knowledge/router/graph_router_new.py

import logging
import sys,os
import traceback
from agent.cdss.capbility import CDSSCapability
from agent.cdss.models.schemas import CDSSInput, CDSSInt, CDSSText
from model.response import StandardResponse
from service.cdss_service import CdssService
from service.kg_node_service import KGNodeService
from utils import DeepseekUtil

current_path = os.getcwd()
sys.path.append(current_path)
import time
from fastapi import APIRouter, Depends, Query
from typing import Optional, List

import sys
sys.path.append('..')
from utils.agent import call_chat_api,get_conversation_id
import json



router = APIRouter(tags=["Knowledge Graph"])
service = CdssService()
logger = logging.getLogger(__name__)
@router.post("/knowledge/disease/recommend", response_model=StandardResponse)
async def recommend(payload: dict):
    try:
        start_time = time.time()
        app_id = "256fd853-60b0-4357-b11b-8114b4e90ae0"
        conversation_id = get_conversation_id(app_id)

        desc = "主诉:" + payload["chief"]
        if "symptom" in payload:
            desc += "\n现病史：" + payload["symptom"]

        prompt = '''## 核心能力
        1. 医学语义理解：
           - 精准解析临床表现描述
           - 将口语化表达转换为ICD-11标准术语
           - 完整提取症状特征（性质/部位/放射等），保持症状描述的临床完整性

        2. 专业能力：
           - 精通ICD-11症状学术语体系
           - 识别症状关联性
           - 具备临床鉴别诊断思维

        ## 输出要求
        - 仅输出JSON格式结果，禁止添加解释性文字
        - 非症状不要抽取
         - 症状术语必须标准化
        - 尽量保留原始症状的临床特征（如部位、性质、放射等）
        - 抽取的症状应该简洁明了，尽量保持在5个字符以内，最多不宜超过7个字符。如果超过可以分多个症状词进行抽取。

        ## 处理流程
        1. 接收患者主诉文本
        2. 识别并提取所有症状描述
        3. 转换为ICD-11标准术语
        4. 结构化输出症状列表

        示例1：

        输入：突然感觉胸口压榨样疼痛，持续不缓解，向左肩和下颌放射，伴大汗、恶心，已经30分钟了。

        输出： { "symptoms": ["胸痛", "左肩放射痛", "下颌放射痛","大汗","恶心"] }
        本次用户输入:
        
        '''
        result = DeepseekUtil.chat(prompt+desc)
        json_data = json.loads(result)
        keyword = " ".join(json_data["symptoms"])

        sex = str(payload["sex"]) if "sex" in payload else None
        age = int(payload["age"]) if "age" in payload else None
        department = payload["dept"][0]["name"] if "dept" in payload and len(payload["dept"]) > 0 else None

        result = await neighbor_search(keyword=keyword, sex=sex, age=age, department=department)
        end_time = time.time()
        print(f"recommend执行完成，耗时：{end_time - start_time:.2f}秒")
        return result
    except Exception as e:
        traceback.print_exc()
        logger.error(f"recommend failed: {str(e)}")
        return StandardResponse(
            success=False,
            errorCode=500,
            errorMsg=str(e)
        )

async def recommend2(
    chief: str,
    present_illness: Optional[str] = None,
    sex: Optional[str] = None,
    age: Optional[int] = None,
    department: Optional[str] = None,
):
    start_time = time.time()
    app_id = "256fd853-60b0-4357-b11b-8114b4e90ae0"
    conversation_id = get_conversation_id(app_id)

    desc = "主诉:"+chief
    if present_illness:
        desc+="\n现病史：" + present_illness
    result = call_chat_api(app_id, conversation_id, desc)
    json_data = json.loads(result)
    keyword = " ".join(json_data["symptoms"])
    result = await neighbor_search(keyword=keyword,sex=sex,age=age,department=department)
    end_time = time.time()
    print(f"recommend执行完成，耗时：{end_time - start_time:.2f}秒")
    return result

@router.get("/neighbor_search", response_model=StandardResponse)
async def neighbor_search(
    keyword: str = Query(..., min_length=2),
    sex: Optional[str] = None,
    age: Optional[int] = None,
    department: Optional[str] = None
):
    """
    根据关键词和属性过滤条件搜索图谱节点
    """
    try:

        keywords = keyword.split(" ")

        record = CDSSInput(
            pat_age=CDSSInt(type="month", value=age),
            pat_sex=CDSSText(type="sex", value=sex),
            chief_complaint=keywords,
            department=CDSSText(type='department', value=department)
        )
        # 使用从main.py导入的capability实例处理CDSS逻辑
        output = capability.process(input=record)

        output.diagnosis.value = [{"name":key,"count":value["count"],"score":value["score"],"symptoms":value["symptoms"]
            } for key,value in output.diagnosis.value.items()]
        result = {}
        if len(output.diagnosis.value)>0:
            result = service.get_disease_detail(output.diagnosis.value[0]['name'], '疾病')
        return StandardResponse(
            success=True,
            data={"可能诊断":output.diagnosis.value,"症状":keywords,"disease_detail":result}
        )
    except Exception as e:
        traceback.print_exc()
        logger.error(f"get_disease_detail failed: {str(e)}")
        return StandardResponse(
            success=False,
            errorCode=500,
            errorMsg=str(e)
        )

@router.get("/knowledge/disease/{disease_name}/detail", response_model=StandardResponse)
async def get_disease_detail(
    disease_name: str
):
    try:
        result = service.get_disease_detail(disease_name,'疾病')
        return StandardResponse(success=True, data=result)
    except Exception as e:
        traceback.print_exc()
        logger.error(f"get_disease_detail failed: {str(e)}")
        return StandardResponse(
            success=False,
            errorCode=500,
            errorMsg=str(e)
        )

capability = CDSSCapability()
graph_router = router