SkyWhal3/STXBP1-RAG-Database

🧬 STXBP1-ARIA RAG Database - (597 Million Tokens)

A pre-built ChromaDB vector database containing 1,194,693 indexed text chunks from 31,786 PubMed Central (PMC) biomedical papers (5947 Million indexed Tokens <100ms) related to STXBP1, Munc18-1, synaptic transmission, epileptic encephalopathy, and related therapeutic research.

📊 Dataset Statistics

Metric	Value
Total Chunks	1,194,693
Source Papers	31,786 PMC articles
Database Size	~17 GB
Embedding Model	all-MiniLM-L6-v2 (384 dimensions)
Chunk Size	~500 tokens with overlap
Token Count	~597,346,500 tokens
Index Type	ChromaDB with HNSW

🎯 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 (14.9 GB)
└── d3ded7f6-11aa-4b46-836f-.../     # Index files
    ├── data_level0.bin               # HNSW index data (2.0 GB)
    ├── header.bin                    # Index header
    ├── index_metadata.pickle         # Index metadata
    ├── length.bin                    # Length data
    └── link_lists.bin                # HNSW links

🔧 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("all-MiniLM-L6-v2")

# 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 dominant negative mechanism therapeutic approaches"
query_embedding = embedder.encode([query]).tolist()

results = collection.query(
    query_embeddings=query_embedding,
    n_results=5,
    include=["documents", "metadatas", "distances"]
)

for doc, meta, dist in zip(
    results['documents'][0],
    results['metadatas'][0],
    results['distances'][0]
):
    score = 1 / (1 + dist)  # Convert L2 distance to similarity
    print(f"[{meta['pmc_id']}] (score: {score:.3f})")
    print(f"{doc[:200]}...\n")

With ARIA Integration

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

📚 Source Literature

The database indexes PMC papers covering:

• STXBP1/Munc18-1 protein function and mutations
• Epileptic encephalopathy and developmental disorders
• Synaptic transmission mechanisms
• Gene therapy approaches (AAV, base editing, prime editing)
• Chemical chaperones (4-phenylbutyrate, etc.)
• Stop codon readthrough compounds
• Protein folding and aggregation
• Clinical trials and case studies

Search Terms Used for Corpus Collection

STXBP1, Munc18-1, syntaxin binding protein, 
epileptic encephalopathy, developmental epilepsy,
synaptic vesicle, neurotransmitter release,
haploinsufficiency, dominant negative,
chemical chaperone, 4-phenylbutyrate,
base editing, prime editing, AAV gene therapy,
stop codon readthrough, ataluren, gentamicin

🏗️ How It Was Built

1. Paper Collection

• Queried PubMed Central (PMC) Open Access subset
• Downloaded full-text HTML/XML for 31,786 papers
• Extracted text, figures, tables, and captions

2. Text Processing

• Chunked documents into ~500 token segments
• Preserved paragraph boundaries where possible
• Added 50-token overlap between chunks
• Retained metadata (PMC ID, title, section)

3. Embedding Generation

• Used sentence-transformers/all-MiniLM-L6-v2
• 384-dimensional embeddings
• Batch processing with GPU acceleration

4. Index Building

• ChromaDB with persistent storage
• HNSW (Hierarchical Navigable Small World) index
• Optimized for semantic similarity search

Processing Time

• Indexing: ~5 hours 42 minutes on AMD 5950X
• Total chunks processed: 1,194,693 (~500 token chunk)

📋 Metadata Schema

Each chunk includes metadata:

{
  "pmc_id": "PMC1234567",
  "title": "Paper title",
  "section": "Introduction",
  "chunk_index": 0,
  "source_type": "text"
}

⚠️ Limitations

• English only - Non-English papers excluded
• PMC Open Access - Does not include paywalled literature
• Static snapshot - Papers published after indexing date not included
• Chunk boundaries - Some context may be split across chunks

🔬 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

📄 Citation

If you use this database in your research, please cite:

@dataset{stxbp1_rag_database_2024,
  author = {Freygang, Adam},
  title = {STXBP1-ARIA RAG Database: A Vector Index of Biomedical Literature for Therapeutic Discovery},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/SkyWhal3/STXBP1-RAG-Database}
}

🔗 Related Resources

• STXBP1-ARIA Space: huggingface.co/spaces/SkyWhal3/STXBP1-Variant-Lookup
• STXBP1 Foundation: stxbp1disorders.org
• ClinVar STXBP1: ncbi.nlm.nih.gov/clinvar/?term=STXBP1

📧 Contact

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

---

Built with ❤️ for the STXBP1 community

Part of the NeuroSenpai v3 + STXBP1-ARIA therapeutic discovery system