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