Mistral AI OCR와 JigsawStack를 사용하여 평면도 데이터를 분류 및 추출

• •HTTP Webhook 엔드포인트(n8n이 자동으로 생성)
• •대상 API의 인증 정보가 필요할 수 있음

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        "content": "## 🚫 Low Quality / Drop\n\n**When:** Confidence < 0.4 or unclear image\n\n**Response:**\n- Tell user file isn't a floorplan\n- Suggest uploading blueprint/CAD\n- List acceptable formats (JPG, PNG, PDF)\n\n**User sees:** \"Unable to Process Your Floorplan\""
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        "content": "## ⚠️ Manual Review Needed\n\n**When:** Confidence 0.4-0.85 (uncertain)\n\n**Response:**\n- File needs human check\n- Promise 2-hour turnaround\n- Set expectation for email follow-up\n\n**User sees:** \"Manual Review Required\""
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        "content": "## ✅ Continue to Step 2\n\n**When:** Confidence ≥ 0.85 (from text analysis)\n\n**Response:**\n- Confirm successful validation\n- Explain next steps (OCR/measurement)\n- Set 30-60 second expectation\n\n**User sees:** \"Floorplan Accepted!\"\n\n*Continues to Jigsaw OCR*"
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        "content": "## Image Classification\n\n**When:** Image files with high AI confidence\n\n**Response:**\n- Premium quality detected\n- Instant processing message\n- List what's being calculated\n\n**User sees:** \"Perfect Quality Floorplan\"\n\n*Fast track to measurements*"
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        "content": "## 📊 Flow Summary\n\n**PDF Path:**\nExtract text → Score confidence → Route by threshold\n\n**Image Path:**\nDirect to AI classification → Route by result\n\n**Thresholds:**\n- < 40%: Not a floorplan\n- 40-85%: Manual review\n- ≥ 85%: Auto-process\n\n**Goal:** Filter bad uploads early, save API costs"
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        "content": "## 📏 File Size/Pages Limit\n\n**When:**\n- File > 10MB\n- PDF > 20 pages\n\n**Response:**\n- Explain size limits\n- Ask to split multi‑floor plans\n- Suggest extracting relevant pages\n\n**User sees:** \"File Too Large\""
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        "content": "# 🏠 Floorplan Classifier & Measurement Extractor  \n\n## ✅ What This Workflow Does  \n\nAutomatically validates uploaded files (**PDFs or images**) to check if they are genuine floorplans. It rejects non‑floorplans early, and prepares valid files for **room measurement & analysis**.  \n\nThe workflow has **two phases**:  \n1. **Classification (Step 1):** Smart filters & classifiers decide if the upload is usable.  \n2. **Calculation (Step 2):** OCR + AI extract structured room measurements (area + wall surfaces) and compile results.  \n\n---\n\n### Supported File Types  \n- **PDFs** → text is extracted for keyword/measurement checks, then passed to AI.  \n- **Images (JPG, PNG, etc.)** → sent directly to the classifier (can’t extract metadata text).  \n\n---\n\n### Quality Filtering (Step 1)  \n1. **Heuristic checks** → detects floorplan indicators:  \n   - Room names (living room, keuken, slaapkamer, etc.)  \n   - Measurements (m², mm)  \n   - Technical symbols (e.g., WCD, TH, WP).  \n2. **AI classification (JigsawStack)** → robustly decides floorplan vs. not.  \n\n---\n\n### Confidence Routing (Step 1)  \n- **< 40% confidence** → ❌ Rejected (not a floorplan).  \n- **40–85% confidence** → ⚠️ Unclear → ask for better upload/manual review.  \n- **> 85% confidence** → ✅ Accepted floorplan → sent to **Measurement Extraction (Step 2)**.  \n\n---\n\n### Measurement Extraction (Paid version ONLY)  \n---\n\n## ⚒ Requirements  \n- **JigsawStack API key** → File storage + classification  \n- **Mistral Cloud API key** → OCR measurement extraction  \n- **n8n instance** → Self‑hosted or Cloud  \n- **Webhook endpoint** → for uploads & responses  \n\n---\n\n## 🔧 Customization  \n- Adjust **heuristic rules** (keywords/symbols) to match your market.  \n- Change **confidence thresholds** (strict vs. lenient filtering).  \n- Set **wall height** or fallback ratio for wall area calculations.  \n- Choose your OCR/AI provider (Mistral, AWS Textract, Azure Vision, etc.).  \n- Extend workflows to **save results to DB, Notion, Slack, CRM**.  \n\n---\n\n## ⚠️ Notes  \n- Never hardcode API keys → use **n8n credential manager**.  \n- All nodes are **renamed by purpose** (easy to follow).  \n- Sticky Notes document both phases clearly.  \n- Designed as **plug‑and‑play end‑to‑end pipeline** for fast onboarding.  \n"
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        "jsCode": "// --- SAFER INPUT HANDLING ---\nconst text = ($json.text || \"\").toLowerCase();\nconst numpages = $json.numpages || 1;\n\nlet score = 0;\nlet reasons = []; \n\n// --- KEYWORD LISTS ---\nconst nlSpaces = [\"woonkamer\", \"keuken\", \"slaapkamer\", \"badkamer\", \"overloop\", \"entree\", \"hal\", \"toilet\", \"berging\", \"techniek\", \"meterkast\", \"mk\"];\nconst enSpaces = [\"living room\", \"kitchen\", \"bedroom\", \"bathroom\", \"landing\", \"entrance\", \"hall\", \"toilet\", \"storage\", \"utility room\"];\nconst floorWords = [\"verdieping\", \"begane grond\", \"zolder\", \"kelder\", \"vliering\", \"doorsnede\", \"gevel\", \"plattegrond\"];\nconst enFloorWords = [\"floor\", \"ground floor\", \"attic\", \"basement\", \"cross-section\", \"facade\", \"floor plan\"];\nconst symbols = [\"wcd\", \"th\", \"wp\", \"v.v.\", \"wm\", \"wd\", \"wtw\", \"pv\", \"rad.\", \"rm\", \"gr\"];\nconst projectWords = [\"bouwnummer\", \"opdrachtgever\", \"project\", \"schaal\", \"datum\", \"bladnummer\", \"formaat\"];\n\n// --- CHECKS ---\n// 1. Spaces\nconst foundNlSpaces = nlSpaces.filter(w => text.includes(w));\nconst foundEnSpaces = enSpaces.filter(w => text.includes(w));\nif ((foundNlSpaces.length + foundEnSpaces.length) >= 3) {\n  score += 0.35;\n  reasons.push(`Found ${foundNlSpaces.length + foundEnSpaces.length} room names`);\n}\n\n// 2. m² matches\nconst m2Matches = text.match(/\\d+(?:[.,]\\d+)?\\s*m(?:²|2)/g) || [];\nif (m2Matches.length >= 3) {\n  score += 0.25;\n  reasons.push(`Found ${m2Matches.length} m² measurements`);\n}\n\n// 3. mm matches (dimensions)\nconst mmMatches = text.match(/\\d{3,}\\s*mm/g) || [];\nif (mmMatches.length >= 3) {\n  score += 0.15;\n  reasons.push(`Found ${mmMatches.length} mm measurements`);\n}\n\n// 4. Symbols\nconst foundSymbols = symbols.filter(s => text.includes(s.toLowerCase()));\nif (foundSymbols.length >= 3) {\n  score += 0.15;\n  reasons.push(`Found ${foundSymbols.length} technical symbols`);\n}\n\n// 5. Floor/plan words\nconst foundFloors = [...floorWords, ...enFloorWords].filter(w => text.includes(w));\nif (foundFloors.length >= 2) {\n  score += 0.10;\n  reasons.push(`Found ${foundFloors.length} floor/drawing parts`);\n}\n\n// 6. Project words\nconst foundProjectWords = projectWords.filter(w => text.includes(w));\nif (foundProjectWords.length >= 3) {\n  score += 0.10;\n  reasons.push(`Found ${foundProjectWords.length} project info words`);\n}\n\n// --- NORMALIZE SCORE ---\nif (score > 1) score = 1;\n\n// ✅ Return one item with JSON + preserve binary\nreturn {\n  json: {\n    ...$json,\n    confidence: parseFloat(score.toFixed(2)),\n    analysis: {\n      reasons,\n      foundNlSpaces,\n      foundEnSpaces,\n      m2Matches,\n      mmMatches,\n      foundSymbols,\n      foundFloors,\n      foundProjectWords,\n      pages: numpages\n    }\n  },\n   binary: item.binary // pass binary unchanged\n};"
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