IA no SQL Server: O que são embeddings

Este é o post 3 de 3 da série IA no SQL Server

Um dos pilares da IA são um conceito chamado embeddings. Praticamente em qualquer modelo de IA, seja de texto, vídeo ou áudio, você tem algo relacionado a embeddings. A partir do SQL Server 2025, esse é um termo que você deve ouvir falar muito, pois o potencial disso para as aplicações é muito grande!

Nesse post, eu quero tentar trazer uma explicação mais didática de embeddings, especialmente para aqueles que ainda não tiveram oportunidade de estudar esse mundo de IA, ou machine learning. Espero que ao final desse post, você consiga finalmente entender o que são os embeddings e conseguir enxergar as possibilidade de uso com o SQL Server (ou qualquer outro banco com suporte).

O objetivo desse post é saí dessa tabelinha simples, para este script!

Começando sem SQL e sem IA

Vamos começar com um exercício simples, e, por enquanto, não vamos envolver IA, nem SQL. O objetivo é que você entenda a ideia por trás dos conceitos e das operações envolvendo os embeddings.

Imagine um carro… Agora, um ônibus. Na sua mente provavelmente você identificou elementos e características padrões dessas duas palavras. Imaginou alguma figura geométrica, texturas, elementos internos, externos, etc. Uma série de propriedades únicas que você aprendeu ao longo da vida que definem esses dois conceitos.

E ambos possuem características em comum, mas em quantidades diferentes. Por exemplo, você pode ter imaginado um carro como uma figura geométrica retangular pequena, e o ônibus também como uma figura geométrica retangular, porém maior. E, ainda, muito provavelmente, ao imaginar um destes dois conceitos, você fez pouco ou zero associação com masculino ou feminino, o que seria bem diferente se eu te pedisse para imaginar uma rainha, por exemplo.

Repare que cada palavra, ou frase, que aprendemos fazemos algum tipo de associação com outros conceitos, mesmo que inconscientemente. Isto é, nós aprendemos a identificar características e o quanto dessa características está presente.

Para que possamos usar isso no computador, precisamos dar um jeito de transformar essas relações em números. Vamos tentar uma abordagem bem simples: pegaremos as frases acima e colocamos cada uma em uma linha numa tabela. Então, vamos mapear essas 2 características: Veículo (indica se a frase se refere a um veículo), pessoa (indica se se refere a uma pessoa)

Texto	Veículo	Pessoa
Um carro	1	0
Um ônibus	1	0
Uma rainha	0	1

Parabéns! Estes são seus primeiros embeddings.

Para carro e ônibus, eu coloquei 1 na coluna Veículo, pois são veículos e 0 para pessoas, pois não são pessoas. simples assim.
Na frase da rainha, o inverso: 0 para veículo, pois não é um veículo, e 1 para pessoa, pois rainha é uma pessoa.

Talvez você tenha outro critério mais elaborado que o meu. Mas, por enquanto, eu quero manter as coisas simples, e por isso me limitei a identificar como 1 ou 0. Já é o suficiente para o nosso primeiro objetivo: conseguimos representar uma frase em números, ainda que poucos e simples números, mas está aí.

Vetores

Podemos representar essa tabela que montamos de uma “maneira mais matemática”: usando vetores (ou, se preferir, chame de arrays). Cada característica que mapeamos (veículo e pessoa) é uma posição no nosso vetor. Damos o nome de cada posição de “dimensão”. Ou seja, se temos 2 características, então, para cada frase, precisamos de uma vetor de 2 dimensões:

Um carro = [1,0]
Um ônibus = [1,0]
Uma rainha = [0,1]

Se fossem 10 características, seriam 10 dimensões, e por aí vai… Isso são os embeddings: São esses vetores que representam cada palavra, frase, etc. Até aqui não usamos nada de IA… Só um pouco de raciocínio humano e matemática! Vamos seguir mais pouco, sem IA ainda…

Comparando Vetores

Agora, como podemos comparar isso? É aqui onde entra um conceito matemático que já mencionei nos posts anteriores: Similaridade por cosseno. Em outros posts, podemos entrar em detalhes do porquê essa cálculo ajuda a comparar, mas, só entenda que essa operação consegue nos dizer o quanto 2 vetores são iguais ou não: você passa 2 vetores e ele devolve um valor entre 0 e 1. Quanto mais próximo de 0 for, significa que são próximos, isto é, iguais, e quanto mais próximo de 1, significa que estão distantes, isto é diferentes. Além desse, existem outros algoritmos que também comparam vetores (como a distância Euclidiana), mas, por enquanto, vamos usar somente o cosseno.

De novo: Aqui eu estou tornando as coisas simples, ok? Se você é um PhD em IA ou matemática, não fica brava comigo.

SQL Server entra no grupo…

Até aqui, você conheceu os principais conceitos: Temos os embeddings (que são esses vetores que representam cada frase, é normal usar o termo “vetores” ou “embeddings”. Se referem a mesma coisa, ok?!), e temos um cálculo para poder compará-los. E, é aqui, onde entra o SQL Server e o novo suporte a isso tudo!

Como eu ainda não tenho acesso a um SQL SERVER 2025, eu subi um Azure SQL DATABASE, que já possui um suporte mínimo ao vector e já é suficiente para explicar o que preciso. Quando o 2025 sair publicamente, fazemos novos testes lá. Então, aqui está um script bem simples que você pode executar em qualquer Azure SQL Database:

DECLARE
	@carro vector(2) = '[1,0]'
	,@onibus vector(2) = '[1,0]'
	,@rainha vector(2) = '[0,1]'

SELECT 
	 [Carro Vs Onibus] = VECTOR_DISTANCE('cosine',@carro,@onibus)
	,[Carro Vs Rainha] = VECTOR_DISTANCE('cosine',@carro,@rainha)
	,[Onibus Vs Rainha] = VECTOR_DISTANCE('cosine',@onibus,@rainha)Code language: SQL (Structured Query Language) (sql)

A query cria uma variável para cada uma das nossas frases, com o novo tipo vector. Esse é o tipo de dados que a Microsoft criou para representar esses arrays. O valor passado nos parênteses é a quantidade de dimensões, que, aqui nosso caso são 2. Eu inicializei cada variável com o seu respectivo vetor, conforme a nossa tabela anterior. Eu poderia ter criado uma tabela com uma coluna varchar e uma coluna vector, mas optei por usar variáveis direto no script pro código ficar mais simples.

Então, logo em seguida, temos um SELECT para comparar esses vectors, usando a nova função: VECTOR_DISTANCE. O primeiro parâmetro é a função matemática de comparação. Aqui eu especifiquei ‘cosine’, pois quero usar a distância por cosseno (‘euclidean’ seria para a distância euclidiana). Os outros dois parâmetros são os vetores a serem comparados, que, no nosso caso, estão nas variáveis (poderiam ser colunas, literais, etc.)

Eu gerei uma comparação entre cada uma das frases o resultado foi esse:

O que esse resultado me diz?

As frases “Um carro” e “Um ônibus” têm uma distância 0, ou seja, os embeddings são idênticos. Isso significa que, no nosso contexto, com essas 2 dimensões, são duas palavras com o mesmo significado.
“Um carro” vs “Uma rainha” ou “Um ônibus” vs “Uma rainha” tem distância 1, isto é, são totalmente diferentes (1 é o valor máximo).

