399 lines
9.8 KiB
JSON
399 lines
9.8 KiB
JSON
{
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"name": "71 Policy QA (reference · Lab 02 example 2)",
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"nodes": [
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{
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"name": "Question webhook",
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"type": "n8n-nodes-base.webhook",
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"typeVersion": 2,
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"position": [
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0,
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300
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],
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"parameters": {
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"httpMethod": "POST",
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"path": "my-policy-qa",
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"responseMode": "responseNode",
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"options": {}
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}
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},
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{
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"name": "Has question?",
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"type": "n8n-nodes-base.if",
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"typeVersion": 2.2,
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"position": [
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200,
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300
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],
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"parameters": {
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"conditions": {
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"options": {
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"caseSensitive": true,
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"leftValue": "",
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"typeValidation": "strict",
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"version": 2
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},
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"conditions": [
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{
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"id": "c0",
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"leftValue": "={{ $json.body.question }}",
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"rightValue": "",
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"operator": {
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"type": "string",
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"operation": "notEmpty",
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"singleValue": true
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}
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}
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],
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"combinator": "and"
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},
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"options": {}
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}
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},
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{
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"name": "Reject empty",
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"type": "n8n-nodes-base.respondToWebhook",
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"typeVersion": 1.1,
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"position": [
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420,
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560
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],
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"parameters": {
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"respondWith": "json",
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"responseBody": "{ \"error\": \"question is required\" }",
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"options": {
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"responseCode": 400
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}
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}
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},
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{
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"name": "Search policies",
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"type": "@n8n/n8n-nodes-langchain.vectorStoreInMemory",
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"typeVersion": 1.3,
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"position": [
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420,
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300
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],
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"parameters": {
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"mode": "load",
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"memoryKey": {
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"__rl": true,
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"mode": "list",
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"value": "my_policy_docs",
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"cachedResultName": "my_policy_docs"
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},
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"prompt": "={{ $json.body.question }}",
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"topK": 3,
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"includeDocumentMetadata": true
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},
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"alwaysOutputData": true
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},
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{
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"name": "Embeddings",
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"type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
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"typeVersion": 1.2,
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"position": [
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380,
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520
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],
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"parameters": {
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"model": "text-embedding-3-large",
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"options": {}
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},
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"credentials": {
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"openAiApi": {
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"id": "openAiCourse0001",
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"name": "OpenAI (.env)"
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}
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}
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},
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{
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"name": "Build context",
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"type": "n8n-nodes-base.code",
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"typeVersion": 2,
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"position": [
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640,
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300
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],
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"parameters": {
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"jsCode": "// Numbered excerpts for the LLM, and the retrieved sources.\nconst question = $('Question webhook').first().json.body.question;\nconst hits = $input.all().filter(item => item.json.document).map(item => ({\n source: item.json.document.metadata?.source, title: item.json.document.metadata?.title,\n score: Math.round((item.json.score ?? 0) * 1000) / 1000, text: item.json.document.pageContent ?? '' }));\nconst context = hits.map((hit, i) => `[${i + 1}] ${hit.text}`).join('\\n\\n');\nconst minScore = Number($('Question webhook').first().json.body.min_score ?? 0.33);\nconst topScore = hits.length ? Math.max(...hits.map(hit => hit.score)) : 0;\nreturn [{ json: { question, hits, context, top_score: topScore, min_score: minScore, relevant: topScore >= minScore } }];"
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}
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},
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{
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"name": "Relevant?",
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"type": "n8n-nodes-base.if",
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"typeVersion": 2.2,
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"position": [
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860,
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300
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],
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"parameters": {
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"conditions": {
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"options": {
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"caseSensitive": true,
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"leftValue": "",
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"typeValidation": "strict",
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"version": 2
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},
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"conditions": [
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{
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"id": "c0",
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"leftValue": "={{ $json.relevant }}",
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"rightValue": "",
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"operator": {
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"type": "boolean",
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"operation": "true",
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"singleValue": true
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}
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}
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],
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"combinator": "and"
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},
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"options": {}
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}
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},
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{
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"name": "Answer from policies",
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"type": "@n8n/n8n-nodes-langchain.agent",
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"typeVersion": 3.1,
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"position": [
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1080,
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200
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],
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"parameters": {
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"promptType": "define",
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"text": "=질문: {{ $json.question }}\n\n규정 발췌:\n{{ $json.context }}",
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"options": {
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"systemMessage": "Answer the employee's question only from the numbered policy excerpts, in Korean, in at most 3 sentences. After each fact, write the number of the excerpt it came from in square brackets, such as [2]. If the excerpts do not contain the answer, answer exactly: 규정에서 답을 찾지 못했습니다.",
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"maxIterations": 2
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}
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}
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},
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{
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"name": "OpenAI chat model",
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"type": "@n8n/n8n-nodes-langchain.lmChatOpenAi",
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"typeVersion": 1.2,
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"position": [
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1040,
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420
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],
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"parameters": {
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"model": {
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"__rl": true,
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"mode": "id",
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"value": "gpt-4.1-mini"
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},
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"options": {
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"temperature": 0
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}
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},
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"credentials": {
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"openAiApi": {
