{"id":16022,"date":"2024-07-16T12:50:20","date_gmt":"2024-07-16T19:50:20","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=16022"},"modified":"2025-06-13T16:36:49","modified_gmt":"2025-06-13T23:36:49","slug":"couchbase-vector-search-in-5-minutes","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/pt\/couchbase-vector-search-in-5-minutes\/","title":{"rendered":"Comece a usar o Couchbase Vector Search em 5 minutos"},"content":{"rendered":"<h2><span style=\"font-weight: 400;\">O que \u00e9 um vetor<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Um vetor \u00e9 um objeto que representa um item do mundo real como um <em>matriz de n\u00fameros flutuantes<\/em>.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Cada item no mundo real \u00e9 representado no formato de vetor (como uma matriz) e tem muitas dimens\u00f5es (atributos) associadas ao objeto com base em suas caracter\u00edsticas.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Por exemplo, se quisermos representar as cores em formato vetorial, podemos criar uma matriz de valores de atributos. Exemplo <\/span><span style=\"font-weight: 400;\">[ \"R\", \"G\", \"B\"]<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Cada cor em uma imagem RGB \u00e9 representada por tr\u00eas valores: a quantidade de luz vermelha, verde e azul presente. Esses valores normalmente variam de 0 a 255, indicando a intensidade de cada componente de cor.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Pure Red= <\/span><span style=\"font-weight: 400;\">[ \"255\", \"0\", \"0\"]<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Onde:<\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span style=\"font-weight: 400;\">R representa a intensidade do vermelho (nesse caso, a intensidade m\u00e1xima, 255),<\/span><\/li>\n<li><span style=\"font-weight: 400;\">G representa a intensidade do verde (nesse caso, 0, ou seja, sem verde),<\/span><\/li>\n<li><span style=\"font-weight: 400;\">B representa a intensidade do azul (nesse caso, 0, ou seja, sem azul).<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>Da mesma forma, voc\u00ea pode representar qualquer cor usando esse formato de vetor RGB, com valores que variam de 0 a 255 para cada canal de cor.<\/p>\n<p><span style=\"font-weight: 400;\">Se quisermos encontrar correspond\u00eancias pr\u00f3ximas para a cor vermelha, podemos descobrir com base no primeiro valor de atributo da cor.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Os objetos do mundo real podem ter muitos outros atributos que precisam ser representados e, portanto, um vetor que representa um objeto do mundo real \u00e9 representado por uma matriz maior de 512, 1028, 1536 ou 2048 valores de atributos.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">O que \u00e9 o Vector Search?<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">A pesquisa vetorial \u00e9 um m\u00e9todo de encontrar itens com base em sua representa\u00e7\u00e3o vetorial. Na pesquisa vetorial, cada item \u00e9 representado em um espa\u00e7o multidimensional em que cada dimens\u00e3o representa o valor do atributo do item.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Mais detalhes podem ser encontrados em:<\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.couchbase.com\/blog\/pt\/products\/vector-search\/\">Recurso de pesquisa vetorial do Couchbase<\/a><\/span><\/li>\n<li><a href=\"https:\/\/www.couchbase.com\/blog\/pt\/tag\/vector-search\/\"><span style=\"font-weight: 400;\">Blogs sobre o Vector Search<\/span><\/a> e <a href=\"https:\/\/www.couchbase.com\/blog\/pt\/vector-databases\/\">Bancos de dados vetoriais<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Casos de uso em todo o setor<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">A pesquisa vetorial pode ser usada em v\u00e1rios setores para diversos casos de uso:<\/span><\/p>\n<ol>\n<li style=\"list-style-type: none;\">\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Gera\u00e7\u00e3o de conte\u00fado<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Detec\u00e7\u00e3o de anomalias<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Pesquisa h\u00edbrida<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Chatbots com IA.<\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<h2><span style=\"font-weight: 400;\">Pesquisa vetorial vs. pesquisa de texto completo?<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">A pesquisa vetorial e a pesquisa de texto completo s\u00e3o m\u00e9todos usados para pesquisar em cole\u00e7\u00f5es de dados, mas operam de maneiras diferentes e s\u00e3o adequadas a diferentes tipos de dados e casos de uso.<\/span><\/p>\n<p><b>Pesquisa de texto completo:<\/b><span style=\"font-weight: 400;\"> \u00e9 uma t\u00e9cnica usada na recupera\u00e7\u00e3o de informa\u00e7\u00f5es para pesquisar e analisar o conte\u00fado textual em documentos ou bancos de dados. Diferentemente dos m\u00e9todos de pesquisa tradicionais que correspondem a frases exatas ou palavras-chave, os mecanismos de pesquisa de texto completo analisam o conte\u00fado de documentos ou registros para corresponder \u00e0s consultas de pesquisa com base no significado e no contexto das palavras<\/span><span style=\"font-weight: 400;\">.