Nattanin
422 lines
5.1 KiB
Markdown
422 lines
5.1 KiB
Markdown
🧠 🎯 เป้าหมาย: AI DMS (Document Management System อัจฉริยะ)
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> จาก “เก็บเอกสาร” → กลายเป็น “ผู้ช่วยควบคุมเอกสาร”
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---
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🏗️ 1. Architecture ภาพรวม
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[User]
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↓
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(Web UI / Chat UI)
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↓
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AI Agent Layer ← (หัวใจ)
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↓
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| RAG Engine (ค้นหาเอกสาร)
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| Workflow Engine (RFA logic)
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| Tool Layer (DB / File / API)
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↓
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[Database + File Storage]
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---
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🔥 2. Core Components (ต้องมี)
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2.1 AI Agent (สมอง)
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ใช้แนวคิดจาก:
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LangChain / OpenClaw / CrewAI
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💡 ทำอะไร:
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เข้าใจคำถาม user
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ตัดสินใจว่าจะ:
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query DB
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search document
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run workflow
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---
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2.2 RAG System (ค้นหาเอกสาร)
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💡 ใช้:
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vector DB (เช่น Qdrant / Chroma)
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💡 ทำอะไร:
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search PDF / Drawing / Spec
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ตอบคำถามจาก content
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👉 use case:
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“Drawing A-101 revision ล่าสุดคืออะไร”
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“RFA นี้เกี่ยวข้องกับ drawing ไหน”
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---
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2.3 Tool Layer (สำคัญมาก)
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AI จะไม่เก่งถ้าไม่มี tools
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tools ที่ต้องมี:
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get_rfa(id)
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get_drawings_by_rfa(rfa_id)
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get_latest_revision(drawing_code)
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search_documents(query)
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get_transmittal_history()
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👉 ตรงนี้ = bridge ระหว่าง AI กับ DB จริงของคุณ
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---
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2.4 Workflow Engine (logic ธุรกิจ)
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💡 ไม่ให้ AI มั่ว
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เช่น:
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RFA status flow
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approval logic
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revision control
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👉 AI = “ช่วยคิด”
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👉 workflow = “ของจริง”
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---
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2.5 File Storage
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เก็บไฟล์จริง (PDF, DWG)
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version control
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naming convention
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👉 ตรงกับ backlog คุณข้อ 9 พอดี
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---
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⚙️ 3. Database Design (ต่อยอดของเดิมคุณ)
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คุณมีอยู่แล้ว:
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RFAs
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Drawings
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Revisions
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👉 เพิ่ม:
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🧩 ai_embeddings
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document_id
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chunk_text
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vector
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🧩 ai_logs
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user_query
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agent_decision
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tool_used
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🧩 ai_tasks (optional)
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background job
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document indexing
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---
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🤖 4. AI Use Cases (ของจริงที่ควรทำ)
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🔎 1. Smart Search
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> “ขอ drawing structural ล่าสุดของ zone B”
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AI:
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เข้าใจ intent
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query DB + RAG
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---
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📄 2. Document QA
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> “สรุป spec นี้”
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AI:
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อ่าน PDF
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summarize
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---
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🔗 3. Relationship Mapping
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> “RFA นี้เกี่ยวกับ drawing อะไร”
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AI:
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join:
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rfa → drawing → revision
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---
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📊 4. Timeline Analysis
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> “RFA นี้ delay เพราะอะไร”
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AI:
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วิเคราะห์ revision timeline
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---
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🧠 5. Auto Classification
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upload file → AI tag:
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type
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discipline
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revision
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---
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⚠️ 6. Alert / Assistant
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“Drawing นี้ outdated”
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“RFA ใกล้ deadline”
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---
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🧩 5. UI Design (สำคัญมาก)
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5.1 Hybrid UI
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Table (DataTables)
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Chat (AI assistant)
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[ Table RFAs ] | [ AI Chat ]
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| > RFA ล่าสุดคืออะไร
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| > Drawing ไหนยังไม่ approve
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👉 best of both worlds
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---
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5.2 Drawing Page (โคตรสำคัญ)
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แสดง:
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revision history
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related RFAs
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AI summary
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---
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⚡ 6. Tech Stack (แนะนำตรงๆ)
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Backend
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PHP (ของเดิมคุณ)
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Python (AI service)
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AI Layer
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LangChain หรือ custom agent
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Ollama (local) หรือ OpenAI
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Vector DB
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Qdrant (แนะนำ)
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Frontend
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DataTables (ตาม backlog)
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Chat UI
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---
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🔥 7. Flow การทำงานจริง
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📥 Upload Drawing
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1. user upload PDF
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2. system:
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extract text (OCR)
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chunk
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embed → vector DB
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3. AI tag metadata
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---
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🔎 Query
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user:
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> “drawing ล่าสุดของ contract นี้”
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AI:
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1. เข้าใจ intent
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2. call tool:
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get_drawings(contract_id)
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3. filter revision ล่าสุด
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4. ตอบ
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---
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🧠 8. Insight สำคัญ (อย่าพลาด)
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❌ อย่าทำแบบนี้
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ให้ AI query DB ตรงๆ
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ให้ AI ตัดสินใจ workflow
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✅ ต้องทำแบบนี้
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AI → call function (tool)
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system → validate logic
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---
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🚀 9. Roadmap (ทำจริง)
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Phase 1 (2–4 สัปดาห์)
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AI chat + query DB
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basic tools
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Phase 2
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RAG (search document)
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embedding
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Phase 3
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automation
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alert
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classification
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---
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🎯 10. ถ้าจะทำให้ “โหดจริง”
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👉 ultimate version:
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AI = Document Controller
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auto check:
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revision mismatch
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missing approval
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outdated drawing
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---
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💡 สรุปสั้น
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AI DMS =
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> “AI + Workflow + Document + Database”
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ไม่ใช่แค่ chatbot
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---
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จากเนื้อหาข้างบน เทียบกับ repo ของผม ขอสรุป แบบละเฮียกทีละข้อ
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