LLMテンプレート
上級
これはEngineering, AI RAG分野の自動化ワークフローで、25個のノードを含みます。主にSet, Agent, ChatTrigger, LmChatOpenAi, RerankerCohereなどのノードを使用。 GPT-4o-miniとQdrantベクトルデータベースを使用した永続のなチャットメモリ
前提条件
- •OpenAI API Key
- •Qdrantサーバー接続情報
使用ノード (25)
カテゴリー
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": "EDZcm0r7Lp2uIkTn",
"meta": {
"instanceId": "48f9e8e7598a73c86aec19069eefaf1e83b51b8858cbb8999ee59d6fa3d9a3f2",
"templateCredsSetupCompleted": true
},
"name": "LLM_TEMPLATE",
"tags": [],
"nodes": [
{
"id": "265bbb29-3ae9-49dd-9d77-4a8230af5f3e",
"name": "Embeddings OpenAI",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
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"parameters": {
"options": {
"dimensions": 1024
}
},
"credentials": {
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"name": "openai - einarcesar@gmail.com"
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},
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{
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"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"width": 324,
"height": 416,
"content": "## 🔤 TEXT VECTORIZATION\n\nConverts conversation text into 1024-dimensional vectors for semantic storage.\n\n### ⚙️ Configuration:\n- **Model**: text-embedding-3-small\n- **Dimensions**: 1024 (must match vector DB)\n\n### 💡 Pro tip: \nThis model offers the best balance between performance and cost for most applications.\n\n### 💰 Costs:\n- ~$0.02 per 1M tokens"
},
"typeVersion": 1
},
{
"id": "ae0a96a7-6cd5-4868-aadb-2b91e3e8f448",
"name": "デフォルトデータローダー",
"type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
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"parameters": {
"options": {}
},
"typeVersion": 1
},
{
"id": "f8685c76-dde4-400a-a359-e52348d9f0ae",
"name": "付箋 2",
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"width": 324,
"height": 371,
"content": "## 📄 DOCUMENT PROCESSOR\n\nPrepares conversation data for vector storage by converting it into a format suitable for chunking.\n\n### 🎯 Purpose:\n- Standardizes data format\n- Prepares for text splitting\n- Maintains metadata integrity\n\n### ⚡ Performance:\n- Processing time: ~10ms per conversation"
},
"typeVersion": 1
},
{
"id": "fd60c06d-0c22-40fc-ab62-7f87b4c6f29a",
"name": "再帰的文字テキスト分割器",
"type": "@n8n/n8n-nodes-langchain.textSplitterRecursiveCharacterTextSplitter",
"position": [
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],
"parameters": {
"options": {},
"chunkSize": 200,
"chunkOverlap": 40
},
"typeVersion": 1
},
{
"id": "487e7425-4d1e-48be-9d92-5398e6328279",
"name": "付箋 3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1296,
848
],
"parameters": {
"width": 340,
"height": 392,
"content": "## ✂️ TEXT CHUNKING STRATEGY\n\n### 🔧 Settings:\n- **Chunk Size**: 200 chars\n- **Overlap**: 40 chars\n\n### 📊 Why these values?\n- Small chunks = Better context precision\n- 20% overlap = Maintains context continuity\n- Optimized for conversation snippets\n\n### ⚡ Performance: \nIdeal for real-time chat applications"
},
"typeVersion": 1
},
{
"id": "922bfcdb-14ce-40cc-a3df-e89be2d59635",
"name": "チャットメッセージ受信時",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"position": [
-112,
448
],
"webhookId": "ef238f10-3af1-409d-b7e8-3bf61cd357e4",
"parameters": {
"options": {}
},
"typeVersion": 1.1
},
{
"id": "a8f24cf0-077f-43ea-a5b6-885ef7069948",
"name": "付箋 4",
"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
"width": 340,
"height": 428,
"content": "## 💬 CHAT INTERFACE\n\n### 🚀 Entry point for user interactions\n\n### 📝 Features:\n- Real-time message processing\n- Session management\n- Context preservation\n\n### 🔗 Integration: \nCan be embedded in websites, apps, or used via n8n's chat widget\n\n### 🌐 Webhook URL:\nAvailable after workflow activation"
},
"typeVersion": 1
