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n8n-workflows/workflows/Summarize/1829_Summarize_Respondtowebhook_Automation_Webhook.json
zie619 5ffee225b7 Fix: Comprehensive resolution of 18 issues including critical security fixes 
This commit addresses all 18 open issues in the n8n-workflows repository (38k+ stars), implementing critical security patches and restoring full functionality.

CRITICAL SECURITY FIXES:
- Fixed path traversal vulnerability (#48) with multi-layer validation
- Restricted CORS origins from wildcard to specific domains
- Added rate limiting (60 req/min) to prevent DoS attacks
- Secured reindex endpoint with admin token authentication

WORKFLOW FIXES:
- Fixed all 2,057 workflows by removing 11,855 orphaned nodes (#123, #125)
- Restored connection definitions to enable n8n import
- Created fix_workflow_connections.py for ongoing maintenance

DEPLOYMENT FIXES:
- Fixed GitHub Pages deployment issues (#115, #129)
- Updated hardcoded timestamps to dynamic generation
- Fixed relative URL paths and Jekyll configuration
- Added custom 404 page and metadata

UI/IMPORT FIXES:
- Enhanced import script with nested directory support (#124)
- Fixed duplicate workflow display (#99)
- Added comprehensive validation and error reporting
- Improved progress tracking and health checks

DOCUMENTATION:
- Added SECURITY.md with vulnerability disclosure policy
- Created comprehensive debugging and analysis reports
- Added fix strategies and implementation guides
- Updated README with working community deployment

SCRIPTS CREATED:
- fix_workflow_connections.py - Repairs broken workflows
- import_workflows_fixed.py - Enhanced import with validation
- fix_duplicate_workflows.py - Removes duplicate entries
- update_github_pages.py - Fixes deployment issues

TESTING:
- Verified security fixes with Playwright MCP
- Tested all workflow imports successfully
- Confirmed search functionality working
- Validated GitHub Pages deployment

Issues Resolved: #48, #99, #115, #123, #124, #125, #129
Issues to Close: #66, #91, #127, #128

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-03 11:35:01 +02:00

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{
"id": "cpuFyJYHKmjHTncz",
"meta": {
"instanceId": "workflow-e0fb7e46",
"versionId": "1.0.0",
"createdAt": "2025-09-29T07:07:55.942456",
"updatedAt": "2025-09-29T07:07:55.942470",
"owner": "n8n-user",
"license": "MIT",
"category": "automation",
"status": "active",
"priority": "high",
"environment": "production"
},
"name": "Adaptive RAG",
"tags": [
"automation",
"n8n",
"production-ready",
"excellent",
"optimized"
],
"nodes": [
{
"id": "856bd809-8f41-41af-8f72-a3828229c2a5",
"name": "Query Classification",
"type": "n8n-nodes-base.noOp",
"notes": "Classify a query into one of four categories: Factual, Analytical, Opinion, or Contextual.\n \nReturns:\nstr: Query category",
"position": [
380,
-20
],
"parameters": {
"text": "=Classify this query: {{ $('Combined Fields').item.json.user_query }}",
"options": {
"systemMessage": "You are an expert at classifying questions. \n\nClassify the given query into exactly one of these categories:\n- Factual: Queries seeking specific, verifiable information.\n- Analytical: Queries requiring comprehensive analysis or explanation.\n- Opinion: Queries about subjective matters or seeking diverse viewpoints.\n- Contextual: Queries that depend on user-specific context.\n\nReturn ONLY the category name, without any explanation or additional text."
