Searchlight

Production-grade document retrieval API with hybrid search combining BM25 keyword matching and HNSW vector similarity using Apache Lucene 9+.

Features

• Hybrid Search: Combine keyword (BM25) and vector (HNSW) search with adjustable weighting
• High Performance: Lucene HNSW for fast approximate nearest neighbor search
• Hexagonal Architecture: Clean separation of domain, ports, and adapters
• Observability: Prometheus metrics, OpenTelemetry instrumentation, Grafana dashboards
• 🔌 Pluggable Embeddings: HTTP-based or ONNX Runtime providers
• 📰 RSS Ingestion: Automated document ingestion from RSS feeds and URLs
• Modern Dashboard: Next.js 14 UI with real-time search
• Docker Ready: Complete docker-compose setup with all services
• 90%+ Test Coverage: Comprehensive test suite with JUnit 5, WireMock, Testcontainers

Architecture

┌─────────────────────────────────────────────────────────┐
│                    REST API Layer                        │
│  (SearchController, AdminController, HealthController)   │
└──────────────────────┬──────────────────────────────────┘
                       │
┌──────────────────────┴──────────────────────────────────┐
│                   Domain Layer                           │
│   Models: DocumentChunk, SearchQuery, SearchResult      │
│   Ports: EmbeddingProvider, Indexer, Searcher           │
└──────────────────────┬──────────────────────────────────┘
                       │
┌──────────────────────┴──────────────────────────────────┐
│               Infrastructure Layer                       │
│                                                          │
│  ┌─────────────────┐  ┌──────────────────────────────┐ │
│  │ Lucene HNSW     │  │ Embedding Providers          │ │
│  │ - Indexer       │  │ - HttpEmbeddingProvider      │ │
│  │ - Searcher      │  │ - OnnxEmbeddingProvider      │ │
│  └─────────────────┘  └──────────────────────────────┘ │
│                                                          │
│  ┌─────────────────────────────────────────────────────┐│
│  │ Ingestion Pipeline                                  ││
│  │ - RssIngestService                                  ││
│  │ - HtmlCleaner (jsoup)                              ││
│  │ - Chunker (token-based splitting)                  ││
│  └─────────────────────────────────────────────────────┘│
└──────────────────────────────────────────────────────────┘

Quick Start

Prerequisites

• Java 21+
• Docker & Docker Compose (optional)
• Node.js 20+ (for dashboard)

Option 1: Docker Compose (Recommended)

# Start all services
docker-compose up --build

# API available at http://localhost:8080
# Dashboard at http://localhost:3000
# Prometheus at http://localhost:9090
# Grafana at http://localhost:3001 (admin/admin)

Option 2: Local Development

# Run API in dev mode (with ONNX stub embeddings)
make dev

# Or manually:
./gradlew bootRun --args='--spring.profiles.active=dev'

Option 3: Using Makefile

# See all available commands
make help

# Build and test
make build
make test

# Run with Docker
make docker-up

Quick Demo

Once the API is running (via make dev or docker-compose up), run the interactive demo:

make demo

This will:

1. Ingest sample documents from Hacker News RSS
2. Run search with alpha=0.0 (pure BM25 keyword search)
3. Run search with alpha=1.0 (pure KNN vector search)
4. Show how rankings differ based on the alpha parameter

Expected output:

🔦 Searchlight Hybrid Search Demo
====================================

📥 Ingesting sample RSS feed (Hacker News)...
✅ Indexed 15 documents, 73 chunks

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🔍 Search 1: Pure Keyword (alpha=0.0, BM25 only)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  [0.95] Best Programming Languages for 2025
  [0.82] Learn to Code: A Beginner's Guide
  [0.71] Programming Paradigms Explained
  ...

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
🔍 Search 2: Pure Vector (alpha=1.0, KNN only)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  [0.91] Machine Learning Fundamentals
  [0.87] Deep Learning in Practice
  [0.79] Building Neural Networks
  ...

✅ Demo complete! Rankings differ based on alpha parameter.

📖 API Documentation

Swagger UI

Once running, access interactive API docs at:

http://localhost:8080/swagger-ui.html

Core Endpoints

Search Documents

curl -X POST http://localhost:8080/api/v1/search \
  -H "Content-Type: application/json" \
  -d '{
    "q": "machine learning",
    "k": 10,
    "alpha": 0.5
  }'

Request Body (copy to Swagger UI):

{
  "q": "machine learning neural networks",
  "k": 5,
  "alpha": 0.5
}

Parameters:

• q (string, required): Query text
• k (int, optional): Number of results (default: 10)
• alpha (float 0-1, optional): Hybrid weight (default: 0.5)
  • 0.0 = pure keyword search (BM25 only)
  • 1.0 = pure vector search (KNN only)
  • 0.5 = balanced hybrid search
• offset (int, optional): Pagination offset (default: 0)

Sample Response:

{
  "query": "machine learning neural networks",
  "results": [
    {
      "id": "doc-1-chunk-0",
      "sourceId": "doc-1",
      "title": "Introduction to Neural Networks",
      "url": "https://example.com/neural-networks",
      "snippet": "Neural networks are the foundation of modern machine learning...",
      "score": 0.85,
      "keywordScore": 0.72,
      "vectorScore": 0.91,
      "source": "hacker-news",
      "timestamp": "2025-10-03T10:30:00Z"
    }
  ],
  "total": 1,
  "took": 45
}

