README.md

metadata

license: cc-by-nc-4.0
task_categories:
  - text-retrieval
  - question-answering
language:
  - en
tags:
  - rag
  - retrieval-augmented-generation
  - biomedical
  - neuroscience
  - rare-disease
  - STXBP1
  - epilepsy
  - chromadb
  - vector-database
  - nvidia
  - nemotron
  - llama
  - sentence-transformers
size_categories:
  - 100K<n<1M
pretty_name: STXBP1 RAG Database v10 - NVIDIA Nemotron Embeddings (Premium)

🧬⚡ STXBP1-ARIA RAG Database v10 - NVIDIA Nemotron Embeddings

The most advanced RAG database for STXBP1 therapeutic research.

A pre-built ChromaDB vector database containing: 571,465 indexed text chunks from ~17,000 curated PubMed Central (PMC) biomedical papers, 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)

🏆 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

📊 Dataset Statistics

Metric	Value
Total Chunks	571,465
Source Papers	~17,000 PMC articles
Curated Entries	24 expert-written
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
Build Date	January 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
• Optional reranking with Nemotron cross-encoder for top-k refinement
• Literature-grounded responses with PMC citations

📁 Contents

STXBP1-RAG-Nemotron/
├── chroma.sqlite3                    # Main database (~7 GB)
├── metadata.json                     # Build info & config
└── [uuid]/                           # HNSW index files
    ├── data_level0.bin               # 2048-dim vectors (~4.6 GB)
    ├── header.bin                    
    ├── index_metadata.pickle         
    ├── length.bin                    
    └── link_lists.bin                

🔧 Usage

Requirements

pip install transformers==4.47.1 sentence-transformers chromadb huggingface_hub torch

Hardware: GPU with 2-4GB VRAM recommended (runs on CPU but slower)

Quick Start

from huggingface_hub import snapshot_download
import chromadb
from chromadb.config import Settings
from sentence_transformers import SentenceTransformer

# Download database
db_path = snapshot_download(
    repo_id="SkyWhal3/STXBP1-RAG-Nemotron",
    repo_type="dataset"
)

# Load Nemotron embedding model (MUST match!)
embedder = SentenceTransformer(
    "nvidia/llama-nemotron-embed-1b-v2",
    trust_remote_code=True
)

# Move to GPU if available
import torch
if torch.cuda.is_available():
    embedder = embedder.to('cuda')

# Connect to ChromaDB
client = chromadb.PersistentClient(
    path=db_path,
    settings=Settings(anonymized_telemetry=False)
)

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

# Search
query = "STXBP1 K196X nonsense mutation prime editing therapeutic approaches"
query_embedding = embedder.encode(query, convert_to_numpy=True).tolist()

results = collection.query(
    query_embeddings=[query_embedding],
    n_results=10,
    include=["documents", "metadatas", "distances"]
)

for doc, meta, dist in zip(
    results['documents'][0],
    results['metadatas'][0],
    results['distances'][0]
):
    pmcid = meta.get('pmcid', meta.get('pmc_id', 'Unknown'))
    print(f"[{pmcid}] (distance: {dist:.4f})")
    print(f"{doc[:200]}...\n")

With Reranker (Maximum Precision)

from sentence_transformers import CrossEncoder

# Load reranker
reranker = CrossEncoder(
    "nvidia/llama-nemotron-rerank-1b-v2",
    trust_remote_code=True
)

# Get more candidates, then rerank
results = collection.query(
    query_embeddings=[query_embedding],
    n_results=100,  # Fetch more for reranking
    include=["documents", "metadatas", "distances"]
)

# Rerank top candidates
docs = results['documents'][0]
pairs = [[query, doc] for doc in docs]
rerank_scores = reranker.predict(pairs)

# Sort by rerank score
ranked = sorted(
    zip(rerank_scores, docs, results['metadatas'][0]),
    key=lambda x: x[0],
    reverse=True
)

# Top 10 after reranking
for score, doc, meta in ranked[:10]:
    pmcid = meta.get('pmcid', 'Unknown')
    print(f"[{pmcid}] rerank_score: {score:.4f}")

