feat: Add Deduplication Agent with embedding and LLM methods

Implement a new Deduplication Agent that identifies and groups similar
transformation descriptions. Supports two deduplication methods:
- Embedding: Fast vector similarity comparison using cosine similarity
- LLM: Accurate pairwise semantic comparison (slower but more precise)

Backend changes:
- Add deduplication router with /deduplicate endpoint
- Add embedding_service for vector-based similarity
- Add llm_deduplication_service for LLM-based comparison
- Improve expert_transformation error handling and progress reporting

Frontend changes:
- Add DeduplicationPanel with interactive group visualization
- Add useDeduplication hook for state management
- Integrate deduplication tab in main App
- Add threshold slider and method selector in sidebar

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

.gitignore

@@ -36,3 +36,6 @@ env/
 .DS_Store
 .idea/
 .vscode/
+
+# Serena (MCP tools)
+.serena/

backend/app/main.py

@@ -3,14 +3,18 @@ from contextlib import asynccontextmanager
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 
-from .routers import attributes, transformation, expert_transformation
+from .routers import attributes, transformation, expert_transformation, deduplication
 from .services.llm_service import ollama_provider
+from .services.embedding_service import embedding_service
+from .services.llm_deduplication_service import llm_deduplication_service
 
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
     yield
     await ollama_provider.close()
+    await embedding_service.close()
+    await llm_deduplication_service.close()
 
 
 app = FastAPI(
@@ -31,6 +35,7 @@ app.add_middleware(
 app.include_router(attributes.router)
 app.include_router(transformation.router)
 app.include_router(expert_transformation.router)
+app.include_router(deduplication.router)
 
 
 @app.get("/")

backend/app/models/schemas.py

@@ -232,3 +232,38 @@ class ExpertTransformationRequest(BaseModel):
     # LLM parameters
     model: Optional[str] = None
     temperature: Optional[float] = 0.7
+
+
+# ===== Deduplication Agent schemas =====
+
+class DeduplicationMethod(str, Enum):
+    """去重方法"""
+    EMBEDDING = "embedding"  # 向量相似度
+    LLM = "llm"              # LLM 成對判斷
+
+
+class DeduplicationRequest(BaseModel):
+    """去重請求"""
+    descriptions: List[ExpertTransformationDescription]
+    method: DeduplicationMethod = DeduplicationMethod.EMBEDDING  # 去重方法
+    similarity_threshold: float = 0.85  # 餘弦相似度閾值 (0.0-1.0)，僅 Embedding 使用
+    model: Optional[str] = None  # Embedding/LLM 模型
+
+
+class DescriptionGroup(BaseModel):
+    """相似描述分組"""
+    group_id: str                          # "group-0", "group-1"...
+    representative: ExpertTransformationDescription  # 代表描述
+    duplicates: List[ExpertTransformationDescription]  # 相似描述
+    similarity_scores: List[float]         # 每個重複項的相似度分數
+
+
+class DeduplicationResult(BaseModel):
+    """去重結果"""
+    total_input: int           # 輸入描述總數
+    total_groups: int          # 分組數量
+    total_duplicates: int      # 重複項數量
+    groups: List[DescriptionGroup]
+    threshold_used: float
+    method_used: DeduplicationMethod       # 使用的去重方法
+    model_used: str                        # 使用的模型

backend/app/prompts/expert_transformation_prompt.py

@@ -90,16 +90,15 @@ def get_single_description_prompt(
 ) -> str:
     """Step 2: 為單一關鍵字生成描述"""
     # 如果 domain 是通用的，就只用職業名稱
-    domain_text = f"（{expert_domain}）" if expert_domain and expert_domain != "Professional Field" else ""
+    domain_text = f"（{expert_domain}領域）" if expert_domain and expert_domain != "Professional Field" else ""
 
     return f"""/no_think
-物件：「{query}」
+你是一位{expert_name}{domain_text}。
-專家：{expert_name}{domain_text}
+
+任務：為「{query}」生成一段創新應用描述。
 關鍵字：{keyword}
 
-你是一位{expert_name}。從你的專業視角，生成一段創新應用描述（15-30字），說明如何將「{keyword}」的概念應用到「{query}」上。
+從你的專業視角，說明如何將「{keyword}」的概念應用到「{query}」上。描述要具體、有創意，15-30字。
 
-描述要體現{expert_name}的專業思維和獨特觀點。
+只回傳 JSON，不要其他文字：
-
+{{"description": "你的創新應用描述"}}"""
-回傳 JSON：
-{{"description": "應用描述"}}"""

backend/app/routers/deduplication.py

@@ -0,0 +1,93 @@
+"""
+Deduplication Router - 使用 Embedding 或 LLM 去重描述
+
+提供 API 端點將相似的創新描述分組，幫助識別重複的想法。
+支援兩種方法：
+- Embedding: 快速向量相似度比較
+- LLM: 精準語意判斷（較慢但更準確）
+"""
+
+import logging
+from fastapi import APIRouter, HTTPException
+
+from ..models.schemas import DeduplicationRequest, DeduplicationResult, DeduplicationMethod
+from ..services.embedding_service import embedding_service
+from ..services.llm_deduplication_service import llm_deduplication_service
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/api/deduplication", tags=["deduplication"])
+
+
+@router.post("/deduplicate", response_model=DeduplicationResult)
+async def deduplicate_descriptions(request: DeduplicationRequest) -> DeduplicationResult:
+    """
+    去重描述
+
+    支援兩種方法：
+    - embedding: 使用向量相似度（快速）
+    - llm: 使用 LLM 成對比較（精準但較慢）
+
+    Args:
+        request: 去重請求，包含描述列表、方法選擇和相關參數
+
+    Returns:
+        DeduplicationResult: 去重結果，包含分組資訊
+
+    Raises:
+        HTTPException: 如果去重處理失敗
+    """
+    method = request.method
+    logger.info(f"Deduplication request: {len(request.descriptions)} descriptions, method={method.value}, threshold={request.similarity_threshold}")
+
+    if not request.descriptions:
+        return DeduplicationResult(
+            total_input=0,
+            total_groups=0,
+            total_duplicates=0,
+            groups=[],
+            threshold_used=request.similarity_threshold,
+            method_used=method,
+            model_used=request.model or ("nomic-embed-text" if method == DeduplicationMethod.EMBEDDING else "qwen3:4b")
+        )
+
+    try:
+        if method == DeduplicationMethod.EMBEDDING:
+            # 使用 Embedding 相似度去重
+            result = await embedding_service.deduplicate(
+                descriptions=request.descriptions,
+                threshold=request.similarity_threshold,
+                model=request.model
+            )
+        else:
+            # 使用 LLM 成對比較去重
+            result = await llm_deduplication_service.deduplicate(
+                descriptions=request.descriptions,
+                model=request.model
+            )
+        return result
+    except ValueError as e:
+        logger.error(f"Deduplication failed: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+    except Exception as e:
+        logger.error(f"Unexpected error during deduplication: {e}")
+        raise HTTPException(status_code=500, detail=f"Deduplication failed: {str(e)}")
+
+
+@router.get("/models")
+async def list_embedding_models():
+    """
+    列出可用的 Embedding 模型
+
+    Returns:
+        dict: 可用模型列表和建議的預設模型
+    """
+    return {
+        "default": "nomic-embed-text",
+        "available": [
+            {"name": "nomic-embed-text", "description": "Fast and efficient embedding model"},
+            {"name": "mxbai-embed-large", "description": "High quality embeddings"},
+            {"name": "all-minilm", "description": "Lightweight embedding model"},
+        ],
+        "note": "Run 'ollama pull <model>' to install a model"
+    }

backend/app/routers/expert_transformation.py

@@ -221,8 +221,27 @@ async def generate_expert_transformation_events(
                     desc_prompt, model=model, temperature=temperature
                 )
 
