Vector search enables you to perform similarity searches on vectors stored in Postgres. With the pgvector extension, you can store and efficiently query vector embeddings, making Postgres a viable option for AI-driven applications like retrieval-augmented generation (RAG) and semantic search.

Steps

  • Install and enable pgvector
  • Create a table with a vector column
  • Insert and retrieve vector data
  • Perform nearest neighbor searches
  • Index using HNSW indexes
  • Insert and retrieve embeddings

Install and enable pgvector

Before using vector search, you need to install the pgvector extension. The pgvector extension adds a vector data type, operators for similarity search (<->, <#>, <=>) , and support for ANN indexes. In Neon, pgvector is already installed, you just need to enable it using the following command.

CREATE EXTENSION IF NOT EXISTS vector;

Create a table with a vector column

To store vector embeddings, create a table with a vector column. You must specify the size (also known as dimensionality) of the vectors when defining the column.

CREATE TABLE embeddings (
  id SERIAL PRIMARY KEY,
  data VECTOR(3) -- 3-dimensional vector example
);

Insert and retrieve vector data

You can insert vectors as arrays using the following command. Under the hood, vectors are just fixed-length arrays of floats.

INSERT INTO embeddings (data)
VALUES ('[0.1, 0.2, 0.3]'),
       ('[0.5, 0.1, 0.8]');

You can retrieve all stored vectors using the following command.

SELECT * FROM embeddings;

Perform nearest neighbor searches

Vector search typically means finding the closest vectors in the database to a given vector. There are different distance metrics to calculate which vector is closest, like Euclidean distance (<->), cosine similarity (<#>), and inner product (<=>).

For example, the following command runs nearest neighbor search to find the most similar vector to [0.2, 0.1, 0.3] using Euclidean distance, which is [0.1, 0.2, 0.3].

SELECT * FROM embeddings
ORDER BY data <-> '[0.2, 0.1, 0.3]'
LIMIT 1;

Index using HNSW indexes

For large datasets, exact nearest neighbor search can be slow. pgvector supports two different indexes for nearest neighbor search: HNSW and IVFFlat.

The following command creates a HNSW index.

CREATE INDEX ON embeddings USING hnsw (data);

Insert and retrieve embeddings

Vector databases are typically used to store embeddings. An embedding is a numerical representation of data in a high-dimensional space that captures semantic relationships and similarities between entities. First, run the following command to recreate the embeddings table to store vectors with dimensionality 512.

DROP TABLE embeddings;

CREATE TABLE embeddings (
  id SERIAL PRIMARY KEY,
  data VECTOR(512)
);

For example, the following command inserts a pair of 512 dimensionality vectors containing text embeddings pulled from the Nomic API. The first embedding represents the string "i like to eat tacos", the second represents the string "An embedding is a numerical representation of data in a high-dimensional space that captures semantic relationships and similarities between entities."

