{"id":15950,"date":"2024-06-24T10:02:09","date_gmt":"2024-06-24T17:02:09","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=15950"},"modified":"2025-06-16T10:43:53","modified_gmt":"2025-06-16T17:43:53","slug":"develop-performant-rag-apps-with-couchbase-and-vectorize","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/es\/develop-performant-rag-apps-with-couchbase-and-vectorize\/","title":{"rendered":"Desarrollar aplicaciones RAG de alto rendimiento con Couchbase y Vectorize"},"content":{"rendered":"

Para los l\u00edderes tecnol\u00f3gicos y los desarrolladores, el proceso de integraci\u00f3n de datos ricos y propios en aplicaciones de IA generativa es a menudo <\/span>lleno de retos<\/span><\/a>. La b\u00fasqueda de similitud vectorial y la generaci\u00f3n aumentada de recuperaci\u00f3n son herramientas poderosas para ayudar con esto, pero un error al extraer, trocear o vectorizar sus datos puede significar la diferencia entre un agente de IA que funcione bien y uno que frustre a los usuarios con respuestas pobres y alucinaciones. Hoy nos complace anunciar que la creaci\u00f3n de \u00edndices vectoriales altamente optimizados en Capella es ahora mucho m\u00e1s f\u00e1cil.<\/span><\/p>\n

Eso es porque los equipos de Couchbase y Vectorize han estado trabajando duro para llevar el poder de los experimentos de Vectorize a Couchbase Capella, una plataforma DBaaS para que empieces r\u00e1pidamente con tu <\/span>RAG<\/span><\/a>-aplicaci\u00f3n GenAI<\/span>. Aprovechar <\/span>esta integraci\u00f3n, puede encontrar r\u00e1pidamente el <\/span>mejor modelo de incrustaci\u00f3n de texto<\/span><\/a>La tecnolog\u00eda de b\u00fasqueda de Google permite a los usuarios acceder a los resultados de b\u00fasqueda de forma r\u00e1pida y sencilla, con estrategias de fragmentaci\u00f3n y par\u00e1metros de recuperaci\u00f3n para maximizar la relevancia de los resultados de b\u00fasqueda sin escribir una sola l\u00ednea de c\u00f3digo.<\/span><\/p>\n

Para cualquiera que est\u00e9 creando aplicaciones basadas en LLM utilizando Capella como base de conocimiento para RAG, esto supone un cambio radical que le permitir\u00e1 crear aplicaciones de IA generativa mejores y m\u00e1s precisas en una fracci\u00f3n del tiempo, sin necesidad de ensayo y error para averiguar cu\u00e1l es la mejor estrategia de vectorizaci\u00f3n para sus datos.\u00a0<\/span><\/p>\n

Caracter\u00edsticas y capacidades<\/span><\/h2>\n

Cuando ejecutas un experimento en Vectorize, construir\u00e1s hasta 4 \u00edndices de b\u00fasqueda vectorial diferentes en Couchbase Capella simult\u00e1neamente. Estos \u00edndices se rellenan a partir de un conjunto de documentos que subes y cada \u00edndice de b\u00fasqueda se rellena utilizando los modelos de incrustaci\u00f3n y las estrategias de fragmentaci\u00f3n que especifiques.\u00a0<\/span><\/p>\n

Bas\u00e1ndose en sus documentos, Vectorize generar\u00e1 un conjunto de preguntas sint\u00e9ticas que le permitir\u00e1n predecir los tipos de preguntas que sus usuarios podr\u00edan hacer sobre sus datos. A continuaci\u00f3n, Vectorize recupera el contexto m\u00e1s relevante para cada pregunta, puntuando los resultados devueltos por Capella mediante un algoritmo de relevancia y una ganancia acumulada descontada normalizada.\u00a0<\/span><\/p>\n

\"\"<\/a>
\nEl resultado neto son datos concretos que muestran con precisi\u00f3n el rendimiento de las distintas estrategias de vectorizaci\u00f3n en sus datos. Pero el viaje no acaba aqu\u00ed.<\/span><\/p>\n

Con Vectorize RAG Sandbox, puede explorar m\u00e1s a fondo sus datos en una interfaz interactiva en vivo que proporciona una interfaz de extremo a extremo para probar sus flujos de Generaci\u00f3n Aumentada de Recuperaci\u00f3n. Puede utilizar una de las preguntas sint\u00e9ticas del experimento o introducir su propia pregunta para ver exactamente qu\u00e9 devuelve Capella e inspeccionar c\u00f3mo responden a su consulta los distintos modelos de lenguaje de gran tama\u00f1o.<\/span><\/p>\n

\"\"<\/a>
\nCon Vectorize y Capella como base de conocimientos, puede construir una <\/span>Canalizaci\u00f3n RAG<\/span><\/a> para garantizar que sus aplicaciones Gen AI y sus agentes AI dispongan siempre del contexto m\u00e1s relevante. Esto significa usuarios m\u00e1s felices y menos alucinaciones de sus LLM.\u00a0<\/span><\/p>\n

Reg\u00edstrate y empieza a experimentar gratis.<\/span><\/h2>\n

Para empezar, <\/span>reg\u00edstrate gratis en Vectorize<\/span><\/a> y un <\/span>Cuenta de prueba de 30 d\u00edas para Capella<\/span><\/a> para realizar su primer experimento hoy mismo. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

For technology leaders and developers, the process of integrating rich, proprietary data into generative AI applications is often filled with challenges. Vector similarity search and retrieval augmented generation are powerful tools to help with this, but one mistake extracting, chunking, […]<\/p>","protected":false},"author":85347,"featured_media":15951,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[10122,3917,9973,9921,9937],"tags":[9924,9980],"ppma_author":[9982,9981],"class_list":["post-15950","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence-ai","category-company","category-generative-ai-genai","category-partners","category-vector-search","tag-rag-retrieval-augmented-generation","tag-vectorize-io"],"acf":[],"yoast_head":"\nDevelop Performant RAG Apps With Couchbase and Vectorize - The Couchbase Blog<\/title>\n<meta name=\"description\" content=\"The teams at Couchbase and Vectorize have been working hard to bring the power of 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