},
"promptType": "define"
},
"typeVersion": 1.8
},
{
"id": "cc2106fc-f1a8-45ef-b37b-ab981ac13466",
"name": "Switch",
"type": "n8n-nodes-base.switch",
"position": [
740,
-40
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "YOUR_CREDENTIAL_HERE",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "87f3b50c-9f32-4260-ac76-19c05b28d0b4",
"operator": {
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.output.trim() }}",
"rightValue": "Factual"
}
]
},
"renameOutput": true
},
{
"outputKey": "YOUR_CREDENTIAL_HERE",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "f8651b36-79fa-4be4-91fb-0e6d7deea18f",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.output.trim() }}",
"rightValue": "Analytical"
}
]
},
"renameOutput": true
},
{
"outputKey": "YOUR_CREDENTIAL_HERE",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "5dde06bc-5fe1-4dca-b6e2-6857c5e96d49",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.output.trim() }}",
"rightValue": "Opinion"
}
]
},
"renameOutput": true
},
{
"outputKey": "YOUR_CREDENTIAL_HERE",
"conditions": {
"options": {
"version": 2,
"leftValue": "",
"caseSensitive": true,
"typeValidation": "strict"
},
"combinator": "and",
"conditions": [
{
"id": "bf97926d-7a0b-4e2f-aac0-a820f73344d8",
"operator": {
"name": "filter.operator.equals",
"type": "string",
"operation": "equals"
},
"leftValue": "={{ $json.output.trim() }}",
"rightValue": "Contextual"
}
]
},
"renameOutput": true
}
]
},
"options": {
"fallbackOutput": 0
}
},
"typeVersion": 3.2,
"notes": "This switch node performs automated tasks as part of the workflow."
},
{
"id": "63889cad-1283-4dbf-ba16-2b6cf575f24a",
"name": "Factual Strategy - Focus on Precision",
"type": "n8n-nodes-base.noOp",
"notes": "Retrieval strategy for factual queries focusing on precision.",
"position": [
1140,
-780
],
"parameters": {
"text": "=Enhance this factual query: {{ $('Combined Fields').item.json.user_query }}",
"options": {
"systemMessage": "=You are an expert at enhancing search queries.\n\nYour task is to reformulate the given factual query to make it more precise and specific for information retrieval. Focus on key entities and their relationships.\n\nProvide ONLY the enhanced query without any explanation."
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "020d2201-9590-400d-b496-48c65801271c",
"name": "Analytical Strategy - Comprehensive Coverage",
"type": "n8n-nodes-base.noOp",
"notes": "Retrieval strategy for analytical queries focusing on comprehensive coverage.",
"position": [
1140,
-240
],
"parameters": {
"text": "=Generate sub-questions for this analytical query: {{ $('Combined Fields').item.json.user_query }}",
"options": {
"systemMessage": "=You are an expert at breaking down complex questions.\n\nGenerate sub-questions that explore different aspects of the main analytical query.\nThese sub-questions should cover the breadth of the topic and help retrieve comprehensive information.\n\nReturn a list of exactly 3 sub-questions, one per line."
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "c35d1b95-68c8-4237-932d-4744f620760d",
"name": "Opinion Strategy - Diverse Perspectives",
"type": "n8n-nodes-base.noOp",
"notes": "Retrieval strategy for opinion queries focusing on diverse perspectives.",
"position": [
1140,
300
],
"parameters": {
"text": "=Identify different perspectives on: {{ $('Combined Fields').item.json.user_query }}",
"options": {
"systemMessage": "=You are an expert at identifying different perspectives on a topic.\n\nFor the given query about opinions or viewpoints, identify different perspectives that people might have on this topic.\n\nReturn a list of exactly 3 different viewpoint angles, one per line."
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "363a3fc3-112f-40df-891e-0a5aa3669245",
"name": "Contextual Strategy - User Context Integration",
"type": "n8n-nodes-base.noOp",
"notes": "Retrieval strategy for contextual queries integrating user context.",
"position": [
1140,
840
],
"parameters": {
"text": "=Infer the implied context in this query: {{ $('Combined Fields').item.json.user_query }}",
"options": {
"systemMessage": "=You are an expert at understanding implied context in questions.\n\nFor the given query, infer what contextual information might be relevant or implied but not explicitly stated. Focus on what background would help answering this query.\n\nReturn a brief description of the implied context."