Um pouco mais real

Estes nossos embeddings estão muito, mas muito simples… Deixei simples de propósito, apenas para você entender a comparação. Agora, vamos deixar algo um pouco mais elaborado. Veja a tabela a seguir:

Texto	Veículo	Pessoa	Cor	Gênero
Um carro azul	0.70	0.01	0.90	1.00
Um ônibus branco	0.80	0.10	1.00	1.00
Um carro branco	0.70	0.01	1.00	1.00
O rei entrou no carro	0.70	1.00	0.00	1.00
A rainha vai de avião	0.95	1.00	0.00	0.50

Agora nós temos 4 dimensões: Veículo, Pessoa, Cor e Gênero. Eu escolhi 4 características, e os valores representam o quanto cada frase possui em cada característica. A seguir as regras que eu estabeleci para definir os valores:

Em veículo indica o quanto do conceito de veículo está presente na frase. E, para diferenciarmos o tipo de veículo, quanto maior o veículo, mais próximo de 1 será este valor.
Em pessoa, também defini o quanto do conceito de pessoa está presente. Para os veículos eu mantive um valor abaixo, mas que representam a capacidade de transportar pessoas (ônibus > carro). E para frases que se referem a pessoas de fato, possui um valor mais próximo de 1.
Em cor, é o quanto de alguma cor está presente. E, para diferenciar tonalidades de cor, cores mais próximo do branco tem um valor mais próximo de 1. 0 significa que não tem nada de relacionado a cores.
Em gênero, quanto mais próximo a frase se referir ao masculino, deixei 1. Quanto mais próximo do feminino, 0.5. 0 seriam para frases que não possuem qualquer distinção de gênero.

Se você não concorda com estes valores, ou acha que eles estão desproporcionais, não se preocupe. O objetivo agora não são os valores em si, mas apenas que você entenda que alguma coisa define estas dimensões e calcula estes valores para cada uma delas. Por enquanto, essa coisa foi eu… Mas poderia ser, por exemplo, uma IA treinada com uma quantidade gigante de dados que ensinam essa relação…

Agora, vamos ver como tudo isso se compara, usando a distância por cosseno:

DECLARE							
	 @CarroAzul vector(4)		= '[0.70,0.01,0.90,1.00]'
	,@OnibusBranco vector(4)	= '[0.80,0.10,1.00,1.00]'
	,@CarroBranco vector(4)		= '[0.70,0.01,1.00,1.00]'
	,@ReiCarro vector(4)		= '[0.70,1.00,0.00,1.00]'
	,@RainhaAviao vector(4)		= '[0.95,1.00,0.00,0.50]'

SELECT
	Carros			= VECTOR_DISTANCE('cosine',@CarroAzul,@CarroBranco)
	,VeiculosBrancos	= VECTOR_DISTANCE('cosine',@OnibusBranco,@CarroBranco)
	,CarroAzulVsOnibus	= VECTOR_DISTANCE('cosine',@CarroAzul,@OnibusBranco) 

	,ReiVsRainha		= VECTOR_DISTANCE('cosine',@RainhaAviao,@ReiCarro) 
	,ReiVsCarro			= VECTOR_DISTANCE('cosine',@CarroAzul,@ReiCarro) 
	,RainhaVsCarro		= VECTOR_DISTANCE('cosine',@RainhaAviao,@CarroAzul)Code language: SQL (Structured Query Language) (sql)

Agora, temos resultados muito mais interessantes do que antes. Vamos explicar cada coluna do resultado:

A coluna Carros é uma comparação entre os dois carros, azul e branco. Note que o resultado é quase 0, mas não é 0. Isso mostra o quanto essas são frases são próximas, mas não exatamente, afinal, são cores diferentes.
VeiculosBrancos, compara ônibus e carro brancos. Também, são muito próximos de 0, mas ainda sim, existe uma diferença. São 2 veículos de mesma cor, porém veículos com características diferentes. E, note que o valor é ligeiramente maior do que a comparação de carros. Enquanto os 2 carros deu 0.0013, este deu 0.0030, o que faz algum sentido , já que um carro é bem diferente de um ônibus.
Comparando um Carro Azul com um ônibus branco produz um valor ligeiramente maior do que o anterior. 0.0031, e, novamente, faz sentido, pois além dos veículos diferentes, as cores são diferentes.
ReiVsRainha compara duas pessoas. Aqui temos um valor bem maior que os anteriores, 0.064, mas ainda sim, muito próximo de zero, indicando o quanto estas frases possuem um semelhança: Ambas falam de pessoas e veículos, mas são pessoas com sexo diferente e veículos diferentes… Por isso, o valor é maior que os demais!
ReiVsCarro mostra uma comparação entre duas frases completamente diferentes. 0.37, um valor muito superior aos demais… Afinal, a única coisa em comum é o veículo.
RainhaVsCarro, segue a mesma linha, mostrando ainda mais diferença, pois enquanto uma tem pessoas e veículos, a outra foca primordialmente no veículo e na cor.

Note como a matemática faz uma mágica interessante nesses resultados. Até aqui não usamos absolutamente nada de IA, só matemática e apenas transformamos conceitos em números.

IA entra no grupo…

E onde que a IA se encaixa nessa história toda? Como adiantei acima, é na hora de gerar os embeddings. A grande maioria dos modelos de linguagens são capazes de gerar embeddings. Na verdade eles precisam dos embeddings, pois é usando embeddings que eles conseguem entender o texto, e assim podem gerar mais texto considerando contexto.

Então, basicamente, todo esse trabalho que eu fiz de escolher dimensões e mapear os valores, é feito por uma IA que foi treinada com muitos dados (ou com dados de uma área específica, o que faz ela gerar os valores desses embeddings muito melhor do que uma IA que “sabe um pouco tudo”).

E, além disso, você tem muito mais do que apenas 4 dimensões. Imagine o quanto poderíamos comparar, se adicionarmos apenas mais uma dimensão: Faça o teste você mesmo.. use sua criatividade, escolha uma dimensão e ajuste no código acima, e atribua alguns valores… Você vai ver como essa dimensão a mais pode mudar completamente os resultados. Agora, considere o fato de as IAs geralmente geram 768, 1536 dimensões…

Pra gerar embeddings, você precisa ter acesso um modelo de IA. Tem várias maneiras de você acessar um modelo de IA… Você pode usar uma API para um modelo na nuvem, uma API para um modelo na sua máquina, pode usar código python, um interface web, etc. Tem muitas e muitas maneiras… A tabela a seguir traz um pequeno resumo de algumas maneiras comuns, com seus prós e contras:

Maneira	Complexidade	Prós	Contras
Script python	Muito alta	Pode automatizar para vários textos	Requer conhecer python e as bibliotecas de IA Precisa conhecer sobre o modelo de IA
Invocar uma API de algum provedor como OpenAI, ollama, etc.	Alta	Pode automatizar facilmente em qualquer linguagem com suporte a HTTP	Precisa de conhecimentos em programação Precisa conhecer sobre o modelo de IA
Módulo PowershAI	Média	Automação fácil Uso fácil em Ambiente Windows	Precisa de um conhecimento básico em powershell Precisa conhecer sobre os modelos
Direto do SQL Server	Baixo	Automação fácil e integrado direto com suas tabelas Não precisa saber outroas linguagens	Não disponível ainda Requer conhecer os novos comandos do SQL 2025 Requer conhecer sobre os modelos
Usando um Space do Hugging Face	Baixo	Não precisa conhecer nenhuma linguagem de programação	ruim para automações e gerar para vários textos Depende de alguém criar o space para você, caso contrário, é o mesmo nível de complexidade de python

Muito mais coisas para avaliar!

Gerando Embeddings Reais

Agora que você conhece o conceito base por debaixo dos panos, vamos gerar embeddings reais usando uma IA, e não mais da minha cabeça. Eu vou usar a API da OpenAI para gerar embeddings de 768 dimensões usando o modelo “text-embedding-3-small”, que é bom e barato (não o melhor).

