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We can filter the routes that the SemanticRouter considers when making a classification. This can be useful if we want to restrict the scope of possible routes based on some context. For example, we may have a router with several routes, politics, weather, chitchat, etc. We may want to restrict the scope of the classification to only consider the chitchat route. We can do this by passing a route_filter argument to our SemanticRouter calls like so: 
sr("don't you love politics?", route_filter=["chitchat"])
In this case, the SemanticRouter will only consider the chitchat route for the classification.

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Full Example

Open In Colab Open nbviewer We start by installing the library: 
!pip install -qU semantic-router
We start by defining a dictionary mapping routes to example phrases that should trigger those routes. 
from semantic_router import Route

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",
        "don't you just hate the president",
        "they're going to destroy this country!",
        "they will save the country!",
    ],
)
Let’s define another for good measure: 
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",
    ],
)

routes = [politics, chitchat]
Now we initialize our embedding model: 
import os
from getpass import getpass
from semantic_router.encoders import CohereEncoder, OpenAIEncoder

os.environ["COHERE_API_KEY"] = os.getenv("COHERE_API_KEY") or getpass(
    "Enter Cohere API Key: "
)
# os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY") or getpass(
#     "Enter OpenAI API Key: "
# )

encoder = CohereEncoder()
# encoder = OpenAIEncoder()
Now we define the SemanticRouter. When called, the router will consume text (a query) and output the category (Route) it belongs to — to initialize a SemanticRouter we need our encoder model and a list of routes. 
from semantic_router.routers import SemanticRouter

sr = SemanticRouter(encoder=encoder, routes=routes)
Now we can test it: 
sr("don't you love politics?")

RouteChoice(name='politics', function_call=None, similarity_score=None)

sr("how's the weather today?")

RouteChoice(name='chitchat', function_call=None, similarity_score=None)
Both are classified accurately, what if we send a query that is unrelated to our existing Route objects? 
sr("I'm interested in learning about llama 2")

RouteChoice(name=None, function_call=None, similarity_score=None)
In this case, we return None because no matches were identified.

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Demonstrating the Filter Feature

Now, let’s demonstrate the filter feature. We can specify a subset of routes to consider when making a classification. This can be useful if we want to restrict the scope of possible routes based on some context. For example, let’s say we only want to consider the “chitchat” route for a particular query: 
sr("don't you love politics?", route_filter=["chitchat"])

RouteChoice(name='chitchat', function_call=None, similarity_score=None)
Even though the query might be more related to the “politics” route, it will be classified as “chitchat” because we’ve restricted the routes to consider. Similarly, we can restrict it to the “politics” route: 
sr("how's the weather today?", route_filter=["politics"])

RouteChoice(name=None, function_call=None, similarity_score=None)
In this case, it will return None because the query doesn’t match the “politics” route well enough to pass the threshold.