{ "name": "2-[Agente-Abogado]-RAG", "nodes": [ { "parameters": { "options": {} }, "id": "f497088e-68b5-4e50-8847-d24a4b711b1c", "name": "OpenAI Chat Model", "type": "@n8n/n8n-nodes-langchain.lmChatOpenAi", "typeVersion": 1, "position": [ -800, 368 ], "credentials": { "openAiApi": { "name": "" } } }, { "parameters": { "jsonMode": "expressionData", "jsonData": "={{ $json.data || $json.text || $json.concatenated_data }}", "options": { "metadata": { "metadataValues": [ { "name": "=file_id", "value": "={{ $('Set File ID').first().json.file_id }}" }, { "name": "file_title", "value": "={{ $('Set File ID').first().json.file_title }}" } ] } } }, "id": "4e50b58a-4229-45ec-9bb6-9d010a376c04", "name": "Default Data Loader", "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader", "typeVersion": 1, "position": [ 1008, 1152 ] }, { "parameters": { "model": "text-embedding-3-small", "options": {} }, "id": "97729448-7e7c-49f5-b985-0294447f09de", "name": "Embeddings OpenAI1", "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "typeVersion": 1, "position": [ 768, 1152 ], "credentials": { "openAiApi": { "name": "" } } }, { "parameters": { "content": "## Agent RAG-Herramientas\n", "height": 528.85546469693, "width": 583.4552380860637, "color": 4 }, "id": "f72e1bc4-11ab-43a3-8708-2f0dfe89e755", "name": "Sticky Note", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ 16, 0 ] }, { "parameters": { "content": "## Agregar archivos como vectores en la base de datos", "height": 867, "width": 3073, "color": 5 }, "id": "f26fc173-aef0-4ecc-9755-f8ec333b7ceb", "name": "Sticky Note1", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -1728, 544 ] }, { "parameters": { "operation": "download", "fileId": { "__rl": true, "value": "={{ $('Set File ID').item.json.file_id }}", "mode": "id" }, "options": { "googleFileConversion": { "conversion": { "docsToFormat": "text/plain" } } } }, "id": "9a032cbb-763e-406a-bcfe-0ccabcf2907e", "name": "Download File", "type": "n8n-nodes-base.googleDrive", "typeVersion": 3, "position": [ -656, 832 ], "executeOnce": true, "credentials": { "googleDriveOAuth2Api": { "name": "" } } }, { "parameters": { "pollTimes": { "item": [ { "mode": "everyMinute" } ] }, "triggerOn": "specificFolder", "folderToWatch": { "__rl": true, "value": "1uWfYKTYjExzm-duQc_U3Klu4B4igd_L5", "mode": "list", "cachedResultName": "Documetos Legales-1", "cachedResultUrl": "https://drive.google.com/drive/folders/1uWfYKTYjExzm-duQc_U3Klu4B4igd_L5" }, "event": "fileCreated", "options": {} }, "id": "7f85937e-ad04-425e-8e93-b846a4a414db", "name": "File Created", "type": "n8n-nodes-base.googleDriveTrigger", "typeVersion": 1, "position": [ -1680, 672 ], "credentials": { "googleDriveOAuth2Api": { "name": "" } } }, { "parameters": { "pollTimes": { "item": [ { "mode": "everyMinute" } ] }, "triggerOn": "specificFolder", "folderToWatch": { "__rl": true, "value": "1uWfYKTYjExzm-duQc_U3Klu4B4igd_L5", "mode": "list", "cachedResultName": "Documetos Legales", "cachedResultUrl": "https://drive.google.com/drive/folders/1uWfYKTYjExzm-duQc_U3Klu4B4igd_L5" }, "event": "fileUpdated", "options": {} }, "id": "087523f7-c3dd-4671-9828-f7f7969a25e8", "name": "File Updated", "type": "n8n-nodes-base.googleDriveTrigger", "typeVersion": 1, "position": [ -1680, 832 ], "credentials": { "googleDriveOAuth2Api": { "name": "" } } }, { "parameters": { "operation": "text", "options": {} }, "id": "8e993fd3-d088-4ead-b01b-6c289e78dc56", "name": "Extract Document Text", "type": "n8n-nodes-base.extractFromFile", "typeVersion": 1, "position": [ 208, 1152 ], "alwaysOutputData": true }, { "parameters": {}, "id": "483d194c-9519-4329-b4f9-3003854d9b11", "name": "Postgres Chat Memory", "type": "@n8n/n8n-nodes-langchain.memoryPostgresChat", "typeVersion": 1, "position": [ -608, 368 ], "notesInFlow": false, "credentials": { "postgres": { "name": "" } }, "disabled": true }, { "parameters": { "operation": "delete", "tableId": "documents", "filterType": "string", "filterString": "=metadata->>file_id=like.