-                desc_data = extract_json_from_response(desc_response)
+                # 嘗試解析 JSON，若失敗則使用原始回應作為描述
-                desc_text = desc_data.get("description", "")
+                desc_text = ""
+                try:
+                    desc_data = extract_json_from_response(desc_response)
+                    # 支援多種可能的 key: description, content, text, desc
+                    desc_text = (
+                        desc_data.get("description") or
+                        desc_data.get("content") or
+                        desc_data.get("text") or
+                        desc_data.get("desc") or
+                        ""
+                    )
+                except ValueError:
+                    # JSON 解析失敗，嘗試清理原始回應作為描述
+                    cleaned = desc_response.strip()
+                    # 移除可能的 markdown 和多餘符號
+                    if cleaned.startswith('"') and cleaned.endswith('"'):
+                        cleaned = cleaned[1:-1]
+                    if len(cleaned) > 5 and len(cleaned) < 100:
+                        desc_text = cleaned
+                        logger.info(f"[DESC] 使用 fallback 描述 for '{kw.keyword}': {desc_text[:50]}")
 
                 if desc_text:
                     descriptions.append(ExpertTransformationDescription(
@@ -231,15 +250,22 @@ async def generate_expert_transformation_events(
                         expert_name=kw.expert_name,
                         description=desc_text
                     ))
+                else:
+                    logger.warning(f"[DESC] Empty description for keyword='{kw.keyword}', parsed_data={desc_data}")
 
-                # Send progress update
+                # Send progress update with success/fail status
-                yield f"event: description_progress\ndata: {json.dumps({'current': idx + 1, 'total': len(all_expert_keywords), 'keyword': kw.keyword}, ensure_ascii=False)}\n\n"
+                yield f"event: description_progress\ndata: {json.dumps({'current': idx + 1, 'total': len(all_expert_keywords), 'keyword': kw.keyword, 'success': bool(desc_text)}, ensure_ascii=False)}\n\n"
 
             except Exception as e:
-                logger.warning(f"Failed to generate description for '{kw.keyword}': {e}")
+                logger.warning(f"[DESC] Failed to generate description for '{kw.keyword}': {e}")
+                yield f"event: description_progress\ndata: {json.dumps({'current': idx + 1, 'total': len(all_expert_keywords), 'keyword': kw.keyword, 'success': False, 'error': str(e)}, ensure_ascii=False)}\n\n"
                 # Continue with next keyword
 
-        yield f"event: description_complete\ndata: {json.dumps({'count': len(descriptions)}, ensure_ascii=False)}\n\n"
+        # 統計成功率
+        success_rate = len(descriptions) / len(all_expert_keywords) * 100 if all_expert_keywords else 0
+        logger.info(f"[DESC] 描述生成完成: {len(descriptions)}/{len(all_expert_keywords)} 成功 ({success_rate:.1f}%)")
+
+        yield f"event: description_complete\ndata: {json.dumps({'count': len(descriptions), 'total': len(all_expert_keywords), 'success_rate': success_rate}, ensure_ascii=False)}\n\n"
 
