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site/guides/binary-quant.md
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@ -24,13 +24,13 @@ binary quantized 8-dimensional vector can be stored in a single byte — one bit
per element. For 1 million vectors, that would be just `1MB`, a 32x reduction!
Though keep in mind, you're bound to lose a lot quality when reducing 32 bits of
information to 1 bit. [Over-sampling and re-scoring](#re-scoring) will help a
information to 1 bit. [Oversampling and re-scoring](#re-scoring) will help a
lot.
The main goal of BQ is to dramatically reduce the size of your vector index,
resulting in faster searches and less resources. This is especially useful in
resulting in faster searches with less resources. This is especially useful in
`sqlite-vec`, which is (currently) brute-force only and meant to run on small
devices. BQ is an easy low-cost method to make larger vector datasets easy to
devices. BQ is an easy low-cost method to make larger vector datasets easier to
manage.
## Binary Quantization `sqlite-vec`
@ -41,7 +41,9 @@ element in a given vector, it will apply `0` to negative values and `1` to
positive values, and pack them into a `BLOB`.
```sqlite
select vec_quantize_binary('[-0.73, -0.80, 0.12, -0.73, 0.79, -0.11, 0.23, 0.97]');
select vec_quantize_binary(
'[-0.73, -0.80, 0.12, -0.73, 0.79, -0.11, 0.23, 0.97]'
);
-- X'd4`
```
@ -51,6 +53,9 @@ The single byte `0xd4` in hexadecimal is `11010100` in binary.
## Demo
Here's an end-to-end example of using binary quantization with `vec0` virtual
tables in `sqlite-vec`.
```sqlite
create virtual table vec_movies using vec0(
synopsis_embedding bit[768]