About

🌟 What is Spector?

The Zero-Overhead, Agent-Ready AI Memory Backbone.

Legacy search engines bolted vectors onto text databases. Spector is designed from the ground up for modern AI — combining vector similarity, keyword search, and hybrid ranking in a single embeddable library with zero external dependencies. Connect any AI agent via the built-in MCP server, or embed directly in your application.

Spector is an open-source, high-performance search engine built entirely on modern Java 25. It's designed for developers who want sub-millisecond search, native AI agent integration, and zero infrastructure complexity. Drop in a JAR, write a few lines of code, and you have production-grade hybrid search with built-in agent support.

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🎯 What It Does

Spector indexes documents with their vector embeddings and text content, then retrieves them using multiple strategies — directly from AI agents or your application code:

graph LR
    subgraph Clients
        MCP["🤖 AI Agent (MCP)"]
        REST["🌐 REST API"]
        SDK["📦 Java SDK"]
    end
    
    subgraph Search Modes
        A[Vector Search] --> D[Results]
        B[Keyword Search] --> D
        C[Hybrid Search] --> D
    end
    
    subgraph Engines
        A --> E[HNSW ANN]
        B --> F[BM25 Scoring]
        C --> E
        C --> F
        C --> G[RRF Fusion]
    end
    
    MCP --> A & B & C
    REST --> A & B & C
    SDK --> A & B & C

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Mode	How It Works	Best For
🧠 Vector Search	HNSW approximate nearest neighbor graphs	Semantic similarity
📝 Keyword Search	BM25 scoring with term frequency saturation	Exact term matching
🧬 Hybrid Search	Combines both via Reciprocal Rank Fusion	Best-of-both-worlds
🤖 RAG Pipeline	Ingest → chunk → embed → retrieve → context assembly	LLM applications
🏛️ SpectorIndex	IVF-HNSW-SVASQ adaptive hybrid index	Scale + recall

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💎 Key Differentiators

🤖 Agent-Native (MCP Protocol)

Includes a built-in Model Context Protocol server with 6 tools. AI agents connect directly via JSON-RPC — no Python frameworks, no network round-trips.

Feature	Python Vector DB MCP	Spector MCP
Search latency	2–10ms	88µs p50 (23–113× faster) †
Network overhead	HTTP/gRPC round-trip	Zero (in-process)
Concurrent queries	Limited by Python GIL	61,000 QPS †
Dependencies	Python framework stack	Single JAR

† Measured. See Benchmarks.

Tip

See the MCP Server Guide to connect Claude Desktop, Cursor, or any MCP client in minutes.

📦 Pure Java, Zero Dependencies

Unlike most vector databases that rely on C++, Rust, or Python bindings, Spector is 100% Java. It uses the JDK's own Vector API for SIMD acceleration — no JNI, no native libraries, no external infrastructure.

Tip

Add the JAR to your classpath and you're done. No Docker, no clusters, no ops.

🚀 Modern JVM Technologies

Technology	Purpose
Java Vector API	SIMD-accelerated math (AVX2/AVX-512/NEON)
Panama FFM	Zero-copy memory-mapped storage, GPU interop
Virtual Threads	Millions of concurrent operations without thread pools
Structured Concurrency	Safe parallel task management

⚡ Sub-Millisecond at Scale

HNSW at 100K documents (128 dimensions, top-10, M=16, efSearch=64):

Search Type	Average Latency	Throughput
Vector	0.13 ms	7,556 QPS
Keyword	0.98 ms	1,019 QPS
Hybrid	1.01 ms	994 QPS

SpectorIndex (IVF-HNSW-SVASQ) at 10K documents (4096-dim real Qwen3 embeddings):

Config	Average Latency	Throughput	Recall@10
nCentroids=128, nProbe=4	0.46 ms	2,173 QPS	1.0000
nCentroids=64, nProbe=4	0.62 ms	1,601 QPS	1.0000
nCentroids=128, nProbe=16	1.26 ms	792 QPS	1.0000

Note

SpectorIndex achieves perfect recall while searching only 3.1% of the data (nProbe=4 out of 128 centroids). Ingestion is 28–160× faster than standalone HNSW. Numbers measured on 24-core x86, AVX2, Java 25, ZGC with Qwen3-embedding real vectors. For comprehensive, multi-centroid sweeps and adaptive HNSW shard promotion benchmarks, see the dedicated Large-Scale Real-Embedding Benchmarks page.

🏠 Dual Deployment Modes

Mode	Description	Best For
Embedded	In-process library, zero network overhead	Microservices, desktop apps, edge
Server	REST API with CORS, auth, and metrics	Teams, multi-language clients

🗜️ Advanced Quantization (SVASQ + IVF-PQ)

Spector offers two quantization paths:

• SVASQ (Vectorized Affine Scalar Quantization): Uses the Fast Walsh-Hadamard Transform to spread variance before INT8 quantization, achieving 4× compression with near-lossless recall (~97–99.5%). Used inside SpectorIndex shards.
• IVF-PQ (Product Quantization): Provides 32× memory compression for billion-scale datasets.