},
{
"id": "58538d83-7b62-47ea-a099-143517886719",
"name": "検索用Embeddings",
"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
"position": [
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896
],
"parameters": {
"options": {
"dimensions": 1024
}
},
"credentials": {
"openAiApi": {
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"name": "openai - einarcesar@gmail.com"
}
},
"typeVersion": 1.2
},
{
"id": "64ee54af-4f43-4b8a-a73b-f0fb02a69fca",
"name": "付箋 5",
"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
"color": 6,
"width": 340,
"height": 260,
"content": "## 🔍 RETRIEVAL EMBEDDINGS\n\nGenerates vectors for semantic search in the memory database.\n\n### ⚠️ Important: \nMust use the SAME model and dimensions as storage embeddings!\n\n### 🎯 Used for:\n- Query vectorization\n- Similarity search\n- Context retrieval"
},
"typeVersion": 1
},
{
"id": "b86d86d8-8595-4c27-bc9f-45e706d08623",
"name": "Reranker Cohere",
"type": "@n8n/n8n-nodes-langchain.rerankerCohere",
"position": [
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],
"parameters": {},
"credentials": {
"cohereApi": {
"id": "7GqfOJcuJFHWeOpS",
"name": "CohereApi account"
}
},
"typeVersion": 1
},
{
"id": "43ef1754-fe7a-4435-be5f-5cc912ef7590",
"name": "付箋 6",
"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
"color": 5,
"width": 340,
"height": 396,
"content": "## 🎯 RELEVANCE OPTIMIZER\n\nRe-ranks retrieved memories by relevance to current context.\n\n### ✨ Benefits:\n- Improves retrieval accuracy by 30-40%\n- Reduces hallucinations\n- Ensures most relevant context is used\n\n### 💰 Cost: \n~$1 per 1000 re-rankings\n\n### 🔧 Optional:\nCan be disabled for cost savings"
},
"typeVersion": 1
},
{
"id": "9df9f1e4-b067-4ec8-8ee1-1d64f256081a",
"name": "RAG_MEMORY",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"onError": "continueRegularOutput",
"position": [
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"parameters": {
"mode": "retrieve-as-tool",
"topK": 20,
"options": {},
"toolName": "RAG_MEMORY",
"useReranker": true,
"toolDescription": "Long-term memory storage for maintaining context across conversations. Use this to recall previous interactions, user preferences, and historical context.",
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "ltm",
"cachedResultName": "ltm"
}
},
"credentials": {
"qdrantApi": {
"id": "IMqj7iGvb0Ko0nCj",
"name": "Qdrant - einar.qzz.io"
}
},
"typeVersion": 1.2
},
{
"id": "a2f24b1e-df0f-4c10-b525-4eeea31edf7e",
"name": "付箋 7",
"type": "n8n-nodes-base.stickyNote",
"position": [
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"parameters": {
"color": 3,
"width": 340,
"height": 428,
"content": "## 🧠 MEMORY RETRIEVAL SYSTEM\n\n### 📊 Configuration:\n- **Collection**: 'ltm' (long-term memory)\n- **Top K**: 20 results\n- **Reranker**: Enabled\n\n### 🔍 How it works:\n1. Searches for similar past conversations\n2. Retrieves top 20 matches\n3. Re-ranks by relevance\n4. Provides context to AI\n\n### ⚡ Performance: \n~50ms average retrieval time\n\n### 💾 Storage:\nQdrant cloud or self-hosted"
},
"typeVersion": 1
},
{
"id": "33b1f48b-9700-4edb-a73b-5889316e7cdf",
"name": "OpenAI チャットモデル",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
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],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini"
},
"options": {
"maxTokens": 2000,
"temperature": 0.7
}
},
"credentials": {
"openAiApi": {
"id": "uHrKvsqlQYyImnjO",
"name": "openai - einarcesar@gmail.com"
}
},
"typeVersion": 1.2
},
{
"id": "866bf74b-dc5b-4bf9-b01d-8fbc2da442c1",
"name": "構造化出力パーサー",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"position": [
544,
112
],
"parameters": {
"autoFix": true,
