
# Create a chain
chain = LLMChain(llm=llm, prompt=prompt)
# Get the response
response = chain.run(user_input="Hello! How are you?")
print(response)
In this code snippet, we set up a basic chatbot that responds to user input. By utilizing the LangChain framework, developers can easily scale and enhance their conversational AI applications!