アダプティブ RAG
上級
これはEngineering, Building Blocks, AI分野の自動化ワークフローで、39個のノードを含みます。主にSet, Switch, Summarize, Agent, RespondToWebhookなどのノードを使用、AI技術を活用したスマート自動化を実現。 適応型RAG戦略：クエリ分類と検索（GeminiとQdrant）
前提条件
- •HTTP Webhookエンドポイント（n8nが自動生成）
- •Qdrantサーバー接続情報
- •Google Gemini API Key
使用ノード (39)
ワークフロープレビュー
ノード接続関係を可視化、ズームとパンをサポート
ワークフローをエクスポート
以下のJSON設定をn8nにインポートして、このワークフローを使用できます
{
"id": "cpuFyJYHKmjHTncz",
"meta": {
"instanceId": "2cb7a61f866faf57392b91b31f47e08a2b3640258f0abd08dd71f087f3243a5a",
"templateCredsSetupCompleted": true
},
"name": "Adaptive RAG",
"tags": [],
"nodes": [
{
"id": "856bd809-8f41-41af-8f72-a3828229c2a5",
"name": "クエリ分類",
"type": "@n8n/n8n-nodes-langchain.agent",
"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": "スイッチ",
"type": "n8n-nodes-base.switch",
"position": [
740,
-40
],
"parameters": {
"rules": {
"values": [
{
"outputKey": "Factual",
"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": "Analytical",
"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": "Opinion",
"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": "Contextual",
"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
},
{
"id": "63889cad-1283-4dbf-ba16-2b6cf575f24a",
"name": "事実戦略 - 精度重視",
"type": "@n8n/n8n-nodes-langchain.agent",
"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": "分析戦略 - 包括的カバー",
"type": "@n8n/n8n-nodes-langchain.agent",
"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": "意見戦略 - 多様な視点",
"type": "@n8n/n8n-nodes-langchain.agent",
"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": "文脈戦略 - ユーザーコンテキスト統合",
"type": "@n8n/n8n-nodes-langchain.agent",
"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": "チャット",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-280,
120
],
"webhookId": "56f626b5-339e-48af-857f-1d4198fc8a4d",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "7f7df364-4829-4e29-be3d-d13a63f65b8f",
"name": "事実プロンプトと出力",
"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
},
{
"id": "590d8667-69eb-4db2-b5be-714c602b319a",
"name": "文脈プロンプトと出力",
"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
},
{
"id": "fa3228ee-62d8-4c02-9dca-8a1ebc6afc74",
"name": "意見プロンプトと出力",
"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
},
{
"id": "c769a76a-fb26-46a1-a00d-825b689d5f7a",
"name": "分析プロンプトと出力",
"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
},
{
"id": "fcd29f6b-17e8-442c-93f9-b93fbad7cd10",
"name": "Gemini 分類",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
360,
180
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash-lite"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "c0828ee3-f184-41f5-9a25-0f1059b03711",
"name": "Gemini 事実",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
1120,
-560
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "98f9981d-ea8e-45cb-b91d-3c8d1fe33e25",
"name": "Gemini 分析",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
1120,
-20
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "c85f270d-3224-4e60-9acf-91f173dfe377",
"name": "分析用チャットバッファメモリ",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
1280,
-20
],
"parameters": {
"sessionKey": "={{ $('Combined Fields').item.json.chat_memory_key }}",
"sessionIdType": "customKey",
"contextWindowLength": 10
},
"typeVersion": 1.3
},
{
"id": "c39ba907-7388-4152-965a-e28e626bc9b2",
"name": "事実用チャットバッファメモリ",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
1280,
-560
],
"parameters": {
"sessionKey": "={{ $('Combined Fields').item.json.chat_memory_key }}",
"sessionIdType": "customKey",
"contextWindowLength": 10
},
"typeVersion": 1.3
},
{
"id": "52dcd9f0-e6b3-4d33-bc6f-621ef880178e",
"name": "Gemini 意見",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
1120,
520
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "147a709a-4b46-4835-82cf-7d6b633acd4c",
"name": "意見用チャットバッファメモリ",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
1280,
520
],
"parameters": {
"sessionKey": "={{ $('Combined Fields').item.json.chat_memory_key }}",
"sessionIdType": "customKey",
"contextWindowLength": 10
},
"typeVersion": 1.3
},
{
"id": "3cb6bf32-5937-49b9-acf7-d7d01dc2ddd1",
"name": "Gemini 文脈",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
1120,
1060
