AI 创作日记 | 摆脱知识孤岛的困境，DeepSeek推动四阶知识增强

# 传统向量检索伪代码
def vector_search(query):
    # 将问句转换为向量
    query_embed = embed(query)  
    
    # 计算相似度  
    similarities = cosine_similarity(query_embed, knowledge_base)
    
    # 返回Top3结果
    return sort(similarities)[:3] 

# 实际查询示例
results = vector_search("头痛伴视力模糊")
# 输出：["偏头痛治疗指南", "青光眼护理手册", "脑肿瘤诊断标准"]

class DeepSeekEnhancer:
    def __init__(self, kb):
        self.knowledge_graph = build_graph(kb)  # 构建知识图谱
        self.retriever = VectorRetriever(kb)    # 向量检索器
        
    def reasoning(self, query, context=None):
        # 混合检索
        candidates = self.retriever.search(query)
        graph_nodes = link_to_graph(candidates) 
        
        # 推理链生成
        chains = []
        for node in graph_nodes:
            chain = self._build_chain(node, context)
            chains.append(chain)
            
        # 路径评估
        scored_chains = self.llm.evaluate(chains)
        return select_best(scored_chains)
    
    def _build_chain(self, node, context):
        """构建多跳推理链"""
        chain = [node]
        for _ in range(3):  # 最大3跳
            relations = self.knowledge_graph.get_relations(node)
            next_node = self.llm.choose_next(node, relations, context)
            if not next_node: break
            chain.append(next_node)
            node = next_node
        return chain

prepare_data(
    format='混合数据',
    sources=['临床指南','电子病历','药品库'],
    preprocess=[
        '实体消歧@ICD-11标准',
        '时序关系提取@TSNE降维'
    ]
)

deploy_component('向量检索引擎',
    config={
        'dim':768,
        'metric':'cosine',
        'encoder':'PubMedBERT'
    })

deploy_component('图计算引擎',
    config={
        'max_hop':3,
        'path_weighting':'时序衰减系数',
        'cache_strategy':'动态预热'
    })

train_llm(
    task='推理链生成',
    dataset='med_chain_dataset',
    params={
        'max_depth':3,
        '约束条件':'符合NCCN指南',
        'reward_model':'诊疗方案安全性评分'
    })

create_api(
    endpoint='/reasoning',
    input_schema={
        'query':'text',
        'context':{
            'patient_info':'脱敏哈希值',
            'device':'体征监测数据流'
        }
    },
    auth='OAuth2.0+HIPAA认证'
)

monitor_system(
    metrics=[
        'accuracy@NLPCC临床评测集',
        'response_time@p99<2s',
        '药品冲突预警率'
    ],
    alert='飞书医疗质控群'
)

update_knowledge(
    cron='0 3 * * *',
    pipeline=[
        '指南更新抓取@CDC官网',
        '电子病历增量向量化',
        'A/B测试模型迭代'
    ]
)

# 不推荐使用的情况
if any([
    query_type == '简单事实查询',  # 如"北京人口数量"
    latency_requirement < 500,  
    domain_knowledge_coverage < 0.3
]):
    print("建议使用传统检索方案")