Create faiss_example.py

```python
import faiss
import numpy as np

# Create a random set of vectors
d = 128  # dimension
nb = 10000  # database size
nq = 100  # number of queries

xb = np.random.random((nb, d)).astype('float32')
xq = np.random.random((nq, d)).astype('float32')

# Build the index
index = faiss.IndexFlatL2(d)
index.add(xb)

# Query the index
D, I = index.search(xq, k=5)  # search for the 5 nearest neighbors

# Print the results
print(I)
print(D)
```

Author: ewdlop

Vector Databases/faiss_example.py

@@ -0,0 +1,21 @@
+import faiss
+import numpy as np
+
+# Create a random set of vectors
+d = 128  # dimension
+nb = 10000  # database size
+nq = 100  # number of queries
+
+xb = np.random.random((nb, d)).astype('float32')
+xq = np.random.random((nq, d)).astype('float32')
+
+# Build the index
+index = faiss.IndexFlatL2(d)
+index.add(xb)
+
+# Query the index
+D, I = index.search(xq, k=5)  # search for the 5 nearest neighbors
+
+# Print the results
+print(I)
+print(D)