*{{ $json.file_id }}*" }, "id": "6ee97cde-e333-4c4c-a4fe-46278e759892", "name": "Delete Old Doc Rows", "type": "n8n-nodes-base.supabase", "typeVersion": 1, "position": [ -1136, 672 ], "alwaysOutputData": true, "credentials": { "supabaseApi": { "name": "" } } }, { "parameters": { "assignments": { "assignments": [ { "id": "10646eae-ae46-4327-a4dc-9987c2d76173", "name": "file_id", "value": "={{ $json.id }}", "type": "string" }, { "id": "f4536df5-d0b1-4392-bf17-b8137fb31a44", "name": "file_type", "value": "={{ $json.mimeType }}", "type": "string" }, { "id": "77d782de-169d-4a46-8a8e-a3831c04d90f", "name": "file_title", "value": "={{ $json.name }}", "type": "string" }, { "id": "9bde4d7f-e4f3-4ebd-9338-dce1350f9eab", "name": "file_url", "value": "={{ $json.webViewLink }}", "type": "string" } ] }, "options": {} }, "id": "de570c61-a9c0-4e82-831c-f376905de495", "name": "Set File ID", "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ -1328, 832 ] }, { "parameters": { "content": "## RAG AI Interfaz de chat\n", "height": 689, "width": 1036 }, "id": "eb6a6d87-b720-4b3d-9819-c201c26c5b31", "name": "Sticky Note2", "type": "n8n-nodes-base.stickyNote", "typeVersion": 1, "position": [ -1040, -160 ] }, { "parameters": { "options": {} }, "id": "0f88fb90-6fbb-41b9-9737-7891de8ad20b", "name": "Respond to Webhook", "type": "n8n-nodes-base.respondToWebhook", "typeVersion": 1.1, "position": [ -208, 80 ] }, { "parameters": { "assignments": { "assignments": [ { "id": "9a9a245e-f1a1-4282-bb02-a81ffe629f0f", "name": "chatInput", "value": "={{ $json.chatInput || $json.body.message || $json.data?.chatInput }}", "type": "string" }, { "id": "b80831d8-c653-4203-8706-adedfdb98f77", "name": "sessionId", "value": "={{ $json.sessionId || $json.body.sessionId || $json.data?.sessionId }}", "type": "string" } ] }, "includeOtherFields": true, "options": {} }, "id": "2bfd19b8-52c8-4f0d-ad65-16a144a6ec78", "name": "Edit Fields", "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ -784, 144 ] }, { "parameters": { "public": true, "options": {} }, "id": "a72bd515-3a17-4bb0-9df9-b39bbcfc5e6e", "name": "When chat message received", "type": "@n8n/n8n-nodes-langchain.chatTrigger", "typeVersion": 1.1, "position": [ -1008, 144 ] }, { "parameters": { "operation": "pdf", "options": {} }, "id": "3b7a2cb4-17e2-4a44-99f1-a74b79bbe96c", "name": "Extract PDF Text", "type": "n8n-nodes-base.extractFromFile", "typeVersion": 1, "position": [ 208, 592 ] }, { "parameters": { "aggregate": "aggregateAllItemData", "options": {} }, "id": "e30e9399-90f1-48f6-a2f4-8c51b758332f", "name": "Aggregate", "type": "n8n-nodes-base.aggregate", "typeVersion": 1, "position": [ 240, 768 ] }, { "parameters": {}, "id": "07553354-4172-4dae-867d-f97e6c18f6a0", "name": "Character Text Splitter", "type": "@n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter", "typeVersion": 1, "position": [ 912, 1264 ] }, { "parameters": { "fieldsToSummarize": { "values": [ { "aggregation": "concatenate", "field": "data" } ] }, "options": {} }, "id": "484caa4b-b6dc-49c5-9faf-a783285f2f1c", "name": "Summarize", "type": "n8n-nodes-base.summarize", "typeVersion": 1, "position": [ 448, 848 ] }, { "parameters": { "promptType": "define", "text": "={{ $json.chatInput || $json.body.chatInput || $json.body.query}}", "options": { "systemMessage": "=Eres un asistente legal especializado en derecho argentino que ayuda a responder consultas legales a partir de un corpus de documentos jur\u00eddicos. Los documentos pueden ser de texto (archivos .txt, .doc, PDFs extra\u00eddos, etc.) o datos tabulares (archivos CSV o Excel).