SkyWhal3/STXBP1-RAG-Nemotron

🧬⚡ 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:

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

📊 Full sweep data available at: 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.

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

🎯 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

🚀 Quick Start

Load in 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

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

📜 License

Apache 2.0

🙏 Acknowledgments

Built for the STXBP1 research community. Special thanks to:

• —STXBP1 Foundation
• —NVIDIA for Nemotron embeddings
• —The families and researchers working toward treatments

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Built with ❤️ for rare disease research

Last Updated: January 6, 2026

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