GPU_IVF_FLAT

The GPU_IVF_FLAT index is a GPU-accelerated version of the IVF_FLAT index, designed exclusively for GPU environments. It partitions vector data into nlist cluster units and computes similarity by first comparing the target query vector with the center of each cluster. By tuning the nprobe parameter, only the most promising clusters are searched, which reduces query time while maintaining a balance between accuracy and speed. For more information on foundational concepts, refer to IVF_FLAT.

Build index

To build a GPU_IVF_FLAT 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_FLAT", # 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={
        "nlist": 1024, # Number of clusters for the index
    } # Index building params
)

In this configuration:

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

• metric_type: The method used to calculate the distance between vectors. For details, refer to Metric Types.

• params: Additional configuration options for building the index.

  • nlist: Number of clusters to divide the dataset.

  To learn more building parameters available for the GPU_IVF_FLAT 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",
    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_FLAT 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.