Ingest Documents

curl -X POST http://localhost:8080/api/v1/admin/ingest \
  -H "Content-Type: application/json" \
  -d '{
    "urls": ["https://news.ycombinator.com/rss"],
    "mode": "RSS"
  }'

Request Body (copy to Swagger UI):

{
  "urls": [
    "https://news.ycombinator.com/rss",
    "https://example.com/blog/feed.xml"
  ],
  "mode": "RSS"
}

Sample Response:

{
  "message": "Ingestion completed",
  "documentsProcessed": 25,
  "chunksIndexed": 142,
  "errors": 0
}

Get Document by ID

curl http://localhost:8080/api/v1/docs/{id}

🔄 Reindex

curl -X POST http://localhost:8080/api/v1/admin/reindex

Health Check

curl http://localhost:8080/api/v1/health

Metrics (Prometheus)

curl http://localhost:8080/actuator/prometheus

Dashboard

The Next.js dashboard provides a beautiful UI for searching:

• Real-time search with results
• Adjustable alpha slider for hybrid search tuning
• Result scoring visualization
• Responsive design

Running Dashboard Locally

cd dashboard
npm install
npm run dev
# Open http://localhost:3000

🧪 Testing

# Run all tests
./gradlew test

# Generate coverage report
./gradlew jacocoTestReport

# View coverage
open build/reports/jacoco/test/html/index.html

Test Categories

• Unit Tests: Domain logic, embeddings, chunking
• Integration Tests: Lucene indexing & searching
• API Tests: Controller endpoints with MockMvc
• E2E Tests: Full stack smoke tests

⚙ Configuration

application.yaml

searchlight:
  index:
    path: data/index
    similarity: COSINE  # COSINE, DOT_PRODUCT, EUCLIDEAN
    hnsw:
      m: 16
      ef-construction: 100
  
  embedding:
    provider: onnx  # http or onnx
    url: http://localhost:8000/embed
    dimension: 384
    timeout: 30000
  
  chunker:
    size: 512
    overlap: 50

Environment Variables

SEARCHLIGHT_EMBEDDING_PROVIDER=http|onnx
SEARCHLIGHT_EMBEDDING_URL=http://embedder:8000/embed
SEARCHLIGHT_EMBEDDING_DIMENSION=384
SPRING_PROFILES_ACTIVE=dev|ci|prod

Performance Benchmarks

Run load tests with k6:

make bench

Sample Results (local machine, mock embeddings):

Metric	Value
Requests/sec	~200 RPS
P50 Latency	45ms
P95 Latency	120ms
P99 Latency	180ms
Error Rate	<0.1%

Note: Performance varies based on index size, hardware, and embedding provider.

Development

Project Structure

searchlight/
├── src/main/java/com/searchlight/
│   ├── app/              # Spring Boot application & config
│   ├── domain/           # Core domain models & ports
│   │   ├── model/
│   │   └── ports/
│   ├── infra/            # Infrastructure implementations
│   │   ├── embeddings/   # Embedding providers
│   │   ├── index/        # Lucene HNSW
│   │   └── ingest/       # RSS/HTML processing
│   └── api/              # REST controllers & DTOs
│       ├── controller/
│       └── dto/
├── src/test/java/com/searchlight/
│   ├── fixtures/
│   ├── infra/
│   ├── api/
│   └── e2e/
├── dashboard/            # Next.js frontend
├── scripts/              # Helper scripts
├── config/               # Prometheus, Grafana configs
└── docker-compose.yml

Adding a New Embedding Provider

1. Implement EmbeddingProvider interface
2. Add @ConditionalOnProperty for configuration
3. Register in Spring context
4. Update configuration

Example:

@Component
@ConditionalOnProperty(name = "searchlight.embedding.provider", havingValue = "custom")
public class CustomEmbeddingProvider implements EmbeddingProvider {
    // Implementation...
}

🔬 Observability

Metrics

Key metrics exposed via Prometheus:

• search_requests_total - Total search requests
• search_latency - Search latency histogram
• embedding_latency - Embedding generation time
• index_docs_count - Total documents in index
• ingest_documents_total - Documents ingested
• ingest_errors_total - Ingestion errors

Grafana Dashboards

Pre-configured dashboards available at http://localhost:3001:

• Request rates and latencies
• JVM metrics (heap, GC, threads)
• Index statistics
• Error rates

🗺 Roadmap

• Real ONNX Runtime integration with MiniLM-L6-v2
• Multi-tenant indexes with namespace isolation
• Synonym expansion for keyword search
• Query rewriting and expansion
• Re-ranking with cross-encoder models
• Postgres integration for source document registry
• Incremental indexing and updates
• Document deduplication
• Faceted search support
• Saved searches and query history
• API rate limiting
• Authentication & authorization

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure ./gradlew build passes
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Apache Lucene - High-performance search library
• Spring Boot - Application framework
• Next.js - React framework for dashboard
• HNSW - Hierarchical Navigable Small World algorithm

📧 Contact

For questions or feedback, please open an issue on GitHub.

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Built with using Java 21, Spring Boot 3, and Apache Lucene