Here's an example of how you can use a vector database in Python, specifically using the Faiss library for similarity search. We'll use a simple dataset of documents and their corresponding vector representations:

In this example, we use the Faiss library, which is a popular library for efficient similarity search on large-scale vector datasets. We create a vector index (IndexFlatL2) and add the document vectors to it. Then, we perform a similarity search by providing a query vector and specifying the number of nearest neighbors (k). Finally, we retrieve the nearest neighbors' indices and distances and print them.

Note that you would need to install the Faiss library (pip install faiss) and import the necessary libraries (import faiss and import numpy as np) before running the code. Also, make sure to modify the documents list with your own data, where each element contains a document ID and its corresponding vector representation.

Feel free to replace the example document vectors with your own IP address vectors or any other relevant data.