This workflow follows the Agent → 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": "Polish AI RAG - Direct Qdrant (NO HF Space)",
"nodes": [
{
"parameters": {
"httpMethod": "POST",
"path": "rag-agent",
"responseMode": "lastNode",
"options": {}
},
"id": "webhook-1",
"name": "Webhook: RAG Question",
"type": "n8n-nodes-base.webhook",
"typeVersion": 2.1,
"position": [
240,
300
]
},
{
"parameters": {
"model": "anthropic/claude-3.5-sonnet",
"options": {
"baseURL": "https://openrouter.ai/api/v1",
"temperature": 0.3
}
},
"id": "openrouter-analyze",
"name": "OpenRouter: Analyze Model",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1.3,
"position": [
560,
420
],
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"description": "Analyzes the user question and determines optimal top_k value (10-15)",
"jsCode": "const question = $fromAI('question', 'User question', 'string');\nconst lowerQ = question.toLowerCase();\nconst result = { query: question, top_k: 10 };\nif (lowerQ.includes('szczeg\u00f3\u0142') || lowerQ.includes('jak')) result.top_k = 15;\nelse if (lowerQ.includes('przyk\u0142ad')) result.top_k = 12;\nreturn JSON.stringify(result, null, 2);"
},
"id": "code-tool-params",
"name": "Code Tool: Build Parameters",
"type": "@n8n/n8n-nodes-langchain.toolCode",
"typeVersion": 1.3,
"position": [
680,
420
]
},
{
"parameters": {
"schemaType": "manual",
"inputSchema": "{\n \"type\": \"object\",\n \"properties\": {\n \"query\": {\"type\": \"string\"},\n \"top_k\": {\"type\": \"number\"}\n },\n \"required\": [\"query\", \"top_k\"]\n}"
},
"id": "output-parser",
"name": "Structured Output Parser",
"type": "@n8n/n8n-nodes-langchain.outputParserStructured",
"typeVersion": 1.3,
"position": [
800,
420
]
},
{
"parameters": {
"promptType": "define",
"text": "={{ $json.body.question }}",
"hasOutputParser": true,
"options": {
"systemMessage": "Jeste\u015b ekspertem analizy zapyta\u0144 u\u017cytkownik\u00f3w dla systemu RAG. Przeanalizuj pytanie i u\u017cyj narz\u0119dzia Build Parameters aby okre\u015bli\u0107 liczb\u0119 wynik\u00f3w (top_k: 10-15). Zwr\u00f3\u0107 JSON z query i top_k."
}
},
"id": "agent-analyze",
"name": "AI Agent: Analyze Question",
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
480,
300
]
},
{
"parameters": {
"method": "POST",
"url": "https://router.huggingface.co/models/nomic-ai/nomic-embed-text-v1.5",
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Authorization",
"value": "={{ \"Bearer \" + $env.HF_API_TOKEN }}"
},
{
"name": "Content-Type",
"value": "application/json"
}
]
},
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={{ { \"inputs\": \"search_query: \" + $json.output.query } }}",
"options": {
"response": {
"response": {
"neverError": true
}
},
"timeout": 30000
}
},
"id": "http-embedding",
"name": "HTTP: Generate Embedding",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.3,
"position": [
720,
300
]
},
{
"parameters": {
"jsCode": "// Przygotuj query dla Qdrant API\nconst embedding = $input.item.json;\nconst params = $('AI Agent: Analyze Question').item.json.output;\n\n// HF API zwraca embedding jako array lub nested array\nconst vector = Array.isArray(embedding[0]) ? embedding[0] : embedding;\n\n// Buduj Qdrant search request\nconst qdrantQuery = {\n vector: {\n name: \"dense\",\n vector: vector\n },\n limit: params.top_k || 10,\n with_payload: true,\n score_threshold: 0.5\n};\n\nreturn {\n json: {\n qdrantQuery,\n originalQuestion: $('Webhook: RAG Question').item.json.body.question,\n top_k: params.top_k\n }\n};"
