Backend: - Add CategoryMode enum with 4 modes (fixed_only, fixed_plus_custom, custom_only, dynamic_auto) - Add Step 0 for LLM category analysis before attribute generation - Implement dynamic prompts for Step 1/2 that work with N categories - Add execute_step0(), resolve_final_categories(), assemble_dynamic_attribute_tree() - Update SSE events to include step0_start, step0_complete, categories_resolved Frontend: - Add CategorySelector component with mode selection, custom category input, and category count slider - Update types with CategoryDefinition, Step0Result, DynamicStep1Result, DynamicCausalChain - Update api.ts with new SSE event handlers - Update useAttribute hook with category parameters - Integrate CategorySelector into InputPanel - Fix mindmap to dynamically extract and display N categories (was hardcoded to 4) - Add CSS styles for depth 5-8 to support more category levels 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
216 lines
6.9 KiB
Python
216 lines
6.9 KiB
Python
from typing import List, Optional, Dict
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import json
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DEFAULT_CATEGORIES = ["材料", "功能", "用途", "使用族群", "特性"]
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CATEGORY_DESCRIPTIONS = {
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"材料": "物件由什麼材料組成",
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"功能": "物件能做什麼",
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"用途": "物件在什麼場景使用",
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"使用族群": "誰會使用這個物件",
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"特性": "物件有什麼特徵",
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}
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def get_attribute_prompt(query: str, categories: Optional[List[str]] = None) -> str:
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"""Generate prompt with causal chain structure."""
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prompt = f"""分析「{query}」的屬性,以因果鏈方式呈現:材料→功能→用途→使用族群。
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請列出 3-5 種材料,每種材料延伸出完整因果鏈。
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JSON 格式:
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{{"name": "{query}", "children": [{{"name": "材料名", "category": "材料", "children": [{{"name": "功能名", "category": "功能", "children": [{{"name": "用途名", "category": "用途", "children": [{{"name": "族群名", "category": "使用族群"}}]}}]}}]}}]}}
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只回傳 JSON。"""
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return prompt
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def get_step1_attributes_prompt(query: str) -> str:
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"""Step 1: 生成各類別的屬性列表(平行結構)"""
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return f"""/no_think
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分析「{query}」,列出以下四個類別的屬性。每個類別列出 3-5 個常見屬性。
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只回傳 JSON,格式如下:
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{{"materials": ["材料1", "材料2", "材料3"], "functions": ["功能1", "功能2", "功能3"], "usages": ["用途1", "用途2", "用途3"], "users": ["族群1", "族群2", "族群3"]}}
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物件:{query}"""
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def get_step2_causal_chain_prompt(
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query: str,
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materials: List[str],
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functions: List[str],
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usages: List[str],
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users: List[str],
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existing_chains: List[dict],
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chain_index: int
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) -> str:
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"""Step 2: 生成單條因果鏈"""
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existing_chains_text = ""
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if existing_chains:
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chains_list = [
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f"- {c['material']} → {c['function']} → {c['usage']} → {c['user']}"
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for c in existing_chains
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]
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existing_chains_text = f"""
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【已生成的因果鏈,請勿重複】
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{chr(10).join(chains_list)}
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"""
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return f"""/no_think
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為「{query}」生成第 {chain_index} 條因果鏈。
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【可選材料】{', '.join(materials)}
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【可選功能】{', '.join(functions)}
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【可選用途】{', '.join(usages)}
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【可選族群】{', '.join(users)}
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{existing_chains_text}
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【規則】
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1. 從每個類別選擇一個屬性,組成合理的因果鏈
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2. 因果關係必須合邏輯(材料決定功能,功能決定用途,用途決定族群)
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3. 不要與已生成的因果鏈重複
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只回傳 JSON:
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{{"material": "選擇的材料", "function": "選擇的功能", "usage": "選擇的用途", "user": "選擇的族群"}}"""
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def get_flat_attribute_prompt(query: str, categories: Optional[List[str]] = None) -> str:
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"""Generate prompt with flat/parallel categories (original design)."""
