import sqlite3
import numpy as np
import time

conn = sqlite3.connect(":memory:")
c = conn.cursor()
c.execute('''CREATE TABLE memory (
    id INTEGER PRIMARY KEY,
    type TEXT,
    content TEXT,
    ts INTEGER,
    embedding BLOB
)''')

def to_blob(x): return x.astype(np.float32).tobytes()
embeddings = np.random.randn(10000, 1536)
start = time.time()
for i in range(10000):
    c.execute("INSERT INTO memory (type, content, ts, embedding) VALUES (?, ?, ?, ?)",
              ('summary', f"content_{i}", i, to_blob(embeddings[i])))
conn.commit()
print(f"SQLite 10k row insert: {(time.time()-start)*1000:.2f} ms")

def search(query_embed, k=5):
    c.execute("SELECT content, embedding FROM memory WHERE type='summary' AND ts BETWEEN 0 AND 5000")
    rows = c.fetchall()
    X = np.array([np.frombuffer(row[1], dtype=np.float32) for row in rows])
    dists = np.linalg.norm(X - query_embed, axis=1)
    topk = np.argsort(dists)[:k]
    return [rows[i][0] for i in topk]

qvec = np.random.randn(1, 1536)
start = time.time()
results = search(qvec[0], k=5)
print(f"SQLite hybrid filter + search latency: {(time.time()-start)*1000:.2f} ms")