},
"id": "code-prepare-qdrant",
"name": "Code: Prepare Qdrant Query",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
960,
300
]
},
{
"parameters": {
"method": "POST",
"url": "={{ $env.QDRANT_URL }}/collections/wordpress_articles/points/search",
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "api-key",
"value": "={{ $env.QDRANT_API_KEY }}"
},
{
"name": "Content-Type",
"value": "application/json"
}
]
},
"sendBody": true,
"specifyBody": "json",
"jsonBody": "={{ $json.qdrantQuery }}",
"options": {
"response": {
"response": {
"neverError": true
}
},
"timeout": 60000
}
},
"id": "http-qdrant",
"name": "HTTP: Qdrant Search",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.3,
"position": [
1200,
300
]
},
{
"parameters": {
"jsCode": "// Format Qdrant results do formatu dla LLM\nconst response = $input.item.json;\nconst originalQuestion = $('Code: Prepare Qdrant Query').item.json.originalQuestion;\n\n// Sprawd\u017a czy s\u0105 wyniki\nif (!response || !response.result || response.result.length === 0) {\n return {\n json: {\n context: 'Brak wynik\u00f3w. System RAG nie znalaz\u0142 odpowiednich artyku\u0142\u00f3w.',\n hasResults: false,\n resultCount: 0,\n results: [],\n originalQuestion\n }\n };\n}\n\nconst results = response.result;\n\n// Buduj formatowany kontekst\nlet context = `Znaleziono ${results.length} wynik\u00f3w z bazy wiedzy:\\n\\n`;\n\nconst formattedResults = [];\n\nresults.forEach((hit, index) => {\n const payload = hit.payload || {};\n \n context += `--- Wynik ${index + 1} (Score: ${(hit.score * 100).toFixed(1)}%) ---\\n`;\n context += `Tytu\u0142: ${payload.title || 'Brak tytu\u0142u'}\\n`;\n context += `URL: ${payload.url || ''}\\n`;\n context += `Typ sekcji: ${payload.section_type || 'content'}\\n`;\n \n if (payload.publication_date) {\n context += `Data publikacji: ${payload.publication_date}\\n`;\n }\n \n if (payload.tags && payload.tags.length > 0) {\n context += `Tagi: ${payload.tags.join(', ')}\\n`;\n }\n \n if (payload.categories && payload.categories.length > 0) {\n context += `Kategorie: ${payload.categories.join(', ')}\\n`;\n }\n \n context += `\\nTre\u015b\u0107:\\n${payload.text || ''}\\n\\n`;\n \n // Zapisz dla sources array\n formattedResults.push({\n title: payload.title || 'Brak tytu\u0142u',\n url: payload.url || '',\n score: hit.score,\n text: payload.text || '',\n section_type: payload.section_type || 'content'\n });\n});\n\nreturn {\n json: {\n context,\n hasResults: true,\n resultCount: results.length,\n results: formattedResults,\n originalQuestion\n }\n};"
},
"id": "code-format-results",
"name": "Code: Format Qdrant Results",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
1440,
300
]
},
{
"parameters": {
"model": "anthropic/claude-3.5-sonnet",
"options": {
"baseURL": "https://openrouter.ai/api/v1",
"temperature": 0.5
}
},
"id": "openrouter-answer",
"name": "OpenRouter: Answer Generator",
"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
"typeVersion": 1.3,
"position": [
1800,
420
],
"credentials": {
"openAiApi": {
"name": "<your credential>"
}
}
},
{
"parameters": {
"promptType": "define",
"text": "={{ $('Code: Format Qdrant Results').item.json.originalQuestion }}",
"options": {