📚 Curated Corpus

The database indexes ~17,000 curated papers filtered for STXBP1 relevance:

Primary Keywords (Auto-include)

• STXBP1, Munc18-1, Munc18, syntaxin binding protein, UNC-18, N-Sec1

Related Topics

• Epilepsy & Encephalopathy: DEE, Ohtahara, West syndrome, Dravet, infantile spasms
• Synaptic Machinery: SNARE complex, syntaxin-1, SNAP-25, synaptotagmin, exocytosis
• Genetics: haploinsufficiency, dominant negative, nonsense/missense mutations
• Gene Therapy: AAV, ASO, CRISPR, base editing, prime editing
• Protein Therapeutics: 4-PBA, chaperones, readthrough compounds
• Neurodevelopment: intellectual disability, autism, developmental delay

Curated Expert Entries

24 hand-written entries covering:

• Guiberson 2018 (4-PBA mechanism)
• Kovacevic 2018 (functional characterization)
• Dominant negative vs haploinsufficiency mechanisms
• Variant-specific therapeutic summaries
• Clinical trial information (NCT04937062)
• Base/prime editing feasibility rules

🏗️ How It Was Built

Pipeline

1. Corpus Curation — Filtered 27K multimodal PMC papers → 17K relevant
2. Chunking — 1500 char chunks with 200 char overlap
3. Embedding — NVIDIA Llama-Nemotron-Embed-1B-v2 (2048 dims)
4. Indexing — ChromaDB HNSW on H100 GPU
5. Validation — Core pack verification & curated injection

Build Stats

Stage	Details
Hardware	NVIDIA H100 80GB SXM5
Batch Size	128
Build Time	~4 hours
Total Chunks	571,465
Index Size	~11.5 GB

⚡ Performance

Metric	Value
Query Embedding	~50ms (GPU)
Retrieval (k=25)	~20ms
+ Reranking (k=100→25)	~200ms
Total Latency	<300ms

GPU Memory Usage

Mode	VRAM
Embedding only	~2-3 GB
Embedding + Reranker	~4-6 GB
T4 (16GB)	✅ Compatible
RTX 3080 (10GB)	✅ Compatible
Free Colab	⚠️ Tight fit

📋 Metadata Schema

{
  "pmcid": "PMC1234567",
  "title": "Paper title (truncated to 500 chars)",
  "chunk_idx": 0,
  "source": "multimodal_corpus | targeted_paper | curated"
}

🔬 Use Cases

1. Precision Therapeutic Queries — "What gene editing approaches work for STXBP1 nonsense mutations?"
2. Variant-Specific Research — "K196X dominant negative mechanism and rescue strategies"
3. Mechanism Differentiation — Distinguish haploinsufficiency from dominant negative
4. Clinical Evidence — Find trial data and case reports
5. Comparative Analysis — Compare therapeutic modalities with nuanced retrieval

🔗 Related Resources

Resource	Link
BGE Version (CPU-friendly)	STXBP1-RAG-Database
STXBP1-ARIA MAX Space	HuggingFace Space
Variant Lookup	STXBP1-Variant-Lookup
STXBP1 Foundation	stxbp1disorders.org
Nemotron Model	nvidia/llama-nemotron-embed-1b-v2

📄 Citation

@dataset{stxbp1_rag_nemotron_2026,
  author = {Freygang, Adam},
  title = {STXBP1-ARIA RAG Nemotron: Premium Vector Database with NVIDIA Embeddings for Therapeutic Discovery},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/SkyWhal3/STXBP1-RAG-Nemotron}
}

📧 Contact

Adam Freygang
AI/ML Engineer & STXBP1 Parent Researcher
SkyWhal3 on HuggingFace

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Built with ❤️ and an H100 for the STXBP1 community

Part of the NeuroSenpai + STXBP1-ARIA therapeutic discovery system

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🙏 Acknowledgments

• NVIDIA for the Nemotron embedding models
• Lambda Labs for H100 GPU access
• STXBP1 Foundation for supporting rare disease research
• Anthropic Claude for development assistance