增加FAISS向量数据库检索存储等功能。

demo/向量索引.py

@@ -0,0 +1,40 @@
+import faiss
+import numpy as np
+
+# 数据归一化函数
+def normalize_vectors(vectors):
+    """对向量进行L2归一化"""
+    norms = np.linalg.norm(vectors, axis=1, keepdims=True)
+    # 避免除零
+    norms = np.where(norms == 0, 1, norms)
+    return vectors / norms
+
+
+data = np.array([[2, 3], [2, 4], [3, 7]], dtype='float32')
+
+# 归一化数据
+data_normalized = normalize_vectors(data)
+print("原始数据:")
+print(data)
+print("归一化后数据:")
+print(data_normalized)
+
+# 普通索引
+# base_index = faiss.IndexFlatL2(2)
+base_index = faiss.IndexFlatIP(2)
+
+# 包一层 IDMap
+index = faiss.IndexIDMap(base_index)
+
+# 指定 ID
+ids = np.array([101, 102, 103])  # 自定义 ID
+# index.add_with_ids(data, ids)
+index.add_with_ids(data_normalized, ids)
+
+# 查询
+query = np.array([[3, 4.5]], dtype='float32')
+query_normalized = normalize_vectors(query)
+# D, I = index.search(query, k=2)
+D, I = index.search(query_normalized, k=2)
+print(D)
+print(I)  # 可能输出 [[101 102]]