Important

SVASQ gives INT8 the precision of INT12–16 by rotating vectors before quantization. See the SVASQ Deep Dive for the full theory.

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📊 How Spector Compares

Latency Comparison (100K docs, 128-dim, top-10)

Engine	Language	Vector Avg	Vector P99
⚡ Spector	Java 25	0.13 ms	0.26 ms
hnswlib	C++	0.1–0.5 ms	~1 ms
FAISS	C++	0.2–0.8 ms	1–2 ms
Lucene 9+	Java	1–5 ms	5–10 ms
Elasticsearch 8+	Java	2–10 ms	10–25 ms
Qdrant	Rust	2–5 ms	10–25 ms
Milvus	Go/C++	3–10 ms	10–35 ms

Note

Spector's vector search latency is competitive with native C++ implementations (hnswlib, FAISS) for in-process workloads. Numbers for external systems are from published benchmarks and ann-benchmarks.com. Hardware and configuration differences apply — these are directional comparisons, not controlled A/B tests.

Feature Comparison

Feature	Spector	Elasticsearch	Qdrant	Milvus	hnswlib
Deployment	Embedded + Server	Cluster only	Server only	Cluster only	Embedded only
MCP Server	✅ Built-in (6 tools)	❌	❌	❌	❌
Hybrid Search	✅ RRF built-in	✅ RRF	✅ Sparse+Dense	✅ RRF	❌
Zero Dependencies	✅ JDK only	❌ Heavy stack	❌ Tokio runtime	❌ etcd, MinIO, Pulsar	✅ Header-only
Virtual Threads	✅ Project Loom	❌ Platform threads	N/A (Rust async)	N/A (Go goroutines)	N/A
GPU Acceleration	✅ CUDA (Panama FFM)	❌	✅ Vulkan (indexing)	✅ CUDA (search + indexing)	❌
Quantization	✅ Scalar INT8 + IVF-PQ	✅ BBQ + Scalar + DiskBBQ (IVF)	✅ Scalar + Binary	✅ IVF-PQ + IVF-SQ	❌
Re-ranking	✅ LLM via Ollama	✅ Elastic Rerank + Inference API	✅ FastEmbed / ColBERT	✅ vLLM Ranker + Cross-encoder	❌
Distributed	✅ gRPC fan-out	✅ Built-in sharding	✅ Raft consensus	✅ gRPC + etcd	❌
SIMD Acceleration	✅ Java Vector API	✅ simdvec (Panama)	✅ Native SIMD	✅ AVX/NEON	✅ AVX/SSE

Note

This comparison reflects publicly available information as of May 2025. Feature availability may vary by version and deployment mode. All products are actively evolving.

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🛠️ Use Cases

🤖 Agentic AI Memory

Connect AI agents (Claude, Cursor, custom) directly to Spector via the built-in MCP server. The agent autonomously ingests documents, searches for relevant context, and retrieves information — all with zero Python glue-code. "Point your LLM at Spector's MCP port, and it instantly has mathematically-perfect long-term memory."

🤖 Retrieval-Augmented Generation (RAG)

Ingest documents (PDF, HTML, Markdown), chunk them with token awareness, generate embeddings, and retrieve relevant context for LLM prompting — all through a single /api/v1/rag endpoint or the rag_query MCP tool.

🔍 Semantic Search Applications

Power product search, documentation search, code search, or any application where meaning matters more than exact keywords.

💡 Recommendation Systems

Use vector similarity to find items similar to what users have engaged with. Sub-millisecond latency makes real-time recommendations practical.

🏢 Hybrid Enterprise Search

Combine keyword precision (finding exact product SKUs, error codes) with semantic understanding (finding conceptually related documents).

📱 Embedded Analytics

Drop Spector into existing Java applications without infrastructure changes. Perfect for desktop applications, microservices, or edge deployments.

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✅ When to Choose Spector

Note

Choose Spector when:

• You want AI agents to autonomously search your data (MCP integration)
• You want sub-millisecond hybrid search without infrastructure complexity
• Your stack is Java/JVM and you want native integration
• You need an embedded search library with server-mode option
• You want GPU acceleration without leaving the JVM
• Zero external dependencies matters to your deployment

Warning

Consider alternatives when:

• You need a managed cloud service with zero ops
• Your team primarily works in Python/Rust/Go
• You need built-in ML model serving

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🚀 Next Steps

• Getting Started — Build and run your first search in 5 minutes

• MCP Server Guide — Connect an AI agent in 3 steps

• Architecture Overview — Understand how it works under the hood

• REST API Reference — Full API documentation

• Core Concepts — Deep dive into the algorithms