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AgentDB Vector Search

Index documents into embeddings and query vectors with HNSW indexing.

4|Updated Mar 16, 2026
Search Tags:#embeddings#rag#retrieval#agentdb#semantic-search#vector-search
One-click install
npx skills add https://github.com/gaberger/hex --skill agentdb-vector-search-gaberger
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/gaberger/hex/tree/main/.claude/skills/agentdb-vector-search
Command: npx skills add https://github.com/gaberger/hex --skill agentdb-vector-search-gaberger

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Semantic search over large document collections is difficult with traditional keyword matching. It delivers inaccurate results and slow discovery when data grows. This skill reduces friction by indexing content into embeddings and enabling fast, context-aware retrieval.

Core Features & Use Cases

  • Semantic indexing with HNSW and quantization for fast similarity search.
  • Retrieval augmented generation (RAG) workflows and knowledge-base search.
  • MCP server integration with Claude Code for seamless operationalization of vector search.

Quick Start

Initialize AgentDB with your documents, generate embeddings, and perform a semantic search 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 build a RAG pipeline with semantic vector search?▼

Building a RAG pipeline with semantic vector search requires indexing document embeddings and retrieving context-aware matches. This skill operationalizes that workflow by generating embeddings and applying HNSW indexing for fast similarity retrieval.

What is the best way to perform semantic search over a large document collection?▼

Semantic search over large document collections is best handled by indexing content into embeddings for context-aware retrieval. This approach overcomes the inaccurate results and slow discovery typical of traditional keyword matching.

How does HNSW indexing and quantization improve vector similarity search?▼

HNSW indexing and quantization improve vector similarity search by optimizing embedding storage and accelerating nearest neighbor queries. This enables fast, semantically aware retrieval across large-scale knowledge bases.

Can I integrate vector search workflows with Claude Code?▼

You can integrate vector search workflows with Claude Code using MCP server integration. This enables seamless operationalization of semantic retrieval directly within your intelligent search systems.

Do I need to generate embeddings before initializing a semantic knowledge base?▼

Generating embeddings is required before initializing a semantic knowledge base. You must process your documents into embeddings to enable HNSW indexing and perform fast similarity search queries.

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