Getting started

The quickest way to get started is to install vectorlite using python.

# Note: vectorlite-py not vectorlite. vectorlite is another project.
pip install vectorlite-py numpy

Vectorlite’s metadata filter feature requires sqlite>=3.38. Python 3.14’s built-in sqlite3 module bundles SQLite 3.50.4 (>= 3.38), so no extra driver is needed. Vectorlite still works with older sqlite versions if metadata filter support is not required. Below is a minimal example of using vectorlite. It can also be found in the examples folder.

import vectorlite_py
import sqlite3
import numpy as np
"""
Quick start of using vectorlite extension.
"""

conn = sqlite3.connect(':memory:')
conn.enable_load_extension(True) # enable extension loading
conn.load_extension(vectorlite_py.vectorlite_path()) # load vectorlite

cursor = conn.cursor()
# check if vectorlite is loaded
print(cursor.execute('select vectorlite_info()').fetchall())

# Vector distance calculation
for distance_type in ['l2', 'cosine', 'ip']:
    v1 = "[1, 2, 3]"
    v2 = "[4, 5, 6]"
    # Note vector_from_json can be used to convert a JSON string to a vector
    distance = cursor.execute(f'select vector_distance(vector_from_json(?), vector_from_json(?), "{distance_type}")', (v1, v2)).fetchone()
    print(f'{distance_type} distance between {v1} and {v2} is {distance[0]}')

# generate some test data
DIM = 32 # dimension of the vectors
NUM_ELEMENTS = 10000 # number of vectors
data = np.float32(np.random.random((NUM_ELEMENTS, DIM))) # Only float32 vectors are supported by vectorlite for now

# Create a virtual table using vectorlite using l2 distance (default distance type) and default HNSW parameters
cursor.execute(f'create virtual table my_table using vectorlite(my_embedding float32[{DIM}], hnsw(max_elements={NUM_ELEMENTS}))')
# Vector distance type can be explicitly set to cosine using:
# cursor.execute(f'create virtual table my_table using vectorlite(my_embedding float32[{DIM}] cosine, hnsw(max_elements={NUM_ELEMENTS}))')

# Insert the test data into the virtual table. Note that the rowid MUST be explicitly set when inserting vectors and cannot be auto-generated.
# The rowid is used to uniquely identify a vector and serve as a "foreign key" to relate to the vector's metadata.
# Vectorlite takes vectors in raw bytes, so a numpy vector need to be converted to bytes before inserting into the table.
cursor.executemany('insert into my_table(rowid, my_embedding) values (?, ?)', [(i, data[i].tobytes()) for i in range(NUM_ELEMENTS)])

# Query the virtual table to get the vector at rowid 12345. Note the vector needs to be converted back to json using vector_to_json() to be human-readable. 
result = cursor.execute('select vector_to_json(my_embedding) from my_table where rowid = 1234').fetchone()
print(f'vector at rowid 1234: {result[0]}')

# Find 10 approximate nearest neighbors of data[0] and there distances from data[0].
# knn_search() is used to tell vectorlite to do a vector search.
# knn_param(V, K, ef) is used to pass the query vector V, the number of nearest neighbors K to find and an optional ef parameter to tune the performance of the search.
# If ef is not specified, ef defaults to 10. For more info on ef, please check https://github.com/nmslib/hnswlib/blob/v0.8.0/ALGO_PARAMS.md
result = cursor.execute('select rowid, distance from my_table where knn_search(my_embedding, knn_param(?, 10))', [data[0].tobytes()]).fetchall()
print(f'10 nearest neighbors of row 0 is {result}')

# Find 10 approximate nearest neighbors of the first embedding in vectors with rowid within [1000, 2000) using metadata(rowid) filtering.
rowids = ','.join([str(rowid) for rowid in range(1000, 2000)])
result = cursor.execute(f'select rowid, distance from my_table where knn_search(my_embedding, knn_param(?, 10)) and rowid in ({rowids})', [data[0].tobytes()]).fetchall()
print(f'10 nearest neighbors of row 0 in vectors with rowid within [1000, 2000) is {result}')

conn.close()

More examples can be found in examples folder.