\n\nTienes herramientas disponibles para:\n- Realizar RAG (b\u00fasqueda sem\u00e1ntica) en la tabla 'documents'\n- Consultar los documentos disponibles en tu base de conocimientos en la tabla 'document_metadata'\n- Extraer todo el texto de un documento espec\u00edfico\n- Consultar archivos tabulares con SQL en la tabla 'document_rows'\n\n**PROTOCOLO DE B\u00daSQUEDA:**\nSiempre comienza realizando RAG, a menos que la pregunta requiera una consulta SQL para datos tabulares (obtener una suma, encontrar un m\u00e1ximo, estad\u00edsticas, o algo para lo cual una b\u00fasqueda RAG ser\u00eda poco confiable). Si RAG no ayuda, entonces consulta qu\u00e9 documentos est\u00e1n disponibles, identifica algunos que creas que contendr\u00edan la respuesta, y luego analiza esos documentos completos.\n\n**REGLA FUNDAMENTAL:**\nSiempre debes decirle al usuario si no encontraste la respuesta. NUNCA inventes informaci\u00f3n solo para complacerlo. La honestidad sobre las limitaciones es fundamental en temas legales." } }, "id": "ce08ef27-33e5-4b65-90d7-13e41d10ff35", "name": "RAG AI Agent", "type": "@n8n/n8n-nodes-langchain.agent", "typeVersion": 1.6, "position": [ -528, 144 ] }, { "parameters": { "rules": { "values": [ { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 1 }, "conditions": [ { "leftValue": "={{ $('Set File ID').item.json.file_type }}", "rightValue": "application/pdf", "operator": { "type": "string", "operation": "equals" } } ], "combinator": "and" } }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 1 }, "conditions": [ { "id": "2ae7faa7-a936-4621-a680-60c512163034", "leftValue": "={{ $('Set File ID').item.json.file_type }}", "rightValue": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet", "operator": { "type": "string", "operation": "equals", "name": "filter.operator.equals" } } ], "combinator": "and" } }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 1 }, "conditions": [ { "id": "fc193b06-363b-4699-a97d-e5a850138b0e", "leftValue": "={{ $('Set File ID').item.json.file_type }}", "rightValue": "=application/vnd.google-apps.spreadsheet", "operator": { "type": "string", "operation": "equals", "name": "filter.operator.equals" } } ], "combinator": "and" } }, { "conditions": { "options": { "caseSensitive": true, "leftValue": "", "typeValidation": "strict", "version": 1 }, "conditions": [ { "id": "b69f5605-0179-4b02-9a32-e34bb085f82d", "leftValue": "={{ $('Set File ID').item.json.file_type }}", "rightValue": "application/vnd.google-apps.document", "operator": { "type": "string", "operation": "equals", "name": "filter.operator.equals" } } ], "combinator": "and" } } ] }, "options": { "fallbackOutput": 3 } }, "id": "c6aae001-25cc-4b99-b0cd-6cdb3dbc6283", "name": "Switch", "type": "n8n-nodes-base.switch", "typeVersion": 3, "position": [ -464, 800 ] }, { "parameters": { "mode": "insert", "tableName": { "__rl": true, "value": "documents", "mode": "list", "cachedResultName": "documents" }, "options": { "queryName": "match_documents" } }, "id": "567bc2ae-e79c-4a2b-b4c4-19fb04e7f025", "name": "Insert into Supabase Vectorstore", "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase", "typeVersion": 1, "position": [ 928, 928 ], "credentials": { "supabaseApi": { "name": "" } } }, { "parameters": { "operation": "xlsx", "options": {} }, "id": "4e3345d2-1d51-4a56-8532-6774847f75b9", "name": "Extract from