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"id": "openAiCourse0001",
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"name": "OpenAI (.env)"
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}
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}
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},
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{
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"name": "Build answer",
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"type": "n8n-nodes-base.code",
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"typeVersion": 2,
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"position": [
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1320,
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200
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],
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"parameters": {
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"jsCode": "// Turn excerpt numbers into document titles and keep which documents were cited.\nconst ctx = $('Build context').first().json;\nconst draft = String($json.output ?? '').trim();\nconst numbers = [...draft.matchAll(/\\[(\\d+)\\]/g)].map(m => Number(m[1])).filter(n => n >= 1 && n <= ctx.hits.length);\nconst cited = [...new Set(numbers.map(n => ctx.hits[n - 1].source))];\nconst answer = draft.replace(/\\[(\\d+)\\]/g, (all, n) => ctx.hits[n - 1] ? `[${ctx.hits[n - 1].title}]` : all);\n\nreturn [{ json: { route: '자동 답변', answer, sources: [...new Set(ctx.hits.map(hit => hit.source))], cited, top_score: ctx.top_score } }];"
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}
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},
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{
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"name": "Not in policies",
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"type": "n8n-nodes-base.set",
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"typeVersion": 3.4,
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"position": [
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1080,
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520
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],
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"parameters": {
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"assignments": {
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"assignments": [
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{
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"id": "a0",
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"name": "route",
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"value": "담당자 연결",
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"type": "string"
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},
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{
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"id": "a1",
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"name": "answer",
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"value": "규정에서 답을 찾지 못했습니다. IT 문의 창구로 연결해 드릴게요.",
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"type": "string"
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},
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{
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"id": "a2",
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"name": "sources",
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"value": "={{ [] }}",
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"type": "array"
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},
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{
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"id": "a3",
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"name": "cited",
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"value": "={{ [] }}",
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"type": "array"
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},
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{
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"id": "a4",
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"name": "top_score",
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"value": "={{ $json.top_score }}",
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"type": "number"
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}
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]
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},
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"options": {}
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}
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},
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{
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"name": "Send result",
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"type": "n8n-nodes-base.respondToWebhook",
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"typeVersion": 1.1,
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"position": [
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1540,
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300
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],
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"parameters": {
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"respondWith": "json",
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"responseBody": "={{ $json }}",
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"options": {}
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}
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}
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],
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"connections": {
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"Question webhook": {
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"main": [
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[
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{
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"node": "Has question?",
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"type": "main",
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"index": 0
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}
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]
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]
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},
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"Has question?": {
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"main": [
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[
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{
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"node": "Search policies",
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"type": "main",
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"index": 0
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}
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],
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[
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{
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"node": "Reject empty",
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"type": "main",
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"index": 0
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}
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]
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]
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},
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"Embeddings": {
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"ai_embedding": [
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[
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{
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"node": "Search policies",
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"type": "ai_embedding",
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"index": 0
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}
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]
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]
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},
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"Search policies": {
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"main": [
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[
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{
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"node": "Build context",
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"type": "main",
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"index": 0
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}
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]
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]
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},
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"Build context": {
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"main": [
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[
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{
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"node": "Relevant?",
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"type": "main",
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"index": 0
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}
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]
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]
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},
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"Relevant?": {
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"main": [
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[
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{
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"node": "Answer from policies",
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"type": "main",
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"index": 0
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}
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],
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[
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{
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"node": "Not in policies",
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"type": "main",
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"index": 0
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}
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]
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]
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},
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"OpenAI chat model": {
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"ai_languageModel": [
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[
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{
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"node": "Answer from policies",
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"type": "ai_languageModel",
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"index": 0
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}
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]
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]
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},
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"Answer from policies": {
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"main": [
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[
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{
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"node": "Build answer",
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"type": "main",
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"index": 0
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}
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]
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]
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},
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"Build answer": {
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"main": [
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[
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{
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"node": "Send result",
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"type": "main",
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"index": 0
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}
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]
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]
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},
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"Not in policies": {
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"main": [
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[
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{
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"node": "Send result",
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"type": "main",
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"index": 0
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}
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]
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]
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}
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},
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"settings": {
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"executionOrder": "v1"
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}
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}
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