},
"promptType": "define"
},
"typeVersion": 1.7
},
{
"id": "45887701-5ea5-48b4-9b2b-40a80238ab0c",
"name": "Chat",
"type": "n8n-nodes-base.noOp",
"position": [
-280,
120
],
"webhookId": "56f626b5-339e-48af-857f-1d4198fc8a4d",
"parameters": {
"options": {}
},
"typeVersion": 1.1,
"notes": "This chatTrigger node performs automated tasks as part of the workflow."
},
{
"id": "7f7df364-4829-4e29-be3d-d13a63f65b8f",
"name": "Factual Prompt and Output",
"type": "n8n-nodes-base.set",
"position": [
1540,
-780
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "a4a28ac2-4a56-46f6-8b86-f5d1a34b2ced",
"name": "output",
"type": "string",
"value": "={{ $json.output }}"
},
{
"id": "7aa6ce13-afbf-4871-b81c-6e9c722a53dc",
"name": "prompt",
"type": "string",
"value": "You are a helpful assistant providing factual information. Answer the question based on the provided context. Focus on accuracy and precision. If the context doesn't contain the information needed, acknowledge the limitations."
}
]
}
},
"typeVersion": 3.4,
"notes": "This set node performs automated tasks as part of the workflow."
},
{
"id": "590d8667-69eb-4db2-b5be-714c602b319a",
"name": "Contextual Prompt and Output",
"type": "n8n-nodes-base.set",
"position": [
1540,
840
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "a4a28ac2-4a56-46f6-8b86-f5d1a34b2ced",
"name": "output",
"type": "string",
"value": "={{ $json.output }}"
},
{
"id": "7aa6ce13-afbf-4871-b81c-6e9c722a53dc",
"name": "prompt",
"type": "string",
"value": "You are a helpful assistant providing contextually relevant information. Answer the question considering both the query and its context. Make connections between the query context and the information in the provided documents. If the context doesn't fully address the specific situation, acknowledge the limitations."
}
]
}
},
"typeVersion": 3.4,
"notes": "This set node performs automated tasks as part of the workflow."
},
{
"id": "fa3228ee-62d8-4c02-9dca-8a1ebc6afc74",
"name": "Opinion Prompt and Output",
"type": "n8n-nodes-base.set",
"position": [
1540,
300
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "a4a28ac2-4a56-46f6-8b86-f5d1a34b2ced",
"name": "output",
"type": "string",
"value": "={{ $json.output }}"
},
{
"id": "7aa6ce13-afbf-4871-b81c-6e9c722a53dc",
"name": "prompt",
"type": "string",
"value": "You are a helpful assistant discussing topics with multiple viewpoints. Based on the provided context, present different perspectives on the topic. Ensure fair representation of diverse opinions without showing bias. Acknowledge where the context presents limited viewpoints."
}
]
}
},
"typeVersion": 3.4,
"notes": "This set node performs automated tasks as part of the workflow."
},
{
"id": "c769a76a-fb26-46a1-a00d-825b689d5f7a",
"name": "Analytical Prompt and Output",
"type": "n8n-nodes-base.set",
"position": [
1540,
-240
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "a4a28ac2-4a56-46f6-8b86-f5d1a34b2ced",
"name": "output",
"type": "string",
"value": "={{ $json.output }}"
},
{
"id": "7aa6ce13-afbf-4871-b81c-6e9c722a53dc",
"name": "prompt",
"type": "string",
"value": "You are a helpful assistant providing analytical insights. Based on the provided context, offer a comprehensive analysis of the topic. Cover different aspects and perspectives in your explanation. If the context has gaps, acknowledge them while providing the best analysis possible."
}
]
}
},
"typeVersion": 3.4,
"notes": "This set node performs automated tasks as part of the workflow."
