NeuronDB - AI Database Extension for PostgreSQL

PostgreSQL extension for vector similarity search (HNSW, IVFFlat), kNN, embeddings, machine learning, and hybrid full-text + vector search in SQL

</div>

Run the system in 5 minutes

From the repository root:

docker compose -f docker/docker-compose.yml up -d neurondb
docker compose -f docker/docker-compose.yml ps   # wait until healthy
docker compose -f docker/docker-compose.yml exec neurondb psql -U neurondb -d neurondb -c "CREATE EXTENSION IF NOT EXISTS neurondb; SELECT neurondb.version();"

Then run your first vector search: Simple Start or Quick Start.

<details> <summary><strong>Expand full table of contents</strong></summary>

Overview
- Key Capabilities
- Performance Metrics
Documentation
Official Documentation
Architecture
Compatibility
Support & Community
Contributing
License
Authors

</details>

Overview

Vectors, embeddings, and ML—inside PostgreSQL. NeuronDB keeps similarity search and models on your live rows, not in a separate database you have to sync and babysit.

HNSW · IVFFlat · kNN · hybrid full-text + vector · RAG pieces · train & predict in SQL—all first-class in the engine.

One extension: same Postgres backups, HA, and security. Start with CREATE EXTENSION neurondb;, then index and query from SQL.

Key Capabilities

<details> <summary><strong>Feature summary</strong></summary>

| Category | Details | |:---------|:--------| | Vector types | vector, vectorp, vecmap, vgraph, rtext, halfvec, binaryvec, sparsevec (8 types) | | Index access methods | HNSW and IVF only. PQ and OPQ are quantization (codebook training), not separate index types. Hybrid and multi-vector search are query-level functions. | | Distance metrics | L2, cosine, inner product, L1, Hamming, Jaccard, and others | | ML | 25+ algorithm families (train/predict/evaluate): linear regression, XGBoost, LightGBM, CatBoost, K-Means, etc. | | SQL | ~650+ functions and operators (vector, ML, embeddings, RAG, indexing). See FEATURES.md and SQL API. | | GPU | CUDA, ROCm, Metal (distance and search; index build is CPU only). See GPU feature matrix. | | Background workers | neuranq, neuranmon, neurandefrag, neuranllm |

</details>

Performance Metrics

NeuronDB provides significant performance improvements over standard PostgreSQL extensions:

Index Build Performance:

The index build time for HNSW follows the relationship:

$$T_{build} = O(N \cdot \log N \cdot m \cdot ef_{construction})$$

Where:

$N$ = number of vectors
$m$ = number of connections per node (typically 16-32)
$ef_{construction}$ = size of candidate list during construction (typically 64-200)

Query Performance:

Query latency for HNSW search:

$$T_{query} = O(\log N + ef_{search} \cdot k)$$

Where:

$ef_{search}$ = size of candidate list during search (typically 40-200)
$k$ = number of results requested

Throughput Calculation:

$$QPS = \frac{1}{T_{query}} = \frac{1}{O(\log N + ef_{search} \cdot k)}$$

[!TIP] For optimal performance, tune ef_search based on your recall requirements. Higher values improve recall but increase latency.

Documentation

Getting Started

Installation - Install NeuronDB extension
Extension packaging - Control file, file layout, CREATE/UPDATE/DROP EXTENSION, dump/restore
Quick Start - Get up and running quickly

Vector Search & Indexing

Vector Types — vector, vectorp, vecmap, vgraph, rtext, halfvec, binaryvec, sparsevec
Indexing — HNSW and IVF indexing
Distance Metrics — L2, cosine, inner product, and more
Quantization — PQ and OPQ compression

ML Algorithms & Analytics

Random Forest - Classification and regression
Gradient Boosting - XGBoost, LightGBM, CatBoost
Clustering - K-Means, DBSCAN, GMM, Hierarchical
Dimensionality Reduction - PCA and PCA Whitening
Classification - SVM, Logistic Regression, Naive Bayes, Decision Trees
Regression - Linear, Ridge, Lasso, Deep Learning
Outlier Detection - Z-score, Modified Z-score, IQR
Quality Metrics - Recall@K, Precision@K, F1@K, MRR
Drift Detection - Centroid drift, Distribution divergence
Topic Discovery - Topic modeling and analysis
Time Series - Forecasting and analysis
Recommendation Systems - Collaborative filtering

ML & Embeddings

Embedding Generation - Text, image, multimodal embeddings
Model Inference - ONNX runtime, batch processing
Model Management - Load, export, version models
AutoML - Automated hyperparameter tuning
Feature Store - Feature management and versioning

Hybrid Search & Retrieval

Hybrid Search - Combine vector and full-text search
Multi-Vector - Multiple embeddings per document
Faceted Search - Category-aware retrieval
Temporal Search - Time-decay relevance scoring

Reranking

Cross-Encoder - Neural reranking models
LLM Reranking - GPT/Claude-powered scoring
ColBERT - Late interaction models
Ensemble - Combine multiple strategies

RAG Pipeline

Complete RAG Support - End-to-end RAG
LLM Integration - Hugging Face and OpenAI
Document Processing - Text processing and NLP

Background Workers

neuranq - Async job queue executor
neuranmon - Live query auto-tuner
neurandefrag - Index maintenance
neuranllm - LLM job processor

GPU Acceleration

CUDA Support - NVIDIA GPU acceleration
ROCm Support - AMD GPU acceleration
Metal Support - Apple Silicon GPU acceleration
Auto-Detection - Automatic GPU detection

Performance & Security

SIMD Optimization - AVX2/AVX512, NEON optimization
Security - Encryption, privacy, RLS
Monitoring - Monitoring views and Prometheus

Configuration & Operations

Configuration - Essential configuration options
Troubleshooting - Common issues and solutions (getting started and operations)

Official Documentation

https://www.neurondb.ai/docs — API reference (~650+ SQL functions), tutorials, deployment, and troubleshooting.

Architecture

NeuronDB follows PostgreSQL's architectural patterns and extends the database with AI capabilities.

System Architecture

graph TB
    subgraph SQL["SQL Interface Layer"]
        FUNC["~650+ SQL Functions"]
        TYPES["Vector Types: vector, vectorp, vecmap, vgraph, rtext, halfvec, binaryvec, sparsevec"]
        OPS["Distance Operators: <->, <=>, <#>"]
    end
    
    subgraph VECTOR["Vector Operations"]
        INDEX["HNSW/IVF Indexes"]
        DIST["Distance Metrics: L2, Cosine,

Neurondb

Install / Use

README