feat: Add dynamic category system for attribute analysis
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>
This commit is contained in:
@@ -1,5 +1,6 @@
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from pydantic import BaseModel
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from typing import Optional, List
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from typing import Optional, List, Dict
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from enum import Enum
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class AttributeNode(BaseModel):
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@@ -46,12 +47,17 @@ class CausalChain(BaseModel):
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class StreamAnalyzeRequest(BaseModel):
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"""多步驟分析請求"""
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"""多步驟分析請求（更新為支持動態類別）"""
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query: str
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model: Optional[str] = None
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temperature: Optional[float] = 0.7
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chain_count: int = 5 # 用戶可設定要生成多少條因果鏈
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# 新增：動態類別支持
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category_mode: Optional[str] = "dynamic_auto" # CategoryMode enum 值
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custom_categories: Optional[List[str]] = None
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suggested_category_count: int = 3 # 建議 LLM 生成的類別數量
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class StreamAnalyzeResponse(BaseModel):
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"""最終完整結果"""
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@@ -59,3 +65,36 @@ class StreamAnalyzeResponse(BaseModel):
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step1_result: Step1Result
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causal_chains: List[CausalChain]
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attributes: AttributeNode
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# ===== Dynamic category system schemas =====
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class CategoryMode(str, Enum):
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"""類別模式"""
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FIXED_ONLY = "fixed_only"
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FIXED_PLUS_CUSTOM = "fixed_plus_custom"
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CUSTOM_ONLY = "custom_only"
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DYNAMIC_AUTO = "dynamic_auto"
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class CategoryDefinition(BaseModel):
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"""類別定義"""
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name: str
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description: Optional[str] = None
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is_fixed: bool = True # LLM 生成的為 False
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order: int = 0
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class Step0Result(BaseModel):
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"""Step 0: LLM 分析建議類別"""
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categories: List[CategoryDefinition]
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class DynamicStep1Result(BaseModel):
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"""動態版本的 Step 1 結果"""
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attributes: Dict[str, List[str]] # {類別名: [屬性列表]}
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class DynamicCausalChain(BaseModel):
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"""動態版本的因果鏈"""
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chain: Dict[str, str] # {類別名: 選中屬性}
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@@ -1,4 +1,5 @@
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from typing import List, Optional
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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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@@ -115,3 +116,100 @@ def get_flat_attribute_prompt(query: str, categories: Optional[List[str]] = None
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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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@@ -11,10 +11,18 @@ from ..models.schemas import (
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Step1Result,
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CausalChain,
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AttributeNode,
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CategoryMode,
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CategoryDefinition,
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Step0Result,
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DynamicStep1Result,
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DynamicCausalChain,
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)
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from ..prompts.attribute_prompt import (
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get_step1_attributes_prompt,
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get_step2_causal_chain_prompt,
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get_step0_category_analysis_prompt,
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get_step1_dynamic_attributes_prompt,
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get_step2_dynamic_causal_chain_prompt,
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)
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from ..services.llm_service import ollama_provider, extract_json_from_response
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@@ -22,6 +30,117 @@ logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api", tags=["attributes"])
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# Fixed categories definition
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FIXED_CATEGORIES = [
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CategoryDefinition(name="材料", description="物件材料", is_fixed=True, order=0),
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CategoryDefinition(name="功能", description="物件功能", is_fixed=True, order=1),
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CategoryDefinition(name="用途", description="使用場景", is_fixed=True, order=2),
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CategoryDefinition(name="使用族群", description="目標用戶", is_fixed=True, order=3),
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]
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async def execute_step0(request: StreamAnalyzeRequest) -> Step0Result | None:
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"""Execute Step 0 - LLM category analysis"""
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if request.category_mode == CategoryMode.FIXED_ONLY:
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return None
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prompt = get_step0_category_analysis_prompt(
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request.query,
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request.suggested_category_count
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)
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temperature = request.temperature if request.temperature is not None else 0.7
