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Here are the steps to add 4 million embeddings to a Faiss index:

1. Define the dimensionality of the embeddings (e.g. 512, 1024)
2. Create an empty Faiss index of the desired type (e.g. IVF, Flat, HNSW) with the corresponding dimensionality
3. Load the 4 million embeddings into memory. This will depend on how the embeddings are stored (e.g. in a file on disk, in a database, in memory).
4. Convert the embeddings to a numpy array (if not already in numpy format).
5. Normalize the embeddings to have unit length using L2 normalization. This will enable efficient cosine similarity calculations.
6. Add the embeddings to the Faiss index using the add method. This should be done in batches for efficient memory usage.
7. Save the Faiss index to disk if you want to reuse it later.

The time it takes to add 4 million embeddings to a Faiss index will depend on the type of index, dimensionality of the embeddings, and hardware resources available. It is recommended to use a GPU for faster indexing.