         # ========== Build final result ==========
         result = ExpertTransformationCategoryResult(

backend/app/services/embedding_service.py

@@ -0,0 +1,250 @@
+"""
+Embedding Service - generates embeddings and performs similarity-based deduplication
+
+使用 Ollama 的 embedding 端點生成向量，並透過餘弦相似度進行去重分組。
+"""
+
+import logging
+from typing import List, Optional
+
+import httpx
+import numpy as np
+
+from ..config import settings
+from ..models.schemas import (
+    ExpertTransformationDescription,
+    DeduplicationResult,
+    DeduplicationMethod,
+    DescriptionGroup,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class EmbeddingService:
+    """Embedding 服務：生成向量並執行相似度去重"""
+
+    def __init__(self):
+        self.base_url = settings.ollama_base_url
+        self.default_model = "nomic-embed-text"  # Ollama 預設的 embedding 模型
+        self.client = httpx.AsyncClient(timeout=120.0)
+
+    async def get_embedding(self, text: str, model: Optional[str] = None) -> List[float]:
+        """取得單一文字的 embedding 向量"""
+        model = model or self.default_model
+        url = f"{self.base_url}/api/embed"
+
+        try:
+            response = await self.client.post(url, json={
+                "model": model,
+                "input": text
+            })
+            response.raise_for_status()
+            result = response.json()
+            return result["embeddings"][0]
+        except httpx.HTTPStatusError as e:
+            logger.error(f"Embedding API error: {e.response.status_code} - {e.response.text}")
+            raise
+        except Exception as e:
+            logger.error(f"Embedding error: {e}")
+            raise
+
+    async def get_embeddings_batch(
+        self,
+        texts: List[str],
+        model: Optional[str] = None
+    ) -> List[List[float]]:
+        """批次取得多個文字的 embedding 向量"""
+        if not texts:
+            return []
+
+        model = model or self.default_model
+        url = f"{self.base_url}/api/embed"
+
+        try:
+            # Ollama 支援批次 embedding
+            response = await self.client.post(url, json={
+                "model": model,
+                "input": texts
+            })
+            response.raise_for_status()
+            result = response.json()
+            return result["embeddings"]
+        except httpx.HTTPStatusError as e:
+            logger.error(f"Batch embedding API error: {e.response.status_code} - {e.response.text}")
+            # 如果批次失敗，嘗試逐一處理
+            logger.info("Falling back to single embedding requests...")
+            embeddings = []
+            for text in texts:
+                emb = await self.get_embedding(text, model)
+                embeddings.append(emb)
+            return embeddings
+        except Exception as e:
+            logger.error(f"Batch embedding error: {e}")
+            raise
+
+    def cosine_similarity(self, a: List[float], b: List[float]) -> float:
+        """計算兩個向量的餘弦相似度"""
+        a_np = np.array(a)
+        b_np = np.array(b)
+        norm_a = np.linalg.norm(a_np)
+        norm_b = np.linalg.norm(b_np)
+        if norm_a == 0 or norm_b == 0:
+            return 0.0
+        return float(np.dot(a_np, b_np) / (norm_a * norm_b))
+
+    def build_similarity_matrix(
+        self,
+        embeddings: List[List[float]]
+    ) -> np.ndarray:
+        """建立成對相似度矩陣"""
+        n = len(embeddings)
+        matrix = np.zeros((n, n))
+
+        for i in range(n):
+            matrix[i][i] = 1.0  # 自己與自己的相似度為 1
+            for j in range(i + 1, n):
+                sim = self.cosine_similarity(embeddings[i], embeddings[j])
+                matrix[i][j] = sim
+                matrix[j][i] = sim
+
+        return matrix
+
+    def cluster_by_similarity(
+        self,
+        similarity_matrix: np.ndarray,
+        threshold: float
+    ) -> List[List[int]]:
+        """
+        貪婪聚類：將相似度 >= threshold 的項目分組
+
+        演算法：
+        1. 從第一個未分配的項目開始
+        2. 找出所有與該項目相似度 >= threshold 的項目
+        3. 歸入同一組
+        4. 重複直到所有項目都已分配
+
+        Returns:
+            List[List[int]]: 每個子列表包含同組項目的索引
+        """
+        n = len(similarity_matrix)
+        assigned = [False] * n
+        groups = []
+
+        for i in range(n):
+            if assigned[i]:
+                continue
+
+            # 開始新的分組，以 item i 為代表
+            group = [i]
+            assigned[i] = True
+
+            # 找出所有與 i 相似的項目
+            for j in range(i + 1, n):
+                if not assigned[j] and similarity_matrix[i][j] >= threshold:
+                    group.append(j)
+                    assigned[j] = True
+
+            groups.append(group)
+
+        return groups
+
+    async def deduplicate(
+        self,
+        descriptions: List[ExpertTransformationDescription],
+        threshold: float = 0.85,
+        model: Optional[str] = None
+    ) -> DeduplicationResult:
+        """
+        主要去重方法
+
+        Args:
+            descriptions: 要去重的描述列表
+            threshold: 相似度閾值 (0.0-1.0)，預設 0.85
+            model: Embedding 模型名稱
+
+        Returns:
+            DeduplicationResult: 去重結果，包含分組資訊
+        """
+        model = model or self.default_model
+
+        # 空輸入處理
+        if not descriptions:
+            return DeduplicationResult(
+                total_input=0,
+                total_groups=0,
+                total_duplicates=0,
+                groups=[],
+                threshold_used=threshold,
+                method_used=DeduplicationMethod.EMBEDDING,
+                model_used=model
+            )
+
+        # 提取描述文字
+        texts = [d.description for d in descriptions]
+        logger.info(f"Generating embeddings for {len(texts)} descriptions using model '{model}'...")
+
+        # 批次取得 embeddings
+        try:
+            embeddings = await self.get_embeddings_batch(texts, model)
+        except Exception as e:
+            logger.error(f"Failed to generate embeddings: {e}")
+            raise ValueError(f"Embedding generation failed: {e}. Make sure the model '{model}' is installed (run: ollama pull {model})")
+
+        # 建立相似度矩陣
+        logger.info("Building similarity matrix...")
+        sim_matrix = self.build_similarity_matrix(embeddings)
+
+        # 聚類
+        logger.info(f"Clustering with threshold {threshold}...")
+        clusters = self.cluster_by_similarity(sim_matrix, threshold)
+
+        # 建立結果分組
+        result_groups = []
+        total_duplicates = 0
+
+        for group_idx, indices in enumerate(clusters):
+            if len(indices) == 1:
+                # 獨立項目 - 無重複
+                result_groups.append(DescriptionGroup(
+                    group_id=f"group-{group_idx}",
+                    representative=descriptions[indices[0]],
+                    duplicates=[],
+                    similarity_scores=[]
+                ))
+            else:
+                # 有重複的分組 - 第一個為代表
+                rep_idx = indices[0]
+                dup_indices = indices[1:]
+                dup_scores = [
+                    float(sim_matrix[rep_idx][idx])
+                    for idx in dup_indices
+                ]
+
+                result_groups.append(DescriptionGroup(
+                    group_id=f"group-{group_idx}",
+                    representative=descriptions[rep_idx],
+                    duplicates=[descriptions[idx] for idx in dup_indices],
+                    similarity_scores=dup_scores
+                ))
+                total_duplicates += len(dup_indices)
+
+        logger.info(f"Deduplication complete: {len(descriptions)} -> {len(result_groups)} groups, {total_duplicates} duplicates found")
+
+        return DeduplicationResult(
+            total_input=len(descriptions),
+            total_groups=len(result_groups),
+            total_duplicates=total_duplicates,
+            groups=result_groups,
+            threshold_used=threshold,
+            method_used=DeduplicationMethod.EMBEDDING,
+            model_used=model
+        )
+
+    async def close(self):
+        """關閉 HTTP 客戶端"""
+        await self.client.aclose()
+
+
+# 全域實例
+embedding_service = EmbeddingService()