INSERT INTO embeddings (data)
VALUES
  /* Embedding representation of "i like to eat tacos" */
  ('[0.017120361,0.09112549,-0.24157715,0.0045776367,-0.024642944,0.0062828064,-0.06707764,0.022094727,-0.022232056,-0.019546509,0.010147095,0.05722046,0.027832031,0.07006836,-0.0051574707,-0.041259766,0.0008292198,-0.08605957,0.014213562,0.10180664,-0.045318604,-0.046447754,-0.002002716,-0.04144287,0.11590576,0.0093688965,-0.019638062,0.08929443,-0.057739258,0.031173706,-0.030471802,-0.07293701,0.019317627,0.100097656,0.017288208,-0.053222656,0.082092285,0.018234253,0.024536133,-0.0541687,-0.027191162,0.038635254,0.05657959,-0.050445557,0.06378174,-0.015579224,0.0736084,0.059173584,0.029037476,-0.03451538,-0.030151367,-0.027633667,-0.038604736,-0.06750488,-0.0038433075,-0.06210327,0.055664062,-0.06677246,-0.01828003,0.025848389,0.10809326,0.021942139,0.016067505,0.08532715,0.02708435,0.031311035,-0.046691895,0.078125,-0.07287598,-0.021347046,0.07159424,-0.0037384033,0.03878784,0.014350891,-0.02381897,-0.04309082,-0.031463623,-0.00541687,-0.03274536,-0.015464783,-0.0046539307,-0.017654419,0.08538818,-0.025238037,0.035949707,-0.012565613,0.0625,-0.057647705,-0.0418396,0.052825928,0.024276733,0.002412796,0.051452637,0.01663208,-0.029724121,0.035247803,-0.025817871,0.046081543,-0.007888794,-0.05114746,-0.036346436,0.017074585,0.009651184,-0.010925293,0.103759766,0.022567749,0.056121826,-0.0058555603,0.0362854,0.0031356812,0.03062439,0.042755127,-0.026870728,-0.05215454,-0.006095886,0.00006586313,0.010673523,-0.09136963,0.033721924,0.040740967,-0.01991272,-0.01953125,0.00033140182,0.05831909,0.015686035,0.024383545,-0.005264282,0.022613525,-0.048858643,-0.028945923,-0.002817154,0.03781128,0.014976501,-0.014030457,0.011795044,0.06008911,0.03262329,-0.066101074,0.015686035,0.008361816,0.005657196,0.06335449,0.051635742,-0.015274048,0.02571106,-0.044281006,0.0140686035,-0.09503174,0.011451721,-0.039886475,-0.02571106,0.0073432922,-0.0067329407,0.042541504,-0.022781372,-0.061798096,0.025634766,-0.05718994,-0.0023117065,0.015312195,0.04937744,-0.029815674,-0.009246826,0.05505371,0.014663696,-0.049468994,-0.0051002502,0.06573486,0.030593872,0.07922363,-0.026885986,-0.019348145,-0.051452637,-0.06427002,-0.04324341,-0.076171875,-0.09637451,-0.03753662,0.04888916,-0.017456055,0.02520752,-0.070129395,0.0022792816,0.08203125,-0.038635254,-0.044769287,-0.0020809174,0.025283813,-0.06549072,-0.028427124,0.011878967,0.010292053,-0.07965088,-0.05239868,-0.03062439,0.025115967,0.033081055,0.0035209656,0.014038086,-0.038909912,-0.023147583,-0.03616333,-0.10192871,0.027648926,-0.054382324,0.030395508,-0.05493164,-0.0048446655,-0.03756714,0.022705078,0.06274414,-0.030807495,-0.023605347,-0.02330017,-0.026519775,-0.034210205,-0.004245758,-0.014305115,-0.014213562,0.03845215,0.045684814,-0.014465332,0.009208679,-0.032562256,0.022567749,-0.027557373,-0.0033683777,-0.038085938,-0.04937744,-0.022033691,-0.014198303,-0.07611084,0.14099121,0.003921509,0.034576416,0.05404663,0.066345215,0.0847168,-0.0026435852,-0.051452637,-0.013175964,0.01701355,0.034820557,-0.039642334,-0.05734253,0.039093018,-0.004928589,-0.052215576,-0.027740479,0.050689697,0.049041748,-0.016693115,0.015731812,-0.01158905,0.024597168,-0.01878357,-0.012107849,0.040100098,0.031158447,-0.06994629,0.045135498,-0.10028076,0.033843994,-0.08734131,-0.021850586,-0.009010315,-0.03894043,0.052642822,-0.015525818,-0.07067871,0.023330688,0.011230469,-0.00018012524,0.046447754,-0.06591797,0.019104004,0.02494812,-0.0345459,-0.03277588,-0.0038433075,0.031051636,-0.03744507,-0.011779785,0.031234741,0.0041542