README.md

---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: docid
    dtype: string
  - name: text
    dtype: string
  - name: url
    dtype: string
  splits:
  - name: train
    num_bytes: 48560880327
    num_examples: 14878084
  download_size: 29752310440
  dataset_size: 48560880327
task_categories:
- text-retrieval
language:
- en
---

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## OpenResearcher Corpus 
This dataset contains a carefully curated **~11B-tokens** corpus, which serves as an offline search engine for our data generation process, eliminating the need for external Search APIs. It was introduced in the paper [OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis](https://huggingface.co/papers/2603.20278). Details on the corpus curation process are available in our [blog](https://boiled-honeycup-4c7.notion.site/OpenResearcher-A-Fully-Open-Pipeline-for-Long-Horizon-Deep-Research-Trajectory-Synthesis-2f7e290627b5800cb3a0cd7e8d6ec0ea?source=copy_link).

## Format      
Each row in the dataset contains the following fields:
+ **docid** (string): A unique identifier for each document in the corpus.
+ **text** (string): The complete text content of the document. Contains the full body of web pages.
+ **url** (string): The source URL where the document was retrieved from.

## How to use this dataset?
You can use this dataset together with its [embeddings](https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Indexes) to build an offline search engine. Below is a pseduo code for **demonstration only** (for production use, consider [Faiss-GPU](https://github.com/facebookresearch/faiss/wiki/Faiss-on-the-GPU)).
```bash
# download index before
huggingface-cli download OpenResearcher/OpenResearcher-Corpus --repo-type=dataset --include="qwen3-embedding-8b/*" --local-dir ./indexes
```
```python
import glob
import pickle
import faiss
import numpy as np
from datasets import load_dataset
from sentence_transformers import SentenceTransformer

# 1. Load corpus
corpus = load_dataset("OpenResearcher/OpenResearcher-Corpus", split="train")
docid_to_doc = {str(doc["docid"]): doc for doc in corpus}

# 2. Load all embedding shards from OpenResearcher-Indexes
index_files = sorted(glob.glob("path/to/indexes/*.pkl"))
all_embeddings = []
all_lookup = []

for file_path in index_files:
    with open(file_path, "rb") as f:
        embeddings, lookup = pickle.load(f)
        all_embeddings.append(embeddings)
        all_lookup.extend(lookup)

all_embeddings = np.vstack(all_embeddings).astype(np.float32)
faiss.normalize_L2(all_embeddings)  # Normalize for cosine similarity

# 3. Build FAISS index
index = faiss.IndexFlatIP(all_embeddings.shape[1])
index.add(all_embeddings)

# 4. Load model and encode query
model = SentenceTransformer("Qwen/Qwen3-Embedding-8B")
query = "What is machine learning?"
query_embedding = model.encode([query], prompt_name="query")

# 5. Search in FAISS
scores, indices = index.search(query_embedding, k=5)

# 6. Print results
for idx, score in zip(indices[0], scores[0]):
    docid = str(all_lookup[idx])
    doc = docid_to_doc.get(docid)
    if doc:
        print(f"Score: {score:.4f}")
        print(f"URL: {doc['url']}")
        print(f"Text: {doc['text'][:200]}...
")
```

## Citation 
```bibtex
@article{li2026openresearcher,
  title={{OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis}},
  author={Li, Zhuofeng and Jiang, Dongfu and Ma, Xueguang and Zhang, Haoxiang and Nie, Ping and Zhang, Yuyu and Zou, Kai and Xie, Jianwen and Zhang, Yu and Chen, Wenhu},
  journal={arXiv preprint arXiv:2603.20278},
  year={2026}
}
```