Is it compatible with OpenAI embeddings?

Yes, it supports any embedding model including OpenAI's text-embedding-ada-002, sentence-transformers, and custom local models via configurable dimensions.

Can I use it for local development?

Absolutely. It supports in-memory databases for rapid testing and persistent local storage for development and production environments.

What is the main benefit of using AgentDB with Claude Code?

It provides sub-millisecond semantic search performance—up to 12,500x faster than traditional solutions—and massive memory savings via quantization for intelligent context retrieval.

Does this skill support RAG?

Yes, it is specifically optimized for building Retrieval Augmented Generation pipelines, allowing developers to retrieve the most relevant context for LLM prompts efficiently.

How does quantization affect memory usage?

AgentDB offers binary, scalar, and product quantization, which can reduce memory footprints by 4x to 32x while maintaining high retrieval accuracy.

AgentDB Vector Search

Name: AgentDB Vector Search
Author: DNYoussef

byDNYoussef

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Data Science & ML

Implements high-performance semantic vector search for intelligent document retrieval and RAG systems using AgentDB.

About

This skill integrates AgentDB's high-performance vector database into Claude Code, providing a sub-millisecond semantic search layer for AI-driven applications. It leverages HNSW indexing and advanced quantization strategies—such as Binary and Scalar—to achieve search speeds up to 12,500x faster than traditional solutions while reducing memory footprints by up to 32x. Ideal for developers building Retrieval Augmented Generation (RAG) pipelines, this skill allows Claude to intelligently query large datasets, perform similarity matching, and retrieve contextually relevant information with near-zero latency.

Key Features

Memory-efficient quantization (Binary, Scalar, Product) for up to 32x reduction
Built-in RAG pipeline support for context-aware generation
6 GitHub stars
Sub-millisecond vector search (<100µs) using HNSW indexing
Hybrid search capabilities combining vector similarity with metadata filtering
Native MCP server integration for seamless use within Claude Code

Use Cases

Implementing semantic code search across massive repositories
Developing intelligent knowledge bases with high-concurrency retrieval needs
Building production-grade Retrieval Augmented Generation (RAG) systems

About

Key Features

Memory-efficient quantization (Binary, Scalar, Product) for up to 32x reduction
Built-in RAG pipeline support for context-aware generation
6 GitHub stars
Sub-millisecond vector search (<100µs) using HNSW indexing
Hybrid search capabilities combining vector similarity with metadata filtering
Native MCP server integration for seamless use within Claude Code

Use Cases

Implementing semantic code search across massive repositories
Developing intelligent knowledge bases with high-concurrency retrieval needs
Building production-grade Retrieval Augmented Generation (RAG) systems