🦹‍♀️ Vector quantization

Vector quantization is a technique to reduce the number of bits required to represent a vector. This can help reduce the storage and memory requirements for vector embeddings.

To enable vector auto-quantization on your embeddings, simply set the quantization field to one of the supported quantization types (scalar or binary) in the vector search index definition.

Fill in any <CODE_BLOCK_N> placeholders and run the cells under the 🦹‍♀️ Enable vector quantization section in the notebook to enable auto-quantization on your embeddings.

The answers for code blocks in this section are as follows:

CODE_BLOCK_17

Answer

{
    "name": ATLAS_VECTOR_SEARCH_INDEX_NAME,
    "type": "vectorSearch",
    "definition": {
        "fields": [
            {
                "type": "vector",
                "path": "embedding",
                "numDimensions": 512,
                "similarity": "cosine",
                "quantization": "scalar",
            },
        ]
    },
}

info

Notice the slight increase in the size of the vector search index upon enabling quantization. This is because full-fidelity vectors are also stored on disk for re-scoring and/or exact nearest neighbors (ENN) search.

In the Atlas UI, the entire index size is displayed, which might be larger than the original index size, since Atlas does not show a break down of the data structures within an index that are stored in RAM and on disk.

The Atlas Search metrics however will show a much smaller index that is held in memory when you enable automatic quantization. Refer to our documentation to learn more about these considerations.