"jsonSchemaExample": "{\n \"sessionId\": \"unique-session-identifier\",\n \"chatInput\": \"User's message\",\n \"output\": \"AI's response\",\n \"timestamp\": \"2024-01-01T12:00:00Z\",\n \"relevanceScore\": 0.95\n}"
},
"typeVersion": 1.3
},
{
"id": "130381f8-4d66-4a5c-b233-5079e3630f71",
"name": "付箋 8",
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"color": 4,
"width": 340,
"height": 344,
"content": "## 📐 OUTPUT FORMATTER\n\nEnsures AI responses follow a consistent structure for storage.\n\n### 🎯 Schema includes:\n- Session ID (conversation tracking)\n- User input & AI output\n- Timestamp (temporal retrieval)\n- Relevance score (optimization)\n\n### ✅ Auto-fix: \nEnabled to handle schema violations"
},
"typeVersion": 1
},
{
"id": "6d7eb364-5c8f-4d6a-9ef6-51e3a1fc45bd",
"name": "レスポンスフォーマット",
"type": "n8n-nodes-base.set",
"position": [
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],
"parameters": {
"options": {},
"assignments": {
"assignments": [
{
"id": "fdd39640-54c5-4ed7-9f37-c8cd4302a212",
"name": "output",
"type": "string",
"value": "={{ $('AI Agent').first().json.output.output }}"
}
]
}
},
"executeOnce": true,
"typeVersion": 3.4
},
{
"id": "cdc23122-cf49-4e54-922e-4990f5a2a5ee",
"name": "付箋 9",
"type": "n8n-nodes-base.stickyNote",
"position": [
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],
"parameters": {
"width": 340,
"height": 304,
"content": "## 🎨 RESPONSE FORMATTER\n\nExtracts and formats the AI response for the chat interface.\n\n### 📤 Output: \nClean response without metadata\n\n### 💡 Purpose:\nEnsures users only see the actual message, not the underlying structure"
},
"typeVersion": 1
},
{
"id": "98430332-8de1-48f9-b883-017e7ee35983",
"name": "AIエージェント",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
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432
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"parameters": {
"options": {
"systemMessage": "# AI Assistant with Long-Term Memory\n\nYou are an AI assistant equipped with a sophisticated long-term memory system. Your RAG_MEMORY tool allows you to recall past conversations, user preferences, and contextual information across sessions.\n\n## Core Capabilities:\n1. **Context Retention**: Remember and reference previous conversations\n2. **User Personalization**: Adapt responses based on learned preferences\n3. **Knowledge Accumulation**: Build upon past interactions\n4. **Intelligent Retrieval**: Access relevant historical context\n\n## Memory Usage Protocol:\n\n### Before Each Response:\n1. Query RAG_MEMORY for relevant past interactions\n2. Analyze retrieved context for applicable information\n3. Integrate historical knowledge into your response\n4. Maintain consistency with previous conversations\n\n### Memory Query Strategies:\n- Use specific keywords from the current conversation\n- Search for user preferences and patterns\n- Look for related topics discussed previously\n- Check for unresolved questions or follow-ups\n\n## Response Guidelines:\n1. **Acknowledge Continuity**: Reference previous conversations when relevant\n2. **Build on History**: Use past context to provide more informed responses\n3. **Maintain Consistency**: Ensure responses align with established facts\n4. **Update Understanding**: Evolve your knowledge based on new information\n\n## Privacy & Ethics:\n- Only reference information from this user's history\n- Respect conversation boundaries\n- Maintain appropriate context separation\n\n## Example Interaction Flow:\n```\nUser: \"What was that book you recommended last week?\"\n1. Query RAG_MEMORY for \"book recommendation\"\n2. Retrieve relevant conversation\n3. Provide specific book title and context\n4. Offer additional related suggestions\n```\n\nRemember: Your memory makes you more than just an AI - you're a continuous conversation partner who learns and grows with each interaction."