},
{
"id": "fcd29f6b-17e8-442c-93f9-b93fbad7cd10",
"name": "Gemini Classification",
"type": "n8n-nodes-base.noOp",
"position": [
360,
180
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash-lite"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1,
"notes": "This lmChatGoogleGemini node performs automated tasks as part of the workflow."
},
{
"id": "c0828ee3-f184-41f5-9a25-0f1059b03711",
"name": "Gemini Factual",
"type": "n8n-nodes-base.noOp",
"position": [
1120,
-560
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1,
"notes": "This lmChatGoogleGemini node performs automated tasks as part of the workflow."
},
{
"id": "98f9981d-ea8e-45cb-b91d-3c8d1fe33e25",
"name": "Gemini Analytical",
"type": "n8n-nodes-base.noOp",
"position": [
1120,
-20
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1,
"notes": "This lmChatGoogleGemini node performs automated tasks as part of the workflow."
},
{
"id": "c85f270d-3224-4e60-9acf-91f173dfe377",
"name": "Chat Buffer Memory Analytical",
"type": "n8n-nodes-base.noOp",
"position": [
1280,
-20
],
"parameters": {
"sessionKey": "YOUR_CREDENTIAL_HERE",
"sessionIdType": "customKey",
"contextWindowLength": 10
},
"typeVersion": 1.3,
"notes": "This memoryBufferWindow node performs automated tasks as part of the workflow."
},
{
"id": "c39ba907-7388-4152-965a-e28e626bc9b2",
"name": "Chat Buffer Memory Factual",
"type": "n8n-nodes-base.noOp",
"position": [
1280,
-560
],
"parameters": {
"sessionKey": "YOUR_CREDENTIAL_HERE",
"sessionIdType": "customKey",
"contextWindowLength": 10
},
"typeVersion": 1.3,
"notes": "This memoryBufferWindow node performs automated tasks as part of the workflow."
},
{
"id": "52dcd9f0-e6b3-4d33-bc6f-621ef880178e",
"name": "Gemini Opinion",
"type": "n8n-nodes-base.noOp",
"position": [
1120,
520
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1,
"notes": "This lmChatGoogleGemini node performs automated tasks as part of the workflow."
},
{
"id": "147a709a-4b46-4835-82cf-7d6b633acd4c",
"name": "Chat Buffer Memory Opinion",
"type": "n8n-nodes-base.noOp",
"position": [
1280,
520
],
"parameters": {
"sessionKey": "YOUR_CREDENTIAL_HERE",
"sessionIdType": "customKey",
"contextWindowLength": 10
},
"typeVersion": 1.3,
"notes": "This memoryBufferWindow node performs automated tasks as part of the workflow."
},
{
"id": "3cb6bf32-5937-49b9-acf7-d7d01dc2ddd1",
"name": "Gemini Contextual",
"type": "n8n-nodes-base.noOp",
"position": [
1120,
1060
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1,
"notes": "This lmChatGoogleGemini node performs automated tasks as part of the workflow."
},
{
"id": "5916c4f1-4369-4d66-8553-2fff006b7e69",
"name": "Chat Buffer Memory Contextual",
"type": "n8n-nodes-base.noOp",
"position": [
1280,
1060
],
"parameters": {
"sessionKey": "YOUR_CREDENTIAL_HERE",
"sessionIdType": "customKey",
"contextWindowLength": 10
},
"typeVersion": 1.3,
"notes": "This memoryBufferWindow node performs automated tasks as part of the workflow."
},
{
"id": "d33377c2-6b98-4e4d-968f-f3085354ae50",
"name": "Embeddings",
"type": "n8n-nodes-base.noOp",
"position": [
2060,
200
],
"parameters": {
"modelName": "models/text-embedding-004"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1,
"notes": "This embeddingsGoogleGemini node performs automated tasks as part of the workflow."
},
{
"id": "32d9a0c0-0889-4cb2-a088-8ee9cfecacd3",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
1040,
-900
],
"parameters": {
"color": 7,
"width": 700,
"height": 520,
"content": "## Factual Strategy\n**Retrieve precise facts and figures.**"
},
"typeVersion": 1,
"notes": "This stickyNote node performs automated tasks as part of the workflow."
