| --- |
| 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 |
| --- |
| |
| <div style="display: flex; align-items: center; justify-content: center; gap: 8px;"> |
| <img src="imgs/or-logo1.png" style="height: 84px; width: auto;"> |
| <img src="imgs/openresearcher-title.svg" style="height: 84px; width: auto;"> |
| </div> |
|
|
|
|
| <div align="center"> |
| <a href="https://huggingface.co/papers/2603.20278"><img src="https://img.shields.io/badge/arXiv-2603.20278-B31B1B.svg?style=for-the-badge&logo=arXiv&logoColor=white" alt="Paper"></a> |
| <a href="https://x.com/DongfuJiang/status/2020946549422031040"><img src="https://img.shields.io/badge/Twitter-000000?style=for-the-badge&logo=X&logoColor=white" alt="Blog"></a> |
| <a href="https://boiled-honeycup-4c7.notion.site/OpenResearcher-A-Fully-Open-Pipeline-for-Long-Horizon-Deep-Research-Trajectory-Synthesis-2f7e290627b5800cb3a0cd7e8d6ec0ea?source=copy_link"><img src="https://img.shields.io/badge/Blog-4285F4?style=for-the-badge&logo=google-chrome&logoColor=white" alt="Blog"></a> |
| <a href="https://github.com/TIGER-AI-Lab/OpenResearcher"><img src="https://img.shields.io/badge/Github-181717?style=for-the-badge&logo=github&logoColor=white" alt="Blog"></a> |
| <a href="https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Dataset"><img src="https://img.shields.io/badge/Dataset-FFB7B2?style=for-the-badge&logo=huggingface&logoColor=ffffff" alt="Dataset"></a> |
| <a href="https://huggingface.co/OpenResearcher/Nemotron-3-Nano-30B-A3B"><img src="https://img.shields.io/badge/Model-FFD966?style=for-the-badge&logo=huggingface&logoColor=ffffff" alt="Model"></a> |
| <a href="https://huggingface.co/spaces/OpenResearcher/OpenResearcher"><img src="https://img.shields.io/badge/Demo-F97316.svg?style=for-the-badge&logo=gradio&logoColor=white" alt="Demo"></a> |
| <a href="https://huggingface.co/datasets/OpenResearcher/OpenResearcher-Eval-Logs/tree/main"><img src="https://img.shields.io/badge/Eval%20Logs-755BB4?style=for-the-badge&logo=google-sheets&logoColor=white" alt="Eval Logs"></a> |
| </div> |
| </div> |
| <p align="center"> |
| 🤗 <a href="https://huggingface.co/collections/TIGER-Lab/openresearcher" target="_blank">HuggingFace</a> ｜ |
| <img src="imgs/notion.svg" width="15px" style="display:inline;"> <a href="https://boiled-honeycup-4c7.notion.site/OpenResearcher-A-Fully-Open-Pipeline-for-Long-Horizon-Deep-Research-Trajectory-Synthesis-2f7e290627b5800cb3a0cd7e8d6ec0ea?source=copy_link" target="_blank">Blog</a> ｜ <img src="imgs/slack.png" width="14px" style="display:inline;"> <a href="https://join.slack.com/t/openresearcher/shared_invite/zt-3p0r32cky-PqtZkVjjWIAI14~XwcRMfQ" target="_blank">Slack</a> | <img src="imgs/wechat.svg" width="14px" style="display:inline;"> <a href="https://github.com/TIGER-AI-Lab/OpenResearcher/blob/main/assets/imgs/wechat_group.jpg" target="_blank">WeChat</a> |
| |
| </p> |
|
|
| ## 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} |
| } |
| ``` |
