from fastapi import APIRouter, Depends, HTTPException from .models import RetrievalRequest, RetrievalResponse, ErrorResponse from .auth import verify_api_key, validate_knowledge_id from services.knowledge_service import KnowledgeService import logging logger = logging.getLogger(__name__) router = APIRouter(tags=["Knowledge"]) knowledge_service = KnowledgeService() @router.post( "/retrieval", response_model=RetrievalResponse, responses={ 403: {"model": ErrorResponse}, 500: {"model": ErrorResponse}, 404: {"model": ErrorResponse} }, summary="知识库检索", description="根据查询文本检索相关的知识库内容" ) async def retrieval( request: RetrievalRequest, api_key: str = Depends(verify_api_key) ): """ 知识库检索API - 兼容Dify外部知识库接口 """ try: logger.info(f"收到检索请求: knowledge_id={request.knowledge_id}, query='{request.query[:50]}...'") # 验证知识库ID if not validate_knowledge_id(request.knowledge_id): raise HTTPException( status_code=404, detail={ "error_code": 2001, "error_msg": "知识库不存在" } ) # 调用服务层处理 result = await knowledge_service.search(request) logger.info(f"检索完成: 返回 {len(result.records)} 条记录") return result except HTTPException: raise except Exception as e: logger.error(f"检索过程中发生错误: {e}") error_msg = str(e) # 检查是否是知识库不存在的错误 if "不存在" in error_msg and "知识库" in error_msg: raise HTTPException( status_code=404, detail={ "error_code": 2001, "error_msg": error_msg } ) raise HTTPException( status_code=500, detail={ "error_code": 500, "error_msg": f"内部服务器错误: {error_msg}" } ) @router.get("/collections", summary="列出所有知识库") async def list_collections(api_key: str = Depends(verify_api_key)): """ 列出所有可用的知识库集合 """ try: collections = await knowledge_service.list_collections() return {"collections": collections} except Exception as e: logger.error(f"获取集合列表失败: {e}") raise HTTPException( status_code=500, detail={ "error_code": 500, "error_msg": f"获取集合列表失败: {str(e)}" } ) @router.get("/collections/{collection_name}/stats", summary="获取知识库统计信息") async def get_collection_stats( collection_name: str, api_key: str = Depends(verify_api_key) ): """ 获取指定知识库的统计信息 """ try: stats = await knowledge_service.get_collection_stats(collection_name) return stats except Exception as e: logger.error(f"获取集合统计信息失败: {e}") raise HTTPException( status_code=500, detail={ "error_code": 500, "error_msg": f"获取统计信息失败: {str(e)}" } ) from services.similarity_service import SimilarityCalculator from fastapi import HTTPException from pydantic import BaseModel, Field from typing import Optional from config.settings import settings import time # 全局相似度计算器实例 similarity_calculator = None # Pydantic模型定义 class SimilarityRequest(BaseModel): x_collection: str = Field(..., description="X轴collection名称") y_collection: str = Field(..., description="Y轴collection名称") x_max_items: Optional[int] = Field(100, description="X轴最大项目数") y_max_items: Optional[int] = Field(100, description="Y轴最大项目数") max_items: Optional[int] = Field(100, description="最大项目数（向后兼容）") def init_similarity_calculator(): """初始化相似度计算器""" global similarity_calculator try: similarity_calculator = SimilarityCalculator() logger.info("✅ 相似度计算器初始化成功") except Exception as e: logger.error(f"❌ 相似度计算器初始化失败: {e}") similarity_calculator = None @router.get("/similarity/health") async def similarity_health_check(): """相似度服务健康检查""" try: calculator_ready = similarity_calculator is not None connection_info = {} if similarity_calculator: connection_info = similarity_calculator.test_connection() return { "status": "healthy", "calculator_ready": calculator_ready, "timestamp": time.time(), "connections": connection_info, "vector_db_type": settings.vector_db_type } except Exception as e: raise HTTPException( status_code=500, detail={ "status": "error", "calculator_ready": False, "error": str(e), "timestamp": time.time() } ) @router.get("/similarity/collections") async def get_similarity_collections(): """获取所有collections""" try: if not similarity_calculator: init_similarity_calculator() if not similarity_calculator: raise HTTPException(status_code=500, detail="相似度计算器未初始化") collections = similarity_calculator.get_collections() return { "success": True, "collections": collections, "count": len(collections), "vector_db_type": settings.vector_db_type } except Exception as e: raise HTTPException( status_code=500, detail={"success": False, "error": str(e)} ) @router.post("/similarity/calculate") async def calculate_similarity_matrix(request: SimilarityRequest): """计算相似度矩阵""" try: if not similarity_calculator: init_similarity_calculator() if not similarity_calculator: raise HTTPException(status_code=500, detail="相似度计算器未初始化") # 解析参数 x_collection = request.x_collection y_collection = request.y_collection x_max_items = int(request.x_max_items or request.max_items or 100) y_max_items = int(request.y_max_items or request.max_items or 100) # 限制最大值 x_max_items = min(x_max_items, 3000) y_max_items = min(y_max_items, 3000) logger.info(f"🎯 收到相似度计算请求:") logger.info(f" X: {x_collection} (最大{x_max_items}项)") logger.info(f" Y: {y_collection} (最大{y_max_items}项)") # 计算相似度矩阵 result = similarity_calculator.calculate_similarity_matrix( x_collection=x_collection, y_collection=y_collection, x_max_items=x_max_items, y_max_items=y_max_items ) return { "success": True, "result": result, "message": f"成功计算 {len(result['y_data'])} x {len(result['x_data'])} 相似度矩阵", "vector_db_type": settings.vector_db_type } except Exception as e: logger.error(f"❌ 相似度计算API失败: {e}") raise HTTPException( status_code=500, detail={"success": False, "error": str(e)} )