GPU_IVF_PQ

The GPU_IVF_PQ index builds on the IVF_PQ concept by combining inverted file clustering with Product Quantization (PQ), which breaks down high-dimensional vectors into smaller subspaces and quantizes them for efficient similarity searches. Exclusively designed for GPU environments, GPU_IVF_PQ leverages parallel processing to accelerate computations and handle large-scale vector data effectively. For more information on foundational concepts, refer to IVF_PQ.

Build index

To build a GPU_IVF_PQ index on a vector field in Milvus, use the add_index() method, specifying the index_type, metric_type, and additional parameters for the index.

from pymilvus import MilvusClient

# Prepare index building params
index_params = MilvusClient.prepare_index_params()

index_params.add_index(
    field_name="your_vector_field_name", # Name of the vector field to be indexed
    index_type="GPU_IVF_PQ", # Type of the index to create
    index_name="vector_index", # Name of the index to create
    metric_type="L2", # Metric type used to measure similarity
    params={
        "m": 4, # Number of sub-vectors to split eahc vector into
    } # Index building params
)

In this configuration:

• index_type: The type of index to be built. In this example, set the value to GPU_IVF_PQ.

• metric_type: The method used to calculate the distance between vectors. Supported values include COSINE, L2, and IP. For details, refer to Metric Types.

• params: Additional configuration options for building the index.

  • m: Number of sub-vectors to split the vector into.

  To learn more building parameters available for the GPU_IVF_PQ index, refer to Index building params.

Once the index parameters are configured, you can create the index by using the create_index() method directly or passing the index params in the create_collection method. For details, refer to Create Collection.

Search on index

Once the index is built and entities are inserted, you can perform similarity searches on the index.

search_params = {
    "params": {
        "nprobe": 10, # Number of clusters to search
    }
}

res = MilvusClient.search(
    collection_name="your_collection_name", # Collection name
    anns_field="vector_field", # Vector field name
    data=[[0.1, 0.2, 0.3, 0.4, 0.5]],  # Query vector
    limit=3,  # TopK results to return
    search_params=search_params
)

In this configuration:

• params: Additional configuration options for searching on the index.

  • nprobe: Number of clusters to search for.

  To learn more search parameters available for the GPU_IVF_PQ index, refer to Index-specific search params.

Index params

This section provides an overview of the parameters used for building an index and performing searches on the index.

Index building params

The following table lists the parameters that can be configured in params when building an index.

Index-specific search params

The following table lists the parameters that can be configured in search_params.params when searching on the index.