Aqui está o código com os embeddings reais:

DECLARE	-- Gerado com OpenaAI, text-embedding-3-small 
	 @CarroAzul vector(768)		= '[0.0034950098,0.008337075,-0.103812024,0.048385914,0.0003430359,-0.07855377,-0.0094255265,0.06258982,-0.03794913,-0.023714347,0.03127947,0.008769368,-0.016921174,-0.035664152,0.046070058,0.012204551,-0.008398831,-0.023853298,0.0059478856,-0.013624942,-0.0034544824,-0.004909611,0.021475688,-0.007823727,0.015825003,-0.03195879,-0.015632015,-0.040882546,-0.03399674,-0.07750392,0.053449914,-0.0530485,0.017754883,-0.03110964,-0.015508504,-0.04196328,0.0146207595,0.011448039,-0.012760356,-0.0024798939,0.040635522,0.011170137,0.08157982,0.06033572,0.080900505,-0.009286575,-0.03557152,-0.024347346,0.028191665,0.051381085,-0.023328371,0.03186615,-0.022201322,0.12122725,-0.04332191,-0.029982591,-0.0348922,-0.010305551,0.051411964,0.010112563,-0.06774645,-0.03350269,-0.023837859,0.07478665,-0.020657418,0.0637323,-0.005446117,0.044248253,0.0017291711,0.007700215,-0.049590155,0.08281495,-0.0034737813,0.013038259,0.06397933,-0.007734953,-0.0006937913,-0.02732708,-0.06496743,0.047397815,-0.035509765,0.02732708,-0.008931478,-0.05906972,-0.047583085,-0.03112508,-0.032329325,-0.0061022756,-0.016905736,-0.045020204,-0.023266615,0.01968476,-0.06743767,-0.026493372,0.040079717,0.09226363,-0.037053667,-0.04112957,0.019916346,0.025798615,0.047459573,-0.01665871,0.00009727794,-0.023915054,-0.019190712,0.00019636513,0.041994154,-0.012644564,0.007318099,0.021352176,0.0641646,-0.010012209,0.04711991,0.08553221,0.0027539367,0.0485403,0.03949303,-0.053264644,-0.016272735,-0.06496743,0.035355374,-0.02999803,-0.03331742,-0.07071075,-0.032792494,0.06095328,-0.072810456,-0.02477964,-0.060150452,-0.0061099953,-0.019638443,-0.022417469,-0.041839764,-0.0024798939,0.016735906,0.014512686,0.009765185,-0.06546148,-0.016442565,0.036621373,0.010220637,-0.03717718,-0.0026072657,-0.02014793,0.0014126711,-0.015994832,0.05224567,0.014095833,0.030214177,0.024964908,0.009981331,0.029318715,-0.053573426,0.005862971,0.00066918536,-0.008337075,-0.03251459,-0.036034692,-0.06348528,-0.02176903,0.015686052,-0.039276887,-0.039307766,-0.04051201,0.0044078426,-0.0072949408,0.02334381,-0.008174965,-0.08621153,0.012050161,-0.03273074,0.055179086,-0.05270884,0.02269537,-0.035972934,-0.0034757112,0.041561864,-0.04400123,0.022803444,0.025428079,0.00041178783,0.011131539,0.03776386,0.049590155,-0.07713338,0.0149912955,-0.023096785,-0.034058496,-0.04400123,0.006638782,0.00009486559,0.07503367,-0.0030762262,0.08405007,-0.0010537136,-0.013146332,-0.04878733,-0.036436107,-0.087323144,0.037671227,0.017909272,-0.005770337,0.036096446,-0.053882208,0.012660002,0.0048362752,-0.005542611,-0.029859079,0.0064843916,-0.020364078,0.12517963,0.00010970153,-0.025613347,0.022757126,-0.04415562,0.01365582,0.04304401,0.012567368,0.02842325,-0.02857764,-0.05033123,0.04116045,0.06712889,-0.013733015,-0.02541264,0.059965182,-0.013879686,-0.045267228,-0.012752637,0.01665871,0.0005746213,-0.03083174,-0.016550638,-0.023930494,0.035139225,0.058668304,-0.0149295395,-0.024192957,0.04078991,-0.025597908,-0.044526156,-0.014520406,0.056939133,-0.013038259,0.052955866,0.026709517,-0.027188128,0.028176226,0.0060752574,0.0070865136,0.02922608,-0.016442565,-0.013578624,0.031835277,0.02572142,0.041376594,0.0059324466,-0.018696662,0.001302668,-0.060119573,-0.03711542,0.010259234,-0.088496506,-0.029889958,0.034429032,0.052616205,0.045699522,-0.03408937,0.021475688,0.05746406,-0.030754544,-0.03016786,0.02857764,0.095969,-0.020348638,0.022463785,0.038844593,-0.010189759,0.027450591,-0.0070247576,0.042673472,0.025675103,-0.004376964,0.019005444,0.038381424,0.037887376,0.019731078,0.016936613,0.022896077,-0.012189113,0.0371463,-0.02427015,-0.03365708,0.0027809548,0.012111917,-0.030368568,-0.013956881,-0.018804736,-0.018403322,-0.0030395587,0.13524589,0.009502722,0.0419324,-0.0055464706,-0.06749943,0.036744885,-0.005816654,-0.011262771,0.0343364,-0.036158204,0.013540028,0.034706935,-0.021012517,-0.01682854,0.012474734,-0.055086453,-0.0247642,0.03510835,0.031372104,-0.069290355,0.06743767,-0.0011338036,-0.0041492386,0.040604644,0.01730715,-0.024501737,-0.023065908,-0.016319053,-0.0059478856,-0.039832693,0.0055194525,-0.018526834,-0.0025281408,-0.012968783,0.10109475,-0.015902199,-0.020348638,-0.018696662,-0.041839764,-0.017260833,-0.019576687,-0.015439028,-0.042457327,0.0025860372,-0.056105427,-0.0758365,0.018619467,-0.06252806,-0.009124465,0.017862955,0.037887376,-0.017554175,-0.039801814,-0.009448685,-0.035015713,0.05919323,-0.037671227,0.031989664,-0.041036937,0.031541932,0.057124402,0.02269537,-0.020765493,-0.024007687,0.019761955,-0.04783011,-0.0074995076,0.0145898815,0.04400123,0.045668643,-0.08022119,-0.055302598,-0.042796984,0.018187175,0.026385298,-0.033749714,-0.01985459,0.02589125,-0.007653898,-0.047737475,-0.01571693,0.022355713,0.035509765,-0.008653575,-0.019020882,-0.017338028,0.004751361,0.022973273,0.038010888,-0.020117054,0.03129491,-0.05064001,0.016874857,0.0018613678,-0.012312625,0.11041993,0.06002694,0.00761916,-0.00056448946,0.019947223,-0.033780593,-0.09627777,-0.0052299704,0.045020204,-0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	,@RainhaAviao vector(768)	= 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SELECT
	Carros				= VECTOR_DISTANCE('cosine',@CarroAzul,@CarroBranco)
	,VeiculosBrancos	= VECTOR_DISTANCE('cosine',@OnibusBranco,@CarroBranco)
	,CarroAzulVsOnibus	= VECTOR_DISTANCE('cosine',@CarroAzul,@OnibusBranco) 

	,ReiVsRainha		= VECTOR_DISTANCE('cosine',@RainhaAviao,@ReiCarro) 
	,ReiVsCarro			= VECTOR_DISTANCE('cosine',@CarroAzul,@ReiCarro) 
	,RainhaVsCarro		= VECTOR_DISTANCE('cosine',@RainhaAviao,@CarroAzul)Code language: CSS (css)

Note que o resultado mudou um pouco, mas, ainda sim, podemos fazer a mesma interpretação:

Os dois carros ficaram com 0.24 de distância, mas a comparação de ônibus e carro continuou maior.
A comparação entre rei e rainha subiu, muito provavelmente por conta dos pesos maiores para os veículos e outras palavras envolvidas que o modelo da OpenAI faz.
Rei vs Carro ficou um pouco menor, provavelmente por ter mais relação com o carro.
Rainha vs Carro segue bem maior que os demais, visto que a maior parte dos conceitos envolvidos são bem diferentes.