backend/app/services/llm_deduplication_service.py

@@ -0,0 +1,252 @@
+"""
+LLM Deduplication Service - 使用 LLM 成對比較進行去重
+
+讓 LLM 判斷兩個描述是否語意重複，透過並行處理加速。
+"""
+
+import asyncio
+import logging
+from typing import List, Tuple, Optional
+
+import httpx
+import numpy as np
+
+from ..config import settings
+from ..models.schemas import (
+    ExpertTransformationDescription,
+    DeduplicationResult,
+    DeduplicationMethod,
+    DescriptionGroup,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class LLMDeduplicationService:
+    """LLM 去重服務：使用 LLM 成對比較判斷語意相似度"""
+
+    def __init__(self):
+        self.base_url = settings.ollama_base_url
+        self.default_model = "qwen3:4b"  # 快速模型，適合簡單判斷
+        self.client = httpx.AsyncClient(timeout=60.0)
+        self.max_concurrent = 5  # 最大並行數，避免 Ollama 過載
+
+    async def compare_pair(
+        self,
+        desc1: str,
+        desc2: str,
+        model: str,
+        semaphore: asyncio.Semaphore
+    ) -> bool:
+        """
+        讓 LLM 判斷兩個描述是否語意重複
+
+        Args:
+            desc1: 第一個描述
+            desc2: 第二個描述
+            model: LLM 模型名稱
+            semaphore: 並行控制信號量
+
+        Returns:
+            bool: 是否為重複描述
+        """
+        async with semaphore:  # 控制並行數
+            prompt = f"""判斷以下兩個創新描述是否表達相同或非常相似的概念：
+
+描述1: {desc1}
+
+描述2: {desc2}
+
+如果兩者描述的創新概念本質相同或非常相似，回答 "YES"
+如果兩者描述不同的創新概念，回答 "NO"
+只回答 YES 或 NO，不要其他文字"""
+
+            try:
+                response = await self.client.post(
+                    f"{self.base_url}/api/generate",
+                    json={
+                        "model": model,
+                        "prompt": prompt,
+                        "stream": False,
+                        "options": {
+                            "temperature": 0.1,  # 低溫度以獲得一致的判斷
+                            "num_predict": 10,   # 只需要短回答
+                        }
+                    }
+                )
+                response.raise_for_status()
+                result = response.json()["response"].strip().upper()
+                is_similar = result.startswith("YES")
+                logger.debug(f"LLM comparison: '{desc1[:30]}...' vs '{desc2[:30]}...' -> {result} ({is_similar})")
+                return is_similar
+            except Exception as e:
+                logger.error(f"LLM comparison failed: {e}")
+                return False  # 失敗時假設不相似
+
+    async def compare_batch(
+        self,
+        pairs: List[Tuple[int, int, str, str]],
+        model: str
+    ) -> List[Tuple[int, int, bool]]:
+        """
+        並行批次比較多個描述對
+
+        Args:
+            pairs: 待比較的配對列表 [(i, j, desc1, desc2), ...]
+            model: LLM 模型名稱
+
+        Returns:
+            比較結果列表 [(i, j, is_similar), ...]
+        """
+        semaphore = asyncio.Semaphore(self.max_concurrent)
+
+        async def compare_one(pair: Tuple[int, int, str, str]) -> Tuple[int, int, bool]:
+            i, j, desc1, desc2 = pair
+            is_similar = await self.compare_pair(desc1, desc2, model, semaphore)
+            return (i, j, is_similar)
+
+        # 使用 asyncio.gather 並行執行所有比較
+        results = await asyncio.gather(*[compare_one(p) for p in pairs])
+        return results
+
+    def cluster_by_similarity(
+        self,
+        similarity_matrix: np.ndarray,
+        threshold: float
+    ) -> List[List[int]]:
+        """
+        貪婪聚類：將相似度 >= threshold 的項目分組
+
+        與 embedding_service 使用相同的演算法
+        """
+        n = len(similarity_matrix)
+        assigned = [False] * n
+        groups = []
+
+        for i in range(n):
+            if assigned[i]:
+                continue
+
+            # 開始新的分組，以 item i 為代表
+            group = [i]
+            assigned[i] = True
+
+            # 找出所有與 i 相似的項目
+            for j in range(i + 1, n):
+                if not assigned[j] and similarity_matrix[i][j] >= threshold:
+                    group.append(j)
+                    assigned[j] = True
+
+            groups.append(group)
+
+        return groups
+
+    async def deduplicate(
+        self,
+        descriptions: List[ExpertTransformationDescription],
+        model: Optional[str] = None
+    ) -> DeduplicationResult:
+        """
+        使用 LLM 成對比較進行去重
+
+        Args:
+            descriptions: 要去重的描述列表
+            model: LLM 模型名稱
+
+        Returns:
+            DeduplicationResult: 去重結果
+        """
+        model = model or self.default_model
+
+        # 空輸入處理
+        if not descriptions:
+            return DeduplicationResult(
+                total_input=0,
+                total_groups=0,
+                total_duplicates=0,
+                groups=[],
+                threshold_used=0.5,  # LLM 方法固定使用 0.5 閾值
+                method_used=DeduplicationMethod.LLM,
+                model_used=model
+            )
+
+        n = len(descriptions)
+        similarity_matrix = np.zeros((n, n))
+
+        # 對角線為 1（自己與自己相似）
+        for i in range(n):
+            similarity_matrix[i][i] = 1.0
+
+        # 建立所有需要比較的配對
+        pairs = []
+        for i in range(n):
+            for j in range(i + 1, n):
+                pairs.append((
+                    i, j,
+                    descriptions[i].description,
+                    descriptions[j].description
+                ))
+
+        total_pairs = len(pairs)
+        logger.info(f"LLM deduplication: {total_pairs} pairs to compare (parallel={self.max_concurrent}, model={model})")
+
+        # 並行批次比較
+        results = await self.compare_batch(pairs, model)
+
+        # 填入相似度矩陣
+        for i, j, is_similar in results:
+            similarity_value = 1.0 if is_similar else 0.0
+            similarity_matrix[i][j] = similarity_value
+            similarity_matrix[j][i] = similarity_value
+
+        # 使用閾值 0.5 聚類（因為 LLM 輸出只有 0/1）
+        logger.info("Clustering results...")
+        clusters = self.cluster_by_similarity(similarity_matrix, 0.5)
+
+        # 建立結果分組
+        result_groups = []
+        total_duplicates = 0
+
+        for group_idx, indices in enumerate(clusters):
+            if len(indices) == 1:
+                # 獨立項目 - 無重複
+                result_groups.append(DescriptionGroup(
+                    group_id=f"group-{group_idx}",
+                    representative=descriptions[indices[0]],
+                    duplicates=[],
+                    similarity_scores=[]
+                ))
+            else:
+                # 有重複的分組 - 第一個為代表
+                rep_idx = indices[0]
+                dup_indices = indices[1:]
+                # LLM 方法的相似度分數都是 1.0（因為是 YES/NO 判斷）
+                dup_scores = [1.0 for _ in dup_indices]
+
+                result_groups.append(DescriptionGroup(
+                    group_id=f"group-{group_idx}",
+                    representative=descriptions[rep_idx],
+                    duplicates=[descriptions[idx] for idx in dup_indices],
+                    similarity_scores=dup_scores
+                ))
+                total_duplicates += len(dup_indices)
+
+        logger.info(f"LLM deduplication complete: {n} -> {len(result_groups)} groups, {total_duplicates} duplicates found")
+
+        return DeduplicationResult(
+            total_input=n,
+            total_groups=len(result_groups),
+            total_duplicates=total_duplicates,
+            groups=result_groups,
+            threshold_used=0.5,  # LLM 方法固定使用 0.5 閾值
+            method_used=DeduplicationMethod.LLM,
+            model_used=model
+        )
+
+    async def close(self):
+        """關閉 HTTP 客戶端"""
+        await self.client.aclose()
+
+
+# 全域實例
+llm_deduplication_service = LLMDeduplicationService()

backend/requirements.txt

@@ -4,3 +4,4 @@ httpx>=0.26.0
 pydantic>=2.5.0
 pydantic-settings>=2.1.0
 python-dotenv>=1.0.0
+numpy>=1.26.0

frontend/src/App.tsx

@@ -1,16 +1,17 @@
 import { useState, useRef, useCallback, useEffect } from 'react';
-import { ConfigProvider, Layout, theme, Typography, Space, Tabs } from 'antd';
+import { ConfigProvider, Layout, theme, Typography, Space, Tabs, Slider, Radio } from 'antd';
-import { ApartmentOutlined, ThunderboltOutlined } from '@ant-design/icons';
+import { ApartmentOutlined, ThunderboltOutlined, FilterOutlined } from '@ant-design/icons';
 import { ThemeToggle } from './components/ThemeToggle';
 import { InputPanel } from './components/InputPanel';
 import { TransformationInputPanel } from './components/TransformationInputPanel';
 import { MindmapPanel } from './components/MindmapPanel';
 import { TransformationPanel } from './components/TransformationPanel';
+import { DeduplicationPanel } from './components/DeduplicationPanel';
 import { useAttribute } from './hooks/useAttribute';
 import { getModels } from './services/api';
 import type { MindmapDAGRef } from './components/MindmapDAG';
 import type { TransformationDAGRef } from './components/TransformationDAG';
-import type { CategoryMode, ExpertSource } from './types';
+import type { CategoryMode, ExpertSource, ExpertTransformationDAGResult, DeduplicationMethod } from './types';
 
 const { Header, Sider, Content } = Layout;
 const { Title } = Typography;
@@ -45,8 +46,14 @@ function App() {
   });
   const [customExpertsInput, setCustomExpertsInput] = useState('');
   const [expertSource, setExpertSource] = useState<ExpertSource>('llm');
+  const [expertLanguage, setExpertLanguage] = useState<'en' | 'zh'>('en');
   const [shouldStartTransform, setShouldStartTransform] = useState(false);
   const [transformLoading, setTransformLoading] = useState(false);
+  const [transformationResult, setTransformationResult] = useState<ExpertTransformationDAGResult | null>(null);
+
+  // Deduplication settings
+  const [deduplicationThreshold, setDeduplicationThreshold] = useState(0.85);
+  const [deduplicationMethod, setDeduplicationMethod] = useState<DeduplicationMethod>('embedding');
 
