---
license: apache-2.0
task_categories:
  - text-retrieval
  - question-answering
language:
  - en
tags:
  - medical
  - biology
  - rare-disease
  - STXBP1
  - SNAREopathy
  - neuroscience
  - epilepsy
  - RAG
  - embeddings
  - chromadb
pretty_name: STXBP1-ARIA RAG Nemotron
size_categories:
  - 10K<n<100K
---

# 🧬⚡ STXBP1-ARIA RAG Database v10.1 - NVIDIA Nemotron Embeddings

**The most advanced RAG database for STXBP1 therapeutic research.**

A pre-built ChromaDB vector database containing:
**571,816 indexed text chunks** from **~17,000 curated PubMed Central (PMC) biomedical papers** + **165 base editing analysis entries**,
(https://huggingface.co/datasets/SkyWhal3/stxbp1-base-editing-sweep),
embedded with NVIDIA's state-of-the-art **Llama-Nemotron-Embed-1B-v2** model featuring **2048-dimensional embeddings**.

> ⚡ **This is the premium GPU-accelerated version** — Nemotron embeddings deliver maximum semantic precision for therapeutic queries, but require a GPU with 2-4GB VRAM. For a lightweight CPU-friendly alternative, see: **[STXBP1-RAG-Database (BGE)](https://huggingface.co/datasets/SkyWhal3/STXBP1-RAG-Database)**

---

## 🆕 v10.1 Update: Base Editing Parameter Sweep Data

**NEW!** This version includes **165 curated entries** from our exhaustive base editing compatibility analysis:

| Metric | Value |
|--------|-------|
| **Total Analyses** | 654,595,200 |
| **Variants Analyzed** | 171 pathogenic STXBP1 variants |
| **Parameters Swept** | PAM position, edit window, enzyme type |
| **Mouse Compatibility** | Wild-type mouse modeling feasibility |

### What's Included

Each variant entry contains:
- ✅ **Compatibility Score** (0-100) for adenine/cytosine base editing
- ✅ **Optimal Parameters** (SpCas9/SpCas9-NG, PAM position, edit window)
- ✅ **Mouse Model Compatibility** — Can a WT mouse model this human variant?
- ✅ **Clinical Context** — ClinVar classification, mutation type

### Key Findings Embedded

| Finding | Details |
|---------|---------|
| **Perfect Score Variants** | 5 variants achieve 100/100 compatibility |
| **K196X (c.586A>T)** | Score: 95.9/100, WT mouse compatible ✅ |
| **ABE-Curable** | 68 variants amenable to adenine base editing |
| **CBE-Curable** | 47 variants amenable to cytosine base editing |

> 📊 Full sweep data available at: [SkyWhal3/stxbp1-base-editing-sweep](https://huggingface.co/datasets/SkyWhal3/stxbp1-base-editing-sweep)

---

## 🏆 Why Nemotron?

NVIDIA's Nemotron embedding model ranks **#2 on MTEB retrieval benchmarks** — distilled from their 8B flagship into an efficient 1B parameter model.

| Feature | BGE (v9) | **Nemotron (v10)** |
|---------|----------|---------------------|
| **Embedding Dims** | 768 | **2048** ⬆️ 2.7x |
| **Model Params** | 110M | **1B** ⬆️ 9x |
| **MTEB Retrieval** | ~63 | **~69** ⬆️ +6 pts |
| **Semantic Precision** | Good | **Excellent** |
| **Hardware** | CPU OK | **GPU recommended** |
| **Optional Reranker** | ❌ | **✅ Available** |

### What 2048 Dimensions Means

```
Semantic Space Visualization:

768 dims (BGE)              2048 dims (Nemotron)
─────────────────           ─────────────────────
     ●                              ●
    /|\                          /  |  \  
   / | \                        /   |   \
  ●  ●  ●                      ●    ●    ●
  
  Good separation           Rich semantic space!
                            Fine-grained distinctions
```

