Add links to paper, GitHub, and project page

README.md

@@ -1,24 +1,25 @@
 ---
+language:
+- en
 license: apache-2.0
+size_categories:
+- n<10K
 task_categories:
 - text-retrieval
-- question-answering
-language:
-- en
 tags:
 - r-language
 - chromadb
 - tool-retrieval
 - data-science
 - llm-agent
-size_categories:
-- n<10K
 ---
 
 # R-Package Knowledge Base (RPKB)
 ![Gemini_Generated_Image_h25dizh25dizh25d (3)](https://cdn-uploads.huggingface.co/production/uploads/64c0e071e9263c783d548178/xXKYApaqL9hZyfSeSN3zP.png)
 
-This database is the official pre-computed **ChromaDB vector database** for the paper: *DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval*. 
+[**Project Page**](https://ama-cmfai.github.io/DARE_webpage/) | [**Paper**](https://huggingface.co/papers/2603.04743) | [**GitHub**](https://github.com/AMA-CMFAI/DARE)
+
+This database is the official pre-computed **ChromaDB vector database** for the paper: *[DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval](https://huggingface.co/papers/2603.04743)*. 
 
 It contains **8,191 high-quality R functions** meticulously curated from CRAN, complete with extracted statistical metadata (Data Profiles) and pre-computed embeddings generated by the **[DARE model](https://huggingface.co/Stephen-SMJ/DARE-R-Retriever)**.
 
@@ -28,61 +29,66 @@ It contains **8,191 high-quality R functions** meticulously curated from CRAN, c
 - **Embedding Model:** `Stephen-SMJ/DARE-R-Retriever`
 - **Primary Use Case:** Tool retrieval for LLM Agents executing data science and statistical workflows in R.
 
-## 🚀 How to Use (Plug-and-Play)
+## 🚀 Quick Start (Zero-Configuration Inference)
 
 You can easily download and load this database into your own Agentic workflows using the `huggingface_hub` and `chromadb` libraries.
 
-### 1. Install Dependencies
+### 1. Installation
 ```bash
-pip install huggingface_hub chromadb sentence-transformers
+pip install huggingface_hub chromadb sentence-transformers torch
 ```
 
-### 2. Download RPKB and Connect
-```Python
+### 2. Run the DARE Retriever
+The following script automatically downloads the DARE model and the RPKB database from Hugging Face and performs a distribution-aware search.
+
+```python
 from huggingface_hub import snapshot_download
+from sentence_transformers import SentenceTransformer
 import chromadb
+import torch
+import os
 
-# 1. Download the database folder from Hugging Face
-db_path = snapshot_download(
-    repo_id="Stephen-SMJ/RPKB", 
-    repo_type="dataset",
-    allow_patterns="RPKB/*"  # Adjust this if your folder name is different
-)
+# 1. Load DARE Model
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = SentenceTransformer("Stephen-SMJ/DARE-R-Retriever", trust_remote_code=False)
+model.to(device)
 
-# 2. Connect to the local ChromaDB instance
-client = chromadb.PersistentClient(path=f"{db_path}/RPKB")
+# 2. Download and Connect to RPKB Database
+db_dir = "./rpkb_db"
+if not os.path.exists(os.path.join(db_dir, "DARE_db")):
+    print("Downloading RPKB Database from Hugging Face...")
+    snapshot_download(repo_id="Stephen-SMJ/RPKB", repo_type="dataset", local_dir=db_dir, allow_patterns="DARE_db/*")
 
-# 3. Access the specific collection
+client = chromadb.PersistentClient(path=os.path.join(db_dir, "DARE_db"))
 collection = client.get_collection(name="inference")
 
-print(f"✅ Loaded {collection.count()} R functions ready for conditional retrieval!")
-```
-
-### 3. Perform a R Pakcage Retrieval
-
-To retrieve the best function, make sure you encode your query using the DARE model.
-
-```Python
-from sentence_transformers import SentenceTransformer
-
-# Load the DARE embedding model
-model = SentenceTransformer("Stephen-SMJ/DARE-R-Retriever")
-
-# Formulate the query with data constraints
-user_query = "I have a high-dimensional genomic dataset named hidra_ex_1_2000.csv in my environment. I need to identify driver elements by estimating regulatory scores based on the counts provided
-in the data. Please set the random seed to 123 at the start. I need to filter for fragment lengths between 150 and 600 bp and use a DNA count filter of 5. For my evaluation, please print the
-first value of the estimated scores (est_a) for the very first region identified."
+# 3. Perform Search
+query = "I have a sparse matrix with high dimensionality. I need to perform PCA."
+query_embedding = model.encode(query, convert_to_tensor=False).tolist()
 
-# Generate embedding
-query_embedding = model.encode(user_query).tolist()
-
-# Search in the database with Hard Filters
 results = collection.query(
     query_embeddings=[query_embedding],
     n_results=3,
-    include=["metadatas", "distances", "documents"]
+    include=["documents", "metadatas"]
 )
 
-# Display Top-1 Result
-print("Top-1 Function:", results["metadatas"][0][0]["package_name"], "::", results["metadatas"][0][0]["function_name"])
+# Display Results
+for rank, (doc_id, meta) in enumerate(zip(results['ids'][0], results['metadatas'][0])):
+    print(f"[{rank + 1}] Package: {meta.get('package_name')} :: Function: {meta.get('function_name')}")
 ```
+
+## 📖 Citation
+
+If you find DARE, RPKB, or RCodingAgent useful in your research, please cite our work:
+
+```bibtex
+@article{sun2026dare,
+      title={DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval}, 
+      author={Maojun Sun and Yue Wu and Yifei Xie and Ruijian Han and Binyan Jiang and Defeng Sun and Yancheng Yuan and Jian Huang},
+      year={2026},
+      eprint={2603.04743},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR},
+      url={https://arxiv.org/abs/2603.04743}, 
+}
+```