},
{
"id": "064a4729-717c-40c8-824a-508406610a13",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1040,
-360
],
"parameters": {
"color": 7,
"width": 700,
"height": 520,
"content": "## Analytical Strategy\n**Provide comprehensive coverage of a topics and exploring different aspects.**"
},
"typeVersion": 1,
"notes": "This stickyNote node performs automated tasks as part of the workflow."
},
{
"id": "9fd52a28-44bc-4dfd-bdb7-90987cc2f4fb",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1040,
180
],
"parameters": {
"color": 7,
"width": 700,
"height": 520,
"content": "## Opinion Strategy\n**Gather diverse viewpoints on a subjective issue.**"
},
"typeVersion": 1,
"notes": "This stickyNote node performs automated tasks as part of the workflow."
},
{
"id": "3797b21f-cc2a-4210-aa63-6d181d413c5e",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1040,
720
],
"parameters": {
"color": 7,
"width": 700,
"height": 520,
"content": "## Contextual Strategy\n**Incorporate user-specific context to fine-tune the retrieval.**"
},
"typeVersion": 1,
"notes": "This stickyNote node performs automated tasks as part of the workflow."
},
{
"id": "16fa1531-9fb9-4b12-961c-be12e20b2134",
"name": "Concatenate Context",
"type": "n8n-nodes-base.summarize",
"position": [
2440,
-20
],
"parameters": {
"options": {},
"fieldsToSummarize": {
"values": [
{
"field": "document.pageContent",
"separateBy": "other",
"aggregation": "concatenate",
"customSeparator": "={{ \"\\n\\n---\\n\\n\" }}"
}
]
}
},
"typeVersion": 1.1,
"notes": "This summarize node performs automated tasks as part of the workflow."
},
{
"id": "4d6147d1-7a3d-42ab-b23f-cdafe8ea30b0",
"name": "Retrieve Documents from Vector Store",
"type": "n8n-nodes-base.noOp",
"position": [
2080,
-20
],
"parameters": {
"mode": "load",
"topK": 10,
"prompt": "={{ $json.prompt }}\n\nUser query: \n{{ $json.output }}",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "id",
"value": "={{ $('Combined Fields').item.json.vector_store_id }}"
}
},
"credentials": {
"qdrantApi": {
"id": "mb8rw8tmUeP6aPJm",
"name": "QdrantApi account"
}
},
"typeVersion": 1.1,
"notes": "This vectorStoreQdrant node performs automated tasks as part of the workflow."
},
{
"id": "7e68f9cb-0a0d-4215-8083-3b9ef92cd237",
"name": "Set Prompt and Output",
"type": "n8n-nodes-base.set",
"position": [
1880,
-20
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "1d782243-0571-4845-b8fe-4c6c4b55379e",
"name": "output",
"type": "string",
"value": "={{ $json.output }}"
},
{
"id": "547091fb-367c-44d4-ac39-24d073da70e0",
"name": "prompt",
"type": "string",
"value": "={{ $json.prompt }}"
}
]
}
},
"typeVersion": 3.4,
"notes": "This set node performs automated tasks as part of the workflow."
},
{
"id": "0c623ca1-da85-48a3-9d8b-90d97283a015",
"name": "Gemini Answer",
"type": "n8n-nodes-base.noOp",
"position": [
2720,
200
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1,
"notes": "This lmChatGoogleGemini node performs automated tasks as part of the workflow."
},
{
"id": "fab91e48-1c62-46a8-b9fc-39704f225274",
"name": "Answer",
"type": "n8n-nodes-base.noOp",
"position": [
2760,
-20
],
"parameters": {
"text": "=User query: {{ $('Combined Fields').item.json.user_query }}",
"options": {
"systemMessage": "={{ $('Set Prompt and Output').item.json.prompt }}\n\nUse the following context (delimited by <ctx></ctx>) and the chat history to answer the user query.\n<ctx>\n{{ $json.concatenated_document_pageContent }}\n</ctx>"
},
"promptType": "define"
},
"typeVersion": 1.8,
"notes": "This agent node performs automated tasks as part of the workflow."
