gbanyan
bc281b8e0a
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>
253 lines
8.0 KiB
Python
253 lines
8.0 KiB
Python
"""
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LLM Deduplication Service - 使用 LLM 成對比較進行去重
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讓 LLM 判斷兩個描述是否語意重複，透過並行處理加速。
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"""
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import asyncio
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import logging
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from typing import List, Tuple, 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 LLMDeduplicationService:
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"""LLM 去重服務：使用 LLM 成對比較判斷語意相似度"""
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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 = "qwen3:4b" # 快速模型，適合簡單判斷
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self.client = httpx.AsyncClient(timeout=60.0)
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self.max_concurrent = 5 # 最大並行數，避免 Ollama 過載
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async def compare_pair(
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self,
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desc1: str,
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desc2: str,
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model: str,
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semaphore: asyncio.Semaphore
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) -> bool:
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"""
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讓 LLM 判斷兩個描述是否語意重複
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Args:
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desc1: 第一個描述
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desc2: 第二個描述
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model: LLM 模型名稱
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semaphore: 並行控制信號量
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Returns:
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bool: 是否為重複描述
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"""
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async with semaphore: # 控制並行數
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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：
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描述1: {desc1}
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描述2: {desc2}
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如果兩者描述的創新概念本質相同或非常相似，回答 "YES"
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如果兩者描述不同的創新概念，回答 "NO"
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只回答 YES 或 NO，不要其他文字"""
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try:
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response = await self.client.post(
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f"{self.base_url}/api/generate",
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json={
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"model": model,
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"prompt": prompt,
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"stream": False,
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"options": {
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"temperature": 0.1, # 低溫度以獲得一致的判斷
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"num_predict": 10, # 只需要短回答
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}
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}
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)
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response.raise_for_status()
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result = response.json()["response"].strip().upper()
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is_similar = result.startswith("YES")
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logger.debug(f"LLM comparison: '{desc1[:30]}...' vs '{desc2[:30]}...' -> {result} ({is_similar})")
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return is_similar
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except Exception as e:
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logger.error(f"LLM comparison failed: {e}")
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return False # 失敗時假設不相似
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async def compare_batch(
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self,
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pairs: List[Tuple[int, int, str, str]],
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model: str
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) -> List[Tuple[int, int, bool]]:
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"""
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並行批次比較多個描述對
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Args:
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pairs: 待比較的配對列表 [(i, j, desc1, desc2), ...]
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model: LLM 模型名稱
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Returns:
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比較結果列表 [(i, j, is_similar), ...]
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"""
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semaphore = asyncio.Semaphore(self.max_concurrent)
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async def compare_one(pair: Tuple[int, int, str, str]) -> Tuple[int, int, bool]:
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i, j, desc1, desc2 = pair
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is_similar = await self.compare_pair(desc1, desc2, model, semaphore)
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return (i, j, is_similar)
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# 使用 asyncio.gather 並行執行所有比較
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results = await asyncio.gather(*[compare_one(p) for p in pairs])
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return results
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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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與 embedding_service 使用相同的演算法
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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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model: Optional[str] = None
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) -> DeduplicationResult:
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"""
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使用 LLM 成對比較進行去重
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Args:
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descriptions: 要去重的描述列表
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model: LLM 模型名稱
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Returns:
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DeduplicationResult: 去重結果
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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=0.5, # LLM 方法固定使用 0.5 閾值
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method_used=DeduplicationMethod.LLM,
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model_used=model
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)
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n = len(descriptions)
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similarity_matrix = np.zeros((n, n))
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# 對角線為 1（自己與自己相似）
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for i in range(n):
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similarity_matrix[i][i] = 1.0
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# 建立所有需要比較的配對
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pairs = []
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for i in range(n):
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for j in range(i + 1, n):
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pairs.append((
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i, j,
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descriptions[i].description,
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descriptions[j].description
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))
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total_pairs = len(pairs)
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logger.info(f"LLM deduplication: {total_pairs} pairs to compare (parallel={self.max_concurrent}, model={model})")
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# 並行批次比較
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results = await self.compare_batch(pairs, model)
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# 填入相似度矩陣
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for i, j, is_similar in results:
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similarity_value = 1.0 if is_similar else 0.0
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similarity_matrix[i][j] = similarity_value
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similarity_matrix[j][i] = similarity_value
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# 使用閾值 0.5 聚類（因為 LLM 輸出只有 0/1）
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logger.info("Clustering results...")
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clusters = self.cluster_by_similarity(similarity_matrix, 0.5)
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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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# LLM 方法的相似度分數都是 1.0（因為是 YES/NO 判斷）
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dup_scores = [1.0 for _ in dup_indices]
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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"LLM deduplication complete: {n} -> {len(result_groups)} groups, {total_duplicates} duplicates found")
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return DeduplicationResult(
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total_input=n,
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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=0.5, # LLM 方法固定使用 0.5 閾值
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method_used=DeduplicationMethod.LLM,
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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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llm_deduplication_service = LLMDeduplicationService()
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