Improved example

Author: marcobambini

README.md

@@ -62,9 +62,12 @@ CREATE TABLE images (
   label TEXT
 );
 
--- Insert a vector (Float32, 384 dimensions)
+-- Insert a BLOB vector (Float32, 384 dimensions) using bindings
 INSERT INTO images (embedding, label) VALUES (?, 'cat');
 
+-- Insert a JSON vector (Float32, 384 dimensions)
+INSERT INTO images (embedding, label) VALUES (vector_convert_f32('[0.3, 1.0, 0.9, 3.2, 1.4,...]'), 'dog');
+
 -- Initialize the vector. By default, the distance function is L2.
 -- To use a different metric, specify one of the following options:
 -- distance=L1, distance=COSINE, distance=DOT, or distance=SQUARED_L2.
@@ -73,10 +76,10 @@ SELECT vector_init('images', 'embedding', 'type=FLOAT32,dimension=384');
 -- Quantize vector
 SELECT vector_quantize('images', 'embedding');
 
--- Optional preload quantized version
+-- Optional preload quantized version in memory (for a 4x/5x speedup) 
 SELECT vector_quantize_preload('images', 'embedding');
 
--- Run a nearest neighbor query (returns top 20 closest vectors)
+-- Run a nearest neighbor query on the quantized version (returns top 20 closest vectors)
 SELECT e.id, v.distance FROM images AS e
    JOIN vector_quantize_scan('images', 'embedding', ?, 20) AS v
    ON e.id = v.rowid;