},
"hasOutputParser": true
},
"typeVersion": 2
},
{
"id": "ef4a29b4-68f4-491e-b44b-3345455907a6",
"name": "付箋 10",
"type": "n8n-nodes-base.stickyNote",
"position": [
48,
-112
],
"parameters": {
"width": 360,
"height": 516,
"content": "## 🧠 INTELLIGENT AI AGENT\n\n### 🎯 Core Features:\n- Long-term memory integration\n- Context-aware responses\n- Tool usage (RAG_MEMORY)\n- Structured output generation\n\n### 📋 System Prompt:\n- Defines memory usage protocol\n- Sets behavioral guidelines\n- Ensures privacy compliance\n\n### ⚡ Performance:\n- Avg response time: 2-3 seconds\n- Memory queries: 1-3 per response\n- Context window: Effectively unlimited\n\n### 💰 Cost:\n- ~$0.15/$0.60 per 1M tokens (in/out)"
},
"typeVersion": 1
},
{
"id": "db4e8e6b-0aee-4a93-8a01-bf38b7de9d98",
"name": "会話の保存",
"type": "@n8n/n8n-nodes-langchain.vectorStoreQdrant",
"position": [
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448
],
"parameters": {
"mode": "insert",
"options": {},
"qdrantCollection": {
"__rl": true,
"mode": "list",
"value": "ltm",
"cachedResultName": "ltm"
}
},
"credentials": {
"qdrantApi": {
"id": "IMqj7iGvb0Ko0nCj",
"name": "Qdrant - einar.qzz.io"
}
},
"typeVersion": 1.2
},
{
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"name": "付箋 11",
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"parameters": {
"color": 2,
"width": 340,
"height": 496,
"content": "## 💾 MEMORY STORAGE\n\n### 📥 What gets stored:\n- User input\n- AI response\n- Conversation metadata\n- Session information\n\n### 🔧 Configuration:\n- **Collection**: 'ltm'\n- **Batch size**: 100 (for efficiency)\n\n### 📈 Storage metrics:\n- Avg storage time: 100ms\n- Vector dimensions: 1024\n- Retention: Unlimited*\n\n### ⚠️ Production tip:\nImplement cleanup policies for scalability"
},
"typeVersion": 1
},
{
"id": "4237b604-513f-463c-b891-cb5bd4d588a6",
"name": "GPT-4o-mini (メイン)",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"position": [
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],
"parameters": {
"model": {
"__rl": true,
"mode": "list",
"value": "gpt-4o-mini",
"cachedResultName": "gpt-4o-mini"
},
"options": {
"topP": 0.7,
"temperature": 0.2,
"presencePenalty": 0.3,
"frequencyPenalty": 0.6
}
},
"credentials": {
"openAiApi": {
"id": "uHrKvsqlQYyImnjO",
"name": "openai - einarcesar@gmail.com"
}
},
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{
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"name": "ワークフロー概要",
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"parameters": {
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"width": 460,
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"content": "## 🚀 WORKFLOW OVERVIEW\n\n### Long-Term Memory for AI Assistants\n\nThis workflow implements a sophisticated memory system that allows AI assistants to remember conversations across sessions.\n\n### 🔑 Key Benefits:\n1. **Persistent Context**: No more repeating yourself\n2. **Personalization**: AI learns user preferences\n3. **Cost Efficiency**: Reduces token usage over time\n4. **Scalability**: Handles unlimited conversations\n\n### 📊 Architecture:\n- **Vector Database**: Qdrant for semantic search\n- **LLM**: OpenAI GPT-4o-mini\n- **Embeddings**: text-embedding-3-small\n- **Reranking**: Cohere for accuracy\n\n### 🛠️ Setup Requirements:\n1. OpenAI API key\n2. Qdrant instance (cloud or self-hosted)\n3. Cohere API key (optional)\n4. n8n instance\n\n### 💡 Use Cases:\n- Customer support bots\n- Personal AI assistants\n- Knowledge management systems\n- Educational tutors\n\n### 📈 Performance Metrics:\n- Response time: 2-3 seconds\n- Memory recall: 95%+ accuracy\n- Cost: ~$0.01 per conversation\n\n### 🔗 Resources:\n- [Documentation](https://docs.n8n.io)\n- [Qdrant Setup](https://qdrant.tech)\n- [OpenAI Pricing](https://openai.com/pricing)"
},
"typeVersion": 1
}
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"active": false,
"pinData": {},
"settings": {
"executionOrder": "v1"
},
"versionId": "6b90a41f-8415-4e59-9082-48bf175e4804",
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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, AI RAG検索拡張
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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RAGアプリケーション向けのGoogle DriveからSupabaseコンテキストベクトルデータベースへの同期
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76 ノードMichael Taleb
AI RAG検索拡張
AI スマートアシスタント: Supabase ストレージと Google Drive ファイルとの対話
AIワンチャットボット：SupabaseストレージとGoogle Driveのファイルと対話
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62 ノードMark Shcherbakov
エンジニアリング
Gmail、GPT-4、ベクター知識ベースを使用した顧客サポートシステムの自動化
自動顧客サポートシステム（Gmail、GPT-4、ベクター知識ベース）
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32 ノードKhair Ahammed
AI RAG検索拡張
シンプルな RAG
PDFベースのRAGシステムをOpenAI、Pinecone、Cohereの再ランキングで構築する
Form Trigger
Agent
Chat Trigger
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Chat Trigger
14 ノードAji Prakoso
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