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Router Chains: Selecting from multiple prompts with MultiPromptChain
Router Chains: Selecting from multiple prompts with MultiPromptChain#
This notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects the prompt to use for a given input. Specifically we show how to use the MultiPromptChain to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt.
from langchain.chains.router import MultiPromptChain
from langchain.llms import OpenAI
physics_template = """You are a very smart physics professor. \
You are great at answering questions about physics in a concise and easy to understand manner. \
When you don't know the answer to a question you admit that you don't know.
Here is a question:
{input}"""
math_template = """You are a very good mathematician. You are great at answering math questions. \
You are so good because you are able to break down hard problems into their component parts, \
answer the component parts, and then put them together to answer the broader question.
Here is a question:
{input}"""
prompt_infos = [
    {
        "name": "physics", 
        "description": "Good for answering questions about physics", 
        "prompt_template": physics_template
    },
    {
        "name": "math", 
        "description": "Good for answering math questions", 
        "prompt_template": math_template
    }
]
chain = MultiPromptChain.from_prompts(OpenAI(), prompt_infos, verbose=True)
print(chain.run("What is black body radiation?"))
> Entering new MultiPromptChain chain...
physics: {'input': 'What is black body radiation?'}
> Finished chain.
rtdocs_stable/api.python.langchain.com/en/stable/modules/chains/examples/multi_prompt_router.html