BIN_FLAT

The BIN_FLAT index is a variant of the FLAT index tailored exclusively for binary embeddings. It excels in applications where vector similarity search demands perfect accuracy on relatively small, million-scale datasets. By employing an exhaustive search methodology—comparing every target input against all vectors in the dataset—BIN_FLAT guarantees exact results. This precision makes it an ideal benchmark for assessing the performance of other indexes that might offer less than 100% recall, although its thorough approach also renders it the slowest option for large-scale data.

Build index

To build a BIN_FLAT index on a vector field in Milvus, use the add_index() method, specifying the index_type and metric_type 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_binary_vector_field_name", # Name of the vector field to be indexed
    index_type="BIN_FLAT", # Type of the index to create
    index_name="vector_index", # Name of the index to create
    metric_type="HAMMING", # Metric type used to measure similarity
    params={} # No additional parameters required for BIN_FLAT
)

In this configuration:

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

• metric_type: The method used to calculate the distance between vectors. Supported values for binary embeddings include HAMMING (default) and JACCARD. For details, refer to Metric Types.

• params: No extra parameters are needed for the BIN_FLAT index.

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.

res = MilvusClient.search(
    collection_name="your_collection_name", # Collection name
    anns_field="binary_vector_field",  # Binary vector field name
    data=[query_binary_vector],  # Query binary vector
    limit=3,  # TopK results to return
    search_params={"params": {}}  # No additional parameters required for BIN_FLAT
)

For more information, refer to Binary Vector.

Index params

For the BIN_FLAT index, no additional parameters are needed either during the index creation or the search process.