Update README.md

README.md

@@ -1,3 +1,102 @@
 ---
+pretty_name: AgentWebBench Corpus
 license: mit
+language:
+  - en
+task_categories:
+  - text-retrieval
+size_categories:
+  - 10M<n<100M
+tags:
+  - information-retrieval
+  - dense-retrieval
+  - faiss
+  - embeddings
+  - clueweb22
+  - agents
+  - benchmark
 ---
+
+# AgentWebBench Corpus
+
+Pre-built **dense-retrieval corpus** for [AgentWebBench](https://arxiv.org/abs/2604.10938) [ICML 2026], a benchmark for Multi-Agent Coordination in Agentic Web over a realistic 100-website slice of
+[ClueWeb22](https://lemurproject.org/clueweb22/) (~18.4M documents).
+
+This repository holds the **embeddings and FAISS indices** the benchmark loads at run time, including per-website indices, a global index, and website-level vectors. It does **not** contain ClueWeb22 text (see [Raw documents](#raw-documents-clueweb22-b)).
+- **Websites:** 100
+- **Documents:** ~18.4M 
+- **Embedding dim:** 1024
+
+
+⚠️ **Derived from ClueWeb22.** These vectors and ID maps are derived from ClueWeb22 documents. Use is subject to the [ClueWeb22 license](https://lemurproject.org/clueweb22/).
+
+## 1. Contents
+
+```
+faiss_indices/
+├── <website>.faiss            # per-website FAISS index (IndexFlatIP over doc vectors)
+└── <website>_doc_ids.npy      # row i of the index → that website's ClueWeb22 doc_id
+website_embeddings.pkl         # {website → 1024-d vector}; used to rank/select websites
+global_doc_index.faiss         # one FAISS index over ALL ~18.4M documents
+global_doc_ids.npy             # row i of the global index → ClueWeb22 doc_id
+```
+
+**How `.faiss` and `_doc_ids.npy` pair up:** a FAISS search returns integer row positions, not document IDs. The `.faiss` stores the vectors; the `_doc_ids.npy` is the row→`doc_id` lookup. They are strictly aligned by position and must be used together.
+
+
+## 2. Usage
+
+### 2.1 With the AgentWebBench code (recommended)
+
+```bash
+git clone https://github.com/cxcscmu/AutoGEO/AgentWebBench
+cd AgentWebBench
+python -m awbench.download --output-dir ./AgentWebBench-corpus
+# then set WEBSITE_DATA_ROOT in awbench/config.py to ./AgentWebBench-corpus
+```
+
+### 2.2 Directly with `huggingface_hub`
+
+```python
+from huggingface_hub import snapshot_download
+
+path = snapshot_download(
+    repo_id="cx-cmu/AgentWebBench-corpus",
+    repo_type="dataset",
+    local_dir="./AgentWebBench-corpus",
+)
+```
+
+### 2.3 Load an index
+
+```python
+import faiss, numpy as np
+
+index = faiss.read_index("faiss_indices/community.spiceworks.com.faiss")
+doc_ids = np.load("faiss_indices/community.spiceworks.com_doc_ids.npy", allow_pickle=True)
+
+# encode your query with MiniCPM-Embedding-Light (1024-d, normalized), then:
+scores, rows = index.search(query_vec.reshape(1, -1).astype("float32"), 10)
+hits = [str(doc_ids[i]) for i in rows[0]]   # ClueWeb22 doc_ids
+```
+
+
+This corpus stores only vectors and `doc_id`s. To read the actual page text for a retrieved `doc_id`, obtain [ClueWeb22 category B](https://lemurproject.org/clueweb22/index.php) (license required) and point `CLUEWEB_ROOT_PATH` at it.
+
+## 3. How it was built
+
+We follow [tevatron](https://github.com/texttron/tevatron) to build embeddings and FAISS indices. Specifically,
+- **Encoder:** [`openbmb/MiniCPM-Embedding-Light`](https://huggingface.co/openbmb/MiniCPM-Embedding-Light)
+- **Dimension:** 1024 
+- **Index:** FAISS `IndexFlatIP`
+
+## 4. Citation
+
+```bibtex
+@article{zhong2026agentwebbench,
+  title={AgentWebBench: Benchmarking Multi-Agent Coordination in Agentic Web},
+  author={Zhong, Shanshan and Shen, Kate and Xiong, Chenyan},
+  journal={arXiv preprint arXiv:2604.10938},
+  year={2026}
+}
+```