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response = await ollama_provider.generate(
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prompt, model=request.model, temperature=temperature
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)
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data = extract_json_from_response(response)
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step0_result = Step0Result(**data)
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# Mark as LLM generated
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for cat in step0_result.categories:
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cat.is_fixed = False
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return step0_result
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def resolve_final_categories(
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request: StreamAnalyzeRequest,
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step0_result: Step0Result | None
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) -> List[CategoryDefinition]:
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"""Determine final categories based on mode"""
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if request.category_mode == CategoryMode.FIXED_ONLY:
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return FIXED_CATEGORIES
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elif request.category_mode == CategoryMode.FIXED_PLUS_CUSTOM:
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categories = FIXED_CATEGORIES.copy()
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if request.custom_categories:
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for i, name in enumerate(request.custom_categories):
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categories.append(
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CategoryDefinition(
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name=name, is_fixed=False,
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order=len(FIXED_CATEGORIES) + i
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)
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)
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return categories
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elif request.category_mode == CategoryMode.CUSTOM_ONLY:
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return step0_result.categories if step0_result else FIXED_CATEGORIES
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elif request.category_mode == CategoryMode.DYNAMIC_AUTO:
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return step0_result.categories if step0_result else FIXED_CATEGORIES
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return FIXED_CATEGORIES
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def assemble_dynamic_attribute_tree(
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query: str,
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chains: List[DynamicCausalChain],
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categories: List[CategoryDefinition]
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) -> AttributeNode:
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"""Assemble dynamic N-level tree from causal chains"""
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sorted_cats = sorted(categories, key=lambda x: x.order)
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if not chains:
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return AttributeNode(name=query, children=[])
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def build_recursive(
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level: int,
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parent_path: dict,
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remaining_chains: List[DynamicCausalChain]
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) -> List[AttributeNode]:
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if level >= len(sorted_cats):
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return []
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current_cat = sorted_cats[level]
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grouped = {}
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for chain in remaining_chains:
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# Check if this chain matches the parent path
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if all(chain.chain.get(k) == v for k, v in parent_path.items()):
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attr_val = chain.chain.get(current_cat.name)
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if attr_val:
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if attr_val not in grouped:
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grouped[attr_val] = []
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grouped[attr_val].append(chain)
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nodes = []
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for attr_val, child_chains in grouped.items():
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new_path = {**parent_path, current_cat.name: attr_val}
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children = build_recursive(level + 1, new_path, child_chains)
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node = AttributeNode(
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name=attr_val,
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category=current_cat.name,
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children=children if children else None
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)
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nodes.append(node)
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return nodes
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root_children = build_recursive(0, {}, chains)
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return AttributeNode(name=query, children=root_children)
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def assemble_attribute_tree(query: str, chains: List[CausalChain]) -> AttributeNode:
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"""將因果鏈組裝成樹狀結構"""
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# 以材料為第一層分組
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@@ -74,14 +193,28 @@ def assemble_attribute_tree(query: str, chains: List[CausalChain]) -> AttributeN
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async def generate_sse_events(request: StreamAnalyzeRequest) -> AsyncGenerator[str, None]:
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"""生成 SSE 事件流"""
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"""Generate SSE events with dynamic category support"""
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try:
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temperature = request.temperature if request.temperature is not None else 0.7
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# ========== Step 1: 生成屬性列表 ==========
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yield f"event: step1_start\ndata: {json.dumps({'message': '正在分析屬性列表...'}, ensure_ascii=False)}\n\n"
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# ========== Step 0: Category Analysis (if needed) ==========
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step0_result = None
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if request.category_mode != CategoryMode.FIXED_ONLY:
