DNA-LLM/virus

Semantic Search API

A production-ready semantic search service built with FastAPI. Upload your data (sequences + metadata), create embeddings automatically, and search using natural language queries.

Features

• Semantic/Latent Search: Find similar sequences based on meaning, not just keywords
• FastAPI Backend: Modern, fast, async Python web framework
• FAISS Index: Efficient similarity search at scale
• Sentence Transformers: State-of-the-art embedding models
• Beautiful UI: Dark-themed, responsive search interface
• CSV Upload: Easy data import via web interface or API
• Persistent Storage: Index persists across restarts

Quick Start

1. Install Dependencies

pip install -r requirements.txt

2. Run the Server

python app.py
# or
uvicorn app:app --reload --host 0.0.0.0 --port 8000

3. Open the UI

Navigate to http://localhost:8000 in your browser.

4. Upload Your Data

• Drag & drop a CSV file or click to browse
• Select the column containing your sequences
• Click "Create Index"
• Start searching!

Data Format

Your CSV should have at least one column containing the text sequences you want to search. All other columns become searchable metadata.

Example:

sequence,category,source,date
"Machine learning is transforming industries",tech,blog,2024-01-15
"The quick brown fox jumps over the lazy dog",example,pangram,2024-01-10
"Embeddings capture semantic meaning",ml,paper,2024-01-20

API Endpoints

Search

POST /api/search
Content-Type: application/json

{
  "query": "artificial intelligence",
  "top_k": 10
}

Upload CSV

POST /api/upload-csv?sequence_column=text
Content-Type: multipart/form-data

file: your_data.csv

Create Index (JSON)

POST /api/index
Content-Type: application/json

{
  "sequence_column": "text",
  "data": [
    {"text": "Hello world", "category": "greeting"},
    {"text": "Machine learning", "category": "tech"}
  ]
}

Get Stats

GET /api/stats

Get Sample

GET /api/sample?n=5

Delete Index

DELETE /api/index

Programmatic Usage

You can also create indexes directly from Python:

from create_index import create_index_from_dataframe, search_index
import pandas as pd

# Create your dataframe
df = pd.DataFrame({
    'sequence': [
        'The mitochondria is the powerhouse of the cell',
        'DNA stores genetic information',
        'Proteins are made of amino acids'
    ],
    'category': ['biology', 'genetics', 'biochemistry'],
    'difficulty': ['easy', 'medium', 'medium']
})

# Create the index
create_index_from_dataframe(df, sequence_column='sequence')

# Search
results = search_index("cellular energy production", top_k=3)
for r in results:
    print(f"Score: {r['score']:.3f} | {r['sequence'][:50]}...")

Configuration

Edit these values in app.py to customize:

# Embedding model (from sentence-transformers)
EMBEDDING_MODEL = "all-MiniLM-L6-v2"  # Fast, 384 dimensions

# Alternatives:
# "all-mpnet-base-v2"  # Higher quality, 768 dimensions
# "paraphrase-multilingual-MiniLM-L12-v2"  # Multilingual support
# "all-MiniLM-L12-v2"  # Balanced quality/speed

Project Structure

semantic_search/
├── app.py              # FastAPI application
├── create_index.py     # Programmatic index creation
├── requirements.txt    # Python dependencies
├── static/
│   └── index.html      # Search UI
├── data/               # Created at runtime
│   ├── faiss.index     # FAISS index file
│   ├── metadata.pkl    # DataFrame with metadata
│   └── embeddings.npy  # Raw embeddings (optional)
└── README.md

How It Works

1. Embedding Creation: When you upload data, each sequence is converted to a dense vector (embedding) using a sentence transformer model
2. FAISS Indexing: Embeddings are stored in a FAISS index optimized for similarity search
3. Search: Your query is embedded using the same model, then FAISS finds the most similar vectors using cosine similarity
4. Results: The original sequences and metadata are returned, ranked by similarity

Performance Tips

• Model Choice: all-MiniLM-L6-v2 is fast and good for most use cases. Use all-mpnet-base-v2 for higher quality at the cost of speed.
• Batch Size: For large datasets, the model processes in batches automatically
• GPU: If you have a CUDA-capable GPU, install faiss-gpu instead of faiss-cpu for faster indexing

License

MIT