Na prática mesmo, na maior parte dos casos, você não vai usar os valores absolutos dessas comparações, mas vai usá-los para um critério de ordenação, por exemplo. Vamos demonstrar isso, agora, inserindo todos esse embeddings em uma tabela e depois fazendo pesquisas:

drop table if exists frases;

create table
	frases (
		texto varchar(500) not null 
		,embeddings vector(768)
	)

insert into frases
	values	
		('Um carro azul'			,'[0.0034950098,0.008337075,-0.103812024,0.048385914,0.0003430359,-0.07855377,-0.0094255265,0.06258982,-0.03794913,-0.023714347,0.03127947,0.008769368,-0.016921174,-0.035664152,0.046070058,0.012204551,-0.008398831,-0.023853298,0.0059478856,-0.013624942,-0.0034544824,-0.004909611,0.021475688,-0.007823727,0.015825003,-0.03195879,-0.015632015,-0.040882546,-0.03399674,-0.07750392,0.053449914,-0.0530485,0.017754883,-0.03110964,-0.015508504,-0.04196328,0.0146207595,0.011448039,-0.012760356,-0.0024798939,0.040635522,0.011170137,0.08157982,0.06033572,0.080900505,-0.009286575,-0.03557152,-0.024347346,0.028191665,0.051381085,-0.023328371,0.03186615,-0.022201322,0.12122725,-0.04332191,-0.029982591,-0.0348922,-0.010305551,0.051411964,0.010112563,-0.06774645,-0.03350269,-0.023837859,0.07478665,-0.020657418,0.0637323,-0.005446117,0.044248253,0.0017291711,0.007700215,-0.049590155,0.08281495,-0.0034737813,0.013038259,0.06397933,-0.007734953,-0.0006937913,-0.02732708,-0.06496743,0.047397815,-0.035509765,0.02732708,-0.008931478,-0.05906972,-0.047583085,-0.03112508,-0.032329325,-0.0061022756,-0.016905736,-0.045020204,-0.023266615,0.01968476,-0.06743767,-0.026493372,0.040079717,0.09226363,-0.037053667,-0.04112957,0.019916346,0.025798615,0.047459573,-0.01665871,0.00009727794,-0.023915054,-0.019190712,0.00019636513,0.041994154,-0.012644564,0.007318099,0.021352176,0.0641646,-0.010012209,0.04711991,0.08553221,0.0027539367,0.0485403,0.03949303,-0.053264644,-0.016272735,-0.06496743,0.035355374,-0.02999803,-0.03331742,-0.07071075,-0.032792494,0.06095328,-0.072810456,-0.02477964,-0.060150452,-0.0061099953,-0.019638443,-0.022417469,-0.041839764,-0.0024798939,0.016735906,0.014512686,0.009765185,-0.06546148,-0.016442565,0.036621373,0.010220637,-0.03717718,-0.0026072657,-0.02014793,0.0014126711,-0.015994832,0.05224567,0.014095833,0.030214177,0.024964908,0.009981331,0.029318715,-0.053573426,0.005862971,0.00066918536,-0.008337075,-0.03251459,-0.036034692,-0.06348528,-0.02176903,0.015686052,-0.039276887,-0.039307766,-0.04051201,0.0044078426,-0.0072949408,0.02334381,-0.008174965,-0.08621153,0.012050161,-0.03273074,0.055179086,-0.05270884,0.02269537,-0.035972934,-0.0034757112,0.041561864,-0.04400123,0.022803444,0.025428079,0.00041178783,0.011131539,0.03776386,0.049590155,-0.07713338,0.0149912955,-0.023096785,-0.034058496,-0.04400123,0.006638782,0.00009486559,0.07503367,-0.0030762262,0.08405007,-0.0010537136,-0.013146332,-0.04878733,-0.036436107,-0.087323144,0.037671227,0.017909272,-0.005770337,0.036096446,-0.053882208,0.012660002,0.0048362752,-0.005542611,-0.029859079,0.0064843916,-0.020364078,0.12517963,0.00010970153,-0.025613347,0.022757126,-0.04415562,0.01365582,0.04304401,0.012567368,0.02842325,-0.02857764,-0.05033123,0.04116045,0.06712889,-0.013733015,-0.02541264,0.059965182,-0.013879686,-0.045267228,-0.012752637,0.01665871,0.0005746213,-0.03083174,-0.016550638,-0.023930494,0.035139225,0.058668304,-0.0149295395,-0.024192957,0.04078991,-0.025597908,-0.044526156,-0.014520406,0.056939133,-0.013038259,0.052955866,0.026709517,-0.027188128,0.028176226,0.0060752574,0.0070865136,0.02922608,-0.016442565,-0.013578624,0.031835277,0.02572142,0.041376594,0.0059324466,-0.018696662,0.001302668,-0.060119573,-0.03711542,0.010259234,-0.088496506,-0.029889958,0.034429032,0.052616205,0.045699522,-0.03408937,0.021475688,0.05746406,-0.030754544,-0.03016786,0.02857764,0.095969,-0.020348638,0.022463785,0.038844593,-0.010189759,0.027450591,-0.0070247576,0.042673472,0.025675103,-0.004376964,0.019005444,0.038381424,0.037887376,0.019731078,0.016936613,0.022896077,-0.012189113,0.0371463,-0.02427015,-0.03365708,0.0027809548,0.012111917,-0.030368568,-0.013956881,-0.018804736,-0.018403322,-0.0030395587,0.13524589,0.009502722,0.0419324,-0.0055464706,-0.06749943,0.036744885,-0.005816654,-0.011262771,0.0343364,-0.036158204,0.013540028,0.034706935,-0.021012517,-0.01682854,0.012474734,-0.055086453,-0.0247642,0.03510835,0.031372104,-0.069290355,0.06743767,-0.0011338036,-0.0041492386,0.040604644,0.01730715,-0.024501737,-0.023065908,-0.016319053,-0.0059478856,-0.039832693,0.0055194525,-0.018526834,-0.0025281408,-0.012968783,0.10109475,-0.015902199,-0.020348638,-0.018696662,-0.041839764,-0.017260833,-0.019576687,-0.015439028,-0.042457327,0.0025860372,-0.056105427,-0.0758365,0.018619467,-0.06252806,-0.009124465,0.017862955,0.037887376,-0.017554175,-0.039801814,-0.009448685,-0.035015713,0.05919323,-0.037671227,0.031989664,-0.041036937,0.031541932,0.057124402,0.02269537,-0.020765493,-0.024007687,0.019761955,-0.04783011,-0.0074995076,0.0145898815,0.04400123,0.045668643,-0.08022119,-0.055302598,-0.042796984,0.018187175,0.026385298,-0.033749714,-0.01985459,0.02589125,-0.007653898,-0.047737475,-0.01571693,0.022355713,0.035509765,-0.008653575,-0.019020882,-0.017338028,0.004751361,0.022973273,0.038010888,-0.020117054,0.03129491,-0.05064001,0.016874857,0.0018613678,-0.012312625,0.11041993,0.06002694,0.00761916,-0.00056448946,0.019947223,-0.033780593,-0.09627777,-0.0052299704,0.045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		,('Um ônibus branco'		,'[-0.03422328,-0.039971605,-0.06512794,0.044890277,-0.0368604,-0.03911232,0.0029723141,0.05010525,0.01444489,-0.0077113495,-0.01234853,0.06566129,-0.016326431,0.04708294,0.01986728,0.0056964722,-0.023126641,0.046105128,-0.023052564,-0.01764499,-0.008348406,0.00019132535,0.019689497,-0.034993675,0.0021834008,-0.055023924,-0.008474336,-0.112121984,-0.034430694,-0.01798574,0.0078446865,-0.016311616,0.052890524,-0.027289731,0.009511405,0.0009991049,-0.028504584,0.012318899,0.012274453,-0.013926356,0.058342542,0.046816263,0.09659558,-0.0035982593,0.05369055,0.019674683,-0.039882716,0.018400569,-0.01002994,0.03970493,0.020356184,-0.0027426775,-0.020163586,0.040356804,-0.060209267,-0.00952622,-0.04115683,-0.025482269,0.056209143,0.011726288,-0.0012278158,-0.04566067,-0.09286213,0.06293528,0.013378191,0.049305227,0.010252168,-0.037808575,-0.07241705,0.00081854383,-0.023985926,0.038816016,0.012104078,-0.05318683,-0.005811291,0.024341494,-0.009800303,-0.09926233,-0.04171981,0.01034106,-0.034608476,0.005052008,-0.069454,-0.05493503,-0.06097966,-0.0039964197,-0.027793452,-0.0031334301,-0.0046186615,0.025423007,0.00488904,0.025363747,-0.040504955,-0.014289331,-0.0052038645,0.00590759,-0.031912096,-0.08474336,0.016859781,0.022311801,0.00952622,-0.050964538,-0.009978086,0.042134635,-0.05111269,-0.047171827,-0.017822772,-0.020000618,-0.018267231,0.062283408,0.020489521,-0.0025371155,0.00018426495,0.05982407,0.009385475,0.057275843,0.069868825,-0.08865459,-0.019289484,-0.035467763,0.063350104,0.00039792896,-0.031971358,-0.07650607,-0.046786632,0.02576376,-0.07727646,0.03849008,-0.0793506,-0.010963302,0.061275966,-0.023645176,-0.023185901,0.036267787,-0.0023408132,-0.054135006,0.022208095,0.02391185,0.004155684,0.0106595885,0.053897962,-0.027837897,0.027941605,-0.052920155,-0.012718911,0.0001713016,0.057868455,0.017437575,0.00786691,0.034667738,0.01112627,0.04954227,-0.0542239,-0.016370876,-0.03629742,-0.0135337515,0.0017778327,0.021022871,-0.036178894,0.027052687,0.0035464058,-0.04029754,-0.0026574896,-0.06382419,0.0040445696,-0.0055927653,0.0076743113,-0.0065224236,-0.093573265,0.025778575,-0.017052379,0.04364579,-0.049157076,0.06483163,-0.0737208,-0.0028037904,0.02927498,-0.03203062,0.01265965,-0.0052001607,-0.031912096,0.003059354,0.034460325,0.011104047,-0.018119078,0.009889195,0.011415168,0.0012454089,-0.023748882,0.018948734,0.03309732,0.009852156,-0.02576376,0.07