   // Available models from API
   const [availableModels, setAvailableModels] = useState<string[]>([]);
@@ -188,9 +195,32 @@ function App() {
                         temperature={transformTemperature}
                         expertConfig={expertConfig}
                         expertSource={expertSource}
+                        expertLanguage={expertLanguage}
                         shouldStartTransform={shouldStartTransform}
                         onTransformComplete={() => setShouldStartTransform(false)}
                         onLoadingChange={setTransformLoading}
+                        onResultsChange={setTransformationResult}
+                      />
+                    </div>
+                  ),
+                },
+                {
+                  key: 'deduplication',
+                  label: (
+                    <span>
+                      <FilterOutlined style={{ marginRight: 8 }} />
+                      Deduplication
+                    </span>
+                  ),
+                  children: (
+                    <div style={{ height: 'calc(100vh - 140px)' }}>
+                      <DeduplicationPanel
+                        transformationResult={transformationResult}
+                        isDark={isDark}
+                        threshold={deduplicationThreshold}
+                        onThresholdChange={setDeduplicationThreshold}
+                        method={deduplicationMethod}
+                        onMethodChange={setDeduplicationMethod}
                       />
                     </div>
                   ),
@@ -206,7 +236,7 @@ function App() {
               overflow: 'auto',
             }}
           >
-            {activeTab === 'attribute' ? (
+            {activeTab === 'attribute' && (
               <InputPanel
                 loading={loading}
                 progress={progress}
@@ -218,7 +248,8 @@ function App() {
                 visualSettings={visualSettings}
                 onVisualSettingsChange={setVisualSettings}
               />
-            ) : (
+            )}
+            {activeTab === 'transformation' && (
               <TransformationInputPanel
                 onTransform={handleTransform}
                 loading={transformLoading}
@@ -229,14 +260,85 @@ function App() {
                 expertConfig={expertConfig}
                 customExpertsInput={customExpertsInput}
                 expertSource={expertSource}
+                expertLanguage={expertLanguage}
                 onModelChange={setTransformModel}
                 onTemperatureChange={setTransformTemperature}
                 onExpertConfigChange={setExpertConfig}
                 onCustomExpertsInputChange={setCustomExpertsInput}
                 onExpertSourceChange={setExpertSource}
+                onExpertLanguageChange={setExpertLanguage}
                 availableModels={availableModels}
               />
             )}
+            {activeTab === 'deduplication' && (
+              <div style={{ padding: 16 }}>
+                <Typography.Title level={5} style={{ marginBottom: 16 }}>
+                  <FilterOutlined style={{ marginRight: 8 }} />
+                  Deduplication Settings
+                </Typography.Title>
+
+                {/* Method Selection */}
+                <div style={{ marginBottom: 20 }}>
+                  <Typography.Text strong style={{ display: 'block', marginBottom: 8 }}>
+                    Method
+                  </Typography.Text>
+                  <Radio.Group
+                    value={deduplicationMethod}
+                    onChange={(e) => setDeduplicationMethod(e.target.value)}
+                    buttonStyle="solid"
+                    style={{ width: '100%' }}
+                  >
+                    <Radio.Button value="embedding" style={{ width: '50%', textAlign: 'center' }}>
+                      Embedding
+                    </Radio.Button>
+                    <Radio.Button value="llm" style={{ width: '50%', textAlign: 'center' }}>
+                      LLM Judge
+                    </Radio.Button>
+                  </Radio.Group>
+                  <Typography.Text type="secondary" style={{ display: 'block', marginTop: 8, fontSize: 12 }}>
+                    {deduplicationMethod === 'embedding'
+                      ? 'Fast vector similarity comparison'
+                      : 'Accurate but slower pairwise LLM comparison'}
+                  </Typography.Text>
+                </div>
+
+                {/* Threshold Slider - Only for Embedding method */}
+                {deduplicationMethod === 'embedding' && (
+                  <div style={{ marginBottom: 20 }}>
+                    <Typography.Text strong style={{ display: 'block', marginBottom: 8 }}>
+                      Similarity Threshold
+                    </Typography.Text>
+                    <Typography.Text type="secondary" style={{ display: 'block', marginBottom: 12, fontSize: 12 }}>
+                      Higher = stricter matching, fewer groups
+                    </Typography.Text>
+                    <Slider
+                      min={0.5}
+                      max={1.0}
+                      step={0.05}
+                      value={deduplicationThreshold}
+                      onChange={setDeduplicationThreshold}
+                      marks={{
+                        0.5: '50%',
+                        0.7: '70%',
+                        0.85: '85%',
+                        1.0: '100%',
+                      }}
+                      tooltip={{ formatter: (val) => `${((val ?? 0) * 100).toFixed(0)}%` }}
+                    />
+                    <Typography.Text type="secondary" style={{ fontSize: 12 }}>
+                      Current: {(deduplicationThreshold * 100).toFixed(0)}% similarity required
+                    </Typography.Text>
+                  </div>
+                )}
+
+                {/* LLM Warning */}
+                {deduplicationMethod === 'llm' && (
+                  <Typography.Text type="warning" style={{ display: 'block', fontSize: 12 }}>
+                    Note: LLM method requires N*(N-1)/2 comparisons. May take longer for many descriptions.
+                  </Typography.Text>
+                )}
+              </div>
+            )}
           </Sider>
         </Layout>
       </Layout>

