feat: Migrate to React Flow and add Fixed + Dynamic category mode
Frontend: - Migrate MindmapDAG from D3.js to React Flow (@xyflow/react) - Add custom node components (QueryNode, CategoryHeaderNode, AttributeNode) - Add useDAGLayout hook for column-based layout - Add "AI" badge for LLM-suggested categories - Update CategorySelector with Fixed + Dynamic mode option - Improve dark/light theme support Backend: - Add FIXED_PLUS_DYNAMIC category mode - Filter duplicate category names in LLM suggestions - Update prompts to exclude fixed categories when suggesting new ones - Improve LLM service with better error handling and logging - Auto-remove /no_think prefix for non-Qwen models - Add smart JSON format detection for model compatibility - Improve JSON extraction with multiple parsing strategies 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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@@ -16,6 +16,10 @@ from ..models.schemas import (
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Step0Result,
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DynamicStep1Result,
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DynamicCausalChain,
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DAGNode,
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DAGEdge,
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AttributeDAG,
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DAGRelationship,
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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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@@ -23,6 +27,7 @@ from ..prompts.attribute_prompt import (
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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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get_step2_dag_relationships_prompt,
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)
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from ..services.llm_service import ollama_provider, extract_json_from_response
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@@ -39,14 +44,21 @@ FIXED_CATEGORIES = [
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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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async def execute_step0(
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request: StreamAnalyzeRequest,
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exclude_categories: List[str] | None = None
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) -> Step0Result | None:
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"""Execute Step 0 - LLM category analysis
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Called only for modes that need LLM to suggest categories:
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- FIXED_PLUS_DYNAMIC
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- CUSTOM_ONLY
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- DYNAMIC_AUTO
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"""
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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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request.suggested_category_count,
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exclude_categories=exclude_categories
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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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@@ -83,6 +95,34 @@ def resolve_final_categories(
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)
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return categories
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elif request.category_mode == CategoryMode.FIXED_PLUS_DYNAMIC:
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# Fixed categories + LLM suggested categories
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categories = [
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CategoryDefinition(
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name=cat.name,
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description=cat.description,
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is_fixed=True,
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order=i
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)
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for i, cat in enumerate(FIXED_CATEGORIES)
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]
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if step0_result:
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# Filter out LLM categories that duplicate fixed category names
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fixed_names = {cat.name for cat in FIXED_CATEGORIES}
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added_count = 0
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for cat in step0_result.categories:
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if cat.name not in fixed_names:
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categories.append(
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CategoryDefinition(
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name=cat.name,
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description=cat.description,
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is_fixed=False,
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order=len(FIXED_CATEGORIES) + added_count
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)
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)
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added_count += 1
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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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@@ -192,17 +232,73 @@ def assemble_attribute_tree(query: str, chains: List[CausalChain]) -> AttributeN
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return root
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def build_dag_from_relationships(
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query: str,
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categories: List[CategoryDefinition],
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attributes_by_category: dict,
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relationships: List[DAGRelationship],
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) -> AttributeDAG:
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"""從屬性和關係建構 DAG"""
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sorted_cats = sorted(categories, key=lambda x: x.order)
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# 建立節點 - 每個屬性只出現一次
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nodes: List[DAGNode] = []
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node_id_map: dict = {} # (category, name) -> id
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for cat in sorted_cats:
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cat_name = cat.name
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for idx, attr_name in enumerate(attributes_by_category.get(cat_name, [])):
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node_id = f"{cat_name}_{idx}"
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nodes.append(DAGNode(
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id=node_id,
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name=attr_name,
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category=cat_name,
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order=idx
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))
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node_id_map[(cat_name, attr_name)] = node_id
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# 建立邊
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edges: List[DAGEdge] = []
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for rel in relationships:
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source_key = (rel.source_category, rel.source)
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target_key = (rel.target_category, rel.target)
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if source_key in node_id_map and target_key in node_id_map:
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edges.append(DAGEdge(
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source_id=node_id_map[source_key],
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target_id=node_id_map[target_key]
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))
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return AttributeDAG(
