下一代RAG实战：TiDB+知识图谱优化医疗问答系统（附Doris集成方案）

-- 创建支持图查询的分区表
CREATE TABLE medical_kg (
  entity_id BIGINT PRIMARY KEY,
  entity_name VARCHAR(255),
  entity_type ENUM('DISEASE','DRUG','SYMPTOM'),
  partition_key DATE  -- 按知识更新时间分区
) PARTITION BY RANGE (YEAR(partition_key)) (
  PARTITION p2023 VALUES LESS THAN (2024),
  PARTITION p2024 VALUES LESS THAN (2025)
);

-- 构建跨类型混合索引
CREATE INDEX idx_entity_compound 
  ON medical_kg(entity_name, entity_type) 
  USING INVERTED;  -- 全文索引+类型过滤

(急性心肌梗死) -[SYNONYM]-> (AMI)
(AMI) -[SYNONYM]-> (急性心梗)

def link_entity(clinical_text):
    # 步骤1：BiLSTM-CRF医学实体识别
    entities = med_ner_model.predict(clinical_text)
    
    # 步骤2：基于本体知识库的消歧
    for entity in entities:
        candidate_ids = query_tidb(
            f"SELECT entity_id FROM medical_kg WHERE entity_name LIKE '%{entity}%'"
        )
        # 计算上下文相似度
        best_match = max(candidate_ids, key=lambda x: 
            calculate_semantic_similar(entity.context, x.definition))
        entity.linked_id = best_match
    return entities

-- 热数据：近3个月更新的核心知识
CREATE PLACEMENT POLICY hot_placement 
  CONSTRAINTS = "[+region=sh-hot]" 
  FOLLOWERS=4;

-- 温数据：1-3年内的指南文献
CREATE PLACEMENT POLICY warm_placement 
  CONSTRAINTS = "[+region=sh-warm]"
  FOLLOWERS=2;

-- 冷数据：5年以上历史资料
CREATE PLACEMENT POLICY cold_placement 
  CONSTRAINTS = "[+region=bj-archive]"
  FOLLOWER=0;

ALTER TABLE medical_knowledge 
  PLACEMENT POLICY = hot_placement 
  WHERE update_time > NOW() - INTERVAL 3 MONTH;

ALTER TABLE medical_knowledge 
  PLACEMENT POLICY = warm_placement 
  WHERE update_time BETWEEN NOW() - INTERVAL 3 YEAR AND NOW() - INTERVAL 3 MONTH;

ALTER TABLE medical_knowledge 
  PLACEMENT POLICY = cold_placement 
  WHERE update_time < NOW() - INTERVAL 3 YEAR;

MATCH (drug:Drug {name:'阿司匹林'})
  -[:CONTRAINDICATED_IN]-> (disease:Disease {name:'瑞氏综合征'})
  <-[:HIGH_RISK_FOR]-(age:AgeGroup {name:'儿童'})
RETURN disease.name, risk_level

EXPLAIN 
SELECT d.drug_name, k.evidence_level
FROM drug_contraindications d
JOIN symptom_associations s1 ON d.condition_id = s1.condition_id
JOIN symptom_associations s2 ON s1.condition_id = s2.condition_id
WHERE s1.symptom = '呕吐' 
  AND s2.symptom = '肝功能异常'
  AND d.population = '孕妇'
  AND k.evidence_level >= 2  -- 证据等级≥2

-- 创建Doris表启用ZSTD压缩和前缀索引
CREATE TABLE drug_usage_analysis (
  hospital_id INT,
  drug_code VARCHAR(20),
  patient_age INT,
  dosage FLOAT,
  event_date DATE
) ENGINE=OLAP
DUPLICATE KEY(hospital_id, drug_code)
PARTITION BY RANGE(event_date)(
  PARTITION p202401 VALUES [('2024-01-01'), ('2024-02-01')))
DISTRIBUTED BY HASH(hospital_id) BUCKETS 16
PROPERTIES (
  "compression"="zstd",  -- 压缩率提升45%
  "prefix_index_columns"="drug_code",  -- 药品编码前缀索引
  "storage_format"="V2" 
);

-- 实时查询当前库存与历史消耗趋势
SELECT 
  t.drug_name, 
  t.current_stock,
  d.avg_monthly_usage
FROM tidb_hospital_db.inventory t
JOIN DORIS_CATALOG.drug_usage_analysis d 
  ON t.drug_code = d.drug_code
WHERE d.event_date > NOW() - INTERVAL 6 MONTH
GROUP BY t.drug_id
HAVING t.current_stock < 1.2 * d.avg_monthly_usage;

T = T_index + N * (T_node + M * T_edge)

总内存需求 = (图数据内存索引 + 热数据缓存) * 副本数
图数据内存索引 ≈ 节点数 * 128B + 边数 * 64B
热数据缓存 ≈ 访问频率TOP10%数据 * 平均大小

sequenceDiagram
    医生->>系统： 标记答案有效性
    系统->>验证模块： 触发证据溯源
    验证模块->>知识图谱： 请求关系验证
    知识图谱-->>验证模块： 返回证据链
    验证模块->>LLM： 生成关系更新建议
    LLM->>TiDB： 提交事务更新
    TiDB->>版本管理： 记录变更历史
    版本管理->>审计系统： 发送变更通知

UPDATE medical_relations
SET weight = weight * 0.7
WHERE relation_id IN (
  SELECT relation_id 
  FROM evidence_updates
  WHERE update_type = 'OVERRIDE'
    AND confidence > 0.9
)

# 联邦关系发现算法
def federated_relation_discovery(hospital_a, hospital_b):
    # Step1: 本地梯度计算
    grad_a = compute_local_gradient(hospital_a)
    grad_b = compute_local_gradient(hospital_b)
    
    # Step2: 安全聚合
    global_grad = homomorphic_encrypt(grad_a) + homomorphic_encrypt(grad_b)
    
    # Step3: 更新全局图谱
    update_global_kg(decrypt(global_grad))
    
    # Step4: 各医院同步更新
    hospital_a.update_model(global_grad)
    hospital_b.update_model(global_grad)