Getting Started Quickstart

🚀 Getting Started

Go from zero to your first search result in under 5 minutes. This guide walks you through building Spector from source, starting the server, ingesting documents, and running your first hybrid search.

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📋 Prerequisites

Tool	Version	How to Check
☕ JDK	25+	java -version
📦 Maven	3.9+	mvn --version
🔧 Git	2.40+	git --version

Important

Spector requires JDK 25 or later with the Vector API incubator module. OpenJDK builds include this by default.

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🏗️ Clone and Build

# Clone the repository
git clone https://github.com/spectrayan/spector.git
cd spector

# Build all modules (includes 316+ tests)
mvn clean test

# Build without tests (faster)
mvn clean package -DskipTests

Tip

The full test suite runs 316+ tests across all modules. Expect ~2 minutes on a modern machine.

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🔬 Verify SIMD Support

Confirm your hardware's SIMD acceleration level:

java --add-modules jdk.incubator.vector -cp spector-core/target/classes \
  com.spectrayan.spector.core.SimdCapability

Expected output (varies by hardware):

SIMD Species: S_256_BIT (AVX2, 8 float lanes)

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🖥️ Start the Server

# Start on default port 7070 with 384 dimensions
mvn exec:java -pl spector-node \
  -Dexec.mainClass="com.spectrayan.spector.server.SpectorNode"

# Start with custom port, dimensions, and API key
mvn exec:java -pl spector-node \
  -Dexec.mainClass="com.spectrayan.spector.server.SpectorNode" \
  -Dexec.args="7070 384 my-secret-key"

Verify it's running:

curl http://localhost:7070/health

{"status": "UP"}

Note

The server starts on virtual threads — it can handle thousands of concurrent requests out of the box with no thread pool configuration needed.

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📄 Ingest Your First Document

curl -X POST http://localhost:7070/api/v1/ingest \
  -H "Content-Type: application/json" \
  -d '{
    "id": "doc-1",
    "title": "Introduction to Vector Search",
    "content": "Vector search finds similar items by comparing their mathematical representations called embeddings.",
    "vector": [0.12, 0.45, 0.78, 0.23, 0.91, 0.34, 0.67, 0.55, 0.11, 0.89]
  }'

{"id": "doc-1", "status": "indexed"}

🤖 Ingest with Auto-Embedding

If you have Ollama running with an embedding model:

curl -X POST http://localhost:7070/api/v1/ingest/auto \
  -H "Content-Type: application/json" \
  -d '{
    "id": "doc-2",
    "title": "HNSW Algorithm",
    "content": "Hierarchical Navigable Small World graphs enable fast approximate nearest neighbor search."
  }'

📦 Bulk Ingest

curl -X POST http://localhost:7070/api/v1/ingest/bulk \
  -H "Content-Type: application/json" \
  -d '{
    "documents": [
      {"id": "d1", "content": "BM25 keyword scoring uses term frequency and document length.", "vector": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]},
      {"id": "d2", "content": "Reciprocal Rank Fusion combines multiple ranked lists.", "vector": [0.5, 0.4, 0.3, 0.2, 0.1, 0.9, 0.8, 0.7, 0.6, 0.5]}
    ]
  }'

---

🔍 Run Your First Search

🧬 Hybrid Search (keyword + vector)

curl -X POST http://localhost:7070/api/v1/search \
  -H "Content-Type: application/json" \
  -d '{
    "text": "nearest neighbor search",
    "vector": [0.15, 0.42, 0.73, 0.28, 0.88, 0.31, 0.62, 0.51, 0.14, 0.85],
    "topK": 5
  }'

{
  "results": [
    {
      "id": "doc-1",
      "score": 0.9234,
      "title": "Introduction to Vector Search",
      "content": "Vector search finds similar items..."
    }
  ],
  "searchMode": "HYBRID",
  "latencyMs": 0.31
}

📝 Keyword-Only Search

curl -X POST http://localhost:7070/api/v1/search \
  -H "Content-Type: application/json" \
  -d '{"text": "BM25 scoring", "topK": 10}'

🧠 Vector-Only Search

curl -X POST http://localhost:7070/api/v1/search \
  -H "Content-Type: application/json" \
  -d '{"vector": [0.15, 0.42, 0.73, 0.28, 0.88, 0.31, 0.62, 0.51, 0.14, 0.85], "topK": 10}'

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📊 Check Engine Status

curl http://localhost:7070/api/v1/status

{
  "status": "RUNNING",
  "simd": "AVX2 (256-bit, 8 lanes)",
  "gpuAvailable": false,
  "rerankerEnabled": false,
  "documentCount": 3,
  "dimensions": 384
}

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💻 Use as an Embedded Library

No server needed — use Spector directly in your Java application:

import com.spectrayan.spector.engine.SpectorEngine;
import com.spectrayan.spector.engine.SpectorConfig;

var config = SpectorConfig.DEFAULT
    .withDimensions(384)
    .withCapacity(100_000);

try (var engine = new SpectorEngine(config)) {
    // Ingest
    engine.ingest("doc-1", "Hello world", new float[]{0.1f, 0.2f, ...});

    // Search
    var results = engine.hybridSearch("hello", queryVector, 10);

    for (var result : results.results()) {
        System.out.printf("%s → %.4f%n", result.id(), result.score());
    }
}

Tip

Embedded mode has zero network overhead — perfect for microservices, desktop apps, and edge deployments.

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🎉 What You've Accomplished

In just a few minutes, you've:

• ✅ Built Spector from source

• ✅ Verified SIMD hardware acceleration

• ✅ Started a search server

• ✅ Ingested documents

• ✅ Run hybrid search queries

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🚀 Next Steps

What to explore	Page
Full API documentation	[[REST API Reference
Type-safe Java client	[[Java SDK Guide
Tune for your workload	[[Configuration Guide
Command-line management	[[CLI Reference
Understand the internals	[[Architecture Overview
Spring AI integration	[[Spring AI Integration