---
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
---
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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. 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]}...\n")
```
## Citation
```bibtex
@misc{li2025openresearcher,
title={OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis},
author={Zhuofeng Li and Dongfu Jiang and Xueguang Ma and Haoxiang Zhang and Ping Nie and Yuyu Zhang and Kai Zou and Jianwen Xie and Yu Zhang and Wenhu Chen},
year={2025},
howpublished={\url{https://www.notion.so/OpenResearcher-A-Fully-Open-Pipeline-for-Long-Horizon-Deep-Research-Trajectory-Synthesis-2f7e290627b5800cb3a0cd7e8d6ec0ea}},
note={Notion Blog}
}
```
