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AgentDB Vector Search

Implement semantic vector search with AgentDB for document retrieval.

1|1|Updated Jan 6, 2026
Search Tags:#semantic search#embeddings#RAG#vector search#AgentDB#HNSW
One-click install
npx skills add https://github.com/Geralt1983/Thanos --skill agentdb-vector-search-geralt1983
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/Geralt1983/Thanos/tree/main/.claude/skills/agentdb-vector-search
Command: npx skills add https://github.com/Geralt1983/Thanos --skill agentdb-vector-search-geralt1983

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of efficiently searching and retrieving information from large datasets based on semantic meaning rather than just keywords, enabling more intelligent applications.

Core Features & Use Cases

  • High-Performance Vector Database: Utilizes AgentDB for sub-millisecond vector search with HNSW indexing and quantization.
  • Semantic Search & RAG: Ideal for building Retrieval Augmented Generation systems, semantic search engines, and knowledge bases.
  • Use Case: A customer support chatbot can use this Skill to find the most relevant documentation articles based on the user's natural language query, even if the exact keywords don't match.

Quick Start

Initialize a new AgentDB vector database at './vectors.db' with default dimensions for OpenAI embeddings.

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 for a RAG system?▼

You can implement semantic search for a RAG system by utilizing AgentDB with HNSW indexing and quantization to retrieve semantically relevant documents via vector similarity matching.

What is the best way to perform semantic document retrieval instead of keyword matching?▼

The best way to achieve semantic document retrieval is using vector search to match user queries based on contextual meaning rather than exact keywords, enabling more intelligent applications.

What do I need to run AgentDB vector search?▼

To run AgentDB vector search, you need Node.js, an initialized AgentDB database, and an OpenAI API key or a custom embedding model to generate vector representations.

Can I use a custom embedding model for AgentDB vector search?▼

Yes, you can use a custom embedding model for AgentDB vector search, or you can provide an OpenAI API key to handle the generation of embeddings for your documents and queries.

How fast is sub-millisecond vector search with HNSW indexing?▼

Vector search with HNSW indexing and quantization in AgentDB achieves sub-millisecond query speeds by efficiently organizing high-dimensional embedding data for rapid similarity matching.

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