AgentDB Vector Search

Perform semantic search over documents using AgentDB vector embeddings.

4|3|Updated Oct 26, 2025

Search Tags:#embeddings #RAG #HNSW #semantic-search #vector-search #document-retrieval

Ready to run

npx skills add https://github.com/natea/fitfinder --skill agentdb-vector-search-natea

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/natea/fitfinder/tree/main/.claude/skills/agentdb-vector-search
Command: npx skills add https://github.com/natea/fitfinder --skill agentdb-vector-search-natea

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Implements vector-based semantic search using AgentDB's high-performance vector database with 150x-12,500x faster operations than traditional solutions. Features HNSW indexing, quantization, and sub-millisecond search (<100µs).

Core Features & Use Cases

Vector storage and storage of embeddings with metadata for document retrieval
Hybrid search combining vector similarity with metadata filters
RAG workflows enabling retrieval-augmented generation
Batch operations for bulk insertion and continuous indexing
MCP integration for Claude Code ecosystem

Quick Start

Index your document set into AgentDB and perform a semantic search for related items.

Frequently Asked Questions about AgentDB Vector Search

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I implement semantic search over my document knowledge base?▼

Semantic search over documents is implemented using vector embeddings and HNSW indexing to find contextually related items. You index your document set and perform similarity searches to retrieve matching records based on semantic meaning.

What is the best way to build a RAG workflow with document retrieval?▼

A RAG workflow is built by combining vector storage with semantic document retrieval to fetch relevant context. This approach supports retrieval-augmented generation by quickly finding and extracting the specific information needed to ground responses.

Does AgentDB vector search support hybrid search combining metadata filters and embeddings?▼

Yes, hybrid search combines vector similarity with metadata filters to refine document retrieval. This allows you to query stored embeddings while simultaneously applying attribute constraints to narrow down the exact results needed.

How do I insert bulk embeddings into a vector database for continuous indexing?▼

Bulk embeddings are inserted using batch operations designed for continuous indexing within the vector database. This functionality enables efficient, large-scale ingestion of document vectors alongside their associated metadata.

Can I use semantic search with the Claude Code ecosystem?▼

Yes, semantic search integrates directly with the Claude Code ecosystem through MCP integration. This connection allows you to leverage fast, context-aware document retrieval within your existing AI assistant workflows.

Why use HNSW indexing for vector similarity search instead of traditional database queries?▼

HNSW indexing enables sub-millisecond search performance, operating significantly faster than traditional database solutions. It achieves this through quantization and optimized graph structures to deliver rapid similarity results.