AgentDB Vector Search

Perform semantic vector search over documents using AgentDB with HNSW indexing.

Updated Jan 31, 2026

Search Tags:#indexing #embeddings #RAG #AgentDB #semantic-search #vector-search #document-retrieval

Ready to run

npx skills add https://github.com/thewoolleyman/home-tech-infrastructure --skill agentdb-vector-search-thewoolleyman

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: AgentDB Vector Search
Source: https://github.com/thewoolleyman/home-tech-infrastructure/tree/main/.claude/skills/agentdb-vector-search
Command: npx skills add https://github.com/thewoolleyman/home-tech-infrastructure --skill agentdb-vector-search-thewoolleyman

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Semantic vector search is essential for rapidly surfacing relevant documents in large corpora, enabling context-aware retrieval and smarter knowledge work.

Core Features & Use Cases

Vector storage, indexing (HNSW) and fast similarity search over documents.
Hybrid search combining vector similarity with metadata for precise results.
RAG integration and Claude Flow compatibility for knowledge bases.
Use Case: Retrieve the top 5 policy documents relevant to a regulatory query across a corporate knowledge base.

Quick Start

Initialize the vector database with AgentDB and run a sample search.

Initialize: npx agentdb@latest init ./vectors.db
Basic search: npx agentdb@latest query ./vectors.db "compliance policy" -k 5

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 vector search over a large document corpus?▼

Semantic vector search over large corpora is performed by storing embeddings and applying HNSW indexing for fast similarity retrieval. This Skill uses AgentDB to surface relevant documents based on vector proximity rather than exact keyword matches.

What is the best way to retrieve relevant policy documents for a RAG pipeline?▼

For RAG pipelines, the best way to retrieve relevant policy documents is using hybrid search that combines vector similarity with metadata filtering. This ensures precise context-aware retrieval from knowledge bases for downstream generation.

Can I use AgentDB with HNSW indexing for fast similarity search?▼

Yes, AgentDB supports HNSW indexing and quantization options to enable fast and accurate similarity search. It provides an API to store, query, and manage vectors efficiently for document retrieval.

How do I initialize and query a vector database for document retrieval?▼

Initialize the vector database using 'npx agentdb@latest init' to create the storage file. Query it by running 'npx agentdb@latest query' with your search string and the desired number of top results.

Does this semantic search approach support filtering by metadata alongside vector similarity?▼

Yes, hybrid search combines vector similarity with metadata filtering to deliver precise document retrieval results. This allows you to narrow down relevant documents within large repositories based on specific contextual constraints.

When should I use quantization options for vector embeddings in AgentDB?▼

Quantization options in AgentDB should be used when managing large-scale vector embeddings to optimize memory usage during storage and retrieval. This maintains fast similarity search performance across extensive document repositories.