build_embeddings.py

from sentence_transformers import SentenceTransformer
import faiss, json, glob, os, numpy as np

model = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
texts=[]; vecs=[]
for f in glob.glob("nyc_ads_dataset/*.json"):
    cap=json.load(open(f))["caption"]
    texts.append(cap); vecs.append(model.encode(cap,normalize_embeddings=True))
vecs=np.vstack(vecs).astype("float32")
index=faiss.IndexFlatIP(vecs.shape[1]); index.add(vecs)
faiss.write_index(index,"prompt.index"); json.dump(texts,open("prompt.txt","w"))