def bootstrap():

# Bootstrap() is called from init_repo() during `feast init`

import pathlib

from datetime import datetime

import numpy as np

import pandas as pd

repo_path = pathlib.Path(__file__).parent.absolute() / "feature_repo"

data_path = repo_path / "data"

data_path.mkdir(exist_ok=True)

# Minimal city data with embeddings (384-d to match feature_store embedding_dim)

embedding_dim = 384

now = datetime.now().replace(microsecond=0, tzinfo=None)

cities = [

(

1,

"New York",

"New York",

"New York City is the most populous city in the United States.",

),

(

2,

"Los Angeles",

"California",

"Los Angeles is the second most populous city in the United States.",

),

(

3,

"Chicago",

"Illinois",

"Chicago is the third most populous city in the United States.",

),

]

rows = []

for city_id, city_name, state, wiki_summary in cities:

vec = np.random.randn(embedding_dim).astype(np.float32)

vec = (vec / np.linalg.norm(vec)).tolist()

rows.append(

{

"city_id": city_id,

"event_timestamp": pd.Timestamp(now),

"vector": vec,

"sentence_chunks": wiki_summary[:200],

"state": f"{city_name}, {state}",

"wiki_summary": wiki_summary,

}

)

df = pd.DataFrame(rows)

parquet_path = data_path / "city_wikipedia_summaries_with_embeddings.parquet"

df.to_parquet(path=str(parquet_path), index=False)

if __name__ == "__main__":

bootstrap()