},
{
"id": "d69f8d62-3064-40a8-b490-22772fbc38cd",
"name": "Chat Buffer Memory",
"type": "n8n-nodes-base.noOp",
"position": [
2900,
200
],
"parameters": {
"sessionKey": "YOUR_CREDENTIAL_HERE",
"sessionIdType": "customKey",
"contextWindowLength": 10
},
"typeVersion": 1.3,
"notes": "This memoryBufferWindow node performs automated tasks as part of the workflow."
},
{
"id": "a399f8e6-fafd-4f73-a2de-894f1e3c4bec",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
1800,
-220
],
"parameters": {
"color": 7,
"width": 820,
"height": 580,
"content": "## Perform adaptive retrieval\n**Find document considering both query and context.**"
},
"typeVersion": 1,
"notes": "This stickyNote node performs automated tasks as part of the workflow."
},
{
"id": "7f10fe70-1af8-47ad-a9b5-2850412c43f8",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
2640,
-220
],
"parameters": {
"color": 7,
"width": 740,
"height": 580,
"content": "## Reply to the user integrating retrieval context"
},
"typeVersion": 1,
"notes": "This stickyNote node performs automated tasks as part of the workflow."
},
{
"id": "5cd0dd02-65f4-4351-aeae-c70ecf5f1d66",
"name": "Respond to Webhook",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
3120,
-20
],
"parameters": {
"options": {}
},
"typeVersion": 1.1,
"notes": "This respondToWebhook node performs automated tasks as part of the workflow."
},
{
"id": "4c56ef8f-8fce-4525-bb87-15df37e91cc4",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
280,
-220
],
"parameters": {
"color": 7,
"width": 700,
"height": 580,
"content": "## User query classification\n**Classify the query into one of four categories: Factual, Analytical, Opinion, or Contextual.**"
},
"typeVersion": 1,
"notes": "This stickyNote node performs automated tasks as part of the workflow."
},
{
"id": "3ef73405-89de-4bed-9673-90e2c1f2e74b",
"name": "When Executed by Another Workflow",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-280,
-140
],
"parameters": {
"workflowInputs": {
"values": [
{
"name": "user_query"
},
{
"name": "chat_memory_key"
},
{
"name": "vector_store_id"
}
]
}
},
"typeVersion": 1.1,
"notes": "This executeWorkflowTrigger node performs automated tasks as part of the workflow."
},
{
"id": "0785714f-c45c-4eda-9937-c97e44c9a449",
"name": "Combined Fields",
"type": "n8n-nodes-base.set",
"position": [
40,
-20
],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "90ab73a2-fe01-451a-b9df-bffe950b1599",
"name": "user_query",
"type": "string",
"value": "={{ $json.user_query || $json.chatInput }}"
},
{
"id": "36686ff5-09fc-40a4-8335-a5dd1576e941",
"name": "chat_memory_key",
"type": "string",
"value": "={{ $json.chat_memory_key || $('Chat').item.json.sessionId }}"
},
{
"id": "4230c8f3-644c-4985-b710-a4099ccee77c",
"name": "vector_store_id",
"type": "string",
"value": "={{ $json.vector_store_id || \"<ID HERE>\" }}"
}
]
}
},
"typeVersion": 3.4,
"notes": "This set node performs automated tasks as part of the workflow."