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yield f"event: step0_start\ndata: {json.dumps({'message': '分析類別...'}, ensure_ascii=False)}\n\n"
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step1_prompt = get_step1_attributes_prompt(request.query)
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step0_result = await execute_step0(request)
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if step0_result:
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yield f"event: step0_complete\ndata: {json.dumps({'result': step0_result.model_dump()}, ensure_ascii=False)}\n\n"
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# ========== Resolve Final Categories ==========
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final_categories = resolve_final_categories(request, step0_result)
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yield f"event: categories_resolved\ndata: {json.dumps({'categories': [c.model_dump() for c in final_categories]}, ensure_ascii=False)}\n\n"
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# ========== Step 1: Generate Attributes (Dynamic) ==========
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yield f"event: step1_start\ndata: {json.dumps({'message': '生成屬性...'}, ensure_ascii=False)}\n\n"
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step1_prompt = get_step1_dynamic_attributes_prompt(request.query, final_categories)
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logger.info(f"Step 1 prompt: {step1_prompt[:200]}")
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step1_response = await ollama_provider.generate(
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@@ -90,29 +223,27 @@ async def generate_sse_events(request: StreamAnalyzeRequest) -> AsyncGenerator[s
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logger.info(f"Step 1 response: {step1_response[:500]}")
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step1_data = extract_json_from_response(step1_response)
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step1_result = Step1Result(**step1_data)
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step1_result = DynamicStep1Result(attributes=step1_data)
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yield f"event: step1_complete\ndata: {json.dumps({'result': step1_result.model_dump()}, ensure_ascii=False)}\n\n"
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# ========== Step 2: 逐條生成因果鏈 ==========
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causal_chains: List[CausalChain] = []
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# ========== Step 2: Generate Causal Chains (Dynamic) ==========
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causal_chains: List[DynamicCausalChain] = []
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for i in range(request.chain_count):
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chain_index = i + 1
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yield f"event: chain_start\ndata: {json.dumps({'index': chain_index, 'total': request.chain_count, 'message': f'正在生成第 {chain_index}/{request.chain_count} 條因果鏈...'}, ensure_ascii=False)}\n\n"
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step2_prompt = get_step2_causal_chain_prompt(
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step2_prompt = get_step2_dynamic_causal_chain_prompt(
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query=request.query,
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materials=step1_result.materials,
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functions=step1_result.functions,
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usages=step1_result.usages,
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users=step1_result.users,
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existing_chains=[c.model_dump() for c in causal_chains],
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categories=final_categories,
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attributes_by_category=step1_result.attributes,
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existing_chains=[c.chain for c in causal_chains],
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chain_index=chain_index,
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)
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# 逐漸提高 temperature 增加多樣性
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# Gradually increase temperature for diversity
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chain_temperature = min(temperature + 0.05 * i, 1.0)
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max_retries = 2
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@@ -125,7 +256,7 @@ async def generate_sse_events(request: StreamAnalyzeRequest) -> AsyncGenerator[s
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logger.info(f"Chain {chain_index} response: {step2_response[:300]}")
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chain_data = extract_json_from_response(step2_response)
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chain = CausalChain(**chain_data)
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chain = DynamicCausalChain(chain=chain_data)
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break
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except Exception as e:
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logger.warning(f"Chain {chain_index} attempt {attempt + 1} failed: {e}")
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@@ -136,13 +267,15 @@ async def generate_sse_events(request: StreamAnalyzeRequest) -> AsyncGenerator[s
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causal_chains.append(chain)
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yield f"event: chain_complete\ndata: {json.dumps({'index': chain_index, 'chain': chain.model_dump()}, ensure_ascii=False)}\n\n"
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else:
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yield f"event: chain_error\ndata: {json.dumps({'index': chain_index, 'error': f'第 {chain_index} 條因果鏈生成失敗'}, ensure_ascii=False)}\n\n"
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yield f"event: chain_error\ndata: {json.dumps({'index': chain_index, 'error': f'生成失敗'}, ensure_ascii=False)}\n\n"
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# ========== 組裝最終結構 ==========
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final_tree = assemble_attribute_tree(request.query, causal_chains)
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# ========== Assemble Final Tree (Dynamic) ==========
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final_tree = assemble_dynamic_attribute_tree(request.query, causal_chains, final_categories)
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final_result = {
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"query": request.query,
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"step0_result": step0_result.model_dump() if step0_result else None,
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"categories_used": [c.model_dump() for c in final_categories],
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"step1_result": step1_result.model_dump(),
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"causal_chains": [c.model_dump() for c in causal_chains],
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"attributes": final_tree.model_dump(),
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Reference in New Issue
Block a user