Sdk Usage Java Client

☕ Java SDK Guide

Type-safe, thread-safe Java access to Spector — as a remote client or embedded engine. Whether you're connecting to a server or embedding search directly in your application, this guide covers everything you need.

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📦 Installation

Remote client (connects to a running server):

<dependency>
    <groupId>com.spectrayan</groupId>
    <artifactId>spector-client</artifactId>
    <version>1.0-SNAPSHOT</version>
</dependency>

Embedded engine (in-process, zero network overhead):

<dependency>
    <groupId>com.spectrayan</groupId>
    <artifactId>spector-engine</artifactId>
    <version>1.0-SNAPSHOT</version>
</dependency>

Tip

Choose embedded for maximum performance (zero latency overhead). Choose client when you want a shared server across multiple services.

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🌐 Client SDK (Remote Server)

🔧 Creating a Client

import com.spectrayan.spector.client.SpectorClient;

SpectorClient client = SpectorClient.builder()
    .host("localhost")
    .port(7070)
    .apiKey("my-secret-key")       // optional
    .connectTimeout(Duration.ofSeconds(10))
    .requestTimeout(Duration.ofSeconds(30))
    .maxConnections(10)
    .build();

Configuration Options:

Option	Default	Description
host	localhost	Server hostname
port	7070	Server port
apiKey	—	API key for authentication
connectTimeout	10s	Connection timeout
requestTimeout	30s	Per-request timeout
maxConnections	10	HTTP connection pool size

Note

SpectorClient is fully thread-safe. It uses Java's HttpClient with internal connection pooling. Share a single instance across all threads.

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📥 Ingesting Documents

// Single document
IngestResponse response = client.ingest(IngestRequest.builder()
    .id("doc-1")
    .title("Java Vector API")
    .content("SIMD-accelerated search engine built on modern JVM")
    .vector(new float[]{0.1f, 0.2f, 0.3f, 0.4f, 0.5f})
    .build());

System.out.println("Indexed: " + response.id());

// Bulk ingest
List<IngestRequest> documents = List.of(
    IngestRequest.builder().id("d1").content("first doc").vector(vec1).build(),
    IngestRequest.builder().id("d2").content("second doc").vector(vec2).build(),
    IngestRequest.builder().id("d3").content("third doc").vector(vec3).build()
);

IngestResponse bulkResponse = client.bulkIngest(documents);

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🔍 Searching

// Keyword search
SearchResponse results = client.search(SearchRequest.builder()
    .text("vector search engine")
    .topK(10)
    .build());

// Vector search
SearchResponse results = client.search(SearchRequest.builder()
    .vector(queryEmbedding)
    .topK(10)
    .build());

// Hybrid search (both text and vector)
SearchResponse results = client.search(SearchRequest.builder()
    .text("search engine")
    .vector(queryEmbedding)
    .topK(10)
    .build());

// Process results
for (SearchResponse.Result result : results.results()) {
    System.out.printf("%s (%.4f): %s%n",
        result.id(), result.score(), result.content());
}

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🗑️ Deleting Documents

client.delete("doc-1");

📊 Status and Metrics

StatusResponse status = client.status();
System.out.println("Documents: " + status.documentCount());
System.out.println("SIMD: " + status.simd());

MetricsResponse metrics = client.metrics();
System.out.println("QPS: " + metrics.queriesPerSecond());

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⚠️ Error Handling

try {
    client.search(request);
} catch (SpectorApiException e) {
    // HTTP 4xx/5xx from server
    System.err.println("HTTP " + e.statusCode() + ": " + e.message());
} catch (SpectorConnectionException e) {
    // Server unreachable
    System.err.println("Cannot connect to " + e.endpoint());
} catch (SpectorTimeoutException e) {
    // Request timed out
    System.err.println("Timeout after " + e.timeout());
}

♻️ Resource Management

The client implements AutoCloseable:

try (SpectorClient client = SpectorClient.builder().build()) {
    // Use client...
} // Connections released automatically

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⚡ SpectorEngine (Embedded Usage)

For applications that want in-process search without network overhead:

🔧 Creating an Engine

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

var config = SpectorConfig.DEFAULT
    .withDimensions(384)
    .withCapacity(100_000)
    .withSimilarityFunction(SimilarityFunction.COSINE)
    .withGpu(true)                                           // optional GPU
    .withReranker("http://localhost:11434", "llama3.2", 20); // optional LLM

try (var engine = new SpectorEngine(config)) {
    // Engine is ready — sub-millisecond search, zero network overhead
}

📥 Ingesting

// With pre-computed vector
engine.ingest("doc-1", "Document content here", embedding);
// The engine handles BM25 indexing, HNSW insertion, and storage automatically

🔍 Searching

// Hybrid search (keyword + vector)
SearchResponse response = engine.hybridSearch("search query", queryVector, 10);

// Keyword-only
SearchResponse response = engine.keywordSearch("exact phrase", 10);

// Vector-only
SearchResponse response = engine.vectorSearch(queryVector, 10);

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

🗑️ Deleting

engine.delete("doc-1");

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🎯 Complete Example

import com.spectrayan.spector.client.SpectorClient;
import com.spectrayan.spector.client.model.*;

public class SpectorExample {
    public static void main(String[] args) throws Exception {
        try (SpectorClient client = SpectorClient.builder()
                .host("localhost")
                .port(7070)
                .build()) {

            // Ingest documents
            client.ingest(IngestRequest.builder()
                .id("java-1")
                .title("Virtual Threads")
                .content("Java virtual threads enable millions of concurrent tasks")
                .vector(new float[]{0.9f, 0.1f, 0.3f, 0.7f, 0.5f})
                .build());

            client.ingest(IngestRequest.builder()
                .id("java-2")
                .title("Vector API")
                .content("The Vector API provides SIMD acceleration for math operations")
                .vector(new float[]{0.2f, 0.8f, 0.4f, 0.1f, 0.6f})
                .build());

            // Search
            SearchResponse results = client.search(SearchRequest.builder()
                .text("SIMD acceleration")
                .topK(5)
                .build());

            System.out.println("Results:");
            for (var r : results.results()) {
                System.out.printf("  %s (%.4f): %s%n", r.id(), r.score(), r.title());
            }

            // Cleanup
            client.delete("java-1");
            client.delete("java-2");
        }
    }
}

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🔗 See Also

• REST API Reference — Underlying API endpoints

• Spring AI Integration — Spring AI VectorStore adapter

• Configuration Guide — All engine parameters

• Getting Started — Quick start guide