# One Command to Compress Your Vector Database: Introducing turboquant-pro autotune

*Andrew H. Bond | San Jose State University*

---

You have a vector database with millions of embeddings. You know you should compress them — memory costs are real. But which compression method? How aggressive? What's the recall impact on *your specific data*?

We just shipped a tool that answers this in 10 seconds.

## The Problem

Every RAG system faces the same decision: compress embeddings to save memory, or keep them full-size to preserve search quality. The optimal tradeoff depends on your data — the eigenvalue spectrum of your embeddings, the density of your corpus, and your application's recall requirements.

Until now, finding the right configuration required manual benchmarking across multiple methods, dimensions, and bit-widths. Most teams either skip compression entirely (wasting 10-100x memory) or pick a configuration blindly.

## The Solution: `turboquant-pro autotune`

```bash
pip install turboquant-pro[pgvector]

turboquant-pro autotune \
  --source "dbname=mydb user=me" \
  --table embeddings --column vector \
  --min-recall 0.95
```

In ~10 seconds, autotune:
- Connects to your PostgreSQL database
- Samples 5,000 embeddings (configurable)
- Evaluates 12 compression configurations (4 PCA dimensions x 3 bit-widths)
- Measures cosine similarity and recall@10 for each
- Identifies the Pareto-optimal configurations
- Recommends the best option for your recall threshold

## Real-World Results

We ran autotune on a production RAG system with 194,000 BGE-M3 embeddings (1024-dim):

| Configuration | Compression | Cosine Sim | Recall@10 |
|--------------|------------|-----------|-----------|
| PCA-512 + 4-bit | 15.8x | 0.995 | 96.3% |
| PCA-384 + 4-bit | 20.9x | 0.991 | 96.0% |
| PCA-384 + 3-bit | 27.7x | 0.982 | 93.7% |
| PCA-256 + 3-bit | 41.0x | 0.970 | 92.0% |
| PCA-128 + 3-bit | 78.8x | 0.939 | 84.0% |
| PCA-128 + 2-bit | 113.8x | 0.924 | 78.7% |

For teams needing 95%+ recall, PCA-384 + 4-bit delivers 20.9x compression — your 758 MB corpus becomes 36 MB while maintaining 96% recall@10. For edge deployments where memory is critical, PCA-128 + 2-bit achieves 113.8x compression — nearly two orders of magnitude — at 0.924 cosine similarity.

## How It Works

The compression pipeline combines two techniques:

1. **PCA-Matryoshka rotation**: A training-free PCA transform reorders embedding dimensions by importance. This makes truncation effective on models (BGE-M3, Cohere, ada-002) that weren't trained with Matryoshka loss. On BGE-M3, naive truncation to 256 dims gives 0.467 cosine (useless); PCA rotation first gives 0.974 (+109%).

2. **TurboQuant scalar quantization**: Each coordinate is quantized to 2-4 bits using Lloyd-Max centroids, with random orthogonal rotation to guarantee near-optimal quantization error.

Autotune sweeps the combined configuration space and evaluates against your actual data, not synthetic benchmarks.

## Why This Matters for Enterprise

For organizations running pgvector at scale, the memory savings translate directly to infrastructure cost:

- **10M embeddings at 1024-dim**: 40 GB full → 2 GB at 20x compression
- **100M embeddings**: 400 GB → 20 GB — fits on a single machine instead of a cluster
- **Edge deployment**: Entire corpus fits in device memory for offline operation

The tool is MIT-licensed, requires only NumPy (+ psycopg2 for PostgreSQL), and has no cloud dependency.

## Get Started

```bash
pip install turboquant-pro[pgvector]
turboquant-pro autotune --source "your_dsn" --table your_table --min-recall 0.95
```

Full details, 15-method benchmark, and IEEE TAI paper at: https://github.com/ahb-sjsu/turboquant-pro

---

*Andrew H. Bond is a consultant and researcher working on distributed AI architectures and embedding compression at San Jose State University.*

#MachineLearning #VectorDatabases #Compression #OpenSource #pgvector #RAG #AI #Embeddings
