自适应RAG
高级
这是一个Engineering, Building Blocks, AI领域的自动化工作流，包含 39 个节点。主要使用 Set, Switch, Summarize, Agent, RespondToWebhook 等节点，结合人工智能技术实现智能自动化。 自适应RAG策略：查询分类与检索（Gemini和Qdrant）
前置要求
- •HTTP Webhook 端点（n8n 会自动生成）
- •Qdrant 服务器连接信息
- •Google Gemini API Key
使用的节点 (39)
工作流预览
可视化展示节点连接关系，支持缩放和平移
导出工作流
复制以下 JSON 配置到 n8n 导入，即可使用此工作流
{
"id": "cpuFyJYHKmjHTncz",
"meta": {
"instanceId": "2cb7a61f866faf57392b91b31f47e08a2b3640258f0abd08dd71f087f3243a5a",
"templateCredsSetupCompleted": true
},
"name": "自适应 RAG",
"tags": [],
"nodes": [
{
"id": "856bd809-8f41-41af-8f72-a3828229c2a5",
"name": "查询分类",
"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": "切换",
"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": "事实策略 - 专注于精确性",
"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": "分析策略 - 全面覆盖",
"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": "观点策略 - 多元视角",
"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": "上下文策略 - 用户上下文整合",
"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": "聊天",
"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": "事实提示和输出",
"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": "上下文提示和输出",
"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": "观点提示和输出",
"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": "分析提示和输出",
"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 分类",
"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 事实",
"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": "聊天缓冲记忆分析",
"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": "聊天缓冲记忆事实",
"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 观点",
"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": "聊天缓冲记忆观点",
"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 上下文",
"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": "聊天缓冲记忆上下文",
"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": "嵌入",
"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": "便签",
"type": "n8n-nodes-base.stickyNote",
"position": [
1040,
-900
],
"parameters": {
"color": 7,
"width": 700,
"height": 520,
"content": "## 事实策略"
},
"typeVersion": 1
},
{
"id": "064a4729-717c-40c8-824a-508406610a13",
"name": "便签1",
"type": "n8n-nodes-base.stickyNote",
"position": [
1040,
-360
],
"parameters": {
"color": 7,
"width": 700,
"height": 520,
"content": "## 分析策略"
},
"typeVersion": 1
},
{
"id": "9fd52a28-44bc-4dfd-bdb7-90987cc2f4fb",
"name": "便签2",
"type": "n8n-nodes-base.stickyNote",
"position": [
1040,
180
],
"parameters": {
"color": 7,
"width": 700,
"height": 520,
"content": "## 观点策略"
},
"typeVersion": 1
},
{
"id": "3797b21f-cc2a-4210-aa63-6d181d413c5e",
"name": "便签3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1040,
720
],
"parameters": {
"color": 7,
"width": 700,
"height": 520,
"content": "## 上下文策略"
},
"typeVersion": 1
},
{
"id": "16fa1531-9fb9-4b12-961c-be12e20b2134",
"name": "连接上下文",
"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": "从向量存储检索文档",
"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": "设置提示和输出",
"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": "聊天缓冲记忆",
"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": "便签4",
"type": "n8n-nodes-base.stickyNote",
"position": [
1800,
-220
],
"parameters": {
"color": 7,
"width": 820,
"height": 580,
"content": "## 执行自适应检索"
},
"typeVersion": 1
},
{
"id": "7f10fe70-1af8-47ad-a9b5-2850412c43f8",
"name": "便签5",
"type": "n8n-nodes-base.stickyNote",
"position": [
2640,
-220
],
"parameters": {
"color": 7,
"width": 740,
"height": 580,
"content": "## 整合检索上下文回复用户"
},
"typeVersion": 1
},
{
"id": "5cd0dd02-65f4-4351-aeae-c70ecf5f1d66",
"name": "响应Webhook",
"type": "n8n-nodes-base.respondToWebhook",
"position": [
3120,
-20
],
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "4c56ef8f-8fce-4525-bb87-15df37e91cc4",
"name": "便签 6",
"type": "n8n-nodes-base.stickyNote",
"position": [
280,
-220
],
"parameters": {
"color": 7,
"width": 700,
"height": 580,
"content": "## 用户查询分类"
},
"typeVersion": 1
},
{
"id": "3ef73405-89de-4bed-9673-90e2c1f2e74b",
"name": "当被其他工作流执行时",
"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
},
{
"id": "0785714f-c45c-4eda-9937-c97e44c9a449",
"name": "合并字段",
"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
},
{
"id": "57a93b72-4233-4ba2-b8c7-99d88f0ed572",
"name": "便签7",
"type": "n8n-nodes-base.stickyNote",
"position": [
-300,
400
],
"parameters": {
"width": 1280,
"height": 1300,
"content": "# 自适应 RAG 工作流"
},
"typeVersion": 1
},
{
"id": "bec8070f-2ce9-4930-b71e-685a2b21d3f2",
