"description": "Good for answering math questions", 
        "prompt_template": math_template
    }
]
llm = OpenAI()
destination_chains = {}
for p_info in prompt_infos:
    name = p_info["name"]
    prompt_template = p_info["prompt_template"]
    prompt = PromptTemplate(template=prompt_template, input_variables=["input"])
    chain = LLMChain(llm=llm, prompt=prompt)
    destination_chains[name] = chain
default_chain = ConversationChain(llm=llm, output_key="text")
LLMRouterChain#
This chain uses an LLM to determine how to route things.
from langchain.chains.router.llm_router import LLMRouterChain, RouterOutputParser
from langchain.chains.router.multi_prompt_prompt import MULTI_PROMPT_ROUTER_TEMPLATE
destinations = [f"{p['name']}: {p['description']}" for p in prompt_infos]
destinations_str = "\n".join(destinations)
router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(
    destinations=destinations_str
)
router_prompt = PromptTemplate(
    template=router_template,
    input_variables=["input"],
    output_parser=RouterOutputParser(),
)
router_chain = LLMRouterChain.from_llm(llm, router_prompt)
chain = MultiPromptChain(router_chain=router_chain, destination_chains=destination_chains, default_chain=default_chain, 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/generic/router.html