053,0.070373535,0.023498535,0.0054016113,-0.011703491,-0.0067710876,0.04724121,0.06185913,-0.025558472,0.040130615,0.03439331,0.013008118,0.08886719,-0.032836914,-0.032958984,-0.043029785,-0.009384155,0.04269409,0.037475586,0.022415161,-0.038513184,0.035064697,0.07702637,-0.057861328,0.06274414,-0.028869629,0.0027332306,-0.024215698,-0.0067977905,0.07885742,-0.047668457,0.03137207,-0.020477295,0.0036449432,0.053375244,-0.002811432,0.03074646,-0.051513672,-0.0021152496,-0.05166626,-0.03869629,0.012924194,0.03878784,0.05831909,0.014884949,-0.07141113,0.001496315,0.01776123,0.03353882,-0.030471802,-0.028747559,0.028167725,0.068725586,0.025894165,-0.030807495,0.05807495,-0.007843018,-0.028762817,0.018737793,-0.04714966,-0.03149414,-0.007259369,-0.057128906,0.014770508,0.095458984,0.016723633,-0.039123535,0.02015686,-0.022628784,0.04852295,-0.0047912598,-0.026687622,0.055267334,-0.048736572,0.014633179,-0.005859375,0.02470398,-0.026916504,0.01083374,-0.010940552,-0.007030487,0.027557373,0.027526855,-0.015853882,0.013328552,0.030960083,-0.048919678,-0.051086426,-0.017242432,0.04147339,-0.004863739,0.017288208,-0.13586426,-0.035247803,0.057891846,-0.037750244,-0.0022220612,0.01576233,-0.057861328,0.039489746,0.055114746,0.037200928,0.04522705,0.0023956299,-0.030136108,-0.004131317,-0.006034851,-0.02619934,-0.07397461,-0.008293152,0.027572632,-0.061828613,0.07537842,-0.038635254,0.031341553,-0.002708435,-0.022384644,-0.057861328,0.00024557114,-0.024810791,-0.047729492,0.06677246,-0.03083801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  /* Embedding representation of "An embedding is a numerical representation of data in a high-dimensional space that captures semantic relationships and similarities between entities." */
  ('[0.043701172,0.09063721,-0.24499512,-0.1385498,0.025177002,-0.020385742,0.0074653625,-0.016143799,-0.08294678,-0.03427124,-0.04864502,0.003490448,0.1060791,0.035461426,-0.023712158,0.04220581,-0.016342163,-0.039001465,-0.06008911,0.034362793,-0.048858643,0.023666382,0.012779236,0.012001038,0.057250977,0.038970947,0.08312988,-0.046936035,-0.02229309,0.00674057,0.04751587,-0.015289307,0.0027866364,-0.028762817,-0.043548584,-0.037200928,-0.0045547485,0.09539795,0.026290894,0.018051147,0.0000140070915,-0.002450943,-0.04751587,0.013076782,-0.031982422,-0.0035572052,0.044952393,-0.04220581,0.08660889,-0.037109375,-0.010673523,-0.013221741,-0.015609741,-0.028411865,0.11138916,0.02218628,0.008255005,0.015991211,0.043395996,-0.044189453,0.09460449,0.1005249,-0.06817627,0.09283447,0.0625,0.026916504,-0.097961426,0.05682373,-0.011253357,-0.085510254,0.10241699,-0.010391235,0.03656006,0.028320312,-0.025604248,-0.0149383545,-0.00881958,0.0362854,-0.002401352,0.052734375,0.04220581,0.03640747,0.09686279,-0.040527344,0.09460449,0.045043945,-0.010475159,0.00006771088,-0.06567383,0.060913086,0.016830444,0.009277344,0.02458191,0.05444336,-0.024734497,0.006401062,-0.00166893,0.028289795,-0.033447266,-0.03704834,-0.055389404,-0.01486969,-0.021697998,0.01322937,-0.005695343,0.053649902,-0.000044941902,0.026565552,-0.06561279,0.022399902,-0.022094727,0.015525818,-0.06402588,-0.06585693,-0.0055732727,-0.018295288,0.09020996,-0.07720947,-0.014472961,0.057434082,0.01537323,-0.041870117,0.042419434,0.05392456,0.007080078,0.011199951,-0.020095825,0.007774353,-0.044433594,-0.04031372,-0.016448975,-0.060394287,-0.009780884,0.010131836,0.005207062,0.038879395,-0.048675537,-0.024917603,-0.0069351196,0.08514404,-0.0041885376,-0.015586853,-0.0029888153,-0.0546875,0.008361816,-0.09490967,0.035705566,-0.02935791,0.009742737,-0.015213013