Sdk Usage Java Client

☕ Java Client SDK & Embedded Usage Guide

Type-safe, thread-safe Java access to Spector. Connect over high-performance HTTP using the lightweight Java Client SDK (spector-client), embed the zero-GC Embedded Memory Engine (spector-memory) directly in your JVM application, or integrate seamlessly with Spring AI.

---

📦 Client Options & Dependencies

Choose the dependency that matches your deployment model:

Lightweight Client SDK (Recommended)

For client applications, microservices, and web servers connecting to a running Spector node over HTTP/REST:

```xml
<dependency>
    <groupId>com.spectrayan</groupId>
    <artifactId>spector-client</artifactId>
    <version>0.1.0-alpha</version>
</dependency>
```

*Requires standard Java 21+ — zero vector flags, zero Panama preview options, zero native dependencies.*

Embedded Memory Engine

For low-latency applications requiring direct in-process off-heap memory access and zero-network overhead:

```xml
<dependency>
    <groupId>com.spectrayan</groupId>
    <artifactId>spector-memory</artifactId>
    <version>0.1.0-alpha</version>
</dependency>
```

*Requires Java 25+ with `--add-modules jdk.incubator.vector` and `--enable-native-access`.*

Spring AI Starter

For Spring Boot applications integrating Spector as a `VectorStore`:

```xml
<dependency>
    <groupId>com.spectrayan</groupId>
    <artifactId>spring-ai-starter-spector-store</artifactId>
    <version>0.1.0-alpha</version>
</dependency>
```

---

🚀 Mode 1: Lightweight Client SDK (SpectorClient)

The lightweight SpectorClient provides a fluent, thread-safe client that connects to any Spector gateway:

Creating the Client & Basic Operations

import com.spectrayan.spector.client.SpectorClient;
import java.util.List;

// Fluent builder with custom timeouts and optional API key
try (SpectorClient client = SpectorClient.builder()
        .baseUri("http://localhost:7070")
        .apiKey("optional-api-key")
        .build()) {

    // 1. Remember a new memory with contextual tags
    var engram = client.memory().store(
        "User prefers dark mode and high-contrast syntax highlighting",
        List.of("preferences", "ui")
    );
    System.out.println("Stored memory ID: " + engram.getId());

    // 2. Recall memories using cognitive scoring
    var results = client.memory().recall("user ui preferences", 5);
    for (var memory : results) {
        System.out.printf("[%s] score=%.4f: %s%n", memory.getId(), memory.getScore(), memory.getText());
    }

    // 3. Forget when obsolete
    client.memory().forget(engram.getId());
}

---

⚡ Mode 2: Embedded Memory (SpectorMemory)

For applications that want direct in-process hybrid search and cognitive memory running in the same JVM process:

🔧 Creating the Embedded Engine

import com.spectrayan.spector.memory.SpectorMemory;
import com.spectrayan.spector.memory.DefaultSpectorMemory;
import com.spectrayan.spector.config.SpectorProperties;
import com.spectrayan.spector.provider.embedding.EmbeddingProvider;
import com.spectrayan.spector.provider.embedding.ollama.OllamaEmbeddingProvider;

// Load default properties (overridden by spector.yml if present in the working directory)
SpectorProperties props = SpectorProperties.load();

// Provide an embedding provider instance
EmbeddingProvider embedder = new OllamaEmbeddingProvider(props.provider().embedding());

// Build embedded SpectorMemory instance
try (SpectorMemory memory = DefaultSpectorMemory.builder()
        .properties(props)
        .embeddingProvider(embedder)
        .build()) {
    // Memory engine is ready — zero network overhead, fully embedded inside the JVM
}

---

📥 Remembering Memories

import com.spectrayan.spector.kernel.api.MemoryType;

// Store a factual/semantic memory
String memoryId = memory.remember(
    "Java virtual threads enable high-throughput concurrent workflows with minimal memory footprint.",
    MemoryType.SEMANTIC
);

System.out.println("Stored memory: " + memoryId);

---

🔍 Cognitive Recall & Search

import com.spectrayan.spector.memory.model.RecallOptions;
import com.spectrayan.spector.memory.model.TextSearchMode;
import com.spectrayan.spector.memory.model.CognitiveResult;

RecallOptions options = RecallOptions.builder()
        .topK(5)
        .textSearchMode(TextSearchMode.FULL_STACK)  // Enable BM25 + dense + ColBERT reranking
        .enableReranker(true)                       // ColBERT MaxSim reranking
        .minImportance(4.0f)                        // Filter out low-importance items
        .build();

List<CognitiveResult> memories = memory.recall("virtual threads concurrency", options);

for (CognitiveResult mem : memories) {
    System.out.printf("%s → %.4f (Tier: %s)%n", mem.id(), mem.score(), mem.memoryType());
}

---

🗑️ Forgetting Memories

memory.forget(memoryId);

---

🎯 Complete Embedded Example

import com.spectrayan.spector.memory.DefaultSpectorMemory;
import com.spectrayan.spector.memory.SpectorMemory;
import com.spectrayan.spector.memory.model.CognitiveResult;
import com.spectrayan.spector.memory.model.MemoryType;
import com.spectrayan.spector.memory.model.RecallOptions;
import com.spectrayan.spector.config.SpectorProperties;

import java.util.List;

public class SpectorEmbeddedExample {
    public static void main(String[] args) throws Exception {
        SpectorProperties props = SpectorProperties.load();

        try (SpectorMemory memory = DefaultSpectorMemory.builder()
                .properties(props)
                .build()) {

            // Store memories
            String id1 = memory.remember(
                "Java virtual threads enable millions of concurrent tasks on modern JVMs.",
                MemoryType.SEMANTIC
            );
            String id2 = memory.remember(
                "The Vector API provides SIMD hardware acceleration for dot product and distance math.",
                MemoryType.SEMANTIC
            );

            // Recall
            List<CognitiveResult> results = memory.recall(
                "SIMD hardware acceleration",
                RecallOptions.builder().topK(5).build()
            );

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

            // Cleanup
            memory.forget(id1);
            memory.forget(id2);
        }
    }
}

---

🔗 See Also

• Spring AI Integration — Spring AI VectorStore adapter
• MCP Server Guide — Connect AI agents to Spector over STDIO / HTTP
• REST API Reference — Synapse HTTP endpoints
• Configuration Guide — All engine configuration parameters
• Getting Started — Quick start guide