| | --- |
| | 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 |
| | --- |
| | <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://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://wandb.ai/dongfu/nano-v3-sft-search"><img src="https://img.shields.io/badge/WandB%20Logs-48B5A3?style=for-the-badge&logo=weightsandbiases&logoColor=white" alt="WandB Logs"></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. 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} |
| | } |
| | ``` |
| |
|
| |
|
