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