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cats = categories if categories else DEFAULT_CATEGORIES
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# Build category list
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category_lines = []
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for cat in cats:
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desc = CATEGORY_DESCRIPTIONS.get(cat, f"{cat}的相關屬性")
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category_lines.append(f"- {cat}:{desc}")
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categories_text = "\n".join(category_lines)
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prompt = f"""/no_think
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你是一個物件屬性分析專家。請將用戶輸入的物件拆解成以下屬性類別。
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【必須包含的類別】
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{categories_text}
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【重要】回傳格式必須是合法的 JSON,每個節點都必須有 "name" 欄位:
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```json
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{{
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"name": "物件名稱",
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"children": [
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{{
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"name": "類別名稱",
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"children": [
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{{"name": "屬性1"}},
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{{"name": "屬性2"}}
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]
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}}
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]
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}}
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```
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只回傳 JSON,不要有任何其他文字。
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用戶輸入:{query}"""
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return prompt
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# ===== Dynamic category system prompts =====
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def get_step0_category_analysis_prompt(query: str, suggested_count: int = 3) -> str:
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"""Step 0: LLM 分析建議類別"""
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return f"""/no_think
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分析「{query}」,建議 {suggested_count} 個最適合的屬性類別來描述它。
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【常見類別參考】材料、功能、用途、使用族群、特性、形狀、顏色、尺寸、品牌、價格區間
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【重要】
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1. 選擇最能描述此物件本質的類別
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2. 類別之間應該有邏輯關係(如:材料→功能→用途)
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3. 不要選擇過於抽象或重複的類別
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只回傳 JSON:
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{{
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"categories": [
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{{"name": "類別1", "description": "說明1", "order": 0}},
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{{"name": "類別2", "description": "說明2", "order": 1}}
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]
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}}
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物件:{query}"""
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def get_step1_dynamic_attributes_prompt(
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query: str,
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categories: List # List[CategoryDefinition]
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) -> str:
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"""動態 Step 1 - 根據類別列表生成屬性"""
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# 按 order 排序並構建描述
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sorted_cats = sorted(categories, key=lambda x: x.order if hasattr(x, 'order') else x.get('order', 0))
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category_desc = "\n".join([
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f"- {cat.name if hasattr(cat, 'name') else cat['name']}: {cat.description if hasattr(cat, 'description') else cat.get('description', '相關屬性')}"
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for cat in sorted_cats
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])
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category_keys = [cat.name if hasattr(cat, 'name') else cat['name'] for cat in sorted_cats]
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json_template = {cat: ["屬性1", "屬性2", "屬性3"] for cat in category_keys}
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return f"""/no_think
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分析「{query}」,列出以下類別的屬性。每個類別列出 3-5 個常見屬性。
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【類別列表】
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{category_desc}
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只回傳 JSON:
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{json.dumps(json_template, ensure_ascii=False, indent=2)}
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物件:{query}"""
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def get_step2_dynamic_causal_chain_prompt(
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query: str,
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categories: List, # List[CategoryDefinition]
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attributes_by_category: Dict[str, List[str]],
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existing_chains: List[Dict[str, str]],
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chain_index: int
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) -> str:
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"""動態 Step 2 - 生成動態類別的因果鏈"""
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sorted_cats = sorted(categories, key=lambda x: x.order if hasattr(x, 'order') else x.get('order', 0))
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# 構建可選屬性
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available_attrs = "\n".join([
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f"【{cat.name if hasattr(cat, 'name') else cat['name']}】{', '.join(attributes_by_category.get(cat.name if hasattr(cat, 'name') else cat['name'], []))}"
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for cat in sorted_cats
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])
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# 已生成的因果鏈
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existing_text = ""
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if existing_chains:
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chains_list = [
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" → ".join([chain.get(cat.name if hasattr(cat, 'name') else cat['name'], '?') for cat in sorted_cats])
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for chain in existing_chains
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]
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existing_text = f"\n【已生成,請勿重複】\n" + "\n".join([f"- {c}" for c in chains_list])
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# JSON 模板
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json_template = {cat.name if hasattr(cat, 'name') else cat['name']: f"選擇的{cat.name if hasattr(cat, 'name') else cat['name']}" for cat in sorted_cats}
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return f"""/no_think
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為「{query}」生成第 {chain_index} 條因果鏈。
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【可選屬性】
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{available_attrs}
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{existing_text}
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【規則】
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1. 從每個類別選擇一個屬性
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2. 因果關係必須合理
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3. 不要重複
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只回傳 JSON:
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{json.dumps(json_template, ensure_ascii=False, indent=2)}"""
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