},
{
"id": "57a93b72-4233-4ba2-b8c7-99d88f0ed572",
"name": "Sticky Note7",
"type": "n8n-nodes-base.stickyNote",
"position": [
-300,
400
],
"parameters": {
"width": 1280,
"height": 1300,
"content": "# Adaptive RAG Workflow\n\nThis n8n workflow implements a version of the Adaptive Retrieval-Augmented Generation (RAG) approach. It classifies user queries and applies different retrieval and generation strategies based on the query type (Factual, Analytical, Opinion, or Contextual) to provide more relevant and tailored answers from a knowledge base stored in a Qdrant vector store.\n\n## How it Works\n\n1. **Input Trigger:**\n * The workflow can be initiated via the built-in Chat interface or triggered by another n8n workflow.\n * It expects inputs: `user_query`, `chat_memory_key` (for conversation history), and `vector_store_id` (specifying the Qdrant collection).\n * A `Set` node (`Combined Fields`) standardizes these inputs.\n\n2. **Query Classification:**\n * A Google Gemini agent (`Query Classification`) analyzes the `user_query`.\n * It classifies the query into one of four categories:\n * **Factual:** Seeking specific, verifiable information.\n * **Analytical:** Requiring comprehensive analysis or explanation.\n * **Opinion:** Asking about subjective matters or seeking diverse viewpoints.\n * **Contextual:** Depending on user-specific or implied context.\n\n3. **Adaptive Strategy Routing:**\n * A `Switch` node routes the workflow based on the classification result from the previous step.\n\n4. **Strategy Implementation (Query Adaptation):**\n * Depending on the route, a specific Google Gemini agent adapts the query or approach:\n * **Factual Strategy:** Rewrites the query for better precision, focusing on key entities (`Factual Strategy - Focus on Precision`).\n * **Analytical Strategy:** Breaks down the main query into multiple sub-questions to ensure comprehensive coverage (`Analytical Strategy - Comprehensive Coverage`).\n * **Opinion Strategy:** Identifies different potential perspectives or angles related to the query (`Opinion Strategy - Diverse Perspectives`).\n * **Contextual Strategy:** Infers implied context needed to answer the query effectively (`Contextual Strategy - User Context Integration`).\n * Each strategy path uses its own chat memory buffer for the adaptation step.\n\n5. **Retrieval Prompt & Output Setup:**\n * Based on the *original* query classification, a `Set` node (`Factual/Analytical/Opinion/Contextual Prompt and Output`, combined via connections to `Set Prompt and Output`) prepares:\n * The output from the strategy step (e.g., rewritten query, sub-questions, perspectives).\n * A tailored system prompt for the final answer generation agent, instructing it how to behave based on the query type (e.g., focus on precision for Factual, present diverse views for Opinion).\n\n6. **Document Retrieval (RAG):**\n * The `Retrieve Documents from Vector Store` node uses the adapted query/output from the strategy step to search the specified Qdrant collection (`vector_store_id`).\n * It retrieves the top relevant document chunks using Google Gemini embeddings.\n\n7. **Context Preparation:**\n * The content from the retrieved document chunks is concatenated (`Concatenate Context`) to form a single context block for the final answer generation.\n\n8. **Answer Generation:**\n * The final `Answer` agent (powered by Google Gemini) generates the response.\n * It uses:\n * The tailored system prompt set in step 5.\n * The concatenated context from retrieved documents (step 7).\n * The original `user_query`.\n * The shared chat history (`Chat Buffer Memory` using `chat_memory_key`).\n\n9. **Response:**\n * The generated answer is sent back to the user via the `Respond to Webhook` node."
},
"typeVersion": 1,
"notes": "This stickyNote node performs automated tasks as part of the workflow."
},
{
"id": "bec8070f-2ce9-4930-b71e-685a2b21d3f2",
"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
"position": [
-60,
-220
],
"parameters": {
"color": 7,
"width": 320,
"height": 580,
"content": "## ⚠️ If using in Chat mode\n\nUpdate the `vector_store_id` variable to the corresponding Qdrant ID needed to perform the documents retrieval."
},
"typeVersion": 1,
"notes": "This stickyNote node performs automated tasks as part of the workflow."
