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add ivf-random
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README_ENVECTOR.md

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@@ -109,9 +109,10 @@ export NUM_PER_BATCH=4096
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Run the provided shell scripts (`./scripts/run_benchmark.sh`) as the following:
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```bash
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./scripts/run_benchmark.sh --type flat # FLAT
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./scripts/run_benchmark.sh --type ivf # IVF
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./scripts/run_benchmark.sh --type ivf-gas # Our ANN (GAS)
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./scripts/run_benchmark.sh --type flat # FLAT
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./scripts/run_benchmark.sh --type ivf # IVF-FLAT with trained centroids via k-means cluster
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./scripts/run_benchmark.sh --type ivf-random # IVF-FLAT with random centroids
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./scripts/run_benchmark.sh --type ivf-gas # IVF-FLAT with Our ANN (GAS)
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```
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For more details, please refer to `./scripts/run_benchmark.sh` or `./scripts/envector_benchmark_config.yml` for benchmarks with enVector with ANN (VCT). Or you can use the following command:
@@ -124,13 +125,12 @@ python -m vectordb_bench.cli.vectordbbench envectorflat \
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--db-label "Performance1536D500K-FLAT" \
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--eval-mode mm
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# IVF-FLAT
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# IVF-FLAT with random centroids
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python -m vectordb_bench.cli.vectordbbench envectorivfflat \
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--uri "localhost:50050" \
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--case-type "Performance1536D500K" \
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--db-label "Performance1536D500K-IVF-FLAT" \
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--eval-mode mm \
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--train-centroids False \ # random centroids
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--nlist 250 \
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--nprobe 6
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