Vector arithmetic utilities and weighted, variably randomized cosine similarity search in Postgres.
This library allows you to perform basic arithmetic on vectors, such as:
SELECT ct_add(
a := ARRAY[1,2,3],
b := ARRAY[4,5,6]
);
-- => {5.0,7.0,9.0}However it's main purpose is facilitating advanced cosine proximity / similarity ranking. Say you're building a Spotify competitor, and you want to offer users a frontpage with recommendations based on tracks they have listened to or liked. If a user disliked a track, similar tracks should also rank lower. Finally you want to show a slightly fresh batch of recommendations every time. That's easy with costop!
Lets build on this example. Each track in our platform's database is represented by a vector generated previously by our magic AI. The vector represents the "feel" or identity of the track, and tracks with a smaller cosine distance between them have a similar feel. We want to optimize the recommendation query, and so next to our vector (which has a cardinality of 100), we also pre-calculate and store it's magnitude. While costop can calculate the magnitude if it is not supplied, that's going to be around 3x slower.
So if we take a look at some sample data:
SELECT vec, magnitude
FROM track_properties
LIMIT 10[
{
"vec": [-4.43744993210000000, 22.10746002200000000, 34.09545898440000000, -79.21794128420000000, -40.66732788090000000, 48.03163909910000000, 7.49958276750000000, 15.56112861630000000, -121.58570098880000000, 6.16820621490000000, -63.39702224730000000, 11.55327510830000000, 50.01103210450000000, 49.56256103520000000, 11.54410839080000000, -70.26428222660000000, 13.63951587680000000, 18.20098876950000000, -5.82873439790000000, -78.48036193850000000, -84.23011016850000000, 67.92200469970000000, -37.82934951780000000, 96.60363006590000000, -48.90142440800000000, -7.87157392500000000, 55.80366134640000000, 36.68728256230000000, 26.20443916320000000, -100.69766998290000000, 14.79865741730000000, -53.33053970340000000, -78.81256866460000000, 25.74527168270000000, 69.50791168210000000, -49.72927474980000000, -122.10723876950000000, 84.24948883060000000, 69.43576812740000000, 34.47189712520000000, -30.75948715210000000, -10.19818115230000000, 61.92003631590000000, 53.65451049800000000, -47.13929748540000000, -33.99538040160000000, -38.53485870360000000, -60.34483718870000000, -24.34590911870000000, 42.64239883420000000, 5.59185695650000000, -127.63845825200000000, -41.23598098750000000, 35.88092422490000000, -76.12630462650000000, -25.35723686220000000, -72.36457824710000000, 1.18507289890000000, -53.30847549440000000, -83.74974823000000000, -82.00662994380000000, -65.07209014890000000, -118.70180511470000000, 57.94058990480000000, -33.31725311280000000, -32.33360672000000000, -81.61341857910000000, -8.05749607090000000, 29.19243812560000000, 20.65240287780000000, 8.01358032230000000, 2.73667764660000000, 33.00947570800000000, 36.34382629390000000, -9.59816360470000000, -11.56176090240000000, -27.19181442260000000, 41.67628097530000000, 98.88591766360000000, -42.40296936040000000, -55.85681152340000000, 101.21887969970000000, -17.46896934510000000, 40.40370941160000000, 12.23396778110000000, -55.25162124630000000, 50.01083374020000000, 8.71825218200000000, 28.61510467530000000, -81.74520111080000000, 51.40452194210000000, -47.99239730830000000, -132.47442626950000000, -64.47586059570000000, 41.36410140990000000, -26.68621063230000000, -68.49066162110000000, -69.74295806880000000, -72.57584381100000000, -42.64236068730000000],
"magnitude": 567.9233
},
{
"vec": [1.48314607140000000, 2.67624783520000000, 8.49940872190000000, 0.09380218390000000, -4.47907876970000000, -0.84740149970000000, 5.83015537260000000, 3.73414492610000000, -10.62790203090000000, 1.04884028430000000, -0.85947906970000000, 0.37547129390000000, 1.00873827930000000, 4.77964591980000000, -4.74055433270000000, -4.94443845750000000, -2.51182174680000000, 5.61057329180000000, 2.58822035790000000, -5.56535911560000000, -9.46886062620000000, 4.96676254270000000, -6.60264492030000000, 8.21836280820000000, -7.04813480380000000, -2.67112159730000000, 4.36127090450000000, 3.65302515030000000, 4.67065143590000000, -4.96347236630000000, -1.49984049800000000, -3.81294393540000000, -16.41612625120000000, 0.63507586720000000, 1.26081514360000000, -0.31485545640000000, -11.84312725070000000, 5.54491901400000000, 4.61627483370000000, 5.56733274460000000, -3.61712503430000000, -1.34634089470000000, 1.23889744280000000, -4.21752643590000000, 2.92170929910000000, 0.04173455390000000, 1.92271137240000000, 0.38014635440000000, 1.25466668610000000, 8.80738639830000000, -1.94717121120000000, -6.26801300050000000, -0.09252355250000000, 0.34039661290000000, -8.17535972600000000, 0.78276950120000000, -3.13373565670000000, -0.06576443460000000, -5.16936540600000000, -1.61087036130000000, -11.18818759920000000, -1.88064920900000000, -5.08728933330000000, 0.49590194230000000, -2.49765181540000000, -0.55674767490000000, -6.37714290620000000, -5.04904317860000000, 3.39625954630000000, 0.20004054900000000, 6.93111324310000000, -0.68639588360000000, 3.31862616540000000, 0.13029925530000000, -1.57837152480000000, 1.59223818780000000, -0.41523024440000000, -2.61051702500000000, 9.10719490050000000, 2.07206869130000000, -2.54725074770000000, 6.49956274030000000, -2.88127255440000000, 2.87980747220000000, 4.91765213010000000, -4.08196544650000000, 8.35620021820000000, 1.47835493090000000, 0.54625040290000000, -4.01129293440000000, 6.81511831280000000, -0.95397168400000000, -7.37796974180000000, -2.64400100710000000, -1.78433191780000000, -2.62039470670000000, -4.13981533050000000, -2.24478054050000000, -3.70239877700000000, -6.29406976700000000],
"magnitude": 48.442005
},
...
