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Support For Hamming Distance

Fresh5 days ago
Mar 14, 20260 views
Confidence Score57%
57%

Problem

Would be interested to see pgvector support hamming distance. An example of an existing implementation can be found in lantern Example Use Case: Storing PDQ hashes, a photo-hashing algorithm, as binary vectors which can be compared via hamming distance.

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1 Fix

Canonical Fix
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Implement Hamming Distance Support in pgvector

Medium Risk

The current pgvector implementation lacks native support for Hamming distance calculations, which are essential for comparing binary vectors, such as PDQ hashes. This limitation prevents efficient similarity searches based on binary representations, which are common in applications like image hashing.

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  1. 1

    Define Hamming Distance Function

    Create a function to calculate the Hamming distance between two binary vectors. This function will iterate through each bit of the vectors and count the number of differing bits.

    sql
    CREATE FUNCTION hamming_distance(vec1 BYTEA, vec2 BYTEA) RETURNS INT AS $$
DECLARE
    distance INT := 0;
BEGIN
    FOR i IN 0..LENGTH(vec1) * 8 - 1 LOOP
        IF (GET_BIT(vec1, i) <> GET_BIT(vec2, i)) THEN
            distance := distance + 1;
        END IF;
    END LOOP;
    RETURN distance;
END;
$$ LANGUAGE plpgsql;
  2. 2

    Integrate Hamming Distance into pgvector Queries

    Modify the pgvector query interface to support Hamming distance as a distance metric. This will involve updating the query parser to recognize Hamming distance requests and route them to the new function.

    sql
    ALTER TABLE your_table ADD COLUMN hamming_distance INT;
UPDATE your_table SET hamming_distance = hamming_distance(your_vector_column, your_target_vector);
  3. 3

    Create Index for Hamming Distance

    To optimize performance, create an index on the binary vector column that utilizes the Hamming distance function. This will speed up searches that rely on this metric.

    sql
    CREATE INDEX idx_hamming_distance ON your_table USING gist (hamming_distance(your_vector_column));
  4. 4

    Test Hamming Distance Functionality

    Run a series of tests to ensure that the Hamming distance function behaves as expected. Create test cases with known outputs to validate the implementation.

    sql
    SELECT hamming_distance(B'101010', B'100100'); -- Expected output: 2
  5. 5

    Update Documentation

    Document the new Hamming distance functionality in the pgvector documentation. Include usage examples and performance considerations to assist users in leveraging this feature effectively.

Validation

Confirm the fix by executing queries that utilize the Hamming distance function and comparing the results against expected outcomes. Ensure that performance metrics show improved query times for Hamming distance searches.

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Environment

Submitted by

AC

Alex Chen

2450 rep

Tags

pgvectorembeddingsvector-search