search.py

import sqlite3
import coremltools as ct
from transformers import AutoTokenizer
import numpy as np
from usearch.index import Index, Matches

def tokenize(text):
    return tokenizer(
        text,
        add_special_tokens=True,
        max_length=512,
        padding='max_length',
        truncation=True,
        return_attention_mask=True,
        return_tensors='np'
    )

def embed(text):
    result = tokenize(text)
    token_ids = result['input_ids'].astype(np.float32)
    mask = result['attention_mask'].astype(np.float32)
    # print(f"Tokens: {token_ids}")
    # print(f"Mask: {mask}")
    predictions = model.predict({"input_ids": token_ids, "attention_mask": mask})
    return predictions['embeddings'][0]

lang = "simple"
date = "20240801"
model = ct.models.CompiledMLModel('./msmarco_distilbert_base_tas_b_512_single_quantized.mlmodelc')
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/msmarco-distilbert-base-tas-b")

precision = "f16"
index_path = f"./{lang}wiki-{date}.{precision}.index"
index = Index.restore(index_path, view=True)

db_name = f"wikipedia_{lang}_{date}.db"
conn = sqlite3.connect(db_name)
cursor = conn.cursor()

query = "what is the capital of AUS?"
# query = "who is the current US president?"
# query = "who was the first paraplegic to fully recover spinal & motor function after severing their spinal cord?"
print(f"🔎 testing search... '{query}'")
qembed = embed(query)
res: Matches = index.search(qembed, 5)
print(f"  - Results:")
for result in res:
    (title, section, text) = cursor.execute("SELECT title, section_name, text FROM article_sections WHERE id = ?;", (f"{result.key}",)).fetchone()
    snippet = text[:280].replace("\n", " ")
    print(f"    - Key: {result.key} | Distance: {result.distance} | Excerpt from '{title}', '{section}': {snippet}")

conn.close()