<\/span><span style=\"font-weight: 400;\">\u00a0<\/span><\/p>\n<table>\n<tbody>\n<tr>\n<td><\/td>\n<td><strong>\u00c1rea de compara\u00e7\u00e3o<\/strong><\/td>\n<td><strong>Pesquisa de texto completo<\/strong><\/td>\n<td><strong>Pesquisa de vetores<\/strong><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">1<\/span><\/td>\n<td><strong>Representa\u00e7\u00e3o de dados<\/strong><\/td>\n<td><span style=\"font-weight: 400;\">Os dados s\u00e3o representados como documentos de texto ou cadeias de caracteres<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Os dados s\u00e3o representados como vetores em um espa\u00e7o multidimensional<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">2<\/span><\/td>\n<td><strong>Crit\u00e9rios de correspond\u00eancia<\/strong><\/td>\n<td><span style=\"font-weight: 400;\">Correspond\u00eancia exata ou difusa<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Partida vizinha mais pr\u00f3xima<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">3<\/span><\/td>\n<td><strong>Pesquisa<\/strong><\/td>\n<td><span style=\"font-weight: 400;\">Pesquisa ou compara\u00e7\u00e3o textual<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Pesquisa ou compara\u00e7\u00e3o contextual com base nos atributos do objeto.<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">4<\/span><\/td>\n<td><strong>Caso de uso<\/strong><\/td>\n<td><span style=\"font-weight: 400;\">Pesquisa em um documento, p\u00e1gina da Web, conte\u00fado de e-mail, etc.<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Pesquisa de \u00e1udio, v\u00eddeo, imagem, texto, etc.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span style=\"font-weight: 400;\">Por que o Couchbase para o Vector Search?<\/span><\/h2>\n<ol>\n<li style=\"list-style-type: none;\">\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Vetor em nossos produtos<\/strong>: Primeiro no setor a anunciar suporte para todas as tr\u00eas implementa\u00e7\u00f5es: nuvem, local e m\u00f3vel.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Capacidades amplas<\/strong>: Cache integrado, pesquisa de texto completo, pesquisa anal\u00edtica, s\u00e9rie temporal, valor-chave, eventos e outros recursos, al\u00e9m de pesquisa vetorial em uma \u00fanica plataforma.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Integra\u00e7\u00e3o do ecossistema<\/strong>: <a href=\"https:\/\/python.langchain.com\/v0.2\/docs\/integrations\/providers\/couchbase\/\">LangChain<\/a> e <a href=\"https:\/\/docs.llamaindex.ai\/en\/latest\/api_reference\/storage\/vector_store\/couchbase\/\">LlamaIndex<\/a> integra\u00e7\u00e3o.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\"><strong>Velocidade e flexibilidade comprovadas<\/strong>: Arquitetura na mem\u00f3ria, formato json flex\u00edvel e indexa\u00e7\u00e3o potente.<\/span><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<p><span style=\"font-weight: 400;\">Mais detalhes podem ser encontrados em nosso <a href=\"https:\/\/www.couchbase.com\/blog\/pt\/announcing-vector-search\/\">An\u00fancio de lan\u00e7amento do vector search<\/a>.\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Pr\u00e9-requisitos<\/span><\/h2>\n<ol>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Couchbase Capella ou Couchbase Server 7.6 EE\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">J\u00e1 criaram um banco de dados<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Dados de amostra:<\/span>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Baixar arquivo <\/span><a href=\"https:\/\/cbc-remote-execution-examples-prod.s3.amazonaws.com\/color_data_2vectors.zip\"><span style=\"font-weight: 400;\">color_data_2vectors.zip<\/span><\/a><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Para este exemplo, usaremos o <em>rgb.json<\/em> arquivo\u00a0<\/span><\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Arquivo de \u00edndice: <em>color-index.json<\/em><\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/p>\n<ul>\n<li><em>color-index.json:<\/em>\n<pre class=\"height:300 nums:false scroll:true minimize:true lang:js decode:true\">{\r\n    \"type\": \"fulltext-index\",\r\n    \"name\": \"color-index\",\r\n    \"sourceType\": \"gocbcore\",\r\n    \"sourceName\": \"vector-sample\",\r\n    \"sourceUUID\": \"789365cccdf940ee2814a5dd2752040a\",\r\n    \"planParams\": {\r\n      \"maxPartitionsPerPIndex\": 512,\r\n      \"indexPartitions\": 1\r\n    },\r\n    \"params\": {\r\n      \"doc_config\": {\r\n        \"docid_prefix_delim\": \"\",\r\n        \"docid_regexp\": \"\",\r\n        \"mode\": \"scope.collection.type_field\",\r\n        \"type_field\": \"type\"\r\n      },\r\n      \"mapping\": {\r\n        \"analysis\" (an\u00e1lise): {},\r\n        \"default_analyzer\": \"standard\",\r\n        \"default_datetime_parser\": \"dateTimeOptional\",\r\n        \"default_field\": \"_all\",\r\n        \"default_mapping\": {\r\n          \"dynamic\": falso,\r\n          \"enabled\": false\r\n        },\r\n        \"default_type\": \"_default\",\r\n        \"docvalues_dynamic\": falso,\r\n        \"index_dynamic\": falso,\r\n        \"store_dynamic\": falso,\r\n        \"type_field\": \"_type\",\r\n        \"types\": {\r\n        \"color.rgb\": {\r\n          \"dynamic\": falso,\r\n          \"enabled\": true,\r\n          \"properties\" (propriedades): {\r\n            \"brightness\" (brilho): {\r\n              \"dynamic\" (din\u00e2mico): false,\r\n              \"enabled\": true,\r\n              \"fields\" (campos): [\r\n                {\r\n                  \"index\": true,\r\n                  \"name\": \"brightness\",\r\n                  \"store\": true,\r\n                  \"type\": \"number\"\r\n                }\r\n              ]\r\n            },\r\n            \"color\" (cor): {\r\n              \"dynamic\" (din\u00e2mico): false,\r\n              \"enabled\" (ativado): true,\r\n              \"fields\": [\r\n                {\r\n                  \"Analyzer\": \"en\",\r\n                  \"index\": true,\r\n                  \"name\": \"color\",\r\n                  \"store\": true,\r\n                  \"type\": \"text\"\r\n                }\r\n              ]\r\n            },\r\n            \"colorvect_dot\": {\r\n              \"dynamic\" (din\u00e2mico): false,\r\n              \"enabled\": true,\r\n              \"fields\": [\r\n                {\r\n                  \"dims\": 3,\r\n                  \"index\": true,\r\n                  \"name\": \"colorvect_dot\",\r\n                  \"similarity\" (similaridade): \"dot_product\",\r\n                  \"type\" (tipo): \"vector\"\r\n                }\r\n              ]\r\n            },\r\n            \"colorvect_l2\": {\r\n              \"dynamic\" (din\u00e2mico): false,\r\n              \"enabled\" (ativado): true,\r\n              \"fields\": [\r\n                {\r\n                  \"dims\": 3,\r\n                  \"index\": true,\r\n                  \"name\" (nome): \"colorvect_l2\",\r\n                  \"similarity\" (similaridade): \"l2_norm\",\r\n                  \"type\" (tipo): \"vector\"\r\n                }\r\n              ]\r\n            },\r\n            \"description\" (descri\u00e7\u00e3o): {\r\n              \"dynamic\" (din\u00e2mico): false (falso),\r\n              \"enabled\" (ativado): true,\r\n              \"fields\": [\r\n                {\r\n                  \"Analyzer\": \"en\",\r\n                  \"index\": true,\r\n                  \"name\": \"description\",\r\n                  \"store\": true,\r\n                  \"type\": \"text\"\r\n                }\r\n              ]\r\n            },\r\n            \"embedding_vector_dot\": {\r\n              \"dynamic\" (din\u00e2mico): false,\r\n              \"enabled\": true,\r\n              \"fields\": [\r\n                {\r\n                  \"dims\": 1536,\r\n                  \"index\": true,\r\n                  \"name\": \"embedding_vector_dot\",\r\n                  \"similaridade\": \"dot_product\",\r\n                  \"type\" (tipo): \"vector\"\r\n                }\r\n              ]\r\n            }\r\n          }\r\n        }\r\n      }\r\n    },\r\n    \"store\": {\r\n      \"indexType\": \"scorch\",\r\n      \"segmentVersion\": 16\r\n    }\r\n  },\r\n  \"sourceParams\": {}\r\n}<\/pre>\n<\/li>\n<\/ul>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Defini\u00e7\u00e3o de pesquisa de amostra:<br \/>\n<\/span><code>{\"fields\": [\"*\"], \"query\": { \"match_none\": \"\" }, \"knn\": [ { \"k\": 2, \"field\": \"colorvect_l2\", \"vector\": [ 0, 0, 128 ] } ] }<\/code><\/li>\n<\/ol>\n<h2><span style=\"font-weight: 400;\">Etapas<\/span><\/h2>\n<h3><span style=\"font-weight: 400;\">Criar dados de amostra<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Abra a Capella UI, v\u00e1 para <em>Banco de dados<\/em>e comece a importar dados usando a importa\u00e7\u00e3o do navegador usando ferramentas de dados:<\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Use o arquivo de dados rgb.json de amostra fornecido nos pr\u00e9-requisitos.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Escolha a op\u00e7\u00e3o <strong>carregar do navegador<\/strong>.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Selecione o arquivo: <em>rgb.json<\/em><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Especifique o nome do novo bucket: <em>amostra de vetores<\/em><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Especifique o novo escopo com o nome: <em>cor<\/em><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Especifique o nome da nova cole\u00e7\u00e3o: <em>rgb<\/em><\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Na etapa 3, <em>visualiza\u00e7\u00e3o<\/em> seus dados,<br \/>\n<\/span>Escolha como o Capella cria identificadores para cada um de seus documentos. Selecione a op\u00e7\u00e3o como campo e especifique o campo: <strong>Id<\/strong> conforme identificado na captura de tela abaixo.