OPERA Retrieval Corpus

Prebuilt dense retrieval corpus used by the OPERA inference pipeline (OPERA: A Reinforcement Learning–Enhanced Orchestrated Planner-Executor Architecture for Reasoning-Oriented Multi-Hop Retrieval).

This release contains two files:

File	Size	Purpose
opera_corpus.index	~7.3 GB	FAISS IndexFlatIP over BGE-M3 dense embeddings
opera_corpus.meta	~394 MB	Pickle list of {id, title, content} aligned 1:1 with the FAISS rows

• Total documents: 1,780,294
• Embedding model: BAAI/bge-m3 (1024-dim, cosine via inner-product on L2-normalised vectors)
• Index type: FAISS IndexFlatIP (exact search, no quantization)

Source

The corpus is composed of Wikipedia paragraphs aggregated from three public multi-hop QA benchmarks evaluated in the OPERA paper:

• HotpotQA (Yang et al., 2018)
• 2WikiMultiHopQA (Ho et al., 2020)
• MuSiQue (Trivedi et al., 2022)

All text in the corpus comes from the Wikipedia paragraphs distributed with these benchmarks. No external web content was added, and no benchmark labels (supporting-paragraph flags, sub-question decompositions, gold answers) are included — fetch those from the upstream datasets if you need them.

Schema

Each record in opera_corpus.meta is a Python dict with exactly three keys:

{
    "id":      "doc_0",                    # sequential, stable across loads
    "title":   "The Big Short (film)",     # source-document title
    "content": "The Big Short is a 2015 American ...",
}

Row i of opera_corpus.meta corresponds to row i of opera_corpus.index. The row order has been shuffled with a fixed seed; the IDs doc_0 … doc_1780293 are simple sequential identifiers and carry no information about which benchmark a paragraph came from.

Content lengths vary across records (paragraph-level and shorter fragments coexist in the index). If your downstream use case prefers a single granularity, filter by len(record["content"]).

Loading

import pickle, faiss

index = faiss.read_index("opera_corpus.index")
with open("opera_corpus.meta", "rb") as f:
    meta = pickle.load(f)

assert index.ntotal == len(meta) == 1780294
print(meta[0])

See load_example.py for an end-to-end retrieval example with BGE-M3.

Reproducing OPERA inference

The OPERA inference pipeline (Plan Agent → BGE-M3 retriever → Analysis-Answer Agent → Rewrite Agent → final synthesis) is released separately at the OPERA repository. Point its --index-path and --metadata-path flags at the two files in this release, or serve them behind the persistent retriever HTTP server shipped with the pipeline.

Evaluation splits. The reproducibility numbers below are produced on random 500-question subsamples of the official dev splits of HotpotQA, 2WikiMultiHopQA, and MuSiQue, drawn with fixed seeds (42 and 53). For MuSiQue we stratify the 500-question sample across hop counts (350 × 2-hop + 100 × 3-hop + 50 × 4-hop) to keep the difficulty distribution close to the full dev set. Two independent seeds are used so variance across runs can be reported.

On these splits, the inference-only pipeline against this corpus reproduces the OPERA (CoT) row of Table 1 of the paper, i.e. the OPERA architecture without MAPGRPO training:

Split	HotpotQA EM	2Wiki EM	MuSiQue EM
seed42	41.8	44.0	24.0
seed53	42.2	42.2	20.8
paper "OPERA (CoT)"	44.9	42.3	21.2

Headline numbers in Table 1 of the paper (the OPERA-MAPGRPO row) require MAPGRPO training of the three agents; the training code is not part of this release.

Citation

@article{Liu2026OPERA,
  author  = {Liu, Yu and Liu, Yanbing and Yuan, Fangfang and Cao, Cong and Sun, Youbang and Peng, Kun and Chen, WeiZhuo and Li, Jianjun and Ma, Zhiyuan},
  title   = {{OPERA}: A Reinforcement Learning--Enhanced Orchestrated Planner-Executor Architecture for Reasoning-Oriented Multi-Hop Retrieval},
  journal = {arXiv preprint arXiv:2508.16438},
  year    = {2025}
}

License

The retrieval index and metadata are released for research use only. Underlying paragraph text comes from HotpotQA, 2WikiMultiHopQA and MuSiQue and remains subject to those datasets' original licenses, each of which derives from Wikipedia under CC-BY-SA 4.0. When publishing results obtained with this corpus, please cite the OPERA paper as well as the original benchmark papers:

• Yang et al., HotpotQA, EMNLP 2018.
• Ho et al., Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps, COLING 2020.
• Trivedi et al., MuSiQue: Multihop Questions via Single-hop Question Composition, TACL 2022.
• Chen et al., BGE M3-Embedding, 2024.

Contact

For questions about the corpus or the OPERA pipeline, please open a discussion on this dataset's HuggingFace page or an issue on the OPERA GitHub repository.