]we're ready to use costop. All vectors in costop are double precision[]. Here's an example of how we could rank tracks:
WITH
liked AS (
SELECT track_id, vec, magnitude
FROM track_properties
WHERE track_id = 'f08507c4-df2e-11ea-ac56-fff589f5856f'
),
dont_like AS (
SELECT vec, magnitude
FROM track_properties
WHERE track_id = 'ecca9126-df2e-11ea-ac56-f7c1f9429c6f'
),
weighted AS (
SELECT *
FROM ct_weigh(
positive := ARRAY(SELECT vec FROM liked)
,negative := ARRAY(SELECT vec FROM dont_like)
,neg_scale := 0.1
,noise := 0.05
)
)
SELECT
tp.track_id,
ct_similarity(
a := (select w.vec from weighted w)
,b := tp.vec
,norm_a := (select w.norm from weighted w)
,norm_b := tp.magnitude
)
FROM track_properties tp
WHERE track_id NOT IN (SELECT track_id FROM liked)
ORDER BY 2
LIMIT 10;That's a lot! Let's find out how this works.
First, liked queries the vectors from the tracks we'd like to see more of. For this example, there's just one track in there. Next, dont_like queries vectors for tracks the user doesn't like. Again, just one as an example.
Without respecting likes and dislikes, and without the random component, we could just use the vector from the liked track directly in the ct_similarity function we'll get to in a moment. But that only really works for a single source vector. If we want to recommend tracks based on say the last 5 tracks a user has liked, we need to use ct_weigh to compact that into one vector.
So now we use ct_weigh to build a weighted vector taking into account user preferences and a bit of chance. positive receives a list of vectors used to bias the weighted vector towards, whereas negative is a list of vectors to bias against. We also have optional neg_scale and pos_scale parameters. In the example above, all positive vectors are counted as they are, but negative vectors are scaled to only have 10% of their original impact.
Finally, we add a bit of chance (5%) using the optional noise parameter.
Now, we use the resulting vector as the origin and try to find similar tracks by using ct_similarity. As mentioned previously we make use of cached magnitude values. norm_a comes from the ct_weigh output, and magnitude from our tracks table. ct_similarity returns a value that when used with the default ORDER BY (the 2 here just references the second SELECTed value) will sort by most similar first.
We also exclude all input tracks with WHERE track_id NOT IN (SELECT track_id FROM liked), because the most similar tracks are of course the same tracks we put in.
Note: double means double precision, precision is omitted for readability.
Returns a new vector which is the sum of vector a and vector b.
Returns a new vector which is the result of subtracting vector b from vector a.
Returns a new vector of size dim filled with random numbers picked from a gaussian normal distribution with a standard deviation of std centered around mean.
Calculates and returns the magnitude of vector.
Scales (/multiplies all values of the) vector vec with constant factor c.
Returns a new vector which represents the dot product of vector a and vector b.
Returns a set of (vector, norm), where vector is the sum of the weight vectors as supplied and scaled by the following parameters, and norm is that vector's magnitude.
| Param | Purpose |
|---|---|
positive double[][] |
An array of vectors to add to the weighted vector |
negative double precision[][] DEFAULT ARRAY[]::double precision[][] |
(optional) An array of vectors to subtract from the weighted vector |
noise double precision DEFAULT NULL |
(optional) Amount of noise to add to the weighted vector (as a constant factor which scales a random vector with a standard deviation of the magnitude of the weighted vector) |
pos_scale double precision DEFAULT NULL |
(optional) A constant factor to scale all positive vectors by before adding them to the weighted vector |
neg_scale double precision DEFAULT NULL |
(optional) A constant factor to scale all negative vectors by before adding them to the weighted vector |
Returns a value indicating the cosine proximity of vector b to vector a, in sort-order. Return values start at -1.0 (most similar). More positive values are less similar. If the magnitudes of the vectors (norm_a and norm_b) are not supplied to this function, they're calculated on the fly, which should be avoided if possible.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent