Initial commit

backend/app/routers/attributes.py

@@ -0,0 +1,178 @@
+import json
+import logging
+from typing import AsyncGenerator, List
+
+from fastapi import APIRouter, HTTPException
+from fastapi.responses import StreamingResponse
+
+from ..models.schemas import (
+    ModelListResponse,
+    StreamAnalyzeRequest,
+    Step1Result,
+    CausalChain,
+    AttributeNode,
+)
+from ..prompts.attribute_prompt import (
+    get_step1_attributes_prompt,
+    get_step2_causal_chain_prompt,
+)
+from ..services.llm_service import ollama_provider, extract_json_from_response
+
+logger = logging.getLogger(__name__)
+router = APIRouter(prefix="/api", tags=["attributes"])
+
+
+def assemble_attribute_tree(query: str, chains: List[CausalChain]) -> AttributeNode:
+    """將因果鏈組裝成樹狀結構"""
+    # 以材料為第一層分組
+    material_map = {}
+
+    for chain in chains:
+        if chain.material not in material_map:
+            material_map[chain.material] = []
+        material_map[chain.material].append(chain)
+
+    # 構建樹狀結構
+    root = AttributeNode(name=query, children=[])
+
+    for material, material_chains in material_map.items():
+        material_node = AttributeNode(name=material, category="材料", children=[])
+
+        # 以功能為第二層分組
+        function_map = {}
+        for chain in material_chains:
+            if chain.function not in function_map:
+                function_map[chain.function] = []
+            function_map[chain.function].append(chain)
+
+        for function, function_chains in function_map.items():
+            function_node = AttributeNode(name=function, category="功能", children=[])
+
+            # 以用途為第三層分組
+            usage_map = {}
+            for chain in function_chains:
+                if chain.usage not in usage_map:
+                    usage_map[chain.usage] = []
+                usage_map[chain.usage].append(chain)
+
+            for usage, usage_chains in usage_map.items():
+                usage_node = AttributeNode(
+                    name=usage,
+                    category="用途",
+                    children=[
+                        AttributeNode(name=c.user, category="使用族群")
+                        for c in usage_chains
+                    ],
+                )
+                function_node.children.append(usage_node)
+
+            material_node.children.append(function_node)
+
+        root.children.append(material_node)
+
+    return root
+
+
+async def generate_sse_events(request: StreamAnalyzeRequest) -> AsyncGenerator[str, None]:
+    """生成 SSE 事件流"""
+    try:
+        temperature = request.temperature if request.temperature is not None else 0.7
+
+        # ========== Step 1: 生成屬性列表 ==========
+        yield f"event: step1_start\ndata: {json.dumps({'message': '正在分析屬性列表...'}, ensure_ascii=False)}\n\n"
+
+        step1_prompt = get_step1_attributes_prompt(request.query)
+        logger.info(f"Step 1 prompt: {step1_prompt[:200]}")
+
+        step1_response = await ollama_provider.generate(
+            step1_prompt, model=request.model, temperature=temperature
+        )
+        logger.info(f"Step 1 response: {step1_response[:500]}")
+
+        step1_data = extract_json_from_response(step1_response)
+        step1_result = Step1Result(**step1_data)
+
+        yield f"event: step1_complete\ndata: {json.dumps({'result': step1_result.model_dump()}, ensure_ascii=False)}\n\n"
+
+        # ========== Step 2: 逐條生成因果鏈 ==========
+        causal_chains: List[CausalChain] = []
+
+        for i in range(request.chain_count):
+            chain_index = i + 1
+
+            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"
+
+            step2_prompt = get_step2_causal_chain_prompt(
+                query=request.query,
+                materials=step1_result.materials,
+                functions=step1_result.functions,
+                usages=step1_result.usages,
+                users=step1_result.users,
+                existing_chains=[c.model_dump() for c in causal_chains],
+                chain_index=chain_index,
+            )
+
+            # 逐漸提高 temperature 增加多樣性
+            chain_temperature = min(temperature + 0.05 * i, 1.0)
+
+            max_retries = 2
+            chain = None
+            for attempt in range(max_retries):
+                try:
+                    step2_response = await ollama_provider.generate(
+                        step2_prompt, model=request.model, temperature=chain_temperature
+                    )
+                    logger.info(f"Chain {chain_index} response: {step2_response[:300]}")
+
+                    chain_data = extract_json_from_response(step2_response)
+                    chain = CausalChain(**chain_data)
+                    break
+                except Exception as e:
+                    logger.warning(f"Chain {chain_index} attempt {attempt + 1} failed: {e}")
+                    if attempt < max_retries - 1:
+                        chain_temperature = min(chain_temperature + 0.1, 1.0)
+
+            if chain:
+                causal_chains.append(chain)
+                yield f"event: chain_complete\ndata: {json.dumps({'index': chain_index, 'chain': chain.model_dump()}, ensure_ascii=False)}\n\n"
+            else:
+                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"
+
+        # ========== 組裝最終結構 ==========
+        final_tree = assemble_attribute_tree(request.query, causal_chains)
+
+        final_result = {
+            "query": request.query,
+            "step1_result": step1_result.model_dump(),
+            "causal_chains": [c.model_dump() for c in causal_chains],
+            "attributes": final_tree.model_dump(),
+        }
+        yield f"event: done\ndata: {json.dumps(final_result, ensure_ascii=False)}\n\n"
+
+    except Exception as e:
+        logger.error(f"SSE generation error: {e}")
+        yield f"event: error\ndata: {json.dumps({'error': str(e)}, ensure_ascii=False)}\n\n"
+
+
+@router.post("/analyze")
+async def analyze_stream(request: StreamAnalyzeRequest):
+    """多步驟分析 with SSE streaming"""
+    return StreamingResponse(
+        generate_sse_events(request),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",
+        },
+    )
+
+
+@router.get("/models", response_model=ModelListResponse)
+async def list_models():
+    """List available LLM models."""
+    try:
+        models = await ollama_provider.list_models()
+        return ModelListResponse(models=models)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))