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "5916c4f1-4369-4d66-8553-2fff006b7e69",
"name": "文脈用チャットバッファメモリ",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
1280,
1060
],
"parameters": {
"sessionKey": "={{ $('Combined Fields').item.json.chat_memory_key }}",
"sessionIdType": "customKey",
"contextWindowLength": 10
},
"typeVersion": 1.3
},
{
"id": "d33377c2-6b98-4e4d-968f-f3085354ae50",
"name": "埋め込み (Embeddings)",
"type": "@n8n/n8n-nodes-langchain.embeddingsGoogleGemini",
"position": [
2060,
200
],
"parameters": {
"modelName": "models/text-embedding-004"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "32d9a0c0-0889-4cb2-a088-8ee9cfecacd3",
"name": "付箋",
"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
},
{
"id": "064a4729-717c-40c8-824a-508406610a13",
"name": "付箋1",
"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
},
{
"id": "9fd52a28-44bc-4dfd-bdb7-90987cc2f4fb",
"name": "付箋2",
"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
},
{
"id": "3797b21f-cc2a-4210-aa63-6d181d413c5e",
"name": "付箋3",
"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
},
{
"id": "16fa1531-9fb9-4b12-961c-be12e20b2134",
"name": "コンテキストの結合",
"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
},
{
"id": "4d6147d1-7a3d-42ab-b23f-cdafe8ea30b0",
"name": "ベクターストアから文書を取得",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"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
},
{
"id": "7e68f9cb-0a0d-4215-8083-3b9ef92cd237",
"name": "プロンプトと出力の設定",
"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
},
{
"id": "0c623ca1-da85-48a3-9d8b-90d97283a015",
"name": "Gemini 回答",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
2720,
200
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.0-flash"
},
"credentials": {
"googlePalmApi": {
"id": "2zwuT5znDglBrUCO",
"name": "Google Gemini(PaLM) Api account"
}
},
"typeVersion": 1
},
{
"id": "fab91e48-1c62-46a8-b9fc-39704f225274",
"name": "回答",
"type": "@n8n/n8n-nodes-langchain.agent",
"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
},
{
"id": "d69f8d62-3064-40a8-b490-22772fbc38cd",
"name": "チャットバッファメモリ",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
2900,
200
],
"parameters": {
"sessionKey": "={{ $('Combined Fields').item.json.chat_memory_key }}",
"sessionIdType": "customKey",
"contextWindowLength": 10
},
"typeVersion": 1.3
},
{
"id": "a399f8e6-fafd-4f73-a2de-894f1e3c4bec",
"name": "付箋4",
"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
},
{
"id": "7f10fe70-1af8-47ad-a9b5-2850412c43f8",
"name": "付箋5",
"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
},
{
"id": "5cd0dd02-65f4-4351-aeae-c70ecf5f1d66",
"name": "Webhookに応答",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
3120,
-20
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "4c56ef8f-8fce-4525-bb87-15df37e91cc4",
"name": "付箋6",
"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
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{
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"name": "別のワークフローから実行された場合",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
-280,
-140
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"parameters": {
"workflowInputs": {
"values": [
{
"name": "user_query"
},
{
"name": "chat_memory_key"
},
{
"name": "vector_store_id"
}
]
}
},
"typeVersion": 1.1
},
{
"id": "0785714f-c45c-4eda-9937-c97e44c9a449",
"name": "フィールドの結合",
"type": "n8n-nodes-base.set",
"position": [
40,
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],
"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
},
{
"id": "57a93b72-4233-4ba2-b8c7-99d88f0ed572",
"name": "付箋7",
"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
},
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"name": "付箋8",
"type": "n8n-nodes-base.stickyNote",
"position": [
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-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
}
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}よくある質問
このワークフローの使い方は？
上記のJSON設定コードをコピーし、n8nインスタンスで新しいワークフローを作成して「JSONからインポート」を選択、設定を貼り付けて認証情報を必要に応じて変更してください。
このワークフローはどんな場面に適していますか？
上級 - エンジニアリング, ビルディングブロック, 人工知能
有料ですか？
このワークフローは完全無料です。ただし、ワークフローで使用するサードパーティサービス（OpenAI APIなど）は別途料金が発生する場合があります。
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