AgentDB Vector Search

Perform semantic vector search and retrieval with AgentDB for RAG workflows.

Updated Jan 7, 2026

Search Tags:#embeddings #rag #mcp #retrieval #semantic-search #knowledge-base #vector-search

Ready to run

npx skills add https://github.com/Aktoh-Cyber/agent-control-plane --skill agentdb-vector-search-aktoh-cyber

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/Aktoh-Cyber/agent-control-plane/tree/main/.claude/skills/agentdb-vector-search
Command: npx skills add https://github.com/Aktoh-Cyber/agent-control-plane --skill agentdb-vector-search-aktoh-cyber

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Vector-based search over large document stores often suffers from slow performance and poor relevance. AgentDB Vector Search provides fast, scalable, vector-based retrieval that yields contextually relevant results.

Core Features & Use Cases

Efficient semantic search with HNSW indexing and quantization for memory efficiency.
Retrieval-augmented generation and context-aware document discovery.
Ideal for knowledge bases, research archives, and enterprise content requiring precise similarity ranking.

Quick Start

Submit a natural language query to retrieve the most relevant documents from your vector store.

Frequently Asked Questions about AgentDB Vector Search

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

FAQPage Schema

How do I perform semantic vector search over enterprise documents?▼

Semantic vector search over enterprise documents is performed by submitting a natural language query to retrieve contextually relevant results through AgentDB, applying HNSW indexing and quantization for fast, memory-efficient similarity matching.

What is the best way to improve retrieval-augmented generation context discovery?▼

Retrieval-augmented generation context discovery is improved by using AgentDB Vector Search to rank enterprise documents and research archives by precise similarity, yielding contextually relevant results for intelligent workflows.

Does AgentDB Vector Search support HNSW indexing and quantization?▼

AgentDB Vector Search supports HNSW indexing and quantization to deliver efficient semantic search and scalable vector storage while maintaining memory efficiency for large knowledge bases.

Can I use this for hybrid search and MCP server integration?▼

Hybrid search and MCP server integration are supported, enabling retrieval-augmented workflows and intelligent document discovery across enterprise content requiring accurate similarity ranking.

Why is my vector search slow over large document stores?▼

Vector search over large document stores is often slow due to poor indexing, but AgentDB Vector Search applies HNSW indexing and quantization to provide fast, scalable, contextually relevant retrieval.

Do I need pre-computed embeddings to run similarity matching?▼

Pre-computed embeddings are handled by the system to perform accurate similarity matching and ranking, powering retrieval-augmented workflows and intelligent document discovery across research archives.