1. Overall Vision of V4
V4 adds a complete vector layer to CortexDB, making it AI-ready. It is divided into two sub-versions:
- V4.1: Vector Core (collections, HNSW, 8-bit quantization, persistence)
- V4.2: Vector DX (advanced filters, hybrid search, batch search, IVF-Lite, 4-bit quantization, ingestion helpers)
Main objectives:
- Enable semantic search capabilities in CortexDB
- Optimize for CPU, not GPU
- Support embeddings and vector search
- Deliver acceptable performance on modest machines
2. V4 Structure
V4.1 — Vector Core
Features:
- Vector collections
- L2, Cosine, and Dot-product distances
- CPU-first HNSW index
- 8-bit quantization
- Snapshot persistence of the index
- Simple filters (post-filtering)
V4.2 — Vector DX
Features:
- Advanced filters with indexing
- Hybrid search (vector plus text)
- Batch search
- IVF-Lite index as an alternative to HNSW
- 4-bit quantization
- Embedding ingestion helpers on the client side
3. V4 Non-Goals
- No GPU support
- No replication (V5)
- No CortexQL (V6)
- No distribution (V7)
- No fine-tuning of embedding models
4. V4 Architecture Overview
┌───────────┐
│ Client │
└─────┬─────┘
│ vector_search(query, k=10)
▼
┌───────────────────┐
│ Vector Engine │
│ - Collections │
│ - HNSW or IVF │
│ - Search │
└─────┬─────────────┘
│
▼
┌───────────────────┐
│ Storage Engine │
│ (V3) │
└───────────────────┘5. V4 Objective
At the end of V4 (V4.1 + V4.2), CortexDB becomes:
- AI-ready: full vector search capabilities
- CPU-optimized: acceptable performance on CPU
- Memory-efficient: quantization to reduce memory usage
- Production-ready: suitable for AI applications
This is the version that makes CortexDB unique through its CPU-first vector search capability.