feat: 分析结果入mysql

cucyuqing/cmd/risk-analyze.py

@@ -26,12 +26,33 @@ async def main():
 
     dbscan_result = await run_dbscan()
     docs = [cluster[0] for cluster in dbscan_result.clusters]
+    print("共有待分析文档:", len(docs), "噪声", len(dbscan_result.noise))
 
+    risks_to_update: dict[str, set[str]] = {}
     analyze_result = await batch_risk_analyze(docs, risk_types)
     for task in analyze_result:
         if "是" not in task.response:
             continue
-        print(f"风险: {task.risk_type.name} 标题: {task.doc.title}")
+        print(f"风险: {task.risk_type.name} 标题: {task.doc.title} {task.doc.id}")
+
+        # 合并每个文档的风险到一个set
+        if task.doc.id not in risks_to_update:
+            risks_to_update[task.doc.id] = set()
+        risks_to_update[task.doc.id].add(task.risk_type.name)
+
+    # 更新数据库
+    for doc_id, risks in risks_to_update.items():
+        await mysql.execute(
+            """
+            UPDATE risk_news
+            SET risk_types = :risk_types, updated_at = now()
+            WHERE es_id = :es_id
+            """,
+            {
+                "es_id": doc_id,
+                "risk_types": json.dumps(list(risks), ensure_ascii=False),
+            },
+        )
 
 
 @dataclass

cucyuqing/dbscan.py

@@ -8,9 +8,12 @@ from sklearn.metrics import pairwise_distances
 
 from cucyuqing.pg import pool, get_cur
 
+
 @dataclass
 class Document:
-    id: int
+    id: str
+    """ID 是 ES 中的 32 为 hex ID"""
+
     title: str
     content: str
     similarity: float = 0.0
@@ -23,13 +26,16 @@ class Document:
         """
         return self.title
 
+
 @dataclass
 class DBScanResult:
     noise: list[Document]
     clusters: list[list[Document]]
 
+
 from sklearn.metrics.pairwise import cosine_similarity
 
+
 async def run_dbscan() -> DBScanResult:
     # 从 PG 数据库获取数据
     async with get_cur() as cur:
@@ -45,16 +51,17 @@ async def run_dbscan() -> DBScanResult:
         )
         rows = await cur.fetchall()
         docs: list[Document] = [
-            Document(row[0], row[1], row[2])
+            Document(str(row[0]).replace("-", ""), row[1], row[2]) for row in rows
-            for row in rows
         ]
         embeddings = [numpy.array(json.loads(row[3])) for row in rows]
 
     # 计算余弦距离矩阵
-    cosine_distances = pairwise_distances(embeddings, metric='cosine')
+    cosine_distances = pairwise_distances(embeddings, metric="cosine")
 
     # 初始化DBSCAN模型
-    dbscan = DBSCAN(eps=0.25, min_samples=2, metric='precomputed')  # Adjust eps as needed
+    dbscan = DBSCAN(
+        eps=0.25, min_samples=2, metric="precomputed"
+    )  # Adjust eps as needed
 
     # 进行聚类
     dbscan.fit(cosine_distances)
@@ -66,7 +73,7 @@ async def run_dbscan() -> DBScanResult:
     ret: DBScanResult = DBScanResult(noise=[], clusters=[])
     unique_labels = set(labels)
     for label in unique_labels:
-        class_member_mask = (labels == label)
+        class_member_mask = labels == label
         cluster_docs = [docs[i] for i in range(len(labels)) if class_member_mask[i]]  # type: ignore
         cluster_embeddings = [embeddings[i] for i in range(len(labels)) if class_member_mask[i]]  # type: ignore
 
@@ -89,14 +96,16 @@ async def run_dbscan() -> DBScanResult:
 
     return ret
 
+
 async def main():
     await pool.open()
     result = await run_dbscan()
     print(f"噪声文档: {len(result.noise)}")
     for i, cluster in enumerate(result.clusters):
-        print('----------------')
+        print("----------------")
         for doc in cluster:
             print(f"聚类 {i} 文档: {doc.title} 相似度: {doc.similarity}")
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     asyncio.run(main())