Toy search tutorial

This tutorial shows the entire workflow with a minimal matrix.

Step 1: create a tiny embedding bank

import numpy as np

embeddings = np.array(
    [
        [1.0, 0.0],
        [0.9, 0.1],
        [0.0, 1.0],
    ],
    dtype=np.float32,
)

Step 2: define metadata and a query

metadata = [
    {"cell_id": "cell-a", "disease": "IPF"},
    {"cell_id": "cell-b", "disease": "IPF"},
    {"cell_id": "cell-c", "disease": "Control"},
]
query = np.array([1.0, 0.0], dtype=np.float32)

Step 3: search

from turbocellatlas import SearchConfig, SearchIndex

index = SearchIndex(embeddings, metadata, SearchConfig(top_k=2))
results = index.search(query, filters={"disease": "IPF"})

Step 4: inspect results

The output is a ranked list of result objects with rank, item_id, score, and metadata. This is enough to plug the package into a larger workflow without keeping large example files in the repository itself.