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query=query,
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categories=sorted_cats,
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nodes=nodes,
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edges=edges
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)
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async def generate_sse_events(request: StreamAnalyzeRequest) -> AsyncGenerator[str, None]:
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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 0: Category Analysis (if needed) ==========
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# Only these modes need LLM category analysis
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needs_step0 = request.category_mode in [
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CategoryMode.FIXED_PLUS_DYNAMIC,
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CategoryMode.CUSTOM_ONLY,
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CategoryMode.DYNAMIC_AUTO,
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]
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step0_result = None
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if request.category_mode != CategoryMode.FIXED_ONLY:
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if needs_step0:
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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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step0_result = await execute_step0(request)
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# For FIXED_PLUS_DYNAMIC, exclude the fixed category names
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exclude_cats = None
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if request.category_mode == CategoryMode.FIXED_PLUS_DYNAMIC:
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exclude_cats = [cat.name for cat in FIXED_CATEGORIES]
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step0_result = await execute_step0(request, exclude_categories=exclude_cats)
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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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@@ -227,58 +323,58 @@ async def generate_sse_events(request: StreamAnalyzeRequest) -> AsyncGenerator[s
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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: Generate Causal Chains (Dynamic) ==========
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causal_chains: List[DynamicCausalChain] = []
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# ========== Step 2: Generate Relationships (DAG) ==========
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yield f"event: relationships_start\ndata: {json.dumps({'message': '生成關係...'}, ensure_ascii=False)}\n\n"
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for i in range(request.chain_count):
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chain_index = i + 1
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step2_prompt = get_step2_dag_relationships_prompt(
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query=request.query,
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categories=final_categories,
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attributes_by_category=step1_result.attributes,
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)
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logger.info(f"Step 2 (relationships) prompt: {step2_prompt[:300]}")
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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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relationships: List[DAGRelationship] = []
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max_retries = 2
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for attempt in range(max_retries):
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try:
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step2_response = await ollama_provider.generate(
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step2_prompt, model=request.model, temperature=temperature
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)
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logger.info(f"Relationships response: {step2_response[:500]}")
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step2_prompt = get_step2_dynamic_causal_chain_prompt(
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query=request.query,
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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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rel_data = extract_json_from_response(step2_response)
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raw_relationships = rel_data.get("relationships", [])
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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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for rel in raw_relationships:
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relationships.append(DAGRelationship(
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source_category=rel.get("source_category", ""),
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source=rel.get("source", ""),
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target_category=rel.get("target_category", ""),
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target=rel.get("target", ""),
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))
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break
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except Exception as e:
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logger.warning(f"Relationships attempt {attempt + 1} failed: {e}")
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if attempt < max_retries - 1:
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temperature = min(temperature + 0.1, 1.0)
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max_retries = 2
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chain = None
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for attempt in range(max_retries):
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try:
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step2_response = await ollama_provider.generate(
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step2_prompt, model=request.model, temperature=chain_temperature
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)
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logger.info(f"Chain {chain_index} response: {step2_response[:300]}")
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yield f"event: relationships_complete\ndata: {json.dumps({'count': len(relationships)}, ensure_ascii=False)}\n\n"
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chain_data = extract_json_from_response(step2_response)
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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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if attempt < max_retries - 1:
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chain_temperature = min(chain_temperature + 0.1, 1.0)
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if chain:
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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'生成失敗'}, ensure_ascii=False)}\n\n"
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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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# ========== Build DAG ==========
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dag = build_dag_from_relationships(
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query=request.query,
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categories=final_categories,
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attributes_by_category=step1_result.attributes,
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relationships=relationships,
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)
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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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"relationships": [r.model_dump() for r in relationships],
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"dag": dag.model_dump(),
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}
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yield f"event: done\ndata: {json.dumps(final_result, ensure_ascii=False)}\n\n"
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