frontend/src/components/DeduplicationPanel.tsx

@@ -0,0 +1,271 @@
+import React, { useEffect, useMemo } from 'react';
+import {
+  Card,
+  Button,
+  Slider,
+  Statistic,
+  Row,
+  Col,
+  Empty,
+  Spin,
+  Alert,
+  Typography,
+  Space,
+  Divider,
+} from 'antd';
+import {
+  FilterOutlined,
+  ReloadOutlined,
+  CheckCircleOutlined,
+  ClusterOutlined,
+  CopyOutlined,
+} from '@ant-design/icons';
+import { useDeduplication } from '../hooks/useDeduplication';
+import { GroupCard } from './deduplication/GroupCard';
+import type {
+  ExpertTransformationDAGResult,
+  ExpertTransformationDescription,
+  DeduplicationMethod,
+} from '../types';
+
+const { Title, Text } = Typography;
+
+interface DeduplicationPanelProps {
+  transformationResult: ExpertTransformationDAGResult | null;
+  isDark: boolean;
+  threshold: number;
+  onThresholdChange: (value: number) => void;
+  method: DeduplicationMethod;
+  onMethodChange?: (method: DeduplicationMethod) => void;  // Optional, handled in App.tsx sidebar
+}
+
+/**
+ * Panel for deduplicating transformation descriptions
+ */
+export const DeduplicationPanel: React.FC<DeduplicationPanelProps> = ({
+  transformationResult,
+  isDark,
+  threshold,
+  onThresholdChange,
+  method,
+  // onMethodChange is handled in App.tsx sidebar
+}) => {
+  const { loading, result, error, progress, deduplicate, clearResult } = useDeduplication();
+
+  // Extract all descriptions from transformation result
+  const allDescriptions = useMemo<ExpertTransformationDescription[]>(() => {
+    if (!transformationResult) return [];
+
+    const descriptions: ExpertTransformationDescription[] = [];
+    for (const categoryResult of transformationResult.results) {
+      descriptions.push(...categoryResult.descriptions);
+    }
+    return descriptions;
+  }, [transformationResult]);
+
+  // Clear result when transformation result or method changes
+  useEffect(() => {
+    clearResult();
+  }, [transformationResult, method, clearResult]);
+
+  const handleDeduplicate = () => {
+    if (allDescriptions.length > 0) {
+      deduplicate(allDescriptions, threshold, method);
+    }
+  };
+
+  const containerStyle: React.CSSProperties = {
+    height: '100%',
+    display: 'flex',
+    flexDirection: 'column',
+    padding: 16,
+    overflow: 'hidden',
+  };
+
+  const headerCardStyle: React.CSSProperties = {
+    marginBottom: 16,
+    background: isDark ? '#1f1f1f' : '#fff',
+    borderRadius: 8,
+  };
+
+  const resultsContainerStyle: React.CSSProperties = {
+    flex: 1,
+    overflow: 'auto',
+    paddingRight: 8,
+  };
+
+  // No transformation data
+  if (!transformationResult) {
+    return (
+      <div style={{ ...containerStyle, justifyContent: 'center', alignItems: 'center' }}>
+        <Empty
+          description={
+            <Space direction="vertical" size={4}>
+              <Text style={{ color: isDark ? '#999' : '#666' }}>
+                No transformation data available
+              </Text>
+              <Text type="secondary" style={{ fontSize: 12 }}>
+                Please run the Transformation Agent first
+              </Text>
+            </Space>
+          }
+        />
+      </div>
+    );
+  }
+
+  // No descriptions found
+  if (allDescriptions.length === 0) {
+    return (
+      <div style={{ ...containerStyle, justifyContent: 'center', alignItems: 'center' }}>
+        <Empty description="No descriptions found in transformation result" />
+      </div>
+    );
+  }
+
+  return (
+    <div style={containerStyle}>
+      {/* Header Card with Controls */}
+      <Card size="small" style={headerCardStyle}>
+        <Row gutter={[16, 16]} align="middle">
+          <Col span={6}>
+            <Statistic
+              title="Total Descriptions"
+              value={allDescriptions.length}
+              prefix={<FilterOutlined />}
+            />
+          </Col>
+          <Col span={6}>
+            <Statistic
+              title="Unique Groups"
+              value={result?.total_groups ?? '-'}
+              prefix={<ClusterOutlined />}
+              valueStyle={{ color: result ? '#52c41a' : undefined }}
+            />
+          </Col>
+          <Col span={6}>
+            <Statistic
+              title="Duplicates Found"
+              value={result?.total_duplicates ?? '-'}
+              prefix={<CopyOutlined />}
+              valueStyle={{ color: result?.total_duplicates ? '#fa8c16' : undefined }}
+            />
+          </Col>
+          <Col span={6}>
+            <Space direction="vertical" size={4} style={{ width: '100%' }}>
+              <Text type="secondary" style={{ fontSize: 12 }}>
+                Similarity Threshold: {(threshold * 100).toFixed(0)}%
+              </Text>
+              <Slider
+                min={0.5}
+                max={1.0}
+                step={0.05}
+                value={threshold}
+                onChange={onThresholdChange}
+                disabled={loading}
+                tooltip={{ formatter: (val) => `${((val ?? 0) * 100).toFixed(0)}%` }}
+              />
+            </Space>
+          </Col>
+        </Row>
+
+        <Divider style={{ margin: '12px 0' }} />
+
+        <Row justify="space-between" align="middle">
+          <Col>
+            <Text type="secondary" style={{ fontSize: 12 }}>
+              {progress.message || 'Ready to analyze'}
+            </Text>
+          </Col>
+          <Col>
+            <Space>
+              {result && (
+                <Button
+                  icon={<ReloadOutlined />}
+                  onClick={clearResult}
+                  disabled={loading}
+                >
+                  Clear
+                </Button>
+              )}
+              <Button
+                type="primary"
+                icon={<CheckCircleOutlined />}
+                onClick={handleDeduplicate}
+                loading={loading}
+              >
+                {loading ? 'Processing...' : 'Deduplicate'}
+              </Button>
+            </Space>
+          </Col>
+        </Row>
+      </Card>
+
+      {/* Error Alert */}
+      {error && (
+        <Alert
+          message="Deduplication Error"
+          description={error}
+          type="error"
+          showIcon
+          closable
+          style={{ marginBottom: 16 }}
+        />
+      )}
+
+      {/* Loading State */}
+      {loading && (
+        <div style={{ textAlign: 'center', padding: 40 }}>
+          <Spin size="large" />
+          <div style={{ marginTop: 16 }}>
+            <Text type="secondary">{progress.message}</Text>
+          </div>
+        </div>
+      )}
+
+      {/* Results */}
+      {!loading && result && (
+        <div style={resultsContainerStyle}>
+          <Title level={5} style={{ marginBottom: 16, color: isDark ? '#fff' : '#000' }}>
+            <ClusterOutlined style={{ marginRight: 8 }} />
+            {result.total_groups} Groups
+            {result.total_duplicates > 0 && (
+              <Text type="secondary" style={{ fontSize: 14, fontWeight: 'normal', marginLeft: 8 }}>
+                ({result.total_duplicates} duplicates removed)
+              </Text>
+            )}
+          </Title>
+
+          {result.groups.map((group, index) => (
+            <GroupCard
+              key={group.group_id}
+              group={group}
+              isDark={isDark}
+              index={index}
+            />
+          ))}
+
+          {result.total_groups === 0 && (
+            <Empty description="No groups found" />
+          )}
+        </div>
+      )}
+
+      {/* Initial State - show prompt */}
+      {!loading && !result && !error && (
+        <div style={{ textAlign: 'center', padding: 40 }}>
+          <FilterOutlined style={{ fontSize: 48, color: '#1890ff', marginBottom: 16 }} />
+          <Title level={4} style={{ color: isDark ? '#fff' : '#000' }}>
+            Ready to Deduplicate
+          </Title>
+          <Text type="secondary">
+            Click the "Deduplicate" button to analyze {allDescriptions.length} descriptions
+            and group similar ones together.
+          </Text>
+        </div>
+      )}
+    </div>
+  );
+};
+
+export default DeduplicationPanel;

frontend/src/components/TransformationInputPanel.tsx

@@ -12,6 +12,11 @@ const EXPERT_SOURCE_OPTIONS = [
   { label: 'Wikidata', value: 'wikidata' as ExpertSource, description: '從 Wikidata 查詢職業 (需等待 API)' },
 ];
 
+const EXPERT_LANGUAGE_OPTIONS = [
+  { label: 'English', value: 'en' as const },
+  { label: '中文', value: 'zh' as const },
+];
+
 interface TransformationInputPanelProps {
   onTransform: () => void;
   loading: boolean;
@@ -26,6 +31,7 @@ interface TransformationInputPanelProps {
   };
   customExpertsInput: string;
   expertSource: ExpertSource;
+  expertLanguage: 'en' | 'zh';
   onModelChange: (model: string) => void;
   onTemperatureChange: (temperature: number) => void;
   onExpertConfigChange: (config: {
@@ -35,6 +41,7 @@ interface TransformationInputPanelProps {
   }) => void;
   onCustomExpertsInputChange: (value: string) => void;
   onExpertSourceChange: (source: ExpertSource) => void;
+  onExpertLanguageChange: (language: 'en' | 'zh') => void;
   availableModels: string[];
 }
 