"name": "便签8",
"type": "n8n-nodes-base.stickyNote",
"position": [
-60,
-220
],
"parameters": {
"color": 7,
"width": 320,
"height": 580,
"content": "## ⚠️ 如果在聊天模式下使用"
},
"typeVersion": 1
}
],
"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "7d56eea8-a262-4add-a4e8-45c2b0c7d1a9",
"connections": {
"Chat": {
"main": [
[
{
"node": "Combined Fields",
"type": "main",
"index": 0
}
]
]
},
"Answer": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
},
"Switch": {
"main": [
[
{
"node": "Factual Strategy - Focus on Precision",
"type": "main",
"index": 0
}
],
[
{
"node": "Analytical Strategy - Comprehensive Coverage",
"type": "main",
"index": 0
}
],
[
{
"node": "Opinion Strategy - Diverse Perspectives",
"type": "main",
"index": 0
}
],
[
{
"node": "Contextual Strategy - User Context Integration",
"type": "main",
"index": 0
}
]
]
},
"Embeddings": {
"ai_embedding": [
[
{
"node": "Retrieve Documents from Vector Store",
"type": "ai_embedding",
"index": 0
}
]
]
},
"Gemini Answer": {
"ai_languageModel": [
[
{
"node": "Answer",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Gemini Factual": {
"ai_languageModel": [
[
{
"node": "Factual Strategy - Focus on Precision",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Gemini Opinion": {
"ai_languageModel": [
[
{
"node": "Opinion Strategy - Diverse Perspectives",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Combined Fields": {
"main": [
[
{
"node": "Query Classification",
"type": "main",
"index": 0
}
]
]
},
"Gemini Analytical": {
"ai_languageModel": [
[
{
"node": "Analytical Strategy - Comprehensive Coverage",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Gemini Contextual": {
"ai_languageModel": [
[
{
"node": "Contextual Strategy - User Context Integration",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Chat Buffer Memory": {
"ai_memory": [
[
{
"node": "Answer",
"type": "ai_memory",
"index": 0
}
]
]
},
"Concatenate Context": {
"main": [
[
{
"node": "Answer",
"type": "main",
"index": 0
}
]
]
},
"Query Classification": {
"main": [
[
{
"node": "Switch",
"type": "main",
"index": 0
}
]
]
},
"Gemini Classification": {
"ai_languageModel": [
[
{
"node": "Query Classification",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Set Prompt and Output": {
"main": [
[
{
"node": "Retrieve Documents from Vector Store",
"type": "main",
"index": 0
}
]
]
},
"Factual Prompt and Output": {
"main": [
[
{
"node": "Set Prompt and Output",
"type": "main",
"index": 0
}
]
]
},
"Opinion Prompt and Output": {
"main": [
[
{
"node": "Set Prompt and Output",
"type": "main",
"index": 0
}
]
]
},
"Chat Buffer Memory Factual": {
"ai_memory": [
[
{
"node": "Factual Strategy - Focus on Precision",
"type": "ai_memory",
"index": 0
}
]
]
},
"Chat Buffer Memory Opinion": {
"ai_memory": [
[
{
"node": "Opinion Strategy - Diverse Perspectives",
"type": "ai_memory",
"index": 0
}
]
]
},
"Analytical Prompt and Output": {
"main": [
[
{
"node": "Set Prompt and Output",
"type": "main",
"index": 0
}
]
]
},
"Contextual Prompt and Output": {
"main": [
[
{
"node": "Set Prompt and Output",
"type": "main",
"index": 0
}
]
]
},
"Chat Buffer Memory Analytical": {
"ai_memory": [
[
{
"node": "Analytical Strategy - Comprehensive Coverage",
"type": "ai_memory",
"index": 0
}
]
]
},
"Chat Buffer Memory Contextual": {
"ai_memory": [
[
{
"node": "Contextual Strategy - User Context Integration",
"type": "ai_memory",
"index": 0
}
]
]
},
"When Executed by Another Workflow": {
"main": [
[
{
"node": "Combined Fields",
"type": "main",
"index": 0
}
]
]
},
"Retrieve Documents from Vector Store": {
"main": [
[
{
"node": "Concatenate Context",
"type": "main",
"index": 0
}
]
]
},
"Factual Strategy - Focus on Precision": {
"main": [
[
{
"node": "Factual Prompt and Output",
"type": "main",
"index": 0
}
]
]
},
"Opinion Strategy - Diverse Perspectives": {
"main": [
[
{
"node": "Opinion Prompt and Output",
"type": "main",
"index": 0
}
]
]
},
"Analytical Strategy - Comprehensive Coverage": {
"main": [
[
{
"node": "Analytical Prompt and Output",
"type": "main",
"index": 0
}
]
]
},
"Contextual Strategy - User Context Integration": {
"main": [
[
{
"node": "Contextual Prompt and Output",
"type": "main",
"index": 0
}
]
]
}
}
}常见问题
如何使用这个工作流？
复制上方的 JSON 配置代码，在您的 n8n 实例中创建新工作流并选择「从 JSON 导入」，粘贴配置后根据需要修改凭证设置即可。
这个工作流适合什么场景？
高级 - 工程, 构建模块, 人工智能
需要付费吗？
本工作流完全免费，您可以直接导入使用。但请注意，工作流中使用的第三方服务（如 OpenAI API）可能需要您自行付费。
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