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AgentDB Vector Search

Store, index, and query embeddings with AgentDB for semantic search.

Updated Mar 5, 2026
Search Tags:#embeddings#rag#agentdb#semantic-search#knowledge-base#vector-search#hybrid-search
One-click install
npx skills add https://github.com/fabri07/Vektor --skill agentdb-vector-search-fabri07
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/fabri07/Vektor/tree/main/.claude/skills/agentdb-vector-search
Command: npx skills add https://github.com/fabri07/Vektor --skill agentdb-vector-search-fabri07

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Efficient, scalable search over large document collections requires semantic understanding and fast retrieval. AgentDB Vector Search provides a high-performance vector store and retrieval mechanism to perform similarity matching, context-aware querying, and hybrid results in knowledge bases and RAG pipelines.

Core Features & Use Cases

  • Vector storage and indexing for fast, scalable retrieval of semantically related documents.
  • Similarity search with configurable metrics and integration with metadata for precise results.
  • Hybrid search that combines vector similarity with document metadata to improve relevance.
  • RAG-style workflows and knowledge-base exploration to surface context-rich answers.
  • MCP integration for Claude Code and automation workflows.

Quick Start

Initialize AgentDB vector database, insert a sample embedding, and run a semantic search to verify setup.

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 search over my document knowledge base?▼

Semantic search over a document knowledge base is performed by storing and indexing document embeddings to retrieve similar results. This Skill uses AgentDB to provide fast similarity matching and context-aware querying.

How do I implement vector similarity search for a RAG pipeline?▼

Vector similarity search for RAG pipelines is implemented by indexing document embeddings and querying for related content. This Skill provides configurable similarity metrics and integrates document metadata for precise retrieval results.

Can I combine vector similarity search with metadata filtering?▼

Yes, hybrid search combines vector similarity with document metadata to improve relevance. This approach filters embeddings using associated metadata to return context-rich and precise results from the knowledge base.

Does AgentDB vector search work with Claude Code and MCP automation workflows?▼

Yes, AgentDB vector search includes MCP integration for Claude Code and automation workflows. This allows semantic search and retrieval actions to be executed directly within automated pipelines.

What's the best way to store and query embeddings for large document collections?▼

The best way to store and query embeddings for large collections is using a dedicated vector store. This Skill provides high-performance vector storage and indexing for fast, scalable retrieval of semantically related documents.

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