What is Semantic Routing in RAG

 Each route has a set of example queries associated with it, that become embedded and stored as vectors. The incoming query gets embedded also, and a similarity search is done against the other sample queries from the router. The route which belongs to the query with the closest match gets selected.

There is in fact a python package called semantic-router that does just this. Let’s look at some implementation details to get a better idea of how the whole thing works. These examples come straight out of that libraries GitHub page.

from semantic_router import Route

# we could use this as a guide for our chatbot to avoid political

# conversations

politics = Route(

    name="politics",

    utterances=[

        "isn't politics the best thing ever",

        "why don't you tell me about your political opinions",

        "don't you just love the president",

        "they're going to destroy this country!",

        "they will save the country!",

    ],

)

# this could be used as an indicator to our chatbot to switch to a more

# conversational prompt

chitchat = Route(

    name="chitchat",

    utterances=[

        "how's the weather today?",

        "how are things going?",

        "lovely weather today",

        "the weather is horrendous",

        "let's go to the chippy",

    ],

)

# we place both of our decisions together into single list

routes = [politics, chitchat]

encoder = OpenAIEncoder()

from semantic_router.layer import RouteLayer

route_layer = RouteLayer(encoder=encoder, routes=routes)

route_layer("don't you love politics?").name

references:

https://towardsdatascience.com/routing-in-rag-driven-applications-a685460a7220