ShashwatShivam commented on PR #13651:
URL: https://github.com/apache/lucene/pull/13651#issuecomment-2356278997
Following up on the above comment by tanyaroosta, the dataset I was using
for benchmarking RaBitQ through Luceneutil (main branch) was amazon's ASIN and
query embeddings (which are 256 dim float vectors). Here are three sets of
results I got:
**Normal HNSW**
```
Results:
recall latency (ms) nDoc topK fanout maxConn beamWidth quantized
index s force merge s num segments index size (MB)
0.721 0.128 1439443 10 6 32 40 no
81.51 42.58 1 1455.25
0.828 0.166 1439443 10 6 64 100 no
202.30 78.03 1 1472.66
0.886 0.202 1439443 10 6 64 250 no
545.83 242.29 1 1493.95
0.914 0.227 1439443 10 6 64 500 no
1111.43 452.62 1 1511.73
```
**RaBitQ HNSW (same parameters) - codec
Lucene912HnswBinaryQuantizedVectorsFormat**
```
Results:
recall latency (ms) nDoc topK fanout maxConn beamWidth quantized
index s force merge s num segments index size (MB)
0.448 0.095 1439443 10 6 32 40 no
86.15 45.45 1 1526.72
0.522 0.130 1439443 10 6 64 100 no
195.14 89.08 1 1555.16
0.542 0.167 1439443 10 6 64 250 no
524.03 207.12 1 1589.50
0.547 0.192 1439443 10 6 64 500 no
1035.65 415.44 1 1618.38
```
**Only RaBitQ - codec Lucene912BinaryQuantizedVectorsFormat**
```
Results:
recall latency (ms) nDoc topK fanout maxConn beamWidth quantized
index s force merge s num segments index size (MB)
0.000 87.658 1439443 10 100 64 40 no
12.39 8.01 1 1471.69
0.000 87.830 1439443 100 100 64 40 no
12.53 7.78 1 1471.69
```
The first issue we're facing is the large recall reduction for RaBitQ HNSW
when compared to pure HNSW. Is this recall regression expected? Secondly, the
pure RaBitQ implementation returns 0 recall, which is definitely suspect.
Perhaps there is a bug on my end when trying to benchmark it using luceneutil?
@benwtrent I'll also try working with your branch of luceneutil to see if it
changes the results
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