Can I use batch operations to speed up data ingestion?

Yes, the batch insert pattern is up to 500x faster than individual inserts, processing 100 vectors in approximately 2ms.

What is the best optimization for mobile or edge devices?

Binary quantization is recommended for memory-constrained environments, as it offers the highest compression (32x) and fastest search speeds.

Does HNSW indexing affect search accuracy?

HNSW provides extremely fast approximate nearest neighbor search. While there is a slight trade-off, AgentDB allows tuning parameters like efSearch to balance speed and accuracy.

How much memory can I save with AgentDB quantization?

Depending on the strategy, you can achieve a 4x reduction with Scalar quantization or up to 32x reduction using Binary quantization.

AgentDB Performance Optimization

Name: AgentDB Performance Optimization
Author: zellycloud

byzellycloud

数据库管理

Optimizes AgentDB vector database performance using quantization, HNSW indexing, and advanced caching strategies.

关于

This skill provides a comprehensive toolkit for scaling AgentDB vector databases, enabling massive performance gains through specialized techniques like binary, scalar, and product quantization. It helps developers achieve up to 32x memory reduction and search speeds up to 12,500x faster by implementing HNSW (Hierarchical Navigable Small World) indexing and intelligent pattern caching. Ideal for applications handling millions of vectors or running on memory-constrained edge devices, it ensures high-speed similarity search and efficient batch processing without sacrificing retrieval accuracy.

主要功能

500x Faster Batch Insert Operations
0 GitHub stars
Multi-level Quantization (Binary, Scalar, Product)
Automatic Memory Consolidation and Pruning
HNSW Indexing for O(log n) Search Complexity
In-memory LRU Pattern Caching

使用场景

Reducing memory footprint for edge and mobile deployments
Improving latency for real-time AI reasoning workflows
Scaling production RAG applications to millions of vectors

关于

主要功能

500x Faster Batch Insert Operations
0 GitHub stars
Multi-level Quantization (Binary, Scalar, Product)
Automatic Memory Consolidation and Pruning
HNSW Indexing for O(log n) Search Complexity
In-memory LRU Pattern Caching

使用场景

Reducing memory footprint for edge and mobile deployments
Improving latency for real-time AI reasoning workflows
Scaling production RAG applications to millions of vectors