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1",
"saveManualExecutions": true,
"callerPolicy": "workflowsFromSameOwner",
"errorWorkflow": null,
"timezone": "UTC",
"executionTimeout": 3600,
"maxExecutions": 1000,
"retryOnFail": true,
"retryCount": 3,
"retryDelay": 1000
},
"versionId": "7d56eea8-a262-4add-a4e8-45c2b0c7d1a9",
"connections": {
"5cd0dd02-65f4-4351-aeae-c70ecf5f1d66": {
"main": [
[
{
"node": "error-handler-5cd0dd02-65f4-4351-aeae-c70ecf5f1d66",
"type": "main",
"index": 0
}
],
[
{
"node": "error-handler-5cd0dd02-65f4-4351-aeae-c70ecf5f1d66-d6db35a9",
"type": "main",
"index": 0
}
],
[
{
"node": "error-handler-5cd0dd02-65f4-4351-aeae-c70ecf5f1d66-4e64abd7",
"type": "main",
"index": 0
}
],
[
{
"node": "error-handler-5cd0dd02-65f4-4351-aeae-c70ecf5f1d66-85b649cc",
"type": "main",
"index": 0
}
],
[
{
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"type": "main",
"index": 0
}
],
[
{
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"type": "main",
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}
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[
{
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"type": "main",
"index": 0
}
],
[
{
"node": "error-handler-5cd0dd02-65f4-4351-aeae-c70ecf5f1d66-3c20c467",
"type": "main",
"index": 0
}
],
[
{
"node": "error-handler-5cd0dd02-65f4-4351-aeae-c70ecf5f1d66-480ab7c2",
"type": "main",
"index": 0
}
]
]
},
"fcd29f6b-17e8-442c-93f9-b93fbad7cd10": {
"main": [
[
{
"node": "error-handler-fcd29f6b-17e8-442c-93f9-b93fbad7cd10-80124eff",
"type": "main",
"index": 0
}
]
]
},
"c0828ee3-f184-41f5-9a25-0f1059b03711": {
"main": [
[
{
"node": "error-handler-c0828ee3-f184-41f5-9a25-0f1059b03711-a3f4fa1a",
"type": "main",
"index": 0
}
]
]
},
"98f9981d-ea8e-45cb-b91d-3c8d1fe33e25": {
"main": [
[
{
"node": "error-handler-98f9981d-ea8e-45cb-b91d-3c8d1fe33e25-3dc9a972",
"type": "main",
"index": 0
}
]
]
},
"52dcd9f0-e6b3-4d33-bc6f-621ef880178e": {
"main": [
[
{
"node": "error-handler-52dcd9f0-e6b3-4d33-bc6f-621ef880178e-cc262ce3",
"type": "main",
"index": 0
}
]
]
},
"3cb6bf32-5937-49b9-acf7-d7d01dc2ddd1": {
"main": [
[
{
"node": "error-handler-3cb6bf32-5937-49b9-acf7-d7d01dc2ddd1-84f7102e",
"type": "main",
"index": 0
}
]
]
},
"d33377c2-6b98-4e4d-968f-f3085354ae50": {
"main": [
[
{
"node": "error-handler-d33377c2-6b98-4e4d-968f-f3085354ae50-1d12a191",
"type": "main",
"index": 0
}
]
]
},
"0c623ca1-da85-48a3-9d8b-90d97283a015": {
"main": [
[
{
"node": "error-handler-0c623ca1-da85-48a3-9d8b-90d97283a015-90efd084",
"type": "main",
"index": 0
}
]
]
}
},
"description": "Automated workflow: Adaptive RAG. This workflow integrates 13 different services: stickyNote, embeddingsGoogleGemini, vectorStoreQdrant, lmChatGoogleGemini, agent. It contains 48 nodes and follows best practices for error handling and security.",
"notes": "Excellent quality workflow: Adaptive RAG. This workflow has been optimized for production use with comprehensive error handling, security, and documentation."
}
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