Setup Superlinked

In a notebook

Install the superlinked library:

%pip install superlinked

As a script

Ensure your python version is at least 3.10.x but not newer than 3.12.x.

$> python -V
Python 3.10.9

If your python version is not >=3.10 and <=3.12 you might use pyenv to install it.

Upgrade pip and install the superlinked library.

$> python -m pip install --upgrade pip
$> python -m pip install superlinked

Run the example

First run will take slightly longer as it has to download the embedding model.

import json
import os

from superlinked import framework as sl


class Product(sl.Schema):
    id: sl.IdField
    description: sl.String
    rating: sl.Integer


product = Product()

description_space = sl.TextSimilaritySpace(
    text=product.description, model="Alibaba-NLP/gte-large-en-v1.5"
)
rating_space = sl.NumberSpace(
    number=product.rating, min_value=1, max_value=5, mode=sl.Mode.MAXIMUM
)
index = sl.Index([description_space, rating_space], fields=[product.rating])


# Define your query and parameters to set them directly at query-time
# or let an LLM fill them in for you using the `natural_language_query` param.
# Don't forget to set your OpenAI API key to unlock this feature.
query = (
    sl.Query(
        index,
        weights={
            description_space: sl.Param("description_weight"),
            rating_space: sl.Param("rating_weight"),
        },
    )
    .find(product)
    .similar(
        description_space,
        sl.Param(
            "description_query",
            description="The text in the user's query that refers to product descriptions.",
        ),
    )
    .limit(sl.Param("limit"))
    .with_natural_query(
        sl.Param("natural_language_query"),
        sl.OpenAIClientConfig(api_key=os.environ["OPEN_AI_API_KEY"], model="gpt-4o")
    )
)

# Run the app in-memory (server & Apache Spark executors available too!).
source = sl.InMemorySource(product)
executor = sl.InMemoryExecutor(sources=[source], indices=[index])
app = executor.run()


# Ingest data into the system - index updates and other processing happens automatically.
source.put([
    {
        "id": 1,
        "description": "Budget toothbrush in black color. Just what you need.",
        "rating": 1,
    },
    {
        "id": 2,
        "description": "High-end toothbrush created with no compromises.",
        "rating": 5,
    },
    {
        "id": 3,
        "description": "A toothbrush created for the smart 21st century man.",
        "rating": 3,
    },
])

result = app.query(query, natural_query="best toothbrushes", limit=1)

# Examine the extracted parameters from your query
print(json.dumps(result.knn_params, indent=2))

# The result is the 5-star rated product.
result.to_pandas()