AgentDB Vector Search

Name: AgentDB Vector Search
Availability: InStock
Author: Slevo

Enable semantic search over documents using AgentDB's vector store.

Updated Apr 8, 2026

Search Tags:#embeddings #rag #retrieval #agentdb #nlp #semantic-search #vector-search

Ready to run

npx skills add https://github.com/SlevoDev/s-tag --skill agentdb-vector-search-slevodev

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Efficient semantic search over large document collections to quickly surface contextually relevant information for knowledge bases, RAG workflows, and AI-assisted research.

Core Features & Use Cases

Semantic search over documents using AgentDB's vector store to return top-ranked, context-relevant results.
Indexing, embedding generation, and hybrid search to combine vector similarity with metadata for refined results.
RAG workflows and MCP server integrations (Claude Code) to streamline AI-assisted document understanding and retrieval.

Quick Start

Provision a vector store, insert documents with embeddings, and run a semantic search query to retrieve relevant context.

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 documentation using a vector database?▼

Semantic search over documentation requires inserting text with embeddings into a vector store to retrieve contextually relevant results. This Skill uses AgentDB to index large text corpora and return top-ranked similarity matches for knowledge bases.

Can I use vector search and metadata filtering together for RAG workflows?▼

Yes, hybrid search combines vector similarity with metadata to refine context-aware results for RAG workflows. This allows you to filter large document collections by specific attributes while still retrieving semantically relevant information.

What is the best way to integrate semantic search into Claude Code workflows?▼

Integrating semantic search into Claude Code workflows requires an MCP server connection to the vector database. This Skill provides MCP server integration with AgentDB, enabling AI-assisted document understanding and streamlined retrieval directly within your coding environment.

Does AgentDB support fast similarity matching for large text corpora?▼

Yes, AgentDB supports fast similarity matching for large text corpora using HNSW indexing and quantization options. These features optimize vector storage and retrieval speed when processing extensive knowledge bases.

How do I set up a vector store for embedding generation and retrieval?▼

To set up a vector store, you provision the storage, insert documents with their generated embeddings, and run a semantic search query. This process enables efficient indexing and rapid context-aware retrieval over your document collections.

When should I use HNSW indexing for semantic search?▼

HNSW indexing should be used when you need fast, accurate similarity matching over large document collections. It optimizes vector storage in AgentDB, ensuring efficient retrieval when scaling semantic search across extensive knowledge bases.