Excel", "type": "n8n-nodes-base.extractFromFile", "typeVersion": 1, "position": [ 32, 768 ] }, { "parameters": { "assignments": { "assignments": [ { "id": "f422e2e0-381c-46ea-8f38-3f58c501d8b9", "name": "schema", "value": "={{ $('Extract from Excel').isExecuted ? $('Extract from Excel').first().json.keys().toJsonString() : $('Extract from CSV').first().json.keys().toJsonString() }}", "type": "string" }, { "id": "bb07c71e-5b60-4795-864c-cc3845b6bc46", "name": "data", "value": "={{ $json.concatenated_data }}", "type": "string" } ] }, "options": {} }, "type": "n8n-nodes-base.set", "typeVersion": 3.4, "position": [ 880, 704 ], "id": "b0fa670c-1016-4468-b041-97f5a2586718", "name": "Set Schema" }, { "parameters": { "options": {} }, "type": "n8n-nodes-base.extractFromFile", "typeVersion": 1, "position": [ 32, 944 ], "id": "86b9925a-c4a5-4f5d-9968-cb98c54ac24c", "name": "Extract from CSV" }, { "parameters": { "content": "## Antes de iniciar crear las siguientes tablas", "height": 300, "width": 680, "color": 3 }, "type": "n8n-nodes-base.stickyNote", "position": [ -1744, 224 ], "typeVersion": 1, "id": "26468d8d-b7dc-4197-a099-1487ff427be7", "name": "Sticky Note3" }, { "parameters": { "operation": "executeQuery", "query": "CREATE TABLE document_metadata (\n id TEXT PRIMARY KEY,\n title TEXT,\n url TEXT,\n created_at TIMESTAMP DEFAULT NOW(),\n schema TEXT\n);", "options": {} }, "type": "n8n-nodes-base.postgres", "typeVersion": 2.5, "position": [ -1488, 320 ], "id": "eed297ed-4916-4210-a80e-02a673f7ec03", "name": "Create Document Metadata Table", "credentials": { "postgres": { "name": "" } } }, { "parameters": { "operation": "executeQuery", "query": "CREATE TABLE document_rows (\n id SERIAL PRIMARY KEY,\n dataset_id TEXT REFERENCES document_metadata(id),\n row_data JSONB -- Store the actual row data\n);", "options": {} }, "type": "n8n-nodes-base.postgres", "typeVersion": 2.5, "position": [ -1264, 320 ], "id": "33620ac8-b30c-44cd-a7ab-ebd24210da0c", "name": "Create Document Rows Table (for Tabular Data)", "credentials": { "postgres": { "name": "" } } }, { "parameters": { "descriptionType": "manual", "toolDescription": "Use this tool to fetch all available documents, including the table schema if the file is a CSV or Excel file.", "operation": "select", "schema": { "__rl": true, "mode": "list", "value": "public" }, "table": { "__rl": true, "value": "document_metadata", "mode": "list", "cachedResultName": "document_metadata" }, "returnAll": true, "options": {} }, "type": "n8n-nodes-base.postgresTool", "typeVersion": 2.5, "position": [ -464, 368 ], "id": "494c9551-30c6-4759-bfd5-e460c61fa52b", "name": "List Documents", "credentials": { "postgres": { "name": "" } } }, { "parameters": { "descriptionType": "manual", "toolDescription": "Given a file ID, fetches the text from the document.", "operation": "executeQuery", "query": "SELECT \n string_agg(content, ' ') as document_text\nFROM documents\n WHERE metadata->>'file_id' = $1\nGROUP BY metadata->>'file_id';", "options": { "queryReplacement": "={{ $fromAI('file_id') }}" } }, "type": "n8n-nodes-base.postgresTool", "typeVersion": 2.5, "position": [ -320, 368 ], "id": "e9b7eefd-5914-4a62-ad98-0fa0851cd2ac", "name": "Get File Contents", "credentials": { "postgres": { "name": "" } } }, { "parameters": { "descriptionType": "manual", "toolDescription": "Run a SQL query - use this to query from the document_rows table once you know the file ID you are querying. dataset_id is the file_id and you are always using the row_data for filtering, which is a jsonb field that has all the keys from the file schema given in the document_metadata table.