-
-
Notifications
You must be signed in to change notification settings - Fork 9
Home
github-actions[bot] edited this page May 24, 2026
·
17 revisions
Ultra-fast, SIMD-accelerated semantic search engine built on Java Vector API + modern JVM technologies.
Welcome to the Spector Search wiki — your central hub for everything about Spector Search, a high-performance pure-Java vector search engine. Whether you're building RAG pipelines, powering recommendation systems, or need sub-millisecond search with zero infrastructure, you're in the right place.
| Metric | Value |
|---|---|
| ⚡ Vector Search Latency | 0.05 ms avg @ 10K docs (128-dim) |
| 🔍 Keyword Search Latency | 0.98 ms avg @ 100K docs |
| 🧬 Hybrid Search Latency | 0.17 ms avg @ 10K docs |
| 🚀 Vector Throughput | 18,800 queries/sec @ 10K |
| 🧵 Concurrent Hybrid | 14,000+ ops/sec @ 16 threads (384-dim) |
| 🗜️ IVF-PQ + TurboQuant | 8–32× memory reduction |
| ✅ Test Suite | 316+ tests, all passing |
| 📦 Dependencies | Zero (JDK only) |
| Page | Description |
|---|---|
| Getting Started | Build, run, and search in 5 minutes |
| What is Spector Search | Product overview, use cases, and comparisons |
| FAQ | Common questions answered |
| Page | Description |
|---|---|
| Architecture Overview | Module diagram, data flow, threading model |
| Core Concepts | HNSW, IVF-PQ, BM25, RRF, SIMD deep-dives |
| Ingestion Pipeline | Document → chunk → embed → index pipeline |
| RAG Pipeline | End-to-end retrieval-augmented generation |
| Distributed Mode | Clustering, sharding, and replication |
| GPU Acceleration | CUDA setup and kernel details |
| Page | Description |
|---|---|
| REST API Reference | All endpoints with curl examples |
| Java SDK Guide | Programmatic usage (client + embedded) |
| Spring AI Integration | Spring AI VectorStore adapter |
| CLI Reference |
spectorctl commands |
| Configuration Guide | All parameters with tuning advice |
| Page | Description |
|---|---|
| Performance Tuning | Benchmarks and optimization strategies |
| Contributing | Development setup and PR process |
graph LR
A[📄 Document] --> B[🧩 Chunking]
B --> C[🧠 Embedding]
C --> D[⚡ HNSW + BM25 Index]
D --> E[🔍 Hybrid Search]
E --> F[🎯 RRF Fusion]
F --> G[🤖 LLM Re-ranking]
G --> H[✨ Results]
Tip
New here? Start with Getting Started to build and run your first search in under 5 minutes.
| Language | Java 25 |
| License | Apache 2.0 |
| Modules | 16 Maven modules |
| Dependencies | Zero (JDK only) |
| SIMD | AVX2 / AVX-512 / NEON |
| GPU | CUDA via Panama FFM |
| Distributed | gRPC fan-out + consistent hashing |
Built with ⚡ by Spectrayan · GitHub · Apache 2.0 License
- Home
- Getting Started
-
Cognitive Memory
- Overview
- Getting Started
- Use Cases & Configuration
- API Reference
- Architecture
- The 6-Phase Scoring Pipeline
- Scoring Regimes & Fusion Modes
- Autonomous Identity & AISME
- Generative Dreaming & Thought Experiments
- Constructive Simulation & Provenance
- Retrieval Stack
- Cognitive Profiles
- Salience & Importance
-
Biological Systems
- Overview
- Cortex — Tier Stores
- Hippocampus — Sleep Consolidation
- Synapse — Tags & Scoring
- Dopamine — Surprise Detection
- Amygdala — Emotional Valence
- 4-Layer Cognitive Graph
- Habituation — Anti-Filter Bubble
- Inhibition — Suppression
- Interference — Deduplication
- Prospective — Future Intents
- Metamemory — Self-Reflection
- Sync — Persistence & Replication
- Performance & Internals
- Cognitive Evaluation
- Synapse & Cortex
- Architecture
- Community