318745,0.02927498,-0.021585852,-0.0002993148,-0.043290228,-0.062105622,0.034667738,-0.003561221,0.067972474,0.005029785,-0.045956977,0.0014167104,0.010415137,0.05866848,0.039912343,0.0008477114,0.033897344,0.10720331,0.030089818,-0.024711875,0.0093113985,-0.023615545,-0.005803883,-0.036593724,0.02194142,0.026963796,0.03217877,-0.00937066,0.005544616,0.009415106,0.018578352,-0.027837897,0.056979537,0.0009328992,-0.01817834,-0.036623355,0.021985864,0.02798605,0.02300812,-0.038875274,-0.02579339,0.05760178,-0.062876016,0.033986237,0.0010333653,0.08243218,-0.037660424,-0.011444798,0.039882716,0.040593848,0.016593106,0.05259422,0.005355721,-0.038253035,0.034549218,-0.041423503,0.017096825,0.05372018,-0.03380845,-0.050134882,0.045216214,-0.02065249,0.070935525,0.024934104,-0.022134017,0.025423007,-0.015067132,-0.048031114,-0.017348684,-0.031141704,0.019067256,0.025097072,-0.058579586,0.0846841,0.0024519276,0.034045495,0.048534833,-0.016459769,-0.00827433,-0.00065094605,0.029912036,-0.058964785,-0.012081855,0.036178894,-0.008592858,0.0028686072,-0.045838453,-0.0084595205,0.006329825,0.0068113217,0.03594185,0.014059694,-0.011918887,0.022830335,-0.004437174,-0.0011435539,-0.019822834,0.026652675,0.006855767,-0.020296924,-0.03807525,0.025985988,-0.06809099,0.025630422,-0.06453533,0.04847557,-0.05831291,0.089247204,-0.008192846,0.06358715,-0.0022352543,-0.03851971,0.039882716,-0.022934042,-0.014615267,0.01517084,-0.05760178,-0.009659558,0.08699528,-0.03908269,-0.030045373,0.035052937,-0.011459613,-0.02823791,0.04346801,-0.017482022,-0.008133585,0.09120282,-0.005744622,-0.036178894,0.011881849,0.04352727,-0.013222631,-0.0325936,-0.052979413,0.01871169,-0.048534833,-0.0014944907,-0.024593353,0.04349764,0.029023118,0.009466959,0.0140893245,0.0135337515,0.00073474494,-0.038253035,-0.018296862,0.013711534,0.0034982562,-0.04900892,-0.020533968,-0.028771259,-0.024741506,0.048149638,-0.045956977,-0.013896726,0.034015868,0.046401434,-0.03034168,-0.04684589,-0.00083798886,-0.0591722,-0.0039964197,-0.016178278,0.030460201,-0.04014939,0.053394243,0.034519587,-0.002383407,0.025615606,-0.01792648,0.014659712,-0.039912343,0.015541221,0.011978148,0.020326555,-0.000012652301,-0.056357298,0.034489956,-0.011570728,-0.0025278558,0.018356122,-0.058757372,-0.043230966,0.05766104,-0.0064261244,-0.02225254,0.014889349,-0.0043519866,0.04290503,0.020000618,-0.015215285,0.009481775,0.059468504,-0.010881818,0.022667367,-0.062164884,0.009674373,-0.024341494,0.021200655,0.019733943,0.026786013,-0.0040445696,0.007389117,0.045986608,-0.06133523,-0.032889906,-0.078520946,-0.06026853,0.0273341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		,('Um carro branco'			,'[-0.0071132025,-0.017085984,-0.10626195,0.040691808,-0.014726832,-0.06050869,0.002679068,0.03008277,0.0018042156,-0.025207188,-0.022433396,0.04603922,-0.036087885,-0.020145733,0.023848888,0.02472106,0.017443432,0.028238341,0.0029918344,0.007306224,-0.010008526,0.005411753,0.0031598348,-0.024735358,0.021947268,-0.048870202,-0.031684134,-0.07823808,0.004936348,-0.014147767,0.040777594,-0.03823257,0.035887714,-0.05847839,0.020874927,0.016628452,0.018544368,0.005883584,-0.013297042,-0.011495507,0.049041778,0.067886405,0.072976455,0.045553092,0.10677668,-0.006548436,-0.04786935,0.0068629896,0.0123390835,0.0038783038,-0.014641044,0.016699942,-0.013347085,0.07011688,-0.072919264,0.012196104,-0.049928248,-0.035344396,0.06308231,0.029124811,-0.0050435825,-0.04257913,-0.076007605,0.06251039,0.024506591,0.009415164,0.008986227,-0.012403424,-0.055647407,-0.0144480225,-0.048326883,0.072919264,-0.0015754492,-0.004503837,0.0008489375,-0.0032241752,0.016328195,-0.096425004,-0.04844127,0.009572441,-0.032627795,0.013347085,-0.05873575,-0.06851551,-0.055790383,-0.022862334,-0.030540302,0.022733653,-0.027680725,0.0023716632,-0.028538598,0.0003156707,-0.062853545,-0.022862334,0.020431692,0.0509291,-0.027308978,-0.067829214,0.027280383,0.047040075,0.026565488,-0.018887518,-0.02314829,-0.016614154,-0.02968243,0.015069981,0.03543018,-0.021017905,-0.032827966,0.03248482,0.046353776,0.0015495344,-0.027080212,0.05235889,-0.037031546,0.06205286,0.029854003,-0.07549288,-0.055733193,-0.08178396,0.050357185,0.009143503,-0.015627598,-0.08578737,-0.05132944,0.07086036,-0.06571312,0.0006353627,-0.06599908,-0.012725126,0.030197153,-0.0041463897,-0.015127173,0.013032531,-0.0054582213,-0.007720863,0.03837555,-0.010137207,-0.0028649406,0.054303404,0.09001955,-0.076236375,0.027180297,-0.031312387,0.0032331115,-0.0016424706,0.04183564,-0.0176579,0.040520232,0.027394766,0.0015915344,0.05627651,-0.04775497,0.027080212,0.004964944,-0.0070238407,0.017271856,-0.02736617,-0.06125218,0.0067736274,-0.016013643,-0.060222734,0.006476946,-0.033056732,0.0017908113,-0.042893685,-0.00030450049,0.010844953,-0.06651381,0.018429985,-0.0057441792,0.06737168,-0.036659803,0.056591067,-0.07520693,0.022590673,0.07749459,0.0010598315,0.03877589,-0.0060837544,-0.03299954,0.013540106,0.019330753,0.016985899,-0.046325177,-0.00862163,-0.017357644,0.004339411,-0.06502683,0.040491637,0.009601036,0.011581294,0.015985046,0.08195553,0.04495258,-0.009193546,-0.031483963,-0.013540106,-0.07137509,0.072690494,0.007928182,0.01263219,-0.010752017,-0.04040585,-0.003561963,0.030740473,0.040749,0.0016817899,-0.034743883,0.002777366,0.113182135,-0.003238473,-0.025922082,0.013883255,-0.06491244,-0.020074245,0.028252639,-0.0027112383,0.022633567,-0.004800518,-0.039433595,0.026408212,0.042093,0.023348462,-0.03254201,0.013754575,0.006330393,-0.047240242,-0.014548108,0.042064406,0.024706762,0.009736867,-0.021875778,-0.04629658,0.06851551,0.0162996,0.024892634,0.016728537,0.055046894,-0.053188168,-0.019573817,0.001721109,0.057477538,-0.011667082,0.03442933,0.028638683,-0.025836296,0.06685696,-0.041578278,0.022933822,0.060623072,-0.066113465,-0.0305689,0.048870202,0.018358497,0.013861809,-0.0064912443,-0.03342848,0.0017559602,-0.0017568538,-0.08876133,-0.04998544,-0.06714291,-0.03354286,0.034057584,0.017800879,0.04460943,-0.014583852,0.021561226,0.019330753,0.022061652,-0.010952188,0.036230866,0.06496964,-0.058564175,-0.008128353,0.051501017,-0.000036526653,0.011502656,-0.033113923,0.039147634,0.0059300517,-0.035458777,0.03042592,0.028081065,0.015184364,0.015127173,0.006423329,0.028009575,-0.015198662,0.023648718,-0.02086063,-0.0027720043,-0.02886745,0.012910998,-0.053331148,0.012110317,-0.042150192,-0.0009990653,-0.045610283,0.10706264,0.03523001,0.06428333,-0.009315078,-0.07223296,0.03171273,-0.022705056,-0.04455224,0.027952384,-0.02016003,0.014927002,0.06353984,-0.04026287,-0.051157866,0.01453381,-0.050700333,-0.028424215,0.03971955,0.023119695,-0.042464748,0.0651984,-0.009944186,-0.050042633,0.047040075,0.030854857,-0.010680527,0.0048183906,-0.034457926,0.05338834,-0.07366275,-0.014498065,0.02051748,0.043494195,-0.020746246,0.049013183,-0.010794911,-0.011538401,-0.00787814,-0.048955992,-0.033028137,0.02274795,-0.0033671542,-0.057477538,0.046010625,-0.034658097,-0.08235587,-0.0048577096,-0.067829214,0.0036852823,-0.008071162,0.019316455,-0.02973962,0.00488988,0.029996984,-0.06405457,0.05052876,-0.051729783,0.028838854,-0.041921426,0.054961108,0.093279466,0.017786581,0.008600184,0.0011643848,-0.023291271,-0.054417785,0.009329376,0.010973634,0.041578278,0.062167246,-0.07423467,-0.02973962,-0.020588968,-0.008750311,0.032970946,-0.046067815,-0.038976062,0.029425068,-0.007427756,-0.07995383,0.019673903,0.0037782188,0.025350166,-0.00033443669,0.0043358365,-0.011102315,0.06897304,0.034715287,0.06365423,-0.034801077,0.006920181,-0.022819439,0.043437004,0.023133993,0.0004923837,0.05398885,0.010022824,0.021160884,-0.031626943,0.0031294518,-0.04169266,-0.08567298,-0.0035297927,0.062967926,-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		,('O rei entrou no 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		,('A rainha vai de 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DECLARE
	@Pesquisa vector(768)