"systemMessage": "Jeste\u015b pomocnym asystentem AI, kt\u00f3ry odpowiada na pytania u\u017cytkownik\u00f3w w j\u0119zyku polskim na podstawie kontekstu z bazy wiedzy RAG.\\n\\n=== PYTANIE U\u017bYTKOWNIKA ===\\n{{ $('Code: Format Qdrant Results').item.json.originalQuestion }}\\n\\n=== KONTEKST Z BAZY WIEDZY ===\\n{{ $('Code: Format Qdrant Results').item.json.context }}\\n\\n=== INSTRUKCJE ===\\n1. Odpowiedz zwi\u0119\u017ale i konkretnie w j\u0119zyku polskim\\n2. Bazuj TYLKO na dostarczonym kontek\u015bcie\\n3. Cytuj \u017ar\u00f3d\u0142a - podawaj tytu\u0142y artyku\u0142\u00f3w i linki\\n4. Uwzgl\u0119dnij score i daty publikacji\\n5. Je\u015bli brak odpowiedzi, powiedz wprost i zasugeruj przeformu\u0142owanie\\n6. U\u017cywaj formatowania markdown\\n7. Wyr\u00f3\u017cnij checklisty i key insights\\n\\nOdpowiedz teraz:"
}
},
"id": "agent-answer",
"name": "AI Agent: Generate Answer",
"type": "@n8n/n8n-nodes-langchain.agent",
"typeVersion": 3.1,
"position": [
1680,
300
]
},
{
"parameters": {
"respondWith": "json",
"responseBody": "={{ {\n \"question\": $('Code: Format Qdrant Results').item.json.originalQuestion,\n \"answer\": $json.output,\n \"sources\": $('Code: Format Qdrant Results').item.json.results.map(r => ({\"title\": r.title, \"url\": r.url, \"score\": r.score})),\n \"sources_count\": $('Code: Format Qdrant Results').item.json.resultCount,\n \"has_results\": $('Code: Format Qdrant Results').item.json.hasResults\n} }}",
"options": {}
},
"id": "respond",
"name": "Respond to Webhook",
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1.5,
"position": [
1920,
300
]
}
],
"connections": {
"Webhook: RAG Question": {
"main": [
[
{
"node": "AI Agent: Analyze Question",
"type": "main",
"index": 0
}
]
]
},
"OpenRouter: Analyze Model": {
"ai_languageModel": [
[
{
"node": "AI Agent: Analyze Question",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"Code Tool: Build Parameters": {
"ai_tool": [
[
{
"node": "AI Agent: Analyze Question",
"type": "ai_tool",
"index": 0
}
]
]
},
"Structured Output Parser": {
"ai_outputParser": [
[
{
"node": "AI Agent: Analyze Question",
"type": "ai_outputParser",
"index": 0
}
]
]
},
"AI Agent: Analyze Question": {
"main": [
[
{
"node": "HTTP: Generate Embedding",
"type": "main",
"index": 0
}
]
]
},
"HTTP: Generate Embedding": {
"main": [
[
{
"node": "Code: Prepare Qdrant Query",
"type": "main",
"index": 0
}
]
]
},
"Code: Prepare Qdrant Query": {
"main": [
[
{
"node": "HTTP: Qdrant Search",
"type": "main",
"index": 0
}
]
]
},
"HTTP: Qdrant Search": {
"main": [
[
{
"node": "Code: Format Qdrant Results",
"type": "main",
"index": 0
}
]
]
},
"Code: Format Qdrant Results": {
"main": [
[
{
"node": "AI Agent: Generate Answer",
"type": "main",
"index": 0
}
]
]
},
"OpenRouter: Answer Generator": {
"ai_languageModel": [
[
{
"node": "AI Agent: Generate Answer",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"AI Agent: Generate Answer": {
"main": [
[
{
"node": "Respond to Webhook",
"type": "main",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "direct-qdrant-v1"
}
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.
openAiApi
Pro
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About this workflow
Polish AI RAG - Direct Qdrant (NO HF Space). Uses lmChatOpenAi, toolCode, outputParserStructured, agent. Webhook trigger; 12 nodes.
Source: https://github.com/a-michalski/wordpress-rag/blob/87141bd131e299f2b80489e37b71036c6922550a/n8n_workflow_direct_qdrant.json — original creator credit. Request a take-down →
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