\n\nExample query:\n\nSELECT AVG((row_data->>'revenue')::numeric)\nFROM document_rows\nWHERE dataset_id = '123';\n\nExample query 2:\n\nSELECT \n row_data->>'category' as category,\n SUM((row_data->>'sales')::numeric) as total_sales\nFROM dataset_rows\nWHERE dataset_id = '123'\nGROUP BY row_data->>'category';", "operation": "executeQuery", "query": "{{ $fromAI('sql_query') }}", "options": {} }, "type": "n8n-nodes-base.postgresTool", "typeVersion": 2.5, "position": [ -160, 368 ], "id": "89203baa-b741-4728-b44b-a0c8ef35bcec", "name": "Query Document Rows", "credentials": { "postgres": { "name": "" } } }, { "parameters": { "mode": "retrieve-as-tool", "toolName": "documents", "toolDescription": "Use RAG to look up information in the knowledgebase.", "tableName": { "__rl": true, "value": "documents", "mode": "list", "cachedResultName": "documents" }, "options": { "queryName": "match_documents" } }, "type": "@n8n/n8n-nodes-langchain.vectorStoreSupabase", "typeVersion": 1, "position": [ 288, 256 ], "id": "0f386875-1b43-4dc9-9533-c2d1c57f5af9", "name": "Supabase Vector Store1", "credentials": { "supabaseApi": { "name": "" } } }, { "parameters": { "options": {} }, "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi", "typeVersion": 1.2, "position": [ 80, 384 ], "id": "66e0af94-2e69-4fc5-9037-3540b6b907cd", "name": "Embeddings OpenAI2", "credentials": { "openAiApi": { "name": "" } } }, { "parameters": { "options": { "reset": false } }, "type": "n8n-nodes-base.splitInBatches", "typeVersion": 3, "position": [ -1504, 672 ], "id": "86518252-8eb5-4576-9f71-c1fc99811e85", "name": "Loop Over Items" }, { "parameters": { "operation": "executeQuery", "query": "-- Enable the pgvector extension to work with embedding vectors\ncreate extension vector;\n\n-- Create a table to store your documents\ncreate table documents (\n id bigserial primary key,\n content text, -- corresponds to Document.pageContent\n metadata jsonb, -- corresponds to Document.metadata\n embedding vector(1536) -- 1536 works for OpenAI embeddings, change if needed\n);\n\n-- Create a function to search for documents\ncreate function match_documents (\n query_embedding vector(1536),\n match_count int default null,\n filter jsonb DEFAULT '{}'\n) returns table (\n id bigint,\n content text,\n metadata jsonb,\n similarity float\n)\nlanguage plpgsql\nas $$\n#variable_conflict use_column\nbegin\n return query\n select\n id,\n content,\n metadata,\n 1 - (documents.embedding <=> query_embedding) as similarity\n from documents\n where metadata @> filter\n order by documents.embedding <=> query_embedding\n limit match_count;\nend;\n$$;", "options": {} }, "type": "n8n-nodes-base.postgres", "typeVersion": 2.5, "position": [ -1680, 320 ], "id": "7fda4a49-ed24-4575-b410-302280bc53a6", "name": "Create Documents Table and Match Function", "credentials": { "postgres": { "name": "" } } }, { "parameters": { "operation": "delete", "tableId": "document_rows", "filters": { "conditions": [ { "keyName": "dataset_id", "condition": "eq", "keyValue": "={{ $('Set File ID').item.json.file_id }}" } ] } }, "type": "n8n-nodes-base.supabase", "typeVersion": 1, "position": [ -976, 832 ], "id": "b34e5974-ff35-4426-9bd0-bd63c5af7382", "name": "Delete Old Data Rows", "alwaysOutputData": true, "executeOnce": true, "credentials": { "supabaseApi": { "name": "" } } }, { "parameters": { "operation": "upsert", "schema": { "__rl": true, "mode": "list", "value": "public" }, "table": { "__rl": true, "value": "document_metadata", "mode": "list", "cachedResultName": "document_metadata" }, "columns": { "mappingMode": "defineBelow", "value": { "id": "={{ $('Set File ID').item.json.file_id }}", "title": "={{ $('Set File