Model: OpenAI-ada2 embeddings with quantized HNSW indexes (using cached queries)
This page describes regression experiments, integrated into Anserini's regression testing framework, using OpenAI-ada2 embeddings on the TREC 2019 Deep Learning Track passage ranking task, as described in the following paper:
Jimmy Lin, Ronak Pradeep, Tommaso Teofili, and Jasper Xian. Vector Search with OpenAI Embeddings: Lucene Is All You Need. arXiv:2308.14963, 2023.
In these experiments, we are using cached queries (i.e., cached results of query encoding).
Note that the NIST relevance judgments provide far more relevant passages per topic, unlike the "sparse" judgments provided by Microsoft (these are sometimes called "dense" judgments to emphasize this contrast). For additional instructions on working with MS MARCO passage collection, refer to this page.
The exact configurations for these regressions are stored in this YAML file.
Note that this page is automatically generated from this template as part of Anserini's regression pipeline, so do not modify this page directly; modify the template instead and then run bin/build.sh to rebuild the documentation.
From one of our Waterloo servers (e.g., orca), the following command will perform the complete regression, end to end:
python src/main/python/run_regression.py --index --verify --search --regression dl19-passage.openai-ada2.parquet.hnsw-int8.cachedWe make available a version of the MS MARCO Passage Corpus that has already been encoded with the OpenAI-ada2 embedding model.
From any machine, the following command will download the corpus and perform the complete regression, end to end:
python src/main/python/run_regression.py --download --index --verify --search --regression dl19-passage.openai-ada2.parquet.hnsw-int8.cachedThe run_regression.py script automates the following steps, but if you want to perform each step manually, simply copy/paste from the commands below and you'll obtain the same regression results.
Download the corpus and unpack into collections/:
wget https://rgw.cs.uwaterloo.ca/pyserini/data/msmarco-passage-openai-ada2.parquet.tar -P collections/
tar xvf collections/msmarco-passage-openai-ada2.parquet.tar -C collections/To confirm, msmarco-passage-openai-ada2.parquet.tar is 75 GB and has MD5 checksum fa3637e9c4150b157270e19ef3a4f779.
With the corpus downloaded, the following command will perform the remaining steps below:
python src/main/python/run_regression.py --index --verify --search --regression dl19-passage.openai-ada2.parquet.hnsw-int8.cached \
--corpus-path collections/msmarco-passage-openai-ada2.parquetSample indexing command, building quantized HNSW indexes:
bin/run.sh io.anserini.index.IndexHnswDenseVectors \
-threads 16 \
-collection ParquetDenseVectorCollection \
-input /path/to/msmarco-passage-openai-ada2.parquet \
-generator ParquetDenseVectorDocumentGenerator \
-index indexes/lucene-hnsw-int8.msmarco-v1-passage.openai-ada2/ \
-M 16 -efC 100 -quantize.int8 \
>& logs/log.msmarco-passage-openai-ada2.parquet &The path /path/to/msmarco-passage-openai-ada2.parquet/ should point to the corpus downloaded above.
Upon completion, we should have an index with 8,841,823 documents.
Topics and qrels are stored here, which is linked to the Anserini repo as a submodule. The regression experiments here evaluate on the 43 topics for which NIST has provided judgments as part of the TREC 2019 Deep Learning Track. The original data can be found here.
After indexing has completed, you should be able to perform retrieval as follows:
bin/run.sh io.anserini.search.SearchHnswDenseVectors \
-index indexes/lucene-hnsw-int8.msmarco-v1-passage.openai-ada2/ \
-topics tools/topics-and-qrels/topics.dl19-passage.openai-ada2.jsonl.gz \
-topicReader JsonIntVector \
-output runs/run.msmarco-passage-openai-ada2.parquet.openai-ada2-hnsw-int8-cached.topics.dl19-passage.openai-ada2.jsonl.txt \
-hits 1000 -efSearch 1000 -threads 16 &Evaluation can be performed using trec_eval:
bin/trec_eval -m map -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-openai-ada2.parquet.openai-ada2-hnsw-int8-cached.topics.dl19-passage.openai-ada2.jsonl.txt
bin/trec_eval -m ndcg_cut.10 -c tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-openai-ada2.parquet.openai-ada2-hnsw-int8-cached.topics.dl19-passage.openai-ada2.jsonl.txt
bin/trec_eval -m recall.100 -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-openai-ada2.parquet.openai-ada2-hnsw-int8-cached.topics.dl19-passage.openai-ada2.jsonl.txt
bin/trec_eval -m recall.1000 -c -l 2 tools/topics-and-qrels/qrels.dl19-passage.txt runs/run.msmarco-passage-openai-ada2.parquet.openai-ada2-hnsw-int8-cached.topics.dl19-passage.openai-ada2.jsonl.txtWith the above commands, you should be able to reproduce the following results:
| AP@1000 | OpenAI-ada2 |
|---|---|
| DL19 (Passage) | 0.479 |
| nDCG@10 | OpenAI-ada2 |
| DL19 (Passage) | 0.703 |
| R@100 | OpenAI-ada2 |
| DL19 (Passage) | 0.623 |
| R@1000 | OpenAI-ada2 |
| DL19 (Passage) | 0.863 |
The above figures are from running brute-force search with cached queries on non-quantized flat indexes. With cached queries on quantized HNSW indexes, observed results are likely to differ; scores may be lower by up to 0.01, sometimes more. Note that both HNSW indexing and quantization are non-deterministic (i.e., results may differ slightly between trials).
❗ Retrieval metrics here are computed to depth 1000 hits per query (as opposed to 100 hits per query for document ranking).
For computing nDCG, remember that we keep qrels of all relevance grades, whereas for other metrics (e.g., AP), relevance grade 1 is considered not relevant (i.e., use the -l 2 option in trec_eval).
The experimental results reported here are directly comparable to the results reported in the track overview paper.
Reproduction Log*
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