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AgentDB Vector Search

Perform semantic vector search against AgentDB stores using embedding similarity.

Updated Jan 27, 2026
Search Tags:#semantic search#embeddings#vector database#RAG#hybrid search#AgentDB#document retrieval
One-click install
npx skills add https://github.com/Awannaphasch2016/agent-kernel-mcp --skill agentdb-vector-search-awannaphasch2016
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/Awannaphasch2016/agent-kernel-mcp/tree/main/assets/skills/agentdb-vector-search
Command: npx skills add https://github.com/Awannaphasch2016/agent-kernel-mcp --skill agentdb-vector-search-awannaphasch2016

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This skill enables fast and accurate semantic retrieval of documents by performing vector-based search against an AgentDB store, surfacing relevant content through embedding similarity.

Core Features & Use Cases

  • Semantic search across large document collections: Retrieve contextually relevant items based on embedding similarity.
  • RAG-enabled retrieval: Integrate with language models to build retrieval-augmented generation pipelines.
  • Hybrid search and filtering: Combine vector similarity with metadata constraints for precise results.

Quick Start

To start a local vector search workflow, install Node.js 18+, install AgentDB, initialize the vector store, and perform a semantic query using the provided CLI/API examples.

Frequently Asked Questions about AgentDB Vector Search

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

FAQPage Schema
How do I build a RAG system with vector search for document retrieval?▼

Building a RAG system with vector search requires storing document embeddings in a vector database and retrieving contextually relevant items through similarity ranking. This skill provides the semantic search layer needed for RAG pipelines.

What is hybrid search and how does it combine vector similarity with metadata?▼

Hybrid search combines vector similarity with metadata constraints to filter and rank documents simultaneously. It improves retrieval precision by restricting semantic search results to items matching specific metadata fields.

How do I perform semantic search across a large document collection using embeddings?▼

Semantic search across large document collections uses embedding similarity to retrieve contextually relevant items. This skill applies HNSW indexing to rank and surface documents based on their vector representations.

Do I need Node.js to use AgentDB for local vector storage?▼

Yes, local vector storage with AgentDB requires Node.js 18+ installed. After setup, you initialize the vector store and perform semantic queries using the provided CLI or API examples.

Can I use API embeddings for HNSW indexing in a knowledge base?▼

Yes, API embeddings are supported for HNSW indexing to build intelligent document repositories. The system stores generated vectors and applies approximate nearest neighbor search for fast retrieval.

When should I use hybrid search instead of standard semantic search?▼

Use hybrid search instead of standard semantic search when you need to combine embedding similarity with metadata filtering. This approach is essential for precise document retrieval in large knowledge bases where contextual relevance alone is insufficient.

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