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Agente AI para chatear con Snowflake y UI

Un agente AI conversacional que interactúa con Snowflake para generar consultas SQL, recuperar datos, y mostrar resultados en un dashboard interactivo HTML con vistas de tabla y gráfico. Soporta análisis de grandes volúmenes de datos.

AI 28 nodos 14 tipos conectado
Cargando workflow...

Nodos

ChatTrigger Agent LmChatOpenAi MemoryBufferWindow SnowflakeTool Webhook Set RespondToWebhook Snowflake Aggregate ExecuteWorkflowTrigger ToolWorkflow StickyNote If

Herramientas

OpenAI Snowflake Webhook Langchain

Detalles

ID
5435
Nodos
28
Conex.
Tipos
14

Pertenece a:

¿Qué hace este workflow?

Este workflow implementa un agente AI conversacional que democratiza el acceso a la información contenida en Snowflake. Los usuarios pueden interactuar con el agente para generar consultas SQL de forma dinámica, extraer datos relevantes y visualizar los resultados instantáneamente en un dashboard HTML interactivo, con opciones de tabla y gráfico. Es ideal para equipos que necesitan realizar análisis de grandes volúmenes de datos sin depender de expertos en SQL, agilizando la toma de decisiones basada en datos. Elimina la barrera técnica del acceso a Snowflake, permitiendo a cualquier usuario formular preguntas y obtener respuestas visuales de manera autónoma. Esto reduce significativamente el tiempo de espera para informes, optimizando recursos humanos y mejorando la eficiencia operativa al ofrecer insights de negocio en tiempo real.

¿Cómo funciona?

Este workflow usa 28 nodos conectados con 14 tipos diferentes: ChatTrigger, Agent, LmChatOpenAi, MemoryBufferWindow, SnowflakeTool y 9 más. La estructura está totalmente conectada — listo para importar.

¿Para quién es?

Diseñado para equipos de IT & DevOps. Nivel avanzado — recomendado para usuarios experimentados. Alto valor de negocio: automatiza una tarea recurrente con impacto directo.

¿Lo quieres en tu empresa?

Lo implementamos por ti end-to-end: integración, deploy, mantenimiento y soporte. Consultoría B2B con Genai Sapiens.

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Dashboard

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Workflow","type":"n8n-nodes-base.executeWorkflowTrigger","position":[1000,320],"parameters":{"workflowInputs":{"values":[{"name":"query"}]}},"typeVersion":1.1},{"id":"3b69b3eb-7a91-43b3-8839-13b5bbdf3203","name":"Retrieve Data","type":"@n8n/n8n-nodes-langchain.toolWorkflow","position":[1400,-80],"parameters":{"workflowId":{"__rl":true,"mode":"list","value":"kqpZSjy0tzRRY4hH","cachedResultName":"My workflow 41"},"description":"Generate custom SQL queries using knowledge about DB schema and table definitions to provide needed response for user request.\nUse ->> operator to extract JSON data.\n\nSupported functions for big data analysis:\n• GROUP BY – for grouping data\n• SUM() – for summing values\n• AVG() – for calculating averages\n• COUNT() – for counting records\n• MIN() – for finding the minimum value\n• MAX() – for finding the maximum value\n• MEDIAN() – for median calculation\n• STDDEV() – for standard deviation\n• VARIANCE() – for variance calculation\n• PERCENTILE_CONT() – for percentile calculations\n• MODE() – for most frequent value\n• TREND() – for trend analysis over time\n• WINDOW FUNCTIONS – for advanced analytics (e.g., ROW_NUMBER(), RANK(), PARTITION BY)\n\nQuery example:\nSELECT * FROM FILES","workflowInputs":{"value":{"query":"={{ $fromAI(\"sql_query\",\"SQL query\") }}"},"schema":[{"id":"query","type":"string","display":true,"removed":false,"required":false,"displayName":"query","defaultMatch":false,"canBeUsedToMatch":true}],"mappingMode":"defineBelow","matchingColumns":["query"],"attemptToConvertTypes":false,"convertFieldsToString":false}},"typeVersion":2.2},{"id":"8968d722-6b26-42d9-b750-0b13c382f7ab","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[940,120],"parameters":{"width":1140,"height":560,"content":"### Tool\n"},"typeVersion":1},{"id":"33544700-b10f-441a-852c-0f985158cb5c","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[940,720],"parameters":{"width":1360,"height":480,"content":"### Report workflow"},"typeVersion":1},{"id":"d279cb96-4fc5-4554-9363-669aea575926","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[940,-360],"parameters":{"width":900,"height":440,"content":"### Agent"},"typeVersion":1},{"id":"fc341443-ec9c-4005-8b67-dce59e8b4119","name":"Execute SQL","type":"n8n-nodes-base.snowflake","onError":"continueErrorOutput","position":[1200,320],"parameters":{"query":"{{ $json.query }}","operation":"executeQuery"},"credentials":{"snowflake":{"id":"YWnLSlN2NAjYvAfU","name":"Snowflake account"}},"retryOnFail":false,"typeVersion":1},{"id":"935f1932-e803-4daf-89ae-e3ce9f322962","name":"Aggregate Data","type":"n8n-nodes-base.aggregate","position":[1420,260],"parameters":{"options":{},"aggregate":"aggregateAllItemData"},"typeVersion":1},{"id":"1c7362be-e338-4976-8a18-a40f11e01ef1","name":"If Count>100","type":"n8n-nodes-base.if","position":[1600,280],"parameters":{"options":{},"conditions":{"options":{"version":2,"leftValue":"","caseSensitive":true,"typeValidation":"strict"},"combinator":"and","conditions":[{"id":"5a485a5a-28fb-4914-8fb6-131a159db08d","operator":{"type":"array","operation":"lengthGt","rightType":"number"},"leftValue":"={{ $json.data }}","rightValue":100}]}},"typeVersion":2.2},{"id":"36440a5e-d254-4995-be8e-2e8313b8bc29","name":"Link to Report","type":"n8n-nodes-base.set","position":[1800,200],"parameters":{"mode":"raw","options":{},"jsonOutput":"={\"output\":\"[Link to report](https://n8n.lowcoding.dev/webhook/87893585-d157-468d-a9af-7238784e814c?sql={{ $('When Executed by Another Workflow').item.json.query.urlEncode() }})\"}"},"typeVersion":3.4},{"id":"c581ea63-c210-4df9-b029-f58c1c8da75d","name":"Return Data","type":"n8n-nodes-base.set","position":[1800,360],"parameters":{"mode":"raw","options":{},"jsonOutput":"={{ $json }}"},"typeVersion":3.4},{"id":"4fa89cd3-64d8-40cf-87f1-d604aca9c7eb","name":"Return Error","type":"n8n-nodes-base.set","position":[1600,460],"parameters":{"mode":"raw","options":{},"jsonOutput":"={{ $json }}"},"typeVersion":3.4},{"id":"c67e5a20-e116-4955-bc66-fe8b4384b284","name":"Error page","type":"n8n-nodes-base.set","position":[1800,1000],"parameters":{"options":{},"assignments":{"assignments":[{"id":"1257d6ca-3c5c-476b-8d26-f8fb84a0c38e","name":"html","type":"string","value":"=\n\n\n Error Status\n \n\n\n
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Error Occurred

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An error occurred while preparing the analysis data.

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Please try again later or contact support if the problem persists.

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Error Code: HTML_PREP_ERROR
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Analysis ID:

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