-- veículos brancos
set @Pesquisa = '
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'

select
	*
	,dist = VECTOR_DISTANCE('cosine',@pesquisa,embeddings)
from 
	frases
order by 
	dist

-- pessoas que foram voando
set @Pesquisa = '
	[0.044711415,0.018551953,0.0076507353,-0.016935356,-0.0078106658,-0.013243984,-0.02631507,0.093295746,0.0316922,0.008281813,-0.019364573,0.012941414,0.0024248941,0.0030408432,-0.021871593,-0.013468752,0.041080557,-0.06967788,0.052733883,-0.00065052864,-0.015206377,-0.000083342224,-0.029980509,-0.053356316,-0.049656298,-0.011385332,0.031069765,0.028562745,0.023790762,0.05114322,0.040388968,-0.05857783,0.006444772,0.01448885,-0.04606002,-0.012033699,-0.046717033,-0.0002297652,0.0009552613,-0.027093112,0.047581524,0.015897969,0.032902483,0.01152365,-0.008359617,0.05221519,-0.015612687,0.020298222,0.017220639,0.08700226,-0.020194484,0.0040933597,-0.019848688,0.13499874,0.0059909145,0.07268631,0.028908541,-0.0063669677,-0.0137367435,-0.06058345,0.04803106,-0.035928197,0.008830764,0.048964705,-0.012094214,0.0666003,0.0035076677,-0.002703692,-0.062381588,0.0023125103,0.05705633,0.067879744,-0.059234846,-0.027335169,0.012984638,-0.08921535,-0.009621772,-0.047374044,-0.026557129,-0.04000859,0.002943588,-0.033801552,-0.01067645,-0.03160575,0.017445406,-0.017324377,-0.055638567,0.0011778674,0.04446936,0.06732647,-0.022632344,0.012552393,0.023600573,-0.005688343,0.0138059035,0.06691152,0.010909862,-0.033576787,0.0024227328,-0.031951547,0.08292187,-0.04854975,0.03233192,0.05895821,-0.026124883,-0.006678184,-0.041149717,0.009500744,0.05384043,0.0063669677,0.03834877,0.0012048827,-0.06469842,0.07071527,0.049932934,0.05124696,0.0147222625,-0.017281152,0.06590871,0.008804829,-0.0015150185,-0.024067398,0.010451683,-0.0633844,0.02716227,0.03917868,-0.08444337,0.00467689,0.0020283093,0.013183471,0.04097682,-0.04785816,-0.00011940766,-0.060963824,0.0003322883,0.053944167,0.00018235332,0.018638402,-0.013451463,0.011981829,0.038002975,0.025398713,0.017039096,-0.01540521,0.042083368,0.027179562,0.029461814,0.05214603,0.025139365,-0.039801113,-0.05716007,0.0147049725,-0.044296462,0.019295413,-0.07282463,0.0018489277,-0.020989815,-0.048238534,-0.047581524,0.05705633,-0.015923902,-0.042187106,-0.036896426,-0.005446286,0.0045083147,-0.017808491,0.0008147817,0.018292606,-0.08195364,-0.08790133,-0.05646848,-0.019364573,-0.041253455,0.06220869,-0.021664117,-0.019226255,0.09889764,-0.005403062,-0.05370211,-0.057644185,-0.03419922,0.033352017,-0.03917868,0.005234486,-0.053632952,0.020177193,-0.035582405,0.047339465,-0.042152524,0.030447332,-0.008636254,-0.04336281,0.014203568,0.04924134,-0.029150598,-0.021802435,0.027732834,-0.006842437,-0.021456638,0.09163593,0.009103078,-0.038106713,0.0411843,-0.0764209,0.0428787,0.030153407,0.07455361,-0.0010346863,0.024413195,-0.0035184738,0.1291202,0.025675349,-0.01870756,-0.04875723,-0.066081606,-0.033939872,0.053563792,-0.025243104,0.020920655,-0.013857773,-0.020955235,0.018171577,0.08603403,0.029686581,0.084789164,0.000023266934,-0.018551953,-0.01448885,-0.053494632,0.042602062,-0.029271627,-0.025485162,0.03824503,0.029703872,0.009889764,0.054808658,-0.022545896,-0.01786036,0.000097862954,-0.0006002801,-0.050486207,-0.054013327,0.034406696,0.009621772,-0.017203348,0.015586752,-0.04612918,0.016382083,-0.0011368041,0.0037194677,0.09495556,-0.018292606,0.00018451456,0.03603194,0.011515005,0.00552409,0.018845879,0.012768515,0.04917218,-0.024637962,-0.011774352,0.0072184904,-0.088592924,0.0681218,-0.03910952,-0.013399593,-0.017315732,-0.01406525,0.001059,0.023237487,-0.076835856,0.0021396123,-0.039144102,0.08022466,-0.05027873,0.08022466,0.07821904,-0.03153659,0.023254778,0.0037907881,0.00875296,0.06988536,-0.00093310873,-0.05864699,-0.025640769,0.003743241,0.008692445,-0.035253897,0.042844117,-0.047270305,0.05622642,-0.028251529,0.02052299,-0.03758802,0.052491825,0.060237654,0.007101784,-0.056606796,-0.037933815,-0.03077584,0.033905294,-0.09675371,0.07005826,-0.009915698,-0.0036892104,0.05038247,0.017160123,0.09516305,-0.027629096,-0.0056407964,0.009941634,0.08437421,-0.0031121634,-0.049275924,-0.024344034,-0.026730027,0.026989374,0.049587138,-0.024862729,-0.0032829002,-0.0010860154,-0.018430924,0.031795938,0.0013075409,0.03824503,0.0151285725,0.0101231765,-0.010520842,0.041945048,-0.004668245,-0.035202026,-0.032798745,-0.025986565,0.035444085,0.047789,0.0037216288,-0.0147222625,0.033109963,-0.017289797,-0.08548076,-0.048238534,-0.03309267,-0.047443204,0.0035833104,-0.005714278,0.045852542,0.040838502,-0.062001213,-0.038417928,0.07441529,0.049967512,0.046474975,-0.049033865,0.05543109,-0.03814129,-0.038556248,0.051627334,0.0015668878,-0.0043246103,0.0444002,-0.07441529,0.0036675984,-0.008502258,-0.020159904,0.027732834,-0.0061811027,-0.040285226,-0.011713837,-0.0027490777,0.011393976,-0.071233965,-0.0063453554,-0.01325263,0.05377127,-0.013399593,-0.002336284,0.021007104,-0.02401553,-0.055154454,0.021145422,0.0151458625,0.02385992,-0.028061341,0.0012913317,-0.08160784,0.0733779,-0.032643136,-0.058335777,0.06324608,0.03333473,-0.0046250205,0.01579423,0.002991135,0.038729146,-0.0013939899,-0.040388968,0.043155335,0.057886243,-0.08160784,-0.050002094,-0.025312264,-0.05712549,-0.021197291,0.026366942,-0.04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'