,-0.00970459,0.08270264,-0.03753662,-0.045074463,0.01612854,-0.0030441284,0.024749756,0.0041542053,0.064697266,-0.007019043,0.038970947,0.04284668,-0.030029297,0.04623413,0.019699097,-0.074523926,-0.0024147034,0.019836426,0.011489868,0.009597778,-0.04751587,-0.03125,-0.023025513,-0.0064201355,0.0007266998,-0.007888794,0.036834717,-0.068359375,0.056671143,0.006175995,0.021530151,-0.04324341,0.07232666,-0.004169464,-0.025619507,-0.019226074,-0.007259369,-0.01902771,-0.060760498,-0.03161621,-0.055877686,0.012390137,-0.031280518,-0.00705719,-0.019470215,-0.00061893463,0.06774902,-0.034301758,-0.003293991,-0.023925781,-0.007820129,0.011604309,-0.024002075,0.05206299,-0.012214661,0.043304443,-0.04232788,-0.0005931854,-0.050872803,0.04647827,0.06555176,-0.017486572,0.001001358,0.010131836,0.04776001,-0.0076560974,0.0063323975,-0.04147339,-0.02243042,-0.00008791685,0.028717041,-0.01927185,0.039794922,-0.0769043,0.03289795,-0.019439697,-0.03137207,0.047088623,-0.045532227,0.0011854172,-0.03768921,-0.04663086,0.044525146,-0.031173706,0.011817932,0.06109619,-0.01701355,0.06524658,0.006614685,0.037841797,0.018707275,0.053833008,-0.02468872,-0.03387451,-0.02897644,0.05923462,0.024429321,-0.060516357,-0.0435791,0.07159424,-0.04446411,-0.036712646,0.012107849,0.007286072,0.07183838,-0.031829834,-0.047790527,-0.07092285,0.014518738,-0.008964539,0.05621338,-0.017486572,-0.0129470825,-0.036499023,-0.06890869,-0.021835327,0.027175903,-0.007709503,0.02960205,-0.02003479,0.01058197,0.017303467,0.018112183,0.019622803,0.011024475,-0.013412476,0.02229309,-0.012329102,-0.05053711,0.01197052,-0.05316162,-0.018341064,-0.07086182,0.0146865845,-0.018798828,0.021240234,0.036895752,0.020812988,0.025863647,0.031097412,0.037475586,-0.042053223,-0.03768921,0.04321289,-0.00054073334,0.045806885,0.06732178,-0.001572609,0.049682617,-0.064086914,-0.010314941,0.049835205,0.099975586,0.011741638,-0.0135269165,-0.033843994,0.040924072,-0.056121826,0.020202637,-0.04135132,-0.06286621,-0.0056762695,-0.054840088,0.0025749207,-0.0647583,0.051208496,0.027694702,-0.00026249886,0.03201294,-0.07409668,-0.005104065,-0.12463379,0.036010742,-0.031173706,0.0036354065,0.07354736,-0.050201416,0.013839722,-0.01612854,0.021835327,-0.039001465,0.012069702,0.055603027,-0.005886078,-0.034606934,0.017791748,-0.02961731,-0.033721924,0.011795044,0.0029697418,0.08337402,-0.008636475,0.02470398,-0.09301758,0.026794434,0.03869629,-0.061767578,-0.004070282,0.04171753,0.021850586,-0.03186035,0.00680542,0.009895325,-0.032104492,0.022888184,-0.0076675415,0.0440979,-0.00548172,-0.006793976,-0.0138168335,0.060913086,-0.0035152435,-0.02609253,-0.053619385,-0.0090789795,0.012084961,0.03604126,0.040924072,0.020462036,0.031585693,0.0057411194,-0.0006456375,-0.060272217,0.042297363,0.04827881,-0.0340271,-0.087646484,-0.06738281,0.005554199,-0.014373779,-0.017181396,0.03753662,0.015686035,0.005493164,0.037750244,-0.0031909943,0.035125732,0.00712204,0.017791748,0.007865906,0.004673004,-0.015129089,-0.052978516,0.01751709,0.026031494,-0.06939697,-0.018112183,0.010276794,0.03741455,-0.010620117,-0.014030457,-0.066223145,0.0015687943,-0.023376465,-0.0043296814,-0.029556274,-0.008255005,-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You can then query for which embeddings are closest to a new vector. For example, the following query finds the closest vector to the embedding for "burgers are tasty" using cosine similarity <#>. Unsurprisingly, Postgres returns the "i like to eat tacos" vector.

SELECT * FROM embeddings
ORDER BY data <#> 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LIMIT 1;