<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Clique em Import.<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image2-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-16023\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image2-1.png\" alt=\"\" width=\"571\" height=\"651\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image2-1.png 571w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image2-1-263x300.png 263w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image2-1-300x342.png 300w\" sizes=\"auto, (max-width: 571px) 100vw, 571px\" \/><\/a><\/p>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image7.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-16024\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image7-1024x423.png\" alt=\"\" width=\"900\" height=\"372\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image7-1024x423.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image7-300x124.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image7-768x317.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image7-1536x634.png 1536w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image7-1320x545.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image7.png 1900w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image6.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-16025\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image6-1024x432.png\" alt=\"\" width=\"900\" height=\"380\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image6-1024x432.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image6-300x127.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image6-768x324.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image6-1536x649.png 1536w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image6-1320x558.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image6.png 1906w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<h3><span style=\"font-weight: 400;\">Criar \u00edndice de pesquisa de vetores\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Em Search Options (Op\u00e7\u00f5es de pesquisa), em <strong>Ferramentas de dados<\/strong>:<\/span><\/p>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><strong>Criar \u00edndice de pesquisa<br \/>\n<a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image9.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-16026\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image9-1024x333.png\" alt=\"\" width=\"645\" height=\"210\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image9-1024x333.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image9-300x97.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image9-768x250.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image9.png 1234w\" sizes=\"auto, (max-width: 645px) 100vw, 645px\" \/><\/a><br \/>\n<\/strong><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"2\"><span style=\"font-weight: 400;\">Selecione <strong>modo avan\u00e7ado<\/strong><\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Clique em <strong>Defini\u00e7\u00e3o do \u00edndice<\/strong> no lado direito da interface do usu\u00e1rio<\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Selecione a op\u00e7\u00e3o <strong>Importar do arquivo<\/strong><\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Escolha o arquivo <em>color-index.json<\/em> especificado nos pr\u00e9-requisitos<\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Especifique o nome do \u00edndice como:\u00a0<em> color-index.json<\/em><\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Escolha a ca\u00e7amba: <em>amostra de vetores<\/em><\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">O escopo ser\u00e1 preenchido automaticamente como <em>cor<\/em><\/span><\/li>\n<li aria-level=\"2\"><span style=\"font-weight: 400;\">Clique em <strong>Criar \u00edndice<\/strong><\/span><span style=\"font-weight: 400;\"><br \/>\n<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image5-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-16027\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image5-1-1024x423.png\" alt=\"\" width=\"900\" height=\"372\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image5-1-1024x423.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image5-1-300x124.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image5-1-768x318.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image5-1-1320x546.