select
	*
	,dist = VECTOR_DISTANCE('cosine',@pesquisa,embeddings)
from 
	frases
order by 
	dist


-- homens e mulheres
set @Pesquisa = '
[0.041974895,0.023487227,0.004571788,0.024329398,0.00906337,-0.040878735,-0.004655337,0.014103031,-0.029382428,-0.054861456,0.02686928,-0.03398095,0.016683016,-0.0070515154,0.0019266343,0.0047221757,0.008562077,0.006276183,0.08095541,0.013093761,-0.03160148,0.016789958,0.042536344,-0.021214701,-0.058444027,0.040958945,-0.017043946,0.056412123,0.073576376,0.038392324,0.048605327,-0.033366032,0.021842988,0.02662866,0.03831212,0.061331473,-0.039247863,0.030425116,0.06951257,-0.009003215,-0.0070849345,-0.024275927,0.0373229,-0.04395333,0.015667064,0.02876751,-0.09346767,-0.0056712897,0.029382428,-0.0057548387,-0.004979506,-0.0036226742,0.010152845,0.032136194,0.05077091,-0.024824006,-0.056091294,-0.008582128,0.021067655,-0.0036159903,0.01629535,-0.014263444,-0.0389003,0.010279839,0.021976665,0.034248307,-0.018714922,-0.056572534,-0.013875778,-0.008588812,0.07870962,0.00013921312,0.032964997,0.0581232,-0.0068977857,-0.005851755,-0.053604882,-0.010921494,-0.031013299,0.022123711,-0.047749788,0.015533385,0.017137522,-0.0028089092,-0.08026028,0.0054239854,-0.04566441,0.00017419916,0.008989846,0.00915026,-0.08828096,0.0076263305,-0.013822307,0.019770978,0.005387224,-0.0828269,-0.019463519,-0.017324671,0.029168542,0.045209903,0.076143,-0.028446682,0.03160148,0.03855274,-0.019958127,-0.024209088,-0.040290553,-0.004254303,0.024596754,0.045557465,0.10854655,0.002357746,-0.030505322,0.011282424,0.06448627,0.063577265,0.037109017,-0.046493214,-0.028660566,-0.021602366,0.024476444,0.01358837,0.016429028,-0.02189646,0.0022441198,0.0042041736,-0.026682131,0.01617504,-0.04042423,0.043365147,-0.0035357834,-0.008288037,-0.033847272,0.001951699,-0.0364941,0.07277431,0.028366474,0.056893364,-0.030745944,0.043766182,0.10683547,-0.050904587,-0.019115957,-0.023821423,0.023901628,0.0052836235,-0.06491404,0.021094391,0.030451851,0.00032792886,-0.014490697,0.019530358,-0.0009641527,0.064379334,-0.062293954,-0.054005917,-0.009370829,-0.084858805,-0.037723936,0.031521276,0.0746458,-0.05732113,0.005073081,0.004772305,0.027256947,-0.011904027,0.0052234684,-0.0052301525,-0.032751113,-0.012104544,-0.06288214,-0.060903702,-0.046840776,0.041680805,-0.055556584,-0.00097417855,0.08555393,-0.01890207,-0.03962216,0.01465111,-0.025278512,0.021883091,0.0013768836,-0.020091806,-0.0034087894,-0.0101996325,-0.06657165,-0.027671348,-0.0007899535,-0.025385454,0.01182382,0.03860621,0.0545941,0.028981393,0.048498385,-0.020840403,-0.02493095,0.013835674,-0.07127712,0.08298731,0.07122365,-0.056091294,0.056091294,-0.054246537,0.026040478,-0.0053671724,0.014664478,-0.03563856,0.009564661,-0.010934861,0.1127173,-0.010774448,0.0025198306,0.0051065003,0.013648525,-0.060689818,-0.04534358,0.034061156,0.008127623,0.025438925,-0.013688629,0.056465592,0.063416846,0.016335454,0.02650835,0.03312541,0.02772482,-0.056572534,-0.06528834,-0.007158458,-0.031200448,0.01854114,-0.03229661,0.00090566854,0.032189667,0.021629103,-0.005434011,-0.013207388,-0.0065669324,0.034087893,-0.028714037,-0.008869536,-0.06780149,0.077854075,0.011543096,0.061866187,0.007960525,-0.004237593,-0.0042776964,-0.010974965,0.078656144,-0.017926222,0.009598081,0.055716995,0.028045647,-0.040049933,0.048284497,0.017712336,0.022658423,0.02930222,-0.063737676,-0.010720977,-0.030024081,-0.023674376,-0.01854114,-0.021695942,-0.0074458653,-0.03841906,0.046466477,-0.019958127,0.038766623,-0.000101093996,-0.04352556,0.07448539,-0.0475359,0.024102146,0.06892438,0.045370318,-0.026588557,0.0271099,-0.0065702745,0.01854114,0.042402666,-0.023420388,0.018514404,0.07031464,0.008060784,0.06999381,0.06117106,-0.09320031,0.026347937,-0.039969727,0.014637742,-0.05325732,0.024329398,0.040852003,0.0018480986,-0.08411021,-0.06122453,-0.010720977,0.048605327,-0.037376374,0.07314861,0.030692471,-0.012178067,0.0005530928,0.04713487,0.021094391,0.01715089,-0.020278955,0.031146977,-0.0044013485,-0.00673403,-0.013982721,-0.04224225,-0.042295724,-0.00086556515,0.065341815,-0.040718324,-0.09298643,0.021575632,-0.014584271,-0.03312541,0.03197578,0.051653184,0.045049492,-0.047081396,-0.056626007,-0.0025883405,-0.04579809,0.0015072196,-0.010660822,-0.024008572,0.026281098,0.10688894,0.033472974,0.017538555,0.11314507,-0.06363074,-0.036761455,-0.00066045293,-0.09368155,-0.054166332,0.015787374,0.008856168,-0.028874451,0.074218035,-0.027698085,0.029088335,0.05034314,0.01787275,-0.021121126,-0.029462634,-0.015346236,-0.06117106,-0.017846014,0.06400503,-0.031761896,0.060957175,0.045878295,-0.019236267,-0.012873192,-0.0361198,-0.037911084,0.033900745,-0.091917,-0.027377257,0.040477704,-0.012017653,-0.03325909,-0.04224225,-0.021736044,0.006419887,0.054326747,-0.027096534,0.0143703865,-0.029034864,-0.01653597,0.019824449,0.0018581244,-0.032991733,0.027403994,0.063256435,-0.02547903,-0.027043061,0.061278,-0.00978523,-0.015600224,-0.005718077,0.014918466,0.033045206,-0.0052903076,0.02085377,0.0006399835,-0.0060689817,-0.048043877,0.081810944,-0.0004837473,-0.05764196,-0.05780237,0.01507888,-0.0062862085,-0.001650088,0.05539617,-0.027203476,-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'

