admin
143 lines
6.8 KiB
JavaScript
143 lines
6.8 KiB
JavaScript
const config = $('Set Configuration').first().json.config;
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const fallbackState = $('Check Fallback State').first()?.json || { is_fallback_active: false, recent_error_count: 0 };
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const isFallback = fallbackState.is_fallback_active || false;
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const model = isFallback ? config.OLLAMA_MODEL_FALLBACK : config.OLLAMA_MODEL_PRIMARY;
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// Read DB Context
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const dbContext = $('Fetch DB Context').all().map(i => i.json);
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const dbProjects = dbContext.filter(d => d.type === 'projects').map(d => ({id: d.id, code: d.text1, name: d.text2}));
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const dbDisciplines = dbContext.filter(d => d.type === 'disciplines').map(d => ({id: d.id, th: d.text1, en: d.text2}));
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const dbOrgs = dbContext.filter(d => d.type === 'organizations').map(d => ({id: d.id, name: d.text1, code: d.text2}));
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const dbTags = dbContext.filter(d => d.type === 'tags').map(d => ({id: d.id, name: d.text1}));
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const dbCorrTypes = dbContext.filter(d => d.type === 'correspondence_types').map(d => ({id: d.id, code: d.text1, name: d.text2}));
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let systemCategories = ['Correspondence','RFA','Drawing','Transmittal','Report','Other'];
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try { systemCategories = $('File Mount Check').first().json.system_categories || systemCategories; } catch (e) {}
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const pdfItems = $('Extract PDF Text').all();
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// File Validator passes all original Excel JSON fields through (sender, receiver, project_code, etc.)
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// Read PDF File overwrites the JSON with binary data, so we must go back one step
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const metaItems = $('File Validator').all();
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return pdfItems.map((pdfItem, i) => {
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const item = metaItems[i] || pdfItem;
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const docNum = String(item.json.document_number || '');
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const title = String(item.json.title || '');
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const legacyNum = String(item.json.legacy_number || '');
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const issuedDate = String(item.json.issued_date || '');
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const receivedDate = String(item.json.received_date || '');
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const corrType = String(item.json.correspondence_type || '');
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const senderCode = String(item.json.sender || '');
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const receiverCode = String(item.json.receiver || '');
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const projectCode = String(item.json.project_code || '');
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// JavaScript pre-mapping
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const findOrgId = (code) => {
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if (!code) return null;
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const match = dbOrgs.find(o => o.code === code || o.name === code);
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return match ? match.id : null;
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};
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const findProjectId = (code) => {
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if (!code) return config.PROJECT_ID; // Fallback to config
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const match = dbProjects.find(p => p.code === code || p.name === code);
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return match ? match.id : config.PROJECT_ID;
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};
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const senderId = findOrgId(senderCode);
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const receiverId = findOrgId(receiverCode);
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const projectId = findProjectId(projectCode);
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// Excel corrType is likely already the ID based on requirements, but fallback matching to ID if needed
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const corrMatch = dbCorrTypes.find(c => String(c.id) === corrType || c.code === corrType || c.name === corrType);
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const corrTypeId = corrMatch ? corrMatch.id : (isNaN(parseInt(corrType)) ? null : parseInt(corrType));
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const isRFA = docNum.includes('-RFA-') || title.toLowerCase().includes('rfa');
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const systemPrompt = `You are an expert Document Controller for a construction project (LCBP3) in Thailand.
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The documents are primarily in THAI and ENGLISH.
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Your task is to classify documents and extract metadata from OCR text.
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Respond ONLY with valid JSON.`;
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// Use pdfItem for the OCR extracted data, NOT the metaItem
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const pdfText = String(pdfItem.json.data || '').substring(0, 3500).replace(/[^a-zA-Z0-9ก-๙\s\.\/\-:\[\]\(\)]/g, ' ');
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const userPrompt = `Analyze this document:
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[EXCEL METADATA]
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Document Number: ${docNum || 'Not provided'}
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Title: ${title || 'Not provided'}
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Issued Date: ${issuedDate || 'Not provided'}
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Received Date: ${receivedDate || 'Not provided'}
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[DATABASE REFERENCES]
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Disciplines: ${JSON.stringify(dbDisciplines)}
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Tags: ${JSON.stringify(dbTags)}
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[OCR TEXT EXTRACTION]
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${pdfText}
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Rules:
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1. Category: Must be one of ${JSON.stringify(systemCategories)}. If Document Number contains "-RFA-", category MUST be "RFA".
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2. Respond with EXACTLY 8 fields in JSON format:
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- "discipline_id": Find 'id' from Disciplines array analyzing text to match 'th' or 'en'. If no match, use ID=64 (from contract LCBP3-C2).
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- "subject": Document subject. If OCR is close to EXCEL METADATA Title, use EXCEL METADATA.
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- "issued_date": Verify from OCR text if it matches ${issuedDate}, format YYYY-MM-DD.
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- "received_date": Verify from OCR text. If empty, default to issued_date.
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- "status": Extract status (e.g., For Information, Approve, Reject, Resubmit). This will be exported as "remark".
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- "summary": 4-5 lines of Thai summary from OCR. This will be exported as "body".
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- "tags": REQUIRED. Identify 2-5 main topics/themes from the document (from Title, subject matter, and OCR text). For each topic, return an object with:
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* "tag_name": short topic name in Thai (2-5 words), e.g. "คอนกรีตผสม", "ทดสอบวัสดุ"
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* "description": one sentence in Thai describing this topic (use key point details). e.g. "การทดสอบค่า slump ของคอนกรีตผสมที่หน้างาน"
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Return as: [{"tag_name": "...", "description": "..."}, ...]
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- "key_points": Array of 3-5 string key points extracted from the document (in Thai).
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3. IMPORTANT: You MUST REPLACE the 'null' values in the template below with the actual Integer IDs or text you found. DO NOT reply with literal 'null' if you found a match!
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Respond ONLY with this EXACT JSON structure:
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{
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"discipline_id": 64,
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"subject": "${title}",
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"issued_date": "${issuedDate}",
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"received_date": "${receivedDate || issuedDate}",
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"status": null,
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"summary": "สรุปเนื้อหา 4-5 บรรทัด...",
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"tags": [{"tag_name": "ชื่อหัวข้อ", "description": "คำอธิบาย key point ของหัวข้อนี้"}],
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"key_points": ["จุดสำคัญที่ 1", "จุดสำคัญที่ 2", "จุดสำคัญที่ 3"],
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"category": "${isRFA ? 'RFA' : 'Correspondence'}",
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"confidence": 0.95
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}`;
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return {
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json: {
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...item.json,
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active_model: model,
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is_fallback: isFallback,
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system_categories: systemCategories,
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pre_mapped: {
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project_id: projectId,
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sender_id: senderId,
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receiver_id: receiverId,
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correspondence_type_id: corrTypeId
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},
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_debug_mapping: {
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excel_project_code: projectCode,
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excel_sender: senderCode,
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excel_receiver: receiverCode,
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excel_corr_type: corrType,
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matched_project: dbProjects.find(p => p.code === projectCode || p.name === projectCode) || null,
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first_org_sample: dbOrgs[0] || null
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},
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ollama_payload: {
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model: model,
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prompt: `${systemPrompt}\n\n${userPrompt}`,
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stream: false,
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format: 'json',
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options: {
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temperature: 0.1,
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num_ctx: 8192
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}
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}
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}
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};
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});
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