ID').item.json.file_title }}", "url": "={{ $('Set File ID').item.json.file_url }}" }, "matchingColumns": [ "id" ], "schema": [ { "id": "id", "displayName": "id", "required": true, "defaultMatch": true, "display": true, "type": "string", "canBeUsedToMatch": true, "removed": false }, { "id": "title", "displayName": "title", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": false }, { "id": "url", "displayName": "url", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": false, "removed": false }, { "id": "created_at", "displayName": "created_at", "required": false, "defaultMatch": false, "display": true, "type": "dateTime", "canBeUsedToMatch": false }, { "id": "schema", "displayName": "schema", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": false, "removed": true } ], "attemptToConvertTypes": false, "convertFieldsToString": false }, "options": {} }, "type": "n8n-nodes-base.postgres", "typeVersion": 2.5, "position": [ -816, 688 ], "id": "6ddf0921-fdff-43aa-a1c7-5e2837892919", "name": "Insert Document Metadata", "executeOnce": true, "credentials": { "postgres": { "name": "" } } }, { "parameters": { "schema": { "__rl": true, "mode": "list", "value": "public" }, "table": { "__rl": true, "value": "document_rows", "mode": "list", "cachedResultName": "document_rows" }, "columns": { "mappingMode": "defineBelow", "value": { "dataset_id": "={{ $('Set File ID').item.json.file_id }}", "row_data": "={{ $json.toJsonString().replaceAll(/'/g, \"''\") }}" }, "matchingColumns": [ "id" ], "schema": [ { "id": "id", "displayName": "id", "required": false, "defaultMatch": true, "display": true, "type": "number", "canBeUsedToMatch": true, "removed": true }, { "id": "dataset_id", "displayName": "dataset_id", "required": false, "defaultMatch": false, "display": true, "type": "string", "canBeUsedToMatch": true }, { "id": "row_data", "displayName": "row_data", "required": false, "defaultMatch": false, "display": true, "type": "object", "canBeUsedToMatch": true } ], "attemptToConvertTypes": false, "convertFieldsToString": false }, "options": {} }, "type": "n8n-nodes-base.postgres", "typeVersion": 2.5, "position": [ 240, 944 ], "id": "0905cf2b-75bd-4d8d-82e3-fd9fe608cfb0", "name": "Insert Table Rows", "credentials": { "postgres": { "name": "" } } }, { "parameters": { "operation": "upsert", "schema": { "__rl": true, "mode": "list", "value": "public" }, "table": { "__rl": true, "value": "document_metadata", "mode": "list", "cachedResultName": "document_metadata" }, "columns": { "mappingMode": "defineBelow", "value": { "id": "={{ $('Set File ID').item.json.file_id }}", "schema": "={{ $json.schema }}" }, "matchingColumns": [ "id" ], "schema": [ { "id": "id", "displayName": "id", "required": true, "defaultMatch": true, "display": true, "type": "string", "canBeUsedToMatch": true, "removed": false }, { "id": "title", "displayName": "title", "required": false, 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\n\u00bfQu\u00e9 es esto? \ud83e\udd14\nEsta plantilla proporciona una implementaci\u00f3n completa de un sistema RAG Ag\u00e9ntico (Generaci\u00f3n Aumentada por Recuperaci\u00f3n) en n8n que puede extenderse f\u00e1cilmente para tu caso de uso espec\u00edfico y base de conocimiento. A diferencia del RAG est\u00e1ndar que solo realiza b\u00fasquedas simples, este agente puede razonar sobre tu base de conocimiento, auto-mejorar la recuperaci\u00f3n y cambiar din\u00e1micamente entre diferentes herramientas seg\u00fan la pregunta espec\u00edfica.