**Real-world impact:**
- "haploinsufficiency" vs "dominant negative" → **better separated**
- "4-PBA chaperone" vs "AAV gene therapy" → **distinct clusters**
- "K196X nonsense" vs "R406H missense" → **clear differentiation**
- "ABE compatible" vs "prime editing required" → **therapeutic routing**

---

## 📊 Dataset Statistics

| Metric | Value |
|--------|-------|
| **Total Chunks** | 571,816 |
| **Source Papers** | ~17,000 PMC articles |
| **Curated Entries** | 24 expert-written |
| **Base Editing Entries** | 165 (v10.1) |
| **Database Size** | ~11.5 GB |
| **Embedding Model** | `nvidia/llama-nemotron-embed-1b-v2` |
| **Embedding Dimensions** | 2048 |
| **Model Parameters** | 1B |
| **Chunk Size** | ~1500 chars with 200 char overlap |
| **Index Type** | ChromaDB with HNSW |
| **Build Hardware** | NVIDIA H100 80GB |
| **Last Updated** | January 6, 2026 |

---

## 🎯 Purpose

This database powers **STXBP1-ARIA MAX**, the premium therapeutic discovery system, enabling:

- **Maximum retrieval precision** for complex therapeutic queries
- **Fine-grained semantic distinctions** between mutation types and mechanisms
- **Base editing feasibility lookups** for specific variants
- **Mouse model compatibility** guidance for preclinical research
- **Optional reranking** with Nemotron cross-encoder for top-k refinement
- **Literature-grounded responses** with PMC citations

---

## 🔗 Related Resources

| Resource | Link |
|----------|------|
| **ARIA MAX** (Live Demo) | [HuggingFace Space](https://huggingface.co/spaces/SkyWhal3/STXBP1-ARIA-MAX) |
| **Base Editing Sweep** | [Dataset](https://huggingface.co/datasets/SkyWhal3/stxbp1-base-editing-sweep) |
| **Evaluation Harness** | [STXBP1-Eval](https://huggingface.co/datasets/SkyWhal3/STXBP1-Eval) |
| **CPU Version (BGE)** | [STXBP1-RAG-Database](https://huggingface.co/datasets/SkyWhal3/STXBP1-RAG-Database) |

---


## 🚀 Quick Start

### Load in Python

```python
import chromadb
from chromadb.config import Settings

# Connect to database
client = chromadb.PersistentClient(
    path="./STXBP1-RAG-Nemotron",
    settings=Settings(anonymized_telemetry=False)
)

collection = client.get_collection("stxbp1_papers")
print(f"Loaded {collection.count():,} chunks")
```

### Query with Nemotron Embeddings

```python
from sentence_transformers import SentenceTransformer

# Load embedding model (requires GPU for best performance)
embedder = SentenceTransformer(
    "nvidia/llama-nemotron-embed-1b-v2",
    trust_remote_code=True
)

# Embed query
query = "What base editing options exist for K196X?"
query_embedding = embedder.encode(query).tolist()

# Search
results = collection.query(
    query_embeddings=[query_embedding],
    n_results=10
)

for doc, meta in zip(results['documents'][0], results['metadatas'][0]):
    print(f"[{meta.get('pmcid', 'N/A')}] {doc[:200]}...")
```

---

## 📁 Files

| File | Size | Description |
|------|------|-------------|
| `chroma.sqlite3` | ~2 GB | Metadata + document store |
| `{uuid}/data_level0.bin` | ~9 GB | HNSW vector index |
| `{uuid}/header.bin` | <1 KB | Index metadata |
| `{uuid}/length.bin` | <1 KB | Vector dimensions |

---

## 📜 License

Apache 2.0

---

## 🙏 Acknowledgments

Built for the STXBP1 research community. Special thanks to:
- [STXBP1 Foundation](https://www.stxbp1disorders.org/)
- NVIDIA for Nemotron embeddings
- The families and researchers working toward treatments

---

<div align="center">

*Built with ❤️ for rare disease research*

**Last Updated: January 6, 2026**

</div>