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SkyWhal3
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STXBP1-RAG-Database

Tasks:
Text Retrieval
Question Answering
Languages:
English
Size:
100K<n<1M
Tags:
rag
retrieval-augmented-generation
biomedical
neuroscience
rare-disease
STXBP1
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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
  - sentence-transformers
  - bge
size_categories:
  - 100K<n<1M
pretty_name: STXBP1 RAG Database v9 - BGE Embeddings

🧬 STXBP1-ARIA RAG Database v9 - BGE Embeddings

A pre-built ChromaDB vector database containing ~570,000 indexed text chunks from ~17,000 curated PubMed Central (PMC) biomedical papers related to STXBP1, Munc18-1, synaptic transmission, epileptic encephalopathy, and therapeutic research.

💡 This is the lightweight version — BGE-base runs efficiently on CPU/system RAM without requiring a GPU, making it ideal for free-tier deployments and local development. For maximum retrieval quality with NVIDIA's state-of-the-art 2048-dimensional embeddings (requires GPU with 2-4GB VRAM), see our premium database: STXBP1-RAG-Nemotron

🆕 What's New in v9

Feature v8 (Previous) v9 (Current)
Embedding Model all-MiniLM-L6-v2 BGE-base-en-v1.5
Dimensions 384 768
Model Params 22M 110M
MTEB Score ~56 ~63
Corpus 31,786 papers (unfiltered) ~17,000 papers (curated)
Chunks 1.19M (58% noise) ~570K (high relevance)
Quality Focus Quantity Precision

Why BGE?

  • 2x embedding dimensions = finer semantic distinctions
  • 5x larger model = better understanding of biomedical terminology
  • Curated corpus = removed irrelevant papers, kept STXBP1-focused content
  • MTEB benchmark leader = proven retrieval performance

📊 Dataset Statistics

Metric Value
Total Chunks ~570,000
Source Papers ~17,000 PMC articles
Database Size ~8-10 GB
Embedding Model BAAI/bge-base-en-v1.5 (768 dimensions)
Chunk Size ~1500 chars with 200 char overlap
Index Type ChromaDB with HNSW
Build Date January 2026

🎯 Purpose

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

  • Literature-grounded responses with PMC citations
  • Semantic search across decades of research
  • Real-time retrieval for AI-assisted variant analysis
  • Evidence-based therapeutic recommendations

📁 Contents 

STXBP1-RAG-Database/
├── chroma.sqlite3                    # Main database
├── metadata.json                     # Build info
└── [uuid]/                           # HNSW index files
    ├── data_level0.bin               # Vector index
    ├── header.bin                    
    ├── index_metadata.pickle         
    ├── length.bin                    
    └── link_lists.bin

🔧 Usage

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-Database",
    repo_type="dataset"
)

# Load embedding model (MUST match indexing model!)
embedder = SentenceTransformer("BAAI/bge-base-en-v1.5")

# 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 (BGE recommends query prefix for retrieval)
query = "STXBP1 dominant negative mechanism therapeutic approaches"
query_embedding = embedder.encode(query, normalize_embeddings=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:.3f})")
    print(f"{doc[:200]}...\n")

With ARIA Integration

See the full retriever implementation at: STXBP1-Variant-Lookup Space

📚 Curated Corpus

Unlike v8's broad collection, v9 uses a curated corpus filtered for STXBP1 relevance:

Primary Keywords (Auto-include)

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

Related Keywords (Relevance filtered)

  • Epilepsy: epileptic encephalopathy, Ohtahara, West syndrome, Dravet, infantile spasms
  • Synaptic: SNARE complex, syntaxin-1, synaptic vesicle, exocytosis, neurotransmitter release
  • Genetics: haploinsufficiency, dominant negative, nonsense/missense/frameshift mutations
  • Therapeutics: gene therapy, AAV, ASO, CRISPR, base editing, prime editing
  • Chaperones: 4-PBA, phenylbutyrate, protein folding, proteostasis
  • Neurodevelopment: intellectual disability, developmental delay, autism

Curated Entries

Includes 24 hand-curated entries covering:

  • Key primary research (Guiberson 2018, Kovacevic 2018, etc.)
  • Therapeutic mechanism summaries
  • Variant-specific knowledge
  • Clinical trial information

🏗️ How It Was Built

1. Corpus Curation

  • Filtered 27,000 multimodal PMC papers by relevance keywords
  • Kept ~17,000 papers with direct STXBP1 relevance
  • Added 41 targeted high-value papers
  • Included 24 curated expert entries

2. Text Processing

  • Chunked documents into ~1500 character segments
  • 200 character overlap between chunks
  • Preserved document metadata (PMC ID, title)

3. Embedding Generation

  • Used BAAI/bge-base-en-v1.5 (768 dimensions)
  • Normalized embeddings for cosine similarity
  • GPU-accelerated batch processing

4. Index Building

  • ChromaDB with persistent storage
  • HNSW index optimized for semantic search
  • Built on RTX 3080 in ~55 minutes

📋 Metadata Schema

Each chunk includes:

{
  "pmcid": "PMC1234567",
  "title": "Paper title",
  "chunk_idx": 0,
  "source": "multimodal_corpus"
}

Source types:

  • multimodal_corpus - Papers from curated PMC collection
  • targeted_paper - High-priority STXBP1 papers
  • curated - Hand-written expert entries

🔬 Use Cases

  1. Therapeutic Research - Find evidence for treatment approaches
  2. Variant Analysis - Locate papers discussing specific mutations
  3. Mechanism Understanding - Search for molecular pathway details
  4. Clinical Context - Find case reports and trial results
  5. Literature Review - Rapid survey of research landscape

⚡ Performance Notes

  • Free Tier Compatible: BGE-base runs on CPU or minimal GPU
  • Query Time: <100ms typical retrieval
  • Memory: ~1-2GB RAM for embedding model

🔗 Related Resources

📄 Citation 

@dataset{stxbp1_rag_database_2026,
  author = {Freygang, Adam},
  title = {STXBP1-ARIA RAG Database v9: BGE-Embedded Biomedical Literature for Therapeutic Discovery},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/SkyWhal3/STXBP1-RAG-Database}
}

📧 Contact

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

Built with ❤️ for the STXBP1 community

Part of the NeuroSenpai + STXBP1-ARIA therapeutic discovery system