Run a router based on a user's initial prompt to determine what category best matches from the given options. If there is no good match, the value "DEFAULT" is returned.

Here's an example:

routes = [
  PromptRoute.new!(%{
    name: "marketing_email",
    description: "Create a marketing focused email",
    chain: marketing_email_chain
  }),
  PromptRoute.new!(%{
    name: "blog_post",
    description: "Create a blog post that will be linked from the company's landing page",
    chain: blog_post_chain
  }),
]

selected_route =
  RoutingChain.new!(%{
    llm: ChatOpenAI.new!(%{model: "gpt-40-mini", stream: false}),
    input_text: "Let's create a marketing blog post about our new product 'Fuzzy Furies'",
    routes: routes,
    default_route: PromptRoute.new!(%{name: "DEFAULT", chain: fallback_chain})
  })
  |> RoutingChain.evaluate()

# The PromptRoute for the `blog_post` should be returned as the `selected_route`.

The llm is the model used to make the determination of which route is the best match. A smaller, faster LLM may be a great choice for the routing decision, then a more complex LLM may be used for a selected route.

The default_route is required and is used as a fallback if the user's prompt doesn't match any of the specified routes. It may also be used in some fallback error situations as well.

Callbacks

The LLMChain used to make the routing decision is built internally, so handlers registered on the llm itself are not used. Pass callbacks to observe the run instead:

RoutingChain.new!(%{
  llm: llm,
  input_text: user_text,
  routes: routes,
  default_route: default_route,
  callbacks: [%{on_llm_token_usage: fn _chain, usage -> log_usage(usage) end}]
})

Handlers are registered on the internally run LLMChain, so the full set of LangChain.Chains.ChainCallbacks events is available.