---
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"])
```