This workflow follows the Chat Trigger → HTTP Request recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"name": "RAG Query - Document Q&A",
"nodes": [
{
"parameters": {
"content": "## RAG Query Workflow\n\nThis workflow handles document Q&A using vector similarity search.\n\n**Flow:**\n1. Receive question from Chat or Webhook\n2. Generate query embedding\n3. Search similar chunks in pgvector\n4. Build context from retrieved chunks\n5. Send to Ollama LLM with context\n6. Return answer",
"height": 300,
"width": 320
},
"id": "sticky-note",
"name": "Instructions",
"type": "n8n-nodes-base.stickyNote",
"typeVersion": 1,
"position": [
-200,
-100
]
},
{
"parameters": {
"options": {
"responseMode": "responseNode"
}
},
"id": "chat-trigger",
"name": "Chat Trigger",
"type": "@n8n/n8n-nodes-langchain.chatTrigger",
"typeVersion": 1.1,
"position": [
0,
200
]
},
{
"parameters": {
"httpMethod": "POST",
"path": "rag-query",
"responseMode": "responseNode",
"options": {}
},
"id": "webhook-trigger",
"name": "Webhook Trigger",
"type": "n8n-nodes-base.webhook",
"typeVersion": 2,
"position": [
0,
400
]
},
{
"parameters": {
"jsCode": "// Get question from either Chat Trigger or Webhook\nlet question = '';\nlet sessionId = '';\n\ntry {\n // Try Chat Trigger first\n const chatData = $('Chat Trigger').first()?.json;\n if (chatData?.chatInput) {\n question = chatData.chatInput;\n sessionId = chatData.sessionId || 'default';\n }\n} catch (e) {\n // Fall back to Webhook\n const webhookData = $('Webhook Trigger').first()?.json;\n if (webhookData) {\n question = webhookData.question || webhookData.query || webhookData.message || '';\n sessionId = webhookData.sessionId || webhookData.session_id || 'webhook';\n }\n}\n\nif (!question) {\n throw new Error('No question provided. Send {\"question\": \"your question\"}');\n}\n\nreturn [{\n json: {\n question,\n sessionId\n }\n}];"
},
"id": "extract-question",
"name": "Extract Question",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
220,
300
]
},
{
"parameters": {
"method": "POST",
"url": "http://192.168.50.49:11434/api/embeddings",
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={{ JSON.stringify({ model: 'mxbai-embed-large:latest', prompt: $json.question }) }}",
"options": {
"timeout": 60000
}
},
"id": "query-embedding",
"name": "Generate Query Embedding",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
440,
300
]
},
{
"parameters": {
"jsCode": "const embedding = $input.first().json.embedding;\nconst question = $('Extract Question').first().json.question;\nconst sessionId = $('Extract Question').first().json.sessionId;\n\nif (!embedding || !Array.isArray(embedding)) {\n throw new Error('Invalid embedding response from Ollama');\n}\n\n// Format embedding as PostgreSQL vector string\nconst vectorString = '[' + embedding.join(',') + ']';\n\nreturn [{\n json: {\n question,\n sessionId,\n queryEmbedding: vectorString\n }\n}];"
},
"id": "format-embedding",
"name": "Format Query Embedding",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
660,
300
]
},
{
"parameters": {
"operation": "executeQuery",
"query": "SELECT \n id,\n document_name,\n chunk_index,\n content,\n 1 - (embedding <=> '{{ $json.queryEmbedding }}') as similarity\nFROM document_chunks\nWHERE embedding IS NOT NULL\nORDER BY embedding <=> '{{ $json.queryEmbedding }}'\nLIMIT 5;",
"options": {}
},
"id": "vector-search",
"name": "Vector Similarity Search",
"type": "n8n-nodes-base.postgres",
"typeVersion": 2.5,
"position": [
880,
300
],
"credentials": {
"postgres": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"jsCode": "const chunks = $input.all();\nconst question = $('Extract Question').first().json.question;\nconst sessionId = $('Extract Question').first().json.sessionId;\n\nif (chunks.length === 0) {\n return [{\n json: {\n question,\n sessionId,\n context: 'No relevant documents found in the knowledge base.',\n hasContext: false\n }\n }];\n}\n\n// Build context from retrieved chunks\nlet context = '';\nconst sources = [];\n\nfor (const chunk of chunks) {\n const data = chunk.json;\n const similarity = (parseFloat(data.similarity) * 100).toFixed(1);\n context += `\\n\\n--- From: ${data.document_name} (Chunk ${data.chunk_index}, Relevance: ${similarity}%) ---\\n${data.content}`;\n \n if (!sources.includes(data.document_name)) {\n sources.push(data.document_name);\n }\n}\n\nreturn [{\n json: {\n question,\n sessionId,\n context: context.trim(),\n sources,\n chunkCount: chunks.length,\n hasContext: true\n }\n}];"
