simsity 0.3.0

Simple Similarity Service

simsity

Simsity is a Super Simple Similarities Service[tm].
It's all about building a neighborhood. Literally!

This repository contains simple tools to help in similarity retrieval scenarios by making a convenient wrapper around encoding strategies as well as nearest neighbor approaches. Typical usecases include early stage bulk labelling and duplication discovery.

Install

You can install simsity via pip.

python -m pip install simsity

It's usually recommended that you also install embetter.

Quickstart

This is the basic setup for this package.

import pandas as pd
from embetter.text import SentenceEncoder

from simsity.datasets import fetch_recipes
from simsity.service import Service
from simsity.indexer import AnnoyIndexer


# Fetch data
df_recipes = fetch_recipes()
recipes = df_recipes['text']

# Create an indexer and encoder
indexer = AnnoyIndexer()
encoder = SentenceEncoder()

# The service combines the two into a single object.
service = Service(indexer=indexer, encoder=encoder)

# We can now build the service using this data.
service.index(recipes)

# And use it
idx, dists = service.query("meat", n_neighbors=10)

res = (pd.DataFrame({"recipe": recipes})
    .iloc[idx]
    .assign(dists=dists)
    .to_markdown(index=False)
)

# Show results
print(res)