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image5-1.png 1323w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<h3><span style=\"font-weight: 400;\">Executar uma pesquisa de vetor\u00a0<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Selecione o <strong>Pesquisa<\/strong> na op\u00e7\u00e3o <em>\u00edndice de cores<\/em> linha (bot\u00e3o pr\u00f3ximo \u00e0 extrema direita)<\/span><\/p>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image3-1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-16028\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image3-1-1024x292.png\" alt=\"\" width=\"900\" height=\"257\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image3-1-1024x292.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image3-1-300x86.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image3-1-768x219.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image3-1-1320x377.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image3-1.png 1380w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Cole o texto de pesquisa da etapa de pr\u00e9-requisito na janela Pesquisar.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Clique em <strong>Pesquisa<\/strong> para obter um resultado (exibido na janela abaixo do texto da pesquisa).<\/span><\/p>\n<p><a href=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image8.png\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-large wp-image-16029\" style=\"border: 1px solid black;\" src=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/2024\/07\/image8-1024x371.png\" alt=\"\" width=\"900\" height=\"326\" srcset=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image8-1024x371.png 1024w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image8-300x109.png 300w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image8-768x279.png 768w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image8-1536x557.png 1536w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image8-1320x479.png 1320w, https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2024\/07\/image8.png 1839w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\" \/><\/a><\/p>\n<p>&nbsp;<\/p>\n<h2><span style=\"font-weight: 400;\">Conclus\u00e3o<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Nesta postagem, abordamos os conceitos b\u00e1sicos do que \u00e9 a pesquisa vetorial e como come\u00e7ar a usar rapidamente a pesquisa vetorial com o Couchbase.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Depois de executar uma pesquisa vetorial b\u00e1sica, \u00e9 poss\u00edvel combinar facilmente consultas SQL com a pesquisa vetorial no Couchbase, o que ajuda a consolidar a pilha de bancos de dados e evita a grava\u00e7\u00e3o de v\u00e1rias consultas para obter um \u00fanico resultado significativo para um aplicativo.<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">Comece gratuitamente<\/span><\/h3>\n<ul>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span style=\"font-weight: 400;\">Inicie sua <\/span><a href=\"https:\/\/cloud.couchbase.com\/sign-up\"><span style=\"font-weight: 400;\">Conta de avalia\u00e7\u00e3o de 30 dias para Capella<\/span><\/a><span style=\"font-weight: 400;\"> para executar seu primeiro experimento hoje mesmo!<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">Refer\u00eancias<\/span><\/h3>\n<ol>\n<li style=\"list-style-type: none;\">\n<ul>\n<li><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.couchbase.com\/blog\/pt\/announcing-vector-search\/\">An\u00fancio de lan\u00e7amento do Vector Search<\/a>\u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\"><a href=\"https:\/\/www.couchbase.com\/blog\/pt\/products\/vector-search\/\">Recurso de pesquisa vetorial do Couchbase<\/a><\/span><\/li>\n<li><a href=\"https:\/\/www.couchbase.com\/blog\/pt\/tag\/vector-search\/\"><span style=\"font-weight: 400;\">Blogs sobre o Vector Search<\/span><\/a> e <a href=\"https:\/\/www.couchbase.com\/blog\/pt\/vector-databases\/\">Bancos de dados vetoriais<\/a><\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>&nbsp;<\/p>","protected":false},"excerpt":{"rendered":"<p>What is a Vector A Vector is an object that represents a real-world item as an array of floating numbers.\u00a0 Each item in the real world is represented in Vector format(as an array) and has many dimensions (attributes) associated with [&hellip;]<\/p>","protected":false},"author":85424,"featured_media":16031,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[10122,1815,2225,1816,9936,9937],"tags":[9963,9995],"ppma_author":[9996],"class_list":["post-16022","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-ai","category-best-practices-and-tutorials","category-cloud","category-couchbase-server","category-search","category-vector-search","tag-langchain","tag-llamaindex"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.1 (Yoast SEO v26.1.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Get Started With Couchbase Vector Search In 5 Minutes - The Couchbase Blog<\/title>\n<meta name=\"description\" content=\"Vector search and full-text search are both methods used for 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