The aim of this project is to enable efficient exploration of a corpus of documents through semantic search and visualization.

Currently, Searchica only supports email archives in an mbox format. The semantic search implementation is BERT embeddings. The stack is Flask/React. There are sample emails that can be used for testing, retrieved from the public release of enron emails.

• Utilizes msmarco-MiniLM-L6-cos-v5 BERT model
• Implements lazy loading pattern for vector computation
• Caches embeddings in SQLite with JSON serialization

• Cosine similarity computation between query and document vectors
• Weighted field scoring across email components
• Result ranking based on similarity score

• PCA dimensionality reduction for visualization mapping
• Normalized score used to color nodes on plot
• Plotly data structure generation

• Search interface
• Plotly.js visualization integration
• REST API integration

• Email parsing (mbox format)
• Content extraction from plain text and HTML
• Vector embedding computation
• SQLite storage with vector caching
• Query processing and similarity scoring
• 2D projection for visualization

Pytest suite covering:

• Document vector operations
• Email parsing and storage
• Search functionality
• Visualization processing

git clone [repository]
pip install -r requirements.txt

# For production

# Build frontend
cd frontend
npm run build

# Start backend
$env:FLASK_ENV = "production"
cd backend
python app.py

# For development

# Start frontend
cd frontend
npm run dev

# Start backend
$env:FLASK_ENV = ""
cd backend
python app.py

# For deployment (Docker)

# Build frontend
cd frontend
npm run build

# Build Docker
docker build -t searchica .

# Deploy to Google Cloud
docker tag searchica [full Google artifact registry address]:latest
docker push [full Google artifact registry address]:latest
# update Google Cloud Run image to be newly uploaded Docker container