#!/usr/bin/env python3

"""

Feather DB - Basic Python Example

This example shows how to create a database, add vectors, and search.

"""

import feather_db

import numpy as np

def main():

print("=" * 60)

print("Feather DB - Basic Example")

print("=" * 60)

# Step 1: Create or open a database

print("\n1. Creating database...")

db = feather_db.DB.open("example.feather", dim=128)

print(" ✓ Database created with 128 dimensions")

# Step 2: Add some vectors

print("\n2. Adding vectors...")

num_vectors = 10

for i in range(num_vectors):

# Create a random vector (in real use, these would be embeddings)

vector = np.random.random(128).astype(np.float32)

db.add(id=i, vec=vector)

print(f" ✓ Added {num_vectors} vectors")

# Step 3: Save the database

print("\n3. Saving database...")

db.save()

print(" ✓ Database saved to disk")

# Step 4: Search for similar vectors

print("\n4. Searching for similar vectors...")

query = np.random.random(128).astype(np.float32)

ids, distances = db.search(query, k=5)

print(f" ✓ Found {len(ids)} similar vectors:")

for i, (id, dist) in enumerate(zip(ids, distances), 1):

print(f" {i}. ID: {id:2d}, Distance: {dist:.4f}")

print("\n" + "=" * 60)

print("Example completed successfully!")

print("=" * 60)

if __name__ == "__main__":

main()