select
	*
	,dist = VECTOR_DISTANCE('cosine',@pesquisa,embeddings)
from 
	frases
order by 
	distCode language: PHP (php)

Na variável pesquisa eu vou colar o embedding gerado para o meu texto de buscar. Importante: Eu gerei cada embedding usando o mesmo modelo. Você (ainda) não pode comprar embeddings gerados por modelos diferentes, pois, afinal, cada modelo tem suas próprias regras. Por exemplo, no nosso primeiro exemplo, a dimensão 0 era veículo. Muito provavelmente para o modelo OpenAI a dimensão 0 não é veículo, e muito menos, se fosse, seria classificado da forma que eu defini.

Aqui estão alguns exemplos de textos e os resultados comentados:

“veículos brancos”

Note como a distância de “Um carro branco” comparado com “carros brancos” é bem menor em relação aos demais. Ordenando pela distância, nós conseguimos trazer os trechos mais parecidos.
“pessoas que foram voando”

Aqui, a distância é bem alta, mas, ainda sim, é a menor das demais.
“homens e mulheres”

Aqui a comparação não foi o que esperava. Este é um exemplo que fiz questão de mostrar para que você entenda que nem sempre essa comparação vai produzir o efeito desejado. Pois isso depende do modelo usado e como ele interpreta o texto e o contexto e transforma nos embeddings. Aqui caberia uma análise mais a fundo para identificar o que poderia ser ajustado para que ele trouxesse a informação.

E isso não é tudo, pessoal!

Um ponto super importante é que eu não precisei saber qual dimensão representa o quê nesse embeddings gerados pela IA. Eu não sei se a posição 0 representa um veículo, não sei o que o valor significa. Eu só sei que esse conjunto todo é suficiente para que eu compare com outro, e que posso usar o resultado dessa comparação para testar a similaridade de duas frases, baseado no contexto e significado. Por isso eu disse anteriormente que, não se preocupe com os valores (pelo menos, não por agora).

A única informação relevante para você é a quantidade de dimensões e a qualidade do modelo em lidar com o assunto que você vai representar. Por exemplo, se o modelo não entende bem português brasileiro, então, seu embeddings será muito ruim para frases em português. Se você vai usar termos comuns de uma área específica, como medicina, ou jurídico, e o modelo não conhece bem, você também terá problemas.

É uma arte!

No caso do SQL Server, especialmente no Azure SQL Database, o suporte ainda é bem básico. Se você tentasse aplicar isso em uma tabela de milhões de linhas, provavelmente levaria um table scan, o que é ruim. É aí onde entra aquele algoritmo que mencionaram pro 2025: o DiskANN, que é um jeito de indexar e buscar os embeddings nesse índices. Falaremos dele quando eu tiver testado.

Esse post trouxe os fundamentos para que você entenda como o SQL Server se encaixa no mundo de IA através dos embeddings. Qualquer dúvida, é só me procurar nos comentários ou nas redes.

Se você quiser mais exemplos práticos de como usar embeddings:

Implementando o vector search com o pgvector e .NET
O MVP Talles Valiatti escreveu nesse post como ele usa .NET, Azure OpenAI e PostgreSQL para implementar busca semântica
Primeiras impressões do suporte nativo à Vector Store no Sql Azure
O MVP Rafael Santos, publicou um vídeo falando das primeiras impressões do suporte ao vector no Azure SQL, e mostrou um exemplo prático de como isso funciona Entity Framework Core

Obrigado pela leitura e até a próxima!

Navegação da série<< IA no SQL Server 2025: Destaques das demos apresentadas no Ignite

Rodrigo Ribeiro Gomes

Apaixonado por tecnologia e veterano em bancos de dados SQL Server, este entusiasta agora se aventura no fascinante universo da Inteligência Artificial.

Atualmente é o Head de Inovação da Power Tuning, onde é o responsável por trazer novas ideias para produtos e serviços, que melhorem a produtividade do time ou a experiência do cliente! Com muita experiência em programação, hardware, sistemas operacionais, e mais, agora quer juntar tudo isso nesse novo mundo e trazer muitas ideias e conhecimento sobre Inteligência Artificial!

Neste blog, vai compartilhar sua jornada de aprendizado e uso da IA, focando em como transformar nossa maneira de resolver problemas e inovar.

Contents

Começando sem SQL e sem IA
Vetores
Comparando Vetores
SQL Server entra no grupo…
Um pouco mais real
IA entra no grupo…
Gerando Embeddings Reais
E isso não é tudo, pessoal!