},
"id": "build-context",
"name": "Build Context",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1100,
300
]
},
{
"parameters": {
"method": "POST",
"url": "http://192.168.50.49:11434/api/generate",
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={{ JSON.stringify({\n model: 'gpt-oss:120b',\n prompt: `You are a helpful assistant that answers questions based on the provided document context. Respond in the same language as the user's question. If the context doesn't contain relevant information, say so.\n\n=== DOCUMENT CONTEXT ===\n${$json.context}\n=== END CONTEXT ===\n\nUser Question: ${$json.question}\n\nProvide a clear, accurate answer based on the context above:`,\n stream: false\n}) }}",
"options": {
"timeout": 300000
}
},
"id": "ask-ollama",
"name": "Ask Ollama LLM",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
1320,
300
]
},
{
"parameters": {
"jsCode": "const ollamaResponse = $input.first().json;\nconst contextData = $('Build Context').first().json;\n\nconst answer = ollamaResponse.response || 'Sorry, I could not generate an answer.';\n\n// Build response object\nconst response = {\n answer,\n question: contextData.question,\n sources: contextData.sources || [],\n chunksUsed: contextData.chunkCount || 0\n};\n\nreturn [{\n json: response\n}];"
},
"id": "format-response",
"name": "Format Response",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1540,
300
]
},
{
"parameters": {
"operation": "executeQuery",
"query": "INSERT INTO chat_memory (session_id, role, content)\nVALUES \n ('{{ $('Extract Question').first().json.sessionId }}', 'user', '{{ $('Extract Question').first().json.question.replace(/'/g, \"''\") }}'),\n ('{{ $('Extract Question').first().json.sessionId }}', 'assistant', '{{ $json.answer.replace(/'/g, \"''\").substring(0, 10000) }}');",
"options": {}
},
"id": "save-to-memory",
"name": "Save to Chat Memory",
"type": "n8n-nodes-base.postgres",
"typeVersion": 2.5,
"position": [
1760,
300
],
"credentials": {
"postgres": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"respondWith": "json",
"responseBody": "={{ JSON.stringify($json) }}"
},
"id": "respond-webhook",
"name": "Respond to Webhook",
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.1,
"position": [
1980,
400
]
},
{
"parameters": {
"respondWith": "text",
"responseBody": "={{ $json.answer }}"
},
"id": "respond-chat",
"name": "Respond to Chat",
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.1,
"position": [
1980,
200
]
}
],
"connections": {
"Chat Trigger": {
"main": [
[
{
"node": "Extract Question",
"type": "main",
"index": 0
}
]
]
},
"Webhook Trigger": {
"main": [
[
{
"node": "Extract Question",
"type": "main",
"index": 0
}
]
]
},
"Extract Question": {
"main": [
[
{
"node": "Generate Query Embedding",
"type": "main",
"index": 0
}
]
]
},
"Generate Query Embedding": {
"main": [
[
{
"node": "Format Query Embedding",
"type": "main",
"index": 0
}
]
]
},
"Format Query Embedding": {
"main": [
[
{
"node": "Vector Similarity Search",
"type": "main",
"index": 0
}
]
]
},
"Vector Similarity Search": {
"main": [
[
{
"node": "Build Context",
"type": "main",
"index": 0
}
]
]
},
"Build Context": {
"main": [
[
{
"node": "Ask Ollama LLM",
"type": "main",
"index": 0
}
]
]
},
"Ask Ollama LLM": {
"main": [
[
{
"node": "Format Response",
"type": "main",
"index": 0
}
]
]
},
"Format Response": {
"main": [
[
{
"node": "Save to Chat Memory",
"type": "main",
"index": 0
}
]
]
},
"Save to Chat Memory": {
"main": [
[
{
"node": "Respond to Chat",
"type": "main",
"index": 0
},
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
}
},
"settings": {
"executionOrder": "v1"
},
"staticData": null,
"tags": [
{
"name": "RAG"
},
{
"name": "Q&A"
}
],
"triggerCount": 2
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
postgres
Pro
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About this workflow
RAG Query - Document Q&A. Uses chatTrigger, httpRequest, postgres. Chat trigger; 13 nodes.
Source: https://github.com/mohsp-99/n8n-ollama/blob/0ba7d1fd1a606f212cfdf346f85fb70f905f5696/n8n-workflows/rag-query.json — original creator credit. Request a take-down →
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