@@ -48,11 +55,13 @@ export const TransformationInputPanel: React.FC<TransformationInputPanelProps> =
   expertConfig,
   customExpertsInput,
   expertSource,
+  expertLanguage,
   onModelChange,
   onTemperatureChange,
   onExpertConfigChange,
   onCustomExpertsInputChange,
   onExpertSourceChange,
+  onExpertLanguageChange,
   availableModels,
 }) => {
   return (
@@ -142,6 +151,19 @@ export const TransformationInputPanel: React.FC<TransformationInputPanelProps> =
             <Text type="secondary" style={{ fontSize: 11 }}>
               {EXPERT_SOURCE_OPTIONS.find((opt) => opt.value === expertSource)?.description}
             </Text>
+
+            {/* Language selector - only for curated source */}
+            {expertSource === 'curated' && (
+              <div style={{ marginTop: 8 }}>
+                <Text style={{ fontSize: 12 }}>職業名稱語言</Text>
+                <Select
+                  value={expertLanguage}
+                  onChange={onExpertLanguageChange}
+                  style={{ width: '100%', marginTop: 4 }}
+                  options={EXPERT_LANGUAGE_OPTIONS}
+                />
+              </div>
+            )}
           </Space>
         </Card>
 

frontend/src/components/TransformationPanel.tsx

@@ -1,7 +1,7 @@
 import { forwardRef, useMemo, useCallback, useEffect } from 'react';
 import { Empty, Spin, Button, Progress, Card, Space, Typography, Tag } from 'antd';
 import { ReloadOutlined } from '@ant-design/icons';
-import type { AttributeDAG, ExpertTransformationInput, ExpertSource } from '../types';
+import type { AttributeDAG, ExpertTransformationInput, ExpertSource, ExpertTransformationDAGResult } from '../types';
 import { TransformationDAG } from './TransformationDAG';
 import type { TransformationDAGRef } from './TransformationDAG';
 import { useExpertTransformation } from '../hooks/useExpertTransformation';
@@ -19,26 +19,33 @@ interface TransformationPanelProps {
     custom_experts?: string[];
   };
   expertSource: ExpertSource;
+  expertLanguage: 'en' | 'zh';
   shouldStartTransform: boolean;
   onTransformComplete: () => void;
   onLoadingChange: (loading: boolean) => void;
+  onResultsChange?: (results: ExpertTransformationDAGResult | null) => void;
 }
 
 export const TransformationPanel = forwardRef<TransformationDAGRef, TransformationPanelProps>(
-  ({ attributeData, isDark, model, temperature, expertConfig, expertSource, shouldStartTransform, onTransformComplete, onLoadingChange }, ref) => {
+  ({ attributeData, isDark, model, temperature, expertConfig, expertSource, expertLanguage, shouldStartTransform, onTransformComplete, onLoadingChange, onResultsChange }, ref) => {
     const {
       loading,
       progress,
       results,
       transformAll,
       clearResults,
-    } = useExpertTransformation({ model, temperature, expertSource });
+    } = useExpertTransformation({ model, temperature, expertSource, expertLanguage });
 
     // Notify parent of loading state changes
     useEffect(() => {
       onLoadingChange(loading);
     }, [loading, onLoadingChange]);
 
+    // Notify parent of results changes
+    useEffect(() => {
+      onResultsChange?.(results);
+    }, [results, onResultsChange]);
+
     // Build expert transformation input from attribute data
     const transformationInput = useMemo((): ExpertTransformationInput | null => {
       if (!attributeData) return null;

frontend/src/components/deduplication/GroupCard.tsx

@@ -0,0 +1,147 @@
+import React, { useState } from 'react';
+import { Card, Tag, Collapse, Typography, Space, Badge } from 'antd';
+import { StarFilled, CopyOutlined, UserOutlined } from '@ant-design/icons';
+import type { DescriptionGroup } from '../../types';
+
+const { Text, Paragraph } = Typography;
+const { Panel } = Collapse;
+
+interface GroupCardProps {
+  group: DescriptionGroup;
+  isDark: boolean;
+  index: number;
+}
+
+/**
+ * Card component for displaying a group of similar descriptions
+ */
+export const GroupCard: React.FC<GroupCardProps> = ({ group, isDark, index }) => {
+  const [expanded, setExpanded] = useState(false);
+  const hasDuplicates = group.duplicates.length > 0;
+
+  const cardStyle: React.CSSProperties = {
+    marginBottom: 16,
+    borderRadius: 8,
+    border: isDark ? '1px solid #303030' : '1px solid #f0f0f0',
+    background: isDark ? '#1f1f1f' : '#fff',
+  };
+
+  const representativeStyle: React.CSSProperties = {
+    background: isDark
+      ? 'linear-gradient(135deg, #1a472a 0%, #2d5a3d 100%)'
+      : 'linear-gradient(135deg, #f6ffed 0%, #d9f7be 100%)',
+    padding: 12,
+    borderRadius: 6,
+    marginBottom: hasDuplicates ? 12 : 0,
+  };
+
+  const duplicateItemStyle: React.CSSProperties = {
+    background: isDark ? '#2a2a2a' : '#fafafa',
+    padding: 10,
+    borderRadius: 4,
+    marginBottom: 8,
+    borderLeft: `3px solid ${isDark ? '#faad14' : '#fa8c16'}`,
+  };
+
+  return (
+    <Card
+      size="small"
+      style={cardStyle}
+      title={
+        <Space>
+          <Badge
+            count={index + 1}
+            style={{
+              backgroundColor: hasDuplicates ? '#52c41a' : '#1890ff',
+            }}
+          />
+          <Text strong style={{ color: isDark ? '#fff' : '#000' }}>
+            {group.representative.keyword}
+          </Text>
+          {hasDuplicates && (
+            <Tag color="orange" icon={<CopyOutlined />}>
+              {group.duplicates.length} similar
+            </Tag>
+          )}
+        </Space>
+      }
+      extra={
+        <Tag color={isDark ? 'geekblue' : 'blue'}>
+          <UserOutlined style={{ marginRight: 4 }} />
+          {group.representative.expert_name}
+        </Tag>
+      }
+    >
+      {/* Representative description */}
+      <div style={representativeStyle}>
+        <Space direction="vertical" size={4} style={{ width: '100%' }}>
+          <Space>
+            <StarFilled style={{ color: '#52c41a' }} />
+            <Text type="secondary" style={{ fontSize: 12 }}>
+              Representative
+            </Text>
+          </Space>
+          <Paragraph
+            style={{
+              margin: 0,
+              color: isDark ? '#e0e0e0' : '#333',
+              fontSize: 14,
+            }}
+          >
+            {group.representative.description}
+          </Paragraph>
+        </Space>
+      </div>
+
+      {/* Duplicates section */}
+      {hasDuplicates && (
+        <Collapse
+          ghost
+          activeKey={expanded ? ['duplicates'] : []}
+          onChange={() => setExpanded(!expanded)}
+          style={{ marginTop: 8 }}
+        >
+          <Panel
+            key="duplicates"
+            header={
+              <Text type="secondary" style={{ fontSize: 12 }}>
+                View {group.duplicates.length} similar description(s)
+              </Text>
+            }
+            style={{ padding: 0 }}
+          >
+            <Space direction="vertical" size={0} style={{ width: '100%' }}>
+              {group.duplicates.map((dup, dupIndex) => (
+                <div key={`${dup.expert_id}-${dupIndex}`} style={duplicateItemStyle}>
+                  <Space direction="vertical" size={2} style={{ width: '100%' }}>
+                    <Space size="small">
+                      <Tag color="default" style={{ fontSize: 11 }}>
+                        {dup.keyword}
+                      </Tag>
+                      <Tag color="cyan" style={{ fontSize: 11 }}>
+                        {dup.expert_name}
+                      </Tag>
+                      <Tag color="orange" style={{ fontSize: 11 }}>
+                        {(group.similarity_scores[dupIndex] * 100).toFixed(0)}% similar
+                      </Tag>
+                    </Space>
+                    <Text
+                      style={{
+                        fontSize: 13,
+                        color: isDark ? '#b0b0b0' : '#666',
+                      }}
+                    >
+                      {dup.description}
+                    </Text>
+                  </Space>
+                </div>
+              ))}
+            </Space>
+          </Panel>
+        </Collapse>
+      )}
+    </Card>
+  );
+};
+
+export default GroupCard;

