[AI学习笔记]医疗知识问答：DeepSeek多跳推理优化实战指南

class MultiHopReasoner(nn.Module):
    def __init__(self, entity_dim, relation_dim, num_hops=4):
        super().__init__()
        self.entity_embedding = nn.Embedding(entity_dim, 128)
        self.relation_embedding = nn.Embedding(relation_dim, 128)
        self.attention_layers = nn.ModuleList([AttentionLayer() for _ in range(num_hops)])
        self.memory = ExternalMemory(128, 100)
        
    def forward(self, initial_entities, query_embedding):
        # 初始状态构建
        current_state = self.entity_embedding(initial_entities)
        hop_results = []
        
        # 多跳推理过程
        for i, attention_layer in enumerate(self.attention_layers):
            # 路径探索
            possible_paths = self._explore_paths(current_state)
            
            # 注意力分配
            weighted_paths = attention_layer(possible_paths, query_embedding)
            
            # 更新状态
            current_state = self._update_state(weighted_paths)
            
            # 存储中间结果
            self.memory.write(current_state)
            hop_results.append(current_state)
        
        # 结果整合
        final_result = self._integrate_results(hop_results)
        return final_result
    
    def _explore_paths(self, current_state):
        # 动态构建可能的推理路径
        # 实现细节：基于当前状态和知识图谱扩展可能的关联实体
        pass
    
    def _update_state(self, weighted_paths):
        # 更新推理状态
        # 实现细节：通过GRU或Transformer更新状态表示
        pass
    
    def _integrate_results(self, hop_results):
        # 整合多跳结果
        # 实现细节：通过注意力机制或加权平均整合各跳结果
        pass

# 创建虚拟环境
python -m venv deepseek-medical
source deepseek-medical/bin/activate

# 安装依赖
pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
pip install transformers==4.20.0
pip install networkx==2.6.3
pip install redis==4.1.0

from deepseek.knowledge_graph import MedicalKG

# 加载医疗知识图谱
kg = MedicalKG(
    entity_file="data/entities.csv",
    relation_file="data/relations.csv",
    embedding_dim=128
)

# 预加载关键实体
kg.preload_entities(["headache", "fever", "stiff_neck", "meningitis"])

from deepseek.reasoner import MultiHopReasoner

# 初始化多跳推理引擎
reasoner = MultiHopReasoner(
    entity_dim=kg.entity_count,
    relation_dim=kg.relation_count,
    num_hops=4
)

# 加载预训练模型
reasoner.load_state_dict(torch.load("models/reasoner_v2.pth"))
reasoner.eval()

from deepseek.api import QuestionAnsweringAPI

# 初始化问答API
qa_api = QuestionAnsweringAPI(kg, reasoner)

# 处理具体问题
question = "患者出现头痛、发热和颈部僵硬，可能是什么疾病？"
answer = qa_api.answer_question(question)

print(f"问题: {question}")
print(f"答案: {answer}")

# 部署示例
docker-compose up -d redis
python scripts/preload_kg.py
uvicorn deepseek.api:app --host 0.0.0.0 --port 8000

import unittest
from deepseek.api import QuestionAnsweringAPI

class TestQASystem(unittest.TestCase):
    def setUp(self):
        self.qa_api = QuestionAnsweringAPI()
    
    def test_common_disease(self):
        question = "感冒的主要症状有哪些？"
        answer = self.qa_api.answer_question(question)
        self.assertIn("发热", answer)
        self.assertIn("咳嗽", answer)
    
    def test_rare_disease(self):
        question = "出现进行性肌无力和吞咽困难可能是什么病？"
        answer = self.qa_api.answer_question(question)
        self.assertIn("肌萎缩侧索硬化", answer)

def test_end_to_end():
    # 启动测试环境
    os.system("docker-compose up -d test-env")
    
    # 初始化API客户端
    client = TestClient(app)
    
    # 提交测试问题
    response = client.post("/answer", json={"question": "头痛和视力模糊可能是什么原因？"})
    
    # 验证结果
    assert response.status_code == 200
    assert "高血压" in response.json()["answer"]
    assert "脑出血" in response.json()["answer"]
    
    # 清理环境
    os.system("docker-compose down")