Create README.md

README.md

@@ -0,0 +1,87 @@
+---
+license: apache-2.0
+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)
+
+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*. 
+
+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)**.
+
+## 📊 Database Overview
+- **Database Engine:** ChromaDB
+- **Total Documents:** 8,191 R functions
+- **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)
+
+You can easily download and load this database into your own Agentic workflows using the `huggingface_hub` and `chromadb` libraries.
+
+### 1. Install Dependencies
+```bash
+pip install huggingface_hub chromadb sentence-transformers
+```
+
+### 2. Download RPKB and Connect
+```Python
+from huggingface_hub import snapshot_download
+import chromadb
+
+# 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
+)
+
+# 2. Connect to the local ChromaDB instance
+client = chromadb.PersistentClient(path=f"{db_path}/RPKB")
+
+# 3. Access the specific collection
+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."
+
+# 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"]
+)
+
+# Display Top-1 Result
+print("Top-1 Function:", results["metadatas"][0][0]["package_name"], "::", results["metadatas"][0][0]["function_name"])
+```