frontend/src/hooks/useDeduplication.ts

@@ -0,0 +1,100 @@
+import { useState, useCallback } from 'react';
+import { deduplicateDescriptions } from '../services/api';
+import type {
+  ExpertTransformationDescription,
+  DeduplicationResult,
+  DeduplicationProgress,
+  DeduplicationMethod,
+} from '../types';
+
+/**
+ * Hook for managing deduplication state and operations
+ */
+export function useDeduplication() {
+  const [loading, setLoading] = useState(false);
+  const [result, setResult] = useState<DeduplicationResult | null>(null);
+  const [error, setError] = useState<string | null>(null);
+  const [progress, setProgress] = useState<DeduplicationProgress>({
+    step: 'idle',
+    message: '',
+  });
+
+  /**
+   * Execute deduplication on a list of descriptions
+   *
+   * @param descriptions - List of descriptions to deduplicate
+   * @param threshold - Similarity threshold (only used for embedding method)
+   * @param method - Deduplication method: 'embedding' (fast) or 'llm' (accurate but slow)
+   */
+  const deduplicate = useCallback(async (
+    descriptions: ExpertTransformationDescription[],
+    threshold: number = 0.85,
+    method: DeduplicationMethod = 'embedding'
+  ) => {
+    if (!descriptions || descriptions.length === 0) {
+      setError('No descriptions to deduplicate');
+      return;
+    }
+
+    setLoading(true);
+    setError(null);
+    setResult(null);
+
+    // 根據方法顯示不同的進度訊息
+    const methodLabel = method === 'embedding' ? 'Embedding' : 'LLM';
+    const pairCount = (descriptions.length * (descriptions.length - 1)) / 2;
+    const progressMessage = method === 'llm'
+      ? `Processing ${descriptions.length} descriptions with LLM (${pairCount} comparisons)...`
+      : `Processing ${descriptions.length} descriptions with ${methodLabel}...`;
+
+    setProgress({
+      step: 'processing',
+      message: progressMessage,
+    });
+
+    try {
+      const deduplicationResult = await deduplicateDescriptions({
+        descriptions,
+        similarity_threshold: threshold,
+        method,
+      });
+
+      setResult(deduplicationResult);
+      setProgress({
+        step: 'done',
+        message: `Found ${deduplicationResult.total_groups} unique groups, ${deduplicationResult.total_duplicates} duplicates (${methodLabel})`,
+      });
+    } catch (err) {
+      const errorMessage = err instanceof Error ? err.message : 'Unknown error';
+      setError(errorMessage);
+      setProgress({
+        step: 'error',
+        message: 'Deduplication failed',
+        error: errorMessage,
+      });
+    } finally {
+      setLoading(false);
+    }
+  }, []);
+
+  /**
+   * Clear results and reset state
+   */
+  const clearResult = useCallback(() => {
+    setResult(null);
+    setError(null);
+    setProgress({
+      step: 'idle',
+      message: '',
+    });
+  }, []);
+
+  return {
+    loading,
+    result,
+    error,
+    progress,
+    deduplicate,
+    clearResult,
+  };
+}

frontend/src/hooks/useExpertTransformation.ts

@@ -14,6 +14,7 @@ interface UseExpertTransformationOptions {
   model?: string;
   temperature?: number;
   expertSource?: ExpertSource;
+  expertLanguage?: 'en' | 'zh';
 }
 
 export function useExpertTransformation(options: UseExpertTransformationOptions = {}) {
@@ -63,6 +64,7 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
             keywords_per_expert: expertConfig.keywords_per_expert,
             custom_experts: expertConfig.custom_experts,
             expert_source: options.expertSource,
+            expert_language: options.expertLanguage,
             model: options.model,
             temperature: options.temperature,
           },
@@ -155,7 +157,7 @@ export function useExpertTransformation(options: UseExpertTransformationOptions
         });
       });
     },
-    [options.model, options.temperature, options.expertSource]
+    [options.model, options.temperature, options.expertSource, options.expertLanguage]
   );
 
   const transformAll = useCallback(

frontend/src/services/api.ts

@@ -10,7 +10,9 @@ import type {
   TransformationCategoryResult,
   ExpertTransformationRequest,
   ExpertTransformationCategoryResult,
-  ExpertProfile
+  ExpertProfile,
+  DeduplicationRequest,
+  DeduplicationResult
 } from '../types';
 
 // 自動使用當前瀏覽器的 hostname，支援遠端存取
@@ -299,3 +301,24 @@ export async function expertTransformCategoryStream(
     }
   }
 }
+
+// ===== Deduplication Agent API =====
+
+export async function deduplicateDescriptions(
+  request: DeduplicationRequest
+): Promise<DeduplicationResult> {
+  const response = await fetch(`${API_BASE_URL}/deduplication/deduplicate`, {
+    method: 'POST',
+    headers: {
+      'Content-Type': 'application/json',
+    },
+    body: JSON.stringify(request),
+  });
+
+  if (!response.ok) {
+    const errorText = await response.text();
+    throw new Error(`API error: ${response.status} - ${errorText}`);
+  }
+
+  return response.json();
+}

frontend/src/types/index.ts

@@ -265,3 +265,37 @@ export interface ExpertTransformationInput {
     custom_experts?: string[];
   };
 }
+
+// ===== Deduplication Agent types =====
+
+export type DeduplicationMethod = 'embedding' | 'llm';
+
+export interface DeduplicationRequest {
+  descriptions: ExpertTransformationDescription[];
+  method?: DeduplicationMethod;  // 去重方法，default: 'embedding'
+  similarity_threshold?: number;  // 0.0-1.0, default 0.85，僅 embedding 使用
+  model?: string;  // Embedding/LLM model
+}
+
+export interface DescriptionGroup {
+  group_id: string;
+  representative: ExpertTransformationDescription;
+  duplicates: ExpertTransformationDescription[];
+  similarity_scores: number[];
+}
+
+export interface DeduplicationResult {
+  total_input: number;
+  total_groups: number;
+  total_duplicates: number;
+  groups: DescriptionGroup[];
+  threshold_used: number;
+  method_used: DeduplicationMethod;  // 使用的方法
+  model_used: string;                // 使用的模型
+}
+
+export interface DeduplicationProgress {
+  step: 'idle' | 'processing' | 'done' | 'error';
+  message: string;
+  error?: string;
+}