Support for different aggregation in (Hybrid)RouteLayer voting?

[andreped]

Current solution

Currently, for k routes and top_k most relevant utterances, the final route classification is determined by class-wise similarity aggregation (SUM) and then selecting which class has the highest aggregated similarity value.

Problem

In many tasks, doing this type of SUM aggregation might be bad, as it is very dependent on how the routes are defined which aggregation method makes the most sense.

For very high values of top_k, I would expect the most optimal class to be the one that has the highest consensus across all utterances class-wise. Statistically, I would expect this to be the MEAN similarity value.

Then again, using MEAN could be bad as well, if the utterances for one routes are extremely similar and then for another one they are more heterogeneous. So for the current HybridRouteLayer, it seems evident that which method that is used is dependent on how the end user has defined the routes.

Proposed solution

Allow developers to set aggregation themselves to allow them to find which solution works best.

This solution would be of relevant both for the RouteLayer and HybridRouteLayer classes.

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EDIT: I can draft a PR to add this feature.

[andreped]

A PR has been made to address this. See PR #202.

Note that I have added this support to both RouteLayer and HybridRouteLayer.
Below is an example on how one will be able to set the aggregation method (supported types are "sum", "mean", and "max").:

from semantic_router.encoders import AzureOpenAIEncoder, TfidfEncoder
from semantic_router.layer import RouteLayer
from semantic_router.hybrid_layer import HybridRouteLayer

model = RouteLayer(
    encoder=AzureOpenAIEncoder(...),
    routes=routes,
    alpha=0.8,  # default = 0.3
    top_k=6,  # default = 5
    aggregation="max",  # default = "sum"
)

hybrid_model = HybridRouteLayer(
    encoder=AzureOpenAIEncoder(...),
    sparse_encoder=TfidfEncoder(),
    routes=routes,
    alpha=0.2,  # default = 0.3
    top_k=3,  # default = 5
    aggregation="mean",  # default = "sum"
)