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

Embedding Creation: When you upload data, each sequence is converted to a dense vector (embedding) using a sentence transformer model
FAISS Indexing: Embeddings are stored in a FAISS index optimized for similarity search
Search: Your query is embedded using the same model, then FAISS finds the most similar vectors using cosine similarity
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