\n\u00bfPor qu\u00e9 RAG Ag\u00e9ntico? \ud83d\udca1\nEl RAG est\u00e1ndar tiene limitaciones significativas:\n\nAn\u00e1lisis deficiente de datos num\u00e9ricos/tabulares \ud83d\udcca\nP\u00e9rdida de contexto debido a la fragmentaci\u00f3n de documentos \ud83d\udcc4\nIncapacidad para conectar informaci\u00f3n entre documentos \ud83d\udd17\nSin selecci\u00f3n din\u00e1mica de herramientas seg\u00fan el tipo de pregunta \u274c\n\n\u00bfQu\u00e9 hace poderosa a esta plantilla? \u26a1\n\nSelecci\u00f3n inteligente de herramientas: Cambia entre b\u00fasquedas RAG, consultas SQL o recuperaci\u00f3n completa de documentos seg\u00fan la pregunta \ud83c\udfaf\nContexto completo del documento: Accede a documentos enteros cuando es necesario en lugar de solo fragmentos \ud83d\udcda\nAn\u00e1lisis num\u00e9rico preciso: Usa SQL para c\u00e1lculos precisos en datos de hojas de c\u00e1lculo/tabulares \ud83d\udd22\nInsights entre documentos: Conecta informaci\u00f3n a trav\u00e9s de toda tu base de conocimiento \ud83c\udf10\nProcesamiento multi-archivo: Maneja m\u00faltiples documentos en un solo bucle de flujo de trabajo \ud83d\udcc1\nAlmacenamiento eficiente: Usa JSONB en Supabase para almacenar datos tabulares sin crear nuevas tablas para cada CSV \ud83d\udcbe\n\nPrimeros Pasos \ud83c\udfc1\n\nEjecuta primero los nodos de creaci\u00f3n de tablas para configurar tus tablas de base de datos en Supabase\nSube tus documentos a trav\u00e9s de Google Drive (o c\u00e1mbialo por una soluci\u00f3n diferente de almacenamiento de archivos)\nEl agente los procesar\u00e1 autom\u00e1ticamente (fragmentando texto, almacenando datos tabulares en Supabase)\nComienza a hacer preguntas que aprovechen los m\u00faltiples enfoques de razonamiento del agente\n\nPersonalizaci\u00f3n \ud83c\udfa8\nEsta plantilla proporciona una base s\u00f3lida que puedes extender:\n\nAjustando el prompt del sistema para tu caso de uso espec\u00edfico \u2699\ufe0f\nAgregando metadatos de documentos como res\u00famenes \ud83d\udcdd\nImplementando t\u00e9cnicas RAG m\u00e1s avanzadas \ud83d\ude80\nOptimizando para bases de conocimiento m\u00e1s grandes \ud83d\udcc8", "height": 1192, "width": 540, "color": 6 }, "type": "n8n-nodes-base.stickyNote", "position": [ -2464, 208 ], "typeVersion": 1, "id": "951402d5-a265-4ca4-af0b-1878227ababe", "name": "Sticky Note9" }, { "parameters": { "inputSource": "passthrough" }, "type": "n8n-nodes-base.executeWorkflowTrigger", "typeVersion": 1.1, "position": [ -1008, -48 ], "id": "336b7705-eb7d-401b-a4eb-edc4e48a1824", "name": "When Executed by Another Workflow" }, { "parameters": { "httpMethod": "POST", "path": "rag-agent-webhook", "responseMode": "responseNode", "options": {} }, "type": 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"node": "Delete Old Data Rows", "type": "main", "index": 0 } ] ] }, "Set File ID": { "main": [ [ { "node": "Delete Old Doc Rows", "type": "main", "index": 0 } ] ] }, "File Updated": { "main": [ [ { "node": "Loop Over Items", "type": "main", "index": 0 } ] ] }, "Edit Fields": { "main": [ [ { "node": "RAG AI Agent", "type": "main", "index": 0 } ] ] }, "When chat message received": { "main": [ [ { "node": "Edit Fields", "type": "main", "index": 0 } ] ] }, "Extract PDF Text": { "main": [ [ { "node": "Insert into Supabase Vectorstore", "type": "main", "index": 0 } ] ] }, "Aggregate": { "main": [ [ { "node": "Summarize", "type": "main", "index": 0 } ] ] }, "Character Text Splitter": { "ai_textSplitter": [ [ { "node": "Default Data Loader", "type": "ai_textSplitter", "index": 0 } ] ] }, "Summarize": { "main": [ [ { "node": "Set Schema", "type": "main", "index": 0 }, { "node": "Insert into Supabase Vectorstore", "type